Genomics: Fundamentals and Applications

about the book… This unique new text delivers a solid foundation for understanding the role of genomics in human health and in advances that promise to help improve the quality of human life. Unlike other works that focus mainly on toxicogenomic techniques, Genomics presents a thorough overview of the field in four major sections: 1) fundamentals of genes and genome structure, function, expression, variations, and genomic technology platforms; 2) applications of genomics in drug discovery and drug development, safety evaluation, genomic database maintenance, mining, and analysis, food safety monitoring, and translational toxicogenomics; 3) how regulatory agencies such as the FDA and EPA use genomic data in their safety evaluation; and 4) a summary of the current state and the future prospect of the science of genomics. With an international perspective and practical case studies, Genomics is the first resource to present essential discussion of theory and application for: � eukaryotic genomes � epigenomics � translational genomics and biomarker development about the editors... SUPRATIM CHOUDHURI is a toxicologist in the Division of Biotechnology and GRAS notice review, Office of Food Additive Safety, U.S. Food and Drug Administration (FDA), College Park, Maryland, USA, and Adjunct Associate Professor at the Department of Pharmacology and Toxicology, University of Kansas Medical Center, Kansas City, KS. Dr. Choudhuri received his Ph.D. and M.Sc. both from the University of Calcutta, Calcutta, India. Dr. Choudhuri is a member of several professional organizations, including a full member of the U.S. Society of Toxicology. Dr. Choudhuri has been a guest reviewer of a number of toxicology and biochemistry journals, and has taught and trained undergraduate and graduate students, and postdoctoral researchers. Dr. Choudhuri has extensively published in the fields of molecular toxicology, genomics and metabolism; his teaching, research, and regulatory expertise includes molecular mechanisms of toxicity, toxicogenomics, comparative toxicology, metabolism and transporters, and regulatory safety assessment. Dr. Choudhuri has been invited to speak and chair sessions at various national and international meetings, and he is the recipient of numerous FDA awards. DAVID B. CARLSON is a toxicologist in the Division of Metabolism and Endocrinology Products, Office of New Drugs, U.S. Food and Drug Administration, Silver Spring, Maryland, and an Adjunct Assistant Professor of Biotechnology, University of Maryland University College, Adelphi, MD. Dr. Carlson received a B.S. from Duke University and a Ph.D. from Oregon State University. Dr. Carlson is an active member of several professional organizations, including the Society of Toxicology and the Society of Environmental Toxicology and Chemistry. Dr. Carlson has extensive publication and peer-review experience in scholarly journals and he has been invited to speak at national and international professional meetings in his area of expertise. Dr. Carlson’s teaching, research, and regulatory expertise includes mechanisms of endocrine disruption and nuclear receptor signaling, comparative toxicology and carcinogenesis, and safety assessment of drugs and food additives. Dr. Carlson is the recipient of numerous academic and FDA scholarships and awards. Printed in the United States of America

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Genomics Fundamentals and Applications

Genomics

Choudhuri � Carlson

Genomics Fundamentals and Applications

Edited by

Supratim Choudhuri David B. Carlson

9/17/2008 9/16/200812:57:16 5:39:15 PM PM

Genomics

Choudhuri_978-1420067057_TP.indd 1

9/15/08 11:40:39 AM

Genomics Fundamentals and Applications Edited by

Supratim Choudhuri FDA/CFSAN College Park, Maryland, USA

David B. Carlson

FDA/CDER Silver Spring, Maryland, USA

Choudhuri_978-1420067057_TP.indd 2

9/15/08 11:40:39 AM

Informa Healthcare USA, Inc. 52 Vanderbilt Avenue New York, NY 10017 © 2009 by Informa Healthcare USA, Inc. Informa Healthcare is an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-10: 1-4200-6705-2 (Hardcover) International Standard Book Number-13: 978-1-4200-6705-7 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequence of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Library of Congress Cataloging-in-Publication Data Genomics: fundamentals and applications/edited by Supratim Choudhuri, David B. Carlson. p.; cm. Includes bibliographical references and index. ISBN-13: 978-1-4200-6705-7 (hardcover : alk. paper) ISBN-10: 1-4200-6705-2 (hardcover : alk. paper) 1. Genomics. I. Choudhuri, Supratim. II. Carlson, David B. (David Byron), 1970[DNLM: 1. Genomics--United States. 2. Drug Toxicity--genetics--United States. 3. Pharmacogenetics--United States. QU 58.5 G3351 2008] QH447.G4653485 2008 616’.042--dc22 2008026281 For Corporate Sales and Reprint Permissions call 212-520-2700 or write to: Sales Department, 52 Vanderbilt Avenue, 7th floor, New York, NY 10017. Visit the Informa Web site at www.informa.com and the Informa Healthcare Web site at www.informahealthcare.com

Preface

In a span of 100 years between 1900 and 2000, three landmark discoveries almost equally spaced apart in time revolutionized the scope of the study of the molecular basis of life. These were: the rediscovery of Mendelism and the laws of heredity in 1900, the discovery of DNA structure in 1953, and the completion of the first draft sequence of the human genome in 2000 (published in 2001). Each of these discoveries represents a major breakthrough that paved the way for further progress in a new direction. With the elucidation of DNA structure in 1953 by James Watson and Francis Crick, and the subsequent deluge of discoveries on DNA replication, genetic code, mRNA synthesis, protein synthesis, and gene regulation, the findings of classical genetics could be explained in terms of molecular biology principles. Thus, the discovery of DNA structure paved the way for the merger of two apparently disparate disciplines—classical genetics and molecular biology, and the birth of molecular genetics.a,b Within another 50 years, the science of molecular biology/molecular genetics has undergone another phase of transformation into the science of genomics, which is much more global in scope and approach. Knowledge of the genomes of humans and other species as well as scientific advances in genome research in general will presumably help us understand the genetic instructions that make us human. From a clinical perspective, by learning about the gene sequences and the functional dynamics of the genome as well as individual genetic differences, scientists hope to understand the molecular basis of the normal state and the diseased state of life on one hand, and develop ways to individualize medicine and nutrition on the other hand. However, the enormous wealth of knowledge that has been accumulated through genome sequencing and genome research has created a new challenge for scientists. The new challenge is to learn how to integrate and interpret the enormous body of genomic data to determine cause-and-effect relationships and how to predict the occurrence and progression of diseases before there are any symptoms. Parallel developments in the science of bioinformatics, genomic regulatory networks, and systems biology will presumably help us add meaning to the enormous body of genomic data, and in so doing will help us understand the molecular basis of biological diversity, organismal complexity, and life in general. a b

Choudhuri S. Tox Mech Meth 16, 2006, 137-159. Choudhuri S. Bull Sci Tech Soc 23, 2003, 360-367.

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Nevertheless, despite real technological progress and much journalistic hyperbole about the promise of genomics, recent advances in research have not yet radically changed the practice of medicine and nutrition. The disconnect between the hope of quick returns from advances in genomics and the reality of the long-drawn path of developing products that will improve the quality of human life may lead to under-appreciation of the progress and achievements of genomics. In this new era, the terms “genome” and “genomics” have gone beyond the boundaries of the laboratory into public life and thus we all need to know something about genomics. The current book is an attempt to present to the reader, a thorough overview of the progress of genomics to its current level. EDITORS’ DISCLAIMER The editorial work on this book was outside the normal duties of the editors’ work at the FDA. The views and opinions expressed by the editors in the articles of this book are the editors’ personal opinions and they do not necessarily reflect those of the FDA, DHHS, or Federal Government. ACKNOWLEDGEMENT We would like to express our sincere appreciation and thanks to all who contributed chapters for the book, thereby making the project successful. We would like to acknowledge our colleagues who provided encouragement to tackle this project, and for stimulating discussion on various aspects of genomics. Thanks are also due to Carolyn Honour, Sherri Niziolek, and all of their colleagues at Informa Healthcare for helping us bring this project to a successful conclusion. Most importantly, we thank our families for their unwavering support throughout this process and to this small contribution to advance science! Supratim Choudhuri David B. Carlson

Contents

Preface . . . . iii Contributors . . . . xi Introduction . . . . xv PART 1: GENES AND GENOMES 1. Fundamentals of Structure–Function Analysis of Eukaryotic Protein-Coding Genes . . . . . . . . . . . . . . . . . . . 3 Supratim Choudhuri Introduction . . . . 5 Structure and Function of Typical Eukaryotic Protein-Coding Gene . . . . 6 Concluding Remarks . . . . 43 References . . . . 44 2. The Eukaryotic Genome: A Brief Overview of Structure, Function, and Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Supratim Choudhuri Introduction—The Scope of Genomics . . . . 51 Nucleoprotein Organization of the Genome . . . . 55 Genomic DNA as a Mosaic of Isochores . . . . 63 Genomic Landscape as Revealed by Genome Sequencing . . . . 67 Chromosomal Synteny and Syntenic Maps in Comparative Genomics . . . . 94 Concluding Remarks . . . . 94 References . . . . 95 3. Epigenetic Regulation of Gene and Genome Expression . . . . . . . . . Supratim Choudhuri Introduction . . . . 102 Molecular Basis of Epigenetic Regulation . . . . 102

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*Disclaimer: The views and opinions expressed in the articles of this book are the authors’ personal opinions and they do not necessarily reflect those of EPA, FDA, DHHS, or the Federal Government.

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Examples of Epigenetic Regulation of Gene and Genome Expression . . . . 111 Conclusion . . . . 124 References . . . . 125

4. Principles of Functional Genomic Analysis . . . . . . . . . . . . . . . . . . . . 129 Scott A. Jackson, Isha Patel, J. Eugene LeClerc, Thomas A. Cebula, Mark K. Mammel, Michael L. Kotewicz, Amit Mukherjee, Sushil K. Mahata, Juan L. Rodriguez Flores, Manjula Mahata, Daniel T. O’Connor, and Jannavi R. Srinivasan I. Use of High Density Gene Arrays for Gene Expression Profiling and Genotyping . . . . 131 II. Pyrosequencing in SNP Discovery and Genotyping in Prokaryotes . . . . 135 III. Use of Optical Mapping for the Rapid Construction of Ordered Restriction Maps for Entire Chromosomes from Single DNA Molecule . . . . 137 IV. Metabolic Profiling in Bacteria Using Phenotypic Microarray . . . . 139 V. From Plus–Minus Sequencing to Next Generation Sequencing with a Focus on Pyrosequencing . . . . 141 Introduction . . . . 141 Sequencing by Synthesis—Sanger Method . . . . 142 Sequencing by Chemical Cleavage—Maxam & Gilbert Method . . . . 142 Sequencing by Hybridization—ED Southern Method . . . . 142 Sequence by Synthesis—Pal Nyren’s Pyrosequencing Method . . . . 143 Applications of Pyrosequencing . . . . 150 Next Generation Sequencing—$1,000 Per Genome? . . . . 154 Concluding Remarks . . . . 156 VI. Principles of Proteomic Analysis with Special Emphasis on Mass Spectrometry . . . . 157 Introduction . . . . 157 Principles of Protein Separation . . . . 158 Principles of Protein Identification . . . . 160 Principles of Protein Structure Determination . . . . 165 Conclusion . . . . 167 References . . . . 167 PART 2: APPLICATIONS OF GENOMICS 5. Toxicogenomics in Non-Clinical Safety Studies: Models of Animal and Human Tissues to Assess Target Organ Injury . . . . Alison Vickers Introduction–Translational Research and Research Models . . . . 175 New Approaches and Models for Mechanistic Insight . . . . 176 Value of In Vitro Models . . . . 177 Toxicogenomics in Translational Research–Animal and Human Models (In Vivo and In Vitro) . . . . 178 Conclusion . . . . 185 References . . . . 185

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6. Mining of Genome Sequence Databases to Identify New Targets for Prostate and Breast Cancer Therapy. . . . . . . . . . . 191 Tapan K. Bera and Byungkook Lee Introduction . . . . 191 Identification of Disease-Related Genes by Database Mining . . . . 192 Concluding Remarks . . . . 203 References . . . . 203 7. Functional Genomic Approaches for Studying Genotoxicity and Carcinogenesis . . . . . . . . . . . . . . . . . Ebru Caba and Jiri Aubrecht Introduction . . . . 207 Transcriptomics . . . . 209 Functional Genomics . . . . 215 Summary . . . . 217 References . . . . 217

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8. Human Genomics in Hypertension . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Pei-an Betty Shih, Daniel T. O’Connor, and Sushil K. Mahata Introduction . . . . 224 Intermediate Phenotypes and Heritability in Hypertension . . . . 224 Systematic Polymorphism Discovery at the Candidate Genetic Loci Susceptible for Hypertension . . . . 225 Genome-Wide Linkage Studies in Hypertension . . . . 228 Genome-Wide Association Studies in Hypertension . . . . 228 Gene-by-Sex Interaction in Hypertension . . . . 229 Adrenergic Pathway Polymorphisms and Hypertension . . . . 230 Renin-Angiotensin-Aldosterone System and Hypertension . . . . 232 Metabolic Syndrome and Hypertension . . . . 233 Endothelium and Vascular Dysfunction in Hypertension . . . . 233 Cardio-Renal Target Organ Susceptibility Genes and Hypertension . . . . 234 Pleiotropy and Hypertension . . . . 235 Epistasis and Hypertension . . . . 235 Heterosis and Hypertension . . . . 237 Pharmacogenetics—Antihypertensive Drugs . . . . 238 Mechanistic Studies on Trait-Associated Genetic Variants . . . . 238 Transcriptomics in Hypertension . . . . 239 Perspectives and Strategies . . . . 239 References . . . . 241 9. Immunotoxicogenomics: Gene Expression and Immunotoxicology in the Age of Genomics . . . . . . . . . . . . . . . . Dori R. Germolec, Leigh Ann Burns-Naas, G. Frank Gerberick, Gregory S. Ladics, Cindy A. Ryan, Stephen B. Pruett, Berran Yucesoy, and Robert W. Luebke Introduction . . . . 248

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Immune Suppression . . . . 249 Specific Application of Genomics Techniques as a Discovery Tool to Investigate Targeted Pathways in Immune Suppression . . . . 251 Screening for Dermal Sensitizers . . . . 254 Relating Genetic Variability and Host Susceptibility to Immune-Mediated Diseases . . . . 257 Genomics Applications in Discovery—A Tool in the Development of Immunomodulatory Therapeutics . . . . 259 Hazard Identification and Risk Characterization . . . . 262 Conclusions . . . . 263 References . . . . 264 10. Statistical Methods for Functional Analysis of ’Omics Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zoltan Dezso, Andrej Bugrim, Richard J. Brennan, Yuri Nikolsky, and Tatiana Nikolskaya Introduction . . . . 269 Enrichment Analysis of High-Content Data . . . . 270 Analyzing Condition-Specific Networks . . . . 274 Network Measures . . . . 281 References . . . . 285 11. Toxicogenomics Data and Databases . . . . . . . . . . . . . . . . . . . . . . . . . Jennifer Fostel Introduction . . . . 289 Public Toxicogenomics Databases . . . . 290 Types of Data . . . . 293 Archiving ’Omics Data . . . . 296 Cheminformatics Data . . . . 296 Sharing Data . . . . 297 Characteristics of “Ideal” Database . . . . 300 References . . . . 301

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12. Phenotypic Microarray Approaches to the Study of Prokaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Amit Mukherjee, J. Eugene LeClerc, and Thomas A. Cebula Introduction . . . . 305 The Phenotypic Microarray System . . . . 306 Phenotypic Microarray Studies with E. coli K-12 Mutants Deleted for all Two-Component Systems . . . . 307 Deciphering the Function of the b1012 Operon in E. Coli K-12 . . . . 307 Investigation of Phenotypic Changes in Rifampicin-Resistant rpoB Mutants of Bacillus Subtilis . . . . 309 Identifying Phenotypic Markers to Distinguish Foodborne Enteric Pathogens . . . . 310 Concluding Remarks . . . . 313 References . . . . 313

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13. Functional Analysis and Genomics of G-Protein-Coupled Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Kausik Ray and John K. Northup Introduction . . . . 315 GPCR Structural Features and Pharmacology . . . . 316 Receptor Functional Screening Strategies . . . . 324 Functional Genomics and Characterization of Orphan GPCRs . . . . 326 GPCR Dimerization and Allosteric Activation . . . . 329 GPCR Mutations in Human Diseases and Natural Variants . . . . 331 Conclusion . . . . 333 References . . . . 334 14. Nutrigenomics: A Genomic Approach to Human Nutrition . . . . . . Dilip Ghosh and Linda C. Tapsell Introduction . . . . 337 Applied Genomics—Pharmacogenomics and Nutrigenomics in Health Care . . . . 338 Nutrient–Gene Interactions . . . . 339 Paradigm Shift from Health-Care to Self-Care and the Current State of Personalized Nutrition . . . . 340 Public Perceptions and the Future of Nutrigenomics . . . . 341 New Applications of Genomic Technologies . . . . 342 Moving Forward . . . . 342 References . . . . 343

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PART 3: USE OF GENOMICS DATA BY THE REGULATORY AGENCIES 15. Collaborations Lay the Critical Path to Acceptance of Genomics in Drug Safety Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peter G. Lord, Alex Y. Nie, and Michael K. McMillian Introduction . . . . 347 Forums for Collaboration . . . . 348 Microarray Technology Performance . . . . 351 Microarray Applications in Risk Assessment and Regulatory Concerns . . . . 353 Advancing the Science . . . . 355 References . . . . 355 16. Application of Pharmacogenetics and Pharmacogenomics in Drug Development and Regulatory Review . . . . . . . . . . . . . . . . . . . . . . . . . Shiew-Mei Huang, Myong-Jin Kim, Federico Goodsaid, Felix Frueh, and Lawrence J. Lesko Introduction . . . . 358 Drug Metabolizing Enzymes and Transporters . . . . 359 Preapproval Evaluation . . . . 361 Postapproval Evaluation . . . . 362

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Type of Genomic Data that Qualifies as Valid Biomarkers . . . . Drug Interactions . . . . 369 Voluntary Versus Required Submissions . . . . 370 Qualification of Biomarkers . . . . 371 Genetic Tests . . . . 372 Drug/Test Codevelopment . . . . 372 Clinical Study Design . . . . 372 Labeling Implications . . . . 374 Conclusion . . . . 375 References . . . . 376

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17. Potential Use of Microarray Technology at the U.S. Environmental Protection Agency . . . . . . . . . . . . . . . . . . 379 Kenneth M. Haymes, Susan D. Hester, William H. Benson, and J. Thomas McClintock Introduction . . . . 380 Applicability of Genomics Technology in Regulatory Application . . . . 381 The Potential of Microarray Data . . . . 383 Limitations of Microarray Technology . . . . 384 Data Submission Considerations . . . . 385 Research Utilizing Microarray Technology . . . . 388 Potential Integration of Microarray Data Into EPA’S Regulatory Applications . . . . 392 Microarray Applications and Challenges in Risk Assessment . . . . 392 Collaborations to Standardize the Field of Microarray Technology . . . . 393 Outlook on Microarray Technology . . . . 393 References . . . . 394 PART 4: THE PROMISE OF GENOMICS 18. Genomics: The State of the Science . . . . . . . . . . . . . . . . . . . . . . . . . . David B. Carlson and Supratim Choudhuri From Genes to Genomics . . . . 399 Genomic Data, Databases, and Statistical Analysis . . . . 400 Current Applications and Future Promises of Genomics . . . . 401 Genomics and Regulatory Agencies . . . . 401 Genomics—The State of the Science . . . . 402 References . . . . 403 Index . . . .

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Contributors

Jiri Aubrecht U.S.A.

Pfizer Global Research and Development, Groton, Connecticut,

William H. Benson U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, Gulf Breeze, Florida, U.S.A. Tapan K. Bera Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A. Richard J. Brennan Andrej Bugrim

GeneGo, Inc., St. Joseph, Michigan, U.S.A. GeneGo, Inc., St. Joseph, Michigan, U.S.A.

Leigh Ann Burns-Naas Drug Safety Research and Development, Pfizer Global Research and Development, San Diego, California, U.S.A. Ebru Caba Pfizer Global Research and Development, Groton, Connecticut, and Vertex Pharmaceuticals, Cambridge, Massachusetts, U.S.A. David B. Carlson U.S. Food and Drug Administration, CDER, Office of New Drugs, Division of Metabolism and Endocrinology Products, Silver Spring, Maryland, U.S.A. Thomas A. Cebula* U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. Supratim Choudhuri U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, OFAS, Division of Biotechnology and GRAS Notice Review, College Park, Maryland, U.S.A. Zoltan Dezso

GeneGo, Inc., St. Joseph, Michigan, U.S.A.

*Current affiliation: Department of Biology, Johns Hopkins University, Baltimore, Maryland, U.S.A.

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Jennifer Fostel Laboratory of Respiratory Biology, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, U.S.A. Felix Frueh U.S. Food and Drug Administration, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Silver Spring, Maryland, U.S.A. G. Frank Gerberick Central Product Safety, Procter & Gamble Company, Cincinnati, Ohio, U.S.A. Dori R. Germolec Toxicology Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, U.S.A. Dilip Ghosh Australia

Smart Foods Centre, University of Wollongong, New South Wales,

Federico Goodsaid U.S. Food and Drug Administration, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Silver Spring, Maryland, U.S.A. Kenneth M. Haymes U.S. Environmental Protection Agency, Office of Science Coordination and Policy, Office of Prevention, Pesticides, and Toxic Substances, Washington, D.C., U.S.A. Susan D. Hester U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Research Triangle Park, North Carolina, U.S.A. Shiew-Mei Huang U.S. Food and Drug Administration, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Silver Spring, Maryland, U.S.A. Scott A. Jackson U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. Myong-Jin Kim U.S. Food and Drug Administration, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Silver Spring, Maryland, U.S.A. Michael L. Kotewicz U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. Gregory S. Ladics U.S.A.

Crop Genetics, DuPont Company, Wilmington, Delaware,

J. Eugene LeClerc U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A.

Contributors

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Byungkook Lee Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A. Lawrence J. Lesko U.S. Food and Drug Administration, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Silver Spring, Maryland, U.S.A. Robert W. Luebke U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Experimental Toxicology Division, Immunotoxicology Branch, Research Triangle Park, North Carolina, U.S.A. Peter G. Lord Johnson & Johnson Pharmaceutical Research & Development, PreClinical Development, Mechanistic Toxicology, Raritan, New Jersey, U.S.A. Manjula Mahata Department of Medicine and Department of Molecular Genetics, University of California, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A. Sushil K. Mahata Department of Medicine and Department of Molecular Genetics, University of California, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A. Mark K. Mammel U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. J. Thomas McClintock U.S. Environmental Protection Agency, Office of Science Coordination and Policy, Office of Prevention, Pesticides, and Toxic Substances, Washington, D.C., U.S.A. Michael K. McMillian Johnson & Johnson Pharmaceutical Research & Development, PreClinical Development, Mechanistic Toxicology, Raritan, New Jersey, U.S.A. Amit Mukherjee U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. Alex Y. Nie Johnson & Johnson Pharmaceutical Research & Development, PreClinical Development, Mechanistic Toxicology, Raritan, New Jersey, U.S.A. Tatiana Nikolskaya Yuri Nikolsky

GeneGo, Inc., St. Joseph, Michigan, U.S.A.

GeneGo, Inc., St. Joseph, Michigan, U.S.A.

John K. Northup Laboratory of Cellular Biology, National Institute on Deafness and Other Communication Disorders (NIDCD), National Institutes of Health, Rockville, Maryland, U.S.A. Daniel T. O’Connor Department of Medicine and Department of Molecular Genetics, University of California, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A.

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Isha Patel U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. Stephen B. Pruett Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, U.S.A. Kausik Ray Laboratory of Cellular Biology, National Institute on Deafness and Other Communication Disorders (NIDCD), National Institutes of Health, Rockville, Maryland, U.S.A. Juan L. Rodriguez Flores Department of Medicine and Department of Molecular Genetics, University of California, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A. Cindy A. Ryan Ohio, U.S.A.

Central Product Safety, Procter & Gamble Company, Cincinnati,

Pei-an Betty Shih Departments of Medicine and Pharmacology, Center for Human Genetics and Genomics, University of California at San Diego, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A. Jannavi R. Srinivasan U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Division of Biotechnology and GRAS Notice Review, College Park, Maryland, U.S.A. Linda C. Tapsell Australia

Smart Foods Centre, University of Wollongong, New South Wales,

Alison Vickers Allergan Inc., Investigative Sciences, Drug Safety Evaluation, Irvine, California, U.S.A. Berran Yucesoy Toxicology and Molecular Biology Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia, U.S.A.

Introduction

This book is an attempt to present to the reader a thorough overview of the progress of genomics to its current level. The book has four parts. Part 1 contains chapters that capture the fundamentals of gene and genome structure, function, and evolution, as well as a discussion of our understanding of epigenetic regulation of genome expression. Part 1 also includes a chapter that discusses various genomic technology platforms. Part 2 contains chapters on the applications of genomics in drug discovery and development, safety evaluation, food safety, nutritional genomics, genomic database maintenance, genomic database mining, and genomic data analysis. Part 3 contains three chapters on how regulatory agencies like FDA and EPA are collaborating with various industries and using genomic data in their safety evaluation. Part 4 contains one chapter (Chapter 18) that provides an editorial view of the state of genomics and briefly discusses how articles in Parts 2 and 3 fit into the editors’ perception of the current status of the science of genomics. The following description is a brief outline of the chapters in Parts 2 and 3. In Part 2, Vickers (Chapter 5) gives an overview of various in vitro models and discusses how toxicogenomics may be used to advance translational research. Vickers concludes that the major impacts of the new technologies include: (1) identifying the underlying molecular and biochemical mechanism(s) of specific adverse effects to better characterize and evaluate drug safety; (2) identifying specific target organ injury biomarkers that are predictive (or diagnostic) of clinically relevant pathology (specific example provided); and (3) improved comparison of differences in species sensitivity and susceptibility to drug-induced toxicity to better define and predict variability of human responses. Bera and Lee (Chapter 6) provide a glimpse of the power of genomics data mining in the identification and validation of disease-associated candidate gene targets. They provide a simple blueprint for mining genome information and identifying putative prostate and breast cancer–related genes. They targeted their search of the human EST database to identify sequences of previously unknown putative prostate and breast cancer– specific genes and then using a variety of techniques verified the expression of these genes in cancer cells. Caba and Aubrecht (Chapter 7) discuss how the identification and risk assessment of non-DNA-reactive genotoxic compounds that show non-linear, thresholded dose–response relationship may be difficult based on the current in vitro tests. They use examples to emphasize that understanding the molecular mechanism of genotoxicity and carcinogenicity using functional genomics approaches will facilitate improved risk assessment of both genotoxic and nongenotoxic carcinogens. Shih and colleagues (Chapter 8) review an extensive array of genomic approaches that have been used to characterize the genetic contributions to hypertension. They describe evidence for the genetic complexity

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of hypertension and discuss the difficulties in determining the heritability of this complex disease. Germolec and colleagues (Chapter 9) provide a detailed discussion of how gene expression data is used by immunotoxicologists. Using various examples, they discuss how microarrays coupled with confirmatory RT-PCR and other mechanistic biochemistry and molecular biology tools are used to identify immune system–related genes, gene sets, and pathways that are altered in response to xenobiotic exposure, such as identification of targeted pathways in immune suppression, screening for dermal sensitizers, understanding the molecular basis of immune-mediated disease, and relating host susceptibility to immune-mediated disease. Dezso and colleagues (Chapter 10) discuss several different types, or levels, of statistical analyses from individual gene expression patterns and gene set enrichment analysis, to gene sets in pre-selected or previously unknown biological networks and sub-networks, allowing identification of “master regulators” and even small but coordinated gene expression changes. Fostel (Chapter 11) describes various publicly available databases, proprietary databases, types of toxicogenomics data, sharing toxicogenomics data, and features of an ideal database. The immense potential of the vast amount of genomic data and the utility of data-sharing for the global community was realized early on and this led to various collaborative efforts between government regulatory agencies, non-governmental science organizations, and industry interests, which solved some of the vexing issues and provided early standards (MIAME) for universal data format and submission. Mukherjee and colleagues (Chapter 12) discuss how high-throughput metabolic profiling of bacterial populations using phenotypic microarray can be used to identify metabolic differences in bacterial populations that are rooted in the genome. The authors discuss the successful use of phenotypic microarray in identifying phenotypic markers in foodborne enteric pathogens (e.g., Escherichia coli O157:H7, Salmonella, and Shigella), including characterizing a virulent mutation in the E. coli strain from a 2006 multistate spinach outbreak in the United States. Ray and Northup (Chapter 13) give an excellent overview of the importance of G-protein signaling in disease. The authors describe a ‘reverse genetics’ approach to identify ligand(s) and biological function of orphan G-proteincoupled receptors. They also note several examples of genetic variability that result in gainof-function and loss-of-function mutants, an area where pharmacogenomic analysis may provide insight into variation in drug sensitivity across populations and among individuals. Ghosh and Tapsell (Chapter 14) discuss the potential of using genomic technologies in nutrition and the future of nutrigenomics. The authors provide examples of nutrient–gene interactions, including nuclear receptor modulation (retinoic acid), epigenetic effects of gene-specific methylation, and the influence of SNPs on folate metabolism and cardiovascular responsiveness to the DASH diet. In Part 3, Lord and colleagues (Chapter 15) describe collaborative efforts between regulatory agencies and the pharmaceutical industry to use genomics to simultaneously improve drug safety assessment and expedite the development of new pharmaceuticals. The authors clearly outline the necessity of continued collaborations between regulatory and regulated communities for the successful use of genomics in drug discovery and development. Huang and colleagues (Chapter 16) describe the current state of affairs for genomic applications in drug development at the US FDA. They note the FDA hopes to use pharmacogenomics to further the FDA mission and that “The FDA has made a commitment and investment in pharmacogenomics with people, time and technology, and with increasing knowledge and the availability of novel tools, the FDA will continue to foster genomics-based research and drug development, supporting translation of the resulting scientific data to clinical practice.” Haymes and colleagues (Chapter 17) summarize the potential influence of genomics at the EPA. The authors note that environmental moni-

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toring, adverse effect reporting, and risk assessment are obvious areas that can benefit from the use of genomics (e.g., through enhanced understanding of hazards, mechanism of action, and species extrapolations). The authors provide several case-study examples of how genomic data may be used to increase knowledge of environmental agents and to complement EPA submissions. Supratim Choudhuri David B. Carlson

Part 1 Genes and Genomes

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Fundamentals of Structure–Function Analysis of Eukaryotic Protein-Coding Genes Supratim Choudhuri U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, OFAS, Division of Biotechnology and GRAS Notice Review, College Park, Maryland, U.S.A. 1. INTRODUCTION 1.1. The Cistron Concept of Gene 1.2. The Split Gene Concept 1.3. The Split Gene Concept Remains Valid Despite the Fluidity of Eukaryotic Gene Structure 2. STRUCTURE AND FUNCTION OF TYPICAL EUKARYOTIC PROTEIN-CODING GENE 2.1. Transcribed Region of the Gene 2.1.1. Sense and Antisense Strand 2.1.2. Cap Site 2.1.3. The 5′ -Untranslated Region 2.1.3.1. 5′-UTR length 2.1.3.2. Introns may be retained in 5′-UTR after RNA processing 2.1.3.3. Role of 5′-UTR in cap-dependent and cap-independent initiation of translation 2.1.3.4. Comparison of eukaryotic and prokaryotic ribosome-binding site (RBS) 2.1.3.5. 5′-UTR and regulation of gene expression 2.1.3.5.1. Regulation of gene expression by 5′-UTR–regulatory protein interactions 2.1.3.5.2. Regulation of gene expression by 5′-UTR riboswitch–nonprotein ligand interactions 2.1.4. Translation Initiation Region 2.1.4.1. The first-AUG rule and its exceptions 2.1.4.2. Cap to first AUG, and first-AUG to second-AUG distance 2.1.4.3. Leaky scanning

5 5 6 6 6 7 8 8 10 10 10 10 11 11 11 13 13 13 13 13

The opinions expressed in this chapter are the author’s own, and they do not necessarily reflect those of the FDA, the DHHS, or the Federal Government.

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2.1.4.4. Reinitiation 2.1.4.5. Non-AUG initiation codons 2.1.4.6. Kozak sequence 2.1.5. The 3′-Untranslated Region and the Poly(A) Tail 2.1.5.1. 3′-UTR length 2.1.5.2. Introns may be retained in 3′-UTR after RNA processing 2.1.5.3. Various roles of 3′-UTR 2.1.5.3.1. Regulation of transcript cleavage and polyadenylation 2.1.5.3.2. Sequences in 3′-UTR determining mRNA stability and regulating gene expression 2.1.5.3.3. Riboswitch in 3′-UTR 2.1.5.3.4. The role of 3′-UTR in mRNA targeting and localization 2.1.6. Interaction Between 5′ - and 3′ -UTRs and the Circular/Closed-Loop Model of mRNA for the Regulation of mRNA Translation and Stability 2.1.7. Exon and Intron Length 2.1.8. Intron Organization, Types, Splicing, and Function 2.1.8.1. Splicing of group I introns, group II introns, and spliceosomal introns 2.1.8.2. Spliceosome composition 2.1.8.3. Splice signals of GT-AG (GU-AG in pre-mRNA) introns 2.1.8.4. Splicing mechanism for GT-AG introns 2.1.8.5. Splicing of AT-AC (AU-AC in pre-mRNA) introns needs a different splicing apparatus 2.1.8.6. Fidelity of splicing 2.1.8.7. Alternative splicing 2.1.8.8. Effect of intron phase on alternative splicing 2.1.8.9. Intronic riboswitch and the regulation of gene expression through regulation of alternative splicing 2.1.8.10. Mutations generating splice sites within introns can activate pseudoexons that are included in the mature mRNA 2.1.9. Silent Mutations in the ORF Do Not Change the Amino Acid Sequence But May Still Alter Protein Activity by Altering Conformation: A New Paradigm Challenging the Traditional Wisdom that Silent Mutations Do Not Affect the Phenotype 2.1.10. Triplet Repeat Expansion in the Gene and Its Effect 2.2. The 5′-Flanking Region of the Gene and Transcription Initiation 2.2.1. The Promoter 2.2.1.1. Core promoter 2.2.1.1.1. TATA box 2.2.1.1.2. Initiator element 2.2.1.1.3. Downstream promoter element 2.2.1.1.4. Formation of the transcription preinitiation complex (PIC) on core promoter and subsequent events

14 14 14 14 15 15 15 15 17 18 19

19 21 21 22 22 23 23 24 24 27 27 28

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31 32 33 34 34 34 35 35 35

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2.2.1.2. Proximal promoter 2.2.1.3. Distal promoter 2.2.1.4. Promoter-like elements in the intron 2.2.2. Enhancers, Silencers, Locus Control Regions, Insulators 2.2.2.1. Enhancers and silencers 2.2.2.2. Locus control region 2.2.2.3. Insulator 2.3. The 3′-Flanking Region of the Gene and Transcription Termination 2.3.1. Transcription Termination in Prokaryotes 2.3.2. Transcription Termination in Eukaryotes 2.3.2.1. Termination of RNA polymerase I-mediated rRNA transcription 2.3.2.2. Termination of RNA polymerase III-mediated tRNA transcription 2.3.2.3. Termination of RNA polymerase II-mediated mRNA transcription 2.3.2.3.1. The antiterminator (or allosteric) model 2.3.2.3.2. The torpedo model 2.3.2.3.3. The unified allosteric—torpedo model 2.3.2.3.4. The revised torpedo model 3. CONCLUDING REMARKS REFERENCES

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1. INTRODUCTION Since the coining of the term “gene” in 1909 by the Danish botanist Wilhelm Johannsen, the concept of gene has evolved. In the beginning, gene was regarded as an abstract entity without any physical/structural connotation. It was more important to naturalists who were concerned with the inheritance of advantageous variations that would provide raw materials for evolution. 1.1. The Cistron Concept of Gene In the early 1950s, Seymour Benzer’s experiments with the rII locus of T4 bacteriophage helped define gene in terms of a unit of function, the “cistron”. The operational definition of cistron depicts it as a continuous stretch of DNA that codes for a polypeptide through the transcription of messenger RNA. Further work by Charles Yanofsky and Harvey Itano on genetic complementation in other experimental models led to the formulation of the “one-cistron-one-polypeptide” hypothesis. The concept of gene–protein colinearity demonstrated independently by Sydney Brenner and Charles Yanofsky and the operon model proposed by Francois Jacob and Jacques Monod in early 1960s were also in agreement with the cistron concept. The operon model explained how the transcription of a cistron could be regulated, whereas the gene–protein colinearity model demonstrated that a mutation in the gene (cistron) resulted in an amino acid change in the protein. Therefore, the model of regulation of cistron (gene) expression through promoter–operator interaction helped unify the structural and functional aspects of the gene into a universal gene concept.

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1.2. The Split Gene Concept This universal gene concept was again revised following independent discoveries published in 1977 by Phillip Sharp and Richard Roberts, followed by a deluge of other similar publications. These discoveries demonstrated that genes do not necessarily exist as continuous DNA stretch but can also exist as interrupted entities, the so-called “split gene concept”. The split gene concept depicts that the coding section of a gene (cistron) is interrupted by noncoding intervening sequences (introns). The gene is transcribed into a long primary transcript called “heterogeneous nuclear RNA” (hnRNA) or “pre-mRNA”, which is complementary to the entire cistron. The pre-mRNA is then processed to form the translatable mRNA. Processing of pre-mRNA involves three events: capping, polyadenylation, and splicing. Capping results in the addition of a “cap” (m7G) to the first base of mRNA at the 5′ end; polyadenylation results in the addition of a long string of adenylate nucleotides (about 200–250 in eukaryotes) to the 3′ end of mRNA; and splicing results in the removal of introns from the mature mRNA. The regions of the gene that are present in pre-mRNA but absent in mature mRNA were termed “introns”, while those that are present in mature mRNA were termed “exons”. The terms exon and intron were coined by Walter Gilbert. Development of the split gene concept did not invalidate the cistron concept; it is still valid for genes lacking introns, such as prokaryotic genes and some eukaryotic genes. The term “cistron” is now often replaced by the term “open reading frame” (ORF). 1.3. The Split Gene Concept Remains Valid Despite the Fluidity of Eukaryotic Gene Structure With an ever-increasing knowledge of gene structure and function, transcriptional, posttranscriptional, translational, and posttranslational processing and regulation, there have been many surprising findings that challenged even the universality of the split gene concept. Some of these findings such as restructured genes, alternative promoters, alternative splicing including splicing of exons flanked by introns of different phases, overlapping and nested genes, mRNA trans-splicing, RNA editing, and protein splicing have further underscored the fluidity of eukaryotic gene structure beyond the exon–intron model. The traditional idea of one-to-one correspondence between gene, mRNA, and polypeptide sequence is no longer a universal theme; it is applicable to many, but not all, eukaryotic genes (1,2). Despite many exceptions to the gene–mRNA–polypeptide sequence relationship, the exon–intron model is still central to understanding the molecular structure and function of eukaryotic genes. This chapter discusses our current understanding of eukaryotic gene structure and function analysis in light of the split gene concept. Regulation of gene expression will not be discussed; readers are referred to a brief review (1) and some recent books (3,4), for in-depth discussion on various aspects of gene regulation. 2. STRUCTURE AND FUNCTION OF TYPICAL EUKARYOTIC PROTEIN-CODING GENE The structure of a typical eukaryotic protein-coding gene is depicted in Fig. 1. There are three main sections: a 5′-flanking region (5′-flank) at the 5′ end of the gene; a transcribed region in the middle; and a 3′-flanking region (3′-flank) at the 3′ end of the gene. The 5′ end of a gene is where its promoter lies, and this always corresponds to the 5′ end of the sense

Eukaryotic Protein-Coding Genes 5′ -Flanking sequence (contains promoter, URS, etc.)

7

Transcribed region

Sense strand of DNA

TATA Box (TATAAA) (-25)

ATG 5′ 3′

STOP

GT

AG

GT

AG

CA

TC

CA

TC

Exon 1

Gene

Intron 1

Exon 2

3′ 5′

Intron 2

Exon 3

Transcription

Transcribed region but not part of exon 3 Part of the nascent transcript (downstream cleavage product) that is degraded during termination

Poly(A) signal AAUAAA AUG

hnRNA

3′ -Flanking sequence (no specific cistermination signals in most proteincoding genes) Poly(A) signal AATAAA

Transcription start site (+1)

5′

GU

Exon 1

GU

Intron 1

3′

STOP

AG

Exon 2

AG

Exon 3

Intron 2

3′

5′ Cleavage/Poly(A) site

hnRNA processing

Poly(A) signal AAUAAA AUG

Mature mRNA

STOP

5′ Cap

Poly(A) tail

-AAA…AAA 3′ 5′ -UTR

ORF

3′ -UTR

Translation

Polypeptide

NH2-

-COOH

Figure 1 The structure of a typical eukaryotic gene. The figure shows the sense strand, template strand, TATA box, cap site (transcription start site), poly(A) signal (AATAAA in DNA and AAUAAA in RNA), splice donor and acceptor sites in the intron (GT…AG in DNA; GU…AG in RNA), as well as pre-mRNA processing. The figure shows that exon 1 and a small part at the 5′ end of the exon 2 is noncoding, and thus, these form the 5′-UTR. The figure also shows that during transcription, the RNA polymerase II runs well beyond the cleavage/poly(A) site before terminating transcription.

strand (discussed below). In the following description, the transcribed region is discussed first, followed by discussions of the 5′- and 3′-flanking regions. [Note: Since the functional sequence of an mRNA is derived from gene, the structure–function relationships of a gene and its mRNA are intertwined. Therefore, the following discussion on structure and function will include both the gene and its product mRNA. The abbreviations for the bases (A, T, G, C) will be used interchangeably to indicate both the bases and the nucleotides. Whenever appropriate, comparison will be made with prokaryotic genes. The word “nucleotide” will be abbreviated as “nt” whenever necessary. To keep the number of references to a minimum, many recent reviews that include the original references and earlier important reviews have been cited.]

2.1. Transcribed Region of the Gene The transcribed region of a gene has three main sections: a 5′-untranslated region (5′-UTR), an amino acid-coding region (also called the open reading frame or ORF), and a 3′-untranslated region (3′-UTR). The expressions 5′- and 3′-UTR are associated with both mRNA and the gene. The 5′-UTR of a gene (and mRNA) refers to the entire sequence from transcription start site (cap site) to the nucleotide before the translation start codon (ATG in the sense

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strand of DNA; AUG in mRNA). Similarly, the 3′-UTR of a gene (and mRNA) refers to the entire sequence beginning after the translation stop codon (TAG/TGA/TAA in the sense strand of DNA; UAG/UGA/UAA in mRNA) to the nucleotide before the poly(A) tail (Fig. 1). Thus, 5′- and 3′-UTRs of a gene include all noncoding exons, noncoding parts of exons, and sometimes introns. 2.1.1. Sense and Antisense Strand For any given gene, one of the two DNA strands is transcribed. The transcribed strand is called “template” or “antisense strand”. The other strand that is not transcribed is called “sense strand” for two reasons: first, the sequence of bases in the nontranscribed strand is the same as that of mRNA (except for T in DNA and U in RNA) so that the sequence of codons in mRNA is reflected in the base sequence of the nontranscribed strand; second, the 5′→3′ polarity of the nontranscribed strand is the same as that of mRNA. All genes that reside in the same chromosomal DNA may not be transcribed from the same DNA strand. For some genes, one strand may serve as the template, while for other genes, the other strand may serve as the template. Because transcription always proceeds in a 5′→3′ direction, and because the template DNA strand and the RNA that is synthesized on it are antiparallel, the location of the promoter automatically determines which of the two DNA strands can serve as template for transcription.

2.1.2. Cap Site The transcription start site, that is, the first DNA base that is transcribed, is known as the “cap site” and designated + 1. The base just preceding the cap site is denoted −1. In most eukaryotic genes, transcription usually begins with a purine (A or G, mostly an A). The expression cap site applies to both the first DNA base that is transcribed and the first base of mRNA. As a result of pre-mRNA processing, the final mRNA has a 7-methyl GTP, called the “cap”, at the 5′ end through a unique 5′-5′ triphosphate linkage (Fig. 2). This is in contrast to the 5′-3′ (phosphodiester) linkage found between the nucleotides of nucleic acids. The cap is designated as m7Gppp and abbreviated as m7G or m7G. The classic m7G cap structure is called “cap0”, which is present in all eukaryotic mRNAs. Cap0 is added in a three-step reaction: first, the terminal phosphate (γ-phosphate) is hydrolyzed by RNA 5′ triphosphatase from the triphosphate of the first nucleotide of pre-mRNA, thereby leaving a diphosphate; second, RNA guanylyltransferase catalyzes the fusion of a GMP moiety from GTP to this diphosphate of the first nucleotide via a 5′-5′ linkage, thus restoring the terminal triphosphate; finally, RNA (guanine-7)-methyltransferase methylates the N7 position of the guanine. In mammals, the triphosphatase activity lies at the N-terminal end, and the guanylyltransferase activity lies at the C-terminal end of the same polypeptide, but in yeast they are catalyzed by separate enzymes. The capping enzymes are targeted to pre-mRNA by binding to phosphorylated carboxy-terminal domain (CTD) of RNA polymerase II (RNA pol II or pol II). The CTD consists of multiple repeats of an evolutionarily conserved heptapeptide with the consensus sequence Tyr-Ser-Pro-Thr-Ser-Pro-Ser (YSPTSPS). The number of repeats ranges from 26 in yeast to 52 in mammals. Five out of seven amino acids in the consensus motif are phosphate acceptors, and phosphorylation is a major posttranslational modification of the CTD in vivo. The inability of the transcripts generated by RNA polymerase I and III to undergo processing is thought to be due to the lack of CTD in these enzymes (5).

Eukaryotic Protein-Coding Genes

9

Cap0

O

7-Methylguanine N

H2N

5¢ CH2

O– HN

P

N O

CH3

5¢-5¢ triphosphate linkage

O

N

O

O P

O–

O

Methyl group at N7position

O–

Target for methylation (N6 position)

NH2

O P

N

O

N

Cap1 O

N

5¢ CH2

N Nucleotide1 ("A")

O

O

N O O

P

NH

OCH3 N

CH2

O

O–

O

P

NH2

Nucleotide 2 ("G")

O

O

N

Cap2

OCH3 O–

O

2¢-O-methylated ribose

To RNA chain

Figure 2 Various cap structures of eukaryotic mRNAs. In lower eukaryotes, the capping process only creates cap0. In higher eukaryotes, the capping process may create cap1 and cap2 by additional modifications, such as 2′-O-methylation of the ribose of nucleotides 1 and 2 of the pre-mRNA, and an additional N6-methylation of the first base of the pre-mRNA, if the first base is an adenine.

In lower eukaryotes, the capping process only creates cap0. In higher eukaryotes, it may involve additional modifications, such as 2′-O-methylation of the ribose. The 2′-O-methylation of the ribose of nucleotides 1 and 2 creates what is called “cap1” and “cap2”, respectively. There is an additional N6-methylation of the first base of premRNA, if it is an adenine (Fig. 2). The cap is added cotranscriptionally; cap0 is added when the RNA is 20–30 nt long. An interesting situation is encountered in parasitic trypanosomes. All mRNA molecules in parasitic trypanosomes are chimeras of two unrelated sequences that are encoded by different genes. The first 39-nt sequence at the 5′ end is common to all mRNAs and is

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referred to as “spliced leader” or “miniexon”. The spliced leader is derived from the 5′ end of a small nuclear RNA (snRNA), which is 120–140 nt long, and it is spliced with the 5′ end of the protein-coding exons in primary transcripts. For trans-splicing of spliced leader with the coding exon, the spliced leader has to be capped. Capping occurs by the addition of m7G followed by 2′-O-methylation of the first four nucleotides (A-A-C-U). The resultant cap (m7Gppp-Am-p-Am-p-Cm-p-Um) is known as “cap4”, and it is essential for trans-splicing to occur (1). All four modified bases of the cap4 structure are part of the 39-nt spliced leader. The cap performs two major functions: (i) it prevents premature degradation of mRNA, thereby increasing mRNA stability; and (ii) it also helps in the initiation of translation, a process called “cap-dependent initiation” (discussed below). In addition, recent findings indicate that the cap also plays a crucial role in microRNA (miRNA)-mediated translational silencing (6). This is discussed in Section 2.1.6. 2.1.3. The 5′ -Untranslated Region For most genes, the first one or two exons are fully or partially noncoding, that is, they do not code for any amino acids in the polypeptide. Sometimes, more noncoding exons can be found in a gene. For example, in mouse oatp2 (organic anion transporting polypeptide 2) gene, the first three exons are fully noncoding, whereas exon 4 is partially noncoding (7). These noncoding exons or noncoding parts of exons at 5′ end of the gene form the 5′-UTR of mature mRNA. Similarly, noncoding exons or noncoding parts of exons at 3′ end of the gene form the 3′-UTR of mature mRNA. 2.1.3.1. 5′ -UTR length A comprehensive analysis of several thousand 5′- and 3′-UTR sequences of various mRNAs and genes from a number of vertebrate, invertebrate, and fungal species revealed that the average length of 5′-UTR is about one-third to one-fifth of that of 3′-UTR, particularly in vertebrates (8). The average 5′-UTR length was found to be 210 nt in humans, 186 nt in rodents, 126 nt in birds, 221 nt in invertebrates, and 134 nt in fungi; the maximum 5′-UTR length was found to be 2,803 nt in humans, 1,786 nt in rodents, 620 nt in birds, 4,498 nt in invertebrates, and 1,088 nt in fungi; whereas the minimum 5′-UTR length was found to vary between 12 nt and 20 nt, with a median length of 16 nt. 2.1.3.2. Introns may be retained in 5′ -UTR after RNA processing Most mature mRNAs are devoid of introns, but a fraction of them contain introns either due to faulty splicing or due to regulated posttranscriptional processing. Sometimes, intron retention in 5′-UTR may increase or decrease mRNA translatability. For example, the retention of intron 1 in proinsulin mRNA in chick embryos decreases its translatability. The generation of such intron-containing proinsulin mRNA is developmentally regulated (9). In both plants and animals, there are many examples of intron retention in mature mRNA as a result of regulated posttranscriptional processing. 2.1.3.3. Role of 5′ -UTR in cap-dependent and cap-independent initiation of translation The 5′-UTR has an important role in translation. During initiation of translation in eukaryotes, the 40S ribosomal subunit, initiator methionyl-tRNA (tRNAmet), several eukaryotic initiation factors (eIFs), and GTP form the 43S complex. Independently, the mRNA becomes associated with some other eIFs. One such eIF, the eIF4F, consists of three subunits: eIF4A (helicase), eIF4E (cap-binding protein), and eIF4G (scaffolding protein).

Eukaryotic Protein-Coding Genes

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The 43S complex binds the mRNA–eIF complex to form the 48S complex, also known as “initiation complex”. In the formation of the initiation complex, the binding of eIF4E subunit with the cap (m7G) is a crucial step because this interaction stabilizes eIF4F, which then recruits the 43S complex at the cap site with the help of eIF4G subunit. The initiation complex starts scanning the mRNA sequence in a 5′→3′ direction to find (usually) the first available AUG codon. This “scanning model” of initiation of translation is called “cap-dependent initiation”. Once the correct AUG is identified, eIF5 recruits the 60S ribosomal subunit to the initiation complex, forming the 80S complex that begins translation. Translation can also begin by “cap-independent initiation”. In this process, specific internal sequence in the 5′-UTR of mRNA is used to recruit ribosomes directly. These sequences are called “internal ribosomal entry sites” (IRESs). Some viral RNAs and cellular mRNAs have IRES in the 5′-UTR. Studies on the structure and the function of IRES elements in picornavirus RNA showed that IRES involves a large sequence (several hundred nucleotides), and IRES function requires the formation of stem-loop structures which are stabilized by various IRES-binding proteins (10,11). Noncanonical translation factors, also known as “IRES-binding trans-acting factors” (ITAFs), by virtue of their cell- and tissue-specific distribution, may determine viral translation, propagation, and pathogenesis. IRES elements differ in primary sequence and nucleotide length (˜130–460 nt), and they display different secondary structures, the only exception being a Y-shaped structure which is shared by most of them (12). Cap-independent initiation of translation of viral RNAs helps viruses to suppress cap-dependent translation of host mRNAs and use the host’s translation machinery for translating viral RNAs. In eukaryotes, many cellular mRNAs that encode proteins critical during cell stress, apoptosis, and cell cycle can be translated using IRES elements when cap-dependent translation is compromised. Under cellular stress including severe DNA damage, cap-dependent translation is globally suppressed through decapping of translatable mRNAs. Under these circumstances, these “survival” mRNAs are preferentially translated using IRES and cap-independent initiation of translation. 2.1.3.4. Comparison of eukaryotic and prokaryotic ribosome-binding site (RBS) In eukaryotes, the ribosome binding and the initiation of translation are facilitated by a combination of the cap and the Kozak sequence (discussed later), and by the interaction between cap and poly(A) tail (discussed in Section 2.1.6). In prokaryotes, the ribosome-binding for translation occurs at a specific RBS. The RBS is a short sequence (5′-AGGAGGU-3′) located 3–10 nt upstream of translation start codon and is also known as “Shine–Dalgarno sequence”. In prokaryotic polycistronic mRNA, the internal ORFs may lack RBS because the ribosomes keep translating the downstream ORFs. In such cases, only the 5′-terminal ORF possesses RBS. 2.1.3.5. 5′ -UTR and regulation of gene expression The 5′-UTR can regulate gene expression by interacting with either regulatory proteins or regulatory nonprotein ligands, as discussed below. 2.1.3.5.1. Regulation of gene expression by 5′ -UTR—regulatory protein interactions A role of the 5′-UTR in controlling gene expression through direct interactions with proteins is well illustrated by the study of iron response element (IRE). IREs are RNA stem-loop structures in the 5′-UTR of certain mRNAs whose expressions are regulated by

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iron; the conserved loop domain sequence is 5′-CAGUGX-3′, where X is usually U or C but can also be an A. Five base pairs 5′ of the loop is a bulge nucleotide region that may vary in size depending on the IRE. Many proteins involved in iron metabolism are regulated by IRE through its interaction with the IRE-binding protein (IRE-BP). There are two IRE-BPs: IRP1 and IRP2. They show equal capability of mediating translational repression by binding to IREs in vitro (13). Therefore, for simplicity, the model of translational repression is discussed in the following text using the general terminology IRE-BP. The IRE–IRE-BP interactions differentially affect translation depending on whether the IRE is located at the 5′-UTR or 3′-UTR (Fig. 3). For example, ferritin is an intracellular iron-binding protein that prevents the accumulation of toxic levels of iron (Fe2+) in cells. When intracellular iron levels are high, IRE-BP binds iron (Fe), and the IRE-BP–Fe complex cannot bind to IRE in the 5′-UTR of ferritin mRNA. This allows ferritin mRNA to be translated to ferritin, which then sequesters excess iron. When intracellular iron levels are low, the IRE-BP does not bind iron, but it binds to IRE and prevents translation of the ferritin mRNA (Fig. 3A). The IRE-BP has a dual role in the cell depending on the availability of iron. The IRE-BP is a homolog of the mitochondrial aconitase enzyme and is identical to the cytosolic form of aconitase (14). The activity of aconitase depends on a [4Fe–4S] cluster in its active site. Under conditions of high cellular iron, the IRE-BP has an intact [4Fe–4S] cluster, high aconitase activity, and no RNA-binding activity. In contrast, under conditions of low cellular iron, the IRE-BP has a disrupted Fe–S cluster, no aconitase activity, but has high RNA-binding activity. Low cytosolic iron

High cytosolic iron Ferritin

A IRE-BP

IRE-BP without Fe IRE-BP

IRE-BP without Fe IRE (in the 5′ -UTR) binds to the IRE and inhibits translation of the ferritin mRNA

IRE-BP with Fe IRE-BP with Fe cannot bind to the IRE, translation of the ferritin mRNA occurs

Fe

No translation

5′ -UTR

B

ORF

Translation

3′ -UTR

IRE-BP without Fe binds to the IRE and stabilizes the transferrin receptor mRNA which is then increasingly translated; greater amounts of transferrin receptor are produced, and cells take up more iron from transferrin receptor

3′ -UTR

ORF

5′ -UTR

IRE-BP with Fe cannot bind to the IRE; transferrin receptor mRNA is degraded; less transferrin receptor is produced; less iron is taken up by cell

Transferrin receptor

IRE-BP with Fe

IRE-BP without Fe IRE-BP IRE-BP

IRE-BP

IRE-BP

IRE-BP

IRE-BP

IRE (in the 3′-UTR) Translation

Translation

5′ -UTR

ORF

3′ -UTR

5′ -UTR

ORF

3′ -UTR

Figure 3 Interactions between the IRE and the IRE-BP result in different translational outcomes depending on whether the IREs are in the 5′-UTR or in the 3′-UTR. (A) The IRE–IRE-BP interactions in the 5′-UTR of ferritin mRNA downregulate translation, resulting in lower availability of the ferritin protein. (B) The same interactions in the 3′-UTR of transferrin receptor mRNA directly increase the half-life of transferrin receptor mRNA by stabilizing it, resulting in its increased translation and increased availability of the transferrin receptor protein.

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2.1.3.5.2. Regulation of gene expression by 5′ -UTR riboswitch—nonprotein ligand interactions The binding of regulatory proteins to specific nucleic acid sequences (DNA or RNA) is an established paradigm of nucleic acid–protein interaction in controlling gene expression. The discovery of riboswitch in prokaryotes demonstrates that 5′-UTR of certain mRNAs can also directly bind nonprotein ligands to regulate gene expression. Even though the current discussion focuses on eukaryotes, a discussion of riboswitch in prokaryotic mRNA is appropriate here. The part of mRNA that binds a small molecule and acts as a genetic on–off switch for translation in response to environment is called a “riboswitch”. The domain of the riboswitch that actually senses the intracellular concentration of a specific metabolite and binds the metabolite as ligand is termed “aptamer”. Such ligand binding to 5′-UTR presumably masks the RBS, and the translation of mRNA is suppressed. When the ligand concentration falls, the binding is disrupted, and the mRNA is translated into protein. Several bacterial riboswitches were described in 2002 (1). In Escherichia coli, synthesis of the cobalamin transport protein BtuB is regulated by direct and selective binding of coenzyme B12 to a region in the 5′-UTR of BtuB mRNA, called “B12 box”. The binding of coenzyme B12 to B12 box prevents translation of BtuB mRNA. Some other examples include flavin mononucleotide (FMN)-binding mRNA riboswitch, thiamine, or thiamine pyrophosphate (TPP)-binding mRNA riboswitch. All these riboswitches supposedly act by similar mechanisms, that is, by inhibiting translation. Many more riboswitches have been described since the original discovery. The existence of metabolite-binding riboswitches was also reported in eukaryotes, such as the putative TPP-riboswitch sequence motif in Arabidopsis thaliana, bluegrass (Poa secunda), rice (Oryza sativa), and in fungi, such as Neurospora crassa and Fusarium oxysporum (15). Unlike bacterial riboswitches, which are in 5′-UTR, the plant riboswitches described are either in 3′-UTR as in A. thaliana and P. secunda, or in intron as in F. oxysporum, and will be discussed in more detail in the relevant sections.

2.1.4. Translation Initiation Region 2.1.4.1. The first-AUG rule and its exceptions For virtually all eukaryotic genes, the translation initiation codon is AUG in mRNA which corresponds to ATG in the sense strand of DNA. For the vast majority of vertebrate mRNAs, the AUG nearest to the 5′ end of mRNA is used as the initiation codon. If there are multiple in-frame AUG codons toward the 5′ end of mRNA, the “first-AUG” rule may be violated, but there are apparent rules for the violation of first-AUG rule (16), as described below. 2.1.4.2. Cap to first AUG, and first-AUG to second-AUG distance If there are fewer than 10 nucleotides between the cap and the first AUG, ribosomes may initiate translation from both the first and the second AUG codon. If the first and the second AUG codon are located very close to each other, and the first AUG is located in the context of full consensus sequence, ribosomes tend to initiate translation from the first AUG; however, even a small departure from the full consensus sequence allows the second AUG to be utilized for initiation (16,17). 2.1.4.3. Leaky scanning If the first AUG is not surrounded by a favorable sequence context (e.g., Kozak sequence), the ribosomes may skip the first AUG and initiate from the second AUG, a phenomenon

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called “leaky scanning”. Multiple upstream AUGs that are in-frame with the true AUG can support low-level translation initiated from more than one AUG through leaky scanning (18). Thus, leaky scanning can help translate two overlapping ORFs. 2.1.4.4. Reinitiation Translation from the true initiation codon can also occur through another phenomenon called “reinitiation”. When an upstream AUG, that is, upstream from the true initiation codon, lies in a favorable sequence context and is in-frame with a termination codon which is also upstream of the true initiation codon, a small upstream ORF is created in the 5′-UTR; ribosomes can translate this small upstream ORF, then resume scanning and reinitiate translation of the second ORF from the true initiation codon. 2.1.4.5. Non-AUG initiation codons In eukaryotes, AUG is the universal intiation codon in mRNA for nuclear genes, but there are exceptions. For example, in Saccharomyces cerevisiae NFS1 gene, when the first AUG (AUG1) was replaced by the triplet UUG by site-directed mutagenesis, and the product was translated in vitro using yeast lysate, initiation of translation from the second AUG (AUG2) was not increased as expected. Instead, the newly generated UUG was used as the initiation codon for translational initiation, albeit with decreased efficiency (19). The authors concluded that translation initiation at AUG2 would have led to a nonfunctional protein, and the AUG1→UUG mutation would have been lethal. In prokaryotes, AUG is the translation initiation codon in most cases, but alternative initiation codons, such as GUG, UUG, and ACG, are also used (20). 2.1.4.6. Kozak sequence An important sequence relevant for translation initiation and identification of the correct AUG codon by translation initiation complex is called “Kozak sequence” (after its discoverer Marilyn Kozak). The original functional Kozak sequence was described as 5′-GCCRCCAUGG-3′ (where R is a purine, which in most vertebrate mRNAs is an A) (16,21,22). Site-directed mutagenesis experiments confirmed the contribution of every nucleotide in positions –1 to –6, as well as the G in position + 4. A shorter and more effective version (5′-ACCAUGG-3′) of the original Kozak sequence was also described later (23). In the original analysis of 153 mRNAs, later extended to 699 mRNAs (24), it was found that most authentic initiator codons are preceded by a purine (mostly an A) in position –3 and a G in position + 4 with respect to the A (+ 1) of AUG, and most nonfunctional AUGs have a pyrimidine in –3 position. The observed asymmetry suggests that purines in positions –3 and + 4 may facilitate recognition of the AUG codon during the formation of translation initiation complex. Mutagenesis studies support this hypothesis because mutations in positions –3 and + 4 have the strongest influence on translational efficiency. Thus, an initiator codon can be designated “strong” or “weak” by considering only those two positions (24). Some mRNAs contain the consensus Kozak sequence, while many others contain some variant of it. 2.1.5. The 3′ -Untranslated Region and the Poly(A) Tail The noncoding exons or noncoding parts of exons at the 3′ end of a gene form the 3′-UTR of mature mRNA. The last exon is usually the longest, and the major part of it is usually

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noncoding. The poly(A) tail in eukaryotes usually contains between 200 and 250 “A” residues. 2.1.5.1. 3′ -UTR length As mentioned above, the average length of 3′-UTR is about three to five times that of 5′-UTR, particularly in vertebrates (8). In the analysis, the average 3′-UTR length was found to be 1,028 nt in humans, 607 nt in rodents, 652 nt in birds, 445 nt in invertebrates, and 237 nt in fungi; the maximum 3′-UTR length was found to be 8,555 nt in humans, 3,354 nt in rodents, 3,990 nt in birds, 9,142 nt in invertebrates, and 1,142 nt in fungi; whereas the minimum 3′-UTR length was found to vary between 13 and 37 nt, with a median length of 21 nt. 2.1.5.2. Introns may be retained in 3′ -UTR after RNA processing Most mature mRNAs are devoid of introns, but sometimes an intron may be retained in the 3′-UTR of mature mRNA, and it may have regulatory functions. For example, migration stimulatory factor (MSF) is a truncated oncofetal isoform of fibronectin. It is expressed in fetal fibroblasts, keratinocytes, vascular endothelial cells, and also in mammary carcinoma cells, tumor-associated stromal fibroblasts, and endothelial cells. Two types of MSF mRNAs have been detected: a shorter 2.1-kb transcript and a longer 5.9-kb transcript, which differ only in the length of their 3′-UTRs. Both transcripts have 1,941 nt of coding sequence derived from the first 12 exons of the Fn1 gene, and a contiguous intron-derived 30-nt coding sequence. In the smaller transcript, the intron-derived 30-nt coding sequence is followed by a 165-nt intron-derived 3′-UTR. This makes a total of 195-nt intron-derived sequence in the smaller transcript (25). This intron-derived 3′-UTR also provides the polyadenylation signal. The smaller transcript is transported to the cytoplasm and eventually secreted, while the larger transcript is retained in the nucleus. 2.1.5.3. Various roles of 3′ -UTR The 3′-UTR plays important functions, such as regulation of transcript cleavage and polyadenylation, determination of transcript stability, regulation of mRNA targeting and localization, and regulation of translation. Recent evidence suggests that 3′-UTR also plays an important role in the regulation of the nuclear export of mRNA. 2.1.5.3.1. Regulation of transcript cleavage and polyadenylation Regulation of transcript cleavage and polyadenylation is driven by the interplay of various signal sequences in 3′-UTR and their interactions with various protein factors, as discussed below. I. Transcript cleavage and polyadenylation signal sequences in 3′ -UTR: The 3′-end processing of pre-mRNA is regulated by sequences in 3′-UTR. Among these, the polyadenylation signal sequence AAUAAA in mRNA is located 10–30 nt upstream of the transcript cleavage site, which is also the polyadenylation or poly(A) site. The polyadenylation signal sequence is highly conserved; the only functional variant known is AUUAAA (26). A second sequence, known as the “downstream element” (DSE), is usually located within the first 30 nt of the cleavage site. Due to the abundance of uracil and guanine residues in these motifs, DSEs are usually referred to as “U- or G/U-rich elements”. The cleavage site also shows a nucleotide bias in the order A > U > C >> G (27). Frequently, a 5′-CA-3′ dinucleotide is found at the cleavage site. A third element UGUA is often present in one or more copies at variable distance upstream of the cleavage site and is recognized by

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cleavage factor I (CFI; discussed below) (28). The spacing of AAUAAA and DSE, together with local sequence preference (e.g., base composition), is thought to determine the precise cleavage site. II. Cleavage and polyadenylation factors necessary for 3′ -end processing: Some crucial protein factors necessary for cleavage and polyadenylation have been identified and characterized, such as the cleavage and polyadenylation specificity factor (CPSF), cleavage stimulation factor (CstF), cleavage factor I and II (CFI and CFII), poly(A) polymerase, and poly(A) binding protein (PABP). These proteins have multiple subunits except for poly(A) polymerase and PABP, and are sufficient for 3′-end cleavage and polyadenylation in vitro (26). There are two types of PABPs in higher eukaryotes: (i) nuclear PABPs that regulate the polyadenylation of mRNAs in the nucleus and (ii) several types of cytoplasmic PABPs that regulate mRNA translation and stability in the cytoplasm, presumably by regulating polyadenylation. Yeast has one PABP that does both the jobs. The nuclear PABPs have one RNA recognition motif (RRM), whereas the cytoplasmic PABPs have four RRMs (29). III. Mechanism of cleavage and polyadenylation: During 3′-end processing in which the cleavage and the polyadenylation are tightly coupled, a cleavage and polyadenylation complex forms that includes various protein factors and the RNA substrate. The complex forms in a cooperative manner in which the CPSF and CstF first bind the RNA independently but cooperatively; CPSF binds the poly(A) signal AAUAAA and CstF binds the DSE. This is followed by the binding of other factors. Cleavage of the 3′ end is followed by polyadenylation, which requires both poly(A) polymerase and PABP. Addition of the A-residues to the 3′ end is carried out by poly(A) polymerase. Proper activity of the poly(A) polymerase depends on its interaction with CPSF. This is because the CPSF stabilizes poly(A) polymerase on the RNA template, thereby significantly increasing its processivity. Without interaction with CPSF, the poly(A) polymerase is marginally active and acts in a distributive mode, that is, it polymerizes a few nucleotides and detaches from the RNA template. Interaction with CPSF makes it processive, and it can complete the addition of the entire poly(A) tail in one catalytic cycle. The PABP can bind to the growing poly(A) tail when it is about 11 nt long, and multiple molecules bind to the same poly(A) tract covering (thus protecting) about 27 nucleotides. In mammals, the poly(A) tail contains about 200–250 A-residues, while in yeast, it contains about 70–90 A-residues. When the growing poly(A) tail reaches a critical length, poly(A) polymerase falls off the template presumably because its stabilizing interaction with CPSF is disrupted beyond this length. PABP-binding stabilizes the mRNA, and it also promotes the 5′–3′-end interactions after the mRNA exits the nucleus and enters into cytoplasm (5,26,29). The only known exception to the polyadenylation of mature mRNAs is histone mRNAs. IV. Alternative polyadenylation as a means of differential gene regulation: An mRNA may contain more than one polyadenylation signal in its 3′-UTR. Because the 3′-UTR is closely associated with the stability of mRNA, alternative polyadenylation can produce mRNAs with very different stability. Alternative polyadenylation has been reported in both viruses and eukaryotes. Alternatively polyadenylated transcripts can be observed in different tissues or different developmental stages where the mRNA half-lives can markedly vary. Alternatively polyadenylated transcripts may also be produced in response to toxic environmental stimuli. Many alternatively polyadenylated transcripts have been reported in the literature, but not all of them have been characterized (1).

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V. Integration of transcription and 3′ -end processing: The current model of integrated transcription-mRNA processing depicts that during transcription preinitiation phase, the CPSF first interacts with transcription factor IID (TFIID), then it gets transferred to the CTD of pol II. During the elongation phase, CPSF rides on the CTD as pol II transcribes the template. As soon as the poly(A) signal sequence (AAUAAA) is generated on the elongating RNA, CPSF is transferred to the poly(A) signal sequence. This is followed by cleavage and polyadenylation of the RNA. Proof for this model came from several direct and indirect lines of evidence: (i) copurification of CPSF with TFIID subunits; (ii) observation that in the preinitiation complex (PIC) CPSF remains associated with TFIID, but after the start of transcription it is associated with CTD; (iii) observation that the deletion of CTD impairs cleavage and polyadenylation; and (iv) observation that transcription termination is dependent on the presence of a functional poly(A) signal sequence (5,26). 2.1.5.3.2. Sequences in 3′ -UTR determining mRNA stability and regulating gene expression The half-lives of eukaryotic mRNAs may vary widely from ˜5 min (e.g., mRNAs of early response genes, many mRNAs in yeast) to several hours (e.g., β-globin mRNA, human dystrophin mRNA, both with half-lives of ˜16 hr). Various exo- and endoribonucleases have been found to degrade RNAs. Degradation of mRNA may begin from either end. Degradation that begins from the 3′ end is initiated by shortening of the poly(A) tail; degradation of such deadenylated mRNA can be carried out by exosome. Exosome is a complex of about ten 3′→5′ exonucleases that are involved in the degradation as well as precise processing of many types of RNA in both nucleus and cytoplasm. Degradation may also be initiated at the 5′ end after the removal of the poly(A) tail. The enzyme Xrn1 (exoribonuclease 1) is thought to be the primary 5′→3′ exoribonuclease in yeast and other eukaryotes; it degrades mRNA in a 5′→3′ direction following removal of the poly(A) tail and the cap. Xrn1 is mainly cytoplasmic but is also found in nucleus. Proteins that bind to the cis-acting elements in 3′-UTR affect the degradation process from both ends. The existence of an AU-rich sequence in 3′-UTR of many mRNAs was identified by analyzing various cDNA sequences (30). The authors identified a consensus sequence (TTATTTAT; hence, UUAUUUAU in mRNA) in 3′-UTR of both human and mouse TNF mRNAs, as well as in mRNAs encoding human lymphotoxin, human colony-stimulating factor, human and mouse interleukin 1, human and rat fibronectin, and most of the sequenced human and mouse interferons. The role of this AU-rich sequence in determining mRNA stability was also experimentally demonstrated (31). The authors introduced a 51-bp-long AT sequence from a human lymphokine gene (GM-CSF ) into the 3′-UTR of rabbit β-globin gene. As a result, the β-globin mRNA, which is otherwise stable, became highly unstable in vivo. Since then, the role of the AU-rich element (ARE) in determining transcript stability has been well established. The minimal sequence motif required to increase the turnover of chimeric mRNAs was shown to be a nonamer UUAUUUA(U/A)(U/A) (32). It is important to note that although the ARE and the U-rich regions have been associated with increased transcript turnover, no unique or consensus sequence has been precisely defined yet. The regulatory function of AREs is driven by its interaction with various ARE-binding proteins (ARE-BPs), such as AUF1, TTP, and proteins of Hu family (see 33 and references therein for various ARE-BPs). In contrast to the AU-rich sequence that increases transcript turnover, there are other interactions in 3′-UTR that stabilize mRNAs, thereby increasing their half-lives. For example, IREs located in 3′-UTR of some mRNA can regulate mRNA half-life and

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expression, such as in transferrin receptor. Transferrin is a major iron transport protein in the plasma (as opposed to ferritin, which is the major iron-binding protein in the cell). Transferrin binds to transferrin receptor located on the plasma membrane. When iron levels are low, transferrin receptor mRNAs are stabilized by the binding of IRE-BP with IRE in its 3′-UTR so that more transferrin receptors can be translated and cells can take up more iron from transferrin. Unlike ferritin mRNA which has a single IRE in 5′-UTR, transferrin receptor mRNA has multiple IREs in 3′-UTR. When iron levels are high, IRE-BP binds iron and cannot interact with IREs; as a result, transferrin receptor mRNA is degraded at a faster rate to prevent over-uptake (thus, over-accumulation) of iron in the cell (Fig. 3B). Therefore, interactions between IRE and IRE-BP may have very different translational outcomes depending on whether the interaction is in 5′-UTR or in 3′-UTR. The IRE– IRE-BP interaction in 5′-UTR of ferritin mRNA directly downregulates its translation, resulting in lower availability of ferritin protein. The same interaction in 3′-UTR of transferrin receptor mRNA directly increases its half-life, resulting in its increased translation and increased availability of transferrin receptor protein. A similar example is a 106-nt GC-rich region in the 3′-UTR of glucose transporter GLUT-1 mRNA. Normally, this region facilitates the decay of GLUT-1 mRNA. Following TNF treatment, this region binds a protein complex; as a result, GLUT-1 mRNA is protected from degradation and stabilized (34). Another important example of the role of 3′-UTR in translational regulation is that of selenoproteins. Selenocysteine (Sec), the 21st amino acid, is incorporated into many proteins in eukaryotes. Selenocysteine-containing proteins are called “selenoproteins”. In humans, at least 25 selenoproteins are known. The tRNA for selenocysteine is charged with serine and then enzymatically selenylated to produce selenocysteinyl-tRNA (Sec-tRNA[Ser]Sec). During selenoprotein translation, Sec-tRNA is brought into the ribosome by a dedicated specific elongation factor known as “eEFsec”. Selenocysteine is incorporated into the protein at UGA codon, which is normally a stop codon, and this process is called “recoding”. Recoding requires Sec-tRNA[Ser]Sec, protein factors, and one cis-acting sequence. The cis-acting sequence, termed “selenocysteine insertion sequence” (SECIS), forms a specific secondary structure in the 3′-UTR of selenoprotein mRNAs. The SECIS element is about 60-nt-long stem-loop structure, which carries large or small apical loops, and binds the SECIS-binding protein 2 (SBP2). 2.1.5.3.3. Riboswitch in 3′ -UTR Riboswitch in 3′-UTR may also contribute to mRNA stability, translatability, and processing. Sudarsan et al. (15) described a putative thiamine biosynthesis gene of A. thaliana, which carries an RNA element in its 3′-UTR that conforms to the consensus TPP-binding domain. The authors obtained the TPP-riboswitch element of A. thaliana by in vitro transcription and subjected this RNA to ligand-binding analyses. They found that it binds TPP with an apparent dissociation constant (KD) of ˜50 nM, which is comparable to that of E. coli TPP-riboswitch. The authors hypothesized that TPP-riboswitch in plants may modulate the processing and stability of riboswitch-containing mRNAs. Recently, the 2.9 Å crystal structure of A. thaliana TPP-riboswitch in complex with TPP was described (35). Similar RNA elements are found in rice (O. sativa) and bluegrass (P. secunda). In plants, riboswitch domains have been found in 3′-UTR, and in fungi, they have been reported in introns. The intronic riboswitch will be discussed in Section 2.1.8.

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2.1.5.3.4. The role of 3′ -UTR in mRNA targeting and localization Accurate targeting and localization of various mRNAs are necessary for the proper expression of proteins in correct cellular compartments. In some cases, mRNA localization in cells is highly polarized/compartmentalized as in neurons, but in most cases, such polarization is not very clear-cut. In many mRNAs that are targeted to a specific subcellular location, the localization and targeting signals are in 3′-UTR. An example is the localization of Oxa1 mRNA in yeast (S. cerevisiae). The Oxa1 protein (of the Oxa1 protein complex) is a ubiquitous constituent of the inner mitochondrial membrane. The Oxa1 protein is a key component in the biogenesis of the inner mitochondrial membrane because it facilitates the integration of both mitochondrial- and nuclear-encoded proteins from the matrix into the inner mitochondrial membrane. As a consequence, Oxa1 is conserved from bacteria to eukaryotes. In S. cerevisiae, a null mutation in Oxa1 gene leads to complete respiratory deficiency in yeast, but this can be rescued by the human homolog OXA1 protein. Using partial deletion analysis of the 3′-UTR of human OXA1 mRNA, it was demonstrated that the last 137 nt possesses targeting and localization signals, which are recognized by the yeast proteins (36). As a result, human OXA1 mRNA is accurately sorted to the vicinity of yeast mitochondria. Once the OXA1 mRNA is properly localized in the vicinity of mitochondria, OXA1 protein is translated and easily targeted to the inner mitochondrial membrane, thereby rescuing yeast cells from respiratory deficiency. Another example of 3′-UTR-mediated localization of mRNA is the metallothionein (MT ) mRNA. Deletion analysis and mutagenesis studies demonstrated that nt 45–86 in 3′-UTR are required for the targeting of MT-I mRNA to its perinuclear localization (37). 2.1.6. Interaction Between 5′ - and 3′ -UTRs and the Circular/Closed-Loop Model of mRNA for the Regulation of mRNA Translation and Stability Translation of mRNAs is frequently correlated with a change in the length of the poly(A) tail. That is, an increase in poly(A) tail length is usually correlated with an increase in mRNA translatability. The classical view of mRNA as an extended thread-like structure passing through the ribosomes during translation is being increasingly challenged. Recent evidence indicates the existence of a possible circular/closed-loop form of mRNA during translation. The observation by Christensen et al. (38) provided the first clue that mRNA may exist as a circular/closed-loop structure during translation. The authors observed an abundance of circular polysomes on the rough endoplasmic reticulum in somatotropes and mammotropes in the rat anterior pituitary. Somatotropes secrete growth hormone, and mammotropes secrete prolactin. It was observed that about 80% of the membrane-bound polysomes in these two cell types have an approximately circular shape, and they contain an average of seven ribosomes, which is appropriate for the translation of the polypeptide hormones produced by these cells. Interaction between the 5′ and the 3′ end of mRNA was later experimentally demonstrated (39). The authors provided evidence that the 5′-UTR contributed to rapid mRNA degradation mediated by the 3′ ARE. They generated two different types of v/c-fos (viral/ cellular-fos) hybrid transcripts, both containing the 3′ AU-rich destabilizing sequence but differing in the length of their 5′-UTRs. The full-length transcripts were found to be unstable, whereas the shorter transcripts lacking part of the 5′-UTR were stable. When the full-length v/c-fos 5′-UTR fragment was inserted into α-globin gene harboring the AU-rich destabilizing element, the splicing and degradation patterns of the heterologous transcript

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paralleled that of the v/c-fos recombinants. These observations strongly suggested that an intact 5′ end of mRNA is required for the activity of the AU-rich destabilizing element at 3′ end to cause rapid mRNA degradation. In other words, interactions between the two ends of mRNA play an important role in determining mRNA stability. It is now becoming increasingly clear that poly(A) tail and m7G cap act synergistically to stimulate translation. The PABP has been shown to interact with one or more eIFs, such as eIF4G, eIF4E, and eIF4B, particularly eIF4G (Fig. 4A). By reconstituting the eIF4E–eIF4G–PABP complex in vitro, it was demonstrated using atomic force microscopy that this complex could circularize capped, polyadenylated RNA (40). PABP is also known to interact with eukaryotic release factor 3 (eRF3). Several examples of the formation of closed-loop conformation of translatable mRNAs through 5′- and 3′-UTR interactions have been described in the literature (41–43). The formation of a closed-loop conformation also aids in determining mRNA stability by bringing together factors that either facilitate or prevent mRNA degradation. An increasing body of evidence has provided support for this model of translatable mRNAs; these include the demonstration of physical interactions between decapping enzyme Dcp1 and PABP (44) and interaction between the m7G cap and deadenylases. Many major

A

Cap 5′ -UTR

eIF4E eIF4G PABP

eIF4B ORF

eIF4A

A

AA

AA

AA

Poly(A) tail 3′ -UTR

B miRNP complex Cap 5′ -UTR

miRNP/Ago–Cap interaction and translational silencing Cap

eIF4E

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eIF4G PABP

eIF4B ORF

eIF4A

3′ -UTR

A

AA AA AA

Poly(A) tail

Ago miRNA

AAA AAAA

Poly(A) tail

Figure 4 Interaction between the 5′-UTR and the 3′-UTR and the circular/closed-loop model of mRNA for the regulation of mRNA translation and stability. The poly(A) tail and the m7G cap act synergistically to stimulate translation. (A) The poly(A) binding protein (PABP) presumably interacts with one or more eIFs, such as eIF4G, eIF4E, eIF4B, particularly eIF4G, and the eIF– PABP complex plays an important role in translation. (B) The cap also plays an important role in miRNA-mediated translational silencing. Once the miRNP is positioned on the 3′-UTR, the Ago protein disrupts the eIF–elF4G–PABP complex, which is required for efficient translational initiation. The cap now interacts with the Ago proteins of the miRNP complex, which effectively inhibits cap-dependent translation.

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deadenylases [the exonuclease Ccr4, Caf1, Pop2, Pan2, Pan3 in yeast, and the poly(A)specific ribonuclease (PARN), CCR4, PAN in vertebrates] were shown to be stimulated by the presence of m7G cap. During deadenylation, when the poly(A) tail length falls below ˜12–15 residues, PABP is released (42,43,45,46). Therefore, the maintenance of a closed-loop conformation of mRNAs during degradation may be necessary so that PABP, various 3′-UTR binding factors, decapping enzymes, and the deadenylases can all interact. Interactions between the two ends of mRNA during degradation may also help flag the decaying mRNAs so that they are prevented from competing with intact translatable mRNAs. The closed-loop conformation of mRNA also helps in RNA splicing. Components that bind to the cap cooperate with the splicing machinery to facilitate recognition of the first exon, and components of the polyadenylation machinery interact with the splicing machinery to define the last exon (discussed in Section 2.1.8). As mentioned under “cap site” earlier in this chapter, the cap plays a crucial role in microRNA (miRNA)-mediated translational silencing. This apparently involves a circular/ closed-loop conformation of mRNA. The miRNAs constitute a class of small noncoding RNAs (ncRNAs) that play important roles in gene silencing (discussed in more detail in Chapter 3). Following transcription and processing, the final functional miRNA is ˜22-nt long which interacts with various proteins including members of the Argonaute (Ago) family to form miRNA-protein (miRNP) complex. The miRNA binds to the 3′-UTR of mRNA and thus positions the miRNP on the 3′-UTR. Efficient miRNA-mediated translational silencing requires functional m7G. In the circular/closed-loop conformation of mRNA, once the miRNP is positioned on 3′-UTR, the Ago protein disrupts the eIF4E–eIF4G–PABP complex, which is required for efficient translational initiation. This disrupts the interaction between the cap and eIF4E. The cap now interacts with Ago proteins of the miRNP complex, which effectively inhibits cap-dependent translation of the mRNA (6) (Fig. 4B). 2.1.7. Exon and Intron Length A detailed study on exon and intron length in various eukaryotes demonstrated that the majority of internal exons in vertebrate genes are less than 300 bp with an average length of 135 bp, and exons larger than 800 bp are rare (47). In vitro studies showed that vertebrate internal exons greater than 300 bp have increased probability of faulty splicing (48,49). Likewise, decreasing the size of an internal exon to less than 51 bp also increases the probability of faulty splicing by exon skipping (50). The first (5′) noncoding exon can be as small as 20 bp. The last (3′ ) exon is usually the longest, and can be well over 1 kb depending on the gene. A major part of the last exon is usually noncoding. In general, exon/intron lengths vary across eukaryotic species. In vertebrates, large intron and small exon seem to be the rule, but in lower eukaryotes, the opposite seems to be true (49). Exon definition and intron definition models that explain the apparent fidelity in splice site selection during splicing together predict that exons and introns of a gene cannot be large at the same time; introns can be large if exons are small and exons can be large if introns are small (discussed in Section 2.1.8.6). 2.1.8. Intron Organization, Types, Splicing, and Function Introns or intervening sequences (IVSs) are common in eukaryotic genes of all types. Three main groups of introns are known: group I introns, group II introns, and spliceosomal (nuclear) introns. In addition, there are group III introns, which are similar to group II introns but smaller and have different three-dimensional structure. Sometimes, one or more

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introns can be embedded in an intron. This complex intron is called “ twintron”. Several twintrons have been reported in Euglena and Drosophila. Group I introns are found in rRNA, tRNA, and mRNA of organelles in fungi, plants, and protists; in tRNA and mRNA of bacteria/bacteriophage; in rRNA of protists and fungi; and in mRNA of animal (sea anemone) mitochondria. Group II introns are found in rRNA, tRNA, and mRNA of organelles in fungi, plants, and protists, and in mRNA in bacteria. Group III introns are very short group II-like introns found in mRNA of plastids of euglenoid protists (51). In total, around 1,500 group I and 200 group II introns have been identified (52). The spliceosomal introns are found in the nuclear genomes of all eukaryotes and are therefore most numerous and ubiquitous. The following discussion will focus on the spliceosomal introns. 2.1.8.1. Splicing of group I introns, group II introns, and spliceosomal introns Group I and group II (and also group III) introns are self-splicing. The RNA itself possesses enzyme activity, hence acting as a ribozyme. No proteins are needed for splicing reactions. For splicing, a series of stem-loop structures that can bring the splice sites together are necessary. Group I introns need free guanine nucleotide for splicing, which attacks the 5′-splice site. Following splicing, group I introns are released as linear introns. In contrast, group II introns are spliced by the lariat pathway and are released as lariat structure. In this respect, the splicing of group II introns shows similarities to that of spliceosomal introns. Both group I and group II introns have distinct RNA structures that facilitate their self-splicing activity. They also contain internal ORFs which facilitate their propagation to intronless sites through reverse transcription. 2.1.8.2. Spliceosome composition Introns that are spliced by spliceosomes are called “spliceosomal introns”. Such splicing takes place through the interactions of various protein factors and cis-acting signals in the substrate pre-mRNA. Spliceosomes are composed of many proteins (splicing factors) and small RNA molecules. More than 100 splicing factors with known functions have been identified so far (53). The RNA component contains several small RNA molecules (less than 200 nt) found only in the nucleus, which are called “small nuclear RNA” (snRNA). The snRNAs important for the splicing of GT-AG introns are U1, U2, U4, U5, and U6. The name U-RNA owes its origin to the modified uridine content of these snRNAs. The combination of proteins and snRNAs forms small nuclear ribonucleoprotein (snRNP) or snurp; thus, U1–U6 snRNAs in combination with the proteins form U1–U6 snRNPs. The core snRNPs include a group of seven proteins known as “Sm proteins” (Sm B/B′, D1, D2, D3, E1, F1, and G1), and seven LSm proteins (LSm = Like Sm; LSm2 through eight) (53). Sm proteins were first identified through precipitation from nuclear extracts by anti-Sm antibodies from patients with the autoimmune disorder systemic lupus erythematosus. Whereas Sm proteins are part of U1, U2, U4, and U5 snRNPs, LSm proteins are part of U6 snRNPs. The seven Sm proteins form a seven-member-ring core structure that encircles the pre-mRNA. The Sm core is essential for the function of snRNP particles. There are many more protein splicing factors that are part of specific snRNPs (53). One such important group of proteins necessary for both constitutive and alternative splicing are the SR proteins (serine-arginine-rich proteins). The SR proteins are so named because their C-terminal ends contain a region rich in arginine and serine. The arginine-serine-rich

Eukaryotic Protein-Coding Genes

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region is called the “RS domain”. In addition to the RS domain, the SR proteins also contain one or two RRMs at the N-terminal end. More than a dozen SR proteins are known in humans, such as SF2/ASF, SC35, 9G8, SRp20, SRp30c, SRp40, SRp46, SRp54, SRp55, SRp75, SFRS10, SRm160, and SRm300 (53,54). Another essential splicing factor is U2AF (U2 snRNP auxiliary factor). It is a heterodimer composed of two subunits, U2AF65 and U2AF35, and is essential for splicing. Both subunits of U2AF are SR-related proteins because they contain RS domains. 2.1.8.3. Splice signals of GT-AG (GU-AG in pre-mRNA) introns Certain splice signals facilitate proper identification of intron sequences and their accurate removal. Most introns in a gene have GT at the 5′-splice site (splice donor site) and AG at the 3′-splice site (splice acceptor site), giving rise to the so-called GT-AG rule for introns (GU-AG in pre-mRNA). There are exceptions to GT-AG introns, such as GC-AG introns and AT-AC introns. The following discussion focuses on GT-AG introns. In most eukaryotic genes, the nucleotides surrounding splice donor and acceptor sites show a great degree of conservation. For example, the last two nucleotides of an exon preceding the intronic splice donor site are usually AG, and the nucleotide following the splice donor site is a purine, mostly an A. Likewise, the nucleotide immediately before the splice acceptor site is a pyrimidine, mostly a C. Upstream from the splice acceptor site, the intronic sequence is rich in pyrimidines. The usual nucleotide distribution around the splice sites is as follows: 5′-splice site: 5′-…NNNAGgtannn…-3′ (gt = splice donor site; N and n = any nucleotide in the exon and intron, respectively. According to convention, when exon and intron sequences are written together, exon sequence is written in capital letters, whereas intron sequence is written in small letters). 3′-splice site: 5′-…nnncagNNN…-3′ (ag = splice acceptor site; N and n = any nucleotide in the exon and intron, respectively). In addition to the signals at the splice donor and acceptor sites, there are other elements, such as exonic splicing enhancer (ESE), intronic splicing enhancer (ISE), exonic splicing silencer (ESS), and intronic splicing silencer (ISS). Other important signals are the branch site located 20–50 nt upstream of the acceptor site, and the polypyrimidine tract (y)n located downstream of the branch site. The consensus sequence of the branch site in the gene is (C/T)(T/C)(A/G)A(C/T) [hence (C/U)(U/C)(A/G)A(C/U) in the pre-mRNA], where the A-residue is conserved in all genes. 2.1.8.4. Splicing mechanism for GT-AG introns Following recognition of the exon to be spliced, there are overall three steps in splicing: (i) the 5′ end of the intron is excised and is covalently attached to the A-residue of the branch site through a 5′-2′ phosphodiester linkage (first transesterification reaction); (ii) the 3′ end of the intron is excised and the exons are joined (second transesterification reaction); and (iii) the intron thus released as a lariat structure is degraded. Splicing reactions are initiated by the following interactions: (i) the U1 snRNP binds to the 5′-splice site—base-pairing between U1 snRNA and the pre-mRNA aids in stabilizing this interaction; (ii) the U2AF binds to the polypyrimidine tract so that the larger subunit (U2AF65) is bound to the polypyrimidine tract, and the smaller subunit (U2AF35) is bound to the 3′-splice site; and (iii) the U2 snRNP binds to the branch site with the help of U2AF—base-pairing between U2 snRNA and the pre-mRNA aids in stabilizing this interaction. Before the binding of U2, the branch site is occupied by another splicing factor called “SF1” in mammals and BBP (branch site-binding protein) in yeast; U2 binding

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displaces SF1/BBP from the branch site. The complex of U1 snRNP, U2 snRNP, and U2AF with the pre-mRNA is called “complex A”. In complex A, the base-pairing between U2 snRNA and branch site excludes the A-residue. As a result, the A-residue bulges out and is positioned as the nucleophile for the first transesterification reaction. The presence of a pseudouridine (ψ) in U2 snRNA results in an altered conformation of the U2 snRNA–pre-mRNA duplex; such altered conformation of the duplex places the 2′-OH of the bulged A-residue in a favorable position as the transesterification reaction center. Next, the complex A undergoes rearrangement to bring all three splice signal sites (5′-splice site, 3′-splice site, and branch site) together. This is achieved through the involvement of U4/U5/U6 tri-snRNP particle. The recruitment of U4/U5/U6 tri-snRNP to complex A turns it into “complex B”. In complex B, the pre-mRNA loops upon recruitment of U4/U5/U6 tri-snRNP. This is followed by the release of U1 from the 5′-splice site, which is then occupied by U6. During the initial binding, U5 snRNP binds to the downstream exon. At this stage of complex B, U4 is in contact with U2. Next, U4 is released from the complex allowing U6 to interact with U2 through RNA–RNA base-pairing interactions. This arrangement is called “complex C”, and it brings the 5′-splice site close to the branch site through U6–U2 interaction. At this phase, U5 snRNP is shifted to the intron to be excised. Splicing then proceeds in two steps: first, the intron and the exon are cut apart at the 5′-splice site; the free 5′ end of the intron loops around and is joined to the A-residue of the branch site (first transesterification); second, the free 3′ end of the exon displaces the intron from 3′-splice site, and the two exons are joined together (second transesterification). The second reaction is aided by U5 snRNP, which helps bring the two exons together. The intron is released as a lariat structure and is subsequently degraded, and the snRNPs get recycled. Fig. 5 shows the principles of splicing, and Fig. 6 shows the sequence of binding and release of various U-snRNPs during splicing. 2.1.8.5. Splicing of AT-AC (AU-AC in pre-mRNA) introns needs a different splicing apparatus Both GT-AG and GC-AG introns are processed by the major or lariat pathway, while most AT-AC introns are processed by a different pathway, the minor pathway. The AT-AC introns have unique and highly conserved 5′-splice site and branch site elements. These are recognized by some unique sets of minor snRNAs, such as U11, U12, U4atac, and U6atac, which play roles analogous to that of U1, U2, U4, and U6, respectively. The only snRNA shared by both pathways is U5 (55). 2.1.8.6. Fidelity of splicing The requirement for multiple splice signal sequences for splicing provides one mechanism of ensuring the accuracy of splicing. However, splice site signal sequences are part of vast intronic sequences, and it is possible for an intron to have multiple splice site consensus sequences. Thus, there may still be errors in splice site recognition, which may result in skipping of legitimate splice site or recognition of illegitimate or cryptic splice sites. The other challenge of splicing is joining all exons in proper order without skipping any exons. To ensure the fidelity of splicing, additional mechanisms have evolved. One such mechanism was termed “exon definition” (48). Exon definition proposes that in pre-mRNAs with large introns, the splicing machinery searches for a pair of closely spaced splice sites in an exonic polarity. When such a pair is recognized, the exon is defined by the binding of U1 and U2 snRNPs and their associated splicing factors. The assembly of U2AF

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SR proteins

SR proteins i

n

t

p-G-U

5′

o

n yyyyy

A

ESE 5′ Exon

r

First transesterification reaction

2′ -OH

A-G-p

Branch point

SR proteins

5′ Exon

ESE

3′ Exon

SR proteins i n t r o n

— 3′ -OH

5′

3′

ESE

3′

A-G-p

A G U

ESE

3′ Exon

Second transesterification reaction

AG A G Intron released as U a lariat structure

5′

3′ Two exons spliced in proper order

Figure 5 Essential reactions in the splicing of spliceosomal intron. The splicing reaction involves two stepwise transesterification reactions. The branch site A-residue plays a critical role in the enzymatic reaction. Following recognition of the exon to be spliced, there are three overall steps: (i) the 5′ end of the intron is excised and is covalently attached to the A-residue of the branch site through a 5′-2′ phosphodiester linkage (first transesterification reaction); (ii) the 3′ end of the intron is excised and the exons are joined (second transesterification reaction); and (iii) finally, the intron thus released as a lariat structure is degraded.

at the 3′-splice site is enhanced by the presence of downstream 5′-splice site even though the downstream 5′-splice site will not be spliced by the same spliceosome (56). The exon definition model derives further support from the circular/closed-loop model of mRNA. Components that bind to the 5′ cap cooperate with the splicing machinery to facilitate recognition of the first exon, and components of the polyadenylation machinery interact with the splicing machinery to define the last exon (55). Therefore, the exon definition model stipulates that when the gene is composed of large introns and small exons, sequences within large introns need not be recognized. In contrast, if the gene is composed of small introns and large exons, the mechanism appears to work through intron definition (57), where a pair of splice sites is recognized at the ends of short introns. Together, the exon definition and intron definition models predict that exons and introns of a gene cannot be large at the same time; introns can be large if exons are small and exons can be large if introns are small. Another mechanism that contributes to the fidelity of splicing is the coupling of transcription and splicing. Many splicing factors, such as the SR proteins and snRNPs, ride on the CTD of RNA pol II (58). When a 5′-splice site is recognized in the newly synthesized RNA, these factors are transferred to the RNA. These factors are then ready to interact with

26

Choudhuri U2AF U1

Complex A

U2

Exon 1

Exon 2 U6

Complex B

U5 (bound to exon) U4

U1 Exon 1

U5 Exon 2 E

U2

U1 U6

Complex B Exon 1

U4

U5 Exon 2

U2

U4 Complex C Exon 1

U5 (shifted to intron) U6

U5

Exon 2

U2

Exon 1 U5 U2 U6

Exon 2 U5

Exon 1

Exon 2

Exons spliced

U2

U6 Intron released as a lariat

Figure 6 Sequence of association and dissociation of U-snRNPs with the pre-mRNA during splicing. Splicing reactions are initiated by the following interactions: (i) U1 snRNP binds to the 5′-splice site; (ii) U2AF binds to the polypyrimidine tract so that the larger subunit (U2AF 65) is bound to the polypyrimidine tract and the smaller subunit (U2AF 35) is bound to the 3′-splice site; and (iii) U2 snRNP binds to the branch site with the help of U2AF. Next, this complex undergoes rearrangement to bring all three splice signal sites (5′-splice site, 3′-splice site, and the branch site) together. This is achieved through the involvement of the U4/U5/U6 tri-snRNP particle and looping of the premRNA. This is followed by the release of U1 from the 5′-splice site, which is then occupied by U6. Next, U4 is released from the complex allowing U6 to interact with U2. This arrangement brings the 5′-splice site close to the branch site through U6–U2 interaction. Splicing then proceeds in two steps, as indicated in Fig. 5.

3′-splice site factors which will be transferred to the 3′-splice site as soon as it is synthesized. Thus, the correct 3′-splice site will be recognized before any other similar sites are synthesized by the advancing RNA pol II. Additionally, there are many nonsplice site sequence elements that serve to define exons, such as the ESE, ESS, ISE, and ISS elements mentioned earlier. Each of these elements binds splicing factors that positively or negatively affect spliceosome assembly. The most studied of these splicing factors are the SR proteins. The SR proteins bind ESE elements within exons (Fig. 5), and they interact with the components of the splicing

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machinery, such as U2AF of the upstream intron and U1 snRNP of the downstream intron. Most exons contain ESEs, which are loosely conserved purine-rich sequences that bind one or more SR proteins. The SR proteins seem to be essential in defining the exon. A recent report (59) suggests that the SR proteins and the ESEs also play a crucial role in the maintenance of exon order during splicing by preventing exon skipping. Several lines of evidence point to such a role for the ESEs and the SR proteins. For example, deletion of ESEs from an exon results in the skipping of the exon during splicing. Since the ESEs are SR protein-binding sites, progressive loss of SR protein-binding to the exon seems to be an important factor in exon skipping. Inactivation of the 3′-splice site alone does not result in skipping of the immediately downstream exon even though the distal 3′-splice sites are intact. In order for this exon to be skipped during splicing, further deletion of ESEs in the upstream exon is necessary. Because the disruption of the SR proteins– spliceosomal machinery interactions seems to be responsible for exon skipping, it strongly suggests an important role of the ESE–SR protein complex in maintaining exon order during splicing. However, exceptions to this paradigm also exist. There are reports of exon skipping in vertebrate genes including in humans as a result of splice site mutations. 2.1.8.7. Alternative splicing Alternative splicing is achieved through the combinatorial interplay of cooperative and competitive binding of various splicing factors to various cis-acting positive and negative regulatory signals in the pre-mRNA substrate. Some of these interactions may cause masking of splice sites resulting in exon skipping. The cis-acting sequences are the ESE or ISE and ESS or ISS elements mentioned above. The splicing factors involved are various members of the hnRNP and SR protein families. After being synthesized, the pre-mRNA associates with a family of polypeptides to form densely packed ribonucleoprotein complex known as “heterogeneous nuclear ribonucleoprotein” (hnRNP). There are many different kinds of hnRNPs, such as hnRNPA1, hnRNPF, hnRNPH, and hnRNPI (PTB). A model that explains the experimental results depicts that exon skipping and alternative splicing may be primarily driven by a balance between SR proteins and hnRNPs. Under limiting concentrations of the SR protein called “alternative splicing factor/splicing factor 2” (ASF/SF2), U1 snRNP binds only to functionally stronger splice sites, whereas higher levels of ASF/SF2 promote full occupancy of all 5′-splice sites. Because the binding of U1 snRNP to 5′-splice site is the first step in a series of splicing reactions, lack of U1 snRNP-binding to certain 5′-splice sites can be an important determinant of which exon will be skipped. The action of ASF/SF2 can be antagonized by hnRNPA1, which binds to the exonic silencer elements. Thus, tissue-specific differences in the relative concentrations of splicing factors and hnRNPs may modulate splice site choice resulting in tissue-specific pattern of alternative splicing. The regulation of alternative splicing can also be sex-specific and developmental stage-specific. Sex-lethal (Sxl) protein in Drosophila is produced only in female flies. It induces a female-specific pattern of alternative splicing. The role of Sxl in mediating alternative splicing involves its interaction with the polypyrimidine tract and prevention of U2AF from binding to this site. Like hnRNPA1, Sxl also promotes exonskipping in its own pre-mRNA by binding to both of the flanking introns (60). 2.1.8.8. Effect of intron phase on alternative splicing Whereas the regulation of alternative splicing may be driven by a balance between SR proteins and hnRNPs, the choice of exons that can be skipped during alternative splicing is driven by intron phase, as explained below.

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Spliceosomal introns can be divided into three types based on “phase”, such as phase 0, phase 1, and phase 2 introns. A phase 0 intron does not disrupt a codon; a phase 1 intron disrupts a codon in between the first and second base; and a phase 2 intron disrupts a codon in between the second and third base. An exon flanked by the same-phase introns at both ends is called a “symmetrical exon”, while an exon flanked by different phase introns is called an “asymmetrical exon”. Thus, a symmetrical exon flanked by phase 1 introns at both ends is designated class 1,1, while an asymmetrical exon flanked by phase 1 intron at the 5′ end and phase 2 intron at the 3′ end, for example, is designated class 1,2. Normally, an exon has to be flanked by the same-phase introns in order to be inserted, deleted, or duplicated without altering the rest of the ORF from the 3′-splice site. Hence, legitimate alternative splicing involves symmetrical exons. Alternative splicing involving an entire asymmetrical exon will lead to a frameshift in the ORF beyond the 3′-splice site (Fig. 7A and B). If the phases of introns in a gene are known, then it can be predicted which exons are expected (or not expected) to be alternatively spliced. An unusual phenomenon of alternative splicing involving an exon flanked by introns of two different phases was reported for rat liver-specific organic anion transporter-1 (lst-1/Oatp-4), a xenobiotic and endobiotic transporter (61). The authors cloned the full-length mRNA of lst-1/Oatp-4 as well as two splice variants. One of the splice variants termed rlst-1c is a normal splice variant that has a deletion of the entire exon 7 which is a symmetrical exon (class 1,1). Thus, the generation of rlst-1c obeys the rule of intron phase limitation of alternative splicing. The other splice variant termed rlst-1b is an unusual splice variant because it involves partial deletion of exon 10. Since exon 10 is an asymmetrical exon (class 1,2), deletion of the entire exon 10 would have caused a frameshift in the ORF beyond the 3′-splice site. This phase limitation is overcome by the use of an alternative splice donor site from within exon 10. As a result, 91 bp from the 5′ end of exon 10 is retained in the final mRNA. The final transcript thus produced is constitutively expressed and is functional albeit with somewhat different substrate specificity compared with the full-length transcript (62). Fig. 7A and B explains the intron phase limitation rule during alternative splicing. The regulation of intron phase limitation, however, is not known yet. 2.1.8.9. Intronic riboswitch and the regulation of gene expression through regulation of alternative splicing Exogenous thiamine, added to growth medium, was shown to reduce thiA gene expression in the fungus Aspergillus oryzae (63). The thiA gene is involved in thiamine (vitamin B1) biosynthesis. The authors identified through sequence analysis that the second intron in the 5′-UTR of thiA mRNA contains the TPP aptamer. The TPP aptamer is highly conserved among the fungal thiamine biosynthesis genes. Deletion experiments with constructs containing GUS reporter gene revealed that the TPP aptamer is involved in thiamine-dependent repression of thiA gene expression. The authors also concluded that splicing of the riboswitch-containing intron is crucial to the thiamine-regulated gene expression. The mechanism of the action of intronic TPP-riboswitch was studied in further detail in N. crassa (64). The authors studied genes known to be involved in thiamine metabolism, NMT1, THI4, and a third gene whose function is unknown. When the concentration of thiamine in the growth medium of N. crassa was high, there was a marked increase in one type of mRNA splice variant. In contrast, less thiamine in the growth medium resulted in a marked increase in another type of mRNA splice variant. Translation of these two splice variants produced different protein products. To understand the mechanism, the authors subsequently focused on NMT1 pre-mRNA splicing mechanism.

Phase 0 Intron Asn Asp … Asp Gly

Phase 0 Intron Leu Thr … Val A

Phase 1 Intron la

Thr … Val

Ar

Phase 2 Intron g Arg … Pro Ala

Exon 2

Exon 3 Transcription

Exon 4

Exon 5

Phase 0 Intron Asn Asp … Asp Gly

Phase 0 Intron Leu Thr … Val A

Phase 1 Intron la Thr

Arg … Pro

…

Ala

C AGG…CCU GCC

Val Ar

Phase 2 Intron g Arg

mRNA (No frameshift)

…

Pro Ala

DNA

Asn Asp … Asp Gly

Exon 2

Exon 3

Leu Thr … Val A

Transcription la Thr

…

Exon 4

Val Ar

g Arg

…

Pro Ala

pre-mRNA

Exon 5

Asn Asp … Asp Gly

Arg Pro … Arg Gln

Gln Ala … Pro Cys ...

CAG GC…CCC UGC C..

mRNA (frameshift)

Alternative splicing involving asymmetrical exon

Figure 7 The effect of intron phase on alternative splicing. (A) Exon 2 (AAU GAU…GAC GGA) is a symmetrical exon (class 0,0, i.e., flanked by phase 0 introns). Alternative splicing involving its removal does not cause a frameshift in the ORF except the deletion of Asn-Asp…Asp-Gly amino acids encoded by it. All other amino acids are maintained in proper order. (B) Exon 3 (CUA ACC GUG G) is an asymmetrical exon (class 0,1, i.e., flanked by phase 0 intron at the 5′ end and by phase 1 intron at the 3′ end). Alternative splicing involving its removal causes a frameshift in the ORF. All amino acids beyond the Gly residues are new (underlined by a parenthesis) and not part of the original ORF.

Leu Pro … Met Val

CUU CCC…AUG GUC AAU GAU…GAC GGA CGA CCG …CGU CAG

Exon 3 (class 0,1) spliced out

CUU CCC…AUG GUC gu…ag AAU GAU…GAC GGA gu…ag CUA ACC…GUG G gu…ag CG ACC G…CC GUC AG gu…ag C AGG C…CC CCU GCC

Leu Pro … Met Val

Exon 1

CTT CCC… ATG GTC gt…ag AAT GAT…GAC GGA gt…ag CTA ACC…GTG G gt…ag CG ACC G…CC GTC AG gt…ag C AGG C…CC CCT GCC

Leu Pro … Met Val

B

CG ACC…GUC AG

Ala Thr … Val Arg

Leu Pro … Met Val

Leu Thr … Val

CUU CCC … AUG GUC CUA ACC…GUG G

Leu Pro … Met Val Asn Asp … Asp Gly Leu Thr … Val A la Thr … Val Ar g Arg … Pro Ala preCUU CCC…AUG GUC gu…ag AAU GAU…GAC GGA gu…ag CUA ACC…GUG G gu…ag CG ACC …GUC AG gu…ag C AGG…CCU GCC mRNA Exon 2 (class 0,0) spliced out Alternative splicing involving symmetrical exon

Exon 1

CTT CCC…ATG GTC gt…ag AAT GAT…GAC GGA gt…ag CTA ACC…GTG G gt…ag CG ACC…GTC AG gt…ag C AGG…CCT GCC DNA

Leu Pro … Met Val

A

Eukaryotic Protein-Coding Genes 29

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Choudhuri

On the basis of their data, the authors have proposed a model to explain the mechanism of generation of such alternatively spliced variants. The model is depicted in Fig. 8. In NMT1 pre-mRNA, there is an intron at the 5′ end of NMT1 ORF. This intron contains the TPP aptamer, and upstream from the aptamer are two splice donor sites (5′-splice sites) that are 60 nt apart. The authors noted that part of the TPP aptamer sequence (the P4 and P5 elements), which is required to bind TPP, is also complementary to most of the nucleotides of the second 5′-splice site. When TPP is low in the medium, the second 5′-splice site is sequestered by TPP aptamer and is not available to the splicing machinery. Therefore, the first 5′-splice site (upstream splice donor site) is preferentially used by splicing machinery, and more NMT1 mRNA with functional NMT1 ORF is produced (Fig. 8A). In contrast, when TPP is high in the medium, it binds to TPP aptamer causing allosteric changes to RNA folding. Under such altered conformation of the pre-mRNA, the splicing machinery preferentially uses the second 5′-splice site. This results in the inclusion of a small 5′ end of the intron (bordered) that contains two splice donor sites and an ORF (uORF)

A

Intron

5′ -UTR

NMT1 ORF GU * * GU

NMT1 pre-mRNA

AG

Upstream 5′ -splice site is used 5′ -UTR

Low TPP concentration NMT 1 ORF translation (TPP aptamer has been drawn arbitrarily)

TPP aptamer without the ligand (TPP) blocks the downstream 5′ -splice site

NMT1 ORF NMT1 mRNA NMT1 ORF translated

Putative translation start codons in the intron represented by (∗)

B GU* *

GU

AG

T Upstream 5′ -splice site is not used

NMT1 mRNA

GU* *

High TPP concentration uORF translation (TPP aptamer has been drawn arbitrarily)

NMT1 pre-mRNA TPP aptamer with the bound ligand (TPP) makes the downstream 5′ -splice site available

uORF translated

NMT1 ORF translation repressed

Figure 8 Regulation of alternative splicing and gene expression by intronic riboswitch in eukaryotes. The figure depicts a model to explain the mechanism of generation of alternatively spliced variants of NMT1 mRNA in Neurospora crassa. The intron upstream of the NMT1 ORF contains the TPP aptamer and two splice donor sites that are both located upstream of the aptamer. (A) When the TPP concentration is low in the medium, the second 5′-splice site (downstream splice donor site) is presumably sequestered by the aptamer and is not available to the splicing machinery. Therefore, the first 5′-splice site (upstream splice donor site) is preferentially used by the splicing machinery, and more NMT1 mRNA with functional NMT1 ORF is produced. (B) In contrast, when the TPP concentration is high in the medium, the binding of TPP to the TPP aptamer causes allosteric changes to RNA folding. Under such altered conformation of the pre-mRNA, the splicing machinery preferentially uses the second 5′-splice site. This results in the inclusion of a small upstream fragment of the intron in the final NMT1 mRNA. This intronic fragment contains two AUG codons and creates a short upstream ORF (uORF). This uORF competes with the NMT1 ORF for translation, resulting in the repression of NMT1 expression.

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upstream fragment of the intron in the final NMT1 mRNA. This intronic fragment contains two AUG codons and creates a short upstream ORF (uORF). This uORF competes with NMT1 ORF for translation resulting in the repression of NMT1 expression (Fig. 8B). 2.1.8.10. Mutations generating splice sites within introns can activate pseudoexons that are included in the mature mRNA Despite all the safeguards to ensure the fidelity of splicing, errors do happen. An increasing number of evidence show that single-nucleotide changes in a gene in places other than splice sites can also affect the splicing process. Potential exonic sequences (pseudoexons) are frequently found within introns, but they are normally not included in mature mRNA because they are not recognized by the splicing machinery. Mutations creating or activating a strong splice site may result in the inclusion of pseudoexon in processed mRNA. Such pseudoexon insertion in various mature mRNAs has been reported in connection with human diseases. For example, an A→G transition within intron 6 of the human growth hormone receptor (GHR) gene results in the insertion of a pseudoexon in mature GHR mRNA. This pseudoexon encodes 36 additional amino acids in the protein. This aberrant protein is associated with atypical growth hormone insensitivity (65). Pseudoexon activation in Duchenne muscular dystrophy (DMD) gene apparently also causes Becker muscular dystrophy (BMD) (66). The authors characterized two intronic mutations in the DMD gene from two unrelated BMD patients. One of these pseudoexons was a 58-bp insertion between exon 62 and exon 63. The activation of this pseudoexon was due to an A→G transition in intron 62, which resulted in the creation of a high quality splice donor site. The other pseudoexon was created by an A→G transition in intron 25, and this resulted in the inclusion of a 95-bp segment in mature mRNA. The insertion of both these pseudoexons leads to truncation of the ORF and premature translational termination. The authors observed some degree of normal splicing in both patients, and they concluded that the detection of these residual full-length transcripts is consistent with the clinical presentation and dystrophin analyses. 2.1.9. Silent Mutations in the ORF Do Not Change the Amino Acid Sequence But May Still Alter Protein Activity by Altering Conformation: A New Paradigm Challenging the Traditional Wisdom that Silent Mutations Do Not Affect the Phenotype Mutations in the gene that do not change the amino acid sequence and function of the protein are known as “silent” or “synonymous mutations”. The traditional wisdom in molecular biology is that silent mutations do not affect the phenotype because they do not alter protein function. If the frequency of a mutation in the population is 1% or greater, it is called a “single-nucleotide polymorphism” (SNP). An exception to this well-accepted paradigm was reported from Michael Gottesman’s laboratory (67). They reported that a synonymous SNP in exon 26 (C3435T; C at position 3435 replaced by T) in human Multidrug Resistance 1 (MDR1) gene resulted in the production of protein with an altered ability to interact with drugs and inhibitors. This SNP (C3435T) had been reported before to be associated with altered P-glycoprotein (P-gp) activity and also with reduced functionality. The authors found that the mutation did not change the levels of mRNA or protein expression, but the expressed protein had altered conformation. MDR1 gene in humans is expressed in small intestine, liver, kidney and brain, and its protein product is known as “P-glycoprotein (P-gp)”. P-gp is an efflux transporter responsible for the tissue elimination of a variety of xenobiotics including pharmacologically distinct drugs used in cancer chemotherapy, hypertension, allergy, infection, immunosuppression,

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neurology, and inflammation (68). The finding of such a profound effect of silent mutation on the protein activity provides a very important clue about the importance of silent mutations in determining individual susceptibilities to drugs and other xenobiotics even when the protein sequence is not altered. The authors hypothesized that the presence of a rare codon affected the timing of cotranslational folding and insertion of the P-gp into the membrane, thereby altering the structure of substrate and inhibitor interaction sites. Thus, a general mechanism how silent mutations might affect protein function may be due to the creation of rare codons that alter the rate of translation. This could be due to limiting concentration of the specific tRNA that would recognize the rare codon, resulting in transient ribosomal pause during polypeptide elongation. Since protein folding is a cotranslational process, altered rate and timing of synthesis may affect the cotranslational folding, leading to altered conformation and altered binding ability for substrates and inhibitors. 2.1.10. Triplet Repeat Expansion in the Gene and Its Effect Even though DNA replication is a high fidelity process, mistakes do occur. Long stretches of repeat sequences sometimes form internal loops during replication. This phenomenon causes “replication slippage” or “slipped strand mispairing” (Fig. 9). Long stretches of

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-

-

A CG G C A A Slippage and mispairing C G G C A A C G 5¢-CAG-CAG-CAG CAG-CAG-CAG-CAG Direction of new strand synthesis 3¢-GTC-GTC-GTC-GTC-GTC-GTC-GTC-5¢ Parent strand (template) with seven triplet repeats 5¢-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-3¢ 3¢-GTC-GTC-GTC-GTC-GTC-GTC-GTC-5¢

This strand replicates normally

This strand with expanded number of triplet repeats (12) will serve as the template during next replication cycle leading to an increase in the number of triplet repeats in the gene

Newly synthesized strand with 12 triplet repeats

5¢-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-CAG-3¢ 3¢-GTC-GTC-GTC-GTC-GTC-GTC-GTC-GTC-GTC-GTC-GTC-GTC-5¢

Further replication slippage can cause further triplet expansion. When hnRNA is synthesized on these templates and processed to mRNA, the mRNA will contain these expanded triplet repeats

Figure 9 Mechanism of replication slippage. The slippage and mispairing of the newly synthesized DNA strand during replication results in an increase in the number of repeats. During the next replication cycle, when the strand with the increased number of repeats serves as a template strand, it results in a permanent increase in the repeat length of the gene.

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5′

5′

3′

5′

3′ Repeat expanded in this strand

Misalignment of two DNA strands from two nonsister chromatids and unequal crossing-over

At the molecular level = CAG-CAG-CAG-CAG-CAG

At the molecular level

N = Any nucleotide

5′ -CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-3′

X

5′ -CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-3′

5′ -CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-3′ 5′ -CAG-CAG-CAG-CAG-CAG-NNNNN-CAG-CAG-CAG-CAG-CAG-3′

Strand with expanded repeats

Figure 10 Mechanism of unequal crossing-over. The existence of long repeat sequences may sometimes result in misalignment of the two DNA strands from two nonsister chromatids involved in crossing-over. As a result, one strand gains part of the repeat sequence from the other strand. Subsequent replication process fixes this increased length in the gene, as described above.

repeat sequences may also cause unequal crossing-over (Fig. 10). In both situations, there is a net increase in the repeat sequence in one strand. Replication slippage seems to be more important for repeat expansion. The expandable repeat sequences can be located in exons, introns, or even in untranslated regions. Altered number of trinucleotide repeats (triplet repeats) are a unique class of mutations associated with a number of heritable genetic disorders in humans. For example, there are 6–35 CAG repeats in human HD gene. When the number of repeats starts increasing, the result is Huntington’s disease (HD). The number of repeats found in various patients of HD varies from 36 to 121. In general, the greater the number of repeats, the earlier the onset, and the more severe the disease. There are many other trinucleotide repeat disorders, such as fragile X syndrome, myotonic dystrophy, and spinal and bulbar muscular atrophy. Transcriptional disruption appears to be associated with such triplet repeat-mediated disease. In HD, the mutated huntingtin protein contains expanded glutamine-rich domain (because CAG codes for glutamine) compared with that of its normal counterpart, and this glutamine expansion in huntingtin protein disrupts transcriptional activation by Sp1 and TAFII130. In the nucleus, the mutant protein accumulates and associates with Sp1 and TAFII130. This prevents the binding of Sp1 to the GC box and also disrupts the interaction between Sp1 and TAFII130. Such disruption presumably leads to specific and pleiotropic dysregulation of transcription. A similar mechanism may be responsible for other diseases such as dentatorubralpallidoluysian atrophy (DRPLA), a neurodegenerative disease similar to HD (69). 2.2. The 5′ -Flanking Region of the Gene and Transcription Initiation The 5′-flanking region of the gene is the long region upstream of the transcription start site (+ 1). It contains most of the cis-acting transcription regulatory sequence elements, the most fundamental of which is the promoter.

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2.2.1. The Promoter The operon model proposed by Jacob and Monod introduced the concept of promoter as an integral part of gene/transcriptional unit to which RNA polymerase binds. With the advances in molecular cloning techniques, various promoter elements (sequences) have been analyzed and defined through deletion analysis. The most salient features of the promoter elements are that they control the initiation of transcription of a specific gene, and their locations are relatively fixed with respect to the transcription start site. Since transcription proceeds in a 5′→3′ direction, and newly synthesized RNA is in antiparallel orientation with the template DNA strand, the location of the promoter automatically determines which of the two DNA strands of a gene will be transcribed. Various regions of the promoter have been termed the “core (or basal) promoter”, “proximal promoter”, and “distal promoter”, based on their functions and distance from the transcription start site. Sometimes, transcriptional regulatory sequences located upstream of the core promoter are collectively referred to as “upstream promoter elements”. In addition to the promoter, other cis-acting DNA sequences that regulate gene expression include enhancer, silencer, locus control region (LCR), and insulator elements. In general, the transcription start site is determined by TATA box and initiator element, or in the case of TATA-less promoters, by initiator element and downstream promoter element, all located within core promoter. Core promoter elements facilitate the formation of transcription preinitiation complex (PIC) close to the transcription start site. The PIC is composed of RNA polymerase and general (basal) transcription factors (GTFs); the GTFs are so named because of their universal requirement in transcription initiation by RNA pol II. However, the efficiency and the specificity with which a promoter is recognized depend on several other sequences (and their interacting proteins) that are located further upstream in the proximal promoter. These proximal promoter elements bind a different group of transcription factors called “transcriptional activators”. These activator proteins interact with the basal apparatus. Some of the activators can be tissue-specific and are called “specific transcription factors or tissue-specific activators”. 2.2.1.1. Core promoter The core promoter is the minimal contiguous sequence that is sufficient to direct the accurate initiation of transcription by RNA pol II. It is the binding site for RNA pol II and the GTFs. Typically, the core promoter is about 35 bp long, and can extend either upstream or downstream of the transcription start site (–35 to + 35). The core promoter may contain two or more of the following sequence motifs: TATA box, initiator (Inr) element, and downstream promotor element (DPE) (70). 2.2.1.1.1. TATA box The TATA box is responsible for the accurate positioning of RNA pol II. It has a consensus sequence TATAAA, which has been highly conserved through evolution. In most eukaryotic genes, TATA box is located approximately 25 nt upstream (usually between –30 and –25) from the transcription start site. In yeast (S. cerevisiae), the location of TATA box may range from 40 to 100 nt upstream of the transcription start site (70). Certain variations of the consensus TATA sequence, such as TATATA, are also present in many genes. The TATA box is usually followed by a few more (˜3) A-residues. This AT-rich sequence facilitates easy melting of the DNA duplex during transcription.

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2.2.1.1.2. Initiator element Even though the TATA box plays an important role in transcription, nearly half of all known gene promoters lack a TATA box (TATA-less promoters). Accurate positioning of RNA pol II in these genes is thought to be mediated by Inr element and DPE. The Inr encompasses transcription start site between –2 and + 5, and it can be found in both TATA-containing and TATA-less promoters. Its consensus sequence in mammals is PyPy-+1-N-T/A-Py-Py, where Py is pyrimidine, + 1 is transcription start site, and N is any nucleotide. However, Inr is not absolutely conserved, and there are variations of this consensus motif. For example, the promoter of rat liver-specific organic anion transporter 1 (rlst-1/Oatp-4/Slc21a10) is a TATA-less promoter, and it contains a noncanonical Inr motif in which the half-site (+ 1-N-T/A-Py) matches that of the consensus sequence. A similar situation was described for mouse lst-1 and human MRP3 (ABCC3) genes (71). The Inr is apparently recognized by various transcription factors, such as TFIID, TFII-I, YY1, as well as RNA pol II itself (see 70 and references therein). The Inr element and the TATA box, when present in the core promoter, usually act synergistically. 2.2.1.1.3. Downstream promoter element The DPE was originally identified as a downstream basal promoter element in some Drosophila TATA-less promoters, and could bind purified Drosophila TFIID (72). In TATAless core promoters, TFIID was found to bind cooperatively to Inr and DPE, and mutation in either Inr or DPE abolished TFIID-binding to the core promoter (72). Thus, DPE-dependent promoters also need Inr for activity. Studies in Drosophila demonstrated that Inr-to-DPE spacing is strictly maintained, and a single-nucleotide alteration in the spacing reduces core promoter activity and TFIID binding (73). The consensus DPE spans between + 28 and + 32 with respect to transcription start site, and the estimated consensus sequence is (A/G)+28G(A/T) (C/T)(G/A/C)+32 (70). The DPE is conserved from Drosophila to humans and is mostly found in TATA-less promoters; however, it has been most well studied in Drosophila (73). 2.2.1.1.4. Formation of the transcription preinitiation complex (PIC) on core promoter and subsequent events Transcription PIC that forms on the core promoter is composed of RNA pol II and multiple GTFs, such as TFIIA, IIB, IID, IIE, IIF, and IIH (TFII = transcription factor for RNA pol II). The sequence of events that goes on the core promoter during transcription initiation can be summarized as follows: PIC formation→Promoter melting→Transcription initiation→Promoter clearance→Transcript elongation→Transcription termination. I. PIC formation: TFIID is the first transcription factor to bind TATA box. This is followed by the binding of other factors in the following order: TFIIA, TFIIB, RNA pol II–TFIIF, TFIIE, and THIIH. TFIID, which is the first one to bind TATA box, is a multiprotein complex composed of TATA-binding protein (TBP) and about 13 other proteins called “TBP-associated factors” (TAFs or TAFIIs). The TBP actually binds the TATA box and it makes contact with the DNA like a saddle so that the inner surface is bound to DNA, and the larger outer surface is available to make contacts with other proteins. The TBP is a universal component of the PIC for all three types of RNA polymerases. Recent evidence suggests that trimethylated lysine 4 of histone H3 (H3K4me3), which is regarded as the hallmark of transcriptionally active promoters, provides a direct binding site for TFIID. The exact protein component of TFIID involved in this interaction is TAF3 (or TAFII3; TBP-associated factor 3), which binds H3K4me3 via its plant

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homeodomain (PHD) finger. Acetylation of H3K9 and H3K14 potentiates TFIID interaction with H3K4me3 (74). TFIIA binds to the upstream part of TFIID, whereas TFIIB binds to TFIIB recognition element (BRE). The BRE is located immediately upstream of TATA box and has a consensus sequence (G/C)(G/C)(G/A)CGCC. Both TFIIA and TFIIB directly interact with TBP. The recruitment of TFIIB is accompanied by dramatic conformational rearrangements; as a result, TFIIB contacts DNA both upstream and downstream of TATA box. After binding, TFIIB provides the platform for recruiting RNA pol II, thereby acting as a bridge between RNA pol II and the rest of the PIC. The interaction between TFIIB and RNA pol II is partially mediated by TFIIF, which is complexed with RNA pol II. TFIIF is also important for elongation. The recruitment of RNA pol II is followed by the binding of TFIIE and TFIIH. Thus, TFIIH is the last factor to bind the complex. II. Promoter melting, transcription initiation, promoter clearance, and elongation: TFIIH, which is the last factor to bind the preinitiation complex, is also involved in DNA repair (nucleotide excision repair). TFIIH has both helicase and kinase activity; it has nine subunits, of which the XPB subunit is a 3′→5′ helicase, the XPD subunit is a 5′→3′ helicase, and the cdk7 subunit is a kinase. TFIIH recruitment to the PIC is facilitated by TFIIE. Using ATP, the helicase activity of TFIIH disrupts base-pairing and helps in promoter melting. This creates an open complex due to the separation of two DNA strands so that RNA pol II and other factors gain access to the bases of the template strand and starts polymerizing a few complementary nucleotides (initiation). Initiation is defined by the formation of the first phosphodiester bond. The PIC can now be called the “initiation complex”. Following initiation, the kinase activity of TFIIH phosphorylates the CTD of RNA pol II (75). Phosphorylation of CTD makes RNA pol II elongation-competent. Without CTD phosphorylation, RNA pol II does not get promoter clearance, and it cycles in the stuttering synthesis mode in which it polymerizes about 8–10 ribonucleotides, aborts the elongation, and then starts the cycle all over again. Once transcription is initiated, CTD phosphorylation by TFIIH helps RNA pol II to undergo promoter clearance by dissociating from all the transcription factors in the initiation complex except TFIIF, escape the promoter, and begin productive synthesis of the RNA chain (elongation). After the beginning of elongation, TFIIH dissociates from pol II. Once RNA pol II begins elongation, TFIIF remains associated with it. Also, various elongation factors, such as SII (TFIIS), SIII (elongin), P-TEFb, and ELL, become associated with pol II and enhance its elongation activity by suppressing its pausing and arrest on the DNA template. In addition, the advancing polymerase also becomes associated with many other factors, such as FACT that facilitate transcription through chromatin. After transcription termination, a TFIIF-associating C-terminal domain phosphatase (FCP1) dephosphorylates the C-terminal domain of RNA pol II, making it available for new PIC. 2.2.1.2. Proximal promoter The proximal promoter is about 250 bp long and can extend either upstream or downstream of the transcription start site (–250 to + 250) (70). In the literature, however, sequences far upstream of –250 have also been referred to as “proximal promoter sequences” (e.g., see 76,77). The proximal promoter typically binds activators/specific transcription factors. Two transcription activating elements found in proximal promoter are the CAAT box and the GC box. The CAAT box binds transcription factor NF-I (nuclear factor I, also known as NF-Y, CTF, and CBF); it is located about 75 nt upstream from the transcription start site and has a consensus sequence GG(T/C)CAATCT. The GC box has a consensus

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sequence GGGCGG; it is located about 90 nt upstream from the transcription start site, and it binds the transcription factor Sp1 (specificity protein 1). The CAAT box and the GC box operate as enhancer elements because they can activate transcription when placed in either orientation in the proximal promoter (hence, orientation-independent). 2.2.1.3. Distal promoter The term “distal promoter” is used to mean sequences that are further upstream of the proximal promoter elements. There are many examples of cooperation between proximal and distal promoter elements in regulating transcription. The proximal and the distal promoters of genes that show tissue-specific expression contain many more unique transcription regulatory elements that are recognized by tissue-specific transcription factors. 2.2.1.4. Promoter-like elements in the intron Sometimes, promoter-like elements in the intron are indispensable in upregulating transcription. For example, maize ubiquitin 1 promoter cassette includes the promoter, exon 1, and intron 1 (promoter–exon 1–intron 1 cassette) (78). Using different combinations of the elements of the cassette, the authors made various constructs and determined the relative effectiveness of each combination in driving the expression of GUS reporter gene. The authors demonstrated that the complete promoter cassette (promoter– exon 1–intron 1) was the most effective in driving maximal GUS expression. The combination of 40 bp of exon1 and complete intron 1 could also drive GUS expression without the promoter. However, the promoter and the 44 bp of exon 1 combination alone (without intron 1) could not drive GUS expression. In other words, the intronic sequence elements were essential for transcription. The authors suggested that the intron has promoter-like features, such as a TATA-like sequence, a canonical CAAT box, and opaque-2 binding motif that may play a role in the promotion of GUS activity without the promoter. 2.2.2. Enhancers, Silencers, Locus Control Regions, Insulators Many transcription regulatory elements can be located several kilobases away from the gene, either upstream or downstream from transcription start site. Some of these elements stimulate transcription, such as enhancers and LCRs, while others act as transcriptional suppressors, such as silencers. These elements contain recognition sequences for a variety of sequence-specific DNA-binding proteins that are involved in transcriptional regulation. 2.2.2.1. Enhancers and silencers Enhancers can enhance the rate of transcription by increasing promoter utilization. They are the binding sites for specific transcriptional activators. A single enhancer can control the transcription of more than one gene in a position- and orientation-independent manner. Enhancers can be located close to the transcription start site, upstream, downstream, and even within introns. The CAAT box and the GC box described above are enhancer elements that are integral part of the proximal promoter of most genes. Enhancers are thought to bring enhancer-bound transcriptional activators in contact with promoter-bound transcription factors by looping of the DNA. In doing so, enhancers increase the concentration of activators near the promoter and these factors directly or indirectly interact with the promoter to initiate transcription. In yeast, upstream activator sequences (UASs) act as enhancers. Studies on tissue-specific transcription revealed the existence of tissue-specific transcriptional activators. These tissue-specific transcriptional activators, when bound to enhancers, interact with components of the basal transcription machinery. Such interaction is

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mediated by other proteins called “coactivators”, which act as a bridge between different transcriptional activators through protein–protein interactions. Some examples of coactivators are as follows: CREB (cAMP-responsive element-binding protein)-binding protein (CBP); p300 (a mammalian transcriptional coactivator closely related to CBP); p300/ CBP-interacting protein (p/CIP), also called “nuclear receptor coactivator 3” (NCoA-3); p300/CBP-associated factor (p/CAF or PCAF); p160; transcriptional intermediary factor 2 (TIF2), also called “glucocorticoid receptor-interacting protein 1” (GRIP1) and “nuclear receptor coactivator-2 (NCoA-2) (TIF2/GRIP1/NCoA-2)”; yeast transcriptional adaptor GCN5; and steroid receptor coactivator-1 (SRC-1), also called “nuclear receptor coactivator-1” (NCoA-1) (SRC-1/NCoA-1). Many coactivators have histone acetylase activity, histone acetylation being a transcription-activating chromatin modification. The opposite of enhancers are silencers, which suppress transcription by binding silencing transcription factors, thereby acting as negative regulatory elements. Silencers can function in orientation-, position-, and distance-independent manner, and they can also be located within introns. Unlike enhancers which recruit transcriptional activators, silencers recruit transcriptional suppressors. An example is the neuron-restrictive silencer element (NRSE), which is a 21-bp sequence found in the regulatory regions of most neuron-specific genes. The transcription suppressor that binds the NRSE is known as “neuron-restrictive silencing factor” (NRSF) or “RE1-silencing transcription factor” (REST), which is highly expressed in nonneuronal tissues. The NRSF interacts with NRSE to suppress the expression of various neuron-specific genes in nonneuronal cells. The mechanism of NRSF action involves the recruitment of corepressor complexes that have histone deacetylase (HDAC) activity, histone deacetylation being associated with transcriptional repression. However, recent evidence suggests that NRSE may play a dual role, acting as a silencer in nonneuronal cells while acting as an enhancer in neuronal cells (79). Many enhancer and silencer elements in vertebrate genes have been reported in the literature. 2.2.2.2. Locus control region Another transcription-enhancing element is called “locus control region (LCR)”. An LCR enhances transcription of a cluster of linked genes by inducing a more open conformation of the chromatin flanking the locus. Thus, the level of activity of a euchromatic region of a chromosome can be strongly influenced by LCRs. LCR was first identified in human β-globin locus that contains a cluster of five functional genes (5′-ε-G γ-A γ-δ-β-3′). The enhancer activity of β-globin LCR is tissue-specific, the expression of globin genes being confined to erythroid cells. However, the tissue specificity is probably not an intrinsic property of LCR itself, but it depends on the LCR and the tissue-specific promoter that interacts with it (80). An LCR consists of many DNAse I-hypersensitive sites (DHSs). Removal of DNA sequences in between these hypersensitive sites does not affect LCR function much, but removal of any of these hypersensitive sites dramatically reduces LCR activity so that expression of the entire locus is affected. Thus, LCRs can be viewed as a collection of these DHSs. The β-globin LCR is located 6–22 kb 5′ to the first globin gene in the locus (ε). It consists of five DHSs, termed 5′DHS1–DHS5. For LCRs, the 5′ hypersensitive sites are primary regulators and those at the 3′ end are of uncertain function. For the β-globin LCR, the enhancer activity of 5′DHS2–DHS4 resides in a 200- to 300-bp core, which contains binding sites for ubiquitous and erythroid-specific transcription factors. A conserved sequence within 5′DHS2 is TGCTGA(C/G)TCA(T/C). This sequence is called the “Maf recognition element” (MARE), and it is critical for strong enhancer activity. The MARE can bind multiple homodimeric and heterodimeric transcription factors in vitro. These factors include Maf homodimers and heterodimers containing a

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Maf subunit and another subunit of bZIP protein (such as NF-E2, Nrf1, Nrf2, Bach1, and Bach2). The MARE can also bind heterodimers lacking a Maf subunit. The transcriptional activator NF-E2 seems to be a major transactivator that binds to the tandem MAREs of 5′DHS2 and upregulates the globin gene expression (80). The most prominent functional property of LCRs is their ability to confer position-independent, copy number-dependent expression of linked genes. Despite numerous studies on the role of LCR in controlling β-globin gene expression, the mechanism of long-range activation is poorly understood. Various models proposed so far point to the ability of LCRs to alter chromatin configuration and conformation. Besides the β-globin locus in various vertebrates (chicken, mouse, human, etc.), only a small number of other loci have been reported to be regulated by LCRs, such as human α-globin locus and human growth hormone (hGH) locus. 2.2.2.3. Insulator An important gene-boundary element is called “insulator”. Insulators are DNA sequence elements that, when bound to insulator-binding proteins, shield a promoter from the effects of nearby regulatory elements. There are two types of insulator functions: enhancer-blocking function and heterochromatin barrier function. Enhancer-blocking function involves blocking the communication between an enhancer and a promoter when the insulator is located in between these two elements, and thus preventing the enhancer from activating the promoter. Since an enhancer can exert long-range influence on more than one promoter, the blocking function may prevent the indiscriminate effect of an enhancer on multiple promoters and force an enhancer to influence only one specific promoter. Heterochromatin barrier function involves shielding a euchromatic region (containing genes with transcriptionally active promoters) from turning into heterochromatin by the inactivating effect of the invading adjacent heterochromatin. Such heterochromatin barrier function can also protect a stably integrated transgene from local position effects, as well as from a gradual decline of the gene’s expression due to heterochromatin propagation (discussed in detail in Chapter 3 in Section 3.1). Some insulators possess both blocking and barrier functions, while some have only one function. Insulators were first identified in Drosophila. Known insulators in Drosophila include the scs and scs′ (specialized chromatin structure) insulators that flank the 87A7 heat shock locus, which contains two hsp70 genes in divergent orientation. Another Drosophila insulator is the “gypsy” insulator, which is an enhancer-blocking insulator. Gypsy (also known as “mdg4”) is a retrotransposon in Drosophila genome, and it binds the protein “suppressor of Hairy wing” [su(Hw)]; [su(Hw)]-bound gypsy acts as the insulator. Various proteins that bind to the scs, scs′, and gypsy insulator sequences have been identified (see 81 and references therein). Of all known vertebrate insulators, the chicken β-globin insulator (cHS4) is the most extensively studied. It has both blocking and barrier function. It is located in the fourth DHS (5′DHS4) of the LCR of chicken β-globin gene. Initially, the cHS4 insulator was reported to be 1.2 kb long, but subsequent deletion analysis revealed that most of the insulating activity lies in a 250-bp CpG island (the core element) that is highly GCrich (82). The sequence-specific binding of CCCTC-binding factor (CTCF) is required for the enhancer-blocking function of cHS4, but it is not necessary for its heterochromatin barrier function. By deleting the CTCF-binding site, it was demonstrated that cHS4 could still maintain the barrier function, suggesting that the enhancer-blocking and heterochromatin barrier functions are not necessarily interdependent (83). The mechanism of enhancerblocking function of insulators may involve looping, but it is not quite clear. However, the

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mechanism of barrier function understandably involves the maintenance of active chromatin configuration through histone modifications at the boundary (84). Not only does the existence of insulators, LCRs, enhancers, silencers, and various promoter elements make the regulation of gene expression a complex process, but the concerted action of these elements also confers precision to the process. 2.3. The 3′ -Flanking Region of the Gene and Transcription Termination The 3′-flanking region of the gene extends far beyond the 3′-UTR, and it contains transcription termination signals. It was realized early on that the maturation of 3′ end of mRNA and transcription termination are closely coupled. 2.3.1. Transcription Termination in Prokaryotes In bacteria, there are two types of terminators: rho-independent or intrinsic terminators and rho-dependent terminators. Rho-independent or intrinsic terminators have two necessary structural features that are both needed for termination: (i) a sequence that can form a stem-loop structure and (ii) a U-rich region at the very end of the stem-loop structure. The stem-loop generally contains a GC-rich region (toward the base of the stem), which is ˜10 bases away from the U-rich region. Formation of the stem-loop apparently causes the RNA polymerase to pause, thus facilitating termination. Rho-dependent transcription termination is observed in about half of the E. coli genes. Rho (ρ) is an RNA-binding termination factor, which is a homohexameric protein with RNA-dependent ATPase and helicase activities. Rho-dependent terminators are typically C-rich and G-poor. During transcription, the rho factor loads onto the elongating RNA at a region that is about 75 nt long and is located upstream of the terminator. Using its ATPase activity, rho translocates along the elongating RNA, probably faster than the polymerase translocates along the template. When the RNA polymerase pauses at the terminator, rho catches up with it and unwinds the DNA–RNA hybrid using its helicase activity, thereby terminating transcription and releasing the RNA and the polymerase. The rho factor terminates transcription but does not degrade the RNA. Rho-mediated transcription termination model is sometimes referred to as the “hot pursuit” model. 2.3.2. Transcription Termination in Eukaryotes Less is known about transcription termination in eukaryotes. However, some generalizations can be made based on the current state of knowledge. Each class of RNA polymerase uses a different mechanism for termination. For pol I and pol III, pausing at the terminator sequence in the 3′-flanking region seems to be important for termination and subsequent release of both the RNA and the polymerase. The factors that aid in pausing may not necessarily be the release factors. Termination signals as well as termination and release factors may be quite different in different species in eukaryotes. 2.3.2.1. Termination of RNA polymerase I-mediated rRNA transcription Work on the mouse showed that transcription of 45S rRNA by RNA pol I terminates at a conserved 18-bp-long terminator element (sense strand sequence: AGGTCGACCAGA/TT/ANTCCG). This element has a Sal I site (italicized) and is therefore called the “Sal box”. The Sal box is located 565 bp downstream of the rRNA coding region, and

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it binds a nucleolar protein called TTF-I (transcription termination factor for polymerase I). Termination occurs when pol I encounters the Sal box-bound TTF-I and pauses. Additional proteins (release factors) that bind a T-rich sequence upstream of the Sal box help in 3′-end formation and subsequent release of both the transcript and pol I. Various pol I terminators and the terminator-binding proteins studied in the mouse, frog, and yeast are all different. For example, the essential terminator sequence in the frog Xenopus borealis is GACTTGCN, which is much smaller than the mouse Sal box (85–87). 2.3.2.2. Termination of RNA polymerase III-mediated tRNA transcription The termination of pre-tRNA transcription by RNA pol III requires a small run of U’s in a GC-rich region of the RNA (hence a small run of T’s in the sense strand of DNA). This model has some apparent similarity to the prokaryotic intrinsic (rho-independent) termination. However, unlike in bacteria, about four U-residues are sufficient, and the GC-rich region of the RNA need not form a stem-loop structure. The mechanism of transcript release by the pol III termination complex has not been defined yet. Some evidence suggests that the RNA-binding protein “La” is involved in pol III transcription termination (88). La is a nuclear phosphoprotein that has a high binding affinity for the 3′-oligo (U). 2.3.2.3. Termination of RNA polymerase II-mediated mRNA transcription Unlike eukaryotic pol I and pol III or prokaryotic polymerases, eukaryotic RNA pol II in most cases does not terminate transcription at definitive termination signals in the DNA. This lack of dependence on cis-acting sequence signals helps pol II synthesize long transcripts without the possibility of premature termination at similar sequences that may occur elsewhere in the coding region of the gene (89). In most mammalian protein-coding genes, pol II transcribes the template strand 500–2,000 nt beyond the poly(A) addition site. The poly(A) addition site is the pre-mRNA cleavage site and is located 10–30 nt downstream from the poly(A) signal sequence AAUAAA in mRNA (Fig. 1). Transcription of the poly(A) signal sequence triggers the endonucleolytic cleavage of the nascent transcript, degradation of the downstream cleavage product, and termination of transcription; the termination event involves the displacement of pol II from the template. Pausing of pol II downstream from the poly(A) site appears to be an obligatory step leading to termination. In rare cases, pausing may be triggered by intrinsic terminator signals (such as AU-rich sequences); pausing can also be triggered by template-bound proteins (such as MAZ termination factor), both occurring in 3′-flanking region of the gene (90). Once the polymerase slows down and pauses, other factors facilitate release of the transcript and polymerase. Traditionally, there are two major models of termination by pol II: the antiterminator model (also called the “allosteric” model) and the torpedo model. Both models are supported by some experimental evidence. Recently, a unified allosteric–torpedo model has been proposed based on studies in transcription termination in yeast (91). 2.3.2.3.1. The antiterminator (or allosteric) model This model was proposed by Logan et al. (92). This model proposes that cleavage of the nascent transcript at polyadenylation site may trigger a conformational change of transcription elongation complex in such a way that the complex is destabilized; alternatively, the complex may carry an antitermination factor that can leave the complex at the polyadenylation site, thereby marking the nascent RNA for cleavage and simultaneously altering the complex so it is released from the template (Fig. 11A).

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A

RNA pol II

Various factors riding on the CTD ORF

3′ -UTR

Antitermination factor leaving the elongation complex and causing allosteric change of the complex Pol II release (Termination)

5′ -UTR

5′ -Flanking region

CTD Cap

AATAAA AAUAAA Poly(A) signal

Cleavage

AAAA

AAAA

AAUAAA

3′ -Flanking region

3′ -Cleavage product being degraded Poly(A) tail

Antitermination/ Allosteric model

Elongating RNA

B CTD 5′ -UTR

RNA pol II

Cleavage/Polyadenylation factors riding on CTD ORF 3′ -UTR

Rat1/Xrn2 (5′ →3′ exonuclease) loaded onto CTD

Pol II release (Termination)

AATAAA AAUAAA 5′ -Flanking region

Cleavage Poly(A) signal

Cap

Torpedo model

AAUAAA

3′ -Flanking AAAA region 3′ -Cleavage product Exonuclease being degraded catching up Poly(A) tail with pol II

AAAA

Elongating RNA

Figure 11 Models of transcription termination in eukaryotes. (A) The antitermination (allosteric) model depicts that the cleavage of the nascent transcript at the polyadenylation site may trigger a conformational change of the transcription elongation complex in such a way that the complex is destabilized; alternatively, the complex may carry an antitermination factor that can leave the complex at the polyadenylation site, thereby marking the nascent RNA for cleavage and simultaneously altering the complex so it is released from the template. (B) The torpedo model depicts that following the endonucleolytic cleavage of the nascent transcript at the polyadenylation site, the downstream (3′) cleavage product is degraded by a 5′→3′ exonuclease while it is still being synthesized and tethered to the polymerase. The exonuclease continues degrading the transcript until it catches up with the polymerase and destabilizes the pol II complex, thereby releasing the enzyme and causing termination.

2.3.2.3.2. The torpedo model This model was proposed by Connelly and Manley (93). This model proposes that following endonucleolytic cleavage of the nascent transcript at the polyadenylation site, the downstream cleavage product is degraded by a 5′→3′ exonuclease while it is still being synthesized and tethered to the polymerase. The exonuclease continues degrading the transcript until it catches up with the polymerase and destabilizes the pol II complex, thereby causing termination (Fig. 11B). Therefore, the torpedo model is analogous to bacterial rho-dependent termination model, the difference being that the rho factor terminates transcription but does not degrade the RNA, whereas the proposed exonuclease in torpedo model degrades the downstream cleavage product RNA and also terminates transcription. The demonstration of such activities in two exoribonucleases, Rat1 in yeast (94) and its homolog Xrn2 (exoribonuclease 2) in humans (95), provided strong support for the torpedo model. Rat1 is a nuclear 5′→3′ exoribonuclease that partners with Rai1 and degrades single-stranded RNA. Cross-linking studies demonstrated that the Rat1–Rai1 complex

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showed greater accumulation at the 3′ end of protein-coding genes. Inactivation of these exonucleases was found to result in a decrease in termination efficiency. Surprisingly, both these studies also demonstrated that a defect in Rat1 and Xrn2 exonuclease activity did not result in any defect in cleavage, indicating that cleavage of the nascent transcript may not be linked to transcription termination. This is not in agreement with the torpedo model, which assumes that cleavage is required for termination (Fig. 11B). The torpedo model also assumes a cotranscriptional degradation of the downstream cleavage product (Fig. 11B), but direct cotranscriptional degradation of the nascent RNA had not been demonstrated in these two studies. This issue has been addressed by Luo et al. (91), who worked with yeast Rat1 and Xrn1. The Xrn1 (exoribonuclease 1) is mainly cytoplasmic but is also found in nucleus, and it is thought to be the primary 5′→3′ exoribonuclease in yeast and other eukaryotes. It degrades mRNA in a 5′→3′ direction following removal of the poly(A) tail and m7G cap. The authors immunoprecipitated pol II cross-linked to the nascent RNA molecules downstream from the cleavage/polyadenylation site. Attempts to detect this RNA by RT-PCR demonstrated that the nascent RNA was not detectable. The nascent RNA was only detectable when Rat1 or Xrn1 was functionally defective. This demonstrated that degradation of the downstream cleavage product RNA by Rat1 or Xrn1 was indeed cotranscriptional. The authors also concluded that although Rat1 and Xrn1 degraded nascent RNA, this degradation did not explain the mechanism of release of the polymerase from the template. This conclusion does not fit the depiction of the classical torpedo model, which states that after cleavage the degradation of the nascent RNA and the termination of transcription (polymerase release) are intertwined. 2.3.2.3.3. The unified allosteric—torpedo model Neither the allosteric model nor the torpedo model is sufficient to explain the mechanism of termination of pol II-mediated transcription (91). Instead, Luo et al. proposed a hybrid of the two models. In this unified allosteric–torpedo model, the authors suggested that the complex that mediates poly(A) site cleavage consists of pol II, which carries cleavage factors on its phosphorylated CTD along with Rat1. The authors speculated that this complex undergoes one or more transitions that result in (i) cleavage at the poly(A) site stimulated by Rat1 and conventional 3′-end processing factors, (ii) nascent RNA degradation 3′ of the cleavage site, and (iii) an allosteric change that is transmitted to the pol II catalytic site ultimately resulting in its release from the template. 2.3.2.3.4. The revised torpedo model A revised torpedo model of transcription termination has also been proposed (95). According to this model, cotranscriptional cleavage of the nascent transcript occurs close to the transcription termination site. Following cleavage, Xrn2 degrades the downstream cleavage product in a 5′→3′ direction, while the 3′→5′ activity of exosome may degrade the upstream cleavage product back to the poly(A) site. It is clear that the current state of understanding of transcription termination of protein-coding genes is still fluid. Although some of the key factors in the process have been identified, many questions about the mechanism are yet to be answered. It will be interesting to find out whether a specific termination helicase exists (96). 3. CONCLUDING REMARKS Proper identification and accurate annotation of genes is a prerequisite for the success of large-scale genome projects. The success of gene expression studies depends upon accurate

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identification of genes so that unique probes can be designed based on unique sequence regions of the genes. Likewise, identification of putative transcription control elements relies on correct identification of transcription start and termination sites. Incomplete or inaccurate gene structure annotation may result in the failure in identifying various genomic variabilities, such as copy number variations (CNVs) and SNPs, which may be important from a clinical genomics perspective. This may also adversely impact molecular evolutionary studies because of the failure to determine (i) genomic divergence between closely related species, (ii) signatures of positive selection in the genome, and (iii) orthologs and paralogs of various genes. Unfortunately, gene structure determination and annotation are particularly difficult for eukaryotic genomes. The depth of this problem is further underscored when the mammoth genes are taken into consideration. For example, the longest eukaryotic protein-coding gene known is human dystrophin gene which is ˜2,400 kbp (i.e., ˜2.4 Mbp) long. It has 79 exons which encode a 14-kb-long mRNA (as a comparison, the human 28S rRNA is 5 kb long). Thus, 99.4% of the dystrophin gene is composed of noncoding sequences. Using quantitative RT-PCR, it was demonstrated that it takes 16 hr to transcribe the dystrophin gene with an average elongation rate of 2.4 kb/min (97), and the dystrophin mRNA has a half-life of ˜16 hr (98). The elongation rate of dystrophin mRNA was found to be different in different regions of the gene; transcription of exon 51–70 was found to have the slowest elongation rate (1.7 kb/min). Splicing of the dystrophin pre-mRNA is cotranscriptional; thus, splicing of exons at the 5′ end becomes complete before transcription is complete. Dystrophin protein has an estimated molecular weight of 427 kDa. Even though the dystrophin gene is the longest known protein-coding gene, its transcript is not the longest known transcript, nor is its protein product the longest known protein. This distinction goes to human titin gene. This gene is 294 kb long; it contains 363 exons, which together encode a 114-kb-long mRNA. The complete predicted titin protein has 38,138 residues with an estimated molecular weight of 4,200 kDa. However, various alternatively spliced tissue-specific isoforms are detected, such as heart muscle-specific N2B isoform (2,970 kDa) and soleus skeletal muscle-specific isoform (3,700 kDa). The gene is located on the long arm of chromosome 2 (2q) in both humans and mice (99). The mammoth genes offer some exceptions to the generally accepted notions about the structure–function relationships of eukaryotic protein-coding genes. Other similar exceptions may complicate proper annotation of such unique, as yet unknown, genes in the genome. An example of such exception is the exon-size rule discussed above. Vertebrate exons are usually less than 300 bp, and exons longer than 800 bp are rare; however, an exon of the titin gene is 6.5 kb long. Nevertheless, the general structure–function relationship and the rules that apply for average eukaryotic genes also apply for these mammoth genes. REFERENCES 1. Choudhuri S. Gene regulation and molecular toxicology. Toxicol Mechan Meth 2005; 15:1–23. 2. Choudhuri S. Some major landmarks in the path from nuclein to human genome. Toxicol Mechan Meth 2006; 16:137–159. 3. Latchman DS. Gene Regulation, 5th ed. New York, NY: Taylor and Francis, 2005:1–374. 4. Perdew GH, Vanden Heuvel JP, Peters JM. Regulation of Gene Expression: Molecular Mechanisms. Totowa, NJ: Humana Press, 2006:1–344. 5. Proudfoot NJ, Furger A, Dye MJ. Integrating mRNA processing with transcription. Cell 2002; 108:501–512.

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The Eukaryotic Genome: A Brief Overview of Structure, Function, and Evolution Supratim Choudhuri U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, OFAS, Division of Biotechnology and GRAS Notice Review, College Park, Maryland, U.S.A.

1. INTRODUCTION—THE SCOPE OF GENOMICS 1.1. Genome Mapping 1.1.1. Genetic Maps of the Genome 1.1.1.1. Principles of genetic mapping were established by research in Drosophila genetics in the early part of the twentieth century 1.1.1.2. Genetic mapping in humans 1.1.2. Physical Maps of the Genome 1.1.2.1. Cytogenetic maps 1.1.2.2. Radiation hybrid maps 1.1.2.3. Sequence maps 2. NUCLEOPROTEIN ORGANIZATION OF THE GENOME 2.1. Chromatin—the DNA-Protein Complex that Stabilizes the Genome and Regulates its Expression 2.1.1. Nucleosome (Chromatin) Assembly 2.1.2. Histone Variants, Deviant Nucleosomes, and Specialized Functions 2.1.3. NonHistone Proteins in the Chromatin—the HMG Proteins 2.1.4. Chromatin Modification 2.1.5. Chromatin Remodeling Complex 2.2. Heterochromatin and Euchromatin 2.2.1. Heterochromatin-Associated Proteins 2.2.2. Heterochromatin Spread and Position Effect Variegation 2.3. Nuclear Matrix, Matrix Attachment Regions, and Chromatin Loops 3. GENOMIC DNA AS A MOSAIC OF ISOCHORES 3.1. Different Families of Isochores and Their Properties—GC-Content, Gene Distribution, and Codon Usage Bias 3.2. In Search of the “Iso” in Isochores

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52 52 53 53 53 54 55 55 55 57 59 59 59 60 61 62 62 63 64 65

The opinions expressed in this chapter are the author’s own, and they do not necessarily reflect those of the FDA, the DHHS, or the Federal Government.

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4. GENOMIC LANDSCAPE AS REVEALED BY GENOME SEQUENCING 4.1. Human Genome 4.1.1. The Protein-Coding Landscape 4.1.2. Gene Birth and Death in Human Lineage 4.1.3. Genomic GC-Content 4.1.4. CpG Islands 4.1.5. Recombination Rate 4.1.6. Repeat Content 4.1.6.1. C-value, C-value paradox, and C0 t analysis 4.1.6.2. Types of repeat sequences in the human genome 4.1.6.2.1. Interspersed (transposon-derived) repeats 4.1.6.2.2. Processed psuedogenes 4.1.6.2.3. Simple sequence repeats 4.1.6.2.4. Segmental duplications 4.1.6.2.5. Tandem repeat blocks 4.1.7. Noncoding RNA (ncRNA) Genes 4.1.8. Human Proteome Analysis 4.1.9. Duplications in the Human Genome and Composite Structure of Human Proteins 4.1.10. Genome-Wide Chromatin Marks and Transcription 4.1.10.1. Pervasive transcription in the human genome 4.1.10.2. Antisense transcription 4.1.11. Human Genomic Variations 4.1.11.1. Single nucleotide polymorphism 4.1.11.2. Copy number variation 4.2. The Chimpanzee Genome 4.2.1. Similarity in Protein-Coding Genes Between Human and Chimpanzee 4.2.1.1. Evolution of the coding sequences 4.2.1.2. Gene expression 4.2.2. Possible Causes of Genome-Wide Nucleotide Divergence Between Human and Chimpanzee 4.2.2.1. Divergence in CpG islands 4.2.2.2. Divergence due to indel mutations 4.2.2.3. Divergence due to transposable elements 4.2.2.4. Divergence due to deletions, repeat sequences, and chromosomal rearrangements 4.2.2.5. Single nucleotide polymorphisms 4.2.3. Positive Selection and Genome Evolution 4.2.3.1. Neutral theory of molecular evolution 4.2.3.2. Signatures of positive selection in the genome 4.2.3.3. Genome evolution in human and chimpanzee 4.2.3.3.1. Molecular evolution of FOXP2 gene 4.2.3.3.2. Molecular evolution of genes regulating brain size 4.2.3.3.3. Molecular evolution of MYH16 gene 4.2.3.3.4. Molecular evolution of CMAH gene 4.3. Rhesus Macaque Genome 4.4. Mouse Genome 5. CHROMOSOMAL SYNTENY AND SYNTENIC MAPS IN COMPARATIVE GENOMICS

67 67 67 68 69 69 70 70 70 71 72 75 76 76 77 78 79 79 80 80 82 82 83 83 84 84 85 85 86 86 86 86 87 87 87 88 88 90 90 91 92 92 92 93 94

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6. CONCLUDING REMARKS REFERENCES

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1. INTRODUCTION—THE SCOPE OF GENOMICS The goal of the science of genomics is to study the structure, function, and evolution of the genome. An extension of this goal is to understand how environmental factors modulate genome function. This has direct relevance to clinical genomics. The study of genome structure involves an understanding of the genome organization. This includes mapping the location of genes and their regulatory elements in the genome; mapping various single nucleotide polymorphisms (SNPs) in the genes; identifying gene copy number variations (CNVs); identifying various unique sequence elements in the genome; mapping various repetitive sequences, pseudogenes, and retrotransposons; identifying various noncoding RNA (ncRNA)–coding genes; identifying various targets of these ncRNAs and so on. The study of genome function, that is functional genomics, involves studying the expression of the whole genome. This includes studying the expression of protein-coding as well as nonprotein-coding parts of the genome and their interactions. A goal of functional genomics is to compile an atlas of gene expression for various genomes. Detailed functional genomic analyses involve manipulation of genome expression, such as genomewide mutagenesis studies. As Dobzhansky pointed out in his now-famous statement that nothing in biology makes sense except in the light of evolution; the full significance of genomic data of an organism or a species can only be realized when viewed in the light of their evolutionary history. In this respect, evolutionary genomics and comparative genomics have overlapping scopes. The intellectual underpinning of comparative genomics is evolutionary biology. The study of genome evolution involves an understanding of the structure and function of genomes of different species. Therefore, advances in comparative genomics will not only help uncover how genome structure and function in different species evolved over time, but also shed light on how structural and functional divergence of genomes during evolution might have contributed to speciation. Researchers have learned a great deal about various human genes with previously unknown function by comparing them to their counterparts with known functions in other model organisms, such as mouse. Thus, comparative genomics relies heavily on the analysis of sequence similarity and sequence divergence. The following discussion provides a very brief account on certain structural, functional, and evolutionary aspects of eukaryotic genomes, with special emphasis on the human genome. This is by no means an extensive overview; it merely touches on some aspects of genome structure, expression, and evolution.a 1.1. Genome Mapping There are two broad categories of genomic maps: genetic map and physical map. Both these maps for human genome were constructed over time and were refined as newer information about genomic landmarks became available. Genomic landmarks used for mapping are of various types, such as sequence tagged site (STS), expressed sequence a In the following text, many recent reviews that include the original references and earlier important reviews have been cited in order to keep the number of references to a minimum. The expressions kb, Mb, or Gb have been used, and in the context of DNA these mean kbp, Mbp, and Gbp, respectively.

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tag (EST), microsatellite, and restriction fragment length polymorphism (RFLP) markers. Microsatellite and RFLP markers are polymorphic markers, whereas STS and EST are nonpolymorphic markers. The following description is a very sketchy account of the principles of some of the common genome mapping techniques. Readers are referred to “Genome Analysis: A Laboratory Manual (Volume 4; Mapping Genomes); Editors: Bruce Birren et al.; CSHL Press, 1999” for the details of human genome mapping and analysis. 1.1.1. Genetic Maps of the Genome Genetic map is also known as “linkage map”. In a genetic map, the relative distance of genes on the chromosome is determined based on the recombination frequency between two genes or two genetic markers located on the same chromosome. The genes in question may have identifiable phenotypes, such as enzyme production, eye color, a physical trait, or a particular clinical symptom. In contrast, the genetic markers flanking the genes may be identified by some nongenic detectable DNA sequences, such as microsatellite, STS, or RFLP. There is one genetic map for each chromosome. 1.1.1.1. Principles of genetic mapping were established by research in Drosophila genetics in the early part of the twentieth century The concept of using recombination frequency between two genes on a chromosome to determine their relative distance and order was first introduced in 1913 by Alfred Sturtevant, a student of Thomas Hunt Morgan. In Drosophila, chromosome maps were made using the relative distance and order of genes along the length of the chromosome, which was determined by three-point (three-loci)-test crosses. The first step involves finding out the middle gene by comparing the noncrossover/parental combination class (highest frequency) with the double crossover class (lowest frequency). Compared to the noncrossover class, the location of only the middle gene is reversed in the double crossover class. Once the middle gene is determined, the next step is to determine the linear distance of the two genes based on recombination frequency. This is a subject discussed in detail in all textbooks of genetics, so it is not repeated here. Nevertheless, it should be emphasized that determination of the recombination frequency between two genetic loci (genes or markers) is of central importance in creating a genetic map. A 1% recombination frequency between two genetic loci means that they are 1 mapunit apart on the chromosome. The map-unit is termed “centiMorgan” (cM). Thus, 1 cM genetic distance between two loci on the same chromosome is equal to a 1% probability that the two loci will be separated by recombination. In other words, 1 cM genetic distance between two loci represents a probability of obtaining one recombinant offspring involving the two loci in 100 progeny. The physical length of DNA that corresponds to 1 cM may vary from species to species. For example, in humans 1 cM = ~1 Mb (=1,000 kb), in mouse 1 cM = ~1.75 Mb (=1,750 kb), in Drosophila 1 cM = ~400 kb, whereas in Escherichia coli 1 cM = ~40 kb. Recombination fractions between two genetic loci never exceed 50% or 0.5, no matter how far apart they are. 1.1.1.2. Genetic mapping in humans The principles of genetic mapping in humans are similar to that of other sexually reproducing diploid experimental organisms. However, the methods used for mapping human genes are different because mating cannot be arranged for experimental convenience and the number of progeny is small.

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Construction of human genetic maps utilized pedigree analysis and polymorphic genomic markers showing Mendelian segregation pattern. Pedigree analysis was done in many large three-generation families, that is, families with living grandparents and large number of siblings (the more the number of generations and the larger the family size, the better). For polymorphic markers, microsatellite markers [also called “short tandem repeat polymorphisms” (STRPs)] were very useful because they are polymorphic, abundant, and dispersed throughout the genome. Using (CA)n repeat markers in a panel of DNA from eight large families, researchers at the Généthon laboratory in France were able to produce a second-generation linkage map of the human genome. As mapping efforts progressed, subsequent maps were produced with ever increasing numbers of genetic markers (especially microsatellite markers) and ever increasing resolution. In complex pedigree analysis, scoring recombinants simply by examining the phenotype and counting the progeny can be misleading. This problem is circumvented by calculating the likelihood of linkage of two loci. This likelihood is expressed as LOD (logarithm of odds) score denoted as Z. The LOD score compares the likelihood that the test data indicates linkage to the likelihood that the test data indicates no linkage (independent assortment of the two loci). By convention, a LOD score of 3 or greater indicates that the two loci are linked, with a 5% chance of error in the conclusion. The source of the number 3 is the log10 value of 1,000 (103). This means that if the LOD score is 3, the odds that two loci are not linked is 1 in 1,000. To ensure the accuracy of LOD scores, linkage data from related families are often combined with that from unrelated families. 1.1.2. Physical Maps of the Genome A physical map gives the distance from one landmark in the genome to another in terms of DNA base pairs. The importance of physical mapping of the genome becomes apparent when some features of the human genome are considered. Approximately 50% of the genome contains repetitive sequences, and some regions have sequences that are up to 98% identical to one another even though these regions are millions of base pairs apart or are even located on different chromosomes. There is one physical map for each chromosome. Physical maps can be of several types: cytogenetic maps, radiation hybrid maps, and sequence maps, in an increasing order of resolution. 1.1.2.1. Cytogenetic maps Cytogenetic maps have the lowest resolution and are the oldest form of chromosomal physical maps available to scientists for humans and other species. Cytogenetic maps represent specific banding patterns of stained chromosomes that can be observed by light microscopy. Specific banding patterns of chromosomes helped scientists distinguish different chromosomes in the cell and different subchromosomal regions of each chromosome. Using in situ hybridization and the knowledge of cytogenetic map, it has been possible to map the location of genes on specific chromosomes in many mammalian species including humans. 1.1.2.2. Radiation hybrid maps Natural chromosome breaks, such as deletion and translocation, have been useful in the past for mapping the location of genes on specific chromosomes. Radiation hybrid (RH) maps are constructed by inducing artificial chromosome breaks by controlled radiation. In this method, chromosome breaks are induced by measured doses of radiation. The average distance between the breakpoints is controlled by varying the strength of the radiation.

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When the markers are known, their separation due to chromosomal breakage can be used to create a RH map that shows the order of markers on the chromosome and their estimated distance. RH mapping can use nonpolymorphic markers. RH mapping was initially done using panels of somatic cell hybrids, that is, hybrids of human and mouse (or hamster) cells containing various human chromosomes, usually a few per cell but preferably only one per cell, as were later obtained in chromosome-specific monochromosomal hybrids. With the discovery of more genomic markers, RH mapping was done by irradiating the whole genome of human diploid cells and fusing the irradiated cells to nonirradiated mouse or hamster cells to create a panel of somatic cell hybrids that represent the whole genome radiation hybrids. In each RH (somatic cell hybrid line), one or more chromosomal fragments are stably incorporated into the recipient genome. Because the hundreds of radiation hybrids thus created are likely to contain different markers and their combinations, and because markers that are closely linked are likely to stay in the same fragment more frequently than markers that are further apart, screening for various markers help place them in proper order on the chromosome and also help estimate their relative distance. However, direct assignment of the location and relative distance of the loci are not possible due to the fact that the number and composition of human DNA fragments within a hybrid are not known. Therefore, the data analysis involves statistical calculation of the likelihood ratio (LOD) as described earlier under genetic mapping. By increasing the radiation dose and causing more breaks, the resolution of RH maps can be increased. RH mapping and genetic mapping are both based on the principle of separation of two genetic loci on the same chromosome: separation through recombination in genetic mapping and separation through chromosome breaks in RH mapping. In both cases, the probability of separation of two loci is directly proportional to their relative distance from each other. As in the case of genetic map unit, the RH map unit is also determined by the frequency of separation of the two loci. The RH map unit is expressed as centiRay (cR); 1 cR represents a 1% probability that two loci on the same chromosome will be separated by radiation-induced breaks. The higher the cR value, the further apart two loci are, and hence the higher the probability of radiation-induced chromosome breaks separating the two loci. 1.1.2.3. Sequence maps Sequence maps are also called “clone contig maps”. To construct a sequence map, the genome is broken down into smaller pieces, and the pieces are cloned into a suitable vector. At the beginning of the human genome sequencing project, yeast artificial chromosome (YAC) was used because very large pieces of DNA (~3,000 kb) could be cloned into a YAC vector. Later it was found that DNA fragments cloned in YAC may undergo spontaneous rearrangement, thereby messing up the true sequence information of the cloned DNA. The discovery of bacterial artificial chromosome (BAC) vectors solved this problem. BAC can easily hold ~300-kb-long DNA fragments, and the cloned fragments are stable. Genomic DNA pieces are cloned into BAC to create a genomic library. The clones are restriction-digested to determine the unique restriction-digestion-pattern fingerprint of each clone. The clones are then sequenced; probes are designed from the known sequence and are hybridized with other clones (genomic library screening) to identify clones that carry the same DNA fragment, and thus can be overlapped. Use of very long probes to screen the genomic library tends to minimize or suppress signals generated due to highly repetitive sequences. Enough overlapping clones (clone contigs) are sequenced to cover the entire genome many times over (about 10 times, referred to as “10 × coverage”) to ensure

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the accuracy of the map. The overlapping sequences (contigs) are assembled back into an intact and complete genomic sequence using computer software and knowledge of each clone’s map position. Different methods of identifying overlaps between clones have been used, but STS markers, which had previously been identified through sequencing and mapped to various chromosomes, have been particularly useful. Each STS is a few hundred bp-long unique DNA sequence that is found in only one place in the genome. An STS may come from anywhere in the genome, coding or noncoding. When STS markers are identified by sequencing cDNA, they are called “expressed sequence tags”. Once STSs are properly placed on the genome map, the genomic location of any unknown piece of DNA containing an STS can be easily determined. 2. NUCLEOPROTEIN ORGANIZATION OF THE GENOME The structure and organization of the genome can be studied at various levels, from chromatin all the way down to specific sequence characteristics of the DNA. Since the nuclear DNA exists in combination with various proteins which, in turn, determines the spatial structure of the DNA-protein complex and also regulates the replication, repair, and expression of the genome, it is logical to start the discussion of the genome at the level of chromatin. 2.1. Chromatin—the DNA-Protein Complex that Stabilizes the Genome and Regulates its Expression DNA in the nucleus exists mainly in combination with histone proteins; the DNA–histone complex is called “chromatin”. Chromatin can undergo changes in its structure in response to various cellular metabolic demands. Chromatin can be envisioned as a repeat of structural units called “nucleosomes”. The nucleosome core particle is composed of histone octamer plus the DNA that wraps around it. The histone octamer contains two molecules each of histones H2A, H2B, H3, and H4. DNA wraps around the octamer in a left-handed supercoil in about 1.75 turns which encloses about 150 bp. Histone H1 is a linker histone that, along with linker DNA (the DNA in between two nucleosome core particles), physically connects the adjacent nucleosome core particles. The length of linker DNA varies with species and cell types. Usually, nucleosome core particle and linker DNA on both sides of the core encompasses between 180- and 200-bp DNA. Between the nucleosome unit structure and the metaphase chromosome structure containing two chromatids, there are several levels of organization and compaction of the chromatin. Each nucleosome has a diameter of 10 nm; the nucleosomes are compacted into a solenoid fiber structure of 30 nm; the 30-nm solenoid fibers are compacted into a 300-nm filament; and finally, the 300-nm filaments are further compacted into a 700-nm chromosome. During cell division, when the chromosomes duplicate, a 1,400-nm metaphase chromosome is produced containing two chromatids, each chromatid being 700 nm (Fig. 1). During transcription, as the RNA polymerase advances, it introduces right-handed supercoil in the DNA ahead of it. This tends to relieve the left-handed supercoil of the DNA that wraps around the histone octamer. 2.1.1. Nucleosome (Chromatin) Assembly The majority of chromatin assembly is tightly coupled to DNA replication in the cell; thus, the assembly of nucleosomes on DNA occurs during DNA synthesis in S-phase. During DNA replication the preexisting core histones are transferred onto the two nascent

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Histone H1 Nucleosome

Chromatin loop

Linker DNA 30 nm solenoid

Extended chromatin filament

300 nm

30 nm

10 nm DNA wraps around the histone octamer in a left-handed supercoil

1,400 nm

Metaphase chromosome

Matrix attachment region (MAR) in the DNA

Nuclear matrix or chromosome scaffold

700 nm

Supercoiled (coiled coil) chromatin filament (chromosome arm)

Figure 1 From nucleosome to chromosome. The histone octamer contains two molecules each of histones H2A, H2B, H3, and H4. The DNA wraps around the octamer in a left-handed supercoil in about 1.75 turns that encloses about 150 bp. Histone H1 is the linker histone that, along with the linker DNA, physically connects adjacent nucleosome core particles. The length of the linker DNA varies with species and cell types. Usually, the nucleosome core particle and the linker DNA on both sides of the core encompasses between 180- and 200-bp DNA. The nucleosomes (10 nm each) are condensed into solenoid fiber structure of 30 nm; the 30-nm solenoid fibers are condensed into 300-nm filament; the 300-nm filaments are further condensed into the 700-nm chromosome. During cell division, when the chromosomes duplicate, a 1,400-nm metaphase chromosome is produced containing two chromatids, each chromatid being 700 nm.

chromatids behind the replication fork, and the other half of the nucleosome complement is made from newly synthesized histones. The first step in the assembly of chromatin is the nuclear import of newly synthesized core histones from cytoplasm. This is mediated by karyopherins (importins) that bind to the nuclear localization signal in the N-terminal domain of each core histone. These newly synthesized histones are acetylated at lysines 5 and 12 of histone H4 (H4K5ac and H4K12ac, and also H3K14ac in Drosophila) prior to their assembly into chromatin. After deposition onto newly replicated DNA, they are rapidly deacetylated by histone deacetylase HDAC1. The functional significance of this specific pattern of histone acetylation and subsequent deacetylation is not understood. Acetylation of newly synthesized histone H3 seems to occur at different sites in different species and has not yet been reported in human cells. In general, the acetylation of H3 and H4 is carried out by Hat1, the only known B-type histone acetyltransferase (B-type HAT), which is distinct from the A-type HATs that acetylate chromosomal histones (1,2). The H3/H4 heterodimers ultimately form the (H3/H4)2 tetramer, which form a stable complex with >120 bp of DNA. During histone deposition onto newly synthesized DNA, the positive charge of histones is shielded by various anionic factors to allow chromatin assembly in a regulated and ordered fashion. Histone H3–H4 tetramers bind to DNA first due to their higher affinity for DNA than H2A–H2B dimers. Subsequent deposition of H2A–H2B dimers is likely to be driven by the higher affinity of H2A–H2B for subnucleosomal particles comprising DNA and H3–H4 (1).

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Nucleosome core assembly continues with the addition of histones H2A and H2B to the (H3/H4)2 tetramer. Histones H2A and H2B form a stable H2A/H2B heterodimer that binds to either side of the (H3/H4)2 tetramer and extend the wrapping of DNA within the nucleosome to >160 bp. This creates a structure comprised of the four histone dimers linked end-to-end in the sequence (H2A/H2B)–(H4/H3)–(H3/H4)–(H2B/H2A). These precise histone–DNA interactions constrain all DNA sequences, regardless of the inherent sequence-dependent structures to adopt a relatively similar conformation in the nucleosome (3,4). Various histone–chaperone proteins are required for the proper deposition of histones onto DNA and chromatin assembly. A heterotrimeric protein complex termed “chromatin assembly factor-1” (CAF-1) which copurifies with histones was isolated from human cell extracts. The structure and sequence of CAF-1 has been conserved among eukaryotic organisms. CAF-1 appears to deposit newly synthesized histones H3 and H4 onto newly replicated DNA; such histone chaperone activity is coupled to DNA synthesis via its interactions with proliferating cell nuclear antigen (PCNA). Because PCNA is the processivity factor for the replicating DNA polymerase in eukaryotes, the coupling of CAF-1 with PCNA serves to localize CAF-1 to the sites of DNA synthesis. Indeed, in human and mouse cells, CAF-1 is abundantly present at the replication foci (4,5). Another novel histone chaperone, termed “replication–coupling assembly factor” (RCAF), was identified by its ability to facilitate CAF-1-mediated assembly of nucleosomes onto newly replicated DNA. Biochemical approaches have led to the identification of H2A–H2B binding protein termed “nucleosome assembly protein-1” (NAP-1), which is highly conserved among eukaryotic species. The closely related human protein is called “NAP-2”. Both NAP-1 and human NAP-2 bind to histone H2A–H2B, and with the help of karyopherins, aid in their transport from cytoplasm to nucleus as cells progress from G1 to S phase. Although histone chaperones play a crucial role in chromatin assembly, they are not sufficient to generate the typical regular arrays of nucleosomes with 180–200 bp spacing; rather, they lead to the assembly of irregularly spaced nucleosomes. Proper chromatin assembly with regular arrays of nucleosomes requires ATP hydrolysis and ATP-dependent chromatin remodeling factors. Some of these factors have also been identified, such as ATP-utilizing chromatin assembly and remodeling factor (ACF) and chromatin accessibility complex (CHRAC). The catalytic component of ACF and CHRAC is the imitation switch (ISWI) ATPase. Both ACF and CHRAC are members of the ISWI family of chromatin remodeling complex (1). 2.1.2. Histone Variants, Deviant Nucleosomes, and Specialized Functions In higher organisms, all core histone subtypes have variants, with the possible exception of histone H4. These variants are nonallelic primary sequence variants. For the most part, the core histones have only a small number of variants, and the amino acid sequence variations within a subtype are limited. However, there are some highly divergent variants which perform specialized functions. Some examples include CENP-A (histone H3 variant), macroH2A, H2AZ, and H2AX (all are histone H2A variants). The C- or N-terminal regions of all these histone variants have amino acid sequences that are >60% identical with that of their respective normal histone types; in addition, these variants also have unique amino acid sequences. Three well-studied centromeric proteins (CENP) are CENP-A, CENP-B, and CENP-C. CENP-A is the histone H3 variant and is specifically localized to the centromeric chromatin in mammals and yeast. CENP-A synthesis is coordinated with centromeric

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replication from mid-S to early G2. Targeted deletion of mouse homolog of CENP-A (Cenpa gene) showed that it is an essential gene for survival. Heterozygous mice were healthy and fertile, whereas homozygous null mutants failed to survive beyond 6.5 days postconception. The affected embryos showed severe mitotic problems, including micronuclei and macronuclei formation, nuclear bridging and blebbing, and chromatin fragmentation and hypercondensation (6). Histone macroH2A, an H2A variant, remains predominantly associated with transcriptionally inactive heterochromatic regions of the genome, such as the inactive X chromosome of female mammals. The association of macroH2A with the inactive X chromosome may be mediated through the interactions of macroH2A and Xist, a ncRNA that is tightly associated with the inactive X chromosome (7). However, the possible role of macroH2A in X-inactivation is unclear since disruption of macroH2A association with the Xist does not affect the maintenance of X-inactivation. It is possible that other proteins, such as HP1 (discussed later), are involved in the maintenance of X-inactivation. Histone H2AZ, a minor H2A variant, is found in a wide range of organisms from yeast to mammals. Swapping experiments using Drosophila H2AZ homolog H2AvD in which regions of H2AvD were replaced with homologous regions from Drosophila histone H2A.1 led to the identification of a distinct region of H2AvD, which was necessary for overcoming the null mutant lethality in null flies (8). H2AX is another histone H2A variant. Following DNA double-strand breaks, H2AX is rapidly phosphorylated forming the gamma-H2AX (γ-H2AX), which accumulates at the site of the double-strand breaks and is associated with the recruitment of various DNA repair proteins. Thus, γ-H2AX is a useful marker of DNA double-strand breaks. In keeping with the proposed role of γ-H2AX in maintaining genomic integrity and stability, H2AXnull mice are radiation-sensitive, growth-retarded, and immune-deficient, and mutant males are infertile. These pleiotropic phenotypes are associated with chromosomal instability, DNA-damage repair defects, and impaired recruitment of various DNA-damage sensor and double-strand break (DSB) repair proteins, such as Nijmegen breakage syndrome protein 1 (Nbs1 or nibrin), p53-binding protein 1 (53bp1), and breast cancer 1 protein (Brca1), but not Rad51 (eukaryotic RecA homologue) (9). The linker histones (H1) are much more variable in structure than the core histones. Metazoan linker histones have a three-domain structure—a central globular domain flanked by N- and C-terminal regions. The preponderance of basic residues within these regions serves to neutralize the polyanionic backbone of DNA, thus facilitating the folding of nucleosomal arrays into higher order structures (3). Among vertebrates, several different types of H1 may exist in the same species at different times of development or in different cell types. In chicken erythrocyte nuclei, for example, the linker histone variant associated with chromatin is H5, whereas in frog erythrocyte nuclei the linker histone variant associated with chromatin is H10 (subtype H10-2) (10). Similarly, in mammals, there are at least six somatic variants (H1a–H1e, H10), a testis-specific variant (H1t), and an oocyte-specific variant (H1oo) (7,11,12). In addition to playing a direct role in stabilizing nucleosomal and higher-order chromatin structure, H1 histones also function as general and specific repressors of transcription by limiting the access of transcriptional activators to chromatin. For example, H1b interacts with the homeodomain protein Msx1, and the complex binds to a regulatory element of the MyoD gene to inhibit its expression (13). For a detailed list and information of histone variants, see the histone database at http://research.nhgri.nih.gov/histones/.

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2.1.3. NonHistone Proteins in the Chromatin—the HMG Proteins As their name suggests, the high mobility group (HMG) proteins have high electrophoretic mobility in polyacrylamide gels. They are the main nonhistone protein components of the chromatin and are ubiquitous in mammalian cells. However, quantitatively they are much less abundant than histones. They are divided into three families: HMGB (formerly HMG1/-2), HMGN (formerly HMG-14/-17), and HMGA (formerly HMG-I/Y/C) family. Within a family, the gene names are numbered in sequential order (e.g., HMGB1, HMGB2). Splice variants are indicated by small letters (e.g., HMGA1a, HMGA1b, HMGA1c). The HMG functional motifs are as follows: HMG-box for the HMGB family, nucleosomal binding domain for the HMGN family, and AT-hook for the HMGA family. Proteins containing any of these functional motifs are known as the “HMG motif proteins”. HMG proteins bind to DNA and decrease the compactness of the chromatin fiber. In doing so, they enhance the accessibility of various regulatory factors to chromatin. HMG proteins were shown to move very rapidly throughout the nucleus, raising the possibility that following transient displacement of histone H1 from the nucleosomal sites, HMG proteins may rapidly occupy these sites (14). Binding of HMG proteins to DNA can cause significant DNA bending because such interaction weakens the binding of histone H1 to nucleosomes by competing for chromatin binding sites. This is achieved by synergistic binding of HMG proteins to the DNA sites that can potentially serve as H1-binding sites. Thus, a network of dynamic and competitive interactions involving HMG proteins and histone H1, and perhaps other structural proteins, constantly modulates nucleosome accessibility and the local structure of chromatin fiber (14). 2.1.4. Chromatin Modification Chromatin modifications associated with gene expression either activate or repress transcription. Most modifications take place on the N-terminal tails of histones by various chromatin-modifying enzymes. Modifications also take place on the C-terminal tails, and some modifications have been reported in the globular domains of histones (15–17). Various types of posttranslational modifications are acetylation, methylation, phosphorylation, ubiquitinylation, ADP-ribosylation, and sumoylation. The amino acid residues that are subject to modification are specific, thus creating specific “marks” in the chromatin. These modifications help recruit other proteins and aid in either relaxation or further compaction of the chromatin which influence transcription. Lysine is the central amino acid substrate for various modifications on histone. Different lysine modifications may have opposite effects on gene expression. All these modifications are directly relevant for epigenetic regulation of gene and genome expression, and therefore are discussed in more detail in Chapter 3. 2.1.5. Chromatin Remodeling Complex Chromatin modification through histone acetylation, phosphorylation, and so forth can relax and decondense chromatin but cannot completely disrupt nucleosome structure. Without disruption of the nucleosomal core particles, transcription cannot proceed efficiently. Therefore, chromatin modification combined with ATP-dependent chromatin remodeling is necessary for efficient transcriptional activation and completion. A chromatin-remodeling complex is a multiprotein complex with ATPase activity that perturbs nucleosome structure in an ATP-dependent manner, exposes nucleosomal DNA to attack by nucleases, catalyzes histone octamer sliding and transfer, and reduces the supercoiling of DNA circles bearing nucleosomes.

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At least four major families of chromatin remodeling complexes are known: SWI/ SNF (SWI/SNF, RSC, Brahma), Mi-2/CHD (Mi-2 complex, NURD), ISWI (ISW1, ISW2, NURF, ACF, CHRAC, RSF), INO80 (18,19). The switching/sucrose nonfermenting (SWI/ SNF) complex was the first chromatin remodeling complex identified and characterized in budding yeast Saccharomyces cerevisiae. The human homolog is referred to as the “hSWI/ SNF”. The ATPase subunit in yeast SWI/SNF complex is known as “SWI2/SNF2”. Mammalian SWI/SNF complex contains either Brahma-related gene-1 (BRG-1) or Brahma (BRM) as its ATPase subunit. Chromatin remodeling factors use the energy derived from ATP hydrolysis to induce a net change in the position of the histone octamer, and it involves the breaking and reestablishment of histone–DNA contacts. Based on various experimental results, two likely mechanisms have been proposed: (i) transfer of histones from one region of DNA to another via a dissociative pathway, and (ii) movement of the histone octamer along DNA by sliding or tracking (20). Because thermodynamic nucleosome repositioning occurs at lower temperatures than that required to disrupt the entire nucleosome, it seems likely that sliding of histone octamer along the DNA will involve the breaking of fewer histone–DNA contacts at any one time (20). During sliding, using the energy from ATP hydrolysis the DNA dissociates from the histone octamer at one edge of the nucleosome, and a neighboring stretch of DNA associates with the octamer to yield a small DNA loop. As this loop spreads along the surface of the octamer, the octamer slides to a new location. Thus, this model depicts that at any given time only a small number of histone–DNA contacts are broken. Chromatin remodeling allows access of the transcriptional activators to DNA. Once transcription begins, the advancement of RNA pol II through chromatin is facilitated by other factors. One such factor, known as “facilitates chromatin transcription” (FACT), has been identified and extensively characterized by Danny Reinberg’s laboratory. FACT is a heterodimer; in humans, the two subunits are hSPT16 and SSRP1. A model of how FACT facilitates the movement of RNA pol II through chromatin shows that it destabilizes the nucleosomal structure so that one histone H2A–H2B dimer is removed transiently during the passage of RNA pol II, thus leaving behind the histone hexamer. After the passage of RNA pol II, FACT redeposits the H2A–H2B dimer using its histone chaperone activity, thereby reassembling the nucleosome traversed by RNA pol II (21). FACT is restricted to transcription that involves nucleosome disassembly mechanisms, but not nucleosome transfer seen with RNA pol III-transcribed genes (22). Importantly, FACT does not require ATP hydrolysis for its action. 2.2. Heterochromatin and Euchromatin The structure of chromatin varies considerably in the nucleus and also in response to cellular metabolic demands. Different levels of chromatin compaction result in the formation of euchromatin and heterochromatin. In general, transcriptionally inactive, highly condensed chromatin that is stained darker is called “heterochromatin”, and transcriptionally active, less condensed chromatin is called “euchromatin”. At the DNA sequence level, heterochromatin contains a great deal of highly repetitive sequence. Heterochromatin and euchromatin can be easily visualized by staining the chromosomes. Constitutive heterochromatin is identified by C banding in which chromosomes are treated with acid (HCl) and base [Ba(OH)2] and then stained with Giemsa stain. Euchromatic chromosome bands are identified by G banding in which chromosomes are subjected to mild proteolysis by trypsin, followed by staining with Giemsa stain.

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Heterochromatin is classified into two types: facultative heterochromatin and constitutive heterochromatin. Facultative heterochromatin occurs in only one of a pair of chromosomes. The DNA base composition of the facultative heterochromatin is the same as that of its euchromatic counterpart in the homologous chromosome, and that is why facultative heterochromatin has all the characters of euchromatin in terms of the expressible genes present. Facultative heterochromatin is found in the inactive X chromosome (Xi) in the somatic cells of female mammals. In female mammals, one of the two X chromosomes is inactivated so that the females with two X chromosomes do not have twice as much X chromosome gene products compared to that of the males, which possess only one X chromosome. This phenomenon is called “dosage compensation”. The inactivation state of the X chromosomes is reversed in the gonad in primary oocytes (diploid) (23) so that after oogenesis all ova (haploid) contain an active X chromosome. The mechanism of dosage compensation through X chromosome inactivation in mammals is discussed in Chapter 3. Readers are referred to the review by Lucchesi et al. (24) for a better understanding of a comparative account of dosage compensation in Drosophila, C. elegans, and mammals. Unlike facultative heterochromatin, constitutive heterochromatin has DNA sequence characteristics that are radically different from that of the euchromatin; there are a lot of repetitive sequence elements in the DNA. Although constitutive heterochromatin occurs in every chromosome in blocks, they preferentially occur at certain parts of the chromosome, such as at the centromeric and telomeric regions. Heterochromatin is condensed and genetically inert but it is still a dynamic entity and can change in response to the nuclear environment, growth, and differentiation status, as well as the metabolic status of the cell. In Drosophila, about 40 genes have been identified in the heterochromatic regions. These include, among other genes, the 18S and 28S ribosomal RNA genes. The heterochromatin apparently also plays some role in homologous chromosome pairing and chromosome segregation. In general, heterochromatin prevents chiasmata formation and crossing-over in its vicinity (25,26). Centromeric heterochromatin apparently plays a role in sister chromatid cohesion and proper segregation of chromosomes during cell division (27). 2.2.1. Heterochromatin-Associated Proteins A number of proteins have been found associated with heterochromatin. Members of HP1 family (HP1α, HP1β, HP1γ) in mammals represent a class of nonhistone chromosomal proteins that play an important role in establishing and maintaining higher-order chromatin structures and nuclear organization, as well as in chromosome segregation and gene silencing (28). HP1 was first described in Drosophila as a heterochromatin-associated protein with dosage-dependent effects on heterochromatin-induced gene silencing. It was demonstrated that HP1 becomes associated with the inactive X chromosome at the interphase and becomes a component of the Barr bodyb in humans (29). The authors suggested that in humans HP1 might be involved in the maintenance of heterochromatin and X-inactivation. Various HP1-like proteins have since been found in species ranging from fission yeast to humans. The N-terminal region of HP1 is called the “chromodomain”, and the C-terminal region is called the”chromo shadow domain”. The two domains are connected through a hinge region. The chromo shadow domain appears to be important in HP1 b

The inactive X chromosome in female mammals with its facultative heterochromatin stains dark and is called the Barr body. It is explained in more detail in Chapter 3.

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dimerization, while the chromodomain actually binds to the heterochromatin through histone H3 dimethylated at lysine 9 (H3K9me2). Establishment of heterochromatin and transcriptional repression involves two successive modifications of the lysine 9 of histone H3: first deacetylation, followed by dimethylation; the latter enables it to recruit HP1. Thus, dimethylation of histone H3K9 and the presence of HP1 serve as chromatin marks for transcriptionally silenced chromatin (30). In addition, the histones in both facultative and constitutive heterochromatin are typically underacetylated. Underacetylation is associated with transcriptional repression. Many other proteins are associated with heterochromatin, but their relevance as legitimate heterochromatin-associated proteins is not established yet (see 31). In addition to HP1, various other proteins associated with heterochromatin also influence the spread of heterochromatin into the adjacent euchromatin. 2.2.2. Heterochromatin Spread and Position Effect Variegation The expression of genes located in close proximity to the heterochromatin, or of transgenes integrated adjacent to the heterochromatin, is sometimes silenced. The silencing is often not complete; the gene is silenced in some cells, but in other cells it is expressed. This results in a mosaic expression, the so-called “variegated (patchy) expression pattern”. Because this expression pattern is brought about by the proximity of the gene to the heterochromatin, the phenomenon is called “position effect variegation” (PEV). Such heterochromatinization of the adjacent euchromatin is a major cause of silencing of transgenes when they are integrated close to the heterochromatin. Indiscriminate expansion of heterochromatin into euchromatin and mass silencing of genes along the length of the chromosome are prevented by various cis-acting elements and trans-acting factors. The most important cis-acting element shielding genes from the silencing effects of heterochromatin is the “insulator” element discussed in Chapter 1. Because heterochromatin spread and PEV are epigenetic effects, it is discussed in more detail in Chapter 3. 2.3. Nuclear Matrix, Matrix Attachment Regions, and Chromatin Loops In the nuclei of eukaryotic cells, the chromatin exists in the “nuclear matrix”, also called “nuclear scaffold”, “chromosome scaffold”, and “nucleoskeleton”. Nuclear matrix is a fibrous network containing various proteins, and it provides structural integrity to the nuclear interior. The chromatin is organized into independent loops that are anchored to the nuclear matrix or chromosome scaffold (Fig. 1). Evidence largely derived from in vitro experiments indicates that nuclear matrix may play a role in genome duplication, RNA synthesis and processing, anchoring of DNA loops, gene expression regulation, protein phosphorylation, mRNA transport, and a variety of other functions (32). An abundant protein in the nuclear matrix is the enzyme topoisomerase II, which is intimately associated with DNA replication. Another important group of proteins is called the “Structural Maintenance of Chromosomes” (SMC) proteins. One type of SMC proteins called “condensins” are essential for normal mitotic chromosome condensation, whereas another type of SMC proteins called “cohesins” are essential for the regulation of sister chromatid pairing (33). For comprehensive information about nuclear matrix-associated proteins in various species, see the nuclear matrix protein-database (NMP-db) at http://cubic.bioc. columbia.edu/db/nmpdb/. The DNA of the chromatin loops that are in physical association with the nuclear matrix contains specific sequences that actually attach to the nuclear matrix. These sequences are called the “matrix attachment regions” (MARs) or “scaffold/matrix attachment regions”

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(S/MARs). The MARs occur exclusively in eukaryotic genomes. The DNA elements of MARs may range from 300 bp to several thousand bp, and they typically occur every 5–200 kb of sequence. Most known MARs are AT-rich. The suitability of AT-rich island sequences to serve as MARs stems from the fact that AT-rich regions are flexible and easily destabilized so that in their single-stranded form, they can bind to specific DNA unwinding proteins. However, AT-richness alone is not sufficient to confer the matrix attachment property; it is also determined by the specific structure of the AT-rich DNA element, such as strong potential for extensive unpairing when subjected to superhelical strain (34,35). Various MAR-binding proteins (MARBPs) bind to the MARs and mediate MAR function (see 36 for a comprehensive list of MARBPs, their tissue distribution and function). Very large MARs are usually associated with heterochromatin. In general, MARs are vital elements of nuclear organization that are critical for DNA replication, regulation of chromatin structure, transcriptional enhancement or repression, cytosine methylation, and even genomic imprinting (37). MARs appear to be functionally conserved, since animal MARs can bind to plant nuclear scaffolds and vice versa (38). Matrix attachment of the chromatin DNA results in the formation of chromatin loops and folds, creating discrete structural and functional chromatin domains (Fig. 1). Such distinct domains create a microenvironment rich in regulatory proteins and enzymes required for their function. Each domain is also shielded from the effects of the neighboring domains (36). By introducing single and multiple copies of MAR-containing constructs (that contain transcriptionally active genes) in transgenic mouse and transfected cell lines, Heng et al. (39) studied the interaction of MARs with the nuclear matrix. Based on their observation, the authors proposed a model which depicts that structural MARs are physically anchored to the nuclear matrix, whereas functional MARs are located close to the genes and are not anchored to the nuclear matrix. In response to transcriptional demand, the functional MARs become anchored to the nuclear matrix containing the transcription machinery. In doing so, a functional MAR brings the gene close to it in contact with the transcription machinery contained in the nuclear matrix, thereby facilitating transcription. Chromatin loop size in various species, as determined by sedimentation and electron microscopy, shows a great degree of variation; usually between 30 and 120 kb (40). However, larger loop size has also been reported; for example, in human leukemia (HL-60) cells, the average loop size was estimated to be 315 kb (41). 3. GENOMIC DNA AS A MOSAIC OF ISOCHORES Early attempts to describe the organization of vertebrate genomic DNA at a macromolecular level led to the development of the “isochore” concept, the word “isochore” literally meaning “equal regions” (42–44). Isochores have been traditionally defined as >300-kb-long DNA segments in warm-blooded vertebrates (birds and mammals) with their characteristic, relatively homogeneous base composition. In other words, warm-blooded vertebrate genome may be viewed as a mosaic of isochores. The characteristic base composition of an isochore refers to the characteristic GC-content of the isochore, which differs significantly from that of the adjacent isochores. The homogeneity in base composition of an isochore refers to the homogeneity of its base composition compared to that of the chromosome in which it resides. Although isochores are often described as >300-kb DNA fragments, the isochore size may vary between 200 kb and 1.3 Mb (44), and the base composition–homogeneity of the isochores is true above 3-kb level (43,44). Thus, isochores (≥200 kb) are larger than individual chromatin loops, which are on an average 60–70 kb (40), but are usually smaller than chromosomal bands, which are usually 2–3 Mb or larger.

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The presence of different classes of isochores makes the genomes of warm-blooded vertebrates nonhomogeneous. This is in contrast to the genomes of cold-blooded vertebrates (fish, amphibians, and reptiles), where the entire genome appears to have homogeneous base composition. The DNA of cold-blooded vertebrates also never attains very high GC levels, whereas the DNA of warm-blooded vertebrates can attain higher GC levels. 3.1. Different Families of Isochores and Their Properties—GC-Content, Gene Distribution, and Codon Usage Bias Mammalian genomes can be fractionated into five families of isochores: L1, L2, H1, H2, and H3. Isochores L1 and L2 are GC-poor, and together they constitute about 62% of the genome, whereas H1, H2, and H3 are GC-rich, and they represent 22%, 9%, and 3% of the genome, respectively. The remaining 4% of the genome is represented by satellite DNA.c In chicken, there is one additional GC-rich isochore, called “H4”. The typical GC-contents of various isochores in humans are as follows: 39% for L1, 41% for L2, 45% for H1, 49% for H2, and 53% for H3. The distribution of genes in isochores is also nonuniform. The GC-poor isochores L1 and L2 have the lowest concentration of genes, while the most GCrich isochore H3 has the highest concentration of genes. The gene concentration (observed/ expected gene frequency) is about 0.55 for the L1 + L2 isochores, 1.2 for the H1 + H2 isochores, and 9.3 for the H3 isochores (45). Because of their higher gene concentrations, the H2 and H3 isochores together have been termed the “genome core”, which makes up about 12% of the genome (9% for H2 and 3% for H3) (43). The genome core contains one gene per 5–15 kb, while the other 84% of the genome consisting of the L1, L2, and H1 isochores (62% for L1 + L2 and 22% for H1) contains one gene per 50–150 kb. The genome core seems to exist in cold-blooded vertebrates as well even though the GC-richness of the genomes of cold-blooded vertebrates is much lower compared to that of warm-blooded vertebrates (46). In the human genome, the least abundant H3 isochore apparently contains 25% of the genes, and the genome core (H2 + H3 isochores combined) contains about 54% of the genes. In contrast, the most abundant GC-poor isochore contains only about 3% of the genes, and the L1 + L2 + H1 isochores combined contain about 46% of the genes (46). Although GC-rich isochores are gene-rich, the genes located in these isochores tend to be smaller in size because of smaller introns. Conversely, the GC-poor isochores are genepoor, but the genes located in these isochores are larger because of larger introns. Comparison of the exon/intron organization of vertebrate genes belonging to different isochore classes, as predicted by their GC-content in the third codon position, revealed that genes coding for long proteins (500 amino acids or longer) are almost twice as frequent in L1 + L2 isochores as in H3 isochores (47). Also, the total intervening sequences (sum of all introns) are on an average three times as long in L1 + L2 isochores as in H3 isochores. This factor varies between species from 2.1 (mouse) to 4 (cow). The authors found these patterns to be true among human, mouse, rat, cow, and even chicken genes, and are therefore likely to be common to all warm-blooded vertebrates. Thus, there is a direct relationship between the GC-richness and gene concentration in an isochore, but an inverse relationship between the GC-richness and gene size. The GC-rich isochores are localized in early replicating, highly transcribing chromatin, suggesting that chromatin has a much more open conformation in the genome core (48). c The completion of human genome sequencing has modified some of these original estimates slightly, and these will be discussed in the relevant sections.

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Genes that are in GC-rich isochores tend to have a higher GC-content in the third codon position, and genes that are in GC-poor isochores tend to have a higher AT-content in the third codon position (43,49). The AT- or GC-content in the third codon position of the genes within an isochore is generally close to that of the entire isochore. It has also been suggested that this genomic influence extends even to the first and second positions of the codon. Such base-composition bias in the gene sequence, which is driven by the isochore location of the gene often results in codon usage bias. The codon usage bias, in turn, may affect the amino acid composition of the protein. For example, increased GC-content is likely to include amino acids encoded by GC-rich codons (Pro, Ala, Arg, Gly). In contrast, increased AT-content is likely to include amino acids encoded by AT-rich codons (Phe, Ile, Met, Tyr, Asn, Lys) (43,49). While analyzing the sequence of the rat lst-1 (rlst-1) gene (Oatp-4, Slc21a10, Slco1b2), it was observed that the AT-content of the third codon position of rlst-1 coding sequence is about 60%, and that of the entire coding sequence is about 64%. Calculation of the AT-content of the entire locus (NC_005103) from the rat genome sequence database also shows a 60% AT-content for the entire locus. Therefore, a bias toward the usage of ATrich codons is likely. Indeed, of all the codons encoding serine, leucine, and threonine in rlst-1 protein, 61%, 80%, and 73%, respectively, are AT-rich codons, and these three amino acids together account for about 25% of total amino acids of rlst-1 protein (50). For comparison, Choudhuri et al. (50) also analyzed the human LST-1 (hLST-1/ OATP2/OATP-C/SLC21A6/SLCO1B1) gene for its AT-content and codon usage bias. The information on hLST-1 locus (NC_000012) was obtained from the human genome sequence database. The entire hLST-1 locus, located in the short arm of chromosome 12 (12p), is about 109-kb long and has an AT-content of about 65%. When only the exon and intron contents of the hLST-1 gene were analyzed, the AT-content was found to be the same as that of the entire locus (65%). In addition, within the coding sequence, the fractions of AT-rich codons as well as the AT-content of the third codon position were both found to be about 68%. Therefore, the overall sequence of hLST-1 gene is AT-rich, and it shows an AT-rich codon bias similar to that of rlst-1 gene. Because of the base composition-driven codon usage preference, the H3 isochore family has an extremely biased codon usage; a number of codons being absent or very scarce because of very high GC levels in the third codon positions (43). The distribution of isochores in the chromosome also tends to show some relationship with various chromosomal bands. For example, G (Giemsa) bands essentially consist of the GC-poor isochores L1 + L2, T (telomeric) bands consist primarily of the GC-richest isochore H3, while R (reverse) bands are formed almost equally by GC-rich isochore H1 and GC-poor isochores L1 + L2 (48). There are three main views on the origin of isochores. They are selectionist hypothesis, mutationist hypothesis, and recombination hypothesis. None of these hypotheses has been proved to be the only mechanism explaining the origin of isochores; there are evidence for and against each of these hypotheses. A detailed discussion on the origin of isochores is beyond the scope of this chapter. 3.2. In Search of the “Iso” in Isochores The publication of the human genome draft sequence (51) and several other genomes, plus some completely sequenced chromosomes (52), has ignited a debate about the validity of the isochore concept in its current form. The most severe criticism questions the very existence of isochores and claimed that the strict notion of isochores as compositionally homogeneous could be ruled out; hence, “. . . isochores do not appear to merit the prefix ‘iso’ ” (51). Nevertheless, the International Human Genome Sequencing Consortium

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(IHGSC) acknowledged that “…the genome clearly does contain large regions of distinctive GC content”, which the authors refer to as “GC content domains” instead of isochores. Even before the completion of the human genome sequencing, Nekrutenko and Li (53) arrived at similar conclusions. They observed that eukaryotic genomes, including mammalian genomes, are compositionally much more heterogeneous than predicted by the isochore model; however, human (or mammalian) genomes still contain long, compositionally homogeneous GC-rich segments (isochores). Interestingly, the other report of human genome sequencing did not come to the same conclusion as the IHGSC (54). They reported: “The most GC-rich fraction of the genome, H3 isochores, constitute more of the genome than previously thought (about 9%), and are the most gene-dense fraction, but contain only 25% of the genes, rather than the predicted 40%. The low GC L isochores make up 65% of the genome, and 48% of the genes.” They also found that chromosomes 17, 19, and 22 have a disproportionate number of H3-containing bands and the highest gene density, whereas chromosomes X, 4, 18, 13, and Y had the fewest H3 bands and the lowest gene density. In subsequent analysis of the human genome sequence by others, the original assertion of IHGSC that the isochore concept could be dismissed has been challenged. In a rebuttal to the notion that “isochores do not merit the prefix iso”, Li et al. (55) published their analysis that “isochores merit the prefix iso” and claimed that the statistical test applied by the IHGSC was not appropriate. A similar argument questioning the methodology used to identify isochores has been put forward by Clay and Bernardi (56) in response to the claim of Cohen et al. (57) that the isochore theory may have reached the limits of its usefulness as a description of genomic compositional structures. Clay and Bernardi (56) claimed that the method followed by Cohen et al. (57) over-fragmented GC-rich isochores (and also many GC-poor isochores) beyond recognition, and thus systematically lost the GC-rich isochore information in their analysis. Recently, using a nonoverlapping 100-kb window-size genome partitioning, an isochore map of the human genome has been created (58). The authors obtained about 3,200 isochores which cover the entire human genome. The reason for using a 100-kb window-size was the finding that the variance of the plots of GC versus window size was high below 100 kb, but tended to plateau beyond 100 kb. The array of GC levels of the 100-kb windows in each chromosome was scanned for jumps (~1%–2%) based on mean GC-content. The isochore map they produced provides information about the size, GC-content, standard deviation, and coordinates of the isochores on the human genome map. The authors found that the isochores are arranged in blocks, and their distribution patterns nicely correspond to the chromosomal bands. The authors suggested that the isochores may constitute the molecular basis of chromosomal bands. In a subsequent publication, the same group (59) expanded and reemphasized the idea that the molecular basis of the classical G and R bands in human chromosomes can be explained by the presence of isochores and isochore boundaries. The authors emphasized that such correspondence between the isochores and the chromosomal bands provides a higher level resolution (100 kb) definition of chromosomal bands than the classical cytogenetic definition with a resolution level of 2–3 Mb. Recent evidence suggests that isochores are gradually disappearing from mammalian genomes. Isochores probably originated in the past due to a strong mutational pressure, which subsequently disappeared leading to the gradual disappearance of the isochores, although the cause of the mutational pressure and its disappearance is not known. A conservative view about the creation of isochore has been that isochores are predominantly the result of accumulation of selection-neutral changes caused by nonuniform mutation or repair biases, such

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as biased gene conversion (BGC) during homologous recombination (60). Precisely defining isochores in terms of size and composition, as well as isochore boundaries, has remained a major challenge. Nevertheless, there are definite and recognizable GC-rich and GC-poor segments in the genome, and such regional base-composition bias may relate to biological properties that are yet to be understood. As suggested by IHGSC, “The correlation between GC-content domains and various biological properties is of great interest, and this is likely to be the most fruitful route to understanding the basis of variation in GC content” (51). 4. GENOMIC LANDSCAPE AS REVEALED BY GENOME SEQUENCING Genome sequencing and analysis of the sequence can immediately reveal some large-scale features of the genome such as gene content, gene size, and gene distribution; distribution of the GC-content and CpG islands; noncoding genomic landscape; recombination rates; information about the repeat content and genomic parasites; rates of nucleotide substitution and genome evolution and so on. In the following discussion, emphasis will be laid on the details of the human genome, which will be compared primarily with the chimpanzee genome, and to a lesser extent with the genomes of rhesus monkey and mouse. The rationale for choosing chimpanzee genome as the primary comparator is its evolutionary relationship with human. Chimpanzee (Pan troglodytes) is a hominid and the closest evolutionary relative of humans. The chimpanzee–human split occurred about 5–6 million years ago. Rhesus monkey (Macaca mulatta) is an old-world monkey, and the Macaca–human split occurred about 25–30 million years ago. Mouse is a rodent and one of the most widely used eutherian mammals in research. The rodent–primate split from the last common ancestor occurred about 75–95 million years ago. Following completion of the human genome sequencing, a deluge of publications are being added everyday to the public database. Most of these efforts are focused on analyzing the human genome and other genome sequences using various novel statistical tools. The proximate goal is to gain insight into the evolution of genome architecture and organismal complexity and identify various regulatory sequences and molecular entities controlling genome expression and integrity. The ultimate goal is to uncover the molecular basis of humanness, as well as identify genomic hallmarks for specific disease susceptibility in individuals, and finally develop individualized medicine and nutrition. In the following discussion, some fundamental information on the structural, functional, and evolutionary aspects of the human genome will be discussed, and comparisons will be made wherever necessary with other genomes.d 4.1. Human Genome The information discussed below is primarily based on the reports of the international human genome sequencing consortium (51,61). These two references have been cited only when relevant and have not repeated again and again. Other references have been cited as necessary. 4.1.1. The Protein-Coding Landscape Genome sequencing revealed that any two humans are about 99.9% identical in their DNA sequence. Thus, it is through studies of the tiny fraction of the human genome, d

A discussion or even an attempt to summarize the deluge of publications in one chapter is an impossible task. No attempt has been made to capture all relevant references. Rather references have been cited to make a point of discussion.

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which constitutes the genetic variation between individuals, that the phenotypic variabilities and disease susceptibilities can be understood in molecular terms (62). The human genome contains about 3 billion base pairs (3 Gb) and is contained in 23 pairs of chromosomes (22 pairs of autosomes + 1 pair of sex chromosomes). The protein-coding sequences comprise about 1.2%–1.5% of the genome. Initial estimates predicted a total of 30,000–40,000 protein-coding genes (51,54), which is about twice as many as in worm or fly. These numbers have since been revised. The current estimate predicts that the human genome contains little more than 22,000 protein-coding genes (61). At least 50% of all human protein-coding genes appear to be subject to alternative splicing involving mainly the coding sequence, each producing two or more alternatively spliced transcripts. About half of the human genome is derived from transposable elements which have contributed dozens of genes. However, there has been a marked decline in the overall activity of both DNA transposons and long terminal repeat (LTR) retrotransposons in the hominid lineage. Hundreds of human genes appear to have resulted from horizontal transfer from bacteria at some point in the vertebrate lineage (51, 54). The average length of human protein-coding genes is about 27 kb (51), and there are an average of nine exons per transcript (61). The average length of 5′-UTR, internal exons, 3′-UTR, and introns is about 300, 145, 770, and 3,365 bp, respectively. On average, the coding sequence in human genome codes for about 447 amino acid residues. The largest number of exons (234) was identified in titin gene (54). For splice site sequence, about 98% of the introns have the canonical GT–AG, while about 0.80% have GC–AG, and 0.10% have AT–AC. The reason AT–AC introns are the least prevalent is probably because they use a completely different splicing machinery than the others (discussed in Chapter 1). More than 40 extremely short exon sequences (less than 19 bp) were detected, and they are all purine-rich (>70% purine) (51). The GC-rich regions are gene-dense containing many compact genes (due to smaller intron size). With an increase in GC-content from 30% to 50%, the relative gene density increases more than 10-fold. 4.1.2. Gene Birth and Death in Human Lineage In the finished sequence of the human genome (61), the birth and death of human genes were estimated. To identify local gene duplication (birth), clusters of nearby homologous gene sequences were searched, and the divergence was assessed at sites that are likely to be selectively neutral. A total of 1,183 genes exhibit such divergence from a neighboring gene. These genes often fall within larger clusters of paralogous genes (genes that are created by duplication). Genes with greater divergence reflect older duplications. Comparison with mouse genes confirms that these genes are more closely related within each species than between the two species in nearly all cases. This is expected for genes arising by duplication after the divergence of the human and rodent lineages. The recent duplications involve genes with immune and olfactory function as well as those likely to be involved in reproductive functions, such as pregnancy-specific beta-1-glycoprotein and choriogonadotropin beta proteins, which may be involved in the extended gestational period in humans. Therefore, either there may have been a true explosion in the rate of gene duplication in the primate lineage, or there may be an ongoing process of gene conversion of older gene duplication events. Gene death is another phenomenon that sheds light on lineage-specific genome evolution. To identify gene death, the genome was scanned for sequences of recent nonprocessed

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pseudogenes, that is, nearly intact duplicated human genes that have recently acquired inactivating mutation. Database search showed that 27 nonprocessed pseudogenes in humans are also found as nonprocessed pseudogenes in chimpanzee, while five human nonprocessed pseudogenes are functional genes in chimpanzee, indicating that these genes were inactivated in human lineage following human–chimpanzee split about 5–6 million years ago. Of these 32 total nonprocessed pseudogenes fixed in the human population, 10 are derived from olfactory receptors. The remaining 22 include a wide variety, such as genes homologous to a cationic amino acid transporter, a serine-threonine kinase, a calreticulin, a putative G-protein-coupled receptor, and a cystatin. 4.1.3. Genomic GC-Content Average GC-content in the human genome is about 41%, but local GC-contents show wide variations. For example, the most distal 48 Mb, that is, toward the telomere of chromosome 1p (short arm p of chromosome 1) there is an average GC-content of about 47%. In contrast, a 40-Mb region of chromosome 13 has only 36% GC-content. There are also examples of huge variations in GC-content between adjacent chromosomal regions. For example, the average GC-content on chromosome 17q (long arm q of chromosome 17) is 50% for the distal 10.3 Mb, but it drops to 38% for the adjacent 3.9 Mb. Even wider variations were observed in regions less than 300 kb, ranging from 33.1% to 59.3%. The authors tried to look for signatures of isochores originally proposed by Bernardi and colleagues and discussed above. The authors concluded that some of the local heterogeneity in GC-content is attributable to transposable element insertions. 4.1.4. CpG Islands CpG islands are genomic regions that contain a high frequency of cytosine–guanine (CG) dinucleotides. To be recognized as a CpG island, a sequence must fulfill three criteria: (i) have a GC-content of 0.50 or greater; (ii) an observed-to-expected CpG dinucleotide ratio of 0.60 or greater; and (iii) both occurring within a sequence window of 200 bp or greater (63). CpG islands are associated with the 5′ends of many housekeeping as well as regulated genes. Identification of potential CpG islands during sequence analysis thus helps define the extreme 5′ends of genes. Often CpG islands extend into the promoter and sometimes even downstream into the transcription unit. For transcriptionally active genes, the C of CpG is not methylated. A striking feature of the human genome is the dearth of CpG dinucleotides interrupted occasionally by CpG islands. Genes whose promoters are especially rich in CpG sequences tend to be expressed in most tissues. About 72% of human promoters have high CpG content, and 28% have low CpG content (64). The CpG in inactive genes are usually methylated at C to suppress their expression. The methyl-C of methyl-CpG dinucleotides tends to mutate to T by occasional spontaneous deamination, thus methyl-CpG dinucleotides on a DNA strand tend to mutate to TpG. Over evolutionary time, such decay of methyl-CpG accounts for the five times lower observed frequency of CpG islands in the human genome than the expected frequency (observed = 0.8%, expected = 4%; the expected frequency is the product of the frequencies of G and C in the genome). Absence of methylation slows CpG decay because the product of normal cytosine deamination is uracil, which is readily recognized as an aberrant DNA base and is repaired by the DNA repair machinery. Therefore, nonmethylated CpG dinucleotides occur at a predicted frequency.

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The human genome contains ~29,000 CpG islands, 65% of which have 60%–70% GC-content. The longest CpG island on chromosome 10 is 36,619-bp long, and 322 of them are longer than 3,000 bp. The role of these large CpG islands remains to be discovered. The number of CpG islands greatly varies among the chromosomes. Most chromosomes have 5–15 CpG islands per Mb (mean = 10.5 per Mb). Chromosome Y has only three CpG islands per Mb, while chromosome 19 has 43 CpG islands per Mb. Interestingly, the relative density of CpG islands was found to correlate with estimates of relative gene density (51). 4.1.5. Recombination Rate Analysis of the recombination rate revealed that the average recombination rate increases as the length of the chromosome arm decreases. Long chromosome arms have an average recombination rate of about 1 cM per Mb, whereas the shortest arms have a rate of 2 cM per Mb. A similar trend has been reported for the yeast genome. Lengthening or shortening of yeast chromosomes results in a compensatory change in recombination rate. The recombination rate is suppressed near the centromeres but is higher in the distal portions of most chromosomes, largely in the terminal 20–35 Mb. Why recombination is higher on smaller chromosome arms is open to conjecture. It should be emphasized in this context that unlike the inverse relationship between recombination rate and chromosome arm length, the relationship between gene-distance and recombination frequency is direct. Within a limit, the closer two genes are, the lower the probability of recombination; similarly, the further apart two genes are, the higher the probability of recombination between them. 4.1.6. Repeat Content Before discussing the repeat content of the human genome as uncovered by genome sequencing, a discussion of some traditional concepts and methods that provided the initial clues to the repeat content of the genome is relevant. 4.1.6.1. C-value, C-value paradox, and C0t analysis An interesting initial observation in genome biology was that genome size does not correlate well with organismal complexity. For example, Homo sapiens has a genome that is 200 times as large as that of the yeast S. cerevisiae, but 200 times as small as that of the single-celled Amoeba dubia. This apparent lack of relationship between the genome size and organismal complexity was termed as “C-value paradox”(65). The term “C-value” is derived from the notation used by Hewson Swift in 1950 to denote the amount of DNA in an organism’s haploid genome. DNA renaturation (annealing) kinetics experiments, known as “C0t” analysis and developed in the 1960s, showed that eukaryotic genomes contain large quantities of repetitive sequence that could bulk-up the genome size (66). The existence of extensive repetitive sequence elements in eukaryotic genomes may provide at least a partial explanation for the C-value paradox. In C0t analysis, samples of sheared genomic DNA are heat-denatured and then allowed to renature over time producing different C0t values [C0t = beginning nucleotide concentration in moles per liter (C0 ) × annealing time in seconds (t) × a buffer factor ( f ) based on the cation concentration that determines the speed of annealing. For example, f = 1.0 for

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0.12M Na-phosphate buffer and f = 5.82 for 0.5M Na-phosphate buffer.e If genomes that are being compared are annealed in the same buffer condition, then f can be ignored]. For each sample, annealed DNA (dsDNA) is separated from single-stranded DNA (ssDNA) using hydroxyapatite chromatography, and the percentage of the sample that has remained single-stranded (% ssDNA; c/c0) is determined. The logarithm of a sample’s C0t values is plotted against the corresponding % ssDNA (c/c0) values to obtain the C0t curve that shows progression from little or no renaturation to near-complete renaturation over time. Hence, it is a semilogarithmic plot. The shape of the C0t curve is determined by sequence complexity, that is, the proportion of single-copy and repetitive sequences present in the DNA (Fig. 2A). Four classes of sequence are found in eukaryotic DNA: single copy, slightly repetitive ( > 1–10 copies), middle repetitive ( > 10 to many thousand copies), and highly repetitive (~100,000 copies or more). Genomes that contain repetitive sequence anneal differently from genomes with single-copy unique sequences, thereby generating different shapes of C0t curves. By comparing the initial concentration of DNA with the fraction showing annealing characteristics of repetitive sequences, the fraction of repetitive sequences in the genome can be determined. This is how it was initially determined that a large fraction (about 50%) of the genomes of higher eukaryotes including humans contain repetitive sequences. One particular value that is useful in C0t analysis is C0t½, which is the C0t value where half of the DNA has annealed. A higher C0t½ value implies a slower annealing reaction. Slower annealing occurs due to greater sequence complexity, that is, due to the occurrence of higher fraction of unique sequences, as well as due to bigger genome size. In prokaryotes where the DNA does not contain much repetitive sequences, C0t½ values are directly related to genome size. For eukaryotes, highly repetitive nature of DNA adds one more layer of complexity in annealing kinetics. In general, the larger the genome size, the longer it takes for any one sequence to encounter its complementary sequence in the crowd of DNA fragments and anneal (higher C0t½ values). Also, the more complex the genome, that is, the higher the proportion of unique sequences in the genome, the longer it takes for any two complementary sequences to encounter each other and anneal (higher C0t½ values). In contrast, if the genome is full of repetitive sequences, a sequence will encounter its complementary sequence faster and the annealing reaction will go faster (lower C0t½ values). Sometimes two genomes may yield similar-shaped C0t curves indicating similar nature of sequence complexity, but the difference in genome size can be understood based on their C0t½ values (Fig. 2B). With the advent of molecular cloning and sequencing techniques, C0t analysis has become an experimental approach of the past. However, C0t analysis provided the first insight of both prokaryotic and eukaryotic genome structure, including sequence complexity and the repetitive nature of higher eukaryotic genomes. 4.1.6.2. Types of repeat sequences in the human genome Repeat sequences account for at least 50% of the human genome. There are five classes of repeats: (i) Interspersed repeats — transposon-derived; (ii) Processed pseudogenes — retroposed copies of cellular genes; (iii) Simple sequence repeats — (A)n, (CA)n, or (CGG)n, etc.; (iv) Segmental duplications — blocks of ~1–200 kb (or longer) sequences that have been copied from one region of the genome and integrated into another region; and (v) Tandem repeat blocks — various blocks of sequence, such as at centromeres, telomeres, the short arms of acrocentric chromosomes, and ribosomal gene clusters. e

Source: www.mgel.msstate.edu/pdf/cot.pdf

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A 100 Highly repetitive DNA C0t ½ = ~ 0.12

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Figure 2 Hypothetical C0t curves. Figure 2A shows a hypothetical C0t curve of a mammalian DNA. The shape of the C0t curve is determined by sequence complexity, that is, the proportion of single-copy and repetitive sequences present in the DNA. The C0t½ values demonstrate the speed with which various types of sequences, such as highly repetitive, middle repetitive, and single-copy sequences anneal. Figure 2B shows two different DNA samples that yield almost identical-shaped C0t curves, suggesting similar nature of sequence complexity of these two DNA samples. However, DNA 1 anneals 100 times faster than DNA 2, indicating that DNA 1 represents a smaller genome size than DNA 2.

Repeats were traditionally described as “junk”. However, recent findings suggest that they actually represent an extraordinary trove of information about biological processes. In addition to providing a rich paleontological record holding crucial clues about evolutionary events and forces, repeats can be used as markers; they can reshape a genome by causing ectopic rearrangements creating entirely new genes, modifying and reshuffling existing genes, and modulating the overall GC content. The human genome was the first repeat-rich genome sequenced. 4.1.6.2.1. Interspersed (transposon-derived) repeats Most human repeat sequences are derived from transposable elements, which constitute about 45% of the human genome.

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In mammals, there are two main types of transposable elements: DNA transposons (class II) and retrotransposons (class I). Retrotransposons are of two types based on the presence or absence of long terminal repeats (LTRs), such as LTR retrotransposons and non-LTR retrotransposons. The non-LTR retrotransposons have two major types: long interspersed elements (LINEs) and short interspersed elements (SINEs). All retrotransposons transpose through RNA intermediates, while the DNA transposons transpose directly as DNA. Transposable elements that carry out their own transposition are called “autonomous”, such as LTR retrotransposons and LINEs, whereas those that cannot are called “nonautonomous”, such as SINEs, and they need the help from autonomous elements for transposition. An at-a-glance classification of transposon-derived repeat elements is presented in Fig. 3. I. DNA transposons: These are autonomous transposable elements, making up about 3% of the human genome. The distribution of DNA transposons does not show any significant bias toward AT- or GC-rich regions of the genome. DNA transposons resemble bacterial transposons; they have terminal inverted repeats and they encode a transposase enzyme that binds near the inverted repeats and mediates mobility through a cut-and-paste mechanism. The transposase makes a staggered cut at the target site of the genomic DNA producing sticky ends, cuts out the transposon, and ligates it into the target site. Following the ligation of one end of the transposon to the target DNA in each strand, DNA polymerase fills in the gap by extending the ssDNA on the other end of each strand, and DNA ligase seals the gap (Fig. 4). The human genome contains at least seven major classes of DNA transposon. II. LTR retrotransposons: These are autonomous transposable elements, making up about 8% of the human genome (~ 450,000 copies). Exogenous retroviruses seem to have arisen from endogenous retrotransposons by the acquisition of a cellular envelope (env) gene. Like DNA transposons, the distribution of LTR retrotransposons (or LTR retroposons) does not show any significant bias toward AT- or GC-rich regions of the genome. LTR retroposons are flanked by direct LTRs, that is, long terminal direct

Transposon-derived repeats

DNA Transposon (Class II)-derived (direct transposition, autonomous)

LTR Retrotransposon-derived (contains LTR, autonomous e.g., HERV)

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Figure 3 genomes.

Retrotransposon (Class I)-derived (transposition through RNA intermediate)

Non-LTR Retrotransposon-derived (does not contain LTR)

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Various types of transposon-derived repeats in the human genome and other eukaryotic

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Target DNA

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Transposase-mediated staggered cut and transposon integration DNA polymerase fills the gap and ligase seals the nick

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Figure 4 DNA transposons have terminal inverted repeats. They encode a transposase that binds near the inverted repeats and makes staggered cuts at the target site of the genomic DNA producing sticky ends. The transposase also cuts out the transposon and ligates it into the target site. Following the ligation of one end of the transposon to the target DNA in each strand, DNA polymerase fills in the gap by extension on both strands and ligase seals the nick. Transposon insertion creates direct repeats that flank the inserted transposon.

repeats that contain all the necessary transcriptional regulatory elements. They contain gag and pol genes, which encode a protease, reverse transcriptase, RNAse H, and integrase. Reverse transcription, primed by a tRNA, occurs in the cytoplasm (in contrast to the nuclear location and chromosomal priming of LINEs as described below). Although there are a variety of LTR retrotransposons, only the vertebrate-specific endogenous retroviruses (ERVs) appear to have been active in the mammalian genome. About 85% of the LTR retrotransposon-derived elements in the genome consist only of isolated LTR where the internal sequence has been lost by homologous recombination between the flanking LTRs. III. Line elements: These are autonomous transposable elements, making up about 20% of the human genome and occurring at much higher density in AT-rich regions of the genome. In humans, the LINEs (L1) are about 6 kb long. L1 is a primate-specific LINE, which is also the major LINE in human genome constituting about 17% of the genome (~ 520,000 L1 elements). A LINE contains a pol II promoter and two open reading frames (ORFs), one encoding the endonuclease and the other encoding the reverse transcriptase. LINE activity proceeds as follows: RNA pol II transcribes the LINE DNA into LINE RNA; the LINE RNA is translated into proteins; the proteins and RNA join together and reenter the nucleus; the endonuclease cuts a strand of the target genomic DNA, often in the intron of a gene; the reverse transcriptase copies the LINE RNA into LINE DNA which is inserted into the target DNA forming a new LINE element there. The length of LINEs may vary because transcription may continue downstream into additional DNA sequence producing a longer transposed element, or reverse transcription of LINE RNA may conclude prematurely producing a shortened transposed element. The LINE machinery is believed to be responsible for most reverse transcription in the genome, including the retrotransposition of the nonautonomous SINEs and the creation of processed pseudogenes. Three distantly

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related LINE families are found in the human genome: LINE1, LINE2, and LINE3. Only LINE1 (L1) is still active. IV. Sine elements: These are nonautonomous transposable elements that need the help of L1 elements for transpostition. SINEs occur at much higher density in GC-rich regions of the genome and they make up about 13% of the human genome. SINEs are short sequences (about 100–400 bp) and they contain an internal pol III promoter but do not encode any proteins. All currently known SINEs are derived from tRNA and 7SL RNA genes. Most nonautonomous SINEs share the 3′ end with a resident LINE. The only active SINE in the human genome is the Alu element, which is the major SINE constituting about 11% of the genome (~1 million Alu elements). Alu elements are primate genomic-specific SINEs, which are derived from the small cytoplasmic 7SL RNA. Each Alu sequence is a dimer of two 7SL RNA-derived monomer units, connected by an A-rich linker. It is surprising that SINEs depend on LINEs for their insertions but they show opposite insertion bias: Alu in GC-rich regions but LINE in AT-rich regions. One explanation is that SINEs had been initially inserted in AT-rich DNA along with LINEs, but the distribution was subsequently reshaped by evolutionary forces. This is evidenced by the observation that recent Alu elements show a preference for AT-rich DNA like that of LINEs, whereas progressively older Alu elements show a progressively stronger bias toward GC-rich DNA. V. Variations in interspersed repeat density: There is wide variation in repeat density in the genome. Some regions are extraordinarily dense in repeats, such as a 525-kb region on chromosome Xp11 (X chromosome, short arm p, region 1, band 1), with an overall transposable element density of 89%. This region contains a 200-kb segment with 98% density as well as a segment of 100 kb in which LINE1 sequences alone comprise 89% of the total sequence. In contrast, some genomic regions are nearly devoid of repeats. The four regions with the lowest density of interspersed repeats in the human genome are the four homeobox gene clusters: HOXA, HOXB, HOXC, and HOXD. The near absence of repeats may be a sign that sequence disruptions cannot be tolerated in this gene cluster. Each locus contains regions of about 100 kb containing less than 2% interspersed repeats. A similar trend is also true for the HOX clusters in mouse, rat, and baboon. Transposons contributed to the birth of many genes as well as various parts of the gene. For example, 20 human genes had already been recognized as transposon-derived. These include the genes encoding RAG1 and RAG2 recombinases and the major centromere-binding protein (CENPB). From the draft human genome sequence 27 more genes were identified bringing the total to 47. All but four are derived from DNA transposons. The transcription terminators of many genes are derived from LTR retroposons. Other genes employ regulatory elements derived from repeat elements. 4.1.6.2.2. Processed psuedogenes There are three types of pseudogenes in the genome: unitary pseudogene (cellular gene inactivated by acquiring mutations); duplicated pseudogene (a gene born by tandem duplication of a cellular gene, then becoming inactivated by acquiring mutations, while the other copy functions normally); and processed pseudogene (retrotransposed pseudogene or retropseudogene). Processed pseudogenes are retrotransposed copies of mRNAs (in the case of protein-coding genes). Because they are reverse transcribed from mRNAs, they are characterized by one or more of the following structural features: (i) presence of a poly(A) tail, (ii) no introns, (iii) loss of upstream regulatory sequences, and (iv) disabled ORF. The ORF can be disabled in one of the following ways: (i) from premature stop codon, (ii) from accumulating various mutations, and (iii) from ORF truncation.

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In the human genome build34, the total number of pseudogenes (processed plus nonprocessed) seems to be around 17,000 (refer to Gerstein group database http://www. pseudogene.org/human/ and links therein), although the reports from various sources vary significantly depending on annotation. Processed pseudogenes are made through LINE-mediated mRNA retrotransposition. In humans, LINEs are the major source of insertional mutagenesis. In addition to its own reverse transcription and transposition, the LINE enzymatic machinery can sometimes be diverted to direct the same (retroposition) of cellular mRNA transcripts. When LINEs are active, mRNA transcripts that are in the vicinity can be swept up and retrotransposed. This results in the creation of processed pseudogenes in the genome (67). Many processed pseudogenes are transcribed even though they do not encode any functional protein. It has been estimated that the human genome contains 166–233 transcribed processed pseudogenes, which represent about 4%–6% of all pseudogenes in the genome (68). Recent evidence indicates that transcribed processed pseudogenes may actually perform some regulatory functions. Hirotsune et al. (69) provided evidence that the transcribed pseudogene Makorin1-p1 in mice stabilizes the Makorin1 mRNA. Based on their experimental results, the authors hypothesized that the first 700-nt region of Makorin1 mRNA contains a recognition site for a putative destabilizing factor. Because this 700-nt region is shared by both the normal gene-encoded Makorin1 and pseudogene-encoded Makorin1-p1 mRNAs, the expression of the pseudogene provides a means of titrating out the destabilizing factor by direct competition. Although Makorin1 is not found in humans, other such examples may be reported in the future. 4.1.6.2.3. Simple sequence repeats Simple sequence repeats (SSRs), also known as “simple sequence tandem repeats” (SSTRs), are perfect or slightly imperfect tandem short repeat sequences. SSRs with shorter repeat units (n = 1–13 bases) are called “microsatellites”, whereas those with longer repeat units (n = 14–500 bases) are called “minisatellites”. SSRs are thought to arise by replication slippage or unequal crossing over (discussed in Chapter 1). SSRs comprise about 3% of the human genome with the greatest contribution coming from dinucleotide repeats (0.5%). There is approximately one SSR per 2 kb of the human genome and the number of nonoverlapping tandem repeats is 437 per Mb. Of the dinucleotide repeats, the share of the AC dinucleotide repeat is the highest (50%), followed by AT (35%), AG (15%), and GC (0.1%). Similarly, the frequencies of the trinucleotides repeats are AAT (33%), AAC (21%), ACC (4.0%), AGC (2.2%), ACT (1.4%), and ACG (0.1%). When the dinucleotide and trinucleotide repeats are combined, the fraction of AC is still the highest (28%) followed by AT (19%), AG (8%), AAT (4%), AAC (2.5%), AGG (1.5%), AAG (1.4%), and the rest being less than 1% each. Thus, trinucleotide SSRs are much less frequent than dinucleotide SSRs. SSRs have been extremely important in human genetic studies because they show a high degree of length polymorphism in human population due to frequent replication slippage, and hence can serve as polymorphic markers in the genome. The repeat CAG in the ORF (encoding glutamine) has been implicated in many “trinucleotide repeat expansion disorders” (discussed in Chapter 1). 4.1.6.2.4. Segmental duplications Segmental duplication involves the transfer of 1–200 kb (or greater) blocks of genomic sequence to one or more locations in the genome. Hence segmental duplications create paralogous loci (loci created by duplication) that may result in gene CNVs; CNVs result

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in altered gene dosage between individuals. The duplicated regions represent low-copy repeats and have >90% identity. Such strong sequence identity suggests that they are relatively recent in origin. The finished sequence of the human genome reported about 5.3% of the genome as segmental duplications with a possibility for further increase because many of the remaining euchromatic gaps are associated with segmental duplications (61). Various other publications that followed have reported slightly higher percentage of segmental duplications in the human genome. Segmental duplications can be of two types: interchromosomal and intrachromosomal duplications. In interchromosomal duplications, segments are duplicated among nonhomologous chromosomes. Many interchromosomal duplications map near the centromeric and telomeric regions. Intrachromosomal duplications occur within a particular chromosome. It involves several duplicated segments that mediate chromosomal structural rearrangements, which is often associated with genetic disease. Intrachromosomal duplications are more frequent (~3.5%) than interchromosomal duplications (~2%). In humans, chromosomes 7, 9, 15, 16, 17, 19, 22, and Y are significantly enriched in both inter- and intrachromosomal duplications (70). There seems to be a strong association between the location of segmental duplications and regions of chromosomal instability. Segmental duplications predispose the regions containing these to recurrent rearrangement by nonallelic homologous recombination (paralogous recombination), resulting in deletion, duplication, or inversion. Segmental duplications have been implicated in >25 human genetic diseases, such as Smith-Magenis syndrome, Charcot-Marie-Tooth syndrome 1A, and Prader-Willi/Angelman syndrome. Segmental duplications provide raw materials for the evolution of genome and are also responsible for interindividual variations because they contribute to large-scale CNVs within the human population. Segmental duplications have apparently contributed more to the genetic difference between chimpanzee and human than single-base mutations (71,72). 4.1.6.2.5. Tandem repeat blocks This class includes large tandem repeat blocks of sequence known as “satellite DNA”, so named because they form a second or satellite band when genomic DNA is separated on a density gradient. There are various types of satellite DNA, such as alpha, beta, and gamma satellites, satellites I, II, and III. In general, satellite DNA shows a great degree of variability among individuals, particularly with regard to the number of repeats at a given locus. The most common satellite DNA type is the alpha satellite (alphoid DNA) with a repeat unit size of 171 bp. It is the major sequence element in centromeric heterochromatin of all human chromosomes and is involved in centromere function, such as microtubule attachment. Some of the 171-bp units contain a 17-bp binding site called the “CENP-B box” that binds the centromere-specific protein CENP-B. The consensus sequence of the CENP-B box is 5′-(C/T)TTCGTTGGAA(G/A)CGGGA-3′. The 171-bp monomer units are arranged in higher order repeats ranging in length from several hundred to several thousand kilobases. The Celera genome sequencing group (54) reported that about 5% of the sequence reads were identified as alpha satellite sequence. In the initial sequencing of the human genome, these highly repetitive sequence regions were not investigated. These regions include the 24 centromeres (heterochromatic) that largely consist of alpha satellite repeats and the secondary constrictions immediately adjacent to the centromere on chromosomes 1q, 9q, and 16q that contains various satellite repeats, such as beta and gamma satellites, satellites I, II, and III.

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4.1.7. Noncoding RNA (ncRNA) Genes The various classes of ncRNA are transfer RNA (tRNA), ribosomal RNA (rRNA), small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), telomerase RNA, 7SL signal recognition particle RNA, Xist RNA, microRNA (miRNA), small-interfering RNA (siRNA), trans-acting siRNA (tasiRNA), small-scan RNA (scnRNA), repeat-associated siRNA (rasiRNA), and Piwi-interacting RNA (piRNA). The functions of some of the ncRNAs are well understood. In the draft sequence of the human genome, about 500 tRNA genes and more than 300 tRNA-derived putative pseudogenes were identified. More than 50% tRNA genes (a total of 280) are found in chromosomes 1 and 6. Out of this, a total of 140 tRNA genes that are clustered in a 4-Mb-long region of chromosome 6 represent more than 70% of the tRNAs required for translation. In this cluster, only four types of tRNAs that decode Asn, Cys, Glu, and selecocysteine are missing. The rRNA genes have not been fully sequenced. Previous studies demonstrated that over 400 copies of human 18S and 28S rRNA genes are present per haploid genome and clustered on the short arms of the five acrocentric chromosomes: 13p, 14p, 15p, 21p, and 22p. Each gene is part of a 43-kb repeat unit that can be divided into two regions: a 13.3-kb transcribed region and a 30-kb nontranscribed spacer. Repeat unit clusters consist of head-to-tail arrays of about 80 repeats. Eukaryotic rRNA is processed and modified in the nucleolus with the help of many snoRNAs which belong to two families: C/D box snoRNAs involved in guiding sitespecific 2′-O-ribose methylations and H/ACA snoRNAs involved in guiding site-specific pseudouridylations. The initial human genome sequencing compiled a set of 97 known human snoRNA gene sequences, the majority of which (87%) are single-copy genes. That number has since increased to over 460 as revealed from the human snoRNA database (http://www.trex.uqam.ca/~snorna/). There are 105–107 methylations and around 95 pseudouridylations in human rRNA. There are also snoRNA-directed modifications on other stable RNAs, such as U6. Only about half of these or less have been tentatively assigned to known guide snoRNAs. Most of the known spliceosomal snRNAs were identified in the human genome but not the expected tandem arrays. Many ncRNA pseudogenes were also found in the genome. Among the various recently discovered noncoding small RNAs, such as miRNA, siRNA, tasiRNA, scnRNA, rasiRNA, and piRNA (73), the miRNA and siRNA have been extensively studied in plants and animals. Both miRNA and siRNA are involved in triggering RNA interference (RNAi) and silencing of gene expression, albeit through different mechanisms. Mature miRNAs are ~22-nt-long single-stranded ncRNAs that silence gene expression through translational repression. siRNAs are also ~22-nt-long, but doublestranded ncRNAs that regulate gene expression by triggering the degradation of target mRNAs. So far, about 475 human miRNA genes have been reported in the miRBase. The miRBase sequence database at Sanger Center (http://microrna.sanger.ac.uk / ) provides a searchable database for published miRNA sequences and associated annotation and functionality. Version 1.0 of this database was released in December 2002 and had 218 entries; version 10.1 released in December 2007 had 5,395 entries. This reflects the explosion in miRNA research and advances made during the last 5 years. Over 700 ncRNA genes were identified in the initial sequencing effort, but that number has since more than doubled because of newer annotation (74). For many of the ncRNA genes, the tandem arrays were underrepresented in the sequencing effort simply because of the restriction enzymes used to construct the BAC libraries and also because of the decision to delay the sequencing of clones with greater number of repeats.

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4.1.8. Human Proteome Analysis In the initial analysis, the human proteome was compared to that of yeast, worm, and mustard weed to understand the mechanisms of functional diversity of proteins, such as invention of protein domains and new protein architecture. Such comparison is supposed to provide insight into the commonalities and differences among eukaryotic proteomes and the proteome evolution. The human proteome encoded by the human genome is more complex than those of invertebrates. This is partly due to the presence of vertebrate-specific protein domains and motifs. However, the proteome complexity is apparently due to the fact that humans, like other vertebrates, appear to have arranged preexisting components into a richer collection of domain architectures (51). About 7% of the protein families in human genome seem to be specific to vertebrates. The majority of the proteins necessary for the maintenance of basic cellular functions evolved early. Compared with the invertebrates, humans appear to have many proteins involved in cytoskeleton, defense and immunity, transcription, and translation. These expansions are clearly related to aspects of vertebrate physiology. Few new protein domains have been invented in the vertebrate lineage because most protein domains can be traced back to the common ancestor. The evolutionary innovation of new proteins in vertebrate lineage has been driven by gene shuffling and adding or deleting already invented domains. In other words, new proteins have been created from already existing parts through new design and architecture. Thus, humans seem to have more complex structures of the proteins because of characteristics such as more domains per protein and novel combinations of domains. Gene duplication seems to be a major force behind such protein evolution. Further combinatorial usage of modest differences generated through alternative splicing, posttranslational modifications and cellular regulatory networks can significantly amplify the phenotypic outcome. Elucidating such system level properties presents one of the greatest challenges for modern biology. The olfactory receptor genes comprise a huge gene family of about 1,000 genes and pseudogenes, underscoring the importance of olfaction in vertebrates. Nevertheless, the hominids appear to have lost about 60% of their olfactory receptors due to disruption of the ORFs, indicating that there has been massive functional gene loss in the last 10 Myr.

4.1.9. Duplications in the Human Genome and Composite Structure of Human Proteins By comparing various human protein sequences, Britten (75) has concluded that gene duplication has played an important role in the evolution of the human genome. In his analysis, the author built a set of all human proteins with known functions after excluding predicted proteins with unknown functions. If multiple variants of a protein had been reported, only the one with maximum length was included in the set. The set thus ended up with a collection of 13,298 proteins. An all-to-all comparison was made in which each member was compared to all other members of the set. The result shows that ~78% of the proteins in the set bear many distinct matches throughout their sequence with other members of the set. In other words, the vast majority of human proteins show relationships to more than one other protein in different regions. The implication is that ancient gene duplications followed by permutations of various duplicated segments resulted in the majority of human proteins to have a composite structure. Such relationships are probably to be expected for sequences that are ancient.

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4.1.10. Genome-Wide Chromatin Marks and Transcription Establishment of an open chromatin conformation is a key early event for the initiation of transcription. Both the initiation of transcription and transcript elongation are associated with specific modifications of the nucleosomal histones that mark the chromatin. Chromatin marks in the promoter are associated with the initiation of transcription, such as trimethylation of lysine 4 of histone H3 (H3K4me3), acetylation of lysine 9 and 14 of histone H3 (H3K9ac, H3K14ac). Chromatin marks that are associated with transcript elongation are within the gene and downstream from the promoter, such as trimethylation of lysine 36 of histone H3 (H3K36me3) and dimethylation of lysine 79 of histone H3 (H3K79me2) (76). It has been recently demonstrated that the H3K4me3 provides a binding site for TFIID, resulting in enhanced recruitment/stability of the transcription preinitiation complex. The protein component of TFIID involved in this interaction is TAF3 (or TAFII3; TBP-associated factor 3), which binds H3K4me3 via its plant homeodomain (PHD) finger. Acetylation of H3K9 and H3K14 potentiates TFIID interaction with H3K4me3 (77). This finding provides a functional/mechanistic basis to explain the association between the histone code H3K4me3 and the transcription. Using chromatin immunoprecipitation coupled with DNA microarray (ChIP-chip) in human embryonic stem (ES) cell, Guenther et al. (76) found that at least 75% of all protein-coding genes bear the H3K4me3 mark. This finding is unusual because H3K4me3 is known to be a histone code associated with actively transcribing genes, and only about 40% of the genes are known to have detectable transcripts. In other words, the chromatin mark for actively transcribed gene was also detected in genes that are known to be inactive. However, the level of H3K4me3 in inactive genes was found to be lower than in genes that are actively transcribed. The authors conducted further ChIP-chip experiments using antibody against the initiating form of RNA pol II. Fewer genes were found to bind pol II compared to the H3K4me3 mark; however, many inactive genes showed low level pol II binding. The authors then determined whether such pol II binding resulted in the synthesis of mature mRNA for the inactive genes. Using RT-PCR, the authors demonstrated that the average level of mature transcript was <1 for these inactive genes. This suggests that many genes initiate transcription that does not result in productive elongation. This hypothesis was further confirmed by another round of ChIP-chip experiments to detect H3K36me3 and H3K79me2, which are chromatin marks within the gene, that is, downstream from the promoter and associated with transcript elongation. Consistent with the finding of the lack of productive transcript elongation for the inactive genes, the authors found that chromatin marks for elongation are present only in genes that are actively transcribed. This study suggests that there is considerable amount of abortive transcription in the genome. The role of such abortive transcription is open to speculation. It appears that differential chromatin marks for pol II recruitment and productive transcript elongation at different places of the same gene add complexity to transcriptional regulation beyond the level of initiation. Having polymerase recruited to the promoter and poised to initiate transcription may be a way of inhibiting potential transcriptional silencing, as well as facilitating rapid transcription on demand, such as under conditions of induction. 4.1.10.1. Pervasive transcription in the human genome The finding of genome-wide chromatin marks for transcription is consistent with the findings that human genome is very extensively transcribed, which includes both sense and antisense transcription.

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To comprehensively identify coding sequences in the human genome, Bertone et al. (78) used genome tiling arrays by designing about 52 million oligoprobes (36 nt long) positioned every 46 nt on average. These probes cover 1.5 Gb of nonrepetitive genomic DNA, both sense and antisense strands. Using poly(A)+ RNA from the liver tissues pooled from several individuals, a total of about 14,000 transcription units were identified, of which about 9,000 were identified as new transcripts. Out of these 9,000, about 1,500 were identified within previously annotated introns and an equal number were identified within the antisense strand introns. About 5,800 were found to be from intergenic regions and were previously unknown. The most commonly used name of these newly identified unannotated transcripts is transcripts of unknown function (TUF), and the array-detected transcribed regions are referred to as “transfrags”. Cheng et al. (79) further studied transcription in 10 human chromosomes (6, 7, 13, 14, 19, 20, 21, 22, X, and Y) in eight-cell lines, using a higher resolution tiling array in which 25-nt-long oligoprobes with an average spacing of 5 nt were used (hence a much higher interrogation resolution compared to the array design used by Bertone et al.). When the maps created by using poly(A)+ RNA from these cell lines were compared to the genome database, a major portion of the array-detected transfrags was found to be unique, that is, they do not overlap with known annotated transcripts. The extent of such newly identified unannotated transcripts varied depending on the chromosome. For example, annotationdense chromosome 19 had a lower proportion (~33%) of unannotated transcripts, while chromosome 13 had ~70% unannotated transcripts. A large part of these unannotated transcripts was from intergenic regions and intronic regions, and a major fraction showed evidence of transcription from both sense and antisense strands. Out of the 768 unannotated transcripts chosen randomly for further analysis, more than half could be amplified by 5′ and/or 3′ RACE to yield 5′ and 3′ RACE products. The recent publication of the findings of the Encyclopedia of DNA Elements (ENCODE) project further confirmed the pervasive nature of transcription in the human genome (80). The ENCODE project, a multiteam collaborative effort, sought to analyze the regulatory landscape of ~1% of the human genome. About 30 Mb of the human genome was analyzed to map promoters, enhancers, silencers, exons, replication origins, transcription start and termination sites, transcription factor binding sites, methylation sites, conserved sequences, and various transcripts. The 30-Mb region was divided among 44 genomic regions; 14 of these genomic regions encompass about 15 Mb for which there is substantial biological knowledge. The other 30 genomic regions encompass the other 15 Mb and were chosen by stratified random sampling method. The results of the ENCODE study show that the human genome is pervasively transcribed, such that the majority of its bases are associated with at least one primary transcript. Many novel nonprotein-coding transcripts have been identified. Some of these nonprotein-coding transcripts overlap with protein-coding loci, while others are in genomic regions previously thought to be transcriptionally silent. In keeping with the identification of hitherto unknown transcripts, numerous previously unrecognized transcription start sites (TSS) have been identified. Many regulatory factor binding sites were found around these novel TSS. Consistent with the pervasive transcription of the human genome, DNAse-I hypersensitive sites were found throughout the studied regions. The presence of DNAseI hypersensitive sites and TSS reflects genomic regions enriched for regulatory information. The ENCODE regions consist of only eight well-characterized ncRNAs (U70, ACA36, ACA56, miR-192, miR-194-2, miR-196, miR-483, and H19) and do not include C/D snoRNA, snRNA, and tRNA. There is also evidence for transcription of six out of

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eight pseudogenes of ncRNA. At least 75% of the bases in the ENCODE regions studied are represented in primary transcripts. 4.1.10.2. Antisense transcription Analysis of transcriptome using computational approach (EST analysis) followed by further experimental validation, such as microarray, RT-PCR, and northern blot, has demonstrated that sense–antisense transcription units are quite common in the genome. A list of 40 mammalian loci reported in the literature to have overlapping sense and antisense transcripts has been compiled (81). The authors also identified 217 additional sense–antisense transcriptional units in the human genome. An extensive analysis of the sense–antisense overlapping transcription in the human genome was carried out using 22 transformed cell lines (82). Using computer analysis followed by experimental validation by microarray and northern blot, the authors identified ~1,600 putative sense– antisense transcription units. These units are transcribed from both DNA strands. Thus, antisense transcription in the human genome seems to be more widespread than previously thought. Although the role of antisense transcripts in regulating gene expression has been established, the biological significance of sense–antisense transcription units in the human genome is yet to be understood. It has been estimated that over 20% of all human transcripts may form sense–antisense pairs (83). A similar widespread antisense transcription in mouse genome was reported by the RIKEN–Genome Science Group–FANTOM Consortium (84). The authors reported that 72% of all transcriptional units in mouse show antisense transcription, of which 87% are protein-coding transcriptional units. This study demonstrated that external stimulus (LPS used in the study) that affects the expression of the sense–antisense transcript pair could result in various patterns of regulation, such as reciprocal regulation (one increases, the other decreases), proportional coregulation (both increase or decrease proportionally), or divergent regulation (the two show different types of changes). The authors also found that siRNA-mediated targeted degradation of the antisense RNA Ddx39 increased the expression of the sense mRNA CD97, but the reciprocal effect was not observed. This example further demonstrates the potential of widespread antisense regulation of genome expression in mammals, probably in eukaryotes in general. The discovery of widespread transcription in the human genome including antisense transcription provides further validity to the emerging idea that the so-called “junk DNA” is really not junk, and the fraction of the genome that is used as an information career is much higher than previously expected. According to the latest estimate, about 3.5% of the human genome is functional but noncoding, that is, they contain various gene regulatory elements and chromosomal functional elements; however, the inventory of these is still poor. 4.1.11. Human Genomic Variations Interindividual genomic variabilities in humans can be attributed to a number of factors, such as SNPs; variable number tandem repeats (VNTRs), such as mini- and microsatellites; variations in transposable elements; and structural alterations, such as insertions/ deletions (indels), segmental duplications, and chromosomal inversions. Large (~1 kb or larger) indels and duplications are generally termed “copy number variations” (CNVs), also referred to as “copy number polymorphisms” (CNPs), if the frequency is ≥ 1% in a given population (62,85). Nonallelic recombination between homologous sequences is probably one mechanism underlying some of these variations (71).

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Initially, SNPs were thought to be the predominant source of interindividual genomic variability, but it is now recognized that the genomes of any two individuals differ more at the structural level than at the nucleotide sequence level. While SNPs account for a variability of ~2.5 Mb (1/1,200 bp), CNVs account for a minimum variability of ~4 Mb (1/800 bp). Some estimates suggest that CNVs account for a variability of ~5–24 Mb. 4.1.11.1. Single nucleotide polymorphism An SNP (pronounced snip) is DNA sequence variation occurring in a single nucleotide in the genome. SNPs not only include variants that affect a single nucleotide (base) but also include single-base indels. For a variation to be considered an SNP, it must occur in at least 1% of the population. In the human genome, >65% of all SNPs involve C→T transition mutation. SNPs can occur in both coding and noncoding regions of genes. SNPs in the coding region may alter the characteristics of the protein while SNPs in the regulatory regions may alter the expression profile of genes. SNPs generally occur about every 1,000 bases in the genome but may occur more frequently in certain regions. In the human genome, SNPs that make up about 90% of all human genetic variations occur every 100– 300 bases along the genome. A set of linked SNPs that tend to inherit together as a unit is referred to as “SNP haplotype”. The extent of association of the SNPs as a haplotype is an indication of how closely associated they are, and linkage disequilibrium (LD) is a quantitative measure of such association. Higher LD values indicate greater association. Thus, SNPs that are in strong LD with disease-causing genes can be used as markers to identify the gene and track its inheritance. Many SNPs have no effect on cell function but it is believed that SNPs can predispose people to disease or influence their response to drugs and xenobiotics. The importance of SNPs in the etiology of disease is demonstrated by the well-known example of SNPs in the apolipoprotein E (ApoE) gene and Alzheimer’s disease. Two SNPs in the ApoE gene result in three possible alleles of the gene: E2, E3 (wild type), and E4. The corresponding protein product of each gene differs by one amino acid, such as ApoE2 with Cys112, Cys158; ApoE3 with Cys112, Arg158; and ApoE4 with Arg112, Arg158. Individuals inheriting two E4 alleles have the highest chance of getting Alzheimer’s disease while those inheriting two E2 alleles are the least likely to get the disease, and the order of risk associated with various ApoE alleles is E4>E3>E2. Apparently, one amino acid change in the ApoE protein alters its structure and function enough to influence the risk of disease development associated with each allele. The International HapMap Project was launched in 2002 with the aim of genotyping at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse populations, such as from subjects in Nigeria, from subjects of northern and western European ancestry living in Utah, and from subjects in China and Japan. Approximately 1.3 million SNPs were genotyped in the Phase I of the project and a description of this resource was published in 2005. Recently, in the Phase II of the HapMap project over 3.1 million human SNPs have been identified and published (86). 4.1.11.2. Copy number variation Because of the very nature of the variants (insertion, deletion, duplication, etc.), CNVs can influence gene dosage, gene expression, and disease phenotypes, as well as various adaptive phenotypes. Some Mendelian traits associated with CNVs are Charcot-Marie-Tooth disease type 1A, which is caused by duplication (and consequently an extra copy) of the

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PMP22 gene, and α-thalassemia, which is caused by deletion of the α-globin gene cluster. Variations in the copy number of the gene encoding the cytokine CCL3L1 results in an altered susceptibility to HIV infection. Many other such examples exist (87). CNVs can also play a role in adaptive evolution. For example, by comparing the human genome reference sequence with a second genome sequence, 297 sites of structural variation were found that include 139 insertions, 102 deletions, and 56 inversion breakpoints (71). Many of these sites encompass genes that are associated with drug and other xenobiotic detoxification, innate immune response, inflammation, tissue surface integrity, and cell surface antigens. These genes, collectively termed “environmental sensor genes” by the authors, may not be crucial for viability but are important for adaptability. Recently, a more direct example of how CNVs may play a role in evolutionary adaptation has been provided (88). Initial studies in 50 European Americans showed a direct correlation between AMY1 gene copy number and salivary amylase content. The authors then examined seven different populations for their salivary amylase content and the mean diploid AMY1 gene copy number. Of these populations, three (European Americans, Japanese, and Hazda hunter-gatherers) are high-starch consuming, and four (Biaka and Mbuti rainforest hunter-gatherers, Datog pastoralists, and Yakut pastoralist and fishing society) are low-starch consuming populations. The authors found that the diploid AMY1 gene copy number in the high-starch samples is greater (high-starch samples—median 7, mean 6.72; low-starch samples—median 5, mean 5.44). Remarkably, the proportion of individuals from the high-starch consuming population with at least six AMY1 gene copy numbers was found to be two times greater than that of the low-starch consuming population. Since the populations studied are geographically separated, the observed AMY1 gene CNVs are not likely to be due to shared ancestry of these populations. Further comparison of chimpanzee, Bonobo, and human AMY1 genes suggest that the expansion of AMY1 gene copy numbers took place in human lineage. Collectively, these findings suggest that during evolution a shift towards high-starch diet resulted in positive selection of the high copy number variants of the AMY1 gene in the population, and the selection appears to be ongoing even after the evolution of H. sapiens. This is an example of the role of CNVs in evolutionary adaptation driven by dietary shifts to meet the physiological demand for high amount of amylase. 4.2. The Chimpanzee Genome The following information is mainly derived from the initial sequence report of the chimpanzee genome (89) as well as subsequent reports on the comparative analysis of the human and chimpanzee genomes (90,91). The chimpanzee genome is very similar in size to the human genome. There are 23 pairs of autosomes numbered 1 through 22, and one pair of sex chromosomes. The reason 23 pairs of chimpanzee autosomes are numbered 1 through 22 is because of chromosomes 2A and 2B. In humans, these have fused end-to-end to give rise to human chromosome 2. 4.2.1. Similarity in Protein-Coding Genes Between Human and Chimpanzee Overall, human and chimpanzee genes are extremely similar with about 30% proteins being identical in the two species. Out of the 70% nonidentical proteins, the median number of nonsynonymous substitutions (causing amino acid change in the protein) and synonymous substitutions (not causing amino acid change in the protein) per gene

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are two and three, respectively, while the means are about three and five, respectively. Thus, typical human and chimpanzee homologous proteins differ by only two amino acids on average. 4.2.1.1. Evolution of the coding sequences In the vast majority of coding regions, synonymous substitutions (KS) occur much more frequently than nonsynonymous ones (KA). This is because most genes are subject to purifying selection with stronger selective constraints for nonsynonymous changes than for synonymous ones. As a result, for coding sequences that are under strong selection pressure the KA is much lower than the KS, resulting in a KA/KS ratio (denoted ω) less than 1. In contrast, when the coding sequence is under weak selection pressure or under continued positive selection, the KA/KS ratio is close to 1 or higher. Comparison of 13,454 human–chimpanzee gene pairs gives an average KA/KS of 0.23. This means that at least 77% of the amino acid alterations are sufficiently deleterious to be eliminated from the population by purifying selection. Nevertheless, about 585 chimpanzee genes may be under strong positive selection. These genes are involved in immune system defense against microbial disease, such as granulysin (protective against Mycobacterium tuberculosis), glycophorin C (involved in Plasmodium falciparum invasion pathway in erythrocytes), protamines and semenogelins (involved in reproduction), and mas-related genes encoding G-protein-coupled receptors involved in nociception (pain perception). Whereas most genes have undergone subtle changes between human and chimpanzee, about 53 human genes are deleted either entirely (36) or partially (17) in chimpanzee lineage. The authors also calculated the KA/KS ratio between mouse and rat which was found to be 0.13, a value about half of that in hominid. The possible explanation is that in murids purifying selection can operate more effectively because of the larger population size, thereby removing deleterious mutations more effectively. In contrast, smaller population size in hominids results in a less effective purifying selection. The very low level sequence divergence seen in the coding regions of orthologous genes between human and chimpanzee may reflect ongoing purifying selection. In contrast, both 5′- and 3′-UTRs show significant sequence divergence, which may explain some of the gene expression differences between human and chimpanzee. In terms of the overall genomic sequence divergence, there is a consistent increase in sequence divergence toward the ends of most chromosomes. On average, the terminal 10 Mb of chromosomes has 15% higher sequence divergence than the rest of the genome. 4.2.1.2. Gene expression Using DNA microarray, genome-wide comparisons were made in the gene expression profiles between human and chimpanzee. The highest number of genes displaying differences in expression was in testis, while the lowest degree of gene expression differences was in the brain. Interestingly, most of the genes in the brain showing differential expression were more highly expressed in humans. No such differences were found in the liver. Such differential expression is probably driven by differences in the 5′ regulatory regions, which show more sequence divergence between human and chimpanzee compared to the coding regions of protein-coding genes. One such example is prodynorphin (PDYN). This gene is highly expressed in humans and is important in human biology because of its apparent involvement in perception, emotion, nociception, and learning. Mutations in the regulatory region upregulating the expression of this gene are under positive selection.

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The evolutionary history of the PDYN gene makes it a good example in which regulatory changes have played significant role in human evolution (90,91). 4.2.2. Possible Causes of Genome-Wide Nucleotide Divergence Between Human and Chimpanzee Based on nucleotide alignments covering 2.4 Gb of human and chimpanzee genomes, the overall genome-wide nucleotide divergence between human and chimpanzee was found to be 1.23%. After correcting for the known species-specific polymorphisms the fixed nucleotide divergence becomes 1.06%. The highest divergence is found in the Y chromosome (due to higher germ-line mutation rates) and the lowest in the X chromosome (0.94%). The divergence is not uniform across the genome; far greater divergence is found in CpGdinucleotide sites and within the terminal 10 Mb of the chromosomes. 4.2.2.1. Divergence in CpG islands The CpG-dinucleotide sites in both human and chimpanzee show a divergence of 0.15 or 15% per site that reflects both the loss of ancestral CpG and the creation of new CpG. The divergence at CpG sites alone constitutes about 25% of all substitutions even though they constitute only 2% of all aligned bases. Assuming an equilibrium between the rate of CpG loss and the rate of new CpG creation, the mutation rates in CpG-dinucleotide sites are 10–12 times higher than the rest of the genome. Similarly, the terminal 10 Mb of the chromosomes show 15% higher divergence rates than the rest of the genome, with the sharpest divergence toward the telomeres. These terminal regions of the chromosomes are GC-rich; they have higher gene density and higher recombination rate. This trend of higher sequence divergence at the chromosomal ends is also seen in mouse. However, for orthologous segments within the terminal 10 Mb of chromosomes, the divergence rates are much higher in hominids (human and chimpanzee) than in murids (mouse and rat). 4.2.2.2. Divergence due to indel mutations Many indel mutations were recorded in the human genome by comparing it to the chimpanzee genome. About 98.5% of these indels are less than 80 bp and the rest are larger indels, as large as 15-kb . The larger indels, although a small fraction of indels, encompass 73% of the affected base pairs. More than one-third of the larger indels are due to repeat sequences and about a quarter of them are newly inserted transposable elements; the rest represents deletion in one genome. Both human and chimpanzee genomes contain 40–45 Mb of euchromatic DNA sequence that is present in one genome but not in the other. Thus, the total divergence of human and chimpanzee genomes derived from the indel mutations alone accounts for about 90 Mb, or 3% of the genome. Therefore, if one considers both the indel mutations and nucleotide differences, the human–chimpanzee genomic divergence becomes about 4%. 4.2.2.3. Divergence due to transposable elements The major transposon insertions in human and chimpanzee genomes are LTR retrotransposons (HERV-K, PtERV1, PtERV2), non-LTR autonomous retrotransposons (LINE, such as L1 elements), and non-LTR, nonautonomous retrotransposons (SINE, such as Alu elements). Human endogenous retrovirus K (HERV-K) sequence insertion is found in both human and chimpanzee genomes. The chimpanzee genome, in addition, has two other retroviral elements—PtERV1 (234 copies) and PtERV2 (45 copies). The human genome has three times

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as many Alu elements as in chimpanzee genome, indicating that there has been a general decline of Alu activity in chimpanzee lineage. While most human Alu elements are of recent origin after the human–chimpanzee split (AluYa5 and AluYb8), most chimpanzee Alu elements are of the ancestral type (AluYc1). A small fraction of human Alu elements are also of ancestral type and are retained in the genome from before the human–chimpanzee split. Older Alu element insertions in both human and chimpanzee are preferentially found in generich (GC-rich) regions while the more recent Alu element insertions are found in gene-poor (AT-rich) regions. As in humans, the L1 element in chimpanzee genome is also found in ATrich regions, and L1-mediated retrotransposition mediated the integration of about 200 and 300 processed pseudogenes in human and chimpanzee genomes, respectively. Another class of nonautonomous retrotransposons is called SVA (SINE-VNTR-Alu), which is present in both human and chimpanzee genomes. SVA represents the youngest family of nonautonomous primate retrotransposons which are probably mobilized in the genome by L1 elements. SVA elements have about 60% GC-content; they end with a poly(A) tail and are flanked by target site duplications such as Alu and L1 elements. Both human and chimpanzee genomes contain about 1,000 copies of SVA but these are lineage-specific. The roles of SVA retroposons in genome function and evolution need further study. 4.2.2.4. Divergence due to deletions, repeat sequences, and chromosomal rearrangements Both human and chimpanzee genomes have undergone deletions (612 in humans and 914 in chimpanzee), resulting from recombination between Alu elements. The human genome also contains about 1.5 times more microsatellite sequences compared to chimpanzee genome. In general, the human genome seems to have undergone a repeat-driven expansion compared to that of chimpanzee (92). Another source of major genome-wide divergence between human and chimpanzee is the large-scale chromosomal rearrangements that were reported through earlier cytogenetic studies. For example, chromosome 2 in humans resulted from a fusion of two ancestral chromosomes that remained separate in chimpanzee (chromosomes 2A and 2B, formerly designated chromosomes 12 and 13, respectively). Earlier cytogenetic studies also reported at least nine pericentric inversions (inversions involving the centromere) that distinguish the human and chimpanzee genomes. Two of these inversions took place on human chromosomes (1 and 18) while the others took place on chimpanzee chromosome (4, 5, 9, 12, 15, 16, 17). Since each pericentric inversion involves one breakpoint on each chromosomal arm, nine pericentric inversions would involve 18 breakpoints. The authors were able to locate 13 such breakpoints. They also found suggestive evidence of a number of smaller inversions not reported before. 4.2.2.5. Single nucleotide polymorphisms The vast majority of identified human SNPs had a recent evolutionary origin in human lineage. Therefore, SNPs that are shared by human and chimpanzee genomes most likely arose independently in both lineages rather than surviving 6 million years since the last common ancestor. 4.2.3. Positive Selection and Genome Evolution Availability of genome sequences of human, chimpanzee, rhesus monkey, mouse, rat, and other species have enabled scientists to look for signs of positive selection in various genomes, particularly in human genome.

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4.2.3.1. Neutral theory of molecular evolution The neutral theory of molecular evolution, proposed by Motoo Kimura (93), has revolutionized the understanding of positive selection in molecular evolution. According to the neutral theory, the majority of new mutations are either deleterious (adversely affect the fitness of the carrier) or neutral (do not affect the fitness of the carrier). Deleterious mutations are removed by purifying selection (negative selection). Neutral mutations, however, will be subject to chance sampling in every generation. In this process, many neutral mutations would be lost from the population within a few generations, but some will be fixed randomly by sheer chance. The frequency of these mutations fixed by chance will increase through generations (genetic drift) leading to polymorphism. In other words, polymorphism is a transient phase of molecular evolution, and the evolutionary rate is directly related to the level of intrapopulation variation (94). The new fixed mutation is called “derived allele”, as opposed to “ancestral allele” from which it is derived. A number of conclusions can be drawn from the neutral theory as follows: polymorphisms are mostly selectively neutral and are maintained by genetic drift; nonsynonymous substitutions in the coding region are usually not selectively neutral (in fact deleterious) because they result in amino acid change; synonymous or silent substitutions resulting in no amino acid change are usually selectively neutral. Therefore, synonymous substitutions typically exceed nonsynonymous substitutions in protein-coding genes, and functionally constrained regions of genes evolve at a slower rate than regions that are not functionally constrained. However, if a nonsynonymous substitution confers some selective advantage, then it will be rapidly fixed in the population by positive selection. 4.2.3.2. Signatures of positive selection in the genome A prediction of the neutral theory is that if the substitutions are all neutral, then for a given protein-coding gene the KA/KS ratio (i.e., ratio of nonsynonymous substitutions and synonymous substitutions) between two species should be very similar to the same ratio within species. This is because neutral substitutions do not confer any selective advantage; hence their fixation is driven by chance and not by natural selection (positive selection). It is the deviation from this prediction that provides support for positive selection (with some exceptions, such as relaxation of purifying selection and population bottleneck). Even though nonsynonymous substitutions are mostly deleterious and eliminated by purifying selection, sometimes they may also improve the function of the protein by introducing advantageous new variations. Thus, selectively advantageous nonsynonymous substitutions undergo rapid fixation in the population by positive selection much more rapidly than neutral synonymous substitutions. In other words, positive selection should significantly increase the proportion of fixed divergence (sequence difference between species) compared to polymorphism (sequence difference within species). Therefore, KA/KS ratio for fixed divergence between species should be much larger than that for polymorphism within species. While studying sequence evolution of the Adh gene in Drosophila melanogaster, Drosophila simulans and Drosophila yakuba, McDonald and Kreitman (95) proposed a simple test for determining positive selection in protein sequence. This method relies on measuring statistically significant deviation from the prediction of neutral theory (the null hypothesis) as mentioned above. Another similar example was quickly provided by the evolution of the G6pd gene (Glucose-6-phosphate dehydrogenase) in D. melanogaster and D. simulans (96). Examples of positive selection show that at synonymous sites polymorphism within species outnumbers fixed divergence between species, whereas at nonsynonymous sites fixed divergence between species outnumbers polymorphism

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within species. The statistical significance of the deviation from null hypothesis is tested using R × C (R = row, C = column; in this case 2 × 2) test of independence (G-test). The McDonald and Kreitman test is illustrated in Table 1. Many other tests to determine positive selection have been described in the literature, such as Hudson–Kreitman–Aguadé (HKA) test, Fu and Li’s test, Tajima’s D test, but they are much more complicated than McDonald–Kreitman test. A well-known example of repeated amino acid change in proteins as a signature of positive selection comes from the studies on vertebrate MHC complex (97,98). MHC proteins are involved in protein–protein recognition; therefore, a limited number of codons encoding the region of the MHC protein associated with peptide-binding coevolved with the pathogens and maintained adaptively important genetic diversity through repeated positive selection.

A Nonsynonymous

Synonymous

Polymorphic D. melanogaster D. simulans D. yakuba Pooled

2 0 0 2

14 11 17 42

Fixed D. melanogaster D. simulans D. yakuba Pooled

1 1 5 7

2 0 15 17

Fixed Polymorphism divergence (within species) (between species) Synonymous (S)

17

42

Nonsynonymous (NS)

7

2

G = 7.43 P = 0.006

2 × 2 table from the data

Data from McDonald and Kreitman (1991)

B

Polymorphic D. melanogaster D. simulans Pooled Fixed

Nonsynonymous

Synonymous

2 0 2

22 14 36

21

26

Data from Eanes et al. (1993)

Fixed Polymorphism divergence (within species) (between species) Synonymous (S) Nonsynonymous (NS)

26

36

21

2

G = 18.96 P < 0.0001

2 × 2 table from the data

Table 1 McDonald–Kreitman Test Exemplified by the Evolution of the Adh Gene in D. melanogaster, D. simulans, and D. yakuba, and the Evolution of the G6pd Gene in D. melanogaster and D. simulans. The test relies on measuring statistically significant deviation from the prediction of the neutral theory that if the substitutions are all neutral, then for a given protein-coding gene, the NS/S ratio (KA/KS ratio) at fixed divergence sites between species should be very similar to the NS/S ratio at polymorphic sites within species. In the upper panel (Table 1A), data from McDonald and Kreitman (1991) show that the NS/S ratio between species is 7/17 (= 0.41), whereas the NS/S ratio within species is 2/42 (= 0.048). Thus, there is more than eightfold excess of nonsynonymous mutations between species compared to polymorphic mutations within species. Similarly, in the lower panel (Table 1B), data from Eanes et al. (1993) show that the NS/S ratio between D. melanogaster and D. simulans is 21/26 (= 0.81), whereas the NS/S ratio within species (D. melanogaster and D. simulans combined) is 2/36 (= 0.06). Thus, there is more than 13-fold excess of nonsynonymous mutations between species compared to polymorphic mutations within species. One would not expect these results if Adh and G6pd genes had evolved in a strictly neutral fashion. The 2 × 2 contingency G-tests of these data are highly significant in both cases, as revealed by the G-values.

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Selective sweep represents another signature of positive selection. If a new mutation increases the fitness of the carrier, it will be fixed in the population due to strong positive selection, and the frequency of this advantageous mutation will increase. Along with it, the frequency of the genes/sequences around it that are very closely linked and not easily separated by recombination will also increase. The net result is the loss of sequence variability around the newly fixed mutation in the population. Such rapid fixation of an advantageous mutation is called “selective sweep”. The result of selective sweep is a reduction or elimination of the sequence divergence around the newly fixed mutation. The increase in frequency of the neighboring genes/sequences, simply because of their close proximity to the newly fixed mutation, is called “hitchhiking effect”. Selective sweep and hitchhiking effect are the results of strong positive selection. A complete selective sweep is expected to eliminate variation and drive the derived allele to high frequency of fixation. This may result in a region of the chromosome where the positively selected haplotype (the derived allele and its flanking sequences that inherit as a unit) is essentially the only sequence that exists in the population. Such a condition results in a large reduction of the total genetic variation in the chromosomal region containing the haplotype. The length of the haplotype that shows the absence of variation depends on a number of factors, such as the extent of selective sweep (complete or incomplete), as well as intrinsic recombination potential of the region. 4.2.3.3. Genome evolution in human and chimpanzee By comparing the human genome to the chimpanzee genome, the authors identified evidence of selective sweep in recent human history. The evidence of possible selective sweep in the last 250,000 years of human history was identified in six regions in the initial analysis. These regions show significantly less variation than would be expected based on the time of divergence between human and chimpanzee (5–6 million years). These regions vary widely in their gene content, and the candidate genes are of interest in this context. 4.2.3.3.1. Molecular evolution of FOXP2 gene A region on chromosome 7 that contains the FOXP2 gene deserves additional analysis. The FOXP2 protein is a highly conserved, forkhead class transcription factor. Molecular analysis reveals that a two-amino acid substitution has been fixed in human lineage since the human–chimpanzee split from the last common ancestor. Compared to chimpanzee, the human FOXP2 gene has a C→A transversion at nt 911 and an A→G transition at nt 977 (both in exon 7) resulting in two amino acids changes in the protein, Thr→Asp and Arg→Ser, respectively. These two amino acid changes in the protein are linked to severe articulation difficulties accompanied by linguistic and grammatical impairment (99). Even inactivation of one FOXP2 copy leads to deficits in orofacial movements and linguistic and grammatical processing ability similar to Broca’s aphasia (100). Since speech is one of the most important and highly evolved human characters, FOXP2 gene was identified as a unique gene that probably contributed to the evolution of human speech through proper development of the relevant anatomical parts necessary for speech. Therefore, the substitutions in FOXP2 gene have been subject to strong positive selection and selective sweep in human lineage. Recently, using a number of Neandertal DNA samples, Krause et al. (100) amplified and sequenced the specific region of FOXP2 gene where the human and chimpanzee sequences differ. In the amplified Neandertal DNA fragments, the authors found the exact two nucleotides that are present in human but absent in chimpanzee. However, they did not analyze the entire Neandertal FOXP2 gene, and thus might have missed any Neandertal-specific substitutions. Nevertheless, the findings suggest that the selective sweep that had

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fixed the two nucleotide changes in FOXP2 gene predates the common ancestor of modern human and Neandertal that existed 300,000–400,000 years ago. 4.2.3.3.2. Molecular evolution of genes regulating brain size A few genes important for determining brain size have also been studied. These genes evolved under positive selection in early primates and continue to show signatures of positive selection in hominid lineage. These are microcephalin (MCPH1) and abnormal spindle-like microcephaly-associated (ASPM) genes. Microcephaly is defined clinically as severe reduction in brain size (cerebral cortex volume) coupled with mental retardation. MCPH1 is one of the six known loci for which recessive mutations lead to microcephaly, and four of these loci have been identified: MCPH1, CDK5RAP2 (MCPH3), ASPM (MCPH5), and CENPJ (MCPH6). Bruce Lahn’s group at the University of Chicago showed that the evolutionary patterns of all four presently known primary microcephaly genes are consistent with the hypothesis that genes regulating brain size during development may also play a role in brain evolution in primates and especially in humans. The two primary microcephaly genes MCPH1 and ASPM have undergone adaptive evolution in primate lineage leading to human, and these genes show evidence of ongoing selective sweep in modern humans as well. In the initial study with MCPH1 gene (101), the authors identified one particularly abundant haplotype designated haplotype 49 with a frequency of 33%. Haplotype 49 is the predominant member of a haplotype group classified as haplotype D. The authors concluded from their analysis that haplotype D swept from a single copy to a high frequency of approximately 70% worldwide in a short period of time. During this selective sweep, minor variants were produced through rare mutations and recombination. The age of haplotype D was determined to be about 37,000 years with a confidence interval of 14,000–60,000 years, and it shows geographic variation with the lowest frequency found in subsaharan Africa. Similar conclusions were drawn about the evolution of ASPM gene, which also has an excess of replacement substitutions in human lineage (102). Using McDonald–Kreitman test, the authors showed that 15 out of 19 nonsynonymous substitutions were driven to fixation by positive selection in the lineage that spans from the last common ancestor of apes to humans. The same group demonstrated that the protein evolutionary rate for CDK5RAP2 gene is significantly higher in primates than in rodents or carnivores, and within primates the evolutionary rate is particularly high in the human and chimpanzee terminal branches (103). The authors concluded that the molecular evolutionary trend of CDK5RAP2 approximately parallels that of MCPH1 and ASPM. The authors also found that the other gene CENPJ exhibits similar higher rate of protein evolution in primates compared to rodents and carnivores. Having recorded the evidence of strong positive selection in four MCPH genes involved in regulating brain size, Lahn’s group (104) studied whether these genes determine intelligence. They studied the association between the derived alleles and IQ. Unfortunately, the study revealed no detectable association between the recent adaptive evolution of MCPH1 or ASPM and the changes in IQ. Thus, proper functional characterization of the adaptive evolution of MCPH1 and ASPM, particularly their role in human brain development, brain physiology, and intelligence, is yet to be performed. There are more examples like that in the primate lineage; 17 genes showing KA/KS ratio disparities between primates and rodents are linked to brain development and physiology. Kehrer-Sawatzki and Cooper (90) have compiled a list of more than 30 genes that have been inactivated in human lineage but are functional in chimpanzee lineage. Two such

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examples will be discussed below: the myosin heavy chain (MYH16) and CMP-Neu5Ac hydroxylase (CMAH) genes. 4.2.3.3.3. Molecular evolution of MYH16 gene The MYH16 gene is expressed in the jaw musculature of Chimpanzee. In human lineage, it is inactivated because of a 2-bp deletion in codon 660 of the mRNA, resulting in a frameshift that truncates the predicted 224-kDa myosin heavy chain to a 76-kDa fragment. The inactivating deletion seems to have appeared in a hominid ancestor about 2.4 million years ago, which generally overlaps with the first fossils of prehistoric humans featuring rounder skulls and weaker jaws. Coinciding with this loss of masticatory strength has been the marked increase in cranial capacity, which might be due to the fact that a decrease in masticatory muscle size and contractile force generated by these muscles removed an evolutionary constraint on increase of cranium size and encephalization (105). 4.2.3.3.4. Molecular evolution of CMAH gene The CMAH gene has been inactivated in the human lineage by Alu insertion. Owing to this inactivation, the functional enzyme (CMAH) is absent and the sialic acid (Sia) N-acetylneuraminic acid (Neu5Ac) cannot be converted to Sia N-glycolylneuraminic acid (Neu5Gc) in humans. This difference appears to be responsible for the observed human– chimpanzee difference in the susceptibility to malaria. Plasmodium reichenowi infects and causes chimpanzee malaria in chimpanzees but does not infect humans, while genetically related P. falciparum infects and causes fatal malaria in humans but not in chimpanzees. Martin et al. (106) presented evidence that the major merozoite-binding protein called “erythrocyte-binding antigen” (EBA)-175 of P. falciparum (PfEBA-175) binds Neu5Ac, but Neu5Gc interferes with this binding. This probably explains why P. falciparum is unable to successfully infect healthy chimpanzees because of the presence of Neu5Gc on chimpanzee erythrocyte membrane. Thus, PfEBA-175 apparently evolved to take selective advantage of the excess Neu5Ac on human erythrocytes. Conversely, the EBA-175 of P. reichenowi (PrEBA-175) strongly prefers Neu5Gc which probably explains why P. reichenowi fails to infect human subjects because of the absence of Neu5Gc on human erythrocyte membrane. There is as yet no evidence to suggest that the large chromosomal rearrangements between human and chimpanzee have influenced either the speciation or the evolution of lineage-specific traits. In contrast, genome comparison reveals that indels have together given rise to at least 150 Mb of genomic DNA sequence that is either present or absent in humans as compared to chimpanzees (91). Because these regions often contain paralogous sequences and members of multigene families, human and chimpanzee genomes differ by a significant fraction of their gene content. It has been suggested that the submicroscopic rearrangements in both genomes, particularly those involving CNV that are unlikely to represent exclusively neutral changes, may facilitate the identification of genes that have been important for human-specific evolution (91). 4.3. Rhesus Macaque Genome Most recently, the genome of the Rhesus monkey (Macaca mulatta) has been sequenced by the Rhesus Macaque Genome Sequencing and Analysis Consortium (107). The following description is kept very brief because of space limitations. The genome of macaque is organized into 20 autosomes and XY sex chromosomes. Overall, about 20,000 protein-coding genes have been predicted. Orthologs (genes that are

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functionally and ancestrally identical in different species) have more than 97.5% identity between human and macaque at both nucleotide and protein level. The average nucleotide sequence identity found through alignment between human and rhesus is greater than 90%. In the aligned sequence, the GC-content in rhesus monkey is very similar to that in human (40.71% in rhesus vs. 40.74% in humans). Repeat elements account for about 50% of the genome of all sequenced primates. A total of 1,358 genes have been gained by duplication in the macaque lineage. The average difference between the human and the macaque protein-coding genes is about 12 nonsynonymous and 22 synonymous substitutions, whereas that between human and chimpanzee is about 3 and 5, respectively. Similarly, greater number of human–macaque orthologs (89%) differ at the amino acid level than the human–chimpanzee orthologs (71%). Thus, the genomes of chimpanzee and human are much more similar than that of human and macaque. About 10,376 protein-coding genes show a 1:1:1 orthology in human, chimpanzee, and macaque. The authors compared human disease genes with macaque and chimpanzee equivalents to identify ancestral alleles that had undergone mutations in human lineage and became disease-associated mutations. By screening 64,251 disease-causing or diseaseassociated mutations, a total of 229 mutations have been identified for which the mutant amino acid is present in humans but the normal counterpart is present in macaques and chimpanzees. The disease phenotypes in humans associated with several of these mutant alleles are often associated with severe phenotypes including mental retardation. Comparative details of expanded gene families in humans, chimpanzees, and macaques have yielded some novel insight into the evolution of macaques and the potential role of these genes. For example, genomic duplication of a region in the telomere of macaque chromosome 9, which is orthologous to a human locus 10p15.3 resulted in copy number increase of the gene phosphofructokinase platelet (PFKP) form in macaque compared to human lineage. Since PFKP is important in fructose metabolism, an increase in its copy number appears to have facilitated the evolutionary adaptation of macaques to high-fruit diet. 4.4. Mouse Genome A comparison of the human genome with mouse genome reveals many similarities that are derived from the common ancestor. For example, the mouse genome contains about 2.5 billion base pairs (2.5 Gb) and about 25,000 protein-coding genes (108). About 80% of mouse genes seem to have an ortholog in the human genome, and the proportion of mouse genes without any homolog in the human genome seems to be less than 1%. Many gene families involved in reproduction, immunity, and olfaction display expansion in the mouse lineage. Repeat sequences in the mouse genome show similarity to that in the human genome. Overall structure between human and mouse orthologous gene pairs is highly conserved: 46% of them have identical coding sequence length, 86% have identical number of coding exons and >90% of the human–mouse exon pairs have identical exon length. The mouse genes contain an average of eight exons (expressed as mean exons/transcript); the median values (exons/transcript) being six for both mouse and humans. However, only about 1% of the introns have identical lengths. The average length of mouse introns is about 15% shorter than human introns. The splice donor and acceptor site signals in mouse genes are similar to that found in human genes. The frequency of occurrence of the canonical GT-AG, and noncanonical GC-AG and AT-AC introns in both genomes is similar. The occurrence of the noncanonical GC-AG and AT-AC introns in both genomes tends to be mostly in orthologous introns (108).

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5. CHROMOSOMAL SYNTENY AND SYNTENIC MAPS IN COMPARATIVE GENOMICS Two genes located on the same chromosome are called “syntenic”, irrespective of their linkage distance. Different species that evolved from a common ancestor are likely to preserve at least some of the same genes in the same order in their chromosomes that reflect ancestral condition. The closer the species to the ancestor in evolutionary scale, the greater should be the preservation of the ancestral order of genes. Syntenic maps reveal the large-scale relationship between genomes that are related through a common ancestor but have diverged during evolution. Syntenic maps provide an evolutionary perspective of how genomes have changed and diverged over time through genome/chromosomal rearrangements and genetic sequence modifications that have accumulated during the process of speciation. Even very closely related genomes that are highly syntenic show genomic divergence that has accrued over time during evolution. The genomes that are being compared are subdivided into regions called “syntenic blocks”. A syntenic block can be regarded as a chromosomal segment that has been inherited from the common ancestral chromosome without major chromosomal rearrangements, that is, without major rearrangements in the order of genes in that segment. Thus, syntenic blocks maintain orthologous genes in the same order. The length of various syntenic blocks may vary greatly, from kilobase to megabase scale. At the nucleotide level, approximately 40% of the human genome can be aligned to the mouse genome. However, over 90% of the mouse and human genomes can be segmented into 342 blocks of conserved synteny. For example, human chromosome 22q11.2 [the long arm (q) of human chromosome 22 (22q), region 1 (22q1), band 1 (22q11), subband 2 (22q11.2)] has an order of genes some of which is syntenic to mouse chromosome 6 (markers 53–55 cM), some of which is syntenic to chromosome 16 (markers 8–14 cM), some of which is syntenic to chromosome 10 (markers 39–49 cM), and so on (http://www.sanger.ac.uk/HGP/Chr22/ Mouse/table.shtml). Therefore, starting from a common ancestral genome about 85 million years ago, the rate of genome shuffling through chromosomal rearrangements in human and mouse was low enough to maintain many local regions of intact gene order.

6. CONCLUDING REMARKS Many unexpected surprises came out of human genome sequencing and analysis. Some of these are as follows: (i) there is only a very small fraction of protein-coding genes than previously estimated; (iii) there is evidence of pervasive transcription in the genome including widespread antisense transcription and transcription of the so-called “junk DNA”; (iii) copy number variants provide an important source of interindividual variations, probably more important than SNPs; and (iv) intraethnic group genomic variability can be greater than interethnic group genomic variability. Sequencing of the chimpanzee genome, the closest relative of humans, has produced similar surprises in terms of gene number and genome architecture. Because the human and chimpanzee genomes are ~96% identical, the obvious question is which elements in the sea of genomic information are crucial in determining humanness. Following the concept of the C-value paradox of yesteryear, Hahn and Wray (109) coined the term “G-value paradox” to underscore the lack of correlation between the number of protein-coding genes and organismal complexity. In regard to the current gold rush to harness the hidden information in the genome to gain evolutionary insight, it is pertinent to mention some cautionary warnings being given by evolutionary biologists. Based on the current paradigms of detecting signatures of positive

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selection, various studies have reported lineage-specific positive selection (such as in human or chimpanzee) or group-specific positive selection (such as skin pigmentation among different human groups). Many of these identified positive selections have not been functionally characterized. Recently, evolutionary biologist Austin Hughes (98), in an article titled, “Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level”, has warned that the widespread quest for identifying signatures of positive selection in the genome has been hampered by the use of inappropriate statistical tools and an erroneous conceptual framework of positive selection. Hughes has argued that many such efforts have failed to address alternative hypotheses, so what is viewed as a signature of positive selection may actually represent the result of relaxation of purifying selection, or population bottleneck. Hughes has also emphasized that repeated amino acid changes in a protein as a signature of positive selection is not necessarily a universal paradigm that can be applied to all protein-coding genes. This model was derived from vertebrate MHC complex whose function predicted the evolution of these proteins with adaptive genetic diversity. In the absence of known similar functions of proteins, applying this paradigm to all protein-coding genes may lead to erroneous conclusions about the extent of positive selection in the human genome. Although the immediate goal of the rapidly evolving science of genomics is to understand the structure, function, and evolution of the genome and to uncover the molecular basis of humanness, applied genomics aims to utilize this knowledge to understand the role of genetic factors in health and disease so that effective prevention, diagnosis, and treatment strategy could be developed to improve the quality of human life. The power of genomic analyses will be greatly facilitated when the genomes of all the other great apes (bonobo, gorilla, orangutan), the lesser apes (gibbon), and other Old World monkeys become available. Nevertheless, it will still be a daunting task to uncover the molecular genetic basis of humanness, such as the evolution of large cranial capacity, speech, bipedalism with erect posture, and advanced brain development. The current thinking on the evolution of organismal complexity emphasizes the role of progressively elaborate and complex regulation of gene expression. For example, a twofold increase in the number of transcription factors can bring about a dramatic expansion in regulatory network complexity compared to a twofold increase in gene number alone (110). Such analysis, combined with finer-scale investigations of the various nonprotein-coding transcribed sequences and nontranscribed sequences including regulatory sequences across the genomes, is expected to provide better insight into the molecular genomic basis of organismal complexity. REFERENCES 1. Tyler J. Chromatin assembly: cooperation between histone chaperones and ATP-dependent nucleosome remodeling machines. Eur J Biochem 2002; 269:2268–2274. 2. Parthun MR. Hat1: the emerging cellular roles of a type B histone acetyltransferase. Oncogene 2007; 26:5319–5328. 3. Wolffe AP, Hayes JJ. Chromatin disruption and modification. Nucl Acids Res 1999; 27: 711–720. 4. Verreault A. De novo nucleosome assembly: new pieces in an old puzzle. Genes Dev 2000; 14:1430–1438. 5. Ito T, Tyler JK, Kadonaga JT. Chromatin assembly factors: a dual function in nucleosome formation and mobilization? Genes Cells 1997; 2:593–600. 6. Howman EV, Fowler KJ, Newson AJ, et al. Early disruption of centromeric chromatin organization in centromere protein A (Cenpa) null mice. Proc Natl Acad Sci USA 2000; 97:1148–1153. 7. Brown DT . Histone variants: are they functionally heterogeneous? Genome Biol 2001; 2 :6.1–6.6 .

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3

Epigenetic Regulation of Gene and Genome Expression Supratim Choudhuri U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, OFAS, Division of Biotechnology and GRAS Notice Review, College Park, Maryland, U.S.A.

1. INTRODUCTION 2. MOLECULAR BASIS OF EPIGENETIC REGULATION 2.1. DNA Methylation 2.1.1. De Novo Methylation 2.1.2. Maintenance Methylation 2.1.3. DNA Methylation and Transcriptional Silencing 2.2. Histone Modifications and the Histone Code 2.2.1. Acetylation 2.2.2. Methylation 2.2.3. Phosphorylation 2.2.4. Ubiquitinylation and ADP-Ribosylation 2.2.5. Sumoylation 2.3. Noncoding RNA (ncRNA) 2.3.1. MicroRNA Biogenesis and Function 2.3.2. Small Interfering RNA Biogenesis and Function 2.3.3. Epigenetic Regulation by ncRNA 3. EXAMPLES OF EPIGENETIC REGULATION OF GENE AND GENOME EXPRESSION 3.1. Heterochromatin Spread and Position Effect Variegation 3.2. Transvection 3.3. Paramutation 3.4. Imprinting 3.4.1. Imprinting in Igf 2–H19 3.4.2. Noncoding RNA (ncRNA)-Mediated Imprinting 3.5. X-Chromosome Inactivation (XCI) 3.5.1. DNA Methylation in XCI 3.5.2. Histone Modifications in XCI

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The opinions expressed in this chapter are the author’s personal opinions, and they do not necessarily reflect those of the FDA, DHHS, or the Federal Government.

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3.5.3. X-Inactivation Center (Xic) and X Inactive-Specific Transcript (Xist) in XCI 3.5.4. Imprinted (Nonrandom) X Inactivation 3.5.5. Random X Inactivation 3.5.5.1. Counting 3.5.5.2. Choice 3.5.6. Pseudoautosomal Regions Escape Inactivation 3.6. Epigenetics of Disease and the Scope of Epigenetic Therapy 4. CONCLUSION REFERENCES

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1. INTRODUCTION The term “epigenetics” was coined by Conrad Waddington in 1942. Historically, the term has been used with different meanings under different contexts. In the context of molecular biology, epigenetics can be defined as the study of mitotically or meiotically heritable changes in gene function that cannot be explained by changes in the DNA sequence (1). The collection and the combination of all epigenetic factors (epigenome) provide information about the spatial and organizational constraints of the genome that complement genetic instructions to influence the outcome of genome expression. Epigenetic inheritance involves the transmission of information (epigenetic mark) not encoded in DNA, from parent cell to daughter cells and from generation to generation. Epigenetic mark is like a bookmark that flags the chromatin state, “on” or “off”, “open” or “closed”, so that they can be identified and maintained in the daughter cells. In the spirit of genomics, the term “epigenomics” has come into existence and is often used synonymously with the term “epigenetics”. However, epigenomics is a new frontier that studies epigenetic changes at the level of the entire genome (2). At the present moment, any discussions on epigenomics and epigenetics are invariably intertwined. 2. MOLECULAR BASIS OF EPIGENETIC REGULATION Factors that chemically modify DNA without altering the sequence may alter chromatin conformation, modulate the accessibility and binding of the transcription machinery, and influence genetic regulatory cross-talk. Since all these events have downstream effects on transcription, they may trigger an epigenetic effect. Three important factors provide the molecular basis of epigenetic regulation of genome expression: (i) DNA methylation, (ii) histone modification, and (iii) noncoding RNA (ncRNA)-mediated regulation. Besides these factors, there are other mechanisms that also affect gene expression epigenetically, such as chromosome pairing-mediated changes in promoter–enhancer interaction (discussed in relation to transvection). 2.1. DNA Methylation In prokaryotes, DNA methylation occurs at both cytosine and adenine bases and is a part of the host restriction system. In multicellular eukaryotes, DNA methylation is confined to cytosine bases. Methylation involves covalent modification of cytosine at C-5 position, the methyl group donor being S-adenosylmethionine (SAM), and the enzyme involved is DNA methyltransferase (Dnmt). Cytosine methylation almost exclusively occurs on

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CG dinucleotide, which is denoted as CpG. The C of CpG is methylated in both strands of DNA. During replication, the parent strand is methylated, but the newly synthesized daughter strand is not methylated, thus creating a temporarily hemimethylated segment of DNA. The hemimethylated segment is recognized by maintenance methyltransferase, which methylates the hemimethylated sites and restores the parental methylation pattern. There are two types of methyltransferases: one responsible for de novo methylation that establishes the methylation pattern, and the other responsible for maintenance methylation once the methylation pattern is established. In mammals, four different Dnmts have been identified: Dnmt1, Dnmt2, Dnmt3a, and Dnmt3b. These are also designated with capital letters (DNMT), especially when referring to the human enzyme. In the following text, the expression Dnmt will be used for general description except when specifically referring to the human enzymes. 2.1.1. De Novo Methylation The de novo methylation establishes the parent-specific methylation pattern. DNMT3A and DNMT3B (in humans) and Dnmt3a and Dnmt3b (in mice) are de novo methyltransferases (3). Dnmt3a and Dnmt3b are required for de novo methylation of embryonic stem (ES) cells and early postimplantation embryos. In humans, mutations in DNMT3B gene are associated with immunodeficiency, centromere instability, and facial anomalies (ICF) syndrome.a A mouse model for the human ICF syndrome has also been created by mutating the Dnmt3b gene; these mice show some of the characteristics of the human ICF syndrome.b The precise recognition of various target genes by Dnmt3a and Dnmt3b seems to be facilitated by Dnmt3L, a protein that lacks the enzymatic activity, but interacts and colocalizes with Dnmt3a and Dnmt3b (4). 2.1.2. Maintenance Methylation In contrast, DNMT1 in humans and Dnmt1 in mice are maintenance methyltransferases (5,6). Mutations in the Dnmt1 gene result in global genome demethylation and embryonic lethality (7). Maintenance methylation involves recognition of hemimethylated CpG sites at the replication foci, followed by the addition of methyl groups to cytosines on the nascent DNA strand to replicate the methylation pattern on the parent strand. Methylation by Dnmt1 also helps modulate chromatin conformation and represses transcription directly or indirectly through methyl-CpG-binding protein 2 (MeCP2) and histone deacetylase (HDAC) (discussed below). Dnmt1 has been shown to be associated with HDAC at the replication foci (8). Various demethylase enzymes have also been identified (9). Mouse oocytes and preimplantation embryos lack Dnmt1 but express an isoform variant called “Dnmt1o”. Like Dnmt1, Dnmt1o is also involved in maintenance methylation on alleles of imprinted genes during oogenesis and in the eight-cell-stage embryo (4). Dnmt2 is not discussed here because it has weak methyltransferase activity, and its targeted deletion does not have any impact on the global DNA demethylation in the cell (8). a Hansen RS, Wijmenga C, Luo P, et al. The DNMT3B DNA methyltransferase gene is mutated in the ICF immunodeficiency syndrome. Proc Natl Acad Sci USA 1999; 96:14412–14417. b Ueda Y, Okano M, Williams C, et al. Roles for Dnmt3b in mammalian development: a mouse model for the ICF syndrome. Development 2006; 133:1183–1192.

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2.1.3. DNA Methylation and Transcriptional Silencing Cell-specific patterns of cytosine methylation occur on the majority of CpG dinucleotides in vertebrates, and these patterns are maintained through replication. In general, cytosine methylation is associated with transcriptional silencing. Studies during the last decade have led to the development of a general model of DNA methylation-induced transcriptional silencing. This is achieved by two general mechanisms: the recruitment of methyl-CpG-binding transcriptional repressors and the disruption of DNA binding of transcriptional activators, both resulting in a more condensed, transcriptionally silent state of chromatin (8,10). The methyl-CpG-binding transcriptional repressors are known as the “methylCpG-binding domain (MBD) Proteins”, which in turn recruit HDAC. The five known MBD proteins (MBD1, MBD2, MBD3, MBD4, and MeCP2) share a conserved MBD. Of these, the best studied is MeCP2, which selectively binds 5-methyl cytosine in symmetrically positioned CpG dinucleotides in mammalian genome. It contains two functional domains: an 85-amino acid MBD essential for binding to 5-methyl cytosine and a 104-amino acid transcriptional repression domain (TRD) that interacts with HDAC and the transcriptional repressor Sin3. Recruitment of HDAC by MeCP2 causes deacetylation of histones in the chromatin, resulting in a more condensed chromatin conformation and transcriptional silencing. In addition, MeCP2-binding to methylated DNA may also directly prevent the access of transcriptional activators to the promoter and other regulatory sequences (8,10). Consistent with the inverse relationship between methylation and gene expression, the CpG islands around the promoters of housekeeping genes and other constitutively expressed genes are not methylated. In contrast, the CpG sequences near the promoter of inactive genes are methylated. The 5′ ends of genes usually have clusters of CpG dinucleotides. Normally, about 70% of the CpG islands in mammalian genome are methylated. Methylated cytosine can undergo occasional spontaneous deamination to give rise to thymine. Because thymine is a normal DNA base, this mutation sometimes escapes repair resulting in methyl-CpG on a DNA strand being replaced by TpG.

2.2. Histone Modifications and the Histone Code Alteration of chromatin conformation through histone modifications is one of the key events in the epigenetic regulation of gene expression. Because of the plethora of histone modifications and their varying effects on gene expression, it has been proposed that distinct histone modifications act sequentially or in combination to form a “histone code” that is read by other proteins to bring about distinct downstream events (11). The combinatorial and interdependent nature of different histone modifications as depicted by the histone code hypothesis has received much attention. An example of the interdependent nature of histone modifications is the methylation of lysine 4 of histone H3 (H3K4) in yeast Saccharomyces cerevisiae, which is dependent on the ubiquitinylation of lysine 123 of histone H2B (H2BK123). The question that is triggered by the stipulations of the histone code hypothesis is, how is the histone code recognized by histone modifying enzymes and how are these enzymes targeted to their correct destination in the chromatin? The answer lies in the specific domains possessed by various chromatin-modifying enzymes and remodeling proteins, such as bromodomain, chromodomain, and SANT domain proteins. For a discussion of the structural aspects and distribution of these domains in various proteins, refer to the review by de la Cruz et al. (12) and references therein.

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Different histone residues are modified by the addition of various chemical groups, and such modifications are necessary for both activation and repression of transcription. Most of the modifications are on the N-terminal tails of histones that stick out of the core octamer and are available to chromatin-modifying enzymes for various types of posttranslational modifications, such as acetylation, methylation, phosphorylation, ubiquitinylation, ADP-ribosylation, and sumoylation. The amino acid residues that are subject to modification are specific, thus creating specific “marks” on the chromatin. These modifications help recruit other proteins and aid in either relaxation or further compaction of chromatin, which influence transcription. 2.2.1. Acetylation Histone acetyltransferase (HAT) acetylates histones by adding acetyl group (–CH3CO) to one or more lysine residues at the ε-amino group. Acetylation reduces the overall positive charge of histones; hence, it decreases the affinity of histones for the negatively charged DNA. Reducing the interaction between histones and DNA helps in the establishment of open, that is, decondensed, relaxed chromatin conformation, which is necessary for transcriptional activators to gain access to the promoter and other regulatory sequences to initiate transcription. In this way, histone acetylation is associated with transcriptional activation. Histone acetylation, such as H4 acetylation at lysine 5 and 12 by histone acetyltransferase 1 (Hat1), is also implicated in nucleosome assembly and higher-order chromatin packing (discussed in Chapter 2 in Section 2.1.1). The lysine residues that are frequently targeted for acetylation are lysine 9, 14, 18, 23, and 56 of histone H3; lysine 5, 8, 13, and 16 of histone H4; lysine 5, 9, and 13 of histone H2A, and lysine 5, 12, 15, and 20 of histone H2B (13,14). Consistent with the association between histone acetylation and transcriptional activation, many transcriptional coactivators have acetylase (acetyltransferase) activity, such as general control of amino acid synthesis 5 (GCN5); p300/CREB-binding protein (CBP)associated factor (PCAF); CBP/p300; TATA-binding protein (TBP)-associated factor for RNA pol II 250 kDa (TAFII250); steroid receptor coactivator-1 (SRC-1); and activator of thyroid and retinoid receptors (ACTRs), also called “amplified in breast cancer 1” (AIB1) and “steroid receptor coactivator-3” (SRC-3). GCN5 by itself acetylates only free histones, but it can also acetylate nucleosomal histones H2B and H3 when it is a part of other protein complexes, such as ADA complex and SAGA complex. Acetylated histones are recognized by bromodomain-containing factors, such as TAFII250, PCAF, and GCN5. Acetylation is removed by HDACs. A list of various enzymes responsible for histone covalent modifications in different species has been compiled by Bhaumik et al. (13). 2.2.2. Methylation Histone methyltransferase (HMT) methylates histone at lysine and arginine, thereby increasing the bulk but not interfering with the charge. Methylation can be mono-, di-, or trimethylation. Histone methylation is linked to both transcriptional activation and repression. Methylation of lysine 4, 36, and 79 of histone H3 (H3K4, H3K36, and H3K79) activates transcription, whereas methylation of lysine 9 and 27 of histone H3 (H3K9 and H3K27) is associated with transcriptional repression. Methylation of lysine 20 of histone H4 (H4K20) is also associated with transcriptional repression. Methylation of arginine 17 and 23 of histone H3 (H3R17 and H3R23) and arginine 3 of histone H4 (H4R3) activates transcription (14). Since H3K9 can be acetylated (activating) as well as methylated

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(repressing), a balance between H3K9 acetylation and methylation may be important in establishing specific chromatin domains, K9 methylation being associated with silent domains and K9 acetylation being associated with active domains. Methylated histone H3 is recognized by heterochromatin-associated chromodomain-containing protein, such as heterochromatin protein 1 (HP1). Dimethylation of H3K9 (H3K9me2) promotes the binding of HP1 to histone H3. It has been demonstrated that the formation of H3K9me2 and HP1-binding spread in tandem with ectopic locations on the chromosome arms (15; discussed in more detail in Section 3.1 in this chapter). Histone methylation is reversed by specific histone demethylase, such as lysine-specific demethylase 1 (LSD1) (16). It has been recently demonstrated that H3K4me3 provides a binding site for TFIID, resulting in enhanced recruitment and stability of the transcription preinitiation complex (17). This finding provides a functional/mechanistic basis to explain the association between the histone code H3K4me3 and transcription. 2.2.3. Phosphorylation Histone phosphorylation is carried out by various kinases, such as Rsk-2 kinase, aurora kinase,c and NIMA kinase in Aspergillus. The addition of negatively charged phosphate groups to the N-terminal histone tails may disrupt electrostatic interactions between histones and DNA, thereby destabilizing local chromatin conformation, and presumably triggering transcriptional activation. Phosphorylation targets are one or more serine and/or threonine residues (3,10,28) of histone H3; serine 10 (H3S10) appears to be a frequent target. Phosphorylated serine/threonine has an activating effect on transcription. Methylation at H3K9 can inhibit phosphorylation at H3S10; therefore, an interplay between these two modifications may be important in determining local chromatin modification and regulating transcription. Another example of the importance of histone phosphorylation is histone H2AX phosphorylation at serine 139 forming γ-H2AX, which accumulates at the site of DNA double-strand breaks and recruits various DNA repair proteins. Increased H1 phosphorylation and relaxed chromatin structure have also been observed in cells expressing several oncogenes (13,14,18,19). 2.2.4. Ubiquitinylation and ADP-Ribosylation Ubiquitinylation and ADP-ribosylation of chromatin are relatively minor and less extensively studied. Whereas polyubiquitinylation flags proteins for degradation by proteasome, monoubiquitinylation may alter protein function without flagging it for degradation. For example, monoubiquitinylation has been found on lysine 119 of H2A (H2AK119ub; repressive) and lysine 120 and 123 of H2B (H2BK120ub in humans and H2BK123ub in yeast; activating) (14,20). In yeast, H2BK123ub is required for histone H3K4 and H3K79 methylation, which in turn activate transcription. Monoubiquitinylation of histone H1 results in its release from the DNA. Release of histone H1 helps reduce chromatin condensation and facilitate transcriptional activation. Mono-ADP-ribosylation involves the transfer of an ADP-ribose moiety of NAD+ to a specific amino acid of the acceptor protein with the simultaneous release of nicotinamide. Chromatin or histone components are subject to mono-ADP-ribosylation on arginine and glutamate residues. The reaction is carried out by ADP-ribosyltransferase. Several studies c

Aurora kinases (three types in mammals, Aurora-A, -B, and -C) also play crucial role in mitosis by regulating chromosome segregation; thus, these kinases could be potential therapeutic targets for anticancer agents.

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indicate that histones are ADP-ribosylated in response to DNA damage. It is possible that mono-ADP-ribosylation, along with other modifications of histone tails, may regulate subsequent steps in DNA damage response pathways (21). 2.2.5. Sumoylation Small ubiquitin-related modifier (SUMO) is a member of a growing family of ubiquitinlike proteins involved in posttranslational protein modifications. Histone sumoylation was reported in 2003 by Shiio and Eisenman in human cell lines. The substrates for sumoylation are lysine residues, and the consensus sequence for sumoylation is ψ-Lys-X-Glu, where ψ is a large hydrophobic residue (such as Ile, Leu, or Val) and X is any amino acid. Both ubiquitinylation and sumoylation require E1-activating (SAE1/SAE2) and E2-conjugating (UBC9) enzymes. The UBC9 interacts with the substrate to catalyze the formation of an isopeptide bond (peptide bond involving the ε-amino group of lysine instead of the usual α-amino group) between the C-terminal end of SUMO and the amino group of the target lysine. Shiio and Eisenman demonstrated that, of the four major core histones, only histone H4 was efficiently sumoylated both in vivo and in vitro, whereas a relatively lower degree of sumoylation was observed for H2A, H2B, and H3. They also presented evidence that histone sumoylation mediates transcriptional silencing through recruitment of HDAC and HP1. Subsequent work from Shelly Berger’s laboratory in S. cerevisiae using tandem mass spectrometry (MS/MS) analysis identified H2AK126, H2BK6, and H2BK7 as confirmed sumoylation sites. In addition, indirect evidence showed H2BK16 and H2BK17 as putative sumoylation sites. Mutagenesis studies indicated that all five lysines (K5, K8, K12, K16, and K20) in the N-terminal region of H4 could be targets of sumoylation. Interestingly, mutating all five lysine residues (to alanine) reduced sumoylation by half or more, but mutating any three lysine residues did not have an effect, suggesting a lack of specificity among H4 lysine residues for sumoylation (see 22 and references therein). It has been suggested that transcription repression signal is provided by acetylation itself. This suggestion came from the observation that histone H4 sumoylation increases with increasing H4 acetylation. The next step is deacetylation followed by methylation, which is required for the binding of HP1; and HP1, in turn, contributes to chromatin structure in a static repressed state. Sumoylation of histone H2AK126, and histones H2BK6 and H2BK7, is associated with transcriptional repression (14,22). Although histone sumoylation antagonizes histone acetylation and serves as a repressive mark on the chromatin, exactly how sumoylation opposes acetylation (whether by competing for adjacent sites or through some other mechanism) is yet to be clearly understood. Table 1 lists all the transcriptional activating and repressing chromatin modifications at different amino acids in various histones that have been discussed in the text. 2.3. Noncoding RNA (ncRNA) There is mounting evidence that ncRNAs play an important role in epigenetic regulation of genome expression. Some of the ncRNAs have been known for some time, such as Xist and Tsix RNA involved in X-chromosome inactivation in mammals (discussed below in Section 3.5), whereas others have been discovered more recently. For example, Air ncRNA is necessary for the silencing of Igf2r/Slc22a2/Slc22a3 gene cluster, and Kcnq1ot1 ncRNA is necessary for the silencing of multiple genes in the Kcnq1 cluster (discussed below in Section 3.4). With the discovery of various noncoding small RNAs, such as microRNA (miRNA), small interfering RNA (siRNA), trans-acting siRNA (tasiRNA),

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Table 1 Some Transcriptional Activating and Repressing Chromatin Modifications and Their Target Amino Acids in Various Histones Activating modifications

Repressing modifications

Acetylation (ac) H2AK5, H2AK9, H2AK13, H2BK5, H2BK12, H2BK15, H2BK20, H3K9, H3K14, H3K18, H3K23, H3K56, H4K5, H4K8, H4K13, H4K16 Methylation (me) H3K4, H3K36, H3K79, H3R17, H3R23, H4R3

Methylation (me) H3K9, H3K27, H4K20

Phosphorylation (ph) H3T3, H3S10, H3S28, H2AXS139 (for DNA repair activity) Ubiquitinylation (ub) H2BK120 H2BK123 (yeast)

Ubiquitinylation (ub) H2AK119 Sumoylation (su) H2AK126 (yeast) H2BK6, H2BK7 (yeast)

small-scan RNA (scnRNA), repeat-associated siRNA (rasiRNA), and Piwi-interacting RNA (piRNA) (23), the scope of ncRNA-mediated epigenetic regulation of genome expression has grown enormously. Of these, the miRNA and the siRNA have been well studied in plants and animals, and thus will be discussed below. Both miRNA and siRNA are involved in triggering RNA interference (RNAi) and silencing of gene expression, albeit through different mechanisms. 2.3.1. MicroRNA Biogenesis and Function miRNAs are single-stranded ncRNAs that silence gene expression through translational repression. Genes for miRNA are found in all chromosomes. They are found in introns of coding and noncoding genes, as well as in exons of noncoding genes. Many of them exist in clusters that are transcribed by RNA pol II into polycistronic primary transcripts. The primary miRNA transcripts (pri-miRNAs) contain 5′ m7G cap and 3′ poly(A) tail, and they may be hundreds to thousands of nucleotides long with hairpin (stem-loop) structures (23). The pri-miRNA transcripts are first processed in the nucleus by Drosha–DGCR8 complex to produce ˜70-nt-long hairpin precursors called “precursor miRNAs” (pre-miRNAs). DGCR8 (DiGeorge syndrome critical region gene 8) protein is known as “Pasha” in Drosophila and Caenorhabditis elegans, hence forming the Drosha–Pasha complex. While Drosha is a RNAse III-like endonuclease, DGCR8/Pasha is a double-stranded RNA (dsRNA)-binding protein that acts as a ruler to measure and determine the Drosha cleavage site at the 11-nt position from the base of the stem structure. Drosha processing of the pri-miRNA leaves a 2- or 3-nt 3′ overhang in the pre-miRNA. This end structure is characteristic of RNAse III cleavage.

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The pre-miRNA is then exported from the nucleus by exportin-5 to the cytoplasm, where it is further processed into ˜22-nt-long duplex mature miRNA (a miRNA/miRNA* duplex) by another RNAse III-like endonuclease Dicer (Dicer1 in Drosophila) (24). The mature miRNA sequence may be located at the 5′ or 3′ end of the pre-miRNA hairpin. The miRNA/miRNA* duplex is loaded onto the RNA-induced silencing complex (RISC); one strand (the passenger strand) is destroyed, whereas the other strand (the guide strand) becomes part of the RISC, referred to as “miRISC”. The single-stranded guide miRNA in the miRISC base-pairs at the 3′ end of target mRNAs. Binding of multiple miRISCs at the 3′-UTR of target mRNAs represses their translation, resulting in the silencing of gene expression. The miRISC complex contains several proteins such as Dicer, TRBP, PACT, and Gemin3, but the components directly associated with miRNAs are Argonaute (Ago) proteins. There are four Ago proteins in mammalian cells, all of which can bind to endogenous miRNAs (25). However, only Ago2 exhibits endonuclease activity to slice complementary RNA sequences between positions 10 and 11 from the 5′ end of the guide strand RNA. Ago2 is a component not only of miRISC but also of siRISC (discussed in the next section). The pairing between the miRNA guide strand and the target strand (mRNA sequence) is imperfect, usually forming a bulge at the mismatched region. The sequence specificity for target recognition of the miRNA guide strand is determined by nucleotides 2–8 of its 5′ region, the so-called “seed sequence”. The requirement of a seed sequence for miRNA function raises the potential for a single miRNA to target multiple mRNAs because the 3′-UTR of many target mRNAs may harbor sequence, which is complementary to a single miRNA seed sequence (26). 2.3.2. Small Interfering RNA Biogenesis and Function siRNAs are double-stranded ncRNAs that regulate gene expression by triggering the degradation of target mRNAs; siRNAs were originally identified as intermediates in the RNAi pathway after induction by exogenous dsRNA. It is now known that dsRNAs are found in cells, and they form the genetic material of some viruses called “dsRNA viruses”. Endogenous dsRNA is produced through conversion of single-stranded RNA into dsRNA by RNA-dependent RNA polymerases, or alternatively by transcription of inverted DNA repeats by DNA-dependent RNA polymerases. Long dsRNAs are processed by the Dicer–TRBP–PACT complex, creating ˜22-nt dsRNA with 2-nt overhang at the 3′ end and phosphate at the 5′ end of each strand (27). In Drosophila, the Dicer involved in siRNA formation is called “Dicer2” (24). The antisense strand of siRNA is known as the “guide strand” because it serves as the template for sequence-specific gene silencing by the RNAi machinery. The sense strand is known as the “passenger strand”, and following unwinding of the dsRNA, it is destroyed. Subsequent to Dicer processing, the ˜22-nt guide strand of duplex siRNA is loaded onto siRISC, which contains Ago2, the endonuclease responsible for the cleavage activity of siRISC. In contrast to miRNA which does not form a perfect duplex with the target sequence, siRNA needs to form perfectly matched sequence to trigger RNAi. With perfect base-pairing between the siRNA guide strand and its target mRNA, siRISC cleaves its target 10–11 nt from the 5′ end of the guide siRNA strand. The mRNAs cleaved by siRISC are subsequently degraded by cellular exonucleases. 2.3.3. Epigenetic Regulation by ncRNA Epigenetic regulation of gene expression by ncRNAs, although an established fact, is still an emerging area of research. Readers are referred to some recent reviews that discuss various

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facets of ncRNA-mediated epigenetic regulation of gene and genome expression (28–30). One common mechanism of epigenetic regulation by ncRNA is the RNAi-mediated gene silencing, which has been reported from ciliate protozoa to mammals. The ncRNAs (both miRNA and siRNA) apparently also play important roles in DNA methylation and chromatin modification, which ultimately affect gene expression. For example, siRNAs have been shown to be involved in both DNA methylation and histone modifications (28–30) by recruiting histone-modifying complexes and subsequent heterochromatin formation and DNA methylation. The mechanisms, however, are yet to be described. Similar functions have also been ascribed to miRNAs. The expression of PHABULOSA (PHB) and PHAVOLUTA (PHV) genes, required for the development of distinct upper and lower half of leaf morphologies and function in Arabidopsis, is downregulated by miRNA. In most wild-type plant cells, the coding sequences of both PHB and PHV are methylated more than 1 kb downstream from the miRNA-binding site. Mutation of the miRNA-binding site causes demethylation of these genes, suggesting a role of miRNAs in such methylation. The mechanism of such miRNA-directed methylation is not clear, but the involvement of chromatin has been proposed (31). More recently, it has been demonstrated that the inactivation of Dicer1 results in a series of related effects in mice (32). These effects include decreased DNA methylation, decreased expression of Dnmt1, Dnmt3a, and Dnmt3b, and increased telomere recombination and telomere elongation. The DNA methylation defects were due to decreased expression of Dnmts. The authors have shown that overexpression of the retinoblastomalike 2 (Rbl2) protein is responsible for the decreased Dnmt expression in Dicer1-null cells, suggesting the existence of Dicer-dependent small ncRNAs that target Rbl2. The miR-290 cluster of miRNAs is downregulated in Dicer1-null cells, and it silences Rbl2, thereby controlling Dnmt expression. These results have identified a pathway in which miR-290 directly regulates Rbl2-dependent Dnmt expression and indirectly affects DNA methylation and telomere-length homeostasis. In carcinogenesis, miRNAs seem to play important roles such that the disturbance of certain miRNA expression and function may contribute to the initiation and maintenance of tumors. Such oncogenic miRNAs are referred to as “onco-miRs”. However, miRNAs can also serve as tumor suppressors. Examples of tumor suppressor miRNAs are miR-15a and miR-16-1. About 65% of the patients diagnosed with B-cell chronic lymphocytic leukemia (B-CLL) have frequent deletions or downregulation of miR-15a and miR-16-1 located in a cluster in chromosome 13q14.3. The CLL cells also have a higher level of Bcl2 protein expression. A follow-up study (33) demonstrated the mechanism of action of these two miRNAs as tumor suppressors. In the study, the authors used a leukemia-derived cell line MEG-01, which has no constitutive expression of miR-15a and miR-16-1 genes, but has a high level of expression of Bcl2 gene and protein (miR-15a–, miR-16-1–, Bcl2+). A 832-bp genomic sequence containing both these miRNA genes was transfected into MEG-01 cells. Expression of miR-15a and miR-16-1 in these cells resulted in a concomitant decrease in Bcl2 protein expression, but no change in Bcl2 mRNA expression. That the downregulation of Bcl2 was specifically due to the expression of miR-15a and miR-16-1 was further demonstrated by antisense knockdown of miR-15a and miR-16-1 sense RNA expression. Antisense-mediated downregulation of miR-15a and miR-16-1 resulted in a concomitant increase in Bcl2 protein, but no change in Bcl2 mRNA expression. These results clearly demonstrate that miR-15a and miR-16-1 are negative regulators of the antiapoptotic protein Bcl2, and the mechanism of action involves miRNA-mediated translational repression of Bcl2 protein. The authors also noted the existence of the 9-bp Bcl2-complementarity sequence (the seed sequence) in these miRNAs. Since Bcl2 is an antiapoptotic protein,

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repression of Bcl2 protein expression by miR-15a and miR-16-1 results in the apoptosis of cancerous cells. A list of various oncogenic as well as tumor suppressor miRNAs, their regulation, and possible role in carcinogenesis has been compiled by Chuang and Jones (34). 3. EXAMPLES OF EPIGENETIC REGULATION OF GENE AND GENOME EXPRESSION In mammals, the two best studied epigenetic phenomena are differential expression of imprinted loci in the paternal and maternal genomes and inactivation of one of the X chromosomes in female. Some of the relatively well-studied epigenetic phenomena in various eukaryotes are discussed below. 3.1. Heterochromatin Spread and Position Effect Variegation Heterochromatin represents transcriptionally inert condensed chromatin. Heterochromatin spread into the neighboring euchromatin results in the silencing of genes that are in close proximity to the heterochromatin. The silencing of adjacent genes is not complete; in some cells the genes are silenced, while in other cells they are not. As a result, genetically identical cells of a tissue have differential gene expression pattern leading to different cellular phenotypes within the tissue. Because heterochromatin spread results in such mosaic expression, the so-called “variegated (patchy) expression pattern”, and this expression pattern is brought about by the proximity of the gene to the heterochromatin, it is called “position effect variegation” (PEV). Therefore, PEV is the result of heterochromatin spread into adjacent euchromatin, and it is an epigenetic effect driven by altered chromatin conformation that causes genetically identical cells to have different phenotypes. Such heterochromatinization of the adjacent euchromatin is a major cause of silencing of transgenes when they are integrated close to the heterochromatin. However, indiscriminate expansion of heterochromatin into euchromatin and mass silencing of genes along the length of the chromosome are prevented by various cis-acting elements and trans-acting factors. The most important cis-acting element shielding genes from the silencing effects of heterochromatin is the insulator element discussed in Chapter 1. In Drosophila, a trans-acting factor that can antagonize the spread of heterochromatin is a mutant of the H3S10 kinase JIL-1, called “Su(var)3-1”. The molecular mechanism of PEV is not well understood. During heterochromatin spread, the invading heterochromatin proteins are thought to impose a closed (condensed) chromatin state on the adjacent euchromatin containing genes that are expressed. Imposition of a condensed conformation results in heterochromatinization of the euchromatic region, which blocks the access of the transcriptional machinery leading to transcriptional silencing. Cytogenetic studies with Drosophila polytene chromosomes have confirmed that the euchromatin adjacent to the heterochromatin–euchromatin breakpoint undergoes visible changes in the banding pattern, which indicates the induction of chromatin structure changes. Various proteins associated with PEV have been identified. These proteins are termed by the general name Su(var) or Suv, meaning “suppressor of variegation”. In Drosophila, more than 50 Su(var) loci exist, of which ˜15 have been molecularly defined. Su(var) genes encode various components of heterochromatin, such as the zinc finger protein Su(var)3-7, the chromodomain protein Su(var)2-5 [also called HP1, Su(var)205], the enzymes HDAC1, and Su(var)3-9 (a histone methyltransferase or HMT). Among these, Su(var)2-5 (HP1),

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Su(var)3-7, and Su(var)3-9 (HMT) are inherent components of the heterochromatin. In a genetic hierarchy, Su(var)3-9 is dominant over the other two. Su(var)3-9 catalyzes the formation of H3K9me2, which then promotes the binding of Su(var)2-5 (HP1) to histone H3 (35). H3K9me2 formation and HP1-binding spread in tandem to ectopic locations on the chromosome arms inducing heterochromatin formation (15). The Su(var)3-9–HP-1 system-mediated heterochromatin spread is a cis-acting linear heterochromatin propagation model. It was demonstrated that a mutant of the euchromatic H3S10 kinase JIL-1, termed “Su(var)3-1”, can antagonize the propagation of heterochromatin in Drosophila, thus severely restricting Su(var)3-9-mediated heterochromatinization and gene silencing (35). JIL-1 is a kinase that normally localizes specifically to euchromatic interband regions of polytene chromosomes. Building on the work of Ebert et al. (35), Zhang et al. (15) showed that reduction in JIL-1 histone H3S10 kinase resulted in the spreading of the major heterochromatin markers H3K9me2 and HP1 to ectopic locations on the chromosome arms, with the most pronounced increase on the X chromosomes. Because phosphorylation of histone in general is an activating modification, H3S10 phosphorylation may interfere with methylation of the adjacent H3K9, which is repressing, thereby counteracting heterochromatin spread. The cis-acting heterochromatin propagation model does not explain some of the long-distance PEV features in Drosophila that influence genes located several megabases away from the heterochromatin–euchromatin breakpoint. Such long-distance PEV effects are probably achieved by interactions of the heterochromatin proteins with the DNA in trans; therefore, it may involve looping of the chromatin (36,37). 3.2. Transvection In Drosophila (and Dipteran insects in general), homologous chromosomes are in intimate synapsis (pairing) in somatic cells. Such chromosome pairing influences the activity of genes located in the paired chromosomes. This phenomenon in which the expression of an allele in one chromosome is influenced by its proximity to the corresponding allele in the homologous chromosome was termed “transvection” by E.B. Lewis in 1954 who first described this phenomenon from his studies of the bithorax complex of Drosophila melanogaster. The defining feature of transvection is its dependence on chromosomal pairing; thus, chromosomal rearrangements that interfere with homologous pairing also interfere with transvection. Molecular and genetic analyses of various transvecting alleles revealed that transvection involves the action of enhancers in trans. Since enhancer looping is thought to activate a promoter in cis, it may impart the same action in trans if the promoter in trans is in close proximity (38). This model is supported by examples in which transvection occurs when one allele is defective in enhancer and the other allele is defective in the protein-coding region but has an intact enhancer (39). Using the yellow locus in Drosophila, it was demonstrated that transvection not only involves enhancer action in trans, but may also involve insulator bypass in cis. The yellow gene produces pigment that imparts color to the wing, body, bristle, and tarsal claws. The expression of the yellow gene in bristles and tarsal claws is regulated by bristle-tarsal claw enhancers located in the only intron of the gene, whereas the expression in the wing and body is regulated by wing-body enhancers located in the 5′-regulatory region. The yellow2 (y2) mutant has reduced pigmentation in the wing and body, but normal pigmentation of bristles and tarsal claws. The y2 mutation is caused by binding of the suppressor of hairy wing [su(Hw)] protein to the gypsy retrotransposon following the insertion of gypsy between the wing-body enhancers and the promoter. The binding of su(Hw) to the

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gypsy establishes an insulator that prevents the wing-body enhancers from communicating with the promoter and activating transcription. Several other yellow mutations complement y2. One such mutation is y1#8, which is caused by the deletion of 0.8 kb that includes the promoter and some other sequences of the yellow gene, but it has functional wing and body enhancers. The y2/y1#8 transheterozygote shows nearly normal pigmentation. Such transvection and intragenic complementation can be explained by the action of wing-body enhancers of y1#8 in trans on the promoter of y2 (40). Fig. 1 shows a general scheme of transvection and intragenic complementation through the action of enhancer in trans. Using two other yellow mutants y82f29 and y3c3 that were identified in a genetic analysis of transvection in yellow locus, Morris et al. (40) tested and confirmed the role of trans-acting enhancers in transvection and also proposed the existence of a second mechanism, the insulator bypass in cis. In y3c3 allele, the wing enhancer is present, but the body enhancer, the promoter, and the bristle-tarsal claw enhancers are all deleted. Thus, y3c3 is a null mutant producing fully mutant pigmentation of wing, body, and other cuticular structures; yet, y3c3 complements y2 to produce normal pigmentation. Because the body enhancer is missing in y3c3, such complementation for normal body pigmentation cannot be explained by the action of enhancers in trans. To explain the finding, the authors proposed a second model of transvection, the insulator bypass in cis.

A Intact promoter

Intact enhancer

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Nonfunctional coding region X X

Very low-level transcription Truncated enhancer

Intact promoter

Functional ORF

Alleles in paired homologous chromosomes

B Enhancer acting in trans X

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Enhancer-bound coactivators

Intragenic complementation

Figure 1 General mechanism of transvection and intragenic complementation through enhancer action. Enhancer looping is thought to activate promoter in trans. This model is supported by examples in which one allele is defective in enhancer, and the other allele is defective in the protein-coding region but has an intact enhancer.

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This model depicts that the body enhancer and promoter of y2 are unpaired, because y3c3 lacks these elements; thus, y3c3–y2 pairing forces the unpaired region of y2 into a loop. This unpaired region contains the body enhancer, the promoter, as well as the insulator [gypsy-bound su(Hw)]. Such positioning presumably prevents the insulator from exerting its repressive effect on y2 promoter, allowing the body enhancer to bypass the insulator and act on its promoter in cis. Fig. 2 shows the insulator bypass and complementation scheme of y2 and y3c3 mutants. Insulator bypass in cis is achieved through what the authors termed “pairing-mediated topology effect” (TOPE). TOPE is envisioned as a change in chromatin conformation influencing complementation and gene expression, and is therefore an epigenetic effect (38,40). 3.3. Paramutation Paramutation is an allelic interaction in which one allele, referred to as “paramutagenic allele”, causes a heritable change in the expression of a homologous “paramutable allele”. The altered paramutable allele is called “paramutated allele”. Alleles unaffected by exposure

Wing enhancer

Body enhancer

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insulator y2 Wing-body enhancers cannot communicate with the promoter in y 2 y 3c3 No body enhancer to complement y 2 body pigmentation in trans

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Pairing-mediated looping out of the nonhomologous part of y 2 Looped-out insulator cannot prevent the body enhancer from communicating with the promoter in cis

Transcription due to insulator bypass in cis

y2

y 3c3

Figure 2 Proposed mechanism of insulator bypass in cis and complementation scheme of y2 and y3c3 mutants. This model depicts that y3c3–y2 pairing forces the unpaired region of y2 into a loop. This unpaired region contains the body enhancer, the promoter, and the insulator [gypsy-bound su(Hw)]. Such positioning prevents the insulator from exerting its repressive effect on y2 promoter, allowing the body enhancer to bypass the insulator and act on its promoter in cis.

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to a paramutagenic allele are called “neutral alleles”. Paramutation thus violates Mendel’s law of dominance. It was first described in maize in 1956. Since then, paramutation has been described for four genes in maize: r1 (red1, reduces seed pigment), b1 (booster1, reduces plant pigment), pl1 (purple1, reduces anther pigment), and p1 (pericarp color1, reduces seed and cob pigment), all of which encode transcription factors that activate the biosynthesis of flavonoid pigments in plant or seed tissues. The example of paramutation in b1 gene is discussed below. There are two steps in paramutation: establishment and maintenance. Establishment of paramutation occurs when the paramutable allele is exposed to the paramutagenic allele, and the paramutable allele takes on an altered expression state (paramutated allele). Maintenance refers to the mitotic or meiotic heritability of the new expression state in subsequent generations (41). Fig. 3 shows paramutation in b1 gene. Two b1 alleles are involved in paramutation: B′ and B-I. The paramutagenic B′ allele is highly stable, and the paramutable B-I allele is unstable. Thus, in B′ /B-I heterozygote, B-I is always changed to B′ and is called “B-I*”. Studies on the molecular mechanism of paramutation in b1 showed the importance of a 6-kb region located ˜100 kb upstream of the transcription start site. This 6-kb region in both B′ and B-I alleles have seven tandem repeats of an 853-bp sequence, whereas the neutral allele has only one copy of the sequence. There are at least three factors that regulate paramutation of the b1 gene: (i) the copy number of the 853-bp sequence, (ii) chromatin conformation of the enhancer region, and (iii) the methylation status of the enhancer. A comparison of various recombinant alleles with different copy numbers of the 853-bp tandem repeats shows that the repeats are required for both paramutation and high levels of transcription. The strength of paramutagenic effect is directly correlated with the number of repeats. Thus, recombinant alleles with reduced number of repeats are weakly paramutagenic. Additionally, B-I and B′ alleles have different methylation status even though they have identical sequence. In transcriptionally active B-I allele, the repeats are hypermethylated, and the chromatin has an open conformation as assessed by DNAse-I sensitivity assay; in contrast, transcriptionally repressed B′ allele has lower levels of methylation and more closed chromatin (42,43).

B-I/B-I (Colored)

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F2 (Colorless) F2

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B ′ Paramutagenic allele

B-I* Paramutated allele F2

Figure 3 A series of crosses depicting paramutation. The B-I allele in maize encodes transcription factor that regulates the synthesis of anthocyanin pigments. A paramutagenic allele B′ allows the production of a very small amount of pigments. Thus, in crosses of B-I/B-I with B′/B′ homozygotes, the resulting heterozygotes are weakly pigmented, appearing like the B′/B′ homozygotes. In a strict Mendelian inheritance, this will imply that B-I is recessive to B′. If that were true, F1 × F1 crosses would have produced homozygous B-I (colored) plants. Instead, only colorless plants are produced in successive generations, indicating that the B-I allele has been paramutated (B-I*).

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An RNA-dependent mechanism also seems to be critical for paramutation in maize, although the exact nature of such regulation is not understood. Transcription occurs on both strands of the tandem repeats upstream of b1, resulting in the formation of dsRNA. An RNA-dependent RNA polymerase called “mediator of paramutation 1” (mop1) is absolutely required for the generation of the dsRNA and silencing of B-I by B′. Recently, siRNAs produced from the repeats have been detected in all three genotypes except in mop1 mutants. In the current model of b1 paramutation, RNA is hypothesized to mediate the communication between B-I and B′ alleles to establish distinct chromatin states within the repeats. RNA is required to maintain the states that determine whether the b1 gene is expressed at high level (B-I) or low level (B′). Nevertheless, siRNAs alone are not sufficient for paramutation, and many questions about the molecular mechanism of paramutation including its heritability and the potential role of RNA remain unanswered (44). 3.4. Imprinting Genomic imprinting is an epigenetic form of gene regulation. Imprinted genes are expressed in a parent-of-origin-specific manner, that is, either the allele inherited from the mother is expressed (e.g., H19 or CDKN1C gene), or the allele inherited from the father is expressed (e.g., IGF2 gene). Therefore, imprinted genes violate the usual rule of inheritance. More than 70 imprinted genes have been described so far (45). Methylation of CpG islands, chromatin organization, and ncRNA are all associated with genomic imprinting. Methylation patterns are largely erased in primordial germ cells and are reestablished in sex-specific patterns during gametogenesis. Each gamete carries sex-specific imprint marks that are required for normal development. Following fertilization, the paternal genome in the mouse is rapidly and actively demethylated, while the maternal genome is slowly and passively demethylated through subsequent divisions. The parental-specific imprint marks are reestablished later during development (45, 46). Most imprinted genes are found in clusters that contain between 3 and 11 imprinted genes (47). Each imprinted cluster is regulated by an imprint control element (ICE), also called imprint control region (ICR). DNA methylation represses the activity of ICE in one parental chromosome, thereby linking imprinting with DNA methylation. 3.4.1. Imprinting in Igf2–H19 The Igf2–H19 imprinting is one of the best studied examples of genomic imprinting. In mice, Igf2 (encoding insulin-like growth factor 2) is expressed from the paternal allele and H19 (fetal liver ncRNA) is expressed from the maternal allele. These genes are on the same chromosome such that Igf2 is located ˜80 kb upstream of H19, and the ICE is located in between them. The ICE is ˜2.4 kb long and is located ˜2 kb upstream of H19 (from –2 to –4.4 kb relative to the transcription start site of H19). The ICE contains an insulator sequence that binds insulator protein to function as an insulator. There are also a set of enhancers located downstream from H19, which are utilized by both Igf2 and H19 for their expression. Monoallelic expression of these two genes is regulated by the methylation status of ICE. On the maternal chromosome, the unmethylated ICE insulator binds the vertebrate insulator protein CTCF. This mediates the silencing of Igf2 in cis by insulating it from the effects of the downstream enhancer. On the paternal chromosome, the ICE is methylated, which inhibits CTCF binding. Methylation of ICE also leads to secondary methylation of

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the H19 promoter. As a result, H19 becomes silenced in the paternal chromosome. Because the methylated paternal ICE lacks insulator activity, the paternal Igf2 promoter can interact with the enhancers to express Igf2 (Fig. 4 ). Recent studies demonstrated that CTCF binding is necessary to maintain but not to establish the differential methylation of ICR (48). 3.4.2. Noncoding RNA (ncRNA)-Mediated Imprinting In any imprinted cluster, the majority of the genes code for protein-coding mRNA, but there is always at least one ncRNA-coding gene in the cluster. In the Igf2–H19 cluster described above, the H19 is the ncRNA. The function of H19 is enigmatic, but it does not appear to play any direct role in silencing (47). Two recently reported examples, however, provide strong evidence that imprinting may also be caused by ncRNA. The first example (49) showed that in mouse chromosome 17, the 108-kb-long Air ncRNA is necessary for the silencing of Igf2r/Slc22a2/Slc22a3 gene cluster (Slc stands for solute career; the trivial names for Slc22a2 and Slc22a3 are Oct2 and Oct3, respectively, where Oct stands for organic cation transporter). The ICE of this cluster has the promoter for Air ncRNA. Air is paternally expressed, and its expression

CTCF Maternal Igf2

ICE

H19

En

Enhancer (En) promotes H19 expression. Enhancer action on Igf2 is prevented by the insulator (CTCF-bound ICE)

Methylation Paternal Igf2

ICE

H19

En

Methylation of ICE abolishes insulator action. Enhancer (En) promotes Igf2 expression. Methylation of ICE blocks the enhancer’s action on H19 expression

Figure 4 In the mouse, Igf2 and H19 are on the same chromosome such that Igf2 is located ˜80 kb upstream of H19, and the ICE is located in between them. The ICE contains an insulator. There are also a set of enhancers located downstream from H19, which are utilized by both Igf2 and H19 for their expression. On the maternal chromosome, the unmethylated ICE insulator binds the vertebrate insulator protein CTCF. This mediates the silencing of Igf2 in cis by insulating it from the effects of the downstream enhancer. On the paternal chromosome, the ICE is methylated, which inhibits CTCF binding. Methylation of ICE also leads to secondary methylation of the H19 promoter. As a result, H19 becomes silenced in the paternal chromosome. Because the methylated paternal ICE lacks insulator activity, the paternal Igf2 promoter can interact with the enhancers to express Igf2.

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is correlated with the silencing of all three genes on the paternal chromosome. When 96% of the Air was truncated through the introduction of a poly(A) signal sequence that caused premature termination of transcription, there was a complete loss of silencing of the Igf2r/ Slc22a2/Slc22a3 gene cluster on the paternal chromosome, suggesting that full-length (or near full-length) Air ncRNA is required for the silencing and genomic imprinting of the Igf2r/Slc22a2/Slc22a3 gene cluster. A more recent example came from the study of Kcnq1ot1 (potassium channel Q1 overlapping transcript1) ncRNA (50). The Kcnq1ot1 is one of the genes in the 800-kb Kcnq1 cluster, which is one of the largest imprinted clusters. The Kcnq1ot1 ncRNA is at least 60 kb long (its 3′ end is not known). Using an approach similar to that of Sleutels et al. (49) of truncating the full-length transcript by introducing poly(A) signal sequence, Mancini-DiNardo et al. (50) showed that premature termination of the Kcnq1ot1 transcript resulted in the loss of imprinting of at least seven genes that are normally silent on the paternal allele. From the above examples, it is apparent that ncRNAs show reciprocal expression pattern with respect to the mRNA genes in the cluster that are subject to silencing. It also appears that full-length or nearly full-length ncRNAs are required for imprinting and gene cluster silencing. The parental allele that lacks ICE methylation expresses the ncRNA and silences multiple mRNA genes. In contrast, the other parental allele that has its ICE methylated does not express the ncRNA but expresses the mRNA genes (47). While the above two examples show “proof-of-concept” for ncRNA-mediated imprinting, the exact mechanism of ncRNA-mediated gene silencing is open to speculation. One common example that has been invoked in this regard is the example of Xist (X-inactive specific transcript) ncRNA, which is involved in X inactivation in mammals. The role of Xist ncRNA is discussed in more detail in the next section. Because genomic imprinting results in monoallelic expression from the imprinted loci, it is paradoxical to the phenomenon of diploidy. Diploidy confers an advantage to the diploid organisms because one intact functional allele in a locus can mask the effects of deleterious mutations in the other allele. Various hypotheses have been put forward to explain the evolution of imprinting and its selective advantage. The conflict hypothesis, also known as the “kinship hypothesis”, is by far the most favored one. A discussion of the evolution of imprinting is beyond the scope of this chapter. 3.5. X-Chromosome Inactivation (XCI) The XX–XY sex-determining mechanism causes the two sexes to differ in their dose of X chromosomes, yet both sexes require equivalent levels of X-chromosome gene products. This equivalence of expression is achieved by a process termed “dosage compensation”, which equalizes X-chromosome transcript levels in both sexes. Failure to establish dosage compensation causes either male- or female-specific lethality. Different species have evolved different mechanisms for dosage compensation. For example, eutherian mammals (true placental mammals) repress transcription of paternal or maternal X chromosome randomly in females; metatherian mammals (marsupials) repress transcription of the paternal X chromosome in females; Drosophila increases the transcription from the single X chromosome in males; C. elegans reduces the transcription of genes by half on both X chromosomes in females. The phenomenon of XCI in eutherian mammals is discussed below as an example of dosage compensation. Normal female mammals possess two X chromosomes; in any given cell, one X remains active (Xa) and the other becomes inactive (Xi). The Xi resides in the nuclear

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periphery, probably attached to the nuclear scaffold. XCI is achieved by packaging the Xi into repressive heterochromatin. The choice of which X chromosome will be inactivated is random; but once an X chromosome is inactivated, it remains inactive throughout the life of the cell. When the chromosomes are stained, the Xi with its facultative heterochromatin stains dark and is called the “Barr body”. The name “Barr body” owes its origin to M.L. Barr who in 1949, along with E.G. Bertram, reported the existence of a darkly stained body in the nucleus that could morphologically distinguish the neurons from males and females. Loss of the Barr body, that is, loss of the heterochromatic state of Xi, represents a common but largely unexplored mechanism of heterochromatic instability, which may lead to widespread misregulation of genome expression. Mary Lyon correctly hypothesized in 1961 that in mammals, each cell maintains only one active X chromosome, while the other is inactivated into darkly stained sex chromatin (the Barr body). That is why inactivation of one X chromosome is also called “lyonization”. Morphological evidence for random XCI is provided by the patchy coat colors of various mammals. For example, in the female calico cat in which one X chromosome encodes one coat color and the other encodes a different one, random XCI in different cells results in a mosaic of patches in the coat color. The precise mechanism of XCI is yet to be fully understood, but it is achieved at the level of chromatin and appears to involve multiple factors. Participation of Xist RNA, DNA methylation of CpG islands, histone modifications, and addition or removal of various histone variants all contribute to a stable heterochromatic structure. 3.5.1. DNA Methylation in XCI Promoters of X-linked housekeeping genes are hypermethylated on the Xi and hypomethylated on the Xa. In contrast, gene-body methylation downstream from promoter displays the opposite pattern, gene-body methylation being more than two times as abundant on Xa than on Xi (51). The fact that promoter is hypermethylated in Xi several days after X inactivation suggests that DNA methylation plays a role in the maintenance of X inactivation. Consistent with this suggestion, deletion of the maintenance methyltransferase Dnmt1 in mice leads to the expression of previously silenced Xi-linked genes, although this effect may not be widespread. In addition, loss of the endogenous de novo DNA methyltransferase DNMT3B was shown to result in Xi hypomethylation in humans ICF syndrome (52). In male somatic tissue with XY chromosome complement, the X is always active and the expression of its Xist allele is repressed. Consistent with the repressed state of Xist expression in active X chromosome, CpGs in the promoter region and extending into the body of the Xist allele are fully methylated on this single active X chromosome (53). DNA methylation therefore seems to play a role in the maintenance of gene silencing and X inactivation. It has been suggested that DNA methylation may act synergistically with Xist RNA expression and histone hypoacetylation to maintain gene silencing in X inactivation, though it is not exactly clear how this occurs (54). 3.5.2. Histone Modifications in XCI The Xi is marked for several different types of histone modifications that include a number of heterochromatic modifications coupled with an absence of euchromatic modifications. Heterochromatic modifications include methylation of lysine 9 and 27 of histone H3. Methylation of H3K9 and H3K27, particularly H3K9, is the hallmark of heterochromatin (52). In addition, cytological studies with fluorescent antisera for

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individual acetylated lysine residues showed that histones H2A, H3, and H4 in Xi are also hypoacetylated in humans, mice, and marsupials except for regions that escape inactivation. The similarities in hypoacetylation in groups as distant as humans and marsupials involving Xi, whose properties greatly vary between species, also argue in favor of a relationship between the hypoacetylation status of Xi and its inactivation (55). In addition to the heterochromatic modifications described above, the histones of Xi are remarkably void of euchromatic modifications, such as methylation of H3K4 or H3R17 or hyperacetylation of H3 and H4, which are strongly correlated with euchromatin and active gene expression. In fact, Xi is hypoacetylated on H3K9 and on histone H4 at several lysine residues. Two histone H2A variants (H2A-Bbd and H2AZ) commonly associated with euchromatin are less abundant on Xi and more abundant on Xa (52). A transcriptionally repressive histone-modifying protein complex, called the “polycomb repressor complex” (PRC) is recruited on Xi. There are two types of PRCs: PRC1 and PRC2. Whereas PRC2 contains proteins, such as embryonic ectoderm development (EED), enhancer of zeste homolog 2 Drosophila (EZH2), and suppressor of zeste 12 homolog Drosophila (SUZ12), and is involved in establishing transcriptional silencing, PRC1 contains proteins that are involved in the maintenance of transcriptional silencing. At the onset of X inactivation, the recruitment of PRC2 complex on Xi results in histone modifications that create transcription-silencing chromatin mark, such as H3K27me3. EZH2 protein of PRC2 is a methyltransferase that methylates lysine 27 of histone H3 to generate the H3K27me3 mark. This signal is probably recognized by proteins of PRC1 complex, and PRC1 is recruited to Xi to help maintain its transcriptional silencing (52). Therefore, DNA methylation, histone modifications, and Xist expression seem to work synergistically to establish X inactivation and maintain the inactivated state throughout the life of the cell. 3.5.3. X-Inactivation Center (Xic) and X Inactive-Specific Transcript (Xist) in XCI A 100-kb-long region in the X chromosome, known as the “X-inactivation center” (Xic) is necessary for X inactivation. In humans, Xic maps to the cytogenetic band Xq12-Xq13. The Xic contains two important nontranslated RNA genes, Xist (XIST in human) and Tsix, which are involved in both nonrandom (imprinted) and random X inactivation. Tsix is in turn regulated by Xite, an upstream locus that contains a Tsix-specific enhancer. The Xic also contains binding sites for both known and unknown regulatory proteins. The Xist gene product is a long nonprotein-coding RNA, about 15 kb in mice (56) and 19 kb in humans (57). Xist expression in undifferentiated and early differentiating ES cells causes chromosomal transcriptional silencing, but not in differentiated ES cells. Thus, ES cells provide a good model for studying the role of Xist in the initiation of transcriptional silencing (58). Before X inactivation, Xist is expressed along with Tsix from both X chromosomes but eventually silenced on Xa and expressed exclusively from Xi. Tsix and Xite play an essential role in the downregulation of Xist RNA expression before and during the XCI process. The Xist RNA coats the entire Xi in cis, spreading from the Xic. The RNA shows intermediate sequence conservation between the mouse and the human. The sequences in Xist RNA that are necessary for its chromosomal association are different from that required for its transcriptional silencing activity. Deletion analysis showed that a 0.9-kb region at the 5′ end is essential for its transcriptional silencing activity, but this region is not essential for its ability to associate with and localize along the chromosome. Computational secondary structure prediction suggested that this 0.9-kb region contains sequence motifs that could fold into two stem-loop structures that are repeated 7.5 times (8 of the first

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and 7 of the second); the stem-loop structures may represent binding sites for other factors aiding in the transcriptional silencing activity of Xist. It was also found that in contrast to the identified definite sequence necessary for silencing, regions of Xist RNA necessary for its chromosomal association and localization are scattered throughout and do not have any common sequence motifs (58). Apparently, species-specific trans-acting protein factors are necessary for the Xist RNA to coat the Xi. Two lines of evidence provide strong support for the requirement of Xist in X inactivation: first, X chromosome bearing a deletion of the Xist gene is not inactivated; second, Xist transgene incorporated into autosome and expressing Xist RNA coats the autosome and triggers transcriptional silencing of the autosomal genes. Xist gene expression and Xist RNA coating of the Xi persist for the life of the cell. This temporal expression pattern also suggests that Xist RNA is involved in the maintenance of the inactive state. The recruitment of transcription-repressing PRC2 on the Xi at the onset of X inactivation is also likely carried out by Xist. However, there is also evidence to the contrary suggesting that Xist alone may not be essential for the maintenance of the inactive state. Coating of the chromosome by Xist RNA provides the trigger for X-chromosome silencing (59,60). This trigger occurs early in development when cellular differentiation has not yet progressed. It has been recently demonstrated (61) that Xist RNA coating of the Xi creates a microenvironment around the Xi that excludes RNA pol II and transcription factors. The rapid exclusion of RNA pol II and transcription factors represents the earliest event following Xist RNA accumulation, and it precedes transcriptional silencing. Genes that escape X inactivation reside more peripherally outside the Xist RNA-coated microdomain, whereas genes that are inactivated are relocated inside the microdomain, probably through changes in local chromatin conformation. Surprisingly, the Xist locus in Xa also seems to contribute to the maintenance of Xi because deletion of Xist from the Xa alters replication timing and heterochromatic structure of the Xi. It also appears that Xist allele in Xa and Xi act synergistically in trans to maintain the Xi and gene silencing (52). The other important gene in the Xist locus is Tsix, which encodes another large ncRNA. The Tsix RNA is transcribed antisense to Xist, that is, on the opposite strand, and is a negative regulator of Xist. Before X inactivation, both X chromosomes weakly express Tsix RNA, but during the onset of X inactivation, the Xi ceases to express Tsix RNA and increases Xist expression, whereas the Xa continues to express Tsix for several days. 3.5.4. Imprinted (Nonrandom) X Inactivation In marsupials, the X chromosome of paternal origin is inactivated in every cell. A similar paternal X-chromosome inactivation is also found in extraembryonic tissues of the mouse. In contrast, X inactivation is random in embryonic tissues. The two schools of thought on the mechanism of imprinted X inactivation are as follows: (i) maternal X chromosome (Xm) is marked to remain active or (ii) paternal X chromosome (Xp) is predisposed to be inactivated. Direct evidence in favor of the maternal activation mark hypothesis was provided by a study using ng/fg embryos, which were produced by transplantation of nuclei from nongrowing (ng) oocytes and fully-growing (fg) oocytes (62). The HMG-lacZ transgene that is known to be subject to X inactivation was used as a marker. In ng/fg embryos, the Xm derived from the ng oocyte was preferentially inactivated in the extraembryonic tissue, whereas the Xm derived from the fg oocyte remained active. The precise molecular basis is yet to be fully understood, but the authors concluded that a maternal imprint is set on the Xm during oocyte growth, and this maternal imprint serves to render the Xm

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resistant to inactivation in the extraembryonic tissues. That would explain why the Xm derived from the fg oocyte remained active. Both Xm and Xp are active during fertilization. The Xist RNA is known to be expressed from two-cell stage, and this early expression is exclusively of paternal origin. The first signs of transcriptional repression of the Xp (exclusion of RNA pol II) could be detected at the four-cell stage just after Xist accumulation. From the eight-cell stage, but not before, downregulation of X-linked genes (Chic1, Smcx) can be detected on the Xp (63). In the trophectoderm, inactivity of the Xp is maintained, but in the inner cell mass (ICM) of the blastocyst, the Xp is reactivated during blastocyst growth. The reactivation of Xp is associated with the following events: loss of Xist RNA expression, loss of EED/EZH2 (PRC2) enrichment, and loss of histone modifications that are characteristic of X-chromosome inactivation. The reactivation of Xp in ICM permits the subsequent random inactivation of either Xm or Xp in the embryo (63). 3.5.5. Random X Inactivation The process of random X inactivation involves “counting” and “choice”. Counting determines how many X chromosomes are present in a diploid chromosome set, whereas choice determines randomly which of the two X chromosomes in a cell will be inactivated. 3.5.5.1. Counting Cells apparently utilize the n – 1 counting rule to determine the number of X chromosomes to be inactivated. In other words, every X chromosome except one is inactivated, and the choice of which X chromosome will remain active is random. The mechanism of counting is not understood, but several models have been proposed. The blocking factor model (64) proposes that an autosomally encoded blocking factor is present in each cell in a quantity such that this factor blocks the Xic of only one X chromosome per cell. Blocking of the Xic marks that X chromosome for activation. The other X chromosome(s) are destined to be inactivated by default. The blocking and competence factor model (65) proposes that in addition to the autosomally encoded blocking factor, cells also contain X chromosome-encoded competence factor which is produced only when there is more than one X in the cell. Whereas the blocking factor binds to the Xic of the future Xa and marks it for activation, the competence factor binds to the Xic of the future Xi and marks it for silencing. Therefore, according to the blocking and competence factor model, X-chromosome inactivation is not a default process. The blocking factor binding site is termed the “counting element” (66). The length of the specific region of X chromosome required for inactivation (counting element) has been defined by deletion analysis. Initial deletion analysis in ES cells showed that deletion of 65 kb 3′ to the Xist locus affects the counting and results in inappropriate X-chromosome inactivation, including in XY cells, thereby suggesting that this region could be the binding site for the putative blocking factor. Cre/loxP-mediated reinsertion of a 37-kb fragment that represents the upstream 37 kb of the deleted 65 kb rescued the counting process. Further analysis identified this counting element-containing region as a 20-kb bipartite domain (66). At the chromatin level, the Xist gene expression corresponds to a peak of H3K4 dimethylation (H3K4me2), which is dramatically and specifically affected by the deletion 3′ to Xist, suggesting that H3K4me2 within Xist may be functionally implicated in the counting process (66). However, the precise molecular mechanism of counting still remains unknown.

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3.5.5.2. Choice The choice seems to be controlled by Tsix and Xite. The Tsix RNA is transcribed prior to the initiation of random X inactivation, and it is an antisense regulator of Xist RNA expression. Studies using targeted deletion of the Tsix gene indicate that the expression of Tsix seems to determine choice. In mice, targeted deletion of 3.7 kb of the Tsix gene that included the CpG island, promoter, and transcription start site resulted in the deficiency of wild-type Tsix RNA expression in both female and male cells (65). In heterozygous female mice, X inactivation primarily affected the mutant X carrying the mutated Tsix gene. In these cells, the ability of the mutant X to block Xist accumulation is compromised. The authors concluded that Tsix determines the choice about which X chromosome will be inactivated, but it does not affect counting and silencing. This is because mutant female cells still inactivated one X chromosome, mutant male cells still prevented inactivation of the X chromosome, and dosage compensation was properly switched on in all mutant cells. The authors also found that while Tsix has a repressive function in differentiating female cells, it seems to serve no obvious function in undifferentiated cells. Surprisingly, inactivating both copies of Tsix in both X chromosomes resulted in what was described as a “chaotic choice” (67). Homozygous Tsix-null mice showed the following effects: first, for every one female mouse born, there were two to three male mice born, thereby distorting the sex ratio; second, X-chromosome inactivation unexpectedly returned to random pattern in surviving homozygous Tsix-null mice. Thus, with respect to choice, mutation of Tsix yields a phenotypic abnormality in heterozygotes, but not in homozygotes. Based on the observation that massive cell death occurred in homozygous Tsix-null ES cells from the ICM during differentiation, and that both X chromosomes were found inactivated in a significant fraction of cells, the author proposed a “chaotic choice” model. According to this model, the loss of both functional copies of Tsix gene resulted in the loss of stochastic choice of X inactivation. As a result, cells randomly inactivated none, one, or both of the X chromosomes, with the result that only cells with one inactivated X chromosome survived, and the others died. Therefore, according to this model, chaotic choice in survivors reflects a fortuitous selection of distinct X chromosomes as active and inactive, which represents complete randomness and not a reversion to stochastic choice. This model also seems to reconcile the sex-ratio distortion. Despite the progress in our understanding of various epigenetic events in mammalian X-chromosome inactivation, the complete picture is yet to emerge. There are a number of missing pieces, such as the regulation of counting and choice, the nature of the counting element, the regulation of nonrandom X inactivation, and the true role of chromatin modification in this process, among others. 3.5.6. Pseudoautosomal Regions Escape Inactivation In the inactive X chromosome, some genes escape inactivation. These genes are located at the tip of both the long and the short arm of X chromosome. These regions are called “pseudoautosomal regions” (PARs); the PAR on the short arm (p) is called “PARp” and that on the long arm (q) is called “PARq”. Genes in PAR are present in both X and Y chromosomes, and therefore, no dosage compensation is required. Crossing-over involving PAR takes place frequently. An example of a gene that escapes inactivation in humans is the steroid sulphatase (STS). The reason for the name “pseudoautosomal” for a section of the sex chromosome is because the genes and mutations in these regions typically show

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autosomal inheritance pattern, that is, not distinguishable between males and females. This pattern is different from the sex-linked (X- or Y-linked) inheritance pattern typical of genes located in sex chromosomes. 3.6. Epigenetics of Disease and the Scope of Epigenetic Therapy Since Feinberg and Vogelstein’s report of altered methylation status (hypomethylation) in human cancers in 1983, a link between epigenetic changes and carcinogenesis has been repeatedly demonstrated in both animal models and humans. For example, alteration of DNA methylation pattern and chromatin conformation status has been reported to be associated with environmental toxicant-induced carcinogenesis. Nickel is a human carcinogen that can alter DNA methylation pattern and also affect histone acetylation status. Arsenic is another important environmental carcinogen that affects DNA methylation status in the cell. Because arsenic is methylated during its metabolism, it has been proposed that arsenic metabolism may deplete intracellular methyl group stores, thereby leading to changes in DNA methylation; however, the data in this regard are equivocal. It is hypothesized that DNA hypo- and hypermethylation are both important in the development of arsenic-induced cancers (68). Indeed, both global hypo- and hypermethylation of DNA have been reported in many human cancers. While hypomethylation is primarily observed in the repetitive elements associated with satellite sequences, hypermethylation is often observed in the CpG islands associated with the gene. Promoter methylation of the normal copy of the tumor suppressor genes is frequently observed in many familial cancers (69). Therefore, consistent with Knudson’s two-hit theory of carcinogenesis, when one copy of the tumor suppressor gene is already damaged (first hit), promoter methylation and the resulting inactivation of the other normal copy will be the second hit necessary for cancer development. Carcinogen adducts in DNA have also been shown to result in decreased DNA methylation. Thus, altered DNA methylation status by environmental carcinogens may hold an important clue to the epigenetic basis of genotoxicity and carcinogenesis. Because DNA methylation and histone acetylation status can be altered, they may also be targets for epigenetic therapeutic intervention. Potential epigenetic drugs can be classified into two major types: DNMT inhibitors and HDAC inhibitors (70,71). Known inhibitors of both DNMT and HDAC are chemically diverse. An example is decitabine [5-aza-2′-deoxycytidine (or 5-aza-CdR)], which is a DNMT inhibitor indicated for the treatment of hematological malignancies (69). Unfortunately, such epigenetic therapies may also have significant side effects due to their ability to cause global demethylation. The pleiotropic effects of 5-aza-CdR include transcriptional activation of many genes, particularly genes involved in interferon signaling, increased histone acetylation, and H3K4 methylation, suggesting that it also induces chromatin remodeling (70). 4. CONCLUSION The molecular basis of epigenetics and the inheritance of epigenetic marks have been studied in a variety of organisms. This has helped understand genome expression and regulation from a standpoint that is much broader than the traditional nucleotide sequence-centric view of genome expression and regulation. Epigenetic regulation of genome expression is crucial to proper development and differentiation of the various cell types in an organism. However, epigenetic state can be disrupted by environmental factors and aging, as exemplified by differential disease susceptibility of homozygous twins. Interestingly, the fact that

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epigenetic marks can be acquired and passed from parents to children has a Lamarckian flavor to it (72). With the advances in genomic technologies, investigation of genome-wide epigenetic marks and their dynamics is becoming a reality (73). New insights about the importance of epigenetics in genome expression and health and disease have also been gained from some recent studies. For example, two recent reports on cyclical DNA methylation of transcriptionally active promoters (74,75) suggest that the prevailing concept of static “epigenetic marks” on the chromatin may be an oversimplification. Periodic methylation and demethylation of transcriptionally active promoters (with a periodicity of tens of minutes), as shown by the two studies, brought to light some unanticipated dynamic role of DNA methylation in gene regulation in human cells. Notably, it was also demonstrated that DNMTs that are known to be involved in methylation are also involved in the demethylation of the methylated promoter (75). Recent evidence also suggests that epigenetic marks laid down early in development may predispose an individual to cancer development later in life (76). The authors reported that genes methylated in cancer cells are specifically enriched in H3K27me3 mark. This chromatin mark is established early in development and maintained in differentiated cell types. In cancer cells, but not in normal cells, this complex helps recruit Dnmts, which cause de novo DNA methylation. A greater understanding of how established epigenetic marks are altered in response to environmental changes, and how such changes predispose the genome toward certain disease, will certainly shed new light on environmental genomics and the molecular/epigenetic basis of disease, susceptibility, as well as therapy. REFERENCES 1. Riggs AD, Martienssen RA, Russo VEA. Introduction. In: VEA Russo, et al. eds. Epigenetic Mechanisms of Gene Regulation. Cold Spring Harbor, NY: CSHL Press, 1996: 1–4. 2. Callinan PA, Feinberg AP. The emerging science of epigenomics. Hum Mol Genet 2006; 15:R95–R101. 3. Robertson KD, Uzvolgyi E, Liang G, et al. The human DNA methyltransferases (DNMTs) 1, 3a, and 3b: coordinate mRNA expression in normal tissues and overexpression in tumors. Nucleic Acids Res 1999; 27:2291–2298. 4. Kierszenbaum AL. Genomic imprinting and epigenetic reprogramming: unearthing the garden of forking paths. Mol Reprod Dev 2002; 63:269–272. 5. Bestor T, Laudano A, Mattaliano R, et al. Cloning and sequencing of a cDNA encoding DNA methyltransferase of mouse cells. The carboxyl-terminal domain of the mammalian enzymes is related to bacterial restriction methyltransferases. J Mol Biol 1988; 203:971–983. 6. Yen RW, Vertino PM, Nelkin BD, et al. Isolation and characterization of the cDNA encoding human DNA methyltransferase. Nucleic Acids Res 1992; 20:2287–2291. 7. Li E, Bestor TH, Jaenisch R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 1992; 69:915–926. 8. Klose RJ, Bird AP. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci 2006; 31:89–97. 9. Bhattacharya SK, Ramchandani S, Cervoni N, et al. A mammalian protein with specific demethylase activity for mCpG DNA. Nature 1999; 397:568–569. 10. Wade PA. Methyl CpG-binding proteins and transcriptional repression. Bioessays 2001; 23:1131–1137. 11. Strahl BD, Allis CD. The language of covalent histone modifications. Nature 2000; 403:41–45. 12. de la Cruz X, Lois S, S nchez-Molina S, et al. Do protein motifs read the histone code? Bioessays 2005; 27:164–175.

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66. Morey C, Navarro P, Debrand E, et al. The region 3′ to Xist mediates X chromosome counting and H3 Lys-4 dimethylation within the Xist gene. EMBO J 2004; 23:594–604. 67. Lee JT. Homozygous Tsix mutant mice reveal a sex-ratio distortion and revert to random X-inactivation. Nat Genet 2002; 32:195–200. 68. Sutherland JE, Costa M. Epigenetics and the environment. Ann NY Acad Sci 2003; 983:151–160. 69. Plass C. Cancer epigenomics. Hum Mol Genet 2002; 11:2479–2488. 70. Laird PW. Cancer epigenetics. Hum Mol Genet 2005; 14:R65–R76. 71. Peedicayil J. Epigenetic therapy – a new development in pharmacology. Ind J Med Res 2006; 123:17–24. 72. Bird A. Perceptions of epigenetics. Nature 2007; 447:396–398. 73. Schones DE, Zhao K. Genome-wide approaches to studying chromatin modifications. Nat Rev Genet 2008; 9:179–191. 74. Kangaspeska S, Carmouche RP, Benes V, et al. Transient cyclical methylation of promoter DNA. Nature 2008; 452:112–115. 75. Métivier R, Gallais R, Tiffoche C, et al. Cyclical DNA methylation of a transcriptionally active promoter. Nature 2008; 452:45–50. 76. Schlesinger Y, Straussman R, Keshet I, et al. Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer. Nat Genet 2007; 39:232–236.

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Principles of Functional Genomic Analysis Scott A. Jackson1, Isha Patel1, J. Eugene LeClerc1, Thomas A. Cebula1,*, Mark K. Mammel1, Michael L. Kotewicz1, Amit Mukherjee1, Sushil K. Mahata2, Juan L. Rodriguez Flores2, Manjula Mahata2, Daniel T. O‘Connor2, and Jannavi R. Srinivasan3 1

U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Molecular Biology, Laurel, Maryland, U.S.A. 2 Department of Medicine and Department of Molecular Genetics, University of California, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A. 3 U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Division of Biotechnology and GRAS Notice Review, College Park, Maryland, U.S.A.

I. USE OF HIGH DENSITY GENE ARRAYS FOR GENE EXPRESSION PROFILING AND GENOTYPING II. PYROSEQUENCING IN SNP DISCOVERY AND GENOTYPING IN PROKARYOTES III. USE OF OPTICAL MAPPING FOR THE RAPID CONSTRUCTION OF ORDERED RESTRICTION MAPS FOR ENTIRE CHROMOSOMES FROM SINGLE DNA MOLECULE IV. METABOLIC PROFILING IN BACTERIA USING PHENOTYPIC MICROARRAY V. FROM PLUS–MINUS SEQUENCING TO NEXT GENERATION SEQUENCING WITH A FOCUS ON PYROSEQUENCING 1. INTRODUCTION 2. SEQUENCING BY SYNTHESIS—SANGER METHOD 3. SEQUENCING BY CHEMICAL CLEAVAGE—MAXAM & GILBERT METHOD 4. SEQUENCING BY HYBRIDIZATION—ED SOUTHERN METHOD 5. SEQUENCE BY SYNTHESIS—PAL NYREN’S PYROSEQUENCING METHOD 5.1. History 5.2. Principle 5.3. Pyrosequencing Technique in Detail 5.3.1. Template Preparation 5.3.2. Solid-Phase Pyrosequencing (Three-Enzyme Mixture)

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The opinions expressed in this chapter are the author’s personal opinions, and they do not necessarily reflect those of the FDA, DHHS, or the Federal Government. *Current affiliation: Department of Biology, Johns Hopkins University, Baltimore, Maryland, U.S.A.

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5.3.3. Liquid-Phase Pyrosequencing (Four-Enzyme Mixture) 5.4. Reagents and Enzymatic Reactions 5.5. Pyrosequencing Enzymes 5.5.1. Klenow DNA Polymerase 5.5.2. ATP Sulfurylase 5.5.3. Luciferase 5.5.4. Apyrase 5.6. Materials and Methods 5.6.1. DNA Template 5.6.2. Performing PCR for Pyrosequencing 5.6.2.1. Primer Design 5.6.2.2. Gradient PCR for Optimization 5.6.2.3. PCR Amplification of Specific Template DNA 5.6.3. Preparation of the PCR Product for Pyrosequencing 5.6.3.1. Preparation of the PCR Plate for Pyrosequencing 5.6.3.2. Preparation of the Pyrosequencing Plate (White Plate) 5.6.3.3. Setting up of a Workstation for Prepping PCR Product/Bead Mix Tray and the Pyrosequencing Primer Tray 5.6.4. Entering Assay Details 5.6.5. Filling the Cartridge with Reagents and Placement in the PSQHS96A Machine 5.6.6. Test Run with the Reagent-Filled Cartridge 5.6.7. New SNP Run in PSQHS96A 5.6.8. Analysis of Pyrosequencing Results 5.6.9. Reporting Pyrosequencing Results 6. APPLICATIONS OF PYROSEQUENCING 6.1. Single Nucleotide Polymorphism Genotyping 6.2. Pharmacogenetics 6.3. Epigenetics 6.4. Medical Genetics 6.5. Microbial Genotyping 7. NEXT GENERATION SEQUENCING—$1,000 PER GENOME? 7.1. Roche 454 7.2. Illumina Solexa 7.3. ABI SOLiD 8. CONCLUDING REMARKS VI. PRINCIPLES OF PROTEOMIC ANALYSIS WITH SPECIAL EMPHASIS ON MASS SPECTROMETRY 1. INTRODUCTION 2. PRINCIPLES OF PROTEIN SEPARATION 3. PRINCIPLES OF PROTEIN IDENTIFICATION 3.1. Edman Degradation 3.2. Mass Spectrometry 4. PRINCIPLES OF PROTEIN STRUCTURE DETERMINATION 4.1. X-Ray Crystallography 4.2. NMR 5. CONCLUSION REFERENCES

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I. USE OF HIGH DENSITY GENE ARRAYS FOR GENE EXPRESSION PROFILING AND GENOTYPING Scott A. Jackson, Isha Patel, J. Eugene LeClerc, and Thomas A. Cebula The genesis of DNA microarrays is found in Southern blotting, a technique wherein fragments of DNA affixed to a solid substratum are assessed for the presence of particular DNA sequences using known, labeled gene- or DNA fragment-probes (1). A DNA microarray, unlike the membrane in Southern blot, contains a large number—literally thousands or tens of thousands—of known, unique DNA sequences that are arranged and attached systematically onto a glass surface. Microarrays afford the investigator the ability, within a single experiment, to detect and measure manifold genomic features in parallel by interrogating a genomic sample either for gene content or for gene expression under particular conditions. Progress in several areas, including advances in desktop computing and laser-based optics, has made microarray-based assays available to even modest research laboratories. Two major ways to fabricate microarray slides are spotting and photolithography. In spotted microarrays, the probes are synthesized prior to deposition on the array surface and are then “spotted” onto glass with the help of fine pins or needles. In photolithographic synthesis, such as Affymetrix GeneChip microarrays, a combination of UV light, masks, and light-sensitive nucleic acid chemistries are used to “build” a sequence one nucleotide at a time across the entire array (2). More recently, a maskless photolithographic array fabrication has been developed by NimbleGen (3). Affymetrix GeneChip microarrays are widely used in transcriptomics studies in both prokaryotes and eukaryotes. The following discussion will focus on the successful use of Affymetrix GeneChip microarrays in our laboratory for gene expression profiling and genotyping of prokaryotes. Affymetrix is the provider of GeneChip microarrays, which are unique in their manufacturing process. The photolithography process that Affymetrix employs permits oligonucleotide probes to be synthesized directly and attached covalently onto the surface of a glass slide. With this process, nearly 10 million probes can be arrayed on a single slide within an area of one square inch. As the largest genomes are known to contain only approximately 22,500 genes (Human), GeneChip arrays clearly can contain several orders of magnitude more probes than genes. It is known that probe hybridization signal can vary dramatically due to sequence differences, and this phenomenon is referred to as the “probe effect”. To overcome the probe effect and to utilize all of the available probe space on an array, Affymetrix has adopted the “probe set” design which utilizes between 11 and 16 probe pairs per gene. A probe pair consists of a “perfect match” (PM) probe and a corresponding “mismatch probe” (MM). Each of the mismatch probes is a 25-mer oligonucleotide sequence, differing from the PM probe only at the central (13th) nucleotide position. The PM probe, matching the target sequence exactly, thus represents signal generated by both specific and nonspecific hybridization, while the MM probe, containing its centrally located mismatch, would measure only nonspecific hybridization signal. Therefore, subtracting MM signal from the PM signal ought to provide an estimate of specific hybridization signal. The data obtained from a probe set are therefore a very accurate estimation of the quantity of a particular target sequence. While some investigators have argued that GeneChip arrays are able to measure the absolute concentrations of a particular transcript, it is generally believed that this is not currently possible. Over the past decade, it has become increasingly clear that the global genomic diversity of various prokaryotic species has been grossly underestimated. Tools and techniques

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that are able to rapidly measure the diversity at the genome level therefore have been sought and developed. This observed diversity has led to the establishment of new scientific disciplines, such as microbial forensics, and has triggered a renewed interest in the field of antimicrobial resistance. Although the examples discussed below demonstrate the successful use of high density gene arrays in prokaryotes in our laboratory, this technique is being successfully used for both eukaryotes and prokaryotes. Our lab as well as others have utilized various novel array designs that have allowed us to interrogate the genomic diversity of various species of prokaryotes. An interesting example is the utilization of the tiling design in which overlapping oligonucleotide sequences are “tiled” along an array in order to measure polymorphism down to the single nucleotide level. These array designs are highly informative for identifying novel single nucleotide polymorphisms (SNPs), deletions, and gaps, and require only the knowledge of a single genome sequence from a related strain that shares > 90% sequence homology. While this tiling design utilizes 40 times fewer probes per nucleotide being interrogated when compared to traditional array-based resequencing applications, it still requires greater than a million probes if one wishes to interrogate an entire genome. Therefore, many tiling designs utilized to date only represent a fraction of the total genome being investigated. Another informative measure of microbial genomic diversity is based on the gene content. It has been observed that relatively large portions of microbial genomes are often found to be horizontally transferred in a single event. Whole genome sequencing has demonstrated that closely related strains of the same species can vary in gene content by over 100 genes. Additionally, alleles within the same strain can vary slightly or significantly in their level of sequence homology. We therefore sought out an efficient array design that allows us to determine the status of each gene (present or absent) in a genome that shared a defined level of sequence homology. In considering such a design, one would ideally want: (i) multiple independent measurements of the same gene target, (ii) short probe sequences in order to provide for the discrimination of diverse sequences (e.g., SNPs), and (iii) a separate independent measurement of the level of nonspecific hybridization for each probe. Such a design was found to be present in the “expression design” provided by Affymetrix GeneChip (discussed above). Here, multiple independent 25-mer oligonucleotide probes are utilized to measure each gene independently, while a MM probe effectively eliminates signal generated from nonspecific hybridization. Using an algorithm developed by Affymetrix, gene targets are determined to be either present, absent, or marginal as determined by a p-value calculated from the discrimination score (R) for each probe pair. The discrimination score is a fundamental property of a probe pair that explains its ability to detect its intended target. It measures the target-specific intensity difference of the probe pair (PM – MM) relative to its overall hybridization intensity (PM + MM). Therefore R is defined as

, and it approaches 1.0 as

the mismatch probe intensity approaches 0. The next step in the calculation of a detection p-value is the comparison of each R to a user-definable threshold value τ. τ is a small positive number that can be adjusted to increase or decrease sensitivity and/or specificity of the analysis. Increasing the threshold τ can lessen the number of false present calls, but may also lessen the number of true present calls. We found that the most accurate gene detection calls were made when using a user-defined τ value of 0.2. The Affymetrix default τ setting of 0.015 gave a very high number of false positive calls. Next, the one-sided Wilcoxon’s Signed Rank test is employed to generate the detection p-value. It assigns each probe pair a rank based on how far the probe pair discrimination score is from τ. The detection p-value is then compared to a user-definable cutoff value that

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results in the final present/absent/marginal call for each gene.a Here again, we evaluated several cutoff values for the detection p-value and found that the default settings of 0.04 and 0.06 provided highly accurate gene detection calls. That is, probe sets with detection p-values <0.04 were scored as “present”, between 0.04 and 0.06 as “marginal”, and greater than 0.06 as “absent”. Here we present an example of the utility of the probe set design to detect and discriminate between different alleles of the mutS gene found in various strains of Escherichia coli. In this example, genomic DNA from the uropathogenic E. coli strain CFTO73 was hybridized to the Affymetrix E. coli v2.0 array. Represented on this array are three different mutS genes. One is the mutS sequence from CFTO73; a second mutS sequence from the lab-attenuated E. coli K-12 strain MG1655; and a third mutS sequence from the enterohemorrhagic E. coli O157:H7 strain EDL933. Sequence alignments showed that the 2,562-bp mutS gene of strain CFTO73 was 94% homologous to that of EDL933 and MG1655. When using the criteria described above, we were able to accurately call only the CFTO73 mutS allele as “present”, while the other two mutS alleles were called as “absent”. Furthermore, as shown in Fig. 1, the hybridization intensity of each probe pair was 10-fold greater in the CFTO73 mutS probe set when compared to the other two mutS probe sets. This example demonstrates the excellent ability of the probe set design to detect and discriminate between closely related gene target sequences. As mentioned above, the probe set design was originally intended to measure the level of particular transcripts for gene expression studies. A basic assumption in all quantitative hybridization assays (including microarray studies) is that the amount of probe is in large excess to the amount of target sequence. Affymetrix arrays contain tens of millions of probe molecules per feature. This allows for the measure of target concentration variation over several orders of magnitude. While the probe set design is an ideal design for measuring target concentrations, the best way to analyze the data that is generated can be disputed. The probe effect, as described above, is an issue that is of major concern when summarizing probe set information. That is, while all of the probe pairs in a particular probe set are measuring the same target transcript, the observed probe intensities can vary dramatically. Accurately summarizing these dramatically different hybridization intensities into a single value that is a precise measure of the target transcript concentration is nontrivial and is currently widely debated within the microarray community. Several of the latest and most accurate algorithms for summarizing probe set intensities utilize a multiarray normalization step to develop a linear model describing each probe. The rational for this is based solely on the observation that probe intensities vary much more dramatically within a probe set than between arrays. Three such algorithms that utilize linear models for probe normalization and summarization are dChip, RMA, and Plier. Each has subtle variations in how probe sets are summarized and each makes different assumptions regarding the arrays contained within a data set of interest. For the reasons discussed above, the probe set design utilized by Affymetrix GeneChip can accurately detect and discriminate between genes that share close sequence homology as well as accurately measure relative transcript concentrations for gene expression studies. In addition, Affymetrix GeneChips provide the highest density arrays that are currently available commercially. This quality has recently allowed our lab to develop a novel microbial array that utilizes the probe set design and represents over 60 whole genome sequences. a

Data Analysis Fundamentals: http://www.affymetrix.com/Auth/support/downloads/manuals/data_analysis_fundamentals_manual.pdf

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Figure 1 Genomic DNA from the uropathogenic E. coli strain CF TO73 was isolated and hybridized to an Affymetrix E. coli v2.0 GeneChip. Included on this array are three different mutS probe sets that each contain 11 independent probe pairs (PM probes = black bars and MM probes = gray bars). The bar graphs show absolute probe intensities for (A) the uropathogenic E. coli CF TO73 mutS gene, (B) the E. coli O157:H7 EDL933 mutS gene, and (C) the E. coli K-12 MG1655 mutS gene. The 25-mer oligonucleotides utilized in the probe set design are clearly capable of discriminating between these alleles which share 94% sequence homology.

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II. PYROSEQUENCING IN SNP DISCOVERY AND GENOTYPING IN PROKARYOTES Mark K. Mammel, J. Eugene LeClerc, and Thomas A. Cebula Pyrosequencing, dubbed “sequencing by synthesis” or “sequencing by light”, is a real-time sequencing technology (4). Pyrosequencing utilizes four enzymes: DNA polymerase, ATP sulfurylase, luciferase, and apyrase. Briefly, synthesis of DNA proceeds 5′→3′, requiring template DNA, primer DNA, the four deoxyribonucleotide triphosphates (dNTPs), metal cofactor, and DNA polymerase. For pyrosequencing, after the sequencing primer is annealed to single-stranded PCR template, individual dNTPs are added to the reaction. Note, however, that deoxyadenosine alpha-thio triphosphate (dATPαS) is used rather than deoxyadenosine triphosphate (dATP), which would activate the luciferase reaction. If the incoming dNTP is complementary to the base in the template strand, it will be incorporated by DNA polymerase into the growing strand, and inorganic pyrophosphate (PPi) will be released stoichiometrically. ATP sulfurylase, in the presence of adenosine phosphosulfate (APS) and PPi, catalyzes the formation of ATP. Luciferase, at the expense of the ATP, then catalyzes the conversion of luciferin to oxyluciferin and the quantal emission of visible light. Each light emission is recorded by a charge-coupled device (CCD) camera. Because of the stoichiometry of each of these reactions, the peak heights of light emission are directly proportional to the number of deoxyribonucleotide monohosphate (dNMP) incorporations. For example, if a sequence has two Gs that occur consecutively in a sequence, the complementary dNTP (dCTP) will be utilized and incorporated across from each of these two positions, yielding a single peak of twice the height to that if only one G were present (Fig. 2). If the dNMPs is not complementary to the template base, it will be degraded by apyrase, and no signal will be produced. As each of the dNMPs is added sequentially, light emission

Figure 2 Pyrogram showing the SNP analysis result for a bacterial strain. The yellow box contains the SNP in question. There is a peak at C but not at T. The initial G and the C following the yellow box are negative controls (no peak). The last G shows a double peak indicating two consecutive Gs in the sequence. The sequence for this portion of DNA is CAGCGGT.

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intensities and times of flash are transformed to peak heights (on the ordinate) and base sequence (on the abscissa) in the final pyrogram. Using Pyrosequencing™, short stretches (˜80 bp) of PCR products can be sequenced rapidly. For pyrosequencing assays, amplicons up to 600 bp can be generated, though 50–200 bp products are optimal. Primers, specific for generating a particular DNA target, as well as a sequencing primer, are designed using the Assay Design software provided with the automated pyrosequencing instrument, the PyroMarkTM ID (distributed by Biotage AB, headquartered in Uppsala, Sweden). As one of the amplicon primers is biotinylated, the biotinylated PCR product is then immobilized onto streptavidin-coated sepharose beads in a 96-well plate. The plate is incubated at room temperature for 10 min, with shaking to keep the beads dispersed. Beads are harvested using a vacuum-assisted sample preparation tool and immersed in 70% ethanol, 0.2 M NaOH, and Tris washing buffer. The beads are then dispensed into wells of a 96-well plate containing annealing buffer to which the sequencing primer has been added. The plate is heated at 80°C on a heat block for 2 min and then cooled for approximately 5 min to allow annealing. The plate and cartridge with the dNTPs, enzyme, and substrate mixtures are then loaded into the PyroMarkTM ID Instrument. To read the data, first a quality control is verified. Both negative and positive controls (bases) are included in the sequence of dNTPs to be added. A positive control is a conserved base flanking the variable sites that should be detected, whereas a negative control is testing for a base that should not be incorporated at a particular position within the sequence. It then becomes a simple matter to read the DNA sequence from the displayed peaks associated with each base incorporated. The software included with the PyroMarkTM ID instrument makes the base calls and displays the quality of each call. To analyze the frequency of alleles of a SNP from a mixed sample, the relative peak heights of the bases incorporated at the SNP site determine the frequency of each allele in the sample; this value is calculated by the Pyrosequencing Allele Frequency Analysis software. As with any technique or technology, there are potential pitfalls in pyrosequencing. For example, base calls that are difficult to interpret can occur if the overall peak height is low (resulting in a low signal-to-noise ratio). This might be due to a poor PCR product yield or poor transfer of the sepharose beads into the reaction plate. Assay designs that produce difficult-to-interpret results might include a SNP position within a run of repeated bases. In eukaryotes (or partially merodiploid prokaryotes), an ambiguous pattern might occur. For example, a peak pattern potentially generated by C(C/T)(C/T)T would be a double C peak and a double T peak, the same as generated by CCTT. In general, however, the peaks are distinct and very easy to interpret as present or absent. A major asset of pyrosequencing is its versatility; it has proved extremely useful, e.g., in identifying quickly SNPs in a large number of samples. In silico analysis of two strains, for example, might show sequence variations at various sites. These potential SNPs may be real, but they might be the result of sequence errors in one or the other sequence. Pyrosequencing affords the opportunity to confirm potential SNPs or delineate errors, as the case may be, within the sequences of closely related strains. For example, pyrosequencing was used to target particular regions of two E. coli O157:H7 strains, EDL933 and Sakai, and these analyses resolved 10 genomic sequencing errors while confirming tens of informative SNPs (5). Pyrosequencing is being used for sequencing both prokaryotic and eukaryotic DNA and SNP discovery. The following examples, however, highlight the use of pyrosequencing of E. coli in our laboratory. Once an informative SNP has been confirmed, pyrosequencing offers a powerful tool for determining the frequency of a particular SNP within the population of bacteria being

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analyzed. When testing a large number of individual samples, pyrosequencing is much faster than conventional sequencing techniques; that is, 96 samples can be run on a microtiter plate in 10–20 min utilizing the pyrosequencing instrument. The results of each SNP determination are made in the context of neighboring genetic sequence. This adds quality control to the process, ensuring that the correct region of the genome is being interrogated. There is significant flexibility in the design and placement of the PCR and sequencing primers, allowing almost any SNP position in the genome to be analyzed directly. Unlike conventional Sanger sequencing, where the sequencing reaction for each sample must be run individually, DNAs pooled from 10 to 100 samples can be pyrosequenced to determine the frequency of alleles found at the SNP position. Allele frequencies within a bacterial population are important parameters in assessing overall diversity. Not all SNPs are created equal, however. A SNP with a frequency of 50% will divide the population clearly into two groups. A series of such SNPs can then be used to further divide the population. In the case of bacteria, this method results in the “binning” of strains into discrete groupings. A SNP that is represented only rarely in the population, on the other hand, does not have the same utility in grouping a collection of strains, though it may serve as a useful marker for distinguishing and perhaps identifying an individual strain. This approach has been used to identify strain-specific SNP signatures that can be used as biomarkers to distinguish between and among individual bacterial isolates (5). The use of both microarray profiling and pyrosequencing, in particular, has helped enhance discovery and identification of SNPs that can be used to discriminate among pathogenic strains of E. coli. Explicitly, when microarray results are able to localize a potential SNP to a discrete region, pyrosequencing can then be used to locate and identify the base-pair change responsible for the SNP within the genome. Using pooled DNAs from a test set of 20 E. coli O157:H7 strains, for example, pyrosequencing assays were used to determine the allele frequency of 10 informative SNPs. This panel of SNPs was then used to screen and characterize a larger subset (˜150 independent isolates) of E. coli O157:H7 isolates from our strain collection. SNP analyses yielded a binary code that allowed the “binning”, i.e., partitioning, of these closely related strains into eight further subgroupings. In several cases, a particular SNP pattern was unique to only one isolate, suggesting that a pyrosequencing approach might prove very useful in molecular epidemiology or microbial forensic investigations (5).

III. USE OF OPTICAL MAPPING FOR THE RAPID CONSTRUCTION OF ORDERED RESTRICTION MAPS FOR ENTIRE CHROMOSOMES FROM SINGLE DNA MOLECULE Michael L. Kotewicz, J. Eugene LeClerc, and Thomas A. Cebula Since restriction enzymes were first employed in 1971 to fragment the Simian Virus 40 (SV40) genome (6), they have held especial value in the physical mapping of DNA. Up until recently, physical mapping of DNA entailed digesting the DNA with an appropriate restriction enzyme(s) and separating the DNA fragments by gel electrophoresis. As the restriction process could be readily standardized, the arduous task then was to fit the DNA fragments together correctly to yield the proper, intact genome. With the advent of optical mapping, this task has become much easier. Optical mapping is a single-molecule technology that is used today to analyze DNA genomes or chromosomes ranging in size from 0.5 to 50 Mbp (1 Mbp = 1 million bp = 1,000 kbp). Optical mapping still makes use of restriction enzymes, but does not employ gel electrophoresis or any other separation technique. Instead, optical mapping restricts DNA attached to a solid substratum, thus

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producing contiguous and accurately sized restriction fragments ranging in numbers from 500 to 5,000 depending upon the size of the chromosomes being analyzed. The restriction maps resemble “bar codes”, and indeed pattern recognition software can be used to “read” and distinguish the optical maps of closely related chromosomes and genomes. The sequencing community recognized early the value of optical mapping in resolving sequence ambiguities, such as DNA gaps and large repeated sequences, and in closing genome and chromosome sequencing projects. In the microbial research area, where a marked interest in comparative genomics has taken hold, optical mapping is also being heralded as a nonsequencing alternative critical in examining chromosomal differences—the result of insertions, deletions, and inversions—between and among closely related microbial strains. Today’s technology allows phages, viruses, and large clones of DNA (50 Kbp – 1 Mbp); bacterial chromosomes (5–7 Mbp); and lengths of yeast, rice, and human DNA (20–50 Mbp) to be immobilized on specially etched and treated glass surfaces, restricted and optically mapped. The transition from the analysis of large DNA molecules in gel matrices (Pulsed Field Gel Electrophoresis or PFGE) to single molecules attached to surfaces was the first critical step toward high-throughput analysis of DNA genomes. Reports demonstrating the use of optical maps for phages and virus chromosomes were published in 1995 and 2005, respectively (7,8). Breakthroughs in microfluidics allowed a ramping up to DNA molecules of much larger size, e.g., the 5 Mbp DNA genome of an individual bacterium (9). Innovations, such as the development of machine vision software, the coupling of automated rapid data acquisition by CCD camera, map-file creation, and new software for large-scale alignments have made it possible to gather sufficient data in a high-throughput manner that allows the rapid assembly of 5–50 Mbp genomes and chromosomes. The first bacterial genomes to be optically mapped were the radiation-resistant bacterium, Deinococcus radiodurans (10), and a foodborne pathogen, E. coli O157:H7 (11); other optical maps, such as those for Yersinia pestis, Rhodobacter sphaeroides, Leishmania major Friedlin, and Rhodospirillum rubrum, appeared in rapid succession. Briefly, the technique of optical mapping encompasses the embedding of cells in agar, cell lysis, and chromosome spreading onto derivatized glass slides, restriction digestion, data collection, and map assembly. The initial embedding of cells in agar reduces shearing, and special microfluidics handling allows lysis and spreading of 0.1–50 Mbp linear molecules. The DNA attached to a surface is digested with a carefully chosen restriction enzyme and dried sufficiently for a gap to appear at the digestion points along the chromosomal spread. The resultant long, contiguous set of restriction fragments is the chromosome map. Advances in automated scanning using a CCD camera and microscopy of fluorescently labeled DNA affords the rapid quantification of DNA fragment sizes to an accuracy of about 2% (600 bp) for fragments in the 20–30 kb range. Multiple reads of chromosome spreads are assembled by software that overlaps sets of fragments to generate a map of the entire bacterial chromosome. The limit of detection of fragments is about 500 bp. The choice of restriction enzyme optimizes the size distribution for a given species. For a typical bacterial chromosome of 4–6 Mbp, depending on the GC-content, six base cutters such as BamHI (GGATCC) for E. coli or BsiWI (CGTACG) for Salmonella species yield 400–600 contiguous restriction fragments. Overall, the chromosomes of bacteria can be sized within a 10–20 kb accuracy for a 5 Mbp genome. For rice, highly accurate optical maps of chromosomes were compared to other genomic maps from yeast artificial chromosomes (YACs), bacterial artificial chromosomes (BACs), and interphase flourescent in situ hybridization (FISH) results.

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As restriction fragments smaller than 500 bp are not detected, a number of informative sites are not mapped. Similarly, genic changes, such as deletions of individual genes, typically 500–2,000 bp, cannot be resolved within a 10–50,000 bp fragment. To characterize genic changes of this order, considerable PCR walking is required to localize the genes (or their absence) within restriction anamolies revealed by the optical maps. As mentioned previously, optical maps have proved critical in the assembly and finishing of genomic sequences, both bacterial and eukaryotic, by helping close sequence gaps. In many cases, hard stops in sequencing reactions prevent assembly. These gaps can range in size from 10 to 10,000 bases. Sequence gaps may also result because of repetitive sequences occurring within a region, thus making it difficult to resolve the correct sequence. Since an optical map allows precise placement of contigs on a sequence scaffold, it, together with the restriction map of flanking contigs, yields an estimate of the size of the intervening gap (between two sequence contigs). PCR primers can then be designed to cross this sequencing gap to verify and define the intervening sequence information. Scaffolds, assembled from previously sequenced genomes, can also be informative guides in reconstructing the genome of a related but unsequenced isolate. The alignment software, developed for optical mapping for contig assembly, permits multiple genome comparisons; such comparisons have proved effective in the analysis of pathogenicity islands in exotoxigenic E. coli and in examining variations at the subspecies (e.g., Shigella, regarded by many as a subspecies of E. coli) as well as at the individual strain level (e.g., E. coli O157:H7). The level of resolution of optical mapping is ideal for the examination of whole chromosomes extending from viruses to humans. Optical mapping is well suited particularly for detecting differences between genetically similar bacteria and has helped reveal the extensive chromosomal rearrangements that occur in pathogens such as E. coli O157:H7.

IV. METABOLIC PROFILING IN BACTERIA USING PHENOTYPIC MICROARRAY Amit Mukherjee, J. Eugene LeClerc, and Thomas A. Cebula Phenotypic microarray (PM) is a high-throughput, cellular phenotype screening technology where in a single experiment, more than 1,000 phenotypes of a bacterial strain can be screened within a period of 24–48 hr (12,13). The multiple screening approaches and the automated monitoring instrument were developed by Biolog (Hayward, CA) using E. coli as the model system. With appropriate modifications of experimental conditions, it can also be used to screen a diverse range of gram-negative and gram-positive bacteria based on their phenotypic difference (bibliography of publications on the Biolog website, www.biolog.com). Although this approach has been employed successfully on some lower eukaryotes such as fungus (Aspergillus nidulans) and yeast (Candida albicans, Saccharomyces cerevisiae) (12), as well as some eukaryotic cell lines (www.biolog.com), it has not been used as widely on eukaryotes as it has been on prokaryotes. Features that can be readily screened by this technique are different phenotypes representing underlying metabolic differences such as the ability to utilize carbon or nitrogen source, and phenotypes that result due to differences in the sensitivity to antibiotics or other inhibitory chemicals. This microarray approach was developed primarily to assign function to a gene by identifying the phenotypic difference(s) in wild type and an isogenic strain in which the gene of interest is deleted (12–14). In our laboratory, PMs are routinely used to identify phenotypic

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differences between and among strains of foodborne gram-negative pathogenic bacteria isolated from food outbreaks. The ultimate aim is to use such differences as phenotypic markers to distinguish isolates from different outbreaks. The PM system introduced by Biolog is based on the principle that cellular respiration in the presence of a reducible tetrazolium dye will cause reduction of the dye and develop a purple color that can be measured readily (12,13). The intensity of the color, which can be quantitatively measured and kinetically monitored, is proportional to the extent of respiration. Thus, cells placed into wells of a 96-well plate containing various substituents are monitored over time for color intensity, the color intensity being a direct reflection of the ability of the cell to respire under a particular condition. For example, cells that can utilize D-fructose efficiently will develop intense purple color; cells that utilize D-fructose poorly will develop less color; and those cells that cannot utilize fructose at all will develop no color above background levels of the assay. In the PM system, there are twenty 96-well plates (PM 1–20) designed to assess the respiration and/or growth of a bacterial strain under various conditions. Each of the wells of the first 10 plates (PM 1–10) contains compounds at a particular concentration, such as carbon sources in PM 1 and 2; nitrogen sources in PM 3; phosphate and sulfur sources in PM 4; micro-nutrients and substrates to test for biosynthetic pathways in PM 5; dipeptides in PM 6–8; and different pH and ionic conditions in PM 9 and PM 10, respectively. There is a negative control well in each of the plates from PM 1–8. The remaining 10 plates, PM 11–20, contain various antibiotics and inhibitory chemicals, and each of these is present in each of the four wells at different concentrations. As 952 metabolic phenotypes can be tested in plates PM 1–10, and 240 phenotypes with respect to sensitivity or resistance to chemicals can be tested in plates PM 11–20, one complete Biolog run results in the simultaneous screening of 1,192 scorable phenotypes. A complete list of the contents in each of the wells of the 20 plates is available on the Biolog website (www.biolog.com). PM experiments are carried out according to the protocol developed by Biolog using PM 1–20 plates; reagents and media are also supplied by Biolog. The protocol and experimental conditions can be manipulated to meet growth conditions of a particular strain. In our laboratory, in a typical PM experiment with gram-negative enteric bacteria, a bacterial strain is freshly streaked out on a Luria Bertani agar plate and incubated overnight at 37°C. The bacterial cells are then scraped from the plate and suspended at defined turbidities in minimal medium (IF0, Biolog) for plates PM 1–8 and rich medium (IF10, Biolog) for plates PM 9–20. To both media, the reducible tetrazolium dye is added prior to suspending the cells. A suitable carbon source, such as succinate or pyruvate, is added to the cell suspension in IF0. The fortified cell suspension is added to plates PM 3–8 only, but not to plates PM 1 and 2 since the wells in these two plates contain various carbon sources. A volume of 100 μL of the cell suspension is added into each well of the 20 PM plates, and the plates are incubated in an Omnilog incubator for 24–48 hr at the desired temperature. The Omnilog incubator developed by Biolog is a vital component of the PM system. This incubator holds fifty 96-well plates and is equipped with a CCD camera which takes readings of the color developed in each of the wells of all the 96-well plates every 15 min, and the readings are stored for subsequent analysis. It is relevant to point out that it is not necessary to do a PM experiment with all the 20 plates, instead it can be restricted to only those plates that are of interest to the investigator. The data from the PM experiment are analyzed by software developed by Biolog. The software displays the data collected for each well of all the plates in the experiment as a kinetic plot. Comparison of phenotypes can be done with two strains at a time. For the ease of visual comparison of the phenotypic differences between two strains, the area under

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the curve for the reference strain is red and that of the test strain is green. Upon overlaying these curves, the resultant area under the curve could have one of the three following colors: yellow, resulting from equal overlapping areas of red and green if there is no change in phenotype between the test and reference strains; green, when the test strain has a phenotype that the reference does not; and red, when the reference strain has a phenotype that the test strain does not. Unlike in the field of genomic microarray where there are competing technologies, high-throughput PM is relatively new, and to the authors’ knowledge the technology developed by Biolog is the only commercially available system at present. PM is a relatively easy-to-use technology where about 1,200 phenotypic data of a bacterial strain can be collected within 48 hr. The data obtained are reproducible, although false signals do appear infrequently. False signals can be readily identified by the shape of the curve, where instead of an increase in the color reading with time there is an abrupt increase from an early time without any further increase during the course of the experiment. Finally, it is important to point out that this technology provides a starting point in the phenotypic analysis of a bacterial strain, and any gain or loss of phenotype observed by this assay that is of interest to the investigator should be confirmed by microbiological, genetic, or biochemical methods.

V. FROM PLUS–MINUS SEQUENCING TO NEXT GENERATION SEQUENCING WITH A FOCUS ON PYROSEQUENCINGa Sushil K. Mahata**, Juan L. Rodriguez Flores, Manjula Mahata, and Daniel T. O’Connor

1. INTRODUCTION Before the development of direct DNA sequencing methods, DNA sequencing was difficult and indirect. The DNA had to be converted to RNA, and limited RNA sequencing could be done by the existing cumbersome procedures. Thus, only shorter DNA sequences could be determined by this method. Using this method, Walter Gilbert and Alan Maxam at Harvard determined that the lac operator (the binding site for the lac repressor) is a 27-bplong sequence. The development of direct DNA sequencing techniques changes the scope of biological research. The evolution of DNA sequencing technology from plus–minus sequencing to pyrosequencing within about 20 years parallels the progress in biology from molecular biology to genomics. The development of DNA sequencing techniques with enhanced speed, sensitivity, and throughput are of utmost importance for the study of biological systems. Sequence determination is most commonly performed using dideoxy chain termination technology. Pyrosequencing, a nonelectrophoretic real-time bioluminometric method for DNA sequencing, has emerged as a state-of-the-art sequencing technology. This technology has the advantage of accuracy, ease-of-use, and high flexibility for different applications. Pyrosequencing allows the analysis of genetic variations including SNPs, insertions/deletions (indels) and short repeats, as well as assessing RNA allelic imbalance, DNA methylation status, and gene copy number. With the advent of the next generation sequencing technology, sequencing individual genomes at an affordable price seems to be an achievable goal in the not-so-distant future. The best-known DNA sequencing techniques are discussed below. a

Grant support: National Institutes of Health and Department of Veterans Affairs. To whom correspondence should be addressed.

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2. SEQUENCING BY SYNTHESIS—SANGER METHOD The first DNA sequencing method devised by Sanger and Coulson (15) was called “plus and minus” sequencing that utilized E. coli DNA pol I and DNA polymerase from bacteriophage T4 with different limiting triphosphates. This technique had a low efficiency. Sanger and co-workers (16) eventually invented a new method for DNA sequencing via enzymatic polymerization that basically revolutionized DNA sequencing technology. Sanger’s method is by far the most widely used sequencing technique which has been used in various genome sequencing including human genome. This technique relies on DNA synthesis with incorporation of normal deoxynucleoside triphosphates (dNTPs) as well as dideoxynucleoside triphosphates (ddNTPs) causing a termination of the newly synthesized DNA molecule. Briefly, a 32P-labeled primer is annealed to a specific known region on the template DNA, which provides a starting point for DNA synthesis. In the presence of DNA polymerase, catalytic polymerization of dNTP onto the DNA occurs. The polymerization is extended until the enzyme incorporates a modified nucleotide, called a “terminator” or “ddNTP” into the growing chain. These prematurely ended fragments can be electrophoretically resolved and analyzed based on their size. Since different dideoxy nucleotides are used in different tubes or alternatively marked with different fluorophores, the DNA sequence can be deduced from these results. The average read length obtained is typically between 500 and 1,000 bases, depending on the separation and resolution of fragments. The caveats thus include purification and separation of the DNA fragments. Although the Sanger sequencing method can be highly automated, the higher analysis time compared to pyrosequencing decreases its suitability when only shorter sequences are required. 3. SEQUENCING BY CHEMICAL CLEAVAGE—MAXAM & GILBERT METHOD In 1977, Maxam and Gilbert described a sequencing method based on chemical degradation at specific locations of the DNA molecule (17). The end-labeled DNA fragments are subjected to random cleavage at adenine, cytosine, guanine, or thymine positions using specific chemical agents, and the products of these four reactions are separated using polyacrylamide gel electrophoresis (PAGE). As in Sanger method, the sequence can be easily read from the four parallel lanes in the sequencing gel. Double-stranded or single-stranded DNA from chromosomal DNA can be used as template. Originally, end-labeling was done with [32P]phosphate or with a nucleotide linked to 32P and enzymatically incorporated into the end fragment (17). The read length is up to 500 bp. The chemical reactions in the Maxam and Gilbert technique are slow and involve hazardous chemicals that require special handling in the DNA cleavage reactions. As in Sanger’s method, additional caveats in Maxam–Gilbert method include purification and separation of the DNA fragments and higher analysis time. Therefore, this technology is also not suitable for high-throughput, large-scale investigations. 4. SEQUENCING BY HYBRIDIZATION—ED SOUTHERN METHOD Ed Southern’s sequencing by hybridization (SBH) technique (18) relies on detection of specific DNA sequences using hybridization of complementary probes. It utilizes a large number of short nested oligonucleotides immobilized on a solid support to which the labeled sequencing template is hybridized. The target sequence is deduced by computer analysis of the hybridization pattern of the sample DNA. DNA sequences can also be analyzed by

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sequencing by synthesis (SBS). SBH makes use of a universal DNA microarray, which harbors all oligonucleotides of length k (called “k-words”, or simply words when k is clear). These oligonucleotides are hybridized to an unknown DNA fragment, whose sequence one would like to determine. Under ideal conditions, this target molecule would hybridize to all words whose Watson–Crick complements occur somewhere along its sequence. Thus, in principle, one could determine in a single microarray reaction the set of all k-long substrings of the target and try to infer the sequence from those data. The average length of a uniquely reconstructible sequence using an 8-mer array is <200 bases, far below a single read length on a commercial gel-lane machine. The main weakness of SBH is ambiguous solutions—when several sequences have the same spectrum, there is no way to determine the true sequence. 5. SEQUENCE BY SYNTHESIS—PAL NYREN’S PYROSEQUENCING METHOD Pyrosequencing method is described in more detail than the rest of the sequencing techniques because of its widespread utility and use. 5.1. History The idea of pyrosequencing was conceived by Pal Nyren in 1986 while doing his postdoctoral studies in the laboratory of Sir John Walker at the MRC Laboratory of Molecular Biology in England. In 1987, Pal Nyren described how DNA polymerase activity is monitored by bioluminescence (19). In 1996, Pal Nyren’s group reported that natural nucleotides could be used to obtain efficient incorporation during a sequencing-by-synthesis protocol (20). The detection was based on the pyrophosphate (PPi) released during the DNA polymerase reaction, the quantitative conversion of pyrophosphate to ATP by sulfurylase, and the subsequent production of visible light by firefly luciferase. The first major improvement was the inclusion of dATPαS in place of dATP in the polymerization reaction, which enabled the pyrosequencing reaction to be performed in homogeneous phase in real time (20). The nonspecific signals were attributed to the fact that dATP is a substrate for luciferase. Conversely, dATPαS was found to be inert for luciferase, yet could be incorporated efficiently by all DNA polymerases tested (20). The second improvement was the introduction of apyrase to the reaction to make a four-enzyme system (21). Apyrase allows nucleotides to be added sequentially without any intermediate washing step. Pal Nyren, Mathias Uhlen, Mostafa Ronaghi, Bertil Petterson, and Bjorn Ekstrom founded the company pyrosequencing AB to commercialize their new DNA sequencing method. 5.2. Principle Pyrosequencing, a nonelectrophoretic real-time DNA-sequencing method, is based on sequencing by synthesis that relies on the detection of PPi released during the DNA polymerization reaction (20,22). In a cascade of enzymatic reactions, visible light is generated that is proportional to the number of incorporated nucleotides. The cascade starts with a nucleic acid polymerization reaction in which PPi is released as a result of nucleotide incorporation by polymerase. The released PPi is subsequently converted to ATP by ATP sulfurylase, which provides the energy to luciferase to oxidize luciferin and generate light; the light generated is captured by a CCD camera and recorded in the form of peaks, known as a “Pyrogram®” (compared with electropherograms in Sanger dideoxy sequencing). Because

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the added nucleotide is known, the sequence of the template can be determined. Standard pyrosequencing uses the Klenow fragment of E. coli DNA pol I, which is a relatively slow polymerase. The ATP sulfurylase used in pyrosequencing is a recombinant version from the yeast S. cerevisiae, and the luciferase is from the American firefly Photinus pyralis. The overall reaction from polymerization to light detection takes place within 3–4 sec at RT. One pmol of DNA in a pyrosequencing reaction yields 6 × 1011 ATP molecules which, in turn, generate more than 6 × 109 photons at a wavelength of 560 nm. This amount of light is easily detected by a photodiode, photomultiplier tube, or a CCD camera. Initially two chemistries were able to produce either SNP sequence for short SQA (5–10 bp) or nucleotide sequence for long SQA (10–60 bp). Longer sequence production requires modified and more robust chemical and substrates. Pyrosequencing technology has been further developed into array-based massively parallel microfluidic sequencing platform (23) by the 454 Life Sciences Corporation (now acquired by Roche). 5.3. Pyrosequencing Technique in Detail 5.3.1. Template Preparation PCR-generated templates are purified prior to pyrosequencing as unincorporated nucleotides and PCR primers in PCR reaction interfere with the pyrosequencing reaction. Two different pyrosequencing strategies are currently available: solid-phase pyrosequencing (20) and liquid-phase pyrosequencing (21). 5.3.2. Solid-Phase Pyrosequencing (Three-Enzyme Mixture) This strategy requires a template-washing step between nucleotide additions to remove the nonincorporated deoxynucleotides and ATP resulting from sulfurylase action. It enables biotinylated PCR product to be captured onto magnetic beads. After sedimentation, the remaining components of the PCR reaction are removed by washing to obtain pure doublestranded DNA followed by alkali denaturation to yield single-stranded DNA (ssDNA). Both the immobilized biotinylated and nonbiotinylated strands in solution are used as pyrosequencing templates (24). 5.3.3. Liquid-Phase Pyrosequencing (Four-Enzyme Mixture) In liquid-phase pyrosequencing, a nucleotide-degrading enzyme apyrase is introduced to make a four-enzyme system that is performed in a closed system in a single well. Apyrase eliminates the need for solid support and intermediate washing, thereby enabling the pyrosequencing reaction to be performed in a single tube as it degrades unused nucleotides not incorporated into the reaction. The processing time is simple and relatively fast (˜20 min/96-well plate), and the cost of the reaction is comparable to other medium-throughput technologies. 5.4. Reagents and Enzymatic Reactions The four enzymes included in the pyrosequencing system are the Klenow fragment of DNA pol I, ATP sulfurylase, luciferase, and apyrase. The reaction mixture also contains the enzyme substrates adenosine 8′-phosphosulfate (APS), d-luciferin and the sequencing template with an annealed primer to be used as starting material for the DNA polymerase. The four nucleotides are added one at a time, iteratively, in a cyclic manner, and a CCD

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camera detects the light produced. The enzymatic reactions exploited in the pyrosequencing technology, with catalyzing enzyme given in the reaction in parentheses are the following. The first reaction, the DNA polymerization, occurs if the added nucleotide forms a base pair with the sequencing template and thereby is incorporated into the growing DNA strand. (DNA)n + deoxynucleotide triphosphates (dNTP) → (DNA)n+1 + PPi (Polymerase). (1) The inorganic pyrophosphate, PPi, released by the Klenow DNA polymerase serves as substrate for ATP sulfurylase, which produces ATP: PPi + adenosine 5′-phosphosulfate (APS) → ATP + SO42– (ATP sulfurylase)

(2)

Through the third and fourth reactions, the ATP is convert ed to light by luciferase, and the light signal is detected. Hence, only if the correct nucleotide is added to the reaction mixture, light is produced. Luciferase + d-luciferin + ATP → luciferase – luciferin – adenosine monophosphate (AMP) + PPi Luciferase – luciferin – AMP + O2 – luciferase + oxyluceferin + AMP + CO2 + light

(3) (4)

Apyrase removes unincorporated nucleotides and ATP between the additions of different bases. ATP → AMP + 2Pi (apyrase)

(5)

dNTP → deoxynucleotide monophosphate (dNMP) + 2Pi (apyrase).

(6)

This degradation between base additions is crucial for synchronized DNA synthesis, asserting that the light signal detected when adding a certain nucleotide only arises from incorporation of that specific nucleotide. 5.5. Pyrosequencing Enzymes The performance of the four enzymes is crucial for the accuracy of pyrosequencing, as described below. 5.5.1. Klenow DNA Polymerase The E. coli DNA pol I possesses both the polymerase activity as well as the 3′→5′ and 5′→3′ exonuclease activity. A larger Klenow fragment possessing both polymerase and 3′→5′ exonuclease activity is generated by proteolytic digestion of the native 109 kDa polymerase by subtilisin. An exonuclease-deficient (exo–) Klenow variant with slower kinetic polymerase activity, created by mutation of two amino acids for use in pyrosequencing, is used for extension of the primer and simultaneous release of PPi. The deficiency of the 3′→5′ exonuclease activity helps to avoid unsynchronized DNA polymerization. This slower kinetic mechanism of exo– Klenow for mismatch incorporation is exploited by the use of apyrase so that mismatch incorporation is efficiently eliminated.

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5.5.2. ATP Sulfurylase In the second enyzymatic reaction, ATP sulfurylase, a 315 kDa homohexamer catalyzes the production of ATP from PPi released upon DNA polymerization. The commercially available ATP sulphurylase was cloned from the MET3 gene on chromosome X of the yeast S. cerevisiae. This enzyme has been successfully produced in E. coli for use in pyrosequencing (25). 5.5.3. Luciferase In the third enzymatic reaction, luciferase, a 61 kDa enzyme, catalyzes the light production from ATP that is detected in pyrosequencing. The commercially available luciferase variant originates from the North American firefly P. pyralis (26), which produces light quite efficiently (0.88 photons per luciferin molecule) in the green–yellow region (550–590 nm) with an emission maximum at 562 nm at physiological pH. Since luciferase produces light from dATP but no other nucleotides, a modified “A” nucleotide, dATP-S, is used instead of dATP in the pyrosequencing polymerization (21). In pyrosequencing technology, the low thermostability of luciferase limits the reaction temperature to 25°C. 5.5.4. Apyrase In the fourth enzymatic reaction, apyrase, a 49 kDa enzyme, is included for degrading unincorporated nucleotides and excess ATP between base additions. In pyrosequencing, the most critical reactions are DNA polymerization and nucleotide removal by either washing or enzymatic degradation. Nucleotide removal (descending curve) competes with the polymerization reaction (ascending curve). Therefore, slight changes in the kinetics of these reactions directly influence the performance of the sequencing system. Since pyrosequencing requires higher ratio between ATP and ADP to increase the efficiency of nucleotide degradation, Pimpernel apyrase from Solanum tuberosum with an ATP/ADP hydrolysis rate of 10 is used in pyrosequencing. 5.6. Materials and Methods 5.6.1. DNA Template DNA for pyrosequencing is extracted from cell lines, blood, serum, plasma, paraffin-embedded tissue, and frozen tissue. cDNA is also used for pyrosequencing. 5.6.2. Performing PCR for Pyrosequencing 5.6.2.1. Primer Design Primers are designed using PSQTM Assay Design software available from Biotage. (i) Search the NCBI Entrez Gene website for the gene of interest. (ii) Click on the SNP GeneView link. (iii) Select a SNP and click on its rs number. (iv) Select and copy the FASTA sequence for the SNP, including flanking nucleotides. (v) Open the PSQ Assay Design software, paste the flanking sequence and click run. (vi) The PSQ software will return a list of alternative three primer sets, double click on a primer set and save as a HTML file. HTML file would be required for a new SNP run. The primers include two PCR primers: (one biotinylated) and one sequencing primer.

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5.6.2.2. Gradient PCR for Optimization The optimal annealing temperature, magnesium concentration, and correct product size are verified for each new primer set using a thermocycler with a 96-well gradient block that allows for PCR with a range of up to 12 different temperatures or eight magnesium concentrations. The conditions that provide the brightest single band in a gel with the correct amplicon size are chosen for subsequent PCR reactions. The master PCR mix for one 96-well plate includes: 130 μL Amplitaq Gold PCR mastermix (Applied Biosystems, Foster City, CA), forward primer (10 pM final concentration), reverse primer (10 pM final concentration), and 13 μL DNA up to 260 μL with 18.2 MΩ-cm water. Then, 20 μL of mastermix is added to each row of the 96-well plate or twelve 0.2 mL tubes. PCR program [based on an MJ Research (Reno, NV) gradient block] is: 93°C 20 min (or appropriate temperature/time to activate Taq); 30 cycles of 94°C 30 sec; 55–72°C 30 sec; 72°C 30 sec; then 72°C 5 min; 4°C storage. 5.6.2.3. PCR Amplification of Specific Template DNA The concentration of DNA that produces a clean, high-yield PCR product is chosen. 1–3 μL of 1–5 ng/μL DNA is aliquoted into an unskirted 96-well PCR tray, leaving 1–4 wells without DNA as a negative control. Aliquot 7–9 μL of PCR mix (containing buffer, magnesium, dNTPs, and hot start Taq polymerase), to a final volume of 10 μL. Hot start Taq polymerase minimizes primer dimerization, and one PCR mix for all plates ensures consistency. Thermal Seal (Excel Scientific, Inc, CA) is applied to plate, centrifuge for 1 min before PCR: 95°C 15 min; 45 cycles of 94°C 30 sec; X°C (X is determined from the gradient PCR) 30 sec; 72°C 30 sec; then 72°C 5 min; 4°C storage. 5.6.3. Preparation of the PCR Product for Pyrosequencing 5.6.3.1. Preparation of the PCR Plate for Pyrosequencing (i) Prepare bead/binding mix: 225 μL streptavidin sepharose bead (2 μL/sample) with 4.1 mL of binding buffer (38 μL/sample). (ii) Centrifuge PCR plate for 1 min, remove adhesive film, and add 30 μL H2O to each well. Add 40 μL of bead/buffer mix to each well and replace adhesive film securely to prevent cross-contamination between wells. (iii) Place plate in the plate shaker for 5 min at RT. In this step, the streptavidin-coated sepharose beads bind to the biotin tag on the PCR primer. 5.6.3.2. Preparation of the Pyrosequencing Plate (White Plate) (i) Label white 96-well plate with SNP ID and DNA sample ID. (ii) Prepare “Master Mix” with annealing buffer (1,260 μL for one plate) and SNP-specific sequence primer (8–10 μL of 100 μm undiluted). Excess polymerase ensures efficient binding, and nucleotide concentration above polymerase Km ensures immediate polymerization. The Km values of enzymes involved in pyrosequencing are as follows: Klenow polymerase: 0.18 μm (dTTP); ATP sulfurylase: 0.56 μm (APS) & 7.0 μm (PPi); firefly luciferase: 20 μm (ATP); and apyrase: 120 μm (ATP) and 260 μm (ADP). (iii) Add 12 μL of the mix to each well. Proceed immediately or cover plate with adhesive film. 5.6.3.3. Setting up of a Workstation for Prepping PCR Product/Bead Mix Tray and the Pyrosequencing Primer Tray We use vacuum prep tool and troughs from Biotage (Westborough, MA). The four troughs are filled with 70% ethanol, 0.2M NaOH, wash buffer, and distilled water.

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The PCR product/bead mix plate and the pyrosequencing primer tray are properly aligned in that the negative control wells are in the same orientation before transferring the samples. (i) Turn on the vacuum and place the filter tips of the vacuum tool into the wells of the PCR product/bead mix plate until all liquid from the plate is sucked up. Place the vacuum in the 70% ethanol trough for 30 sec, in 0.2 M NaOH for 15 sec to denature the DNA (leaving only biotinylated strand), followed by 25 sec in washing buffer (cleans and neutralizes the PCR product). Turn off the vacuum and disconnect the hose. It is crucial that no negative pressure remains in the vacuum before placing it in the pyrosequencing plate containing the primer/annealing buffer mix. Gently shake/rock the tips of the vacuum in the wells of the pyrosequencing plate to release the PCR product. Remove the vacuum tool from the pyrosequencing plate once the shaking/rocking is complete. Reconnect the vacuum tool to the hose, turn on vacuum, and place the tool into clean water to cleanse it for the next plate. Place the pyrosequencing plate on the heat block for 2 min at 80°C. After 2 min, remove the plate from the heat block and place on a surface to cool to RT before placing into the pyrosequencing machine. 5.6.4. Entering Assay Details (i) Click “Simplex Entry” on the left side of the screen to enter a new assay. Right-click “Simplex Entry” in the menu tree and select “New Entry”. (ii) Enter a unique name for the assay, such as “gene_rs number_primer number”. (iii) Click primer and paste the following from the primer folder: (a) Copy and paste sequencing, forward and reverse primers from the already saved HTML primer folder from the primer design. (b) Copy and paste the sequence to be analyzed from the primer folder. (iv) Click dispensing order. (v) Click histogram and save. If the dispensing order and histogram are not visible, then redo the above steps from the beginning. (vi) Go to SNP RUNS folder, click “New SNP Run”, and write SNP name. (vii) Click instrument parameter. (viii) Select HS002 from drop-down menu. (ix) Click “Set Up”, go to “Simplex”, find the new SNP ID and double click. (x) Highlight 96-well plate with the pen popped-up on the desktop, and save the folder. 5.6.5. Filling the Cartridge with Reagents and Placement in the PSQHS96A Machine (i) Clean both the nucleotide/capillary and reagent tips before use. Fill the tips with water and apply pressure over the top of the tip to check for any tip blockages. Label the tips with enzyme, substrate, nucleotides A, C, G, and T. (ii) The pyrogold reagent box includes nucleotides in solution, lyophilized enzyme, and lyophilized substrate. Add 1,600 μL H2O to enzyme and substrate bottles for reconstitution. Do not shake or mix as air bubbles can result causing tip blockages or inconsistent dispensation. Unused reconstituted enzyme and substrate are stored at –20°C. For capillary-dispensing tips, dilute nucleotides (1:1) with TE buffer pH 8.0 in a microfuge tube. Mix well and centrifuge before use. Since fiber can cause tip blockages it is essential that only the nonbarrier pipet tips are used. (iii) Fill the nucleotide tubes with nucleotides (100 μL/sample) and capillary tubes with enzyme (150 μL/sample) or substrate (150 μL/sample), and gently dispense liquid down the sides of the tips to prevent pipetting air bubbles into them. Check that the tips are dispensing by

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gently pressing the capillary dispenser. Gently push the cartridge in the cartridge chamber of PSQHS96A machine. 5.6.6. Test Run with the Reagent-Filled Cartridge (i) Select the “Instrument” tab on the left side of the screen, then click “Manage.” (ii) Select the instrument from the drop-down menu. (iii) Click “Test”, and a warning will appear on the desktop asking you to check that the test plate has been placed in the instrument. (iv) Click “OK”. The test takes approximately 30 sec. (v) Once complete, remove the test plate, and in the middle of the plate, there should be liquid dots over six of the wells representing four nucleotides, an enzyme, and a substrate. If there are less than six small dots, a blockage has occurred, and the tips should be checked, and any blockages should be cleared. 5.6.7. New SNP Run in PSQHS96A (i) Place the plate in the pyrosequencing 96-well plate platform and close all levers. (ii) Click the “SNP Run”, find the SNP to be pryosequenced, and select “Duplicate Set” from the drop-down menu. (iii) Right-click “SNP Run” on the menu tree, and select “New SNP Run”. (iv) Enter a unique “Name” (e.g., gene_rs number_population identification_plate number). Select the appropriate “Instrument Parameters” from the drop-down menu. Capillary- or nucleotide-dispensing tip parameters are not interchangeable. The default plate map is for a full 96-well plate. Individual wells are selected by holding down the control key and clicking on the selected wells for use of a partial plate. Click “Activate Wells”, and the unused wells will turn gray, while all active wells remain white. (v) Select the instrument from the drop-down menu, and click “Run” on the individual plate run setup. The enzyme, substrate, and nucleotide will dispense in the predetermined order. Click on any of the wells to view the progress of the run. 5.6.8. Analysis of Pyrosequencing Results (i) Click “Finished” when the run is complete. (ii) Open the SNP just sequenced, and click on “Analyze All” for the program to automatically analyze the data. (iii) Blue wells indicate successful genotype, orange wells are for manual correction, and red wells are for negative control or unsuccessful genotype (Figs. 3–5). The pyrogram for a well is viewed and edited by clicking on the well of interest. Open the expected pyrogram by clicking the “Histogram” tab. Compare the expected pyrogram for each possible genotype, and select that which best matches the observed pyrogram for manual correction. Select a new genotype by right-clicking the genotype above the observed pyrogram. Once a well is edited (in any way), a dark circle will show on the plate map to show that it has been edited (Figs. 3–5). (iv) Negative controls (any wells without DNA present) should automatically be scored as negative. Nonspecific peaks in the negative control wells are due to looping of the internal primer. 5.6.9. Reporting Pyrosequencing Results The data is exported as a report, a tab-delimited file, or an XML file (for whole plates and/ or selected wells). To export a file, click “Report” and then save as the appropriate file type. The report structure is available in forms that transfer to most database and spreadsheet systems. Pyrogram charts can also be saved and printed.

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Figure 3 (see color insert) Pyrogram and histogram of a major allele homozygote genotype (C/C) for CHGA_rs9658634 in TUFTS AKI (acute kidney injury) samples. Out of 96 wells (well overview on the top left corner) 93 wells showed successful genotypes (appeared blue), 2 wells (E10 and G12) were manually corrected (initially appeared orange which after manual correction turned blue with a black ring), and H12 was a control with no DNA (appeared red). Histograms are shown in the middle panel and pyrogram in the bottom panel.

6. APPLICATIONS OF PYROSEQUENCING Pyrosequencing can be applied to identify indel mutations, short tandem repeats, human leukocyte antigen (HLA) typing, gene copy number, allelic imbalance in RNA, methylation status, and to calculate pooled allele frequencies, microbial genotyping, SNP genotyping, pharmacogenetics, and epigenetics, as well as medical genetics. Some of the applications are discussed below.

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Figure 4 (see color insert) Pyrogram and histogram of a heterozygote genotype (C/T) for CHGA_ rs9658634 in TUFTS AKI (acute kidney injury) samples. Genotypes are shown on the top left corner. See Fig. 3 legend for the details.

6.1. Single Nucleotide Polymorphism Genotyping Several types of variants exist in the human genome, including SNPs, microsatellite, or indel polymorphisms. SNPs are a molecular form of scattered genetic variations. Microsatellites are tandem repeats of multiple copies of the same base sequence motif on a chromosome. Although microsatellites have been used in linkage analysis in the past, it is becoming increasingly popular to use SNP markers because they are the most frequent DNA sequence variations found in the human genome. SNPs with minor allele frequencies of at least 1% are found at a frequency rate of 1 in every 200–500 bp in the genome,

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Figure 5 (see color insert) Pyrogram and histogram of a minor allele homozygote (T/ T) genotype for CHGA_rs9658634 in TUFTS AKI (acute kidney injury) samples. See Fig. 3 legend for the details.

suggesting that perhaps as many as 15 million SNPs are ultimately available for study. SNPs are located in the coding (27–29) and noncoding (30–33) sequences of genes as well as in the intergenic (29,34) sequences. SNP genotyping and analysis is one of the major applications of pyrosequencing, as the results are based on DNA sequencing, ensuring high-quality and accurate results. A feature of pyrosequencing in typing SNPs is that each allele combination (homozygous, heterozygous) confers a specific pattern compared to the other two variants. Thus, it is rather easy to score the allelic status by pattern recognition software. Utilizing the pyrosequencing allele quantification software, pooling individual samples allows a rapid and cost-effective process for screening allele frequencies in

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affected and control polulations. Using a case-control design to screen 91 SNPs from a 7.3-Mb region in pooled samples of 150 individuals with diabetes and 150 controls from the same population, Permutt et al. (35) performed only 182 assays that allowed him to make a direct comparison of allele frequencies between cases and controls. Otherwise, it would have been necessary to run 27,300 assays to screen all of the individuals for each polymorphism separately (35). 6.2. Pharmacogenetics Pyrosequencing is extensively used in pharmacogenetics that examines the genetic basis for individual variations in response to therapeutics and promises to develop individualized medicines tailored to patient’s genotypes. For example, a functional dinucleotide (TA) repeat in the UGT1A1 gene is associated with severe toxicity in cancer patients treated with the chemotherapy drug, irinotecan (36). The majority of people have six copies of the TA repeat. The UGT1A1*28 polymorphism corresponds to seven copies of the repeat, and this is the allele associated with toxicity. An accurate pyrosequencing assay has been developed that screens this polymorphism in a 96-well plate format and significantly reduces analysis time (37). Copy-number polymorphisms can also have pharmacogenetic relevance. CYP2D6 is another metabolizer of commonly used drugs. Gene duplication events lead to polymorphic CYP2D6 gene copy numbers between individuals. Amplification of the CYP2D6 gene is associated with increased enzyme levels and consequently increased CYP2D6-mediated metabolism. Time-consuming and labor-intensive methods such as long-range PCR can be used to quantify the number of CYP2D6 genes in a patient. However, the development of a pyrosequencing assay to quantify gene copy numbers allows rapid and reliable data to be generated. For CYP2D6 the assay takes advantage of a known pseudogene (CYP2D8P) and the allele quantification software to compare the ratio of peak heights and determine the number of CYP2D6 genes present (38). 6.3. Epigenetics This growing field of research studies all heritable phenotypes of a cell not encoded in its genomic DNA, with a focus on DNA or histone modifications controlling gene expression. The pyrosequencing methodology described above can be used to identify the methylation status of DNA and cytosine nucleotides in particular. Sodium bisulfite treatment of methylated genomic DNA deaminates unmethylated cytosine to uracil. After PCR amplification and pyrosequencing, the methylated cytosine nucleotides will remain as cytosine, while the unmethylated nucleotides will be converted to thymine (39). After pyrosequencing, comparison with a reference sequence will illuminate methylated nucleotides. However, this method is unable to distinguish between a cytosine to thymine SNP and a methylated cytosine nucleotide. Regardless, a number of successful applications of the technique are mentioned below. Hypermethylation of CpG islands 300–3000 bp length of DNA with > 55% located within or close to the 5′ region of genes is associated with inhibition of gene expression. This can be particularly important in tumors where hypermethylation can “switch off” tumorsuppressor genes and/or DNA damage repair genes, which can lead to sensitivity or resistance to chemotherapy drugs (40). The procedure for detecting methylation using pyrosequencing was recently applied to determine the methylation status in oral squamous cell carcinoma (41), leading to the identification of cytoglobin as a novel putative tumor suppressor gene (41).

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At the whole-genome scale, this method can be used to study the effect of epigenetic modifications on HIV integration sites. HIV is a retrovirus with a RNA genome, and an important step in its infection life cycle is reverse transcription to DNA and integration into its host genome. In order to ensure maximum viral particle production, HIV preferentially integrates in actively transcribed genomic regions. Although the mechanism of site selection is unknown, a negative association was found between CpG islands and viral integration (http://completegenomics.com/index.html). Although this research was primarily bioinformatic, it could be conducted experimentally on a genome-wide scale using next generation pyrosequencing. 6.4. Medical Genetics Pyrosequencing stands to play a significant role in the diagnosis, prevention, and treatment of disease, including both genetic diseases and diseases with genetic risk factors. Furthermore, as the cost of whole-genome next generation methods continues to drop, direct-toconsumer genetic testing and personalized medicine are becoming a reality. At the level of diagnosis, pyrosequencing can be applied to the identification of pathogens and is equally effective, while significantly more efficient than sequencing for identifying which human papilloma virus type a patient is infected with. At the level of treatment, the use of pyrosequencing to rapidly and accurately type HLA loci is of valuable clinical importance (42,43). The matching of patient and donor for HLA significantly reduces the chance of rejection following organ transplantation. Other medical applications of pyrosequencing include the typing of mutations conferring antibiotic resistance to bacteria or differentiation between multiple bacterial or viral strains (44), and screening for target phage ligands for novel drug development (45). These protocols demonstrate the utility of pyrosequencing for clinical assessment and drug development applications. The technique also has far-reaching applications to fields outside of human/medical genetics. Pyrosequencing technology can be utilized to determine X and Y copy numbers in cattle sperm sorting, or coat color variation gene copies in pigs, demonstrating that the same technique and protocols can be applied to a wide variety of disciplines. 6.5. Microbial Genotyping Pyrosequencing is now widely used in several fields of microbiological research. A number of different organisms have been characterized and typed using pyrosequencing. These include the classification, identification, and subtyping of bacteria based on 16S rDNA (46), the identification and subtyping of Helicobacter pylori (47), the grouping of Listeria monocytogenes strains based on SNPs (48), the DNA-based diagnosis evaluation of the pertussis toxin in Bordetella infection (49), the identification of lactobacilli (50), the grouping, typing, and subtyping of meningococci, the typing of human papillomavirus (51), and many other bacterial, viral, and fungal typing schemes (52). Other applications of pyrosequencing involve genotyping of hepatitis C virus (53) and hepatitis B virus (54). Utility of pyrosequencing in microbial genotyping has been discussed in a previous section in this chapter by Mammel et al. 7. NEXT GENERATION SEQUENCING—$1,000 PER GENOME? The Archon Genomics X PRIZE Foundation will award $10,000,000 to a group who can sequence 100 human genomes in 10 days at a cost of $10,000 per genome. This goal is the personal genomics “space race” of the twenty-first century. An order of magnitude beyond

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that goal is a $100.00/genome goal, which would make personalized genomic data accessible to the majority of individuals in developed countries (http://genomics.xprize.org/). The successful $1,000/genome platform must innovate beyond the limitations of mainstream human genome sequencing methodology. 7.1. Roche 454 The 454 method (9) is a high-throughput, nanoscale single-molecule improvement to pyrosequencing. It reduces the scale and complexity of each step in the conventional pyrosequencing protocol, while increasing productivity to a genomic scale. Instead of designing a new pair of primers for PCR-amplification of individual genomic fragments, 454 shatters the human genome into millions of fragments and end-ligates two “universal adapter” primers (A and B). This blunt-ended ligation is nonspecific, and fragments that did not successfully incorporate both primers are removed using special primer-coated beads. What follows is a PCR amplification reaction known as “emulsion PCR”, where primer B and the template fragment are free in solution, while primer A is embedded in the bead. Sepharose beads coated with millions of A primers are mixed with the template DNA solution, primer B, and an oil–water solution. The beads are added in excess, such that only one template DNA molecule and one bead end up in each water droplet. This step is critical, since every DNA fragment has the same two adapter sequences and PCR primers. After PCR thermocycling, each bead has millions of copies of a single DNA sequence. These beads are poured over a picotiter plate (PTP) that fits exactly one bead per well (400,000 total wells). The rest of the process is identical to pyrosequencing, with enzyme and substrate poured over the plate, nucleotides A, C, G, and T added sequentially, and fluorescence intensity captured by a CCD camera. 454 Life Sciences completed the whole genome sequencing of Nobel Laureate Dr James Dewey Watson in May 2007. 7.2. Illumina Solexa The Solexa platform (55) is similar to 454 in that it also performs sequencing by synthesis on random fragments blunt-end-ligated to adapter primers and mutiplexes the process with massive arrays of reactions derived from single molecules. However, during synthesis, the Solexa platform incorporates 3′ OH-inactivated fluorescent trinucleotides. A “GGGG” sequence in a 454 read would incorporate four nucleotides in a row, producing a 4× indirect fluorescence peak. In contrast, Solexa would incorporate the nucleotides one at a time, taking a photograph of the four-color fluorescence intensity after each cycle followed by chemical activation of the 3′ OH. All the four nucleotides are added simultaneously, and each is fluorescently tagged with a different color. Instead of a picotiter plate, Solexa sequencing occurs in microfluidic flow cells, which are coated with two different chemically ligated primers. These primers hybridize with blunt-end-ligated genomic DNA prepared in a method similar to that described above for 454. When the fragments attach to the flow cell interior both ends of the fragment bind, thus forming a “bridge”. After bridge formation an isothermal polymerase amplifies the sequence using the same generic primers and unlabeled nucleotides. Denaturation results in a flow cell coated with single-stranded template DNA, ready for the iterative nucleotide incorporation, fluorescent imaging, and 3′ OH activation.

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7.3. ABI SOLiD The SOLiD platform is similar to 454 and Solexa in that it is a massive multiplex platform which avoids bacterial cloning and amplifies fragmented DNA ligated to two generic primers in single-template emulsion PCR with primer-coated beads. Beads without template and beads with two different templates are removed by an enrichment step before sequencing and an error-checking step during analysis. However, the SOLiD platform has a unique protocol that reads every nucleotide twice in successive dinucleotide sequencing reactions, an error-checking mechanism not offered by 454 and Solexa. The sequencing reaction is repeated five times on each template strand with four-color flourescence of 8-mer probes. Each of the five sequencing-by-ligation runs is preceded by hybridization of a universal sequencing primer. This primer is specific to the bead-bound primer used during emPCR amplification, but the five primers hybridize to different overlapping segments of the 5′ end of the bead-bound primer. The 5′ end of primer 1 is at the last nucleotide of the bead-bound primer (position n), while the 5′ end of primer 2 is at n – 1, etc., and the 5′ end of primer 5 is at n – 4. The first ligation step joins the 5′ end of the universal primer to one of four fluorescentlabeled 8-mer oligonucleotides consisting of three random nucleotides (64 combinations), a sequencing dinucleotide (matched to the fluorescent color), and a universal trinucleotide identical in all 8-mers. Each step adds 256 (4 × 64) 8-mers, and fluorescent signal measured indicates the sequencing dimer. The 8-mer is ligated to the universal primer, and in preparation for the next cycle a cleavage step removes the universal trinucleotide and 5′ fluorescent tag. The process is repeated until the end of the DNA fragment, resulting in two out of every five nucleotides sequenced. In order to sequence the rest of the DNA fragment, the reaction is “re-set” and begins again with universal sequencing primer two. After the five iterations of this process, every nucleotide in the sequence has been read twice. Since there are only four fluorescent colors, the process must be repeated four times in order to cover all possible sequencing dimers.

8. CONCLUDING REMARKS Genome sequencing has become the mainstay in biological research in the twenty-first century, and genome sequencing projects are becoming more widespread and increasingly ambitious. One of the main advantages of pyrosequencing is parallel processing as no electrophoresis is involved and the reactions can be performed in real time. Disadvantages of pyrosequencing include the cost for the biotinylated primer that is normally used in PCR and the design of a specialized instrument dedicated to this analysis. Recent advances in methodology, including multiplex and universal primer applications, have reduced assay cost and improved the throughput. Nevertheless, pyrosequencing has been successfully used in a broad range of applications as discussed above. Hundreds of thousands of pyrosequencing reactions are currently being performed on a single chip by 454 Life Sciences (www.454.com), thereby reducing the cost of sequencing. Since genome sequencing projects are becoming more widespread and increasingly ambitious, sequencing is becoming increasingly cheaper and less time-consuming. Consequently, the next generation sequencing technologies are generating a lot of excitement because of their high-throughput nature and lower cost. All three platforms described above intend to make personalized genomic medicine and genome-wide genetic research

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technologically and financially feasible. ABI SOLiD, Illumina Solexa, and Roche 454 all harness single-molecule techniques, nanoscale instrument fabrication, state-of-the-art image analysis, and supercomputer bioinformatic power. However, only SOLiD provides error checking as a part of the sequencing process, while Solexa and 454 guarantee high quality but do not implement quality control measures. On the other hand, the SOLiD platform requires massive computational and data storage resources due to the complexity of the approach. Newer systems that do not require PCR amplification of template and initiate sequencing on single DNA molecules are under development, such as the Helicos Biosystem’s True Single Molecule Sequencing technology (56) and the Complete Genomics platform (http://www.helicosbio.com/), which propose visualization of exact location where a fluorescent 5-mer oligonucleotide is incorporated into a long single DNA strand. Since the publication of Sanger’s dideoxy chain termination method about 30 years ago, sequencing technology has come a long way and undergone true revolution in scope and scale. Such evolution has been triggered and accelerated by the human genome sequencing initiative. The genome of James Watson is the first human genome to have been sequenced recently using the next generation sequencing and reported in the 17th April, 2008 issue of Nature. It took only two months and one hundredth of the original cost of human genome sequencing to achieve a 7.5× coverage of a complete human genome. At the current pace of advances in sequencing technology, $1,000 a genome seems to be an achievable goal in the not-so-distant future. VI. PRINCIPLES OF PROTEOMIC ANALYSIS WITH SPECIAL EMPHASIS ON MASS SPECTROMETRY Jannavi R. Srinivasan 1. INTRODUCTION Proteomics is the study of proteome of a cell or tissue. A proteome is the entire complement of all the proteins expressed by the genome at a given time. The analytical aspect of proteomics includes the isolation, identification, and structural characterization of proteins to determine their interaction, expression, and function in a biological system. Proteomic applications include understanding the protein-state in studies of protein–protein interaction, determination of biomarkers for disease, identification of posttranslational modifications in proteins, and application of proteins in drug development, to name a few. The field of proteomic analytics has grown tremendously in the past decade with the successful completion of genome sequencing of major species (57) and the advent of mass spectrometry as an amenable tool in the laboratory (58). Advances in biophysical techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy (59), as well as in genetic engineering techniques such as yeast two-hybrid (60), and protein microarray technologies (61) have further added to such development. Analysis in a proteomic experiment includes purification of the protein(s) of interest, enzymatic digestion of the protein(s), identification, quantitation, and characterization of the protein and peptide fragments. Protein analysis historically has been accomplished via classical methods such as gel electrophoresis and chromatography (62). More recently, mass spectrometry has become the mainstream technique to characterize various proteins. The unique combination of classical separation tools, mass spectrometry-based detection,

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along with the application of bioinformatics and molecular modeling has opened avenues to fast, reliable, and elegant proteomic analysis (63). [Note: The focus of this chapter is mass spectrometry since it has become an indispensable tool in modern proteomics research. Most of the classical methods of protein separation, purification, and identification have been used by protein chemists and molecular biologists for a long time. Therefore, techniques other than mass spectrometry will be discussed very briefly.]

2. PRINCIPLES OF PROTEIN SEPARATION Efficient “bottom-up” proteomic strategies can be gel-based or liquid-based separations culminating in direct or indirect mass spectrometric detection. The method or combination of methods for a specific experiment depends largely on sample availability and purity. Figure 6 is a schematic showing the different strategies for mass spectrometry-based proteomic analysis. Gel- and liquid-based separations have been the two common analytical tools available to the bench protein chemist. One-dimensional gel electrophoresis (1DE) includes nondenaturing and denaturing polyacrylamide gel electrophoresis (PAGE). Nondenaturing PAGE preserves the integrity of the protein complexes; therefore, this technique is useful in the analysis of protein–protein and protein–ligand complexes. In contrast, proteins can be separated by denaturing PAGE, popularly known as “sodium dodecyl sulfate-polyacrylamide gel electrophoresis” (SDS-PAGE). In SDS-PAGE, proteins are treated with the detergent sodium dodecyl sulfate (SDS), which binds most polypeptides in a constant weight ratio (˜1.5 parts SDS:1 part polypeptide). SDS binding helps in the denaturation of the proteins and makes them negatively charged. Because SDS

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treatment confers the same charge-density to each protein in a protein mixture, during electrophoresis the proteins essentially resolve in a solid phase (gel) based on their size and molecular mass. Thus, SDS-PAGE is a useful technique in determining the size and molecular mass of proteins. Although commonly used for its ease of use and qualitative reliability, 1DE has its limitations in its poor resolving power, low throughput, and poor automation. Two-dimensional gel electrophoresis (2DE) (64) is a multidimensional gel electrophoresis method that fractionates the proteins based on net charge in the first dimension (isoelectric focusing, IEF) followed by size in the second dimension (1DE). The analytes during IEF are allowed to migrate to their intrinsic isoelectric points (pI), focusing the protein to highly resolved spots which are then separated in the second dimension based on their mass. This technique provides better resolution than 1DE and has been the main tool in qualitative and semiquantitative proteomics studies for many years. Challenges faced by the method include lack of sensitivity, specificity, controls, accuracy, and poor resolution of complex protein samples. For example, the bands from the gels have to be manually excised to be analyzed by offline mass spectrometry, thus highlighting limitations in automation of the technique. Improvements to the technique include high resolution imaging for improved quantitation and speed and automation in gel excision, digestion, and sample preparation. In the case of liquid-based separations, the underlying principles of the methods are based on differences in the intrinsic properties of the proteins such as amino acid sequence, molecular weight, and posttranslational modifications. These differences cause the individual protein moieties to behave differently in a given environment. In general, liquid chromatography (LC) seeks to separate a mixture into its individual components, as it partitions between a stationary and a mobile phase. Size exclusion chromatography (SEC) separates the analytes (protein, in this case) based on differences in molecular size. The mechanism is based on the ability of the molecule of interest to be “included” in or “excluded” from the stationary phase media as it migrates through the mobile phase. This is a low-resolution technique and ideal for determining protein qualities such as aggregation. Ion exchange chromatography (IEC), on the other hand, is based on separating the protein of interest from other components in a mixture based on its overall charge. The method utilizes stationary phases (resins) containing functional groups that carry an overall charge opposite to that of the proteins. Resins can be cationic or anionic depending on the charge of the analyte of interest. Separation of the target protein is achieved by its adsorption to the stationary phase and “washing out” of all other proteins in the mixture. The adsorbed protein is then eluted by creating the ideal ionic strength in the mobile phase through manipulation of its salt concentration or pH. IEC is ideal for monitoring differences in primary structure of the protein. A widely used chromatographic method for the determination of peptide maps and posttranslational modifications is reversed phase chromatography (RPC). This method separates protein and peptide moieties in a sample based on their hydrophobicity. The hydrophobic-binding interaction between the analyte in the mobile phase and the stationary phase ligands is the basis for the partitioning mechanism. In an RPC method, the hydrophobicity of the mobile phase composition is gradually changed to accommodate the efficient adsorption and elution of the analyte as it migrates in and out of the stationary phase. Such a variation in mobile phase composition is called “gradient elution” and is typical for use in protein and peptide analyses. RPC is preferred to other chromatographic methods for its high resolution in complex protein analysis and its ability to generate robust peptide maps.

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Another predominant separation method in protein analysis, affinity chromatography (AC), is based on the binding capacity of the protein analyte to the ligands in the stationary phase. AC is very protein-specific and is ideal for large-scale purification of key proteins from complex biological systems (e.g., antibody purification). Unlike RPC, the SEC, IEC and AC routes are not amenable to online mass spectral detection, described in the following sections, due to their high salt requirements in the mobile phase. These strategies, though, have found applications more recently in multidimensional chromatographic experiments in combination with downstream mass spectrometric identification of proteins (Multidimensional Protein Identification Technology or MudPIT) (65). Overall LC methods are limited by the detection system used for their sensitivity, but offer more quantitative prospects than their gel counterpart. 3. PRINCIPLES OF PROTEIN IDENTIFICATION 3.1. Edman Degradation The earliest attempts to identify protein sequence include Edman presenting a cyclic protein degradation technique to cleave amino acids chemically from the protein’s N-terminus. The released mixtures of amino acids, up to 20 residues, are then separated and the de novo protein sequence is constructed. The technique has sensitivities in the low picomolar to femtomolar ranges. Though N-terminal protein sequencing continues to be a very important tool for protein identification, it is excessively labor-intensive as a strategy to analyze the proteome. Some of its limitations include the need for pure and short protein fragments and an inability of the technique to identify N-terminal protein modifications. 3.2. Mass Spectrometry The mass spectrometer has become a key tool of proteomic research in recent years (66). This has been accomplished by two different routes, namely peptide mass fingerprinting and amino acid sequencing. The advantages of mass spectrometry (MS) in its many forms stem from its reputation for high sensitivity, mass accuracy, resolution, range, and speed. An important aspect of the technique besides automation is its tolerance for complex protein mixtures, typical of cell and tissue samples. The increasing capabilities of bioinformatics (67) have also kept up with the steady improvements to MS. The MS experiment from cell line to sequence can be divided into three different stages: sample preparation, sample analysis, and data interpretation. The sample collection, preparation and handling have been by far the biggest challenge in proteomics applications. Factors such as sample quantity, availability, purity, and stability greatly affect the reproducibility of the experiment. Gel and liquid-based protein separation and fractionation strategies discussed above have helped solve these roadblocks to a large extent. The technique of MS can be used for whole protein or peptide analyses, but MS is more suitable for the latter due to its higher accuracy and resolution at lower molecular weight ranges. Sample preparation to identify a protein in a MS experiment usually involves proteolysis (chemical or enzymatic) to generate peptides. These peptides are then, (i) separated by gel electrophoresis and/or LC prior to mass spectral detection (e.g., LC–ESI MS), or (ii) loaded directly into the mass spectrometer (e.g., in-gel digestion and MALDI–MS). In both scenarios the outcome is a peptide map. A mass spectrometric detector (MSD) is an analytical tool that detects samples based on their molecular mass after converting the sample into its ionic state in vacuum.

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The MSD consists of three distinct parts namely, ionizer, mass analyzer, and detector, all maintained in vacuum. The ionizer or the ion source, as the name suggests, converts the samples to gas phase ions (positive or negative). The analyzer resolves the ions based on their mass-to-charge ratio (m/z ratio). The detector can be visualized as an ion counter that generates the signal that is converted into a mass spectrum of a given sample. The two commonly used MSD ion sources for proteomic analysis are electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). Both ionization routes are considered “soft” because they retain the sample “state” intact during the process (little to no fragmentation) and have high practical mass range (70 kDa for ESI and 300 kDa for MALDI) and sensitivity (picomolar (pM)–femtomolar (fM)). ESI has the ability to create multiply charged ions which further extends its dynamic mass range. MALDI is tolerant to salts (millimolar, mM) and suitable for complex mixture analysis. In ESI, the sample ions are formed by spraying a sample in liquid form via a narrow bore metal capillary maintained under the influence of an electric field. The mechanism by which the electrospray produces intact, multiply charged ions has been highly debated (68). Fig. 7 is a schematic of the electrospray ion source in a mass spectrometer. Sample delivery in ESI is typically done by manually loading the sample in a microcapillary tube. But, the ability of ESI to use liquid samples has allowed the successful utilization of LC systems for sample delivery, making the technique of liquid chromatography–mass spectrometry (LC–MS) easily amenable to automation. A significant improvement to sample delivery engineering came in the form of nanospray ionization (69). This allowed for analyses of low-abundance proteins, a common scenario in most proteomic experiments. In nanospray, the flow rates are drastically reduced (low nanoliters) as is the spraying orifice (1–2 μm). These modifications to the electrospray source have in turn triggered many innovations to low-flow and small-bore chromatography. While the presence of LC is important for complex mixture analysis, ESI is not tolerant to the salts and ion-pairing agents such as trifluoroacetic acid (TFA) used in the LC separation. Another key feature of ESI, namely multiple charging of analyte ions, can cause confusion in data interpretation, especially in mixtures or with co-eluting peptides. The ionization of the analyte in the MALDI–MS source is achieved using energy transmitted from a laser to the sample via a “matrix”. The matrix is a small, energy-absorbing molecule that is mixed to dryness with the sample. Commonly used matrices for protein

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analysis include 2,5-dihydroxybenzoic acid and α-cyano-4-hydroxycinnamic acid. The “crystalline” mixture is spotted onto a metal probe (a 96-well plate format) and placed inside the MS system in vacuo. The laser, with a wavelength well removed from the sample absorption frequencies, impinges on the matrix and promotes the formation and ejection of intact sample ions in a vacuum. Fig. 8 depicts a schematic of a MALDI source. The MALDI–MS technique is fast, accurate, and easily automated from sample preparation to analysis (70). The technique also has high tolerance for contaminants, which makes it an ideal choice for detection, for example, in analysis of bands cut out from 2DE separations (71). A disadvantage of the technique originates from the matrix background, which can interfere with low molecular weight (MW) analyses such as low MW peptides < 700 amu (i.e., < 700 Da). Mass analyzers are placed sequentially in space, after the ion source inside a mass spectrometer. The ions generated by the ion source are focused and extracted towards the mass analyzer. The function of a mass analyzer is to resolve these ions based on their m/z ratio. Different mass analyzers have been employed in identifying protein and peptide masses. Typical to proteomic analyses are the time-of-flight (TOF) (72), quadrupole, and ion trap (IT) (73) systems. In its simplest form, the linear TOF analyzers operate based on the principle that two ions produced at the source and having the same initial kinetic energy traverse at different times through a known length of a field-free region (time-of-flight), and this is based on their m/z ratio. When a spatially defined group of ions of differing m/z ratio are accelerated to the same kinetic energy and allowed to drift in a region of “free-field”, their times of arrival at the detector is directly proportional to their m/z ratio. Therefore, in timeof-flight mass spectrometry (TOF–MS), at a given kinetic energy, ions of smaller mass reach the detector faster than high-mass ions. Various improvements have been made to

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Schematic of MALDI ion source in a mass spectrometer.

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the TOF analyzer to meet the demands of increased resolution in complicated mixture analyses. Briefly, in a reflectron TOF (reTOF) design, the flight tube is essentially extended to increase the mass resolving power of the analyzer. This is accomplished by incorporating ion optics at the opposite end from the ion source in order to bend the ion trajectories in the flight tube before their detection. Delayed extraction, on the other hand, involves changes to the ion optics in the source region to obtain similar improvements to mass resolution. A schematic of the engineering involved in the construction of simple TOF and reTOF type mass analyzers is shown in Fig. 9. A quadrupole design involves four parallel, linear rods maintained at an electric field (rf and dc voltages). Stable ions of a specific m/z produced in the source region of the mass spectrometer are transmitted through the rods by sweeping the applied voltages. All the ions from the source or a selected type are thus separated in space prior to detection. When the quadrupole is used to select a single ion species, it is considered more as a mass filter. Such use has been typical for the quadrupole in proteomic applications (74). The IT design is similar in concept to a quadrupole mass analyzer. Here, the ions from the source are “trapped” in an applied electric field, but essentially in 3D. A scanning radiofrequency (rf) voltage is applied to select and eject the ion of interest (of a specific m/z) from the trap. Unlike in the case of a quadrupole, all the ions are stored in the trap, and only the ions of interest are ejected out. Advances in instrumentation engineering have brought about various types of mass analyzer configurations, all showing immense promise for applications in proteomics (75). The single-stage instrument for early proteomic applications, such as protein

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Design of a linear TOF and reTOF mass analyzer in a mass spectrometer.

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identification from 2D gels and LC–MS-based peptide mapping, used all three mass analyzer types. With the advent of tandem instrumentation (76) came the ability to create experiments using hybrid mass analyzers such as triple quadrupole (QqQ), iontrap–time-of-flight (IT–TOF), quadrupole–ion trap (q–IT), time-of-flight–time-of-flight (TOF–TOF), and quadrupole–time-of-flight (Q–TOF). Tandem mass spectrometry is when two or more mass analyzers are placed sequentially to perform MS/MS (or MSn) experiments. In tandem mass spectrometry, ions can be selected in time and space in the presence of contaminants and complex matrices for subsequent identification and/or characterization. Such designs are devised by exploring the diverse properties of the protein or peptide samples to further resolve a mass ion, once generated by the ion source. In a tandem experiment, the first analyzer can be envisioned as fractionating or isolating the ions to tighter packets, while the second analyzer fragments the “parent” (precursor) ions into “daughter” (product or fragment) ions. The m/z values for the product ions are then detected. Fragmentation is induced by using a quadrupole or an IT as one of the filters before the mass spectrum is acquired. The most common fragmentation treatment is referred to as “collision-induced dissociation” (CID). Here, the precursor ion is trapped in a collision cell where a high-pressure inert collision gas such as Argon (Ar) or Helium (He) is passed. The ions undergo repeated collisions with the collision gas until fragmentation occurs. The product ions formed are extracted toward the detector, and the fragment masses are determined. For a protein analyte, the parent constitutes the intact protein or peptide ion (if enzymatically digested prior to mass analysis) and the daughter ions form the short amino acid sequence fingerprints of the original parent primary structure. Fig. 10 is a simple representation of a tandem mass spectrometry (MS/ MS/MS) experiment. The analyzer combinations with the two ion sources (MALDI and ESI) provide an endless array of mass spectrometers that can be chosen based on the proteomic problem at hand; they promise varying degrees of high mass accuracy, resolution, sensitivity, and dynamic range. Table 1 describes analytical attributes of different mass analyzers for a proteomic experiment.

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Schematic of a tandem MS/MS/MS experiment.

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Comparison of features available in different mass analyzers

Mass analyzer Quadrupole IT TOF re-TOF Q – TOF

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* Resolution indicates the ability of a mass spectrometer to distinguish between ions of different m/z ratios. Resolution is calculated as M/FWHM, where M = m/z ratio, and FWHM (“full width at half maximum”) is a measure for quantifying image sharpness. Thus, if the M is 1,800 amu, and the FWHM of the analyte peak is 0.6 amu, then the resolution is 1,800/0.6 = 3,000. ** Mass accuracy is a measure of how close the observed (experimental) m/z correlates with the expected (theoretical) m/z of the analyte. The mass accuracy of the spectrometer is critical in protein and peptide mixture studies, typical to proteomic analysis.

Depending on the experimental design, the results from a mass spectrometer can be a peptide map or fingerprint (single-stage MSD) or a peptide/protein sequence (tandem MSD). The peptide mapping or peptide mass fingerprinting approach has been applied to detect posttranslational modifications (e.g., deamidation, phosphorylation) in purified samples or complex protein mixtures. Using MSn strategies, researchers can obtain detailed sequence information. This next leap of translating information from a mass spectrum to an accurate protein sequence has been as challenging as proteomics methods development itself. Rapid, accurate comparisons of actual spectral data are possible using comprehensive protein sequence databases (e.g., SWISSPROT, TrEMBL, UniProt, NCBI-nr, ExPASy). Fig. 11 shows a flow chart describing the confluence of mass spectral results and computational identification in a proteomic experiment. The field is rapidly growing to keep pace with mass spectral information generated. But the data size, levels of false positives, and extraction of biological meaning from the probability-based methods are some of the drawbacks still being addressed in the field. The powerful combination of MS and bioinformatics has found a niche in the research lab as a rapid, sensitive, accurate, and automated tool for proteomic identification and characterization. In spite of all its positive attributes, the limitations of MS reside in its need for high sample purity and its inherent qualitative nature. Application of MS as a quantitative proteomic tool is the next step in its evolution. Efforts include protein profiling (quantitation of relative protein levels between diseased and normal state) (77), isotope labeling (to distinguish peptides of similar chemical nature from different biological origins) (78), and computer-aided algorithms (quantitation based on ion count). 4. PRINCIPLES OF PROTEIN STRUCTURE DETERMINATION The area of structural proteomics forms the other arm of proteome analytics. The ultimate goal is to determine the 3D structures of proteins, which in turn will help predict protein interactions and proteome function. Two widely used techniques in this regard are X-ray crystallography and NMR. These, along with complementary biophysical tools such as analytical ultracentrifugation, dynamic laser light scattering (LLS), and circular dichroism (CD), have served as a reliable battery of techniques for proteomic characterization (79). The historic techniques, X-ray crystallography, and NMR will be given due mention while discussion of all the techniques is beyond the scope of this chapter.

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Computational digestion of protein(s) sequence within the protein database for a given enzyme

Calculation of theoretical masses of generated peptide(s) Results from the MS experiment Match similar experimental and computational peptide masses. Select identical masses

Computational fragmentation of selected masses using defined mass fragmenting rules Results from the MSn experiment Compare theoretical and experimental fragmentation patterns

Report score-based protein/peptide sequence information

Figure 11 Application of bioinformatics tools to interpret data from a mass spectrometric experiment.

4.1. X-Ray Crystallography X-ray crystallography has long been a useful tool to determine three-dimensional structures of proteins at high resolution. Using high resolution instruments, proteomic information such as molecular architecture, protein recognition, folding, and binding have been elucidated for small proteins. X-ray crystallography determines the arrangement of atoms within a crystal. Therefore, the molecule or macromolecule to be analyzed by X-ray diffraction has to be in crystallized form. The principle of X-ray crystallography lies in the ability of atoms to scatter X-rays and provide a diffraction pattern characteristic of the structure of the protein in question. This occurs by the interaction of the electron clouds in the atoms of the crystal with incident X-rays to produce interference. The diffraction pattern produces an image of the electron cloud that represents the distribution of electrons in the molecule. This is called “electron density map”. Since the electrons are tightly localized around the nuclei, the electron density map is a reflection of the molecule itself. Bragg’s Law of X-ray diffraction is used to determine the spacing between the atomic planes. The equation is nλ = 2d sinθ (or, d = nλ /2 sinθ); where d = spacing (in Angstrom) between the atomic planes, λ = wavelength (in Angstrom) of the X-rays going into the crystal, θ = the angle of the diffracted X-rays coming out of the crystal. Though a very powerful tool, X-ray crystallography still has many hurdles to overcome. A major bottleneck is protein crystallization, which is labor-intensive, time-consuming and largely depends on protein purity and hydrophobicity. Technological advances such as robotics and automation have enabled rapid growth of protein structure data.

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4.2. NMR NMR spectroscopy is a widely used experimental technique to determine 3D protein structures in solution and at atomic resolution. Its ability to analyze proteins close to physiological conditions makes it very suitable for proteomic applications. The specific, nonselective, and noninvasive nature of the method makes it a good complement to X-ray crystallography. The principle of NMR is based on measuring the change in the magnetic resonance of a molecule in a strong magnetic field by bombarding it (atomic nuclei) with radio waves. The individual resonance frequencies, or “chemical shifts”, that are observed depend on the chemical environment of the atom and in principle are unique to the analyte (protein). However, structure determination by NMR is currently limited by protein size, solubility constraints, lengthy data collection, and analysis times. Efforts in the areas of automation, design of cryogenic probes, and data management are keys for its success as a high-throughput characterization tool. 5. CONCLUSION Proteomics studies can vary in their scope and context, such as protein structure, expression, function, and interactions. Accordingly, proteomics studies can be subdivided into structural proteomics, expression proteomics, functional proteomics, and interaction proteomics, the last two aspects overlapping in scope. Once the protein is identified and characterized, the next goal is to understand its function, as well as its interactions with other proteins. Studies of protein–protein interactions are of central importance in understanding the functioning of biological systems. Two screening techniques to study protein–protein interactions are phage display and yeast two-hybrid assay. Recently protein microarray has expanded the scope of protein–protein interaction screening because of its high-throughput nature. Protein–protein interactions are involved in every biological phenomenon from DNA replication, transcription, and translation to formation of cellular structures, cell growth, enzyme complexes, signal transduction, and regulation of many cellular processes. Disruptions of protein–protein interactions contribute to many diseases. Understanding the interactions of all proteins in a cell or tissue, i.e., the protein interactome, in normal and diseased state has twofold utilities. From an academic perspective, this will help understand the protein interaction network in normal state and how the network is altered during diseased state. One can only hope that specific diseased state would have an identifiable protein interaction fingerprint. From a translational research standpoint, this may aid in the development of targeted therapy. REFERENCES 1. Southern EM. Detection of specific sequences among DNA fragments separated by gel electrophoresis. J Mol Biol 1975; 98:503–517. 2. Pease AC, Solas D, Sullivan EJ, et al. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci USA 1994; 91:5022–5026. 3. Nuwaysir EF, Huang W, Albert TJ, et al. Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. Genome Res 2002; 12:1749–1755. 4. Ronaghi M. Pyrosequencing sheds light on DNA sequencing. Genome Res 2001; 11:3–11. 5. Cebula TA, Brown EW, Jackson SA, et al. Molecular applications for identifying microbial pathogens in the post-9/11 era. Expert Rev Mol Diagn 2005; 5:431–445. 6. Danna K, Nathans D. Specific cleavage of Simian Virus 40 DNA by restriction endonuclease of Hemophilus Influenzae. Proc Nat Acad Sci USA 1971; 68:2913–2917.

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Part 2 Applications of Genomics

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Toxicogenomics in Non-Clinical Safety Studies: Models of Animal and Human Tissues to Assess Target Organ Injury Alison Vickers Allergan Inc., Investigative Sciences, Drug Safety Evaluation, Irvine, California, U.S.A.

1. INTRODUCTION—TRANSLATIONAL RESEARCH AND RESEARCH MODELS 2. NEW APPROACHES AND MODELS FOR MECHANISTIC INSIGHT 3. VALUE OF IN VITRO MODELS 4. TOXICOGENOMICS IN TRANSLATIONAL RESEARCH—ANIMAL AND HUMAN MODELS (IN VIVO AND IN VITRO) 4.1. Rat In Vivo Model 4.2. Cell Lines as In Vitro Model 4.3. Rat Hepatocytes as In Vitro Model 4.4. Human Hepatocytes as In Vitro Model 4.5. Liver Slices as In Vitro Model 4.6. Tissue Repair and Fibrosis Models 4.7. Hepatic Inflammation Models 4.8. Extra-Hepatic Models 4.8.1. In Vitro Models of Nephrotoxicity 4.8.2. In Vitro Models of Hemolysis 5. CONCLUSION REFERENCES

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1. INTRODUCTION—TRANSLATIONAL RESEARCH AND RESEARCH MODELS Drug discovery and other similar innovations always lag behind the pace of discoveries in basic sciences. This gap between scientific discoveries and clinical practice is often referred to as the “bench-to-bedside gap”. The goal of translational research is to bridge this gap so that scientific innovations can be translated into health gain with minimum delay. A major obstacle in translational research is the lack of appropriate research models that can be used to extrapolate data to humans reliably. A major breakthrough in translational research models seems to be at hand due to the recent advances in various

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genomic, proteomic, and metabolomic techniques. The ability to interrogate thousands of signals (genes, proteins, metabolomic) in response to toxicant challenge in one experiment has opened up new possibilities in mechanistic research and redefined the scope of translational research in toxicology. 2. NEW APPROACHES AND MODELS FOR MECHANISTIC INSIGHT Drug-induced organ injury continues to be a significant clinical issue. The current biomarkers of organ injury are generally non-specific and often reflect the extent of organ injury rather than the cause. Recent developments in gene expression profiling techniques (i.e., toxicogenomics) now allow us to detect signs of injury at the molecular level long before there are any frank clinical symptoms. Gene expression profiling techniques also help characterize injury types and thus yield information about the key pathways leading to organ dysfunction prior to detectable changes in morphology. This is a major change of paradigm in preclinical drug development. The ability to identify adverse effects at the molecular level long before any clinical symptoms are visible will not only help reduce compound liabilities, but also minimize or stop the tissue damage to the subject. Preclinical rodent and non-rodent studies predict human target organ toxicities relatively well for hematological, gastrointestinal, cardiovascular, and urinary tract toxicities (~70%), but only ~50% for hepatic, skin, and neurological toxicities (1,2). Most of these toxicities are identified in studies of 1-month duration or less (1). Clearly, preclinical models fail to predict many drug-induced toxicities in humans, particularly low-incidence toxicities and immunologically mediated or idiosyncratic reactions (3). Furthermore, the current animal models cannot fully account for the wide variation of human responses to drugs and the environment (4). Adverse drug reactions typically appear only after the drug has been on the market for some time and several thousand individuals have taken it, confirming that individual variations, health status, and lifestyle contribute to adverse drug reactions. The extrapolation of animal studies to humans is contingent upon the site of toxicity within the organ, the presence and levels of protective mechanisms, and the sensitivity of the cells to drug exposure. Species differences in the rate and extent of compound biotransformation and other physiological differences play a role in the body burden of the drug and target organ injury. This is reflected in the toxicity of various marketed drugs to humans, which was not seen in animal models. One of the most frequent target organs of drug-induced injury is the liver. Drug-induced liver toxicity is frequently cited for the withdrawal of a pharmaceutical from the market, along with hematological, cardiovascular, dermatological, and renal toxicities (5,6). Therefore, a greater understanding of the mechanism of action of drug-induced organ injury and addressing whether the key events in the animals are plausible in humans, together with dose–response and exposure assessments, will contribute to identifying issues and key research needs early in drug development to facilitate the overall risk assessment (7). Use of toxicogenomics is expected to provide key insight into most aspects of the molecular mechanisms of toxicity. Toxicogenomic data are particularly valuable when the information is phenotypically anchored to alterations in tissue function and morphology. The regulatory agencies, including US Food and Drug Administration (FDA), support an efficient and rapid translation of new scientific developments into applications that enable the development of safe and effective medical products (8). The European Medicines Agency (EMEA) has issued a guidance document for the detection of early signs of

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hepatotoxicity and encourages the use of metabolically competent liver cell cultures and the application of toxicogenomics to probe for mechanisms of hepatotoxicity (9). Cellular models that allow pathway characterization and species comparisons can define mechanisms of organ injury and help in decision making about the safety and in vivo outcomes. The cellular pathways leading to organ-specific toxicities are often multifaceted, involving several cell types and biochemical networks of cell–cell and cell–matrix interactions. The new models and technologies can facilitate the identification of apoptosis and necrosis thresholds, changes in the metabolic state of the cell, the impact of oxidative stress and the extent of neutralization by enzymes (e.g., glutathione peroxidase, catalase, peroxiredoxins), or the activation of proteases (e.g., cross talk between caspases and calpain, or lysosomal cathepsins). Elucidating the signaling pathways as to how chemicals interfere with cell signaling and cross talk, as well as recent advances in understanding the formation of drug-derived reactive intermediates, covalent binding, oxidative stress, and activation of cellular pathways of proliferation and death are all relevant to understanding the mechanism of action of drugs on cellular targets (6,10,11). There is also a need for long-term in vitro models to investigate injury and to study the reversibility and repair processes following drug exposure. In vitro models that incorporate the various cell types—organotypic cultures that mimic in vivo complexity or multi-organ co-cultures—should be more successful in identifying the mechanisms of organ injury that have in vivo relevance. In order to discriminate compounds that are similar in cytotoxicity profiles, more relevant data can be generated with organotypic cultures than single-cell cultures. The expectation is that such models will more successfully identify the pertinent mechanisms about organ-specific toxicities in vivo. Use of gene expression profiling studies in elaborate human organotypic models earlier in the development process will help identify the true profile of the drug candidate more reliably, and thus add value to the safety decision process (12,13). One of the challenges of toxicogenomic studies that use in vitro models is how to detect inter-individual variations in drug-metabolizing ability. From a technical standpoint, cross-species comparison of transcriptomics also remains fraught with technical limitations (14). Species-specific microarrays exist for both rat and human; however, comparable genes may not be on the microarray chip for each species. Furthermore, a number of signaling events cannot be monitored by gene expression due to post-transcriptional modifications. Hence, endpoints assessing functional competence of cells/tissues must complement the gene expression work. New research opportunities to integrate the new technologies, including toxicogenomics, proteomics, and metabolomics, along with cell function and morphology will influence future mechanistic studies for preclinical drug safety evaluation. The major impacts of the new technologies will include: (i) identifying the underlying molecular and biochemical mechanism(s) of specific adverse effects to better characterize and evaluate drug safety; (ii) identifying specific target organ injury biomarkers that are predictive (or diagnostic) of clinically relevant pathology; (iii) improved comparison of differences in species sensitivity and susceptibility to drug-induced toxicity to better define and predict variability of human responses. 3. VALUE OF IN VITRO MODELS In vitro models help gain insight into the mechanisms of drug-induced cell stress or insult leading to organ injury, as well as compare species susceptibilities. An additional advantage of in vitro models is that less compound is required to obtain a mechanistic insight

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into toxicity, which would allow application of such models earlier in the drug candidate selection process. In vitro models vary in complexity, number of cell types present, and the state of differentiation. Some of these models may be useful for screening purposes, while the more complex organotypic models mimicking in vivo tissues have the greatest potential to identify mechanistic, diagnostic, or predictive biomarkers of organ injury. Organs involve many cell types, mediators, and cell–cell and cell–matrix interactions. Organ cellular diversity, as demonstrated by the liver, reveals the presence of at least 15 different cell types. Hepatocytes are the major cell type, comprising 60% of the total cells, whereby 80% of the volume is in contact with either bile canaliculi epithelia or the perisinusoidal space (which contains endothelial cells). Other cell types in the liver include Kupffer cells, stellate cells, lymphocytes, progenitor cells, fibroblasts, and smooth muscle cells (15). Exposure to drugs can lead to the activation of these various cells. For example, liver Kupffer cells represent about 14% of the hepatic cellular mass and secrete a wide array of biologically active molecules such as nitric oxide, fibronectin, inflammatory cytokines, growth factors, bioactive lipids, hydrolytic enzymes, reactive oxygen, and nitrogen intermediates. The consequences of Kupffer cell activation can either be the release of pro-inflammatory mediators and activation of death signaling pathways, or may result in a dampening of response by the release of anti-inflammatory cytokines and activation of survival pathways. Although Kupffer cells play an important role in the normal physiology and homeostasis of the liver, their response to various drug-induced stress and the effects on hepatocytes, stellate cells, biliary cells, and neutrophils will be the key to understanding mechanisms of liver injury (16,17). Organ slices as in vitro models represent a three-dimensional multicellular, complex system with structural and functional features of in vivo tissue. The cell–cell and cell–extracellular matrix interactions maintain cell differentiation and regulate organ function to changes in the microenvironment. Cell models with combined matrices or co-culturing cell models, such as hepatocytes with non-parenchymal cells, demonstrate the recapitulation of tissue function and differentiation in three-dimensional environments (18,19). Various in vitro models can also be used to study drug uptake and metabolism. For example, purified membrane preparations as well as Xenopus oocytes can be used to study the kinetics of drug uptake and efflux. Stably transfected cell lines expressing a specific transporter are also useful for these types of study. Studies with Xenopus oocytes involve microinjection of specific transporter mRNA that is translated in the cytoplasm followed by targeting of the transporter to the membrane. 4. TOXICOGENOMICS IN TRANSLATIONAL RESEARCH—ANIMAL AND HUMAN MODELS (IN VIVO AND IN VITRO) In vivo models provide the best approximation of the complexity of the human body and its countless cellular, biochemical, and molecular systems involved in drug and chemical activity and metabolism. In contrast to the in vivo complexity or even to intact tissues that are heterogeneous complexes of different cell types, in vitro models such as cell cultures or tissue slices provide a pure system in which transcriptomic studies can be performed. 4.1. Rat In Vivo Model Toxicogenomic studies initially focused on hepatotoxicity in rat models because of the enormous literature and the historical data that exist. Liver samples collected from rat

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in vivo studies were profiled for gene expression. The gene expression data is typically anchored to clinical chemistry or histopathology data. These studies have demonstrated the feasibility of using compound-specific transcriptional profiling to identify toxicity alerts early, that is, prior to changes in histopathology. For example, peroxisome proliferators produce similar gene expression profiles, yet this profile is different from the profile produced by other chemicals such as enzyme inducers (20). The real utility of toxicogenomics is its use in identifying specific gene expression patterns that distinguish different subtypes of toxicity. For example, rat in vivo studies associated with different histopathological endpoints of hepatotoxicity (microvesicular lipidosis, hyperlipidemia, inflammation, necrosis, bile duct hyperplasia, and fibrosis) show different gene expression profiles (21–27). Even the severity of hepatotoxicity can be defined from the pathways deduced from gene expression profiles (21). Transcriptomics may also reveal target versus non-target effects of compounds. For example, the effects of methapyrilene on liver are associated with thousands of gene expression changes whereas the kidney, a non-target tissue, has few gene expression changes (28). Also, high single doses of model compounds can elicit gene expression changes to discriminate acute hepatotoxic from nonhepatotoxic compounds (29). Parallel studies have been performed using liver preparations in vitro to provide insight into mechanisms of cell injury and death (30,31). 4.2. Cell Lines as In Vitro Model Using the human liver cell line HepG2, some initial studies demonstrated that toxicogenomic profiling could discriminate classes of compounds such as cytotoxic anti-inflammatory drugs, DNA-damaging agents, and hepatocarcinogens (32,33). Studies have also helped gain insight into the mechanism of toxicity. For example, gene expression pathways have been linked with the induction of drug-induced phospholipidosis, alteration in lysosomal function and cholesterol metabolism (34). The HepG2 responses should be further compared to other in vitro models to determine whether responses are similar across different systems and whether the HepG2 model could be a screening model for certain types of toxicities. While choosing cell lines, considerations should be given to models that express drug metabolizing enzymes and transporters. Xenosensor nuclear receptors like pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are integral to the regulation of CYP2B, CYP3A, glucuronyl transferases, glutathione-S-transferases, and some clinically important transporters such as MDR1 and MRP2. These nuclear receptors play a major role in the metabolism of drugs and drug-drug interactions, but they are strongly downregulated or absent in cell lines such as HepG2 (35). Cryopreserved human hepatocytes have been used to determine the relative importance of specific uptake transporters (36). To elucidate the contribution of transporters to drug metabolism, excretion, and organ injury, a model that maintains the physiological expression of most if not all transporters in a polarized localization is the liver slice model (described below). 4.3. Rat Hepatocytes as In Vitro Model Primary hepatocytes are isolated from freshly isolated liver. They exhibit a greater extent of cellular differentiation and functionality than cell lines. As the major cell type of the liver, hepatocytes are metabolically competent, providing the opportunity to study toxicity in conjunction with drug metabolism and disposition to develop an integrated understanding of the relationship between metabolism and organ injury. This is valuable for understanding mechanisms and comparing species differences of hepatotoxicity.

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Both the isolation and culturing procedures for hepatocytes induce stress leading to physiological and morphological responses. Such changes include changes in shape from a cuboidal differentiated form to a more flattened dedifferentiated structure that represents an adaptation to the changed environment in order to preserve cell viability. This adaptation results in the induction and repression of genes that respond to cell survival in the new environment. In general, the cytochrome P450 enzymes are downregulated, which in part can be preserved via the addition of ligands, such as dexamethasone, to the culture medium. The transition of hepatocytes from the quiescent environment of the intact liver to an artificial culture environment is reflected in adaptive response that includes changes such as transcriptional changes in transporter genes, glutathione metabolism, a shift from gluconeogenesis to glycolysis, the advancement of hepatocytes through the cell cycle, and alterations in cytoskeleton and extracellular matrix genes to reflect the severing and then reestablishing of cellular contacts (37,38). Gene expression patterns change most rapidly following cell isolation and the initiation of the culture, yet stabilize in culture with time (39). Additional studies have shown that hepatocytes display differences in gene expression patterns which are dependent on the attachment status, as well as differences between cold storage and cryopreservation compared to monolayer cultures (38,40). Hepatocyte profiling following exposure to hepatotoxic and non-hepatotoxic drugs has had success via the utilization of databases to characterize the compounds (37,39,40). Even though hepatocyte profiles may vary from in vivo liver samples, in vitro signatures which predict in vivo pathologies can be acquired, thereby adding value to the prioritization of early stage compounds without the need for large-scale chemical syntheses (41). Evaluation of hepatotoxicity using toxicologically relevant biomarkers, specifically oxidative stress-related markers including manganese-superoxide dismutase (Mn-SOD), has shown that there is a good correlation between in vitro and in vivo test systems in predicting hepatotoxicity (42). Specific gene signatures discriminating aryl hydrocarbon receptor agonists and peroxisome proliferator-activated receptor agonists have been identified in rat hepatocytes (43). Some of the observed gene expression changes, such as that of CYP2B and 3A1 in rat primary hepatocytes, are in good agreement with the effects in livers of treated animals (44). Gene expression signatures may also help understand idiosyncratic reactions in man. For example, compounds eliciting oxidative stress gene expression signatures (reflecting Nrf2 activation) and covalent binding may identify drugs with a high potential for idiosyncratic reactions (45). However, in order to provide better insight into the mechanism of drug-induced organ injury, more comprehensive gene expression databases covering more toxicity types and more compound differences are needed (46). 4.4. Human Hepatocytes as In Vitro Model Prediction of hepatotoxicity at the early stages of drug development is initiated by comparing candidate drug-induced gene expression profiling in human and animal hepatocytes (31,40,47,48). A comparison of human hepatocyte exposure and gene expression response with Aroclor 1254 revealed altered expression of predominantly xenobiotic defense enzymes, which agreed well with previous findings using rat hepatocyte cultures (9,49). Human variations are evident in the basal gene expression profiles. Human liver tissue and freshly isolated hepatocytes are reported to be distinctively different from one culture to another, regardless of the culture conditions (38). A greater variation in donor heterogeneity, as well as the quality of the tissue, contributes more to the variability in gene expression profiles in the in vitro models than variation due to the technology (47,48,50).

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4.5. Liver Slices as In Vitro Model Organ slices are a particularly promising model for in vitro toxicogenomic studies. The multicellular architecture and extracellular matrix composition of organ slices maintain the biologically relevant structural and functional features of in vivo tissue. Organ slices derived from liver and extra-hepatic organs of various species broaden the capability to investigate organ injury beyond, or in conjunction with, liver and to address species differences. Comparison of gene expression patterns in rat liver slices and hepatocytes following exposure to hepatotoxicants or inducers reveals ≥ 80% concordance with in vivo liver gene expression patterns (51). Liver slices exhibit the strongest similarity to in vivo liver as compared to hepatocytes in conventional monolayer or sandwich cultures (39,51). The similarity of organ slices to in vivo tissue is due to the fact that organ slices possess the tissue architecture, various cell types, and extracellular matrix interactions of in vivo tissue. Differences in liver slice gene expression patterns compared to hepatocytes or whole liver include the expression of collagens, other structural proteins, inflammatory mediators, and cytokines, which are greatly reduced in hepatocytes (39). The quality of studies using organ slices can be further improved by ensuring the quality of organ slices and improving their preservation conditions and viability (52,53). RNA quality and integrity, which are critical for microarray toxicogenomic studies, correlate with organ slice viability (52–54). The increased complexity of liver slice function is reflected by the efficient uptake of bile acids and stable expression of genes relevant for bile acid metabolism and transport, cytochromes P450, and various transcription factors (55). Interruption of bile acid transport is linked with some clinical symptoms of hepatotoxicity, and liver slices may prove to be a suitable model to investigate effects on bile production and elimination. In contrast, hepatocyte cell lines like HepG2 only express a partial array of proteins found in normal differentiated hepatocytes, and primary isolated hepatocytes exhibit rapid decreases in the expression levels of transport proteins. It has also been demonstrated in human liver slices that bile acid production does not induce proinflammatory cytokines (55). A mechanistic study to understand the safety concerns of mitochondrial dysfunction, lipid accumulation, inflammation, and necrosis induced by compounds which inhibited fatty acid oxidation was characterized in rat and human liver slices and compared to in vivo rat liver samples (54). Mitochondrial dysfunction was evident in vitro by decreased ATP levels, and mitochondrial injury was verified by ultrastructural mitochondrial changes in vitro and in vivo. Prior to changes in morphology, an upregulation of mitochondrial and peroxisome genes involved in β-oxidation was evident in liver slices and rat liver. Oxidative stress contributed to the mitochondrial dysfunction by a decline in glutathione (GSH) levels and an upregulation of oxidative stress associated genes. Progression of mitochondrial dysfunction was marked by the induction of apoptosis and inflammatory response. In liver slices, the activation of the endothelial cell pro-inflammatory cytokine IL-8 was marked by an upregulation of gene and protein expression. Both human and rat liver slices exhibited similar effects to the inhibitors of mitochondrial β-oxidation, and the liver slices revealed that the mitochondrial injury was associated with apoptosis and inflammation.

4.6. Tissue Repair and Fibrosis Models Tissue repair plays a critical role in the progression of organ injury and is orchestrated by the release of various chemokines, cytokines, growth factors, and the activation of nuclear receptors, leading to a pro-mitogenic gene expression response. Both cell proliferation and the regeneration of extracellular matrix can restore organ architecture and function. Tissue repair

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following organ injury is a dose-dependent process, increasing with dose until a threshold dose is reached, beyond which repair is delayed and impaired. In general, a rapid onset and repair response enhance overall recovery. Assessment of tissue repair following compound exposure or multiple dosing may provide additional mechanistic information, which is valuable for drug selection in the drug development process (56). Repair processes are evident in liver slices by the activation of stellate cells, the proliferation of myofibroblast-like cells, mitosis in hepatocytes, and an increased deposition of collagen. Extracellular matrix changes are evidenced by the upregulation of cytoskeleton, filaments, collagens, and actin genes. Stellate cell activation is marked by the upregulation of marker genes, α-smooth muscle actin and collagen 1a1, and the positive staining for α-smooth muscle actin protein (52). The repair and regenerative response in organ slices (with two-cut surfaces) becomes evident at 48–72 hr of culture in the absence of drug exposure. These findings correspond to the stimulation of liver regeneration pathways following a partial hepatectomy. Liver injury resulting in a repair response has been demonstrated in rat liver slices by a time- and dose-dependent increase in mRNA expression of stellate cell markers following CCl4 exposure of rat liver slices for 16 hr (57). In rat lung slices, fibrinogenesis marked by extracellular matrix accumulation, myofibroblast transdifferentiation, and activation of microvascular endothelial cells are induced following treatment with CdCl2 plus TGF-β1 (58). Markers of hepatic stellate cell activation, collagen and α-smooth muscle actin, are suppressed by PPARγ ligands suggesting the importance of PPARγ in the maintenance of the quiescent hepatic stellate cell phenotype (59). The phenotypic reversal to quiescent hepatic stellate cells results in an increased expression of adipogenic transcription factors such as CCAAT/enhancer-binding proteins (C/EBPα, β, δ, γ), liver X receptor α (LXRα), sterol regulatory element-binding protein1c (SREBP-1c), and increased expression of adipocytespecific genes (PPARγ and SREBP-1c). Adipogenic transcriptional regulation appears to be important in the regulation of hepatic stellate cell quiescence (60). 4.7. Hepatic Inflammation Models The association of inflammatory mediators with enhanced susceptibility to drug toxicity is not unique to the liver and is evident for other organs, including the lung and kidney. Episodes of inflammation that exist at the time of drug therapy could decrease the threshold of organ injury, thereby enhancing organ sensitivity to the drug and its metabolites (61,62). A model for idiosyncratic drug reactions showed that Kupffer cells are activated when rats are treated with bacterial lipopolysaccharide (LPS), which triggers an inflammatory response resulting in the sensitization of the liver. Subsequent exposure of the sensitized liver to a non-hepatotoxic dose of certain drugs can still precipitate a hepatotoxic reaction (61,63). Microarray analysis of rat liver gene expression from such studies identified an upregulation of neutrophil chemokines, such as CINC-1, MIP-2, GM-CSF, indicating activation and accumulation of neutrophils in the liver. Serum protein levels of these chemokines were elevated in the treated animals. Prior depletion of the Polymorphonuclear cells (PMN) by administration of antiserum against the PMN adhesion molecule CD18 protected the liver from such injury or attenuated the severity of the injury (64–66). Liver slices can mimic the LPS-induced inflammatory response in vitro because LPS action activates the Kupffer cells, resulting in increased cytokine release to evoke an inflammatory response. LPS-stimulated rat liver slices show increased levels of TNFα, IL-1β, and IL-10, and an upregulation of inducible nitric oxide synthase (iNOS ) mRNA. Inhibition of TNFα and IL-1β production resulted in a lack of iNOS induction and

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consequently nitric oxide production (50). In human liver slices, iNOS mRNA expression is also induced by LPS. Gene expression studies further revealed that the LPS-induced cholestasis is associated with downregulation of several transporters. The basolateral Na + -dependent taurocholate transporter (Ntcp) mRNA levels are downregulated in both rat and human liver slices following LPS treatment. A species difference was revealed in that the multidrug resistance-associated protein 2 (Mrp2) mRNA was downregulated in the rat but not human liver slices. The canalicular bile salt export pump (Bsep) mRNA was not affected in either species. By measurement of the protein levels, it was revealed that the MRP2 and BSEP protein levels were reduced in human liver slices, suggesting that post-transcriptional mechanisms played a more prominent role in LPS-induced regulation of MRP2 and BSEP compared with the rat transporter proteins (67). Liver biopsies of patients with inflammation-induced icteric cholestasis exhibit no change in MRP2 mRNA levels, yet protein levels are greatly reduced (68). The liver in animals and humans is selectively enriched in Kupffer cells, natural killer (NK) cells, natural killer T (NKT) cells, and neutrophils, which are components of the innate immune system. Hepatic sinusoidal endothelial cells lack a basement membrane, providing these cells access to underlying hepatocytes. Activation and infiltration of NK/NKT cells have been associated with the progression and severity of acetaminophen hepatotoxicity. It is speculated that the depletion of GSH by acetaminophen, which causes oxidative stress, triggers the activation of both the protective mechanisms (Nrf2-mediated) and injurious mechanisms (JNK-mediated) resulting in the release of inflammatory mediators from Kupffer cells and activation of NK/NKT cells. Sensitization of the hepatocytes to the toxic actions by the released mediators of inflammation or infiltrating inflammatory cells will ultimately determine the threshold for toxicity (69). 4.8. Extra-Hepatic Models Gene expression profiling to characterize nephrotoxicants and studies to characterize subtypes of kidney injury have been done with model compounds (70–72). A toxicogenomic approach to identify biomarkers of proximal tubular toxicity and progression has been demonstrated with seven agents (73). Identification of a specific gene expression signature for hemolytic anemia will also help in the early stage of drug discovery and development. 4.8.1. In Vitro Models of Nephrotoxicity A mechanistic in vitro study with cisplatin, a model nephrotoxicant, employed toxicogenomics. Concentration- and time-dependent changes in gene expression in kidney slices preceded an altered change in both function and morphology (53). A comparison of gene expression patterns in rat kidney slices exposed to cisplatin and in intact kidney from an in vivo cisplatin study revealed that similar cellular pathways were affected (53,74). Gene expression changes indicative of transcription, DNA damage, cell cycle, proliferation, and apoptosis were in agreement with the known mechanism of action of cisplatin-induced DNA damage, growth arrest, and apoptosis, while genes indicative of protein damage, disruption of transport and calcium homeostasis, cellular metabolism, and oxidative stress are pathways linked with cisplatin binding to various cellular proteins and macromolecules. In general, the cellular pathways which are disrupted by drug exposure are similar in vitro and in vivo. Some differences between in vitro and in vivo gene expression profiles likely reflect differences in drug exposure, the time point used for analysis, and the extent of injury between the two models. More genes within a given pathway may be evident in vitro

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because of the multitude of concentrations and time points incorporated into a study. Furthermore, the dynamics of gene expression also plays a role as seen by the upregulation of a particular gene within a pathway in one system and its downregulation in another system. The direction of gene expression changes, upregulation versus downregulation, can change at the time point of sample collection and can occur in either the in vitro- or the in vivoderived samples (53). Additional cisplatin studies showed the p53 pathway was upregulated in cisplatin-induced kidney injury; cytokine increases were indicative of neutrophil infiltration and an inflammatory response. Cisplatin-induction of p53 and apoptosis in proximal and tubular cells can be ameliorated in p53-deficient mice or caspase-deficient mice, as well as by caspase inhibitors or a p53 inhibitor, pifithrin-alpha (75–77). Gene expression studies can also reveal the subcellular targets of organ injury. For example, there is increasing evidence that the endoplasmic reticulum (ER) is a subcellular target of nephrotoxicants. The nephrotoxic agents gentamicin, p-aminophenol, and cisplatin have been shown to induce markers of ER stress and ER-mediated cell death in rat kidneys. The XBP1 transcription factor that regulates both the ER-stress response element and the unfolded-protein response element was reported to be upregulated by the three nephrotoxicants. Caspase-12 cleavage and activation, a marker of ER-mediated cell death, was also increased by the nephrotoxicants (78). Biomarkers of kidney injury are emerging thanks to gene expression studies. The kidney injury molecule-1 (Kim-1) gene expression and urinary protein levels are upregulated by renal proximal tubule injury in rats and humans following ischemia and exposure to nephrotoxicants. Kim-1, synthesized in response to renal injury, is detected at lower doses of nephrotoxicants than blood urea nitrogen, serum creatinine, and urinary N-acetyl-β-D-glucosaminidase, and the Kim-1 response levels correlate with the severity of renal histopathology (79). Other biomarkers exhibiting regional differences in cellular status and integrity of both the kidney and the liver, which have been used clinically, include the release of α- and π-glutathione S-transferases (80,81). 4.8.2. In Vitro Models of Hemolysis A variety of pharmaceutical agents cause hemolytic anemia, posing a significant adverse effect and potential restriction of their clinical utility. A toxicogenomic approach to investigate hepatic gene expression changes following administration of the hemolytic agents phenylhydrazine and phenacetin was assessed in parallel with hepatic histopathology, hematology, and blood chemistry data. The hepatic events characteristic of hemolytic anemia included hemoglobin biosynthesis, heme metabolism, and phagocytosis. Six genes were selected as putative biomarkers that are mechanistically linked to the hematotoxicity: Alas2, beta-glo, Eraf, Hmox1, Lgals3, and Rhced (82). An in vitro model of hemolysis using a precision-cut liver slice co-incubated with blood-medium for up to 72 hr has been established for both rat and human. The model can be utilized to screen compounds for drug-induced hemolysis and provide insight into the mechanism of hematotoxic effects. The expression levels of liver marker genes linked with heme degradation and biosynthesis aid in identifying compounds with potential adverse drug reactions and the concentrations at which the hemolytic effect will occur. Reference compounds used to verify the model included the antithyroid agent methimazole (MMI) and the chemical aniline. Both MMI and aniline are known to require metabolism to induce hematotoxicity. Both compounds exhibited a time- and concentration-dependent hemolysis. A depletion of blood glutathione levels, suggestive of oxidative-stress response, was evident prior to hemolysis. Liver genes representing heme homeostasis were upregulated in Kupffer cells prior to hemolysis. Both heme oxygenase 1 (Hmox1) and ferroportin-1

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(Slc40a1) gene expression levels were upregulated by MMI at a time preceding hemolysis. Corresponding with the onset of hemolysis, heme binding protein 23 (prdx1) gene expression was upregulated, followed by an upregulation of the hemoglobin biosynthesis pathway aminolevulinic acid synthase 2 (Alas2) expression. This likely indicates a compensatory mechanism following MMI-induced hemolysis in which the hepatocyte synthesizes more Alas2 to deal with rising levels of iron and the demand for hematopoesis (83,84). 5. CONCLUSION Drug-induced organ injury continues to be a significant clinical issue and it is a major reason for post-marketing labeling or drug withdrawal. New technologies like toxicogenomics provide researchers with a means to better understand the molecular and biochemical cellular pathways leading to drug-induced organ dysfunction, injury, and repair. Application of toxicogenomics to animal and human in vitro models enhances the comparison of species susceptibility and the evaluation of human response. Furthermore, it is possible to address whether the mechanism of the side effect is similar in humans compared to animals, and at which concentration the drug-induced side effects will occur in humans compared to animals. Organ injury covers a broad spectrum of cellular processes, cell types, and regions within an organ. Organotypic models exhibit the structural complexity and functional features of in vivo tissues for better assessment of pathways, cellular networks, and interactions underlying drug-induced injury. Combining toxicogenomics with such models reveal time- and concentration-dependent effects on sub-cellular targets and organelles and the molecular pathways involved in organ injury. In summary, toxicogenomics applied to preclinical animal and human models will provide insight into the mechanisms of organ injury and provide more information for drug candidate selection. It will augment correct extrapolation and translation of animal and human in vitro results into clinical applications and predictions and also identify sensitive and discriminating biomarkers of organ injury. REFERENCES 1. Olson H, Betton G, Robinson D, et al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regulatory Toxicol Pharmacol 2000; 32:56–67. 2. Greaves P, Williams A, Eve M. First dose of potential new medicines to humans: how animals help. Nat Rev Drug Discov 2004; 3:226–236. 3. Peters TS. Do preclinical testing strategies help predict human hepatotoxic potentials? Toxicol Pathol 2005; 33:146–154. 4. Aldridge JE, Gibbon JA, Flaherty MM, et al. Heterogeneity of toxicant response: sources of human variability. Toxicol Sci 2003; 76:3–20. 5. Fung M, Thornton A, Mybeck K, et al. Evaluation of the characteristics of safety withdrawal of prescription drugs from worldwide pharmaceutical markets- 1960 to 1999. Drug Info J 2001; 35:293–317. 6. Guengerich FP. Mechanisms of cytochrome P450 substrate oxidation: MiniReview. J Biochem Mol Toxicol 2007; 21:163–168. 7. Seed J, Carney EW, Corley RA, et al. Overview: using mode of action and life stage information to evaluate the human relevance of animal toxicity data. Crit Rev Toxicol 2005; 35:663–672. 8. Huang S-M, Goodsaid F, Rahman A, et al. Application of pharmacogenomics in clinical pharmacology. Toxicol Mechan Meth 2006; 16:89–99. 9. Thum T, Borlak J. Detection of early signals of hepatotoxicity by gene expression profiling studies with cultures of metabolically competent human hepatocytes. Arch Toxicol 2008; 82:89–101.

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10. Van deWater B, de Graauw M, LeDévédec S, et al. Cellular stress responses and molecular mechanisms of nephrotoxicity. Toxicol Lett 2006; 162:83–93. 11. Gunawan BK, Kaplowitz N. Mechanisms of drug-induced liver disease. Clin Liver Dis 2007; 11:459–475. 12. Farkas D, Tannenbaum SR. In vitro methods to study chemically-induced hepatotoxicity: a literature review. Curr Drug Metab 2005; 6:111–125. 13. Vickers AE, Fisher R. Precision-cut organ slices to investigate target organ injury. Expert Opin Drug Metab Toxicol 2005; 1:687–699. 14. Mattes WB. Cross-species comparative toxicogenomics as an aid to safety assessment. Expert Opin Drug Metab Toxicol 2006; 2:859–874. 15. Malarkey DE, Johnson K, Ryan L, et al. New insights into functional aspects of liver morphology. Toxicol Pathol 2005; 33:27–34. 16. Laskin DL, Weinberger B, Laskin JD. Functional heterogeneity in liver and lung macrophages. J Leukocyte Biol 2001; 70:163–170. 17. Roberts RA, Ganey PE, Ju C, et al. Role of the Kupffer cell in mediating hepatic toxicity and carcinogenesis. Toxicol Sci 2007; 96:2–15. 18. Guillouzo A, Morel F, Ratanasavanh H, et al. Long-term culture of functional hepatocytes. Toxicol In Vitro 1990; 4:415–427. 19. Schmeichel KL, Bissell MJ. Modeling tissue-specific signaling and organ function in three dimensions. J Cell Sci 2003; 116:2377–2388. 20. Hamadeh HK, Bushel PR, Jayadev S, et al. Gene expression analysis reveals chemical-specific profiles. Toxicol Sci 2002; 67:219–231. 21. Huang Q, Jin X, Gaillard ET, et al. Gene expression profiling reveals multiple toxicity end points induced by hepatotoxicants. Mut Res 2004; 549:147–167. 22. Steiner G, Suter L, Boess F, et al. Discriminating different classes of toxicants by transcript profiling. Environ Health Perspect 2004; 112:1236–1248. 23. Heijne WH, Slitt AL, vanBladeren PJ, et al. Bromobenzene-induced hepatotoxicity at the transcriptome level. Toxicol Sci 2004; 79:411–422. 24. Heijne WH, Lamers RJ, vanBladeren PJ, et al. Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis. Toxicol Pathol 2005; 33:425–433. 25. Ruepp S, Boess F, Suter L, et al. Assessment of hepatotoxic liabilities by transcript profiling. Toxicol Appl Pharmacol 2005; 207:161–170. 26. Beyer RP, Fry RC, Lasarev MR, et al. Multicenter study of acetaminophen hepatotoxicity reveals the importance of biological endpoints in genomic analyses. Toxicol Sci 2007; 99:326–337. 27. Lum PY, He YD, Slatter JG, et al. Gene expression profiling of rat liver reveals a mechanistic basis for ritonavir-induced hyperlipidemia. Genomics 2007; 90:464–473. 28. Auman JT, Chou J, Gerrish K, et al. Identification of genes implicated in methapyrilene-induced hepatotoxicity by comparing differential gene expression in target and nontarget tissue. Environ Health Perspect 2007; 115:572–578. 29. Zideck N, Hellmann J, Kramer P-J, et al. Acute hepatotoxicity: a predictive model based on focused illumina microarrays. Toxicol Sci 2007; 99:289–302. 30. DeLongueville F, Atienzar FA, Marcq L, et al. Use of a low-density microarray for studying gene expression patterns induced by hepatotoxicants on primary cultures of rat hepatocytes. Toxicol Sci 2003; 75:378–392. 31. Castro RE, Solá S, Ma X, et al. A distinct microarray gene expression profile in primary rat hepatocytes incubated with ursodeozycholic acid. J Hepatol 2005; 42:897–906. 32. Burczynski ME, McMillian M, Ciervo J, et al. Toxicogenomics-based discrimination of toxic mechanism in HepG2 human hepatoma cells. Toxicol Sci 2000; 58:399–415. 33. Harries HM, Fletcher ST, Duggan CM, et al. The use of genomics technology to investigate gene expression changes in cultured human liver cells. Toxicol In Vitro 2001; 15:399–405. 34. Sawada H, Takami K, Asahi S. A toxicogenomic approach to drug-induced phospholipidosis: analysis of its induction mechanism and establishment of a novel in vitro screening system. Toxicol Sci 2005; 83:282–292.

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35. Pascussi JM, Gerbil-Chaloin S, Drocourt L, et al. The expression of CYP2B6, CYP2C9 and CYP3A4 genes: a tangle of networks of nuclear and steroid receptors. Biochem Biophys Acta 2003; 1619:146–154. 36. Hirano M, Maeda K, Shitara Y, et al. Contribution of OATP2 (OATP1B1) and OATP8 (OATP1B3) to the hepatic uptake of pitavastatin in humans. J Pharmacol Exp Therap 2004; 311:139–146. 37. Baker TK, Carfagna MA, Gao H, et al. Temporal gene expression analysis of monolayer cultured rat hepatocytes. Chem Res Toxicol 2001; 14:1218–1231. 38. Richert L, Liguori MJ, Abadie C, et al. Gene expression in human hepatocytes is suspension after isolation is similar to the liver of origin, is not affected by hepatocyte cold storage and cryopreservation, but is strongly changed after hepatocyte plating. Drug Metab Disp 2006; 34:870–879. 39. Boess F, Kamber M, Romer S, et al. Gene expression in two hepatic cell lines, cultured primary hepatocytes, and liver slices compared to the in vivo liver gene expression in rats: possible implications for toxicogenomics use of in vitro systems. Toxicol Sci 2003; 73:386–402. 40. Waring JF, Ciurlionis R, Jolly RA, et al. Isolated human hepatocytes in culture display markedly different gene expression patterns depending on attachment status. Toxicol In Vitro 2003; 17:693–701. 41. Fielden MR, Kolaja KL. The state of the art in predictive toxicogenomics. Curr Opin Drug Discov Dev 2006; 9:84–91. 42. Kikkawa R, Fujikawa M, Yamamoto T, et al. In vivo hepatotoxicity study of rats in comparison with in vitro hepatoxicity screening system. J Toxicol Sci 2006; 31:23–34. 43. Yang Y, Abel SJ, Ciurlionis R, et al. Development of a toxicogenomics in vitro assay for the efficient characterization of compounds. Pharmacogenomics 2006; 7:177–186. 44. Boess F, Durr E, Schaub N, et al. An in vitro study on 5-HT6 receptor antagonist induced hepatotoxicity based on biochemical assays and toxicogenomics. Toxicol In Vitro 2007; 7:1276–1286. 45. Leone AM, Kao LM, McMillian MK, et al. Evaluation of felbamate and other antiepileptic drug toxicity potential base on hepatic protein covalent binding and gene expression. Chem Res Toxicol 2007; 20:600–608. 46. Hultin-Rosenberg L, Jagannathan S, Nilsson KC, et al. Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes. Xenobiotica 2006; 36:1122–1139. 47. Harris AJ, Dial SL, Casciano DA. Comparison of basal gene expression profiles and effects of hepatocarcinogens on gene expression in cultured primary human hepatocytes and HepG2 cells. Mut Res 2004; 549:79–99. 48. Liguori MJ, Anderson LM, Bukofzer S, et al. Microarray analysis in human hepatocytes suggests a mechanism for hepatotoxicity induced by trovafloxacin. Hepatology 2005; 41:177–186. 49. Borlak J, Thum T. Induction of nuclear transcription factors, CYP monooxygenases and GSTA2 gene expression in Aroclor 1254 treated rat hepatocyte cultures. Biochem Pharmacol 2001; 61:145–153. 50. Olinga P, Merema MT, de Jager MH, et al. Rat liver slices as a tool to study LPS-induced inflammatory response in the liver. J Hepatology 2001; 35:187–194. 51. Jessen BA, Mullins JS, de Peyster A, et al. Assessment of hepatocytes and liver slices as in vitro test systems to predict in vivo gene expression. Toxicol Sci 2003; 75:208–222. 52. Vickers AE, Saulnier M, Cruz E, et al. Organ slice viability extended for pathway characterization: an in vitro model to investigate fibrosis. Toxicol Sci 2004a; 82:534–544. 53. Vickers AE, Rose K, Fisher R, et al. Kidney slices of human and rat to characterize cisplatininduced injury on cellular pathways and morphology. Toxicol Pathol 2004b; 32:577–590. 54. Vickers AE, Bentley P, Fisher RL. Consequences of mitochondrial injury induced by pharmaceutical fatty acid oxidation inhibitors is characterized in human and rat liver slices. Toxicol In Vitro 2006; 20:1173–1182. 55. Jung D, Elferink MGL, Stellaard F, et al. Analysis of bile acid-induced regulation of FXR target genes in human liver slices. Liver Int 2007; 1:137–144. 56. Mehendale HM. Tissue repair: an important determinant of final outcome of toxicant-induced injury. Toxicol Pathol 2005; 33:41–51.

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57. Van de Bovenkamp M, Groothuis GMM, Draaisma AL, et al. Precision-cut liver slices as a new model to study toxicity-induced hepatic stellate cell activation in a physiologic milieu. Toxicol Sci 2005; 85:632–638. 58. Kasper H-U, Dries V, Drebber U, et al. Precision cut tissue slices of the liver as morphological tool for investigation of apoptosis. In Vivo 2005; 19:423–432. 59. Hazra S, Miyahara T, Rippe RA, et al. PPAR gamma and hepatic stellate cells. Comp Hepatol 2004; 3:S7. 60. She H, Xiong S, Hazra S, et al. Adipogenic transcriptional regulation of hepatic stellate cells. J Biol Chem 2005; 280:4959–4967. 61. Roth RA, Luyendyk JP, Maddox JF, et al. Inflammation and drug idiosyncrasy—Is there a connection? J Pharmacol Exp Therap 2003; 307:1–8. 62. Ganey PE, Luyendyk JP, Maddox JF, et al. Adverse hepatic drug reactions: inflammatory episodes as consequence and contributor. Chem Biol Interact 2004; 150:35–51. 63. Liguori MJ, Waring JF. Investigations toward enhanced understanding of hepatic idiosyncratic drug reactions. Expert Opin Drug Metab Toxicol 2006; 2:835–846. 64. Deng X, Stachlewitz RF, Liguoro MJ, et al. Modest inflammation enhances diclofenac hepatotoxicity in rats: role of neutrophils and bacterial translocation. J Pharmacol Exp Therap 2006; 319:1191–1199. 65. Waring JF, Liguori MJ, Luyendyk JP, et al. Microarray analysis of lipopolysaccharide potentiation of trovafloxacin-induced liver injury in rats suggest a role for proinflammatory chemokines and neutrophils. J Pharmacol Exp Therap 2006; 316:1080–1087. 66. Deng X, Luyendyk JP, Zou W, et al. Neutrophil interaction with the hemostatic system contributes to liver injury in rats cotreated with lipopolysaccharide and ranitidine. J Pharmacol Exp Therap 2007; 322:852–861. 67. Elferink MG, Olinga P, Draaisma AL, et al. LPS-induced down regulation of MRP2 and BSEP in human liver is due to a posttranscriptional process. Am J Physiol Gastrointest Liver Physiol 2004; 287:G1008-G1016. 68. Zollner G, Fickert P, Zenz R, et al. Hepatobiliary transporter expression in percutaneous liver biopsies of patients with cholestatic liver diseases. Hepatology 2001; 33:633–646. 69. Liu Z-X, Kaplowitz N. Role of innate immunity in acetaminophen-induced hepatotoxicity. Expert Opin Drug Metab Toxicol 2006; 2:493–503. 70. Huang Q, Dunn RT 2nd, Jayadev S, et al. Assessment of cisplatin-induced nephrotoxicity by microarray technology. Toxicol Sci 2001; 63:196–207. 71. Amin RP, Vickers AE, Sistare F, et al. Identification of putative gene-based markers of renal toxicity. Environ Health Perspect 2004; 112:465–479. 72. Fielden MR, Pearson C, Brennan R, et al. Preclinical drug safety analysis by chemogenomic profiling in the liver. Am J Pharmacogenomics 2005; 5:161–171. 73. Thukral SK, Nordone PJ, Hu R, et al. Prediction of nephrotoxicant action and identification of candidate toxicity-related biomarkers. Toxicol Pathol 2005; 33:343–355. 74. Thompson KL, Afshari CA, Amin R, et al. Identification of platform-independent gene expression markers of cisplatin nephrotoxicity. Environ Health Perspect 2004; 112:488–494. 75. Wei Q, Dong G, Yang T, et al. Activation and involvement of p53 in cisplatin-induced nephrotoxicity. Am J Physiol Renal Physiol 2007; 293:1282–1291. 76. Yano T, Itoh Y, Matsuo M, et al. Involvement of both tumor necrosis factor-alpha-induced necrosis and p53-mediated caspase-dependent apoptosis in nephrotoxicity of cisplatin. Apoptosis 2007; 12:1901–1909. 77. Faubel S, Lewis EC, Rezmikov L, et al. Cisplatin-induced acute renal failure is associated with an increase in the cytokines interleukin IL-1β, IL-18, IL-6, and neutrophil infiltration in the kidney. J Pharmacol Exp Therap 2007; 322:8–15. 78. Peyrou M, Hanna PE, Cribb AE. Cisplatin, gentamicin and p-aminophenol induce markers of endoplasmic reticulum stress in the rat kidneys. Toxicol Sci 2007; 99:346–353. 79. Zhou Y, Vaidya VS, Brown RP, et al. Comparison of kidney injury molecule-1 and other nephrotoxicity biomarkers in urine and kidney following acute exposure to gentamicin, mercury, and chromium. Toxicol Sci 2008; 101:159–170.

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80. Trull AK, Facey SP, Rees GW, et al. Serum α-glutathione S-transferases: a sensitive marker of hepatocellular damage associated with acute liver allograft rejection. Transplantation 1994; 58:1345–1351. 81. Vickers AE, Alegret M, Jimenez RM, et al. Changes in human liver and kidney slice function related to potential side-effects in the presence of biotransformation of four cyclosporine derivatives- CSA, IMM, OG, PSC. In Vitro Mol Toxicol 1998; 11:119–132. 82. Rokushima M, Omi K, Araki A, et al. A toxicogenomic approach revealed hepatic gene expression changes mechanistically linked to drug-induced hemolytic anemia. Toxicol Sci 2007; 95:474–484. 83. Sinclair JR, Way W, Vickers AE. Evaluating drug-induced hemolysis effects using whole bloodliver slice co-culture model. The Toxicologist 2007; 249. 84. Vickers AE, Fisher RL, Sinclair JR, et al. Human in vitro hemolysis model to assess drug-induced hemolytic effects. The Toxicologist 2008; 102:2083.

6 Mining of Genome Sequence Databases to Identify New Targets for Prostate and Breast Cancer Therapy Tapan K. Bera and Byungkook Lee Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A.

1. INTRODUCTION 2. IDENTIFICATION OF DISEASE-RELATED GENES BY DATABASE MINING 2.1. Identification of NGEP as a Therapeutic Target for Prostate Cancer Utilizing the EST Database 2.2. Identification of POTE Gene Family by Using EST and Genome Sequence Database 2.3. Identification of ANKRD26 as an Ancestral Gene for POTE by Comparative Genomics 2.4. TARP: A New Gene Transcribed from the T Cell Receptor Gamma Gene Locus 2.5. Identification of PAGE-4: A Cancer-Testis (CT) Antigen, Using the EST Database 2.6. Identification of XAGE Gene Family by Homology Walking Using the PAGE-4 Sequence 2.7. Identification of MRP-8 and MRP-9 Utilizing the EST and the Genome Sequence Databases 2.8. Generation of ESTs from Membrane-Bound Polyribosomal RNA to Identify Genes Encoding Membrane Proteins 2.9. Identification of CAPC from MAPcL EST Database 3. CONCLUDING REMARKS REFERENCES

192 192 195 198 198 199 200 201 201 201 202 203 203

The opinions expressed in this chapter are the author’s personal opinions, and they do not necessarily reflect those of the NIH, DHHS, or the Federal Government.

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1. INTRODUCTION The availability of the genome sequences of several organisms has opened up an enormous opportunity for researchers to study and identify protein and other therapeutic targets for a wide range of diseases, including cancer. The Human Genome Project (HGP), which includes the expressed sequence tag (EST) database and the genome sequence database, provides detailed information about the structure, organization, and function of the identified human genes. Initial estimates of the HGP predicted approximately 30,000–40,000 protein-coding genes in the human genome, but that number has since been revised to about 20,000–25,000 protein-coding genes. However, there are many unidentified nonprotein-coding genes in the genome, and it could be years before a truly reliable gene count can be determined. Expression-based sequence databases such as EST and serial analysis of gene expression (SAGE) tag provide a unique opportunity to obtain information on as-yet-unidentified genes that are transcribed by the human genome. At the same time, a tremendous effort has been devoted to develop strategies by which these databases can be mined efficiently to identify the hidden therapeutic treasures. We have utilized the EST and genome sequence databases, selected bioinformatic tools, and several experimental methods to identify tissue-specific genes for prostate and breast cancers. The genes that are identified can be used as novel targets for the diagnosis and treatment of prostate and breast cancers. Prostate cancer is a major public health problem and the second leading cause of death for men in the United States. According to the American Cancer Society (ACS) estimate (http://www.cancer.org/docroot/home/index.asp), there were about 220,000 new cases of prostate cancer in the United States in 2007, from which about 27,000 people will die from this disease. Breast cancer, on the other hand, is the most common type of epithelial cancer among women in the United States. An estimated 180,000 women were diagnosed with breast cancer during the same period, from which about 40,000 will die from this disease (1). At present, there are no curative therapies available for prostate or breast cancers after the cancer has metastasized from its site of origin. Thus, there is an urgent need for identifying new targets for prostate and breast cancers, which can be used as early detection markers or for targeted therapy.

2. IDENTIFICATION OF DISEASE-RELATED GENES BY DATABASE MINING Several experimental methods have been used to identify genes that are selectively expressed in a particular cancer. These include differential display (2), subtractive hybridization (3), serial analysis of gene expression (4), microarray analysis (5), and serological identification of antigens by recombinant expression cloning (SEREX) analysis (6). In recent years, publicly available genome sequence databases, as well as curated information on individual genes, provide opportunities for researchers to identify and validate new targets for cancer research (7). The description and the URL for some of these online resources are summarized in Table 1. Our laboratory is interested in developing antibody-based targeted therapies for the treatment of cancer (8). Ideal targets for the immuno-based therapy of cancers are proteins that are expressed on cancer cells at high levels but not on any essential normal tissues like the heart, brain, liver, kidney, or others. The target proteins must also be located on the cell surface, where they would be accessible to antibodies or antibody-based therapeutics. We have utilized various publicly available databases to identify, validate, and characterize several candidate genes that could be used as targets for immuno-based therapy of

New Targets for Immuno-based Therapy Table 1

Online Resources on Various Databases

Site name

URL address

UniGene

http://www.ncbi.nlm.nih.gov/ UniGene/ http://www.ncbi.nlm.nih.gov/ entrez/query.fcgi?db = OMIM http://www.genecards.org/

OMIM GeneCard CGAP SAGE database BLAT

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Description

Cluster of EST and cDNA sequences for a particular gene Genes and genetic disorders linked to specific chromosomal loci Database of collective information on human genes and protein products www.ncbi.nlm.nih.gov/ncicgap Gene expression profile in various cancers www.ncbi.nlm.nih.gov/SAGE Comparative analysis of gene expression in different tissues http://genome.cse.ucsc.edu/cgi-bin/ Alignment of a specific gene into the hgBlat genome

prostate and breast cancers. We also employed various strategies to look for EST clusters that represent genes encoding membrane proteins. A comprehensive list of genes identified by utilizing EST and genome sequence databases is described in Table 2. The Cancer Genome Anatomy Project (CGAP) along with other consortia supports sequencing of ESTs from many cancers and their corresponding normal tissues. An EST is a short sub-sequence of a transcribed, spliced RNA from protein-coding or noncoding transcript. ESTs are typically produced by one-shot sequencing of a cloned cDNA and the resulting sequences are often a relatively low quality sequence whose length is limited by current technology to approximately 500–800 nucleotides. To search for new genes and the proteins they encode, we utilized the human EST database (which now contains the sequences of over 8 million ESTs) for DNA sequences that have the following properties: (i) expressed in prostate cancers, (ii) not associated with known genes, and (iii) not expressed in any essential organ or tissue. These sequences were then arranged into clusters based on sequence identity, and we generated a consensus sequence from the overlapping EST sequences (23). After the human genome sequence (7) became available, the clustering was made on the basis of alignment of the ESTs to the region of the genome and the consensus sequence was taken from the corresponding genomic sequence. We use this consensus sequence to carry out several experiments. First, we design polymerase chain reaction (PCR) primers to determine if the RNA is expressed in essential normal tissues by reverse transcriptase-PCR (RT-PCR), using a panel of normal tissue samples. This is followed by hybridization with a 32P-labeled cDNA probe on multi-tissue dot blots. If no expression in essential tissues is found, we isolate a full-length cDNA after determining the transcript size by performing a northern blot analysis. In addition, we determine the nucleotide sequence of the transcript and that of the predicted protein it encodes. Finally, we examine mRNAs from a set of prostate cancers by RT-PCR to determine if the gene is expressed in these cancers. If expression is frequently observed, we perform in situ hybridization of prostate cancer samples to be certain that the expression is in the cancer cells and not in the extra-cellular matrix or other cell types in the specimens. A flow diagram of the strategy for searching new genes from the EST database is shown in Fig. 1. Using this approach, we have identified several new genes, a few of which encode membrane proteins and are potential candidates for immuno-based therapy. Others are either attractive vaccine candidates or possible candidate genes for further biological studies. We will describe a few of these genes that were identified utilizing the approaches mentioned above.

2q37.3

21q11.2

7p14.1 4q21.1 11q24 17q21 17q21 16q13 Xp11 Xp11.23 16q12.1 16q12.1 16q12.1

20q11.21 9q34.3

NGEP

POTE

TARP GDEP PATE PRAC PRAC2 TEPP PAGE 4 XAGE-1 MRP8 MRP9V1 MRP9V2

BASE CAPC

19.5 37

7 4.0 & 7.1 14 6.0 10.5 30.7 16 9 150 100 20

66

14 & 95

Protein (kDa)

+ – – + – – + + + + + +

+ + + + + + + + – + – + – –

–

+

– +

TE

PR

– –

– – – – – + + – – – –

+

–

PL

Breast Retina Adrenal Rectum Rectum Colon – Uterus Lung, Breast Breast, Liver Breast Brain, Ovary, SK muscle Salivary gland Breast, Salivary gland

Ovary

–

Other

Note: PR, Prostate; TE, Testis; PL, Placenta; BR, Breast; ND, Not determined.

Location

Normal tissue expression

– +

+ + + + + – + + + ND ND

+

+

PR

ND ND Colon, Pancreas, Lung Membrane

+ +

Mitochondria Nuclear Secreted Nuclear Nuclear ND Cytosol ND Plasma membrane Plasma membrane ND

Plasma membrane and Cytosol Membrane

Localization

ND ND

Colon, Lung, Pancreas, Ovary ND – ND Rectum, Colon Rectum, Colon – Uterine Lung, Ewing’s

ND

Other

+ – – – – – – + + + ND

+

ND

BR

Cancer expression

Genes Discovered in Prostate and Breast Cancer Using EST and Genome Sequence Databases

Gene

Table 2

21 22

11 12 13 14 15 16 17 18 19 20 20

10

9

Reference

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ESTs from custom libraries (MAPcL)

ESTs from public database

Use ESTs that are from prostate/prostate cancer to generate EST cluster

Exclude the ESTs from the known genes

Exclude the ESTs that are also expressed in other organs

Homology walking

Gene prediction

EST cluster

Design PCR primers from the consensus sequence to generate labeled probe

Dot blot analysis to test the specificity of expression in various tissues

Northern blot analysis to determine the transcript size

RACE-PCR and cloning of the full-length transcript

Protein sequence analysis

Comparative genomics

Figure 1 A flow diagram showing the overall procedure for searching and analyzing new genes from EST and genomic databases.

2.1. Identification of NGEP as a Therapeutic Target for Prostate Cancer Utilizing the EST Database NGEP was discovered as a cluster of five ESTs (three from normal prostate and two from a prostate cancer) and assembled into a 593-bp overlapping sequence (Fig. 2A). From the assembled sequence, we designed PCR primers to generate a 32P-labeled probe. The multi-tissue dot blot analysis on various tissue RNAs reveals that the cluster is very specific and expressed only in prostate (Fig. 2B). The northern blot showed a small RNA (0.9 kb) and two larger RNAs around 3.5 kb in size, suggesting that these are splice variants (Fig. 2C). Rapid amplification of cDNA ends (RACE)-PCR

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Figure 2 EST clusters and expression analysis for NGEP. (A) Schematics of the EST cluster for NGEP. Each line represents one EST with a unique identifier. The cluster consists of five ESTs and the composite cluster is about 593 nucleotides in length. (B) RNA hybridization of a multiple tissue dot blot containing mRNA from 76 normal human cell types or tissues using a cDNA probe. Expression is only observed in prostate (E8) with no detectable expression in any other tissues including brain (A1), heart (A4), kidney (A7), liver (A9), lung (A8), and colon (A6). (C) Northern blot analysis of NGEP transcript in different normal tissues. Human multiple tissue northern blot was probed with 32P-labeled NGEP cDNA. The expression of NGEP mRNA is detected only in the prostate lane (Pr), and there is no detectable signal in spleen (Sp), thymus (Th), testis (Ts), ovary (Ov), intestine (In), colon (Co), and peripheral blood leukocyte (Pb).

analysis on prostate cDNA determined the 5′ and 3′end sequence of the full-length transcript. Subsequent cloning and sequencing of the transcript showed the short variant (NGEP-S) is derived from four exons and it encodes a 20 kDa protein, which is predicted to be an intracellular protein. The long form (NGEP-L) is derived from 25 exons and encodes a 95 kDa protein, which is predicted to contain eight membrane-spanning regions (Fig. 3A and 3B). Comparative genomic analysis of NGEP-L sequence identified mouse and rat orthologs with conserved exon–intron boundary (Fig. 3A). In situ hybridization shows that NGEP mRNA is localized in epithelial cells of normal prostate and prostate cancers. Immunocytochemical analysis of cells transfected with NGEP cDNAs containing a Myc epitope tag at the C-terminus shows that the protein encoded by the long transcript is present on the plasma membrane (Fig. 4A). Immunohistochemical studies using anti-NGEP antibodies on several prostate cancer specimens showed that the NGEP protein is expressed in the epithelial cells of cancer at an elevated level (Fig. 4B). With its selective expression of NGEP in prostate cancer and its presence on the cell surface, NGEP-L protein is a promising target for the immunotherapy of prostate cancer (9,24).

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Figure 3 Schematics showing the genomic organization of NGEP-L gene and the predicted topology of the protein. (A) Genomic organization of NGEP gene and its rodent orthologs. There are 25 exons and the exon numbers are given at the top. Filled boxes represent the coding exons. The figure is not drawn to scale. (B) Predicted topology of the protein encoded by the NGEP-L transcript. There are eight predicted membrane-spanning regions for NGEP-L protein. Both the amino- and the carboxyl-terminus are predicted to be inside the cell.

Figure 4 (see color insert) Localization of NGEP protein in cells and in prostate tissues. (A) Cellular localization of NGEP-L protein in transfected cells. HEK 293T cells were transfected with a plasmid encoding EGFP-NGEP-L fusion protein. After 24 hours, cells were stained with anti-NGEP antibody and visualized for fluorescence. Fluorescence of EGFP-NGEP in the 293T cells (left panel) completely co-localized (right panel) with the anti-NGEP antibody staining (center panel). Nuclei were stained with DAPI as shown in right panel with blue color. (B) The expression of NGEP protein in prostate tissue. Tangential section of the formalin-fixed, paraffin-embedded prostate specimen was stained with anti-NGEP antibodies. The signal is visible in both normal (left panel) and malignant prostate glandular epithelia (right panel).

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2.2. Identification of POTE Gene Family by Using EST and Genome Sequence Database POTE was discovered as a prostate-specific cluster of only two ESTs. After subsequent experimental analysis, it was found to be expressed in prostate, testis, ovary, and placenta, and it encodes a protein of three distinct domains (10). They are an amino-terminal cysteinrich domain, a central domain with ankyrin repeats, and a carboxyl-terminal domain with spectrin-like helices. A Basic Local Alignment Search Tool (BLAST) search of the POTE gene using the “finished” human genome sequence (Build 35) results in the identification of 12 members of the POTE gene family on eight different chromosomes with preservation of open reading frames and splice junctions (10,25). The paralogs are 88%–98% identical at the DNA level. Another unique feature is that POTE paralogs are found in primates but not in mice, suggesting POTE is a primate-specific gene family. Expression of POTE paralogs is restricted to a few normal tissues including embryonic stem cells (26). However, POTE expression is induced in many cancers, probably by some epigenetic changes during cancer development (27). The POTE protein contains seven ankyrin repeats between amino acids 140 and 380. The presence of ankyrin repeats suggests that POTE interacts with other proteins, probably to convey signals from the cell surface to the interior. In addition, we identified an actin retroposon insertion at the carboxyl-terminus of one of the POTE paralog groups (Fig. 5A and 5B). This insertion results in a POTE-actin fusion protein in a breast cancer cell line (28). The selective expression of POTE in reproductive cells and gametes and the maintenance of multiple functional genes suggest an important role in reproduction and perhaps speciation. Expression of POTE in various cancers, including prostate and its undetectable expression in normal essential tissues, makes POTE a candidate for prostate cancer immunotherapy.

2.3. Identification of ANKRD26 as an Ancestral Gene for POTE by Comparative Genomics As we described above, the POTE family gene appears to be primate-specific and there are no homologous genes found in non-primate mammalian species. The analysis of the mutation rates of different POTE paralogs indicates that this gene family diverged from each other relatively recently. New genes often arise by a mechanism involving transfer or duplication of pre-existing genes from an organism. We utilized a BLAST search of the protein sequence database to identify a number of possible distant homologs of the POTE family proteins. We then examined genomic organization (exon-by-exon) as well as sequence identity to establish the relation of the POTE gene family to the distant homolog candidate genes. The dot blot analysis and exon-by-exon comparison between POTE-8 and ANKRD26 show highly conserved genomic segments including some intronic regions, indicating that these genes are evolutionarily related (Fig. 6A and 6B). The ANKRD26 gene is located on chromosome 10p12 in human and encodes a protein with ankyrin repeats and spectrin helices but is missing the cysteine-rich amino terminal domain of POTE. A BLAST search of the mouse genome using the human ANKRD26 protein sequence enabled us to identify a genomic region in mouse chromosome band 6qF1. After assembling several ESTs from the region into a contig and comparing it with bacterial artificial chromosome (BAC) clone sequences, we have generated a virtual cDNA sequence for mouse Ankrd26 gene (29). The sequence analysis of ANKRD26 protein with genome sequence databases from various species shows that the gene is well conserved from zebra fish to human. In mouse, the Ankrd26 protein is expressed in almost all organs including the brain, where

New Targets for Immuno-based Therapy

A

CRDs 1

ANKs 2

3

199

β-Actin

coiled coil (helical region) 4

5

6 7 8

9

6′ 7′ 8′ 9′ 9a 9b 9c

10

LINE, LTR

11

2α 2β 2γ 2δ 8 14α 14β 15 18 21 22

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Figure 5 Genomic organization and phylogenetic analysis of POTE gene family. (A) Genomic and domain organization of POTE paralogs. Domain organization and the exon numbers are given at the top. Exons are depicted as rectangle boxes, and the coding regions are in different shades of gray. POTE paralogs are numbered according to their chromosome location followed by Greek letters if there is more than one paralog in the same chromosome. (B) Phylogenetic analysis of POTE family and related genes. The multiple sequence alignment of selected conserved exons of POTE family genes, ANKRD26, and mouse Ankrd26 were used to construct a neighbor-joining tree. The tree is a bootstrap consensus tree based on 1000 replicates, and the values at or above 70% are shown on the nodes. The three groups of POTE genes are indicated.

it is expressed in the hypothalamic and limbic areas as well as in other locations. The protein is associated with the inner aspect of the plasma membrane of the cell. A mutant mouse with partial inactivation of the Ankrd26 gene developed extreme obesity, insulin resistance, and an increase in body and organ size (30). Studies on these mutant mice may shed light on the function of POTE genes in primates. 2.4. TARP: A New Gene Transcribed from the T Cell Receptor Gamma Gene Locus TARP was initially identified as a cluster of 23 ESTs derived from six cancer and two normal prostate cDNA libraries. TARP is a very unusual gene because it is located within the TCR gamma locus. It is expressed in normal prostate, prostate cancer, and breast cancer. The transcript identified in prostate contains the J gamma1.2 exon and the three C gamma1 exons (11). However, the reading frame used to make the TARP protein is different from

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Figure 6 A schematic showing exon organization and protein domains of POTE family and related protein. (A) Exon organization of selected POTE family gene and ANKRD26. The homologous exons are aligned along the dotted lines and the horizontal lines are drawn between the exon boxes of each gene. The open reading frame for each gene is shown in gray shade. Black boxes represent the degenerated exons that are not present in mRNA sequences but exist in the corresponding genomic region. (B) Domain organization of the selected POTE family and ANKRD26 proteins. Ankyrin repeats are shown as gray boxes and the coiled-coil regions are indicated by hatched boxes.

that used for the TCR gamma protein and it encodes a small 7 kDa protein with a leucine zipper motif and a basic domain (31). TARP RNA is expressed in almost all primary prostate cancers and its level of expression is comparable to the expression of prostate specific antigen (PSA) in prostate tissue. Because of its wider expression in prostate and breast cancers, TARP is now being developed as a vaccine target for prostate and breast cancer therapies (32).

2.5. Identification of PAGE-4: a Cancer-Testis (CT) Antigen, Using the EST Database PAGE-4 was identified by EST database mining and has been shown to be expressed in normal prostate, testis, uterus, placenta, and prostate cancers (17). The PAGE-4 gene is located on the human X chromosome and is homologous to the CT antigen family of genes encoding GAGE/MAGE-like proteins. CT antigens are a distinct class of differentiation antigens that have a restricted pattern of expression in normal tissues but are overexpressed in many cancers. CT antigens are currently viewed as attractive candidates for cancer vaccines. Some widely studied CT antigens are MAGE, BAGE, GAGE, and LAGE antigens, which are primarily expressed in germ cells and spermatogonia in the normal testis. PAGE-4 encodes a 16 kDa protein that is localized in the cytoplasm of cells (33). Because of its selective expression in prostate cancer, PAGE-4 is a possible vaccine-based therapeutic target for prostate cancer (34).

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2.6. Identification of XAGE Gene Family by Homology Walking Using the PAGE-4 Sequence To identify PAGE-4-related genes, the sequence homology programs FASTA (FAST-ALL) and BLASTN (BLAST-Nucleotide) were used to perform homology walking in the human EST database. Homology walking is a computational iterative procedure that finds members of a gene family using the EST database (18). Although programs like PSI-BLAST and ENTREZ can also identify gene relatives from these databases, both programs utilize full-length sequences rather than the EST database, which often contains partial sequences of genes. The use of the EST database is advantageous because many more genes are represented in this database. By homology walking, we identified three new genes that belong to the CT antigen family: XAGE-1, XAGE-2, and XAGE-3 (18). We have characterized XAGE-1 in detail as a possible cancer vaccine target. The XAGE-1 gene represents 13 ESTs from normal testis and two cancer libraries. Experimental analysis showed that XAGE-1 was expressed in many Ewing’s sarcoma cell lines as well as cancer samples from patients with this disease and with osteosarcoma (35). Subsequent analysis showed that XAGE-1 is expressed in many prostate, lung, breast, and other cancers (36). It has also been reported that XAGE-1 is one of the dominant immunoreactive antigens in serum from patients with lung adenocarcinoma (37). XAGE-1 is being evaluated as a possible target for vaccine-based cancer therapy. 2.7. Identification of MRP-8 and MRP-9 Utilizing the EST and the Genome Sequence Databases In this approach, we identify EST clusters that are breast– or prostate cancer–specific as described in the previous approach (Fig. 1), and then align the assembled cluster sequence into the human genome using the “Golden Path” human genome browser. We then analyze the genomic sequence around the cluster for genes predicted to encode membrane proteins using different gene and transmembrane motif prediction programs. After the identification of candidate genes, we validate our finding experimentally as described in Fig. 1. The cluster Br-001 was initially identified as a breast cancer–specific cluster of six ESTs. GenomeScan gene prediction program identified two genes that are highly homologous to MRP5, a member of multidrug resistance-associated protein family (MRP). There were seven reported members in the MRP family at the time, so we designated these genes as MRP8 and MRP9 (Fig. 7). Although the EST cluster was very specific for breast cancer, experimental data showed that MRP8 was expressed at a low level in liver and brain (19), making it less attractive for immunotoxin target. MRP9, on the other hand, was expressed as two splice variants. The long transcript (4.5 kb) encodes a protein with six transmembrane domains. The mRNA is detected only in breast cancer and testis (20). The short transcript (1.3 kb) is present in brain. The long MRP9 transcript was detected in 9 out of 12 breast cancer samples examined and it was weakly expressed in the CRL1500 breast cancer cell line, in which a 100 kDa protein was detected. 2.8. Generation of ESTs from Membrane-Bound Polyribosomal RNA to Identify Genes Encoding Membrane Proteins The ESTs from the publicly available databases originate from various cDNA libraries of different sources and represent genes encoding mostly cytosolic proteins that are abundant in cells. Our effort to identify genes that encode membrane proteins by these databases

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mRNA AL117406 AK026799 ESTs in Unigene Hs.200102/Br001 AI401832 AI676121 BF447217 AW372855 AW372856 AW372862

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Breast Testis

Figure 7 Schematics showing the alignment of breast-specific EST cluster Br-001 on human chromosome 16. GenomeScan gene prediction program predicted two genes (MRP8 and MRP9) in this region. All six ESTs from the Br-001 cluster aligned at the 3′-end of MRP8. Two cDNA sequences derived experimentally are marked as breast and testis.

did not produce many breast cancer candidate genes. Since there are many breast cancer cell lines that accurately represent the properties of the primary cancer, we decided to use these cell lines to search for new membrane-associated proteins. We made membranebound polysomal RNAs, which are enriched for membrane-encoding as well as secretory proteins, from several estrogen receptor (ER)-positive, ER-negative, erbB2-positive, and erbB2-negative breast cancer cell lines. These RNAs were then used to make a large, high quality cDNA library with an average insert size of 2 kb. The cDNA library was then subtracted from five essential normal tissues (brain, liver, kidney, lung, and skeletal muscle) with RNA to remove RNAs that are widely expressed. We obtained a large “subtracted” cDNA library with 1.3 × 107 colony-forming units and an average insert size of 1.8 kb. We designated this cDNA library as membrane-associated polysome cDNA library (MAPcL). We then sequenced over 25,000 clones and compared their sequences with the human genome sequence and the EST database. Analysis of the sequencing results confirms that we successfully made a library enriched with genes encoding membrane and secreted proteins that are highly expressed in breast cancer (21). The sequences of these 25,000 ESTs were then analyzed and clustered in the same way as described above and in Fig. 1. Using this approach, we have identified many new candidate genes, which need to be verified experimentally as breast cancer–specific. The following is an example of a candidate which we have recently characterized. 2.9. Identification of CAPC from MAPcL EST Database CAPC was initially identified as a cluster of 34 ESTs out of 25,000 EST sequences from the MAPcL. CAPC expression is approximately one-third the level of kallikrein 3 (KLK3), the most abundant cDNA in the library. Full-length cloning and subsequent characterization of the EST reveal that the gene is composed of two exons with an unusually small intron. RTPCR analysis showed CAPC mRNA expression in normal prostate, salivary gland, and in

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many breast cancer specimens. The CAPC gene encodes a 37 kDa protein; the N-terminal portion of the CAPC protein is composed of leucine-rich repeats with a predicted membrane-spanning region at the carboxyl-terminus (C-terminus) (22). Precise location of the CAPC protein in the cell, as well as its function, is yet to be determined. 3. CONCLUDING REMARKS In summary, our effort to identify new molecular targets for the treatment of breast and prostate cancers has produced many promising candidates. Our strategy of identifying new genes is different from the conventional approaches, many of which are time-consuming. In addition to identifying several therapeutically important targets, we have also identified several prostate- and breast-specific genes which do not encode any proteins. These nonprotein-coding genes may have noncoding RNA-mediated regulatory roles in genome expression. ACKNOWLEDGMENTS The authors acknowledge the present and former members of the Gene Discovery Group from Drs Ira Pastan’s and B.K. Lee’s sections of the Laboratory of Molecular Biology, NCI, for their contributions in various aspects of this project. REFERENCES 1. Jemal A, Siegel R, Ward E, et al. Cancer statistics. CA Cancer J Clin 2007; 57:43–66. 2. Liang P, Pardee AB. Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science 1992; 257:967–971. 3. Hara T, Harada N, Mitsui H, et al. Characterization of cell phenotype by a novel cDNA library subtraction system: expression of CD8 alpha in a mast cell-derived interleukin-4-dependent cell line. Blood 1994; 84:189–199. 4. Velculescu VE, Zhang L, Vogelstein B, et al. Serial analysis of gene expression. Science 1995; 276:1268–1272. 5. Schena M, Shalon D, Davis RW, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995; 270:467–470. 6. Alsóe L, Stacy JE, Fosså A, et al. Identification of prostate cancer antigens by automated highthroughput filter immunoscreening. J Immunol Methods 2008; 330(1–2):12–23. 7. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001; 409:860–920. 8. Pastan I, Hassan R, FitzGerald DJ, et al. Immunotoxin therapy of cancer. Nature Rev Can 2006; 6:559–565. 9. Bera TK, Das S, Maeda H, et al. NGEP, a gene encoding a membrane protein detected only in prostate cancer and normal prostate. Proc Natl Acad Sci USA 2004; 101:3059–3064. 10. Bera TK, Popescu N, Zimonjic D, et al. POTE, a highly homologous gene family located on numerous chromosomes and expressed in prostate, ovary, testis, placenta, and prostate cancer. Proc Natl Acad Sci USA 2002; 99:16975–16980. 11. Essand M, Vasmatzis G, Brinkmann U, et al. High expression of a specific T-cell receptor gamma transcript in epithelial cells of the prostate. Proc Natl Acad Sci USA 1999; 96:9287–9292. 12. Olsson P, Bera TK, Essand M, et al. GDEP, a new gene differentially expressed in normal prostate and prostate cancer. Prostate 2001; 48:231–241. 13. Bera TK, Maitra R, Iavarone C, et al. PATE, a new gene expressed in prostate cancer, normal prostate and testis identified by a functional genomic approach. Proc Natl Acad Sci USA 2002; 99:3058–3063.

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14. Liu XF, Olsson P, Wolfgang CD, et al. PRAC: a novel small nuclear protein that is specifically expressed in human prostate and colon. Prostate 2001; 47:125–131. 15. Olsson P, Motegi A, Bera TK, et al. PRAC2: a new gene expressed in human prostate and prostate cancer. Prostate 2003; 56:123–130. 16. Bera TK, Hahn YS, Lee BK, et al. TEPP, a new gene specifically expressed in testis, prostate, and placenta and well conserved in chordates. Biochem Biophys Res Commun 2003; 312:1209–1215. 17. Brinkmann U, Vasmatzis G, Lee B, et al. PAGE-1, an X chromosome-linked GAGE-like gene that is expressed in normal and neoplastic prostate, testis, and uterus. Proc Natl Acad Sci USA 1998; 95:10757–10762. 18. Brinkmann U, Vasmatzis G, Lee B, et al. Novel genes in the PAGE and GAGE family of tumor antigens found by homology walking in the dbEST database. Cancer Res 1999; 59:1445–1448. 19. Bera TK, Lee S, Salvatore G, et al. MRP8, a new member of ABC transporter superfamily, identified by EST database mining and gene prediction program, is highly expressed in breast cancer. Mol Med 2001; 7:509–516. 20. Bera TK, Lee S, Iavarone C, et al. MRP9, an unusual truncated member of the ABC transporter superfamily, is highly expressed in breast cancer. Proc Natl Acad Sci USA 2002; 99:6997–7002. 21. Egland KA, Vincent JJ, Strausberg R, et al. Discovery of the breast cancer gene BASE using a molecular approach to enrich for genes encoding membrane and secreted proteins. Proc Natl Acad Sci USA 2003; 100:1099–1104. 22. Egland K, Liu X-F, Squires S, et al. High expression of a cytokeratin-associated protein in many cancers. Proc Natl Acad Sci USA 2006; 103:5929–5934. 23. Vasmatzis G, Essand M, Brinkmann U, et al. Discovery of three genes specifically expressed in human prostate by expressed sequence tag database analysis. Proc Natl Acad Sci USA 1998; 95:300–304. 24. Das S, Hahn Y, Nagata S, et al. NGEP, a prostate-specific plasma membrane protein that promotes the association of LNCaP cells. Cancer Res 2007; 67:1594–1601. 25. Bera TK, Huynh N, Maeda H, et al. Five POTE paralogs and their splice variants are expressed in human prostate and encode proteins of different lengths. Gene 2004; 337:45–53. 26. Bera TK, Saint-Fleur A, Ha D, et al. Selective POTE paralogs on chromosome 2 are expressed in human embryonic stem cells. Stem Cell Dev 2008, 17:325–332. 27. Bera TK, Saint-Fleur A, Lee Y, et al. POTE paralogs are induced and differentially expressed in many cancers. Cancer Res 2006; 66:52–56. 28. Lee Y, Ise T, Ha D, et al. Evolution and expression of chimeric POTE-actin genes in the human genome. Proc Natl Acad Sci USA 2006; 103:17885–17890. 29. Hahn Y-S, Bera TK, Pastan I, et al. Duplication and extensive remodeling shaped POTE family genes encoding proteins containing ankyrin repeat and coiled coil domains. Gene 2006; 366:238–245. 30. Bera TK, Liu X-F, Yamada M, et al. A new model for obesity and gigantism due to disruption of the Ankrd26 gene. Proc Natl Acad Sci USA 2008; 105:270–275. 31. Wolfgang CD, Essand M, Vincent JJ, et al. TARP: a novel protein expressed in prostate and breast cancer cells derived from an alternate reading frame of the TCRγ locus. Proc Natl Acad Sci USA 2000; 97:9437–9442. 32. Oh S, Terabe M, Pendleton CD, et al. Human CTL to wild type and enhanced epitopes of a novel prostate and breast tumor-associated protein, TARP, lyse human breast cancer cells. Cancer Res 2004; 64:2610–2618. 33. Iavarone C, Wolfgang C, Kumar V, et al. PAGE4 is a cytoplasmic protein that is expressed in normal prostate and prostate cancer. Mol Cancer Ther 2002; 1:329–335. 34. Yokokawa J, Bera TK, Palena C, et al. Identification of cytotoxic T-lymphocyte epitope(s) and its agonist epitope(s) of a novel target for vaccine therapy (PAGE4). Int J Cancer 2007; 121:595– 605. 35. Liu XF, Helman LJ, Yeung C, et al. XAGE-1, a new gene that is frequently expressed in Ewing’s sarcoma. Cancer Res 2000; 60:4752–4755.

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36. Egland KA, Kumar V, Duray P, et al. Characterization of overlapping XAGE-1 transcripts encoding a cancer testis antigen expressed in lung, breast, and other types of cancers. Mol Cancer Ther 2002; 1:441–450. 37. Eldib AMA, Ono T, Shimono M, et al. Immunoscreening of a cDNA library from a lung cancer cell line using autologous patient serum: identification of XAGE-1b as a dominant antigen and its immunogenicity in lung adenocarcinoma. Int J Cancer 2004; 108:558–563.

7

Functional Genomic Approaches for Studying Genotoxicity and Carcinogenesis Ebru Caba1 and Jiri Aubrecht2 1

Pfizer Global Research and Development, Groton, Connecticut, and Vertex Pharmaceuticals, Cambridge, Massachusetts, U.S.A. 2 Pfizer Global Research and Development, Groton, Connecticut, U.S.A.

1. INTRODUCTION 2. TRANSCRIPTOMICS 2.1. Application of Transcriptomics in Genetic Toxicology and Evaluating Chemical Carcinogenesis 2.2. Advantages and Challenges of Transcriptomics-Based Approaches 3. FUNCTIONAL GENOMICS 4. SUMMARY REFERENCES

207 209 211 213 215 217 217

1. INTRODUCTION The discipline of genetic toxicology emerged with the widespread acceptance of the bacterial reverse mutation assay (Ames test) as a tool for assessing chemical mutagenesis (1). The premise that mutagens, chemicals capable of causing mutations in bacteria, are also carcinogens in animals (1,2) resulted in the introduction of genetic toxicity testing into safety evaluation paradigms for drugs and chemicals required by regulatory agencies worldwide. Current standardized genotoxicity testing batteries consist of relatively simple short-term assays: a bacterial reverse mutation assay, an in vitro test to evaluate chromosomal damage in mammalian cells, and an in vivo test to evaluate chromosomal damage in rodent hematopoietic cells (3). On the other hand, the evaluation of carcinogenicity, the potential of chemicals to induce tumors in vivo, is assessed using a 2-year bioassay that is very costly, time-consuming, and requires a large number of experimental animals. Over the past several decades, it has been shown that the correlation between genotoxicity, the induction of mutation and/or chromosomal damage, and carcinogenicity is complicated by additional nongenotoxic mechanisms of carcinogenesis and the limitations of in vitro genetic toxicity assays and endpoints. Because of the nature of genetic toxicity assays that provide only a limited insight into genotoxic mechanisms, it is often difficult to assess risk and relevance of the positive findings in the in vitro assays to humans. Indeed, for

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marketed drugs with negative carcinogenicity bioassays but positive genotoxicity findings, the majority of positive genotoxicity findings were obtained from in vitro mammalian mutation and/or chromosome damage assays (4). Therefore, the interpretation and risk assessment of positive findings in the in vitro mammalian mutation and/or chromosome damage assays is a major challenge to both industry and regulatory agencies. Although many factors may contribute to cancer development, in theory, a single mutation may initiate cancer development. Therefore, the risk assessment of positive findings observed in genotoxicity assays has been typically based on linear extrapolation methods. The linear extrapolation methods are appropriate for agents that directly interact with DNA, such as those that form adducts with genetic material. However, substantial evidence exists indicating that some chemicals may exhibit a nonlinear, thresholded dose– response (5,6) and such findings in the in vitro assays might not pose relevant risk to humans. A threshold dose–response implies that genotoxic effects occur exclusively as a consequence of cytotoxicity via non-DNA-reactive mechanisms (reviewed in 7,8). Indirect or non-DNA-reactive genotoxic mechanisms may include enzyme inhibition, imbalance of DNA precursors, energy depletion, production of reactive oxygen species, lipid peroxidation, sulphydryl depletion, nuclease release, inhibition of protein synthesis and denaturation, and ionic imbalance (7,8). To clearly differentiate a true thresholded dose–response from those results obtained simply because of reaching an assay’s detection limit, a proper understanding of the underlying mechanisms of toxicity is absolutely critical (7). This concept is also reflected in the current Food and Drug Administration (FDA) draft guidance genotoxicity for industry (9). Taking into account the variety of mechanisms manifesting as chromosome damage and lack of broad mechanism-based assays, the differentiation of DNA-reactive and DNA-nonreactive mechanisms is often a complex research endeavor translating into significant delays in the introduction of new medicines to patients. Therefore, the development of experimental approaches enabling mechanism-based risk assessment is extremely important. The cellular response to stress triggers the activation of a complicated web of physiological pathways involved in repair, survival, and/or cell death. The cellular stress response is influenced by the state of the cell, the nature of the damage, the availability of protein targets, and kinetic/thermodynamic factors. Such response can be regulated at the transcriptional or post-transcriptional levels that include translational levels and beyond, or by specific molecular interaction. As a result of the complex nature of the cellular stress response, traditional cellular and molecular methods/approaches are limited by the fact that they typically detect only a single parameter. The recent progress in sequencing and genomic technologies has enabled the investigation of the response to cellular stresses within the whole genome in a single experiment. For instance, the gene expression profile analysis, transcriptomics, was proposed for evaluating the mode of action of chemical agents (10,11) including genotoxicants (12,13), predicting clinical efficacy of cancer therapy (14), and for the biological monitoring of exposure to ionizing radiation (15). The gene expression profile analysis provides a snapshot of transcriptional processes at the time the mRNA is isolated. As such, the molecular pathways affected by the toxic stimulus must be decoded using bioinformatics tools. The recent progress in molecular biology and automation has enabled the development of functional genomic approaches that might provide additional information on molecular targets of toxicants. This chapter will focus on several of the recent approaches for evaluating gene expression profiles and highlight the new developments enabling functional analysis. Furthermore, we will provide a perspective for application of these genomic technologies in genetic toxicology.

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2. TRANSCRIPTOMICS The concept of investigating genotoxicity and predicting chemical carcinogenesis by measuring the expression of DNA damage-sensitive genes has been known and exploited for several decades (see review 13 and Table 1). Initially, the assay systems utilized biosensors, promoter–reporter constructs, that detected the activation of the SOS stress response components in bacteria (16,18,19,20) or DNA damage sensitive promoter RAD54 in Saccharomyces cerevisiae (23). The discovery of genes induced in response to DNA damage, growth arrest, and DNA damage-inducible GADD genes (27,28), and the central role of p53 in response to genotoxic stress (29), provided the groundwork for the development of biosensor-based assays in mammalian cells. For instance, biosensors containing the GADD45 promoter (30) or p53 response element (31) were used to detect DNA damage. Alternative approaches utilized monitoring mRNA levels of GADD45 gene in Hep G2 cells (24) or measuring the accumulation of p53 protein in mouse fibroblast cell lines (32,33). When GADD45 transcript levels were monitored as a measure of genotoxicity for the screening of a combinatorial library, there was a clear structure–activity relationship. However, the GADD45 gene induction did not correlate with mutagenicity detected using the standard Salmonella reverse mutation assay (24). The fact that besides DNA damaging agents, the p53 pathway, including the induction of GADD genes, has been shown to be upregulated by hypoxia, nutrient starvation, alteration of ubiquitin pathway, or ribonucleotide depletion (26,34–36) makes it apparent that monitoring a single gene or pathway is not sufficient for studying genotoxic mechanisms. The complexity of interpreting data from genotoxicity studies using a single gene or pathway resulted in the development of biosensor-based arrays using bacterial (22) or mammalian cells (24–26). One such example is the mammalian Hep G2 cell-based array, CAT-tox (L), which included biosensors associated with DNA damage, protein biosynthesis perturbations, heavy metal damage, and oxidative stress. The simultaneous treatment of these biosensor carrying cell lines with the test-agents provided a stress-associated profile (fingerprint) that was successfully used for studying environmental pollutants and organometallic cytostatics (22,24–26) (see Table 1). Recent technological progress resulting in the development of cDNA or oligonucleotide array technologies (microarrays) has made biosensor approaches almost obsolete. Microarray technology enables the monitoring of gene expression in large gene sets or even whole genomes such as S. cerevisiae, mouse, rat, and human (10). The analysis of gene expression patterns produced by cells and treated with toxic agents provides an unprecedented tool for studying mechanisms of toxicity. For instance, toxicogenomic analysis was used for evaluating the mechanism of action of chemical agents (10,11) including genotoxicants (37–40), predicting efficacy of cancer therapy (14) and for biological monitoring of exposure to ionizing radiation (15). The potential of toxicogenomics has been widely recognized, and various international initiatives such as Health and Environmental Sciences Institute (HESI) Genomics committee (41) and other organizations including Environmental Protection Agency (EPA), Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), and Toxicogenomics Research Consortium (TRC) at National Institute of Environmental Health Sciences (NIEHS) (42–45) are all spearheading the validation of toxicogenomic approaches for the safety evaluation of chemicals. Importantly, the FDA issued a draft guidance for industry regarding pharmacoand toxicogenomic data submissions (42). Despite the pivotal role of microarrays in toxicogenomics, other technologies such as the serial analysis of gene expression (SAGE) and real-time polymerase chain reaction

Umu-test /umu Rec-lac test /recA

SOS-lux test /cda, recA VITOTOX/recN micF, lon, fabA, lac, katG, uspA, micF

Pro-tox (C)/katG, micF, osmY, uspA, recA, zwf, umuDC, merR, ada, dinD, soi28, nfo

S. typhimurium

E. coli

E. coli

E. coli,S. typhimurium

E. coli

E. coli

GreenScreen/rad54 Cat-tox (L)/cyp1A1, gstYa, hmtIIA, Fos, xhf, hsp70, gadd153, gadd45, grp78, xre, NF-kBRE, gre, p53RE, RARE Cat-tox (D)/DNA polb, p53, gadd153, gadd45, cfos, tPARE, tPA

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Human liver cells

Human colon cells

Eukaryotic

SOS chromotest /sfiA

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Table 1 Selected Vectors for Detecting Genotoxicity

DNA-damaging agents (UV irradiation, MMS, EMS, MNNG, DMN, MMC, actinomycin D, Hydroxyurea)

DNA-damaging agents (vanadocene complexes, cisplatin, 3-MC, PMA, RA, MMS, heavy metals, arsenic)

DNA-damaging agents

(DNA damage) trivalent chromiun

sodium azide, 4-nitrophenol, phenol, 3,5-DCP, proflavine hemi sulfate, NiSO4, CTAB, fluoranthene, PbCl2, HgCl2, CdCl2, H2O2, KH2AsO4, ZnCl2, 2,4-DNP, PCP, 2,4,5-T, 2,4,6-TCP, 2,4-D, benzidine, methyl viologen, parathion, malthion, SDS, propiconazole

PAHs

DNA-damaging agents (radiation or chemical), MMC, MNNG, Nalidixic Acid, DMS, H2O2, formaldehyde, UV and gamma irradiation

DNA-damaging agents (radiation or chemical), 4NQO, MMC, MNNG, Nalidixic Acid, DMS, H2O2, formaldehyde, UV radiation, tert-butyl hydroperoxide, cumene hydroperoxide, and streptonigrin

DNA-damaging agents (radiation or chemical)

benzofurans, naphtafurans, fungal toxins, MMC, NCS, MMS, EMS, DMS, DES, b-propiolactone, propane sultone, DMN, DEN, MNNG, B[a]P, 4NQO, DMSO, NaCl, Caffeine, Asprin

Stressors detected

26

24, 25

23

22

21

20

19

18

17

16

Reference

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(RT-PCR) are also being pursued. SAGE differs from microarrays in that it can be used to identify unknown gene sequences (for review, see 46). There are only a limited number of studies that have used SAGE in toxicology applications (47–50). The utility and specificity of SAGE was corroborated in a study in which SAGE identified over 200 transcripts that changed following chronic exposure to uranyl nitrate in mice, while conventional endpoint analyses were unable to significantly distinguish treated vs. control animals (47). In another study, cyclosporine A toxicity was evident in biopsies probed using SAGE prior to an inflammatory cell infiltration (50). Potential therapeutic targets following irradiationinduced pulmonary fibrosis were also identified using SAGE technology (49). Since RT-PCR has become the gold standard in quantifying mRNA levels (51,52), the technology is being used for quantitative characterization of a given gene’s expression change and for the confirmation of microarray results. The general availability of RT-PCR reagents for a majority of genes in the form of single-gene assays, or arrays of assays on card-like matrices, has enabled the design of custom RT-PCR arrays consisting of specific genes of interest. For instance, RT-PCR has been successfully used to examine toxicantinduced CYP gene expression changes in hepatocytes (53) and cytokine expression in mouse and human cells (54–56) and to confirm microarray results (57). 2.1. Application of Transcriptomics in Genetic Toxicology and Evaluating Chemical Carcinogenesis The potential of toxicogenomics to provide insight into the mechanisms of action of genotoxicants was first investigated using ionizing radiation and prototypical alkylating agents in mammalian cells and yeast (S. cerevisiae). In yeast, the monitoring of mRNA expression of the whole genome revealed the enormous complexity of genotoxic stress response (see review 58) and its relationship to the general environmental stress response (ESR) (59–61). For instance, the treatment of yeast cells with the alkylating agent methyl methanesulfonate (MMS) induced transcriptional changes in 20%–30% of the yeast genome in a dose- and time-dependent manner (38,62,63). Although the MMS treatment resulted in the alteration of the transcripts from genes previously shown to be alkylation-sensitive, the majority of MMS-induced mRNA expression changes were detected for genes that participate in the stereotypical ESR (59,61) including genes involved in metabolism, cell growth, and maintenance, as well as genes of unknown function (38,63). Surprisingly, only 8%–10% of genes belonged to a category of stress response (38,62). Additional studies with yeast pointed out the intricacy and the potential impact of experimental design on the outcome of the toxicogenomic analysis of genotoxic stress response when comparing data between studies at an individual gene level. Treatment of yeast with six DNA damaging agents comprising of the alkylating agents MMS, N-methyl-N′-nitro-N-nitrosoguanidine (MNNG), bis-chloroethyl-nitrosourea (BCNU), the oxidizing agent tert-butyl hydroxyperoxide (t-BuOOH), 4-nitroquinoline-N-oxide (4-NQO), and γ-radiation provided agent-specific profiles; however, only 21 transcripts responded to all of the agents in a similar fashion. Surprisingly, none of them has been shown to be involved in DNA repair (63). On the other hand, the analysis of gene expression profiles induced by the genotoxicants cisplatin, MMS, and bleomycin, and the environmental stressors sodium chloride and ethanol at strictly equitoxic doses identified 19 biologically relevant genes specific to genotoxic treatments (38). The set included the ultraviolet (UV) and γ-radiation-induced HUG1 gene, which is involved in DNA damage response (64). In mammalian cells, the exposure to ionizing radiation resulted in cell cycle arrest and apoptosis as a consequence of the activation of several pathways, including the p53

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pathway, mitogen-activated protein kinase (MAPK) cascades, nuclear factor-κB (NF-κB), and activation of adaptor protein (AP-1) transcription complex (29, see reviews 65,66). There is increasing experimental evidence that the gene expression profile analysis in mammalian cells can differentiate genotoxic mechanisms (see review 13). For instance, the gene expression profile of DNA-reactive cisplatin was clearly distinguishable from the profiles of DNA-nonreactive inflammatory agents (67) and sodium chloride (37). In various cell systems, genotoxic, DNA-reactive agents were differentiated from nongenotoxic, DNA-nonreactive agents in HepG2 cells (68), LY5178 cells (37,40,69), and TK6 cells (70) based on gene expression profiles. Gene expression profiling might also provide essential information on the complex nature of signaling pathways involved in chemical carcinogenesis. The liver is considered a major target organ for chemical carcinogenesis in rodents; therefore, a number of published reports have focused on toxicogenomic analysis of rat liver after exposure to a variety of carcinogens and noncarcinogens. When faced with positive genotoxicity or carcinogenicity findings, the distinction between genotoxic and nongenotoxic mechanisms of carcinogenesis has a profound effect on risk assessment. Because of the implications for risk assessment, several laboratories have focused on evaluating gene expression profiles of genotoxic and nongenotoxic carcinogens. For instance, livers from rats treated with two genotoxic and five nongenotoxic carcinogens were compared to treatment with two noncarcinogens using cDNA microarrays (71). The study indicated that downregulation of TGF-β stimulated clone-22 (TSC-22) and upregulation of NADPH-cytochrome P-450 reductase (CYP-R) correlated with the estimated carcinogenic potential; however, only TSC-22 mRNA changes could be confirmed using RT-PCR (71). The most comprehensive studies aiming to identify a toxicogenomic signature capable of differentiation between genotoxic and nongenocotoxic carcinogens were recently reported by EllingerZiegelbauer (72–74). In these studies, rats were treated with tumor-producing doses of test agents for 14 days. The gene expression profile analysis of livers from these animals indicated that treatments with genotoxic carcinogens triggered pathways mainly involved in DNA damage response, represented by p53 target genes and proliferation signaling. On the other hand, the expression profile from these livers indicated that oxidative stress, protein damage, regeneration, and cell cycle progression pathways were hallmarks of the toxicogenomic signature for nongenotoxic carcinogens. This is in agreement with published data in the in vitro assay systems (reviewed in 13) and confirms the biological relevance of observed changes. Other studies focused exclusively on the identification of toxicogenomic signatures for nongenotoxic carcinogens. In a study by Nie et al. (75), gene expression profiles were assessed in livers from rats treated with 24 nongenotoxic carcinogens and 28 noncarcinogens for 1 day. The analysis yielded a set of six genes capable of identifying nongenotoxic carcinogens with a prediction accuracy of 88.5%. These genes were: nuclear transport factor 2 (NUTF2), progesterone receptor membrane component 1 (PGRMC1), phenobarbital-inducible liver uridine diphosphate glucuronyltransferase (UDPGTr2), metallothionein 1A (MT1A), suppressor of lin-12 homolog (Sel1h), and methionine adenosyltransferase 1 alpha (Mat1α). Furthermore, the bioinformatic evaluation of the whole dataset yielded five networks linked to the proto-oncogene c-myc, suggesting that the observed transcript changes contribute to the development of cancer. Recently, the most comprehensive evaluation of nongenotoxic carcinogens included the evaluation of 100 structurally and mechanistically diverse nongenotoxic hepatocarcinogens and noncarcinogens (76). The study yielded a signature consisting of 37 genes that were capable of identifying nongenotoxic hepatocarcinogens with an 86% sensitivity

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and a specificity of 81%. Although the components of the signature provide very limited mechanistic insight, when comparing the signature profiles with known carcinogenic mechanisms of tested chemicals, the authors were able to segregate tested compounds into mechanistic classes. Several other studies focused on the lung as a target organ for chemical carcinogenesis. A combined transcriptomic and metabolomic investigation in lungs/livers of mice after 13-week treatment with two carcinogens and noncarcinogens identified a biomarker capable of differentiating carcinogens from noncarcinogens (77). In another study, the gene expression profiles of seven carcinogens and six noncarcinogens were evaluated in lungs of mice after a subchronic 13-week exposure (77). Statistical analysis of the data yielded a set of six genes whose expression correctly predicted the increase in lung tumor incidence with 93.9% accuracy. The genes comprising the predictive signature were enzymes involved in endogenous or xenobiotic metabolism and a growth factor receptor involved in lung development. 2.2. Advantages and Challenges of Transcriptomics-Based Approaches The complex nature of the stress-associated gene expression in mammalian cells indicates a substantial cross-talk among various pathways complicating the interpretation of data. The fact that gene expression profile provides only a snapshot of the cellular response to the stress at the time the mRNA was collected and that the response can be influenced by the genotype of the cells used for studies further contributes to the complexity of the analysis. Furthermore, the key components of DNA repair machinery may be readily available at the site of the damage to counteract a low-grade injury (78), reiterating the importance of a substantial DNA damage to invoke a global DNA damage response which includes cell cycle arrest and gene expression (78). Therefore, it is essential to develop appropriate experimental protocols including dosing and time points for sample collection. In the case of in vivo studies, the absorption, distribution, metabolism, and elimination (ADME) properties of test agents must also be taken into account. A comparison of published datasets at the individual gene level reveals substantial inter-laboratory variation (reviewed in 13). The observed variations are most likely attributed to differences in experimental design including dose levels and time points for sample collection, cell lines used and their genotypes (e.g., p53 status), and analytical technologies. Despite the differences identified at the individual gene level, the common feature of genotoxic stress response is the activation of pathways involved in cell cycle regulation, apoptosis-mediated DNA repair, and cellular signaling (Fig. 1). Therefore, the pathway analysis is crucial for evaluating genotoxic mechanism. This has also become apparent in studies performed by participating laboratories (37,39,40,57,69,79–82) and others (reviewed in 13). Although the HESI Genomic consortium studies were performed under similar conditions in p53-deficient L5189Y and p53-proficient TK-6 cell lines, considerable differences were evident at the individual gene level. On the other hand, the treatment with DNA-reactive compounds exhibited some common features so that gene expression profile analysis was capable of differentiating DNA-reactive vs. DNA-nonreactive agents (37,40). For instance, the DNA-reactive agents resulted in the downregulation of genes involved in the NF-κB pathway, indicating decreased cell survival, while DNAnonreactive agents resulted in the induction of genes involved in survival pathways (40). Treatment with DNA cross-linking cisplatin resulted in the induction of mRNA levels of biologically relevant genes such as the DNA damage gene GADD45 and immediate early response genes c-fos and HSP40 (37). In p53-proficient TK-6 cells, the exposure to DNA-

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Functional group

Response to stress

• Hspa14 • Hspa5 • Dnajb1 • Hsp70

Signal transduction

Response to DNA damage

• Ddit3 • Hspa5 • Gadd45g • Gadd45b • II1b • Tob1 • Fra-1 • IL-8 • Ccne1

Cell death • Ddit3 • Gadd45a • Gadd45b • McI1 • Bax • Cdkn1a • Bcl-xL • Hsp70 • Ccne1

Cellular process • JunB • Fos • Ccng1

Metabolism

• Pcna • Gadd45a • Bax • Apex • Cdkn1a • Ddb2 • Xpc • Hsp70 • Ccne1 • IL-8

Respone to wounding • Gadd45g • II1b • Bax

Respone to pathogen or pest • Gadd45g • II1b

Catabolism • Hspa14 • Mdm2

Biosynthesis • Gadd45g • II1b • Atf3 • Sui1

Figure 1 Representative genes altered in response to a genotoxic stress in mice and humans. Adapted from Ref. 13.

reactive agents Benzo[a]pyrene diol epoxide (BPDE), bleomycin, or MMS resulted in the activation of genes involved in p53 pathway (39,57). The pivotal role of p53 pathway in the manifestation of genotoxic stress response at the transcriptional level was further confirmed in a recently published study by Amundson et al. (70). The fact that BPDE adducts were detected at lower doses than those needed to induce global gene expression changes suggests that microarray technologies may not be as sensitive as classical genotoxic endpoint assays (39). On the other hand, the treatment with cisplatin increased mRNA levels in key biologically relevant genes that correlated with measurements of DNA adducts, while the induction of micronuclei did not correlate (37). This indicates that despite lower sensitivity, gene expression profiles may provide a robust tool for the mechanistic investigation of genotoxicity (i.e., identifying the pathways involved in the toxicity). In addition, the published data indicate that common elements of gene expression profiles in both in vitro assay systems and in vivo studies are capable of differentiating genotoxic and nongenotoxic carcinogens. These profiles contain genes that are ultimately involved in underlying genotoxic or carcinogenic mechanisms. Although more data are

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needed, there is a great potential for toxicogenomics to generate biomarkers that can be translated from in vitro systems to animals in vivo and, ultimately, to humans. 3. FUNCTIONAL GENOMICS The characteristics of cellular responses to stress depend on the nature of the lesion, abundance of target proteins, state of the cell, and kinetic/thermodynamic factors. The fact that the stress response can be regulated at the transcriptional or translational level, during post-translational modification or by specific molecular interactions, which includes a significant amount of cross-talk among biological pathways, makes the development of experimental approaches for investigating stress response challenging. Although the transcriptomics-based approaches provide useful information leading to the identification of biological pathways involved in the cellular response to stress, the potential of transcriptomics to identify target molecules involved in toxic mechanisms is limited. For instance, the functional analysis of yeast mutant strains treated with DNA damaging agents identified target proteins which in most cases differ from the genes with altered mRNA levels (82). Therefore, the functional genomic approaches used to complement transcriptomics approaches might be useful for investigating genotoxic mechanisms. The availability of collections of the yeast deletion mutants (YDM) covering the whole yeast genome enabled the systematic investigation of toxic mechanisms on a functional level (58,82–86). The YDM collections have been developed by replacing each gene with a “molecular barcode” (unique sequence tag) used for identifying and tracking individual strains (83,85,87). The collection of YDM is commercially available as a pool or individually spotted on to liquid or agar plates as homozygous or heterozygous strains. In principle, the functional genomic approach in S. cerevisiae, also known as genomic phenotyping, includes the treatment of homozygous or heterozygous YDM with the test agent followed by the monitoring of the growth of each strain relative to their untreated counterparts. The lack of growth of a specific mutant in the presence of the test agent can be exploited to identify molecular targets of toxicity. Such treatment of YDM strains with DNA-damaging agents led to the identification of toxicologically relevant proteins, pathways, and molecules (58,84,86,88). In these studies, DNA-reactive agents such as bleomycin, MMS, 4-nitroquinoline-N-oxide (4-NQO), tert-butyl hydroperoxide (t-BuOOH), and UV irradiation have been studied. In accordance with expectations, the genomic phenotyping clearly identified genes involved in DNA damage responses (i.e., nucleotide excision repair, mismatch repair, or DNA damage checkpoints) as well as proteins involved in transcription, regulation, and chromatin remodeling. The data provided an opportunity for studying toxicity-modulating networks. In the most comprehensive study to date, the potential of functional genomic analysis using YDM to identify mode of action of drugs was evaluated using 78 compounds. In this study, the authors corroborated the target genes associated with well-characterized compounds and proposed the approach for identifying potential novel drug targets (89). Although the genomic phenotyping in yeast using YDM is available, development of similar gene deletion approaches in mammalian cells had not been technically feasible until recently. The discovery of RNA interference (RNAi) and the commercial availability of RNAi reagents have enabled functional genomic analyses in mammalian cells. In principle, the RNAi leads to downregulation of a given gene by using the cell’s own machinery to degrade the mRNA bound by the short interfering RNA (siRNA) (see reviews 90,91). RNAi has revolutionized functional genomic studies in mammalian cells. Conversely, unlike the complete “knock-out” achieved in YDM strains, RNAi results in a decrease of mRNA

Specific response High throughput Whole genome Parallel analysis of large sets of genes Unknown sequences/genes can be characterized High throughput Available in standardized format as low density arrays/cards Quantitative Sensitive Quantitative Functional analysis of genes Represents whole genome Target identification Counterparts of mammalian genes Functional analysis of genes Mammalian cells

Biosensor arrays

Microarrays

SAGE

RT-PCR

YDM

RNAi

Advantages Disadvantages

Incomplete silencing Difficult to interpret data

Labor-intensive High-risk of contamination Species-specific

Limited to selected genes Detection of both specific and nonspecific PCR products

Inconsistent tag production Low throughput mRNA and protein expression levels do not always correspond

Possible cross-hybridization Requires complex data analysis tools Limited throughput

Labor-intensive to develop Data limited to specific genes/pathways

The Advantages and Disadvantages of Various Approaches Utilized in Genetic Toxicology

Technology

Table 2

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levels, or “knock-down”, resulting in incomplete gene silencing which can complicate data interpretation. Since the RNAi technology has been made available only recently, its application in toxicology has not been fully exploited. The potential of the RNAi technology for functional genomic studies of genotoxic stress has been demonstrated in the past few years (92). Using retroviral vectors carrying a human RNAi library, Berns and colleagues (92) identified five components of the p53 pathway in human cells. This approach has the potential to provide great insight into the underlying mechanism of genotoxicity in mammalian cells and needs to be exploited further. 4. SUMMARY Understanding mechanisms of toxicity is crucial for evaluating the risk associated with exposure to drugs or environmental chemicals. In genetic toxicology and chemical carcinogenesis studies, differentiating DNA-reactive from DNA-nonreactive mechanisms is an important component of risk management strategies. Advances in the genomics field have yielded exciting technologies that can enable the study of toxicity mechanisms. These technologies have both advantages and disadvantages (see Table 2). Transcriptomic approaches offer unprecedented means to investigate cellular responses and to identify molecular pathways to toxic stimuli at the genome level. In addition, the rapidly evolving functional genomic approaches might further complement transcriptomics in mechanistic genetic toxicity assessment. Although the transcriptomics approaches show a great potential for investigating genotoxic mechanisms, several factors including the nature of the genotoxic stress response and the practical aspects associated with development of appropriate experimental protocols have to be taken into account and more validation is needed to gain a wider acceptance of gene expression profile analysis by regulatory agencies. Collaborations among scientists in academia, industry, and regulatory agencies such as the HESI Genomics committee, and initiatives spearheaded by NIEHS and others will facilitate the development of reliable experimental paradigms and include public reference databases. The development of biomarkers of genotoxic and carcinogenic mechanisms is expected to integrate genetic toxicity and carcinogenicity testing and might eventually replace the current testing paradigm. REFERENCES 1. Ames BN. Carcinogens are mutagens: their detection and classification. Environ Health Perspect 1973; 6:115–118. 2. Ames BN, Durston WE, Yamasaki E, et al. Carcinogens are mutagens: a simple test system combining liver homogenates for activation and bacteria for detection. Proc Natl Acad Sci USA 1973; 70:2281–2285. 3. Müller L, Kikuchi Y, Probst G, et al. ICH-harmonised guidances on genotoxicity testing of pharmaceuticals: evolution, reasoning and impact. Mutat Res 1999; 436:195–225. 4. Snyder RD, Green JW. A review of the genotoxicity of marketed pharmaceuticals. Mutat Res 2001; 488:151–169. 5. Henderson L, Albertini S, Aardema M. Thresholds in genotoxicity responses. Mutat Res 2000; 464:123–128. 6. Kirsch-Volders M, Vanhauwaert A, Eichenlaub-Ritter U, et al. Indirect mechanisms of genotoxicity. Toxicol Lett 2003; 140–141:63–74. 7. Kirkland DJ, Müller L. Interpretation of the biological relevance of genotoxicity test results: the importance of thresholds. Mutat Res 2000; 464:137–147.

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Human Genomics in Hypertension Pei-an Betty Shih1, Daniel T. O‘Connor2*, and Sushil K. Mahata2* 1 Departments of Medicine and Pharmacology, Center for Human Genetics and Genomics, University of California at San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A. 2 Department of Medicine and Department of Molecular Genetics, University of California, San Diego, and VA San Diego Healthcare System, San Diego, California, U.S.A.

1. INTRODUCTION 2. INTERMEDIATE PHENOTYPES AND HERITABILITY IN HYPERTENSION 3. SYSTEMATIC POLYMORPHISM DISCOVERY AT THE CANDIDATE GENETIC LOCI SUSCEPTIBLE FOR HYPERTENSION 4. GENOME-WIDE LINKAGE STUDIES IN HYPERTENSION 5. GENOME-WIDE ASSOCIATION STUDIES IN HYPERTENSION 6. GENE-BY-SEX INTERACTION IN HYPERTENSION 7. ADRENERGIC PATHWAY POLYMORPHISMS AND HYPERTENSION 8. RENIN-ANGIOTENSIN-ALDOSTERONE SYSTEM AND HYPERTENSION 9. METABOLIC SYNDROME AND HYPERTENSION 10. ENDOTHELIUM AND VASCULAR DYSFUNCTION IN HYPERTENSION 11. CARDIO-RENAL TARGET ORGAN SUSCEPTIBILITY GENES AND HYPERTENSION 12. PLEIOTROPY AND HYPERTENSION 13. EPISTASIS AND HYPERTENSION 14. HETEROSIS AND HYPERTENSION 15. PHARMACOGENETICS — ANTIHYPERTENSIVE DRUGS 16. MECHANISTIC STUDIES ON TRAIT-ASSOCIATED GENETIC VARIANTS 17. TRANSCRIPTOMICS IN HYPERTENSION 18. PERSPECTIVES AND STRATEGIES 18.1. Human Genetic Study Design 18.2. Statistical Confidence 18.3. Relative Pairs — Twins and Pedigrees (Families)

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Support: National Institutes of Health and Department of Veterans Affairs. * To whom correspondence should be addressed.

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1. INTRODUCTION Essential hypertension, a quintessential example of human complex (multifactorial) disease, is characterized by chronically elevated blood pressure (BP) with no specific underlying medical cause explaining this elevation. Compelling epidemiological data on family history of affected individuals coupled with disease concordance rate in twins has firmly established that both genetic and environmental factors play significant roles in susceptibility to hypertension. The fraction of a trait that can be explained by genetics is termed “heritability” (h2); the value of h2 for a character can range from 0 to 1. The heritability of hypertension is often cited in the range of ˜ 30%–60% (˜ 0.3–0.6), with multiple contributory genes. Additionally, ethnic and genetic heterogeneity participate in variable clinical presentation and drug response in hypertension, rendering genetic study of this disease a challenging task. Human and animal studies have implicated several important etiological pathways contributing to the clinical presentation of essential hypertension that enable functional candidate gene association studies in addition to more comprehensive genome-wide scan/linkage or association studies. Recent results also suggest the existence of a complex genetic architecture for hypertension and its associated risk traits, including evidence for pleiotropy (one gene → multiple traits), epistasis (gene-bygene interaction), and heterosis (a more extreme phenotype for heterozygotes than either homozygote class). Here, we highlight recent findings on the genomics of hypertension that may lead to new approaches to investigating the pathogenesis, diagnosis, treatment, and prognosis of the disease. 2. INTERMEDIATE PHENOTYPES AND HERITABILITY IN HYPERTENSION Although BP and hypertension display substantial heritability, typically reported at ˜30%–60% (1), hypertension is likely to be a clinically heterogeneous phenotype (trait). Applying the concept of “intermediate phenotypes” (2,3) might enhance risk assessment for the development of future hypertension and its consequences, thus enabling more timely diagnosis (and perhaps treatment) of even “pre-hypertensive” individuals (4). Such intermediate phenotypes may be influenced earlier and more proximately by genomic effects than are disease/clinical traits such as hypertension, and therefore may assist in the discovery of hypertension-predisposition loci (2,3). Several investigators have examined the genetic susceptibility toward hypertension, employing several such intermediate phenotypes in twin pairs (1). Intermediate traits in physical, physiological/hemodynamic (5), autonomic/sympathetic (6–9), metabolic (10), inflammatory (10), oxidative (6), endothelial (11,12), and renal (13) pathways displayed significant heritability (e.g., Fig. 1). Indeed, heritability estimates for microalbumin excretion, endothelin, C-reactive protein, norepinephrine, glutathione, and other cardiovascular and autonomic traits ranged from ~45%–70% in twin studies, with h2 typically exceeding that reported for BP alone (3,6,10,12,13). With the rapid advances in genome technologies in the post-genomic era, informed, systematic phenotyping of sufficiently large number of subjects for genetic studies may now be the rate-limiting step in permitting substantive future advances in complex trait genetics.

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100 Physical Renal

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Figure 1 Heritability (h2) of physical, cardiovascular, and renal traits derived from studies in twin-pairs. Heritability is the percentage of trait variance accounted for by additive genetic variance. The cardiovascular and renal traits may be “intermediate phenotypes” for essential hypertension. BMP: body-mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; GFR: glomerular filtration rate. Reproduced from Rao et al. 2007. Hypertension 49:1015–1031.

3. SYSTEMATIC POLYMORPHISM DISCOVERY AT THE CANDIDATE GENETIC LOCI SUSCEPTIBLE FOR HYPERTENSION The completion of human genome project and the advances in public single nucleotide polymorphism (SNP) databases (www.ncbi.nlm.nih.gov/SNP) provide investigators with a screening set of putative variants within genes of interest for candidate gene studies. However, less than half of the ˜11.8 million variants in dbSNP have been validated as being actually polymorphic, indicating that the true extent of variation in the human genome and across populations is still uncertain (14), although the second phase of the HapMap initiative (15) has established a framework of >3.1 million such markers to anchor case–control studies across the genome (16). Systematic SNP discovery efforts can identify the actual extent of variation of any given candidate gene in the population of interest for hypertension association studies, especially for relatively uncommon alleles, which are likely to be underrepresented in the existing public databases. Chromogranin A (CHGA) regulates catecholamine storage and release through intracellular and extracellular mechanisms. Systematic polymorphism discovery was performed on CHGA using an ABI 3100 automated sequencer, resulting in the discovery of previously unknown functional variants (Fig. 2) (17). Another example of systematic polymorphism discovery was for Secretogranin II (SCG2) gene, whose product is important in the formation of catecholamine storage vesicles and whose alleles are associated with hypertension in African-Americans (18) (Fig. 3). GTP cyclohydrolase 1 (GCH1), SCG2, and tyrosine hydroxylase (TH) are examples of functional candidate genes on which systematic polymorphism discovery by resequencing was accomplished in up to 180 ethnically diverse individuals (or 360 chromosomes) (9,11,18). Upon association analyses of the discovered SNPs, significant associations were found between TH promoter (9) variant C-824T for urinary catecholamine excretion and for BP response to environmental stress in twin pairs; and between SCG2 intronic SNP G735A and hypertension in African-Americans (18). The 3′-untranslated region (3′-UTR) C243T diploid genotype of GCH1 had a significant overall effect on systolic blood pressure (SBP), and a gene-by-sex interaction effect on diastolic blood pressure (DBP), with the predominant effect in women (11). Dickson and Sigmund (19) resequenced the Angiotensin, (AGT) promoter in 11 ethnically diverse individuals (22 chromosomes)

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Figure 2 (see color insert) Systematic polymorphism discovery at a candidate genetic locus for hypertension: Chromogranin A (CHGA) sequence variation in humans. Sequences conserved between mouse and human CHGA are shown in the top panel. Location of common (upper) and rare (lower) SNPs relative to exons and conserved noncoding sequences is indicated by position. Nonsynonymous SNPs are shown as red rods and black rods represent synonymous SNPs. Nucleotides in red in the chimpanzee haplotype indicate the minor allele in the human sequence. Computationally, reconstructed haplotypes along with their relative frequencies in ethnogeographic groups within our sample are shown in the middle panel. Nucleotide deletions in haplotype sequences are indicated by an asterisk (*). Reproduced with permission from Elsevier Limited (Wen et al. 2004. Am J Hum Genet 74:197–207).

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Figure 3 Systematic polymorphism discovery at a candidate genetic locus for hypertension: secretogranin II (SCG2) sequence variation in humans. SCG2 is a major core protein in catecholamine secretory vesicles, whose allele frequencies differ in hypertension within the black population. Sequence conservation (human/mouse; human/dog) is plotted for 100 bp windows. Arrows indicate initial resequencing reads for variant detection. Boxes indicate exons, with coding region in black. Identified sequence variations are indicated by vertical bars. X indicates SNPs for which the minor allele appears to be ancestral in comparison with other primates. Haplotypes were computationally constructed from four common SNPs and their distribution frequencies in four populations are indicated. Reproduced with permission from the Oxford University Press (Wen et al. 2007. Hum Mol Genet 16:1752–1764).

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to discover common variants that alter hepatic transcription of this initiator of the reninangiotensin-aldosterone system (RAAS) cascade. In other disease states, such as obesity (20) or dyslipidemia (21), even more systematic resequencing at physiological pathways in large numbers of trait-extreme individuals has given rise to the picture that multiple rare alleles may contribute to variation in common quantitative disease traits. 4. GENOME-WIDE LINKAGE STUDIES IN HYPERTENSION Identification of genes involved in complex genetic diseases, such as essential hypertension, might be difficult because of such factors as the number of loci involved (in a polygenic trait) and the possibility of small effects for each gene involved. Nonetheless, genomewide linkage (co-segregation of anonymous markers with disease trait in relative pairs) offers the possibility of scanning the entire genome for contributory loci. Hottenga et al. (22) used an ˜ 400–800 microsatellite (˜ 5–10 cM; in humans 1 cM = ˜ 1 Mb) genome scan in 922 predominantly normotensive sibling pairs (especially dizygotic twins) to position loci with suggestive linkage for DBP (LOD score of 2–3) on chromosomes 5p13, 14q12, and 17p12. FBPP Investigators (23) from the National Heart, Lung, and Blood Institute (NHLBI) Family Blood Pressure Program (FBPP) used a 391 microsatellite (˜10 cM) genome scan in sibling pairs from >10,000 individuals in >3,000 families to establish significant linkage (LOD score >3) for loci influencing pulse pressure on chromosomes 1, 7, 17, 19, 20, 21, with pulse pressure h2 estimates of 0.25–0.33. Using a sibling-pair linkage study based on families ascertained on probands with established hypertension before the age of 60 with a family history of hypertension, Guzman et al. (24) found excess allele sharing at the D18S474 marker on chromosome 18q21.1 in 56 pedigrees. Their follow-up case–control analysis using 112 unrelated hypertensive patients and 156 normotensive subjects revealed evidence of association of hypertension with the Ring finger and KH-Domain-containing-2 gene (RKHD2), which maps in the vicinity of the marker on chromosome 18 that displayed significant linkage. RKHD2’s mechanistic involvement with hypertension remains unknown (24). Using 21,42 sibling pairs, Munroe et al. (25) confirmed evidence of linkage on 5q13 for hypertension; however, the subsequent transmission disequilibrium test (TDT) analysis revealed only borderline-significant evidence (p < 0.07) for linkage and association at marker D5S2019 on 5q13. The data taken together suggest this region may harbor a susceptibility gene with modest effects on hypertension (25). In the NHLBI FBPP, Chang et al.’s (26) linkage and subsequent positional candidate-based association studies demonstrated that chromosome 1q contains at least three genes associated with BP in multiple samples: Na,K-ATPase beta-1 polypeptide (ATP1B1), Regulation of G-protein Signaling 5 (RGS5), and Selectin E (SELE). Of the nine original positional candidate genes examined (RGS4, RGS5, ATP1B1, Selectin P (SELP), Selectin L (SELL), SELE, Podocin (NPHS2), Adenosine A2 receptor (ADORA2), and REN), these three genes remained significantly associated with hypertension after multiple testing corrections. Individual variants in these three genes accounted for ~2–5 mmHg differences in mean SBP levels, with the cumulative effect reaching as high as ~8–10 mmHg (26). 5. GENOME-WIDE ASSOCIATION STUDIES IN HYPERTENSION Especially in the setting of substantial locus heterogeneity, allelic association studies may offer an increase in statistical power to detect disease predisposition genes, as compared

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with linkage approaches (27). Microsatellites (MS) are DNA polymorphisms that consist of variably repeating units of 1–5 base pairs in length. Using a three-stage genome-wide scan of three independent Japanese case–control samples, Yatsu et al. (28) performed association tests with 18,977 MS markers to identify susceptibility genes for essential hypertension. Fifty-four MS markers were found to be significant in all three stages of the pooling experiments, but only 19 (35%) of them were confirmed to be significant when individual typing was performed (28). In the NHLBI FBPP, Gu et al. (29) performed family-based association analysis using 384 MS markers and demonstrated 21 candidate loci for hypertension. Two loci, D3S2459 and D10S1412, were additionally confirmed in two linkage scans (GENOA and SAPPHIRe) (29). However, MS markers may be too sparsely distributed to detect the great majority of genetic associations, given a typical extent of linkage disequilibrium (LD) at ˜ 30–50 kbp in subjects of European ancestry and ˜ 3–10 kbp in subjects of African ancestry (30). Even 18,977 MS markers represent an average spacing of only ˜174 kbp across the ˜ 3.3 Gbp human genome, while the customary 400–800 MS markers used in genome-wide linkage scanning are spaced every ˜4000–8000 kbp (˜ 4–8 Mbp) across the genome. A recent genome-wide association study at high SNP density for susceptibility to several common diseases was conducted by the UK Wellcome Trust (31); 500,568 SNPs on Affymetrix gene chips resulted in an average marker spacing of ˜ 6 kbp across the ˜3.3 Gbp human genome. About 2,000 individuals with hypertension and ˜3,000 shared controls were studied to ensure adequate sample size for the detection of even modest effects of susceptibility loci. In hypertension, the most significant SNPs, displaying a “moderate” level of association (p-values from 1 × 10–5 to 5 × 10–7), were not in genes from physiological pathways previously implicated, but instead included regions on chromosomes 1q43, 8q24, 10q11, 12p12, 12q23, 13q21, and 15q26. Of note for studies of such a large number of SNPs (here: ˜0.5 × 106), the significance threshold to avoid false positive conclusions in an unreplicated (initial) report must be adjusted appropriately; here, “significance” was defined at p < 5 × 10–7. However, replication (repeat studies) provides a potential way to avoid such remarkably stringent significance thresholds, since the probabilities of independent replications are multiplicative. Indeed, in the Wellcome Trust study (31), previous hypertension associations (32) at the AGT (chromosome 1q42–q43) and ADRB2 (5q32–q34) loci were replicated at the p < 0.05 level. The Framingham Heart Study of the NHLBI (33) completed a dense SNP scan on >1,300 individuals with SBP and DBP followed for >25 years, as well as arterial stiffness phenotypes. At >7 × 106 SNPs, the average inter-marker map density was ˜ 40 kbp. Although none of the associations achieved genome-wide significance (here stated as 4.4 × 10–8), at a more moderate level of stringency (p < 10–5) there were seven associations for SBP or DBP and five for arterial stiffness traits. At pre-specified candidate genes within the RAAS (Angiotensin-3 converting enzyme, AGT, Angiotensin receptor 1, Steroid 11/18-beta-hydroxylase (cYp11B2), Mineralocorticoid receptor (NR3C2), Renin (REN)), there were nominal (p < 0.05) associations with both SBP and DBP for AGT and NR3C2; the arterial stiffness traits displayed nominal (p < 0.05) associations with each of these loci. As a positive control, the Framingham study did replicate previous associations of chromosome 9p21 with coronary heart disease (34). 6. GENE-BY-SEX INTERACTION IN HYPERTENSION Applying a physiological pathway and an extreme-phenotype joint study design, Rana et al. (32) analyzed subjects falling within upper and lower ˜ 5th percentiles of DBP

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distribution among >50,000 individuals in a primary care population, to assess the role of genetic variation at 35 loci with known physiological roles in adrenergic or renal regulation of BP. In addition to confirming genotype-on-phenotype associations found previously (e.g., AGT, ADRB2), they discovered that gene-by-sex interaction may also influence BP (Fig. 3). In females, variants ADRB1 and ADRA2A were found to be hypertension susceptibility genes, whereas in males, the ADRB2 and AGT genes were associated with BP. Consistent with the individual SNP analyses, an ADRA2A haplotype influenced BP only in women, while two AGT haplotypes affected BP only in men (32). Sex-dependent genetic effects in hypertension were also noted for genetic variants at TH (9), CHGA (35), and GCH1 (11). Such findings raise the possibility that previous genetic studies failing to find associations with hypertension might have overlooked the importance of gene-by-sex interaction effects. The precise mechanism whereby sex and autosomal genes interact to influence susceptibility to hypertension is yet to be determined. 7. ADRENERGIC PATHWAY POLYMORPHISMS AND HYPERTENSION The sympathetic nervous system is the primary regulator of acute changes in BP. Adrenergic receptors have critical roles in regulating sympathetic neurotransmitter release and actions; hence, genes encoding these receptors serve as functional candidate genes for hypertension. Among the many genes involved in this complex pathway, Riddle et al. (36) demonstrated the regulator of G protein signaling 2 (RGS2) gene as an ethnicity-specific susceptibility gene for hypertension in blacks, focusing on an intronic insertion/deletion variant (36). Etzel et al. (37) identified 25 SNPs in the alpha-2B adrenergic receptor (ADRA2B); despite considerable polymorphism in this gene, no evidence was found to support ADRA2B as a major determinant of response to alpha-2-adrenergic blockade or as a susceptibility gene to essential hypertension (37). Li et al. (38) examined the alpha-2A and alpha-2C adrenergic receptor (ADRA2A, ADRA2C) genes in 3,398 men (52% black) for association with hypertension; they also found no evidence of association with hypertension, untreated BP, or cardiac functional alterations (38). In a study examining associations between serum uric acid (UA) and alpha-2-, beta-2-, and beta-3-adrenoceptor (ADRA2A, ADRA2B, ADRB3) polymorphisms, Masuo et al. (39) found the Asn418 allele of the alpha-2-adrenoceptor to be associated with elevated UA and plasma neropinephrine (NE), but not with insulin resistance. Bao et al. (40) examined 10 common variants across the beta-2-adrenoceptor (ADRB2) locus. While no individual SNP showed association with hypertension, there was a significant interaction between age and one common ADRB2 haplotype in white subjects. This haplotype was associated with protection against hypertension in younger (≤50 years) but not older (>50 years) subjects, suggesting that age is an important modifier for the effects of ADRB2 on the development of hypertension (Fig. 4) (40). Rao et al. (9) documented the effect of polymorphism in the TH promoter (position C-824T) as well as the CHGA coding region (catestatin Gly364Ser) (35) on basal BP in the population. In another disease population, Jacob et al. (41) examined ADRB2 using patients with postural tachycardia syndrome (POTS). Even though no association with the POTS trait was identified, higher catecholamine levels were observed in POTS patients compared with healthy controls. Further analysis showed that POTS patients who are homozygous in either codon 16 (Gly16Gly) or codon 27 (Glu27Glu) had lower catecholamine levels, with higher supine and upright BPs compared with other genotypes, suggesting that

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Figure 4 Gene-by-sex interaction on blood pressure (BP). Examples of gene-by-sex interactions at five loci to influence diastolic blood pressure (DBP) in population extremes of BP: AGT, ADRB1, ADRB2, ADRA2A, and CHGA. Reproduced from Rana et al. 2007. Hypertension 49:96–106 and Rao et al. 2007. Circulation 115: 2271–2281.

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decreased beta-2-adrenoceptor-related vasodilation may contribute to the hemodynamic diversity of POTS (41). 8. RENIN-ANGIOTENSIN-ALDOSTERONE SYSTEM AND HYPERTENSION The RAAS is a BP-regulating pathway/cascade in series, beginning with the renin substrate AGT. In a study of AGT promoter variants coupled to transfected luciferase reporters, Dickson et al. (42) found that promoter variation at positions –20 and –217 had the largest influence on AGT gene transcription in cell lines, whereas other promoter SNPs had substantially smaller impact. Their data support a hypothesis that SNPs in the AGT promoter may act cell-specifically to differentially regulate the level of AGT gene transcription, thereby affecting hypertension risk (19,42). Using a classical twin study, Ge et al. (43) examined 13 SNPs at four major loci in the RAAS pathway: AGT, ACE, AGTR1, and CYP11B2 (aldosterone synthase). They demonstrated that AGT coding region variant Met235Thr and ACE promoter variant A-239T were individually associated with BP; but together these two SNPs accounted for only ˜1% of the SBP variation, whereas genetic modifier effects from gene-by-gene (AGT-by-ACE; Fig. 5), gene-by-gender, and gene-by-body mass index interactions accounted for ˜2.5% of resting BP and ˜7.3% of stress BP variations (43). Iwai et al. (44) performed association studies between the plasma aldosterone concentration and 13 polymorphisms in the CYP11B1 (steroid 11-beta-hydroxylase) and CYP11B2 loci in 1,443 Japanese subjects. Aldosterone levels were associated with the following polymorphisms in CYP11B2 gene: T-344C (promoter SNP), C595T, T-4837C (C at position 4,837 upstream from the transcription start site), and G4936A polymorphisms in multivariate analyses. The T-344C polymorphism remained significantly associated after Bonferroni multiple-testing correction; T-344C and renin activity interaction significantly predicted aldosterone, while T-344C and salt intake interaction determined BP. In vitro experiments showed that promoter activity of the T-344C but not T-344 allele was significantly influenced by angiotensin II stimulation, supporting the functional significance of T-344C in angiotensin II reactivity and hence salt sensitivity in this Japanese sample (44). At the human type 1 angiotensin II receptor (AGTR1) locus (on chromosome 3q), variant A1166C (rs5186) within the 3′-UTR is a target site for the endogenous micro-RNA hsa-miR-155, encoded by a locus on chromosome 21. Sethupathy et al. (45) showed that hsamiR-155 downregulates the expression of only the A1166 allele, but not the 1166C allele.

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Figure 5 Age-specific odds ratio of beta-2adrenergic receptor (ADRB2) common variation on risk for hypertension. Age-specific odds ratio (ORs) are shown for the effect of haplotype 1 on hypertension. ORs were calculated on the basis of the natural logarithm. Reproduced from Bao et al. 2005. Hypertension 46:301–307.

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The reported 1166C allelic association with hypertension in many studies suggests that the 1166C allele may be functionally associated with hypertension through abrogating regulation by hsa-miR-155, thereby elevating AGTR1 expression (45). Two novel SNPs upstream of the cap site (G-1889C and A-1859G) in the steroid 11-beta-hydroxylase gene (CYP11B1) were examined by Barr et al. (46), who found that G-1889C was associated with decreased 11-beta-hydroxylase efficiency, as well as altered transcriptional response to stimulation by adrenocorticotropic hormone (46). Upon examining heritability of angiotensin I converting enzyme (ACE), angiotensin I converting enzyme 2 (ACE2), and neprilysin (NEP), Rice et al. found genetic factors accounted for 24.5%, 67%, and 22.7% of the phenotypic variation in circulating ACE, ACE2, and NEP. While ACE and NEP were associated with SBP and DBP in univariate analyses, only ACE was independently associated with BP after accounting for covariates and shared childhood household environment (47). 9. METABOLIC SYNDROME AND HYPERTENSION The metabolic syndrome is a cluster of traits (obesity, atherogenic dyslipidemia, elevated BP, insulin resistance, and a pro-thrombotic state) that may cooperate to confer increased risk of cardiovascular disease (10). Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1-alpha) is a transcription factor that regulates several metabolic processes, including mitochondrial biogenesis and respiration, hepatic gluconeogenesis, and muscle fibertype switching. Andersen et al. (48) showed that in Danish subjects, the Ser allele of Gly482Ser in PGC1-alpha associated with reduced risk of hypertension, as well as SBP and DBP (48). In the context of insulin sensitivity, Guo et al. (49) demonstrated that 14 SNPs among hypertensive candidate genes (ACE, AGT, AGTRI, Adducin 1 (ADD1), Natriuretic peptide precursor A (NPPA), ADDRB2, Non-voltage-gated sodium channel alpha subunit (SCNNIA), Guanine-nucleotide-binding protein, beta3 (GNB3), and Nitric oxide synthase 3 (NOS3)) were associated with insulin sensitivity in 100 Mexican families (49). In a twinpair study, Wessel et al. (10) found that C-reactive protein (CRP) concentration was substantially heritable (h2 = 56 ± 0.07%), and shared joint genetic determination (pleiotropy, evidenced by genetic covariance) with several other features of the metabolic syndrome, including BMI, leptin, triglycerides, and BP. Type 2 iodothyronine deiodinase (DIO2) activates thyroid hormone by converting the prohormone thyroxine to bioactive 3,3′,5-triiodothyronine; therefore, it may serve as a hypertension susceptibility gene. Gumieniak et al. (50) indeed showed an excess of Ala92 carriers in hypertensive euthyroid adults (50). In a sample of type 2 diabetes patients, Canani et al. (51) however found no evidence of association between DIO2 and arterial hypertension. The follicle-stimulating hormone receptor (FSHR) gene was examined by Nakayama et al. (52); out of five SNPs examined, A allele of 5′ region variant rs1394205 showed association with decreased transcriptional activity in FSHR promoter activity construct, lower serum estradiol levels, and was found to be a susceptibility allele for EH in women (52). 10. ENDOTHELIUM AND VASCULAR DYSFUNCTION IN HYPERTENSION Endothelial dysfunction is associated with conventional risk factors for atherosclerosis, such as hypertension. In an association study of endothelin (ET) system genetic polymorphisms and arterial-stiffness phenotypes, Iemitsu et al. (53) demonstrated that ECE-1 influences basal BP but not brachial-ankle arterial pulse wave velocity (baPWV), while ET-A and ET-B polymorphisms significantly affected baPWV (53). In a meta-analysis of four SNPs in the

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endothelial nitric oxide synthase 3 (NOS3) gene, Zintzaras et al. (54) found an association between 4a/b and hypertension, since allele b evidenced protection against hypertension (54). In the Japanese population, Iwai et al. (55) studied 161 SNPs in 10 candidate genes selected from vessel mechanical pathways; four SNPs in three genes (SLC9A2, UMOD, ELN) were nominally associated with hypertension, while the heterozygous genotype at a 3′-UTR variant (SNP-160) in the elastin gene (ELN) showed association with hypertension, BP, and pulse wave velocity (55). In a twin-pair study, Lillie et al. (12) found that the plasma concentration of ET-1 was substantially heritable (h2 = 58 ± 0.05), sharing joint genetic determination (pleiotropy) with plasma CHGA, and was predicted by common variants in the CHGA promoter region. In a follow-up experiment, the CHGA protein itself could directly trigger secretion of ET-1 from isolated endothelial cells. In a twin-pair study of urinary nitric oxide metabolite (nitrate/nitrite) excretion, Zhang et al. (11) found that nitrate/nitrite excretion was substantially heritable (h2 = 39.7 ± 0.07) and was influenced by common variation in the 3′-UTR of GCH1, encoding the rate-limiting enzyme in formation of the nitric oxide synthase cofactor tetrahydrobiopterin. 11. CARDIO-RENAL TARGET ORGAN SUSCEPTIBILITY GENES AND HYPERTENSION Since hypertension is an established risk factor for cardiovascular disease as well as endstage renal disease, it is perhaps not surprising that essential hypertension shares common susceptibility genes with cardio-renal end-organ damage in the setting of hypertension. Goldenberg et al. (56) observed the frequency of the AGT Thr allele at Met235Thr to be significantly higher in blacks than whites and independently associated with increased coronary events in post-myocardial infarction black patients. Pilbrow et al. (57) observed that in patients with heart failure, AGT codon 235 homozygous for 235 Thr allele were admitted to the hospital 3 years younger in age and 10 years earlier after initial hypertension diagnosis. They also showed an association between the AGT 174 Met allele and history of heart failure as well as increased mortality at follow-up. High-risk combination genotypes were predictive of mortality (57). Rasmussen-Torvik et al. (58) found that only one 3′ noncoding SNP (rs943580) in AGT was associated with trans-mitral early peak filling velocity. Meyers studied 395 SNPs in 80 cardiovascular candidate genes and found Apolipoprotein E (APOE), Voltage-gated sodium channel alpha subunit VII (SCN7A), and Phosphate transporter 1 (SLC20A1) to associate with ventricular mass, while ADRB1 associated with relative wall thickness in blacks (59). Peter et al. (60) showed that the S/S genotype of rs20077647 ([TA] repeat polymorphism) in the promoter region of the estrogen receptoralpha (ESR1) gene associated with left ventricular mass (LVM) and wall thickness. Corin activates pro-A-type and pro-B-type natriuretic peptides into biologically active molecules, and the natriuretic peptide system is known for its antihypertrophic effects; Rame et al. (61) found that the Ile allele of the Thr555Ile variant in Corin was an independent predictor of LVM in subjects with high SBP. Interaction between Ile allele of Thr555lle and SBP was also a significant predictor of LVM. Among renal disease candidate genes, the NEDD4L ubiquitin ligase influences sodium reabsorption in the distal nephron by controlling endocytosis of the epithelial Na+ channel in principal cells. Russo et al. (62) found several variants in NEDD4L ubiquitin ligase to be associated with BP. In a study by Hunt et al. (63), the intronic variant rs10177833 within the NaHCO3 co-transporter gene (SLC4A5) was associated with SBP and DBP both at baseline and at 10-year follow-up in 96 Utah pedigrees.

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The GH secretagogue receptor (GHSR) is involved in the release of growth hormone, which can influence left ventricular myocardial growth, structure, and function. Baessler et al. (64) demonstrated association between two GHSR common haplotypes with LVM and geometry, independent of BP and body mass in the general population, suggesting the involvement of GHSR variation in the pathogenesis of left ventricular hypertrophy (LVH). 12. PLEIOTROPY AND HYPERTENSION Single genes may affect multiple observable traits, a phenomenon known as “pleiotropy” (65). Upon associating particular genetic variants with these traits, a twin-study design further demonstrated effects of pleiotropy for polymorphism Thr431Asn in ROCK2 gene on renin, BP, systemic vascular resistance (SVR), and aldosterone; in particular, Thr431Asn seemed to alter the coupling between renin and three of its downstream targets: aldosterone, SVR, and BP (Fig. 6) (5). In another example of genetic pleiotropy, Greenwood et al. (7) exploited bivariate (two phenotypes, chromogranins A and B, as composite dependent/trait variables) genetic linkage in a genome-side study of the sympathochromaffin system, to document the presence of three novel trans-QTLs influencing exocytosis in twin and sibling pairs, on chromosomes 2, 7, and 13 [Note: QTLs or quantitative trait loci affect a polygenic trait. QTLs are important determinants of continuous traits, such as height, BP, developmental dyslexia, and behavioral traits (as opposed to discrete traits, such as blue vs. brown eye color). QTLs for a trait usually map to different chromosomal regions. QTL analysis shows the contribution of various genes on the trait in question; for example, whether a trait is controlled by the small effects of many genes or the large effects of a few genes. QTL analysis requires statistics and uses pedigree data, linkage data, and molecular marker data. The use of microarray and expression QTL (e-QTL) analysis can help identify the cisQTL elements (when the target trait gene and the QTL are tightly linked) and trans-QTL elements (when the target trait gene and the QTL locations are different), and such studies are useful in the investigation of disease-associated loci. Genetic pleiotropy in the determination of CRP secretion was demonstrated in a twin-pair study (10), using the genetic covariance (shared heritability) for CRP along with several other features of the metabolic syndrome: BP, body mass index, and lipids. Finally, the authors demonstrated that variation at a single adrenergic locus, ADRB2, influenced both CRP and triglycerides. Pleiotropy was observed in a twin-pair study of GCH1 polymorphism (11), the ratelimiting enzyme in tetrahydrobiopterin synthesis. Common variation within the GCH1 3′-UTR influenced not only nitric oxide metabolite excretion, but also heart rate variability and minimum heart rate, both risk predictors for premature mortality. Finally, a twinpair study established pleiotropy for albumin excretion (13), an indicator of glomerular permeability alteration-altering risk for cardiorenal disease with catecholamine excretion. SNP genotyping subsequently established the effects of genetic variation at multiple points in the adrenergic pathway (TH, CHGA, Dopamine receptor D1 (DRD1), Sorting nexin 13 (SNX13), Rho-associated-coiled-coil-containing protein kinase 2 (ROCK2)) upon the albumin excretion trait. 13. EPISTASIS AND HYPERTENSION Epistasis (gene-by-gene interaction) (66), first defined by the English geneticist William Bateson in 1907, was demonstrated for circulating CRP levels in a twin-pair

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Figure 6 Pleiotropy. Rho kinase (ROCK2) variant Thr431Asn changing the coupling between renin and its targets: systemic vascular resistance, aldosterone, and BP. Bivariate (two phenotype) genetic analyses in twin pairs established pleiotropy (one gene → more than one trait). Reproduced from Seasholtz et al. 2006. Hypertension 47:937–947.

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Figure 7 Epistasis. Adrenergic pathway gene-by-gene diploid genotype interactions (epistasis) in determination of C-reactive protein (CRP) in twin pairs. The significance of gene-by-gene interactions was determined in SOLAR. Interactions are shown between beta-2-adrenergic receptor (ADRB2) promoter variant T-47C and tyrosine hydroxylase (TH) promoter variant C-824T. Reproduced with permission from Wolters Kluwer Health/Lippincott, Williams & Wilkins (Wessel et al. 2007. J Hypertens 25:329–343).

study (10), as illustrated in Fig. 7. With increasing numbers of the TH T-824 allele (from C/C, through C/T, to T/T), CRP declines incrementally if the ADRB2 T-47C genotype is either T/C or C/C. However, on a background of ADRB2 T-47C T/T homozygosity, the effect of increasing TH T-824C minor (C) allele copy number was a sharper decline in CRP only for TH C/C homozygotes. Twin-pair studies have also established the interactive effect (epistasis) of alleles at multiple loci encoding the RAAS (Fig. 5) in determination of the BP response to environmental stress (43). 14. HETEROSIS AND HYPERTENSION Heterosis (more extreme phenotype for heterozygotes than either homozygote class, sometimes called “overdominance”) (67) was noted in a study of ACE locus polymorphisms in response to ACE inhibition in hypertension variants (68). Detection of heterosis explicitly requires analysis of diploid genotype (rather than simply allele) effects on a trait (67). To assess the genotype effect of the ACE locus on ACE inhibitor responsiveness in hypertensive patients in the African American Study of Kidney Disease and Hypertension (AASK) trial taking ACE inhibitor ramipril (n = 347), subjects were genotyped at three intronic polymorphisms (G12269A, C17888T, and G20037A) spanning the biologically active regions of the ACE locus. Either of two homozygous genotypes at G12269A (G/G or A/A) responded significantly faster than G/A heterozygotes; similar associations were seen comparing homozygous to heterozygous ACE haplotypes, suggesting that the genetic phenomenon of heterosis may be an important determinant of responsiveness to an ACE inhibitor. Since the ACE enzyme may typically function as a homodimer (69), the possibility of ACE heterodimerization arises as a potential explanation for phenotypic heterosis.

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15. PHARMACOGENETICS — ANTIHYPERTENSIVE DRUGS Pharmacogenetic studies focus on determining how genetic variation affects drug response, with the goal of individualized (or “personalized”) antihypertensive therapy. In general, antihypertensive pharmacogenetics seems to be at a relatively early stage, without clear consensus yet on genetic predictors to be employed routinely in clinical practice (70). Mello et al. (71) conducted a low-molecular weight heparin (LMWH) treatment trial for pregnant women with history of preeclampsia; here LMWH reduced preeclampsia, poor outcomes, resistance of uteroplacental flow, and BP in patients who were ACE Deletion/ Deletion homozygotes (71). Padmanabhan et al. (72) conducted nonparametric linkage in hypertensive-therapy-nonresponder groups. Chromosome 2p was linked to RAAS agent (ACE inhibitors, angiotensin II type-1 receptor blockers, or β-blockers) nonresponsiveness, while chromosome 10q showed suggestive linkage to the calcium channel blocker/ diuretic nonresponsiveness (72). Turner et al. (73) examined associations between thiazide diuretic response and genes in renal sodium transport systems. They demonstrated that polymorphisms in WNK1, ADRB2, and the epithelial sodium channel gamma-subunit (SCNN1G) predicted inter-individual differences in antihypertensive responses to hydrochlorothiazide (73). As noted above under genetic complexity, Bhatnagar et al. (68) discovered that ACE locus genotype influenced the rate at which African-American hypertensive patients achieve the goal BP after ACE inhibition with ramipril.

16. MECHANISTIC STUDIES ON TRAIT-ASSOCIATED GENETIC VARIANTS After marker-on-trait associations are found for susceptibility to hypertension, mechanistic/functional studies are warranted to understand how such variants influence susceptibility or resistance to the disease. Dickson et al. (42) studied the transcriptional activities of eight human AGT haplotypes and found that variants at promoter positions –20 and –217 had the greatest effect in all cell lines examined. However, cell-specific differential transcription activities were also observed, which suggest that measurements of circulating AGT may not be a sufficient predictor of the contribution of AGT polymorphisms to BP (42). After systematic polymorphism discovery at the human TH locus, promoter haplotype/reporter plasmids were transfected into chromaffin cells by Rao et al. (9) to evaluate the functional importance of promoter variation; common promoter variants (haplotypes 1 and 2) differed in transcriptional strength in a manner explanatory of the in vivo associations conferred by promoter haplotypes with catecholamine secretion and BP (9). Common GCH1 variant C243T in the 3′- UTR was found to predict NO excretion as well as several autonomic traits such as baroreceptor coupling, maximum pulse interval, and pulse interval variability. In a functional study with transfected 3′-UTR/luciferase reporter plasmids, the single C243T pyrimidine/pyrimidine transition in the ˜2,000 bp 3′-UTR was sufficient to alter gene expression in a direction compatible with the in vivo marker-on-trait associations (11). CHGA Gly364Ser is a natural nonsynonymous coding variant of the catecholamine release-inhibitory peptide catestatin (CHGA352–372) encoded by CHGA. Rao et al. (35) found associations between Gly/Ser heterozygosity and increased baroreceptor slope, increased cardiac parasympathetic index, decreased cardiac sympathetic index, and decreased

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renal norepinephrine excretion. In functional studies with synthetic variant catestatin peptides, Gly364Ser displayed diminished inhibition of nicotinic cholinergic-stimulated catecholamine secretion from cultured central neurons, documenting a potential central mechanism for the genotypic–phenotypic associations observed (35). 17. TRANSCRIPTOMICS IN HYPERTENSION Utilizing microarray technology and an extreme phenotype approach, Friese et al. (74) compared gene expression patterns between the hereditary hypertensive “blood pressure high” (BPH/2) mouse and its control, the “blood pressure low” (BPL/1) mouse, to explore adrenal gene expression patterns in genetic hypertension. Global BPH/2 transcriptional changes were compatible with: (i) increased sympathetic activity, (ii) prohypertensive RAAS changes, (iii) differential expression in vasoconstrictor systems/ responsive changes in vasodilator systems, (iv) increased cholesterol production, (v) depression in intermediary (especially glucose) metabolism, and (vi) enhanced oxidative stress responses (74). Using transcriptomic approach to identify shared, common genetic mechanisms of two independent rodent models of human essential hypertension — the spontaneously hypertensive rat (SHR) and the blood pressure high mouse (BPH), involvement of several biochemical/physiological systems were found in adrenal catecholamines and sympathetic function; steroid hormone synthesis, catabolism, and enhanced glucocorticoid sensitivity in the SHR; while oxidative stress and intermediary metabolism with global but mechanistically different perturbations in both SHR and BPH (74). Thus, a transcriptomic approach enabled discernment of a broad picture of functional derangements that might contribute to genetic susceptibility for hypertension in mammals (74). 18. PERSPECTIVES AND STRATEGIES 18.1. Human Genetic Study Design Recent genetic studies have identified or confirmed several chromosome regions containing susceptibility alleles for essential hypertension. In the case of candidate genetic locus studies, the rationale for involvement of a specific gene product in hypertension may be apparent, while in hypothesis-free, genome-wide linkage or association studies, establishing the responsible variant in the associated chromosomal interval may be quite challenging (24). Particular problems in the linkage or association studies of the hereditary basis of complex traits (including hypertension) include likely phenotypic and genetic heterogeneity, complicated by problematic statistical power, sometimes resulting in inconsistent results or lack of replication. 18.2. Statistical Confidence Achieving sufficient statistical power in newly emerging genome-wide association studies is particularly challenging as a consequence of very large number of genomic regions simultaneously examined for association; indeed, contemporary chip-based association strategies can now simultaneously interrogate upwards of ˜ 5 × 10 5 SNP loci (31) spanning ˜ 60 × 103 to ˜ 1 × 106 relatively independent linkage disequilibrium (LD) blocks (30) over the ˜ 3.3 Mbp human genome. Even denser SNP mapping is now in the works. Such density provokes a severe adjustment of stringency thresholds for avoidance of Type I

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(false-positive) statistical errors; indeed, in a recent study (31) the threshold of significance for ˜ 5 × 105 SNP comparisons was set at p < 5 × 10–7. While such stringent multiple comparisons standards are useful, ultimately more realistic standards may need to be employed, such as the principles of the False Discovery Rate (75,76), to minimize falsenegative conclusions. Such methods have already been successfully implemented for the multiple comparisons arising from transcriptomic studies (77,78). A practical experimental alternative to extreme statistical stringency is replication (79), since the likelihood of finding significance in two independent studies represents the joint probability; hence, the p-values are essentially multiplicative. However, some studies may be difficult (or even impossible) to replicate, because of size, cost, or unique patient populations. 18.3. Relative Pairs — Twins and Pedigrees (Families) The role of relative pair (twins or pedigrees) studies in complex trait genetics is evolving, as association methods (allele-on-trait) increasingly supplant linkage (co-segregation of locus with trait). However, even in the age of large-scale genomic association studies, family members may provide useful information and enable valuable strategies, such as combined linkage with association analyses, or family-based case/control approaches to minimize the potentially artifactual effects of population stratification, such as the TDT (transmission disequilibrium test) or QTDT (quantitative TDT) (80). In addition, twin and family studies offer the additional advantage of the ability to determine h2 for any trait, an important initial step in determining whether a trait is likely to be tractable to genetic investigation (2). The NHLBI has invested substantially in its FBPP since 1994 (23,29; http://www. sph.uth.tmc.edu/hgc/fbpp/), an extensive series of four cooperating networks: GenNet (University of Michigan; emphasizing pre-hypertension), GENOA (University of Texas at Houston; arteriopathy), HyperGEN (University of Utah; intermediate phenotypes in pedigrees), and SAPPHIRe (Stanford; insulin resistance in Asians). FBPP data are archived for collaborative studies at http://www.biostat.wustl.edu/fbpp/FBPP.shtml. These nuclear families have already been subjected to genome-wide microsatellite linkage scans (29,81). Classical twin studies (82) offer many advantages of the twin-pair approach, including estimation of heritability, pleiotropy, structural equation modeling, linkage, association, gene-by-environment interactions, and extension into pedigrees. Twin registries around the world have been summarized extensively by Busjahn (83). Twin studies actively pursuing the heredity of BP include the Medical College of Georgia (1), the Free University of Amsterdam (22), and the University of California, San Diego (9–11,13). 18.4. Phenotypic Stratification of Hypertension Essential hypertension is a common trait with late penetrance (note: penetrance is the proportion of individuals with a specific genotype expressing the character in the phenotype); multiple and heterogeneously correlated phenotypes (e.g., obesity, autonomic dysfunction, endothelial dysfunction, renal dysfunction, oxidative stress); phenocopies (note: phenocopy is an individual whose phenotype under a particular environmental condition is identical to that of another individual whose phenotype is determined by the genotype) as a result of secondary hypertension or environmental perturbation (e.g., dietary salt excess); and abrogation by other environmental influences (e.g., antihypertensive medications). As compared to classical Mendelian syndromes, such “complex traits” (84) may be only variably tractable to standard genetic investigation. An alternative approach to characterization of

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the trait is to employ “intermediate phenotypes” (2,3,85–87), genetically simpler traits that may prove advantageous because of superior heritability, earlier and more consistent penetrance, and rational involvement in the pathogenesis of the disease state. Indeed, recent twinpair studies document that intermediate traits in physical, physiological/hemodynamic (5), autonomic/sympathetic (6–9), metabolic (10), inflammatory (10), oxidative (6), endothelial (11,12), and renal (13) pathways display significant heritability (e.g., Fig. 1). 18.5. Genetic Complexity Finally, genetic complexity in the form of potential effect modifiers such as interactions (gene-by-sex, gene-by-environment, gene-by-gene [epistasis]), pleiotropy (multiple traits consequent upon one genetic variant), heterosis (extreme phenotypic values in heterozygotes), and population stratification (admixture) will continue to necessitate consideration in experimental design, as well as appropriate stratification or covariate adjustment in genetic analyses of the hereditary basis of human BP variation. REFERENCES 1. Kupper N, Ge D, Treiber FA, et al. Emergence of novel genetic effects on blood pressure and hemodynamics in adolescence: the Georgia Cardiovascular Twin Study. Hypertension 2006; 47:948–954. 2. O’Connor DT, Insel PA, Ziegler MG, et al. Heredity and the autonomic nervous system in human hypertension. Curr Hypertens Rep 2000; 2:16–22. 3. Lillie EO, O’Connor DT. Early phenotypic changes in hypertension: a role for the autonomic nervous system and heredity. Hypertension 2006; 47:331–333. 4. Jones DW, Hall JE. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure and evidence from new hypertension trials. Hypertension 2004; 43:1–3. 5. Seasholtz TM, Wessel J, Rao F, et al. Rho kinase polymorphism influences blood pressure and systemic vascular resistance in human twins: role of heredity. Hypertension 2006; 47:937–947. 6. Kennedy BP, Rao F, Botiglieri T, et al. Contributions of the sympathetic nervous system, glutathione, body mass and gender to blood pressure increase with normal aging: influence of heredity. J Hum Hypertens 2005; 19:951–969. 7. Greenwood TA, Rao F, Stridsberg M, et al. Pleiotropic effects of novel trans-acting loci influencing human sympathochromaffin secretion. Physiol Genomics 2006; 25:470–479. 8. Valle A, O’Connor DT, Taylor P, et al. Butyrylcholinesterase: association with the metabolic syndrome and identification of 2 gene loci affecting activity. Clin Chem 2006; 52:1014–1020. 9. Rao F, Zhang L, Wessel J, et al. Tyrosine hydroxylase, the rate-limiting enzyme in catecholamine biosynthesis: discovery of common human genetic variants governing transcription, autonomic activity, and blood pressure in vivo. Circulation 2007; 116:993–1006. 10. Wessel J, Moratorio G, Rao F, et al. C-reactive protein, an “intermediate phenotype” for inflammation: human twin studies reveal heritability, association with blood pressure and the metabolic syndrome, and the influence of common polymorphism at catecholaminergic/beta-adrenergic pathway loci. J Hypertens 2007; 25:329–343. 11. Zhang L, Rao F, Zhang K, et al. Discovery of common human genetic variants of GTP cyclohydrolase 1 (GCH1) governing nitric oxide, autonomic activity, and cardiovascular risk. J Clin Invest 2007; 117:2658–2671. 12. Lillie EO, Mahata M, Khandrika S, et al. Heredity of endothelin secretion: human twin studies reveal the influence of polymorphism at the chromogranin A locus, a novel determinant of endothelial function. Circulation 2007; 115:2282–2291. 13. Rao F, Wessel J, Wen G, et al. Renal albumin excretion: twin studies identify influences of heredity, environment, and adrenergic pathway polymorphism. Hypertension 2007; 49:1015–1031.

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38. Li JL, Canham RM, Vongpatanasin W, et al. Do allelic variants in alpha2A and alpha2C adrenergic receptors predispose to hypertension in blacks? Hypertension 2006; 47:1140–1146. 39. Masuo K, Katsuya T, Fu Y, et al. Lys418Asn polymorphism of the alpha2-adrenoceptor gene relates to serum uric acid levels but not to insulin sensitivity. Hypertension 2005; 46:144–150. 40. Bao X, Mills PJ, Rana BK, et al. Interactive effects of common beta2-adrenoceptor haplotypes and age on susceptibility to hypertension and receptor function. Hypertension 2005; 46:301–307. 41. Jacob G, Garland EM, Costa F, et al. Beta2-adrenoceptor genotype and function affect hemodynamic profile heterogeneity in postural tachycardia syndrome. Hypertension 2006; 47:421–427. 42. Dickson ME, Zimmerman MB, Rahmouni K, et al. The -20 and -217 promoter variants dominate differential angiotensinogen haplotype regulation in angiotensinogen-expressing cells. Hypertension 2007; 49:631–639. 43. Ge D, Zhu H, Huang Y, et al. Multilocus analyses of renin-angiotensin-aldosterone system gene variants on blood pressure at rest and during behavioral stress in young normotensive subjects. Hypertension 2007; 49:107–112. 44. Iwai N, Kajimoto K, Tomoike H, et al. Polymorphism of CYP11B2 determines salt sensitivity in Japanese. Hypertension 2007; 49:825–831. 45. Sethupathy P, Borel C, Gagnebin M, et al. Human microRNA-155 on chromosome 21 differentially interacts with its polymorphic target in the AGTR1 3′ untranslated region: a mechanism for functional single-nucleotide polymorphisms related to phenotypes. Am J Hum Genet 2007; 81:405–413. 46. Barr M, MacKenzie SM, Friel EC, et al. Polymorphic variation in the 11beta-hydroxylase gene associates with reduced 11-hydroxylase efficiency. Hypertension 2007; 49:113–119. 47. Rice GI, Jones AL, Grant PJ, et al. Circulating activities of angiotensin-converting enzyme, its homolog, angiotensin-converting enzyme 2, and neprilysin in a family study. Hypertension 2006; 48:914–920. 48. Andersen G, Wegner L, Jensen DP, et al. PGC-1alpha Gly482Ser polymorphism associates with hypertension among Danish whites. Hypertension 2005; 45:565–570. 49. Guo X, Cheng S, Taylor KD, et al. Hypertension genes are genetic markers for insulin sensitivity and resistance. Hypertension 2005; 45:799–803. 50. Gumieniak O, Perlstein TS, Williams JS, et al. Ala92 type 2 deiodinase allele increases risk for the development of hypertension. Hypertension 2007; 49:461–466. 51. Canani LH, Leie MA, Machado WE, et al. Type 2 deiodinase Thr92Ala polymorphism is not associated with arterial hypertension in type 2 diabetes mellitus patients. Hypertension 2007; 49:e47; e48. 52. Nakayama T, Kuroi N, Sano M, et al. Mutation of the follicle-stimulating hormone receptor gene 5′-untranslated region associated with female hypertension. Hypertension 2006; 48:512–518. 53. Iemitsu M, Maeda S, Otsuki T, et al. Polymorphism in endothelin-related genes limits exerciseinduced decreases in arterial stiffness in older subjects. Hypertension 2006; 47:928–936. 54. Zintzaras E, Kitsios G, Stefanidis I. Endothelial NO synthase gene polymorphisms and hypertension: a meta-analysis. Hypertension 2006; 48:700–710. 55. Iwai N, Kajimoto K, Kokubo Y, et al. Extensive genetic analysis of 10 candidate genes for hypertension in Japanese. Hypertension 2006; 48:901–907. 56. Goldenberg I, Moss AJ, Ryan D, et al. Polymorphism in the angiotensinogen gene, hypertension, and ethnic differences in the risk of recurrent coronary events. Hypertension 2006; 48:693– 699. 57. Pilbrow AP, Palmer BR, Frampton CM, et al. Angiotensinogen M235T and T174M gene polymorphisms in combination doubles the risk of mortality in heart failure. Hypertension 2007; 49:322–327. 58. Rasmussen-Torvik LJ, North KE, Gu CC, et al. A population association study of angiotensinogen polymorphisms and haplotypes with left ventricular phenotypes. Hypertension 2005; 46:1294–1299. 59. Meyers KJ, Mosley TH, Fox E, et al. Genetic variations associated with echocardiographic left ventricular traits in hypertensive blacks. Hypertension 2007; 49:992–999.

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9 Immunotoxicogenomics: Gene Expression and Immunotoxicology in the Age of Genomics Dori R. Germolec1, Leigh Ann Burns-Naas2, G. Frank Gerberick3, Gregory S. Ladics4, Cindy A. Ryan5, Stephen B. Pruett6, Berran Yucesoy7, and Robert W. Luebke8 1

Toxicology Branch, National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, U.S.A. 2 Drug Safety Research and Development, Pfizer Global Research and Development, San Diego, California, U.S.A. 3 Central Product Safety, Procter & Gamble Company, Cincinnati, Ohio, U.S.A. 4 Crop Genetics, DuPont Company, Wilmington, Delaware, U.S.A. 5 Central Product Safety, Procter & Gamble Company, Cincinnati, Ohio, U.S.A. 6 Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, U.S.A. 7 Toxicology and Molecular Biology Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia, U.S.A. 8 U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Experimental Toxicology Division, Immunotoxicology Branch, Research Triangle Park, North Carolina, U.S.A.

1. INTRODUCTION 2. IMMUNE SUPPRESSION 3. SPECIFIC APPLICATION OF GENOMICS TECHNIQUES AS A DISCOVERY TOOL TO INVESTIGATE TARGETED PATHWAYS IN IMMUNE SUPPRESSION 4. SCREENING FOR DERMAL SENSITIZERS 4.1. Transcript Profiling 4.2. Evaluation of Selected Genes

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This report has been reviewed by the Environmental Protection Agency’s Office of Research and Development and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Agency nor does mention of trade names or commercial products constitute endorsement or recommendation for use. Additionally, this research was supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences. Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

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5. RELATING GENETIC VARIABILITY AND HOST SUSCEPTIBILITY TO IMMUNE-MEDIATED DISEASES 5.1. Genetic Variations 5.2. SNP-Disease Associations 5.3. Approaches for Studying Complex Genetic Traits 6. GENOMICS APPLICATIONS IN DISCOVERY—A TOOL IN THE DEVELOPMENT OF IMMUNOMODULATORY THERAPEUTICS 6.1. Defining New Therapeutic Targets 6.2. Understanding Immune-Related Diseases 6.3. Understanding Susceptibility to Disease, to Novel Therapeutics, and to Drug-induced Toxicities 7. HAZARD IDENTIFICATION AND RISK CHARACTERIZATION 8. CONCLUSIONS REFERENCES

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1. INTRODUCTION The human genome project and a number of systems biology studies have begun to characterize gene families, genetic polymorphisms, and biological pathways responsible for inflammatory processes, immune-mediated disease, and host defense (1–3). Genomic analysis has become one of the most powerful tools available to biomedical scientists, allowing the simultaneous measurement of thousands of distinct mRNAs and identification of complex patterns of gene expression associated with specific exposure paradigms. High-throughput technology allows for genome-wide assessment of single nucleotide polymorphisms (SNPs) in hundreds of individuals in very short periods of time. The data generated from this technology can be used to identify genes associated with phenotypic variation in responses to exogenous factors including infectious agents, therapeutic drugs, and environmental chemicals. The complexity of the immune response, which involves multiple cells, tissues, and regulatory factors, presents unique challenges with regard to the design and interpretation of array studies and complicates the functional characterization of genetic polymorphisms associated with immunological disease. Evolving strategies to deal with these challenges are discussed in the following sections. Global gene expression analysis may be investigated in a number of ways, including fullgenome microarrays, which are specialized array panels that contain specific series of DNA or RNA sequences that may be directly or indirectly related to the immune system and the highthroughput platforms for genotyping and haplotype mapping. While genome-wide, large-scale arrays may be useful for generalized toxicological screening, the management and interpretation of the large numbers of gene changes which may or may not be relevant to the immune system is difficult. “Data overload” may be minimized by using commercially available pathway mapping tools that focus on patterns of gene expression in subpopulations of T and B lymphocytes, macrophages, and dendritic cells (DC) which encompass signaling pathways for differentiation and proliferation that would (i) allow for the monitoring of the progress of the immune response and (ii) help identify defects at the cellular and molecular level (4). This chapter provides an overview of current and potential uses of gene expression data by immunotoxicologists for identification of chemical hazards and susceptible subpopulations and the current potential for using immunotoxicogenomic data for risk assessment and regulatory decision making. We also examine specific examples of the proven utility of genomics techniques in investigating mechanisms of immunotoxicant

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action. There has been considerable progress in these areas with regard to immunosuppression and hypersensitivity. However, the use of genomics as a screening tool to identify those chemicals that modulate autoimmune disease has been limited and is not discussed in this chapter. In contrast, many studies have evaluated the mechanisms of autoimmune disease using genomic techniques, leading to the identification of susceptibility loci and molecular targets for therapeutic intervention, which are discussed below. 2. IMMUNE SUPPRESSION The primary goal of immunotoxicological hazard identification is to identify substances or agents that have the potential to adversely affect immune function. In current practice, if specific functional data (e.g., antigen-specific responses) are not available, immunotoxicological risk must be inferred from changes in lymphoid organs, peripheral blood, or nonspecific immune cell function. Genomic analysis has promise as an alternative to current nonspecific indicators of immunotoxicity, as a means to flag potential immunotoxicants for functional evaluation by identifying exposure-related changes in gene transcription, and simultaneously suggest potential modes of toxicity by analyzing results at the pathway level. Genomic analysis also has the potential to inform species-to-species extrapolation and the relationship between immune suppression and host resistance. Immunologists have identified gene families and polymorphisms that are associated with susceptibility to immune system dysfunction, including immune-mediated diseases. The identified host-specific factors, including heritable factors that increase or decrease sensitivity to environmental chemicals or therapeutics in individuals, subpopulations, or lab animal strains, may prove useful in identifying susceptible subgroups in the general population. Host resistance assays have been used in immunotoxicology testing to evaluate resistance to infectious disease and/or neoplasia and to relate functional alterations detected in primary screens with disease endpoints that may be more relevant to what is experienced in the human population (5–7). In general, these tests are performed only when changes in specific immune parameters are observed because resistance models must be matched to the suspected functional defect, and because the assays are labor-intensive and require large numbers of animals. However, considerable progress has been made in the mapping of immune responses to specific pathogens and tumors such that changes in gene expression may be used to predict resistance to disease (8–11). Systematic examination of global gene changes following exposure of macrophages/monocytes to different microorganisms demonstrated a highly consistent pattern of gene expression, with a core of stereotyped responses related to proinflammatory cytokines and their receptors (12,13). In addition, pathogen-specific responses have also been identified. An extensive database, funded by the National Institutes of Health and specifically devoted to immune epitope data, is publicly available (http://www.immuneepitope.org/home.do) and contains antibody and T cell epitopes from infectious pathogens, experimental and self-antigens in humans, nonhuman primates, and rodents (11). This information will be used to understand the specificity of immune responses and the potential differences across species. Identification of speciesspecific genes involved in similar immune responses will greatly increase our ability to extrapolate rodent-derived data to humans. As our knowledge of the mechanisms of immune surveillance increases, altered expression of major histocompatibility complex (MHC) transcripts or genes encoding immune factors which suppress tumor growth may provide information on the ability of the immune system to eliminate neoplastic cells (9). Furthermore, a collection of 874 full

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length genes and their protein products, determined to be essential for correct function of the human immune system and not widely expressed in cells and tissues outside the immune system, was recently published (14). The set includes genes controlling innate and adaptive immunity, inflammation, and cellular markers (CD molecules). The immunome data are publicly available (http://bioinf.uta.fi/Immunome/), regularly updated, and may provide a valuable resource in constructing targeted arrays. Analytic approaches have shown that the clustering of genes based on similar expression patterns in immune versus all tissues can be used to establish associations between specific genes and processes such as antigen processing and chemokine-mediated responses (15). A number of laboratories are using genomics as a tool to investigate the underlying mechanisms of immunotoxicity (16–19), although to date there have been a few attempts to evaluate the utility of genomic analyses as a screening tool for immunotoxicity. One such effort is being conducted at the National Institute of Environmental Health Sciences (NIEHS) in studies comparing functional endpoints and gene expression changes in the spleen and thymus from B6C3F1 mice following in vivo treatment with well-characterized immunosuppressive agents including diethystilbesterol (DES), dexamethasone, cyclophosphamide, and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (20). All of the chemicals examined induce similar pathophysiological changes in the thymus and modify host resistance; however, their putative modes of action and cellular targets differ. Preliminary results suggest that while the majority of differentially expressed genes were associated with individual chemicals, xenobiotic-induced immune system perturbations may also share common patterns of altered gene expression. Many of the differentially expressed genes are known to play a role in apoptosis, host defense, cell growth, differentiation, and adhesion. In the spleen, these include upregulation of IL-18, lymphotoxin B, and colony stimulating factor receptors and downregulation of RANTES and histocompatibility antigens. In the thymus, gene expression changes common to all four compounds included downregulation of nuclear factor of activated T cells, interferon gamma receptor, and T cell transcription factor 7, and upregulation of caspase 1 and apolipoprotein E (ApoE). These findings are consistent with observed alterations in immune function. Microarray analysis has also been used to evaluate the effects of tributyltin oxide, an environmental immunotoxicant that causes thymic atrophy and reduced immune function. Expression of genes associated with thymic cell surface determinants, T-cell receptor chains, and cell proliferation was reduced in exposed mice (21). Gene expression profiling also holds promise to detect and characterize effects on the human immune system following chemical exposure. For example, Wang et al. (22) took blood samples from welders immediately before and after exposure to welding fumes and isolated total RNA. Gene expression associated with the inflammatory response, oxidative stress, intracellular signal transduction, the cell cycle, and programmed cell death was altered following exposure. Their work not only provides insight into early events of exposure to welding fumes, but also indicates that easily obtainable human samples, from subjects acting as their own controls, are a viable approach to monitor for adverse effects in the workplace. Genome-wide arrays were also used to evaluate expression in human peripheral blood lymphocytes isolated from women living in Seveso, Italy, 20 years after background or high level exposure to TCDD (23). Pathway analysis of transcription profiles identified changes in immunological and hematological diseases, cell proliferation, and apoptosis from highly exposed individuals compared to individuals with only background exposure. These results indicate that microarray analysis of blood samples obtained long after exposure to immunotoxicants with long in vivo half-lives may be informative in follow-up studies.

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3. SPECIFIC APPLICATION OF GENOMICS TECHNIQUES AS A DISCOVERY TOOL TO INVESTIGATE TARGETED PATHWAYS IN IMMUNE SUPPRESSION Mechanistic studies in immunotoxicology have often been based on an initial observation in a screening or safety assessment study. Typically, the cellular and molecular targets investigated in subsequent studies are selected on the basis of an understanding of the immunological cell type or function most affected by the toxicant. This has been quite effective in providing a useful understanding of selected molecular pathways and products and how they are altered by toxicant action (24–27). However, these studies have necessarily been limited in scope because of the time, labor, and quantity of samples required to analyze the full range of pathways and molecules typically involved even in the simplest acquired or innate immune responses. High-throughput methods such as microarray analysis of gene expression now provide a practical approach to understand mechanisms of immunotoxicity in a more global manner. An ultimate goal will clearly be to integrate all available high-throughput data in a systems biology approach not only to understand mechanisms of toxicant action, but also to quantitatively model and predict them. However, until sufficient data sets and computational tools are available to routinely take a systems approach, data sets from high-throughput methods can be used to increase the thoroughness of mechanistic studies by identifying particular molecular pathways and resulting sets of immunological mediators that are altered by a toxicant. Gene expression has also proven valuable in identifying groups of genes or pathways that are altered by exposure to a xenobiotic that otherwise may not be identified (Fig. 1). For example, in a study to evaluate the mechanisms by which ethanol inhibits innate immune mechanisms induced by the toll-like receptor 3 (TLR3), genes whose expression was increased or decreased two-fold or more by ethanol were identified (19). The identified genes were evaluated by the investigator and those genes of particular interest with regard to innate immunity were highlighted for further consideration. Genes related to signaling and regulation of transcription were also included, and those that seemed particularly relevant to inhibition of key immunological mediators were subjected to literature searches. These searches revealed a previously described self-amplifying signaling pathway activated by viruses and polyinosinic-polycytidylic acid (poly I:C, a synthetic analog of viral double-stranded RNA) in which almost all key components were downregulated by ethanol. Examining activation of signaling molecules and transcription factors by western blot or other methods as well as evaluating cytokines produced as a result of transcription factor activation suggested a clear mechanism of suppression of the poly I:C response that began at early stages of the signaling process (19). It is unlikely that this set of effects would ever have been identified by other methods. However, the manual approach used in this study was very time-consuming and inefficient, and failed to detect other pathways that were also substantially modulated. The network of responses inhibited by ethanol in the studies described above was centered on IL-1β, which directly or indirectly affects the expression of the other proteins (19). This interacting network includes a number of chemokines, the expression of which is suppressed by ethanol. In addition, IL-16 expression was increased, a process associated with upregulation of some important signaling molecules. The net effect of IL-16 modulation is not clear; however, because this cytokine is chemotactic for Th cells (28), yet anergizes them (29), the effects of ethanol on this pathway were not identified during manual analysis of the results. This was primarily due to the lack of familiarity of relationships between the molecules in question. These results highlight the potential value of

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Figure 1 Effects of ethanol (6 g/kg orally) on gene expression in unstimulated mouse splenocytes (gray) from mice treated only with ethanol or stimulated peritoneal macrophages (white) from mice treated with ethanol plus poly I:C. Values less than 0 indicate fold decreases in gene expression compared to untreated mice, whereas values greater than 0 indicate fold increases in gene expression compared to untreated mice. (A) Genes regulated in a similar direction in unstimulated or stimulated cells. (B) Genes regulated differently in unstimulated or stimulated cells.

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pathway analysis software that queries a database of such relationships when determining the effects of immunotoxicants on selected molecular pathways. Similarly, a pathway in which gene expression was predominantly upregulated by ethanol was not detected upon manual inspection of the raw data. It is interesting that changes in this pathway, which involve metabolism of amino acids and other small molecules, could exert global effects that influence other pathways. This was not suspected as a potential mechanism of ethanol action in the immune system and illustrates the utility of microarray data in identifying pathways that merit further investigation to determine whether these changes are actually associated with immune suppression. Genomics analysis has been employed to study other well-known immunotoxicants. Although the mechanisms by which TCDD inhibits immune responses have been investigated for many years, microarray analysis recently revealed a potentially important new mechanism involved in the inhibition of B cell responses (24). The results indicate that TCDD activates the transcription of suppressor of cytokine signaling 2, which then functions to inhibit B cell activation, which in turn is partly mediated by cytokines. In another intriguing study, microarray analysis indicated some common molecular pathways (e.g., cellular proliferation) targeted by disparate immunotoxicants (30), thus identifying biomarkers that may be useful for immunotoxicity screening and identification of new, potentially shared mechanisms of immunotoxicity that were not previously suspected. Transcription analysis has proven to be useful in identifying immunotoxic effects other than suppression. For example, inflammation was identified as a mechanism of toxicity of ricin in the lungs on the basis of microarray analysis of human airway cells (31). These results were not anticipated, and it is unlikely they would have been detected by methods other than microarray analysis. The results discussed here demonstrate how genomics can be used as a discovery tool to target pathways worthy of investigation. However, the various software packages for pathway analysis are not without shortcomings, as the efficacy of the programs may be limited by the skill and knowledge of persons who annotate databases or computer programs that annotate them in an automated manner. In both cases, some interactions, associations, and functional relationships may not always be correct or may vary in different cell types. In addition, some relationships that exist have simply not yet been discovered and reported, so pathways cannot be regarded as complete. For example, IL-1, which is reported to increase expression of IL-16 (32), is downregulated by ethanol, whereas IL-16 is upregulated (19). Apparently, there are other proteins whose expression is altered by ethanol or direct effects of ethanol that overcome the expected downregulation of IL-16 gene expression and lead to upregulation. Such cases represent a limitation of this methodology but they also represent another way to use microarray data in the discovery mode to identify pathways that may be incomplete or mechanisms by which a chemical overcomes the typical actions of a pathway. An important consideration in many microarray studies involving the immune system is the availability of cell populations that are sufficiently pure to allow generalizations. The use of model systems such as cell lines is probably the most straightforward approach to this problem (24). In most of the ethanol exposure studies described above, peritoneal macrophages were obtained by lavage from mice and were not purified further. Macrophages constituted 85%–95% of cells in these preparations, and this seemed adequate in that many of the genes induced by poly I:C are known to be produced by macrophages, and some are relatively unique to macrophages (19). However, there were also significant changes in gene expression for genes not known to be expressed in macrophages (e.g., CD3). It is not clear if this is due to changes in gene expression in T cells in the peritoneal cavity or if the number of T cells (or NKT cells) in the peritoneal cavity increased. In any case, the role of different cell types should be considered in designing these experiments and interpreting results.

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Another question that is often asked is whether it is necessary to induce an immune response to identify mechanisms of immunotoxicity or if unstimulated cells or experimental animals are relevant models. A priori, one would expect that immunotoxicant-induced changes in expression of immune system–related genes would be more effectively identified under conditions in which the immune system is stimulated, because suppression of gene expression normally upregulated during an immune response could be detected in animals or cells exposed to stimulus. However, in the ethanol studies, analysis of a limited data set revealed a surprising degree of concordance between the effects of ethanol on unstimulated spleen cells and stimulated macrophages (Fig. 1A). Of genes whose expression was altered twofold or more, a total of 26 genes were affected in the same direction (increase or decrease) by ethanol with or without stimulus (poly I:C), whereas only 18 genes were altered in different directions. However, more genes that are clearly relevant to the immune system were included among the 18 genes differently regulated (Fig. 1B). Thus, some useful information can be obtained without stimulation, but it will probably be necessary to use an immunological stimulus to most effectively identify key pathways affected by immunotoxicants. Of course, different stimuli will induce different pathways, so the selection of stimulus should probably be based on inhibition of particular types of immune or inflammatory responses. 4. SCREENING FOR DERMAL SENSITIZERS The evaluation of whether or not a chemical has the potential to induce allergic contact dermatitis is critical in the assessment of new chemicals introduced into consumer products or in occupational setting. The identification of the skin sensitization hazards and risks of chemicals has relied on the use of animals. However, an increasingly sophisticated appreciation of the cellular and molecular mechanisms of chemical allergy and recent technological and methodological advances are providing new opportunities to consider realistic in vitro alternatives to the toxicological evaluation of skin sensitization hazards and risks. Epidermal DC, also called Langerhan’s cells (LC), are considered to be the principal antigen-presenting cells in the skin, and they play a key role in the development of allergic contact sensitization (33). Following application of a skin sensitizing chemical, a proportion of LC local to the site of exposure becomes activated and migrates away from the skin, traveling to draining lymph nodes via the afferent lymphatics. By the time these cells arrive at the lymph nodes, they have acquired the functional characteristics of mature DC and are able to interact with, and present antigen to, responsive T lymphocytes (reviewed in 34). Given the importance of LC in the initiation of skin sensitization, it seems reasonable to explore opportunities for the development of alternative test methods based upon chemicalinduced changes in phenotype or function of these cells. However, for some time the possibility of developing alternative test methods based on LC activation remained academic; isolating LC from the epidermis in sufficient numbers was a limiting factor, and to date, no LC cell line has been established. This problem was resolved with the discovery that DC with characteristics similar to those of LC could be grown in culture from progenitor cells in human blood [i.e., peripheral blood mononuclear cell-derived DC (PBMC-DC) (35,36) or CD34+ precursors obtained from neonatal cord blood (37)]. This paved the way for the use of cultured human DC in the design of alternative approaches to skin sensitization testing. In addition to the expression of typical DC phenotypic markers, these cultured DC have been shown to respond to chemical allergens in a manner similar to LC, exhibiting changes in cell surface markers and cytokine production (38–40). However, based on data obtained to date by various investigators, it appeared that measurement of allergen-induced changes in phenotype or cytokine expression in PBMC-DC or cord blood CD34+ progenitor-derived

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DC lacked the sensitivity and dynamic range to be robust endpoints for the identification of potential contact sensitizers in an in vitro method. Therefore, a genomics-based approach to elucidate pathways in DC, which are involved in the “immune recognition” process of contact sensitization, has been utilized. The objective is to develop endpoint measures based on changes in gene expression in vitro, which will aid in the prediction of the contact sensitization potential of new chemical entities. 4.1. Transcript Profiling While DC gene expression profiles induced by various stimuli such as viral and bacterial pathogens, lipopolysaccharide (LPS), and cytokines (41–45) have been determined, there are limited reports of genome-wide analysis of the changes induced in human DC upon contact with skin sensitizers (46,47). To address this data gap, an effort was begun to identify gene expression changes that may have the potential for use in the development of an in vitro method for predicting the skin sensitization potential of chemicals (46). PMBC-DC were exposed to two different concentrations of dinitrobenzenesulfonic acid (DNBS), the water soluble analog of the experimental contact allergen 2,4-dinitrochlorobenzene. A total of 1,304 genes of the approximately 12,000 characterized sequences represented on the microarray were differentially regulated (p ≤ 0.001) in the DNBS-treated cells versus untreated controls, at 1 mM, 5 mM, or both based on a statistical comparison of the mean signal values of each treated group to the control group mean signal value. Of the 118 genes that were significant in both the 1 mM and 5 mM DNBS treatment groups, 60 were upregulated and 58 were downregulated. A number of the gene expression changes induced by allergen exposure were found to be consistent with what is known of the DC maturation process and thus provide support for the theory of contact allergen-induced DC maturation. These genes were identified as probable targets to further analyze by real-time reverse transcription polymerase chain reaction (RT-PCR) for their predictive potential. Using CD34+ cord blood progenitor-derived DC and microarray slides, Schoeters et al. (47) identified 25 genes with allergen-altered expression patterns from the 11,000 genes represented on their arrays. The selection criteria for these 25 genes were that they had to induce at least a twofold change in allergen-, but not irritant-treated, cells relative to control in at least two out of three donors for at least three out of the four allergens tested. Schoeters et al. (47) proposed further evaluation of these 25 genes for their predictive potential. 4.2. Evaluation of Selected Genes As early as 1997, investigators began to examine selected genes or small panels of genes as potential indicators of the skin sensitizing capacity of chemicals. One of the first attempts was by Reutter et al. (48) who investigated changes in IL-1β gene expression by RT-PCR in PBMC-DC following culture for 30 min in the presence of five contact sensitizers and one irritant. These authors reported that the sensitizers induced 1.5- to 3.5-fold increase in IL-1β gene expression while irritant treatment resulted in no change in expression. Pichowski et al. (49,50) also evaluated changes in IL-1β gene expression by RT-PCR and found only modest increases (two- or threefold over control) with some variability in response amongst PBMC-DC donors. Aeby et al. (51) reported the use of IL-1β and aquaporin P3 gene expression measured by quantitative RT-PCR in combination with flow cytometric analysis of CD86+ cells to

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characterize the sensitizing potential of chemicals. In order to minimize the donor-to-donor variability in response reported by other investigators, they used DC derived from PBMCs pooled from four different donors. The allergen trinitrobenzenesulfonic acid and two sensitizing aromatic amines induced concentration-dependent increases in the three parameters, whereas the irritant sodium lauryl sulfate did not. Verheyen et al. (52) explored changes in mRNA expression for a number of cytokines and chemokines as potential markers for sensitization testing. The mRNA levels of the cytokines IL-1β, IL-6, and IL-8, and the chemokines CCL2, CCL3, CCL3L1, and CCL4 were examined by RT-PCR following allergen or irritant exposure of CD34+ progenitor-derived DC. Significant interindividual variations in response to chemical treatment were observed, and the authors concluded that IL-1β, IL-6, and IL-8 mRNA expressions were not appropriate indicators of sensitizing potential. However, they observed twofold or greater increases relative to control for the allergens, but not for the irritant, in CCL2, CCL3, and CCL4 mRNAs and suggested further investigation of these chemokine genes. Of those 118 significant genes identified by Ryan et al. (46), Gildea et al. (53) selected 60 genes for verification by real-time RT-PCR and for evaluation of donor-to-donor variability and the dynamic range of expression. To determine whether those gene changes are regulated specifically by contact allergens, 50 out of the 60 genes were also examined in DC derived from single donor elutriated monocytes with a small set of chemicals that included three allergens, one irritant, and one nonallergen. Based on that work, the number of candidate genes was narrowed down to 29 genes (53) that were further evaluated using RT-PCR and an initial test set of 15 chemicals consisting of 11 contact allergens and 4 irritants. Some of the 29 genes appeared to have a greater degree of predictive potential than others. The genes ABCA6, AK1RC2, ARHGDIB, BLNK, CCL23, CD1E, CYP27A1, HML2, NOTCH3, S100A4, and SLAM were affected by all of the allergens tested; others (CCL2, CCL4, EPB41L2, FCER1A, FGL2, SH3BP5, SPN, TRIM16, TTRAP, and TXN) showed an association with the majority of allergens tested (53). Of the 29, two genes, ABCA6 and FGL2, were among the 25 candidate genes of Schoeters et al. (47) and both CCL2 and CCL4 were suggested by Verheyen et al. (52). In conclusion, it is not likely that any one gene will be capable of predicting skin sensitization alone, but rather a select panel of perhaps ten or so genes will be used to characterize the allergenic potential of a test chemical. From the work of Gildea et al. (53) described above, some of the 29 listed candidate genes appear to be more robust in terms of their selectivity and reproducibility. However, before a final predictive gene panel is selected, further analysis is needed with an expanded set of test chemicals. While this further evaluation can be accomplished using RT-PCR, the technology can be both time- and sample-consuming. A high-throughput screening (HTS) method to examine changes in gene expression would provide a quicker, easier, and perhaps more cost-effective way to evaluate the effect of large numbers of chemicals. For this purpose, a commercially available multiplex gene expression platform is being used to further evaluate the gene list of Gildea et al. (53). Initial results with this method are promising. For the 29 candidate genes tested in the multiplex assay, there was agreement with real-time RT-PCR data for both the direction and magnitude of the change in gene expression. In addition, the multiplex assay data are very reproducible, with little run-to-run variability. The development of a gene-based HTS method for evaluating the skin sensitization potential of chemicals would go a long way in helping us to achieve our goal of having available a battery of assays to replace the need for animals for this important toxicological endpoint.

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5. RELATING GENETIC VARIABILITY AND HOST SUSCEPTIBILITY TO IMMUNE-MEDIATED DISEASES 5.1. Genetic Variations Genetic differences are responsible for a considerable degree of interindividual variability in response to chemical exposures, drug treatments, or predisposition to the development of many chronic diseases. Most of the variation in the human genome is in the form of SNPs, which result from single-base changes that substitute one nucleotide for another. SNPs that affect phenotype are referred to as “functional” polymorphisms or variants. Most functional SNPs do not directly affect expression, but rather alter the function of regulatory sequences that control gene expression by changing the rate of mRNA synthesis and degradation (54). Common SNPs have a minor allele frequency greater than 1% in the population. They generally possess low or incomplete penetrance, and consequently some individuals do not express the phenotype, even though they carry a specific allele. They can interact with other genes or environmental factors and show low risk associations in epidemiological studies (e.g., odds ratios ˜1.5–2). “Susceptibility profiles” reflecting a unique combination of multiple common risk variants can help define interindividual variability in the general population and more susceptible groups for risk assessment purposes. The “common disease–common variant (CD–CV)” hypothesis states that genetic susceptibility to common diseases, such as asthma, diabetes, and cardiovascular disease, is largely due to common variants with moderate frequency (>1%) in the population (55). Although debate continues over the plausibility of CD–CV hypothesis, several examples of associations between common variants and common diseases have been established, such as APOE*E4 and Alzheimer’s disease (56) and PPARγ Pro12Ala in type II diabetes (57). To fully understand the underlying allelic spectrum of complex diseases, denser SNP maps and new approaches capturing both common and rare alleles as well as statistical models that allow incorporation of this information are required. While any one common variant may have small effects on disease risk, it may be a strong modifier of risk when combined with other genetic variants and environmental factors. For example, deltaF508 mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene is known to play a major role in cystic fibrosis (58) [ΔF508 or F508del means deletion of the codon (3 bp deletion) encoding phenylalanine (F) at position 508 in the protein]. However, despite the presence of a predominant mutation, there is a large variation in the clinical severity of this disease. In humans, a modifying locus linked to 19q13 has been identified, and candidate genes in this region are being studied. The transforming growth factor beta 1 (TGFβ1) codon 10 CC genotype was found to be a disease modifier as it confers a 2.2-fold increased risk in association with more severe forms of cystic fibrosis (59). 5.2. SNP-Disease Associations Genetic association studies evaluate associations between genetic variants and phenotypes, relying on linkage disequilibrium (LD) between genotyped markers and multiple disease loci. The candidate gene approach uses biological information with regard to disease pathogenesis and selects candidate genes based on prior information from animal models, in vitro/in vivo experiments, or data-mining for disease-associated genes (60). SNPs with frequencies of at least 5% are more likely to be relevant candidate genes in association studies (61). On the other hand, the whole genome approach provides opportunity for

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Table 1 Examples of Associations Between Genetic Variants in Immune/Inflammatory Mediators and Environmental/Occupational Diseases Disease Asthma Allergic contact dermatitis Chronic beryllium disease (CBD) Chronic obstructive pulmonary disease (COPD) Coal workers’ pneumoconiosis (CWP) Farmer’s lung disease

Polymorphism

References

IL-4Ra R576, IL-13-1111; TGFβ1-509 IL-16-295; TNFα-308 TGFβ1 (codon 25) TNFα-308; TGFβ1 codon 10

(64–66) (67, 68) (69) (70, 71)

TNFα-308; IL-18-137 TNFα-308; CD14-159

(72, 73) (74, 75)

studying dense sets of SNPs across the genome simultaneously for associations. Since there is no a priori hypothesis or selected candidate genes, this approach has the potential to identify and test new candidate SNPs and regions. Complex diseases result from interactions between multiple genes, lifestyle, and environmental factors. Exposure characteristics (e.g., dose, duration, etc.) are of paramount consideration in genetic association studies and, in some cases, may overwhelm differences associated with the polymorphism. Environmental/occupational molecular epidemiology investigations have primarily focused on examining hypothesis-driven associations between exposures and specific polymorphisms. In this respect, genetic markers related to the immune system have been identified in several occupational lung diseases such as silicosis and chronic beryllium disease (CBD) (62,63). While a strong association was found between the frequency of the TNFα-238 variant and silicosis severity, numerous studies have also implicated the HLA-DP1 (Glu69) variant in CBD (63). Table 1 provides examples of associations between SNPs in genes encoding immune/inflammatory mediators and selected occupational or environmental diseases. 5.3. Approaches for Studying Complex Genetic Traits In the past years, the integration of high-throughput genetic techniques and approaches has greatly improved our understanding of the genetic basis of complex diseases. Haplotypebased analysis is one of the more promising approaches for disease gene mapping. Recent studies suggest that the human genome consists of haplotype blocks of variable length in which there is a strong LD and limited haplotype diversity (76). Since common haplotypes share a number of SNPs, it is possible to identify a minimum subset of “tagging” SNPs that characterize the most common haplotypes and genetic information in a region (77). This approach will allow characterization of the majority of chromosomes in a population in association studies. Using this strategy, for example, asthma-related risk haplotypes were identified on chromosome 7p (78). Genome-wide linkage studies using microarray technology are now recognized as a powerful approach to mapping genes responsible for modest effects on various diseases. Using this approach, a number of candidate regions/genes for complex phenotypes have been identified. For example, chromosomal regions 5q31–33, 6p21, 11q13, 12q, 13q, and 16p12 were found to be associated with asthma/allergy and related phenotypes (79), and genetic markers on chromosomes 8 and 12 were found to contribute to the susceptibility to chronic obstructive pulmonary disease (COPD) (80,81).

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Recently, differential expression and proteome analyses have been applied along with genetic association studies (60). These studies have shown that allele-specific expression differences in the rate of transcription are more common than previously believed and may contribute significantly to human variation (82,83). Gene-expression patterns may also allow for the identification of new genes involved in disease pathogenesis. Using this approach, genes that might be important in the pathology of certain cancers have been identified (84). Studying intermediate (early, sub-clinical) phenotypes has also allowed a better understanding of complex traits. Genetic variants may have a closer relationship to an intermediate phenotype (such as increased IgE levels in asthma or accelerated decline in FEV1 in COPD) than to the end-stage complex phenotype. In most cases, the genetic factors that contribute to complex traits may also be shared at a molecular level (85). For example, functional SNPs in the IL-4 and IL-4RA genes have been associated with total serum IgE (86), asthma (87), and asthma severity (64). The use of intermediate phenotypes may allow identification of subjects at high risk before experiencing clinical symptoms. In summary, genetic epidemiology offers a powerful approach to identify genetic risk profiles associated with immune or inflammatory mechanisms that can be related to environmental or occupational exposures and diseases. It may lead to an improvement in diagnostic, therapeutic, and preventive/educational strategies. SNP studies also provide an opportunity to obtain more accurate quantitative information on the interindividual variability that can be used in risk assessment. 6. GENOMICS APPLICATIONS IN DISCOVERY–A TOOL IN THE DEVELOPMENT OF IMMUNOMODULATORY THERAPEUTICS Potential applications of genomic technology in the development of therapeutics targeting the immune system include the identification of: (i) targets and pathways for vaccine and therapeutic development; (ii) compounds that cause immunomodulation or immune-mediated idiosyncratic drug-related hypersensitivity (IDRH); (iii) biomarkers of disease states including autoimmunity, hematopoietic cancers, and human immunodeficiency virus infection; (iv) biomarkers indicative of drug efficacy; (v) predictive genetic markers of susceptibility to specific diseases or toxicities involving the immune system; and (vi) predictive genetic markers of beneficial responses to specific therapies. Genomics offers the promise of understanding immune-mediated diseases, monitoring the immune system following therapeutic intervention, and direction in the development of “designer drugs” or personalized therapies to combat diseases or adverse effects of therapeutic agents. The next few paragraphs consider examples of both specific and speculative ways this technology may be used in drug discovery and development involving the immune system. These topics have been considered in more detail elsewhere (88). Evaluation of specific toxicities is not considered as methodologies, and opportunities are the same as those previously described for nondrug chemicals. 6.1. Defining New Therapeutic Targets As reviewed in Orth et al. (89) and Jayapal and Melendez (90), microarray technology has been used to identify potential therapeutic agents that target inflammatory and autoimmune-related illnesses, and a variety of oncologic disorders. Tissues from normal or diseased humans and animals, or from animal models of disease, can be probed for differential

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evaluation of targeted genes either responding to a disease such as arthritis or COPD (e.g., cytokine pathways and mechanisms of inflammation), or having altered regulation or differential expression as a result of disease (e.g., cancer). Mining genomic data on the pathophysiology and pharmacology of disease can also identify potential therapeutic targets. Once identified, target genes are then validated using RT-PCR, in situ hybridization, or other technologies. This complimentary validation can often localize tissue and/or cellular responses and identify genes of interest, and can be confirmed in animal models or patient samples. These methodologies have led to a better understanding of immune-mediated diseases and suggest novel therapeutic targets for asthma, lupus, COPD, severe acute respiratory syndrome, leukemia, and multiple myeloma among others (90). 6.2. Understanding Immune-Related Diseases Essential to improving identification of novel targets for drug development is a better understanding of immune-related diseases such as autoimmunity and the role of the immune system in the pathophysiology of other diseases such as cancer (reviewed in 88). Autoimmunity comprises a set of complex, multigenic diseases with enigmatic clinical and molecular profiles for which treatment strategies remain limited. Microarray analysis has been used to define cytokine and chemokine genes differentially expressed in PBMC of patients with systemic lupus erythematosus, scleroderma, multiple sclerosis, rheumatoid arthritis, and Sjogren’s syndrome. Common patterns have emerged across various diseases. Gene clustering analysis of genes identified by microarray in patients with various autoimmune diseases showed that regardless of the disease, when compared to healthy controls, patients had significant differences in particular pathways, including apoptosis, cell cycle control, proteosome function, and cell differentiation (91). These differences often reflected a downregulation, rather than upregulation, of gene expression. Although microarray studies have usually been applied to identify inflammatory pathways in autoimmunity, this technique has also yielded valuable information about autoantigens. Expression profiling of cultured fibroblasts from scleroderma patients showed increased expression of several known scleroderma autoantigens, suggesting a possible mechanism in which overexpression of these proteins led to lost tolerance to certain epitopes and the development of autoantibodies to them (92). The use of genomics to gain enhanced understanding of the multifactoral nature of autoimmune diseases will help untangle the complexity and interactions between genes, environment, and various disease manifestations. Molecular signatures in different tissues at different times in various autoimmune diseases will provide spatial and temporal data regarding the coordination and cooperation of events regulating their progression and specific pathophysiology. The data will then open possibilities for diagnostic criteria, therapeutic interventions, and perhaps, even prevention of autoimmune diseases. 6.3. Understanding Susceptibility to Disease, to Novel Therapeutics, and to Drug-induced Toxicities Genomic technology has shown potential to define populations of individuals who may be more likely to develop immune-related diseases (infectious or autoimmune), who may benefit from therapeutic interventions, or who may experience adverse effects and, therefore, in whom a specific therapy should be avoided. As reviewed in Burns-Naas et al. (88), certain genetic polymorphisms are associated with increased susceptibility to Mycobacterium tuberculosis infection (IL-12 and IFN-γ), tuberculosis and leprosy

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(Nramp1), the lack of susceptibility to HIV (individuals homozygous for a 32 base-pair deletion in CCR5), and the differential susceptibility to HIV disease progression (low vs. high CCR5 expression). Using gene clustering analyses of microarray data, Maas et al. (91) showed that changes in gene expression between autoimmune patients and controls were independent of standard treatments, such as methotrexate for rheumatoid arthritis, prednisone for systemic lupus erythematosus, insulin for type I diabetes, and IFN-β for multiple sclerosis. The identified genes therefore become markers of genetic susceptibility that can be subsequently analyzed for association with autoimmune disease or for SNPs that affect either expression or function of the gene or gene product (93). Important SNPs have been identified in rheumatoid arthritis (94,95), lupus (96,97), and scleroderma (98) that may lead to the identification of novel therapies or the tailoring of therapies specific for an individual’s genetic background (99). In addition to predicting susceptibility to disease, susceptibility to therapeutic intervention is also being evaluated. Treatment of hepatitis C with IFN-α is intended to stimulate a Th1-mediated antiviral response in the host. Individuals with low expression of IL-10, a stimulator of Th2 response, appear to respond more favorably to IFN-α treatment than high expressors (100). Similarly, polymorphisms in thiopurine S-methyltransferase that result in low expression of this enzyme are associated with a higher risk of myelotoxicity associated with azothioprine treatment of autoimmune disorders (101). Asthma is often treated with corticosteroids, leukotriene antagonists, or long-acting β2-adrenergic receptor agonists to suppress the inflammatory response. However, as with many therapies, some individuals do not respond or do not respond well to these therapies. Recent pharmacogenetic studies indicate that multiple polymorphisms in the alleles for the β2-adrenergic receptor influence the response to albuterol in nearly 15% of asthmatics, suggesting that another agent may be better suited as a rescue inhaler for these individuals. Likewise, individuals who possess the five tandem repeats of the binding motif for the transcription factor Sp1 in the promoter region of the 5-lipoxygenase enzyme gene (ALOX5) (considered to be the wild-type) showed better responsiveness to leukotriene antagonists than individuals who possessed a smaller or greater number of repeats (102). Idiosyncratic drug-related hypersensitivity (IDRH) can have significant impact in drug development, particularly when the effect is not identified until after the drug is on the market and a larger, more diverse patient population is exposed (as is often the case). Though not universal, many IDRH reactions appear to be immune-mediated (e.g., allergic hepatitis, Stevens-Johnson syndrome, toxic epidermal necrolysis) and current nonclinical testing methodologies have been ineffective in identifying these potential serious effects (103,104). In the absence of an understanding of the mechanism of IDRH, immunotoxicogenomics may prove useful in screening for candidate genes involved in mediating this effect. Of note, the incidence of IDRH is too low to simply be accounted for by a polymorphism in a single gene (104). Sensitivity of the methodology may be limiting, however, as small changes in gene expression may not be observed, especially when the tissue evaluated contains multiple cell types and an immune-mediated effect may be confined to one or two cell types. Other ’omics technologies (e.g., proteomics or metabonomics) are also under study in an attempt to identify patterns of changes in protein or metabolite expression that might suggest that a drug is capable of producing an allergic response. As noted by Kaplowitz (104), however, limited knowledge of the mechanisms underlying IDRH and the relative contribution of drugs to an adverse effect is currently problematic. It is possible that there is a lack of induced genes or proteins in individuals experiencing IDRH (105), making the search much more difficult, particularly in light of the fact that general

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animal models for IDRH are not well-characterized. Recent immunopharmacogenomics studies in humans suggest an association between HLA-B alleles and IDRH that occurs with abacavir (106,107), allopurinol (108), and carbamazepine (109). A similar association between HLA-DRB1 and IDRH has been demonstrated with nevirapine (110). These associations may represent a starting point for a focused evaluation of IDRH in nonclinical species for drugs suspected of causing IDRH. In a recent review, MHC variations between donors and recipients (both Class 1 and Class 2), and cytokine–chemokine genes were also considered clear candidates for defining susceptibility to acute graft versus host disease (GVHD) (111). It was observed that in addition to MHC polymorphisms, polymorphisms in both the donor and recipient IL-10 genes were independently associated with acute graft rejection, and polymorphisms in the recipient IL-6 genes were associated with chronic GVHD. It was concluded that a more thorough understanding of both HLA (including variations in minor histocompatibility antigens) and non-HLA immunogenetics is needed to better predict rejection or infection risk. Genes affecting the disposition of specific immunosuppressive drugs that impact targets of immunosuppressive therapy (e.g., the glucocorticoid receptor) or that control transcription factors which regulate expression of candidate genes that could impact therapeutic approaches and outcomes have yet to be considered in much published detail for the development of immunosuppressive therapies (112). 7. HAZARD IDENTIFICATION AND RISK CHARACTERIZATION A number of issues must be addressed before toxicogenomic data can be reliably interpreted with confidence and utilized in the safety assessment of chemicals. The biggest challenge to the routine use of such technology is to determine the predictive value of changes in gene expression for immunotoxicological effects (allergy, immunosuppression, or autoimmunity) observed in vivo or in vitro. Continuing research efforts will help to clarify whether there are similarities in gene induction or suppression for a particular class of chemicals, and whether unique gene expression “fingerprints” (i.e., characteristic responses) exist for particular immunotoxic chemicals. The correlation of gene array to in vivo data represents a unique challenge to the immunotoxicologist, as questions still remain regarding what constitutes a chemically induced adverse effect to the immune system in vivo. For example, are increases in the primary humoral immune or cell-mediated immune response considered adverse? The current challenge is to determine whether dose-responsive changes in functional data correlate with qualitative or quantitative changes in gene expression. Care must also be taken in interpreting results obtained following genomic analysis of immune tissues (e.g., spleen, thymus, lymph nodes), as each tissue contains a heterogeneous population of immune and nonimmune cells, each with its own set of genes that may be expressed or suppressed. Such heterogeneity will be reflected in the RNA that is isolated from the composite tissue. Furthermore, it is important to determine the appropriate source of immune cells to analyze (i.e., spleen, thymus, peripheral blood, and/or lymph nodes). Traditional observational data may also be required for correct analysis of genomic data. For example, if exposure results in an increase or decrease in numbers of one cell type, microarray analysis can not necessarily distinguish between upregulated gene expression and more cells expressing the gene of interest as a basal level. In addition, isolation of nonimmune cells together with immune cells could lead to a “dilution” effect of the isolated RNA and subsequent masking of alterations in immune cell mRNA levels. Ideally, experiments directed toward understanding the molecular basis of immunotoxicity should not be add-ons to standard animal immunotoxicology studies, but should be designed to measure endpoints in an appropriately timed manner. Alterations

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in immune cell gene expression may occur very early (i.e., minutes to hours) following chemical exposure. However, altered gene expression observed at a later time following chemical exposure may be associated with compensatory mechanisms or recovery of the immune response. In addition to kinetics, the dose of chemical exposure also needs to be considered. Therefore, a dose response should be conducted with array studies, as the gene alterations observed may vary both qualitatively and quantitatively with the administered dose. Though expensive, an ideal experimental design would incorporate variations in both time and dose. As gene array technology is still relatively new, there are a number of validation experiments that must be done to assess the specificity, sensitivity, and reproducibility of the assays, particularly in relation to immunotoxicity observed in vivo, to enable reliable data interpretation. Additionally, consistent statistical analysis methods utilizing normalization and data management are essential for obtaining relevant biological information from array data. To date, there has not been agreement on what fold change in gene expression (e.g., two- vs. threefold) compared to vehicle-treated controls indicates a significant alteration. There is also a lack of consensus on statistical models or scoring (e.g., Bernoulli) that should be employed to ascertain differences between chemical-treated and control samples. An additional limitation of the gene array technology is the necessity to evaluate and interpret the large amounts of data that are generated. Advances in informatics, with a particular focus on immune and inflammatory pathways, should improve data handling and the ability to identify relevant gene changes. Ultimately, the goal is to develop routine gene-array screens that can be used to determine the potential toxicity of previously untested chemicals. Our ability to distinguish between immunotoxic and nonimmunotoxic chemicals depends on the robustness of the databases generated and in our ability to interpret the changes in gene expression in the context of immune function (113). These data, for the most part, are currently not available. And, while genomics data may be considered in the process for setting priorities and ranking chemicals for further testing, in general, regulatory agencies consider that genomic data alone are insufficient as a basis for making regulatory decisions. 8. CONCLUSIONS For the near future, genomic arrays are likely to continue to play a role in identifying modes or mechanisms of action and the biochemical pathways that are altered following chemical exposure rather than supplying data appropriate for hazard identification and risk assessment. Additional studies addressing specificity, sensitivity, and reproducibility are needed to determine whether any of the molecular changes observed at a particular time and dose of chemical are predictive of toxicological effects observed in vivo or in vitro. There is no doubt, however, that further development, evaluation, and implementation of gene array technology have the potential to reduce animal usage and ultimately reduce the cost of conducting toxicology studies in general. ACKNOWLEDGMENTS The authors wish to thank Dr. MaryJane Selgrade and Dr. Scott Auerbach for their insightful suggestions and comments on this work. This work was supported in part by an Interagency Agreement with the NIEHS (Y1-ES-0001) and the Intramural Research Program of the NIEHS, NIH.

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Figure 4.3 Pyrogram and histogram of a major allele homozygote genotype (C/C) for CHGA_rs9658634 in TUFTS AKI (acute kidney injury) samples. Out of 96 wells (well overview on the top left corner) 93 wells showed successful genotypes (appeared blue), 2 wells (E10 and G12) were manually corrected (initially appeared orange which after manual correction turned blue with a black ring), and H12 was a control with no DNA (appeared red). Histograms are shown in the middle panel and pyrogram in the bottom panel. (See p. 150.)

4 2 0 A

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2,800 2,600 2,400 2,200 2,000 1,800 1,600 1,400 1,200 E

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Figure 4.4 Pyrogram and histogram of a heterozygote genotype (C/T) for CHGA_rs9658634 in TUFTS AKI (acute kidney injury) samples. Genotypes are shown on the top left corner. See Fig. 3 legend for the details. (See p. 151.)

4 2 0 A

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Figure 4.5 Pyrogram and histogram of a minor allele homozygote (T/ T) genotype for CHGA_rs9658634 in TUFTS AKI (acute kidney injury) samples. See Fig. 3 legend for the details. (See p. 152.)

Figure 6.4 Localization of NGEP protein in cells and in prostate tissues. (A) Cellular localization of NGEP-L protein in transfected cells. HEK 293T cells were transfected with a plasmid encoding EGFP-NGEP-L fusion protein. After 24 hours, cells were stained with anti-NGEP antibody and visualized for fluorescence. Fluorescence of EGFP-NGEP in the 293T cells (left panel) completely co-localized (right panel) with the anti-NGEP antibody staining (center panel). Nuclei were stained with DAPI as shown in right panel with blue color. (B) The expression of NGEP protein in prostate tissue. Tangential section of the formalin-fixed, paraffin-embedded prostate specimen was stained with anti-NGEP antibodies. The signal is visible in both normal (left panel) and malignant prostate glandular epithelia (right panel). (See p. 197.)

100% Conserved region

75% 50%

Resequencing coverage CHGA gene structure Common SNPs Common haplotype distribution in each ethnic group

0.5 Minor allele frequency 0.4 0.3 for common SNPs 0.2 0.1 Major haplotypes

Chimp haplotype

Asian Black Hispanic White Global (n = 88) (n = 114) (n = 56) (n = 102) (n = 360)

AATTGTCC

∗T G

G GGGT C G

G C

0.102

0.193

0.161

0.245

0.181

GATTGTCC

∗T G

G GGGT T G

G C

0.261

0.061

0.161

0.069

0.128

GAGGATAC

ACA

G GGGT C G

A T

0.045

0.053

0.250

0.186

0.119

GATTGTCC

∗T G

G AGGT C G

G C

0.170

0.070

0.071

0.069

0.094

GTTTGCCT

ACG

G C

0.080

0.096

0.054

0.098

0.086

GATTGCCC

ACG

G G∗AT C G G G∗AC C A

G C

0.170

0.0

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∗ ∗ G G AC C G

G C

0.068

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ACA

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A T

GATG GTCC

ACG

G GGGT C G

G C

0.023 0.018 0.054 0.078 0.042 (0.920) (0.518) (0.804) (0.824) (0.747)

Rare SNPs

Functional peptides

Vasostatin I (1–76)

Pancreastatin Catestatin (250–301) (352–372)

Figure 8.2 Systematic polymorphism discovery at a candidate genetic locus for hypertension: Chromogranin A (CHGA) sequence variation in humans. Sequences conserved between mouse and human CHGA are shown in the top panel. Location of common (upper) and rare (lower) SNPs relative to exons and conserved noncoding sequences is indicated by position. Nonsynonymous SNPs are shown as red rods and black rods represent synonymous SNPs. Nucleotides in red in the chimpanzee haplotype indicate the minor allele in the human sequence. Computationally, reconstructed haplotypes along with their relative frequencies in ethnogeographic groups within our sample are shown in the middle panel. Nucleotide deletions in haplotype sequences are indicated by an asterisk (*). Reproduced with permission from Elsevier Limited (Wen et al. 2004. Am J Hum Genet 74:197–207). (See p. 226.)

Figure 10.1 Canonical pathway analysis of cholesterol metabolism. Cholesterol metabolism is the top-scored map of the enrichment analysis using canonical pathways. Gene expression changes are visualized on the map as thermometer-like figures. Downregulation of the genes (blue color) by HC and LC is indicated with (1) and (2), respectively. The rate-limiting step in the pathway to cholesterol synthesis (and a major site of regulation) is the conversion of hydroxymethylglutaryl-CoA to mevalonate, a reaction catalyzed by HMG-CoA reductase (HMDH). Metabolites are depicted as hexagons. (See p. 274.)

Figure 10.3 Biological networks of differentially expressed genes for HC allowing the identification of transcriptional (master) regulators. (A–C ) Red dots in the right corner of a gene indicate upregulation, while blue dots indicate downregulation of a particular gene. The networks shown are representative networks used to identify transcriptional (master) regulators that control the gene expression changes under HC conditions (threshold of significance for networks p < 0.01). Metabolites are indicated as hexagons. (D) Network analysis of metabolomic and expression data reveals consistency between elevated level of eicosapentaenoic acid (red circle), an suppresor of SREBP1, and underexpression of genes whose transcription is activated by SREBP1 (blue circles). (See p. 277.)

10

Statistical Methods for Functional Analysis of ’Omics Experimental Data Zoltan Dezso, Andrej Bugrim, Richard J. Brennan, Yuri Nikolsky, and Tatiana Nikolskaya GeneGo, Inc., St. Joseph, Michigan, U.S.A.

1. INTRODUCTION 2. ENRICHMENT ANALYSIS OF HIGH-CONTENT DATA 3. ANALYZING CONDITION-SPECIFIC NETWORKS 3.1. Subnetworks Generated from a List of Differential Genes 3.2. Identifying Differential Response Subnetworks from Gene Expression 4. NETWORK MEASURES 4.1. Degree of Nodes 4.2. Average Clustering Coefficient 4.3. Average Shortest Paths 4.4. Centrality of Nodes 4.5. Statistical Test for Topological Quantities 4.6. Over- and Underconnected Nodes in Subnetworks REFERENCES

269 270 274 276 278 281 281 282 282 282 282 283 285

1. INTRODUCTION The development of a variety of high-content systems biology techniques has allowed the simultaneous interrogation of cellular components at any given time and condition. Gene expression is the primary source of high-content molecular data; however, proteomic, metabolomic, and genotyping information is also becoming increasingly available. Techniques, including serial analysis of gene expression (SAGE) (1), oligonucleotide arrays (2), and cDNA microarrays (3,4), provide surveys of gene expression patterns for thousands of genes simultaneously. Statistical methods have been developed to extract biological information from such large sets of genome-wide data (5). For analyzing the similarity of gene expression patterns, commonly used techniques include clustering (6), self-organizing maps (7), principal component analysis (8), and support vector machine (9) techniques. Many statistical tools aim to identify genes whose expressions are significantly altered under different conditions. This is generally based on the statistical analysis of the individual gene expression pattern (such as ANOVA and t-test). The application of thresholds for fold-change and/or p-value

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generates a smaller set of genes whose expressions vary between different conditions, and, therefore, is of interest for further analysis. One common approach is to apply gene enrichment analysis across knowledge-based functional categories. We show an example of such an enrichment analysis, investigating a set of differentially expressed genes under high and low cholesterol diets to understand the biological processes involved in atherosclerosis. Gene expression can also be analyzed in the context of underlying biological networks, providing a systems level approach to understanding biological function. Differentially expressed gene sets can be used as an input list for network building algorithms, and subnetworks can be generated to identify key biological processes likely to have been affected by the experiment. We have built subnetworks from the set of genes differentially expressed under high and low cholesterol diets and identified transcription regulators influencing the most affected subnetworks. In other network approaches, rather than using a preselected differential set of genes, high-content data and network topology information can be combined to detect significant changes at the pathway level (10–13). We describe a method which uses statistical techniques to identify sets of “differential” pathways, featuring highly correlated expression among multiple repeats of the same treatment, while showing strong anticorrelation across different treatments. We applied this method to microarray expression data derived from livers of rats exposed to phenobarbital, mestranol, and tamoxifen. Finally, we review some of the analysis tools available to characterize the properties of biological networks. We discuss the most common network measures in the context of condition-specific subnetworks and apply them to characterize the network properties of mutated genes in breast and colorectal cancers. 2. ENRICHMENT ANALYSIS OF HIGH-CONTENT DATA In the functional analysis of differentially expressed gene sets, a common approach is to calculate enrichment within the set of genes representing predefined categories (such as Gene Ontology or GO, pathways, and diseases). There are a few interrelated statistical tests to detect significant enrichments, referred to as the “binomial”, “chi square”, “Fisher’s exact”, or the “hypergeometric distribution” (14,15). This section illustrates how enrichment analysis can reveal important biological functions by analyzing differentially expressed genes under increasing dietary cholesterol loads in the context of atherosclerosis. Atherosclerosis is a multifactorial disease of the large arteries and the leading cause of morbidity and mortality in industrialized countries (16). There is ample evidence that hypercholesterolemia (i.e., elevated plasma levels of VLDL and LDL) induced by genetic modification or enhanced intake of dietary lipids is a major causative factor in atherogenesis (17,18). It is equally clear that from the very beginning of lesion formation, atherogenesis involves an inflammatory component thought to drive the progression of the disease (19,20). Indeed, some of the variations in the rate of lesion progression in different individuals may relate to variations in their basal inflammatory state (21,22). However, while the inflammatory processes in the complex evolution of the lesion from the early fatty streak to a fibrous plaque are considered self-perpetuating phenomena, the initial trigger and origin of the inflammatory component in hypercholesterolemia remains enigmatic (21,23). Recent observations suggest that the liver plays a key role in the inflammatory response evoked by dietary constituents (reviewed in 23,24). For example, liver-derived inflammation markers such as C-reactive protein (CRP) and serum amyloid A increase rapidly (within days) after consumption of an excess amount of dietary lipids (23,25) and thus by far precede the onset of early aortic lesion formation (23). These findings suggest

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that nutritional cholesterol itself may contribute to the evolution of the inflammatory component of atherogenesis. In recent work (26), we showed that pro-atherogenic inflammatory factors originate at least partly from the liver and hypothesized that these factors come into play at high dietary cholesterol doses because of the exponential rather than linear nature of the relationship between cholesterol intake (cf. cholesterol plasma levels) and atherosclerotic lesion size (27,28). We sought evidence for the hypothesis that inflammation and hypercholesterolemia are not separate factors, but closely related features of the same trigger—dietary cholesterol. We addressed the question of how the liver responds to increasing dietary cholesterol loads at the gene transcription level and analyzed how hepatic cholesterol metabolism is linked to the hepatic inflammatory response, including four underlying regulatory mechanisms. Notably, all analyses were performed at a very early stage of the atherogenic process (i.e., after 10 weeks of cholesterol feeding) to limit potential feedback reactions from the vessel wall. An established model for cholesterolinduced atherosclerosis, ApoE3Leiden transgenic (E3L) mice, allowed the application of experimental conditions that mimic the human situation. E3L mice display a lipoprotein profile similar to that of humans suffering from dysbetalipoproteinemia and develop atherosclerotic lesions that resemble human lesions with regard to morphology and cellular composition (29,30). E3L mice were exposed to increasing doses of dietary cholesterol (as the only dietary variable modulated), and liver genome and metabolome datasets were analyzed in a unique context, i.e., at the time point of first lesion development. To gain insight into the complex traits underlying the (patho)physiological response of the liver to dietary cholesterol, whole-genome and metabolome measurements were performed. Compared to cholesterol-free controls (Con), a relatively small number of genes (551) significantly changed with low cholesterol (LC) treatment. High cholesterol (HC) treatment modulated most (440 out of 551) of these same genes and, additionally, affected 1,896 other genes. Standard GO biological process annotation allowed categorization of 52% of the differentially expressed genes based on their biological function (Table 1). Table 1

Overview of Genes that are Differentially Expressed in Response to Cholesterol* LC

GO category Lipid and lipoprotein metabolism (including cholesterol and steroid metabolism) Protein metabolism (including protein folding and breakdown) Other metabolism (including carbohydrate metabolism) Generation of precursor metabolites and energy Transport Immune and stress response/Inflammation Cell proliferation/apoptosis Cell adhesion/cytoskeleton

HC

Up

Down

Total

Up

Down

Total

8

50

58

37

114

151

34

14

48

143

98

241

32

19

51

122

130

252

10

15

25

24

47

71

31 19 9 10

15 7 3 1

46 26 12 11

125 99 37 76

77 49 18 8

202 148 55 84

* Differentially expressed genes of LC and HC groups (ANOVA FDR < 0.05 and t-test compared to control group p < 0.01) were analyzed according to standard GO biological process annotation and grouped into functional categories. Abbreviations: LC – Low cholesterol treatment, HC – High cholesterol treatment, ANOVA – Analysis of variance, FDR – False discovery rate.

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LC treatment predominantly affected genes belonging to lipid and lipoprotein metabolism, protein metabolism, carbohydrate metabolism, energy metabolism, and transport. HC affected the same GO groups but, additionally, genes relevant to immune and inflammatory responses, cell proliferation, apoptosis, cell adhesion, and cytoskeleton integrity (Table 1). To refine the liver transcriptome data analysis and to define which biological processes are switched on/off with increasing dietary cholesterol loads, we performed gene enrichment analysis in four different functional ontologies: biological processes, canonical pathway maps, cellular processes, and disease categories (MetaCore™, GeneGo, Inc). This allowed us to analyze functionally related genes (e.g., genes belonging to a specific biochemical process) as a whole. Table 2 summarizes the significantly changed biological processes for LC and HC diets. Four key (“master”) process categories were affected by cholesterol feeding: lipid metabolism, carbohydrate and amino acid metabolism, transport, and immune and inflammatory responses. In the LC group, most significant effects occurred within the master process of lipid metabolism. Important subprocesses (i.e., processes in which more than 10% of process-related genes changed significantly) were lipid biosynthesis, lipoprotein metabolism, cholesterol metabolism, and cholesterol biosynthesis (Table 2). The overall functional effect for LC can be summarized as a substantial downregulation of cholesterol and lipid metabolism. This adaptive response of the liver indicates metabolic liver resilience up to doses of 0.25% (w/w) cholesterol. A further increase of dietary cholesterol (1% w/w; HC) intensified the changes in gene expression seen with LC indicating further metabolic adaptation. Enrichment analysis identified cholesterol metabolism as the top scoring pathway. All individual genes of the cholesterol biosynthesis pathway were downregulated to a greater extent by HC than by LC; the gene of the rate-limiting enzyme of this pathway, HMG-CoA reductase (HMDH) was downregulated 2.8-fold and 10.6-fold by LC and HC, respectively (Fig. 1). Similarly, genes relevant to lipid and lipoprotein metabolism were dose-dependently modulated; for example, LDL receptor (LC 1.3-fold down, HC 1.9-fold down) and lipoprotein lipase (LC 1.8-fold up, HC 5.5-fold up). Besides marked effects on “lipid metabolism”, HC treatment induced significant changes in the master processes: “general metabolism”, “transport”, and “immune and inflammatory response” (Table 2). In particular, HC enhanced the subprocesses involved in translational initiation, Golgi vesicle transport, mitochondrial transport, antigen presentation, antigen processing, and acute phase response by affecting the expression of more than 35% of the genes in these subprocesses. The enrichment analysis also revealed that HC, but not LC, induced dietary stress that activated specific inflammatory pathways [i.e., the platelet-derived growth factor (PDGF), interferon-γ (IFN-γ), interleukin-1 (IL-1), and tumor necrosis factor-α (TNF-α) signaling pathways]. Activation of these inflammatory pathways in HC leads to a significant upregulation of MAP kinases, complement factors, and acute phase proteins such as serum amyloid A. More generally, HC treatment induced many genes, the products of which reportedly or putatively initiate or mediate inflammatory events; these included genes encoding proteases, complement components, chemokines and their receptors, heat shock proteins, adhesion molecules and integrins, acute phase proteins, and inflammatory transcription factors. Such an altered gene expression profile indicates a profound reprogramming of the liver toward an inflammatory state not observed for LC. We compared the genes differentially expressed under high and low cholesterol diets to disease categories in MetaCore™. Disease categories represent sets of genes associated with certain diseases. Gene enrichment analysis shows relative enrichment of the up- and downregulated genes with the genes from different disease categories. As in the case of process enrichment, this procedure is carried out by p-value distribution. Enrichment analysis

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Table 2 Analysis of Processes that are Changed Significantly upon Treatment with Dietary Cholesterol*

Master process

Subprocess (child term)

Lipid metabolism Fatty acid metabolism, fatty acid beta-oxidation Triacylglycerol metabolism Cholesterol metabolism Cholesterol biosynthesis Lipoprotein metabolism Lipid biosynthesis Immune response Antigen presentation, exogenous antigen Antigen processing Acute-phase response General metabolism Cellular polysaccharide metabolism Polysaccharide biosynthesis Cofactor metabolism Regulation of translational initiation Amino acid metabolism Transport Intracellular protein transport Golgi vesicle transport Mitochondrial transport

Number of genes measured

Differentially expressed (%) LC

HC

264 8

8.7† 0.0

24.2† 50.0†

7 27 7 18 105

0.0 33.3† 71.4† 16.7† 11.4†

57.1† 33.3† 57.1† 44.4† 23.8†

297 10

3.0 10.0

12.1† 70.0†

17 11

5.9 9.1

35.3† 36.4†

3,600 19

3.3 5.3

13.1† 26.3†

9 116 9

0.0 5.2 0.0

33.3† 21.6† 44.4†

103

2.9

20.4†

1,119 161 16 11

2.9 3.7 6.3 18.2†

14.3† 19.9† 37.5† 54.5†

* Master processes and their subprocesses are listed together with the number of genes measured (third column). Percentages reflect the fraction of genes differentially expressed (within a specific process or pathway) in the LC and HC groups as compared to controls. Relevant biological processes were identified in GenMAPP by comparison of the set of differentially expressed genes (ANOVA; p < 0.01, and FDR < 0.05) with all genes present on the array. † Biological processes with a Z-score >2 and a Permute P < 0.05. Abbreviations: LC – Low cholesterol treatment, HC – High cholesterol treatment, ANOVA – Analysis of variance, FDR – False discovery rate.

with these disease-related categories confirmed activation of many signaling and effector pathways relevant to inflammation and immunity by HC, but not by LC. The most affected (i.e., activated at the gene expression level) disease categories for HC were interrelated cardiovascular disorders and (auto)immune diseases including cerebral and intracranial arterial diseases, cerebral amyloid angiopathy, hepatocellular carcinoma, and hepatitis (Fig. 2).

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Figure 1 (see color insert) Canonical pathway analysis of cholesterol metabolism. Cholesterol metabolism is the top-scored map of the enrichment analysis using canonical pathways. Gene expression changes are visualized on the map as thermometer-like figures. Downregulation of the genes (blue color) by HC and LC is indicated with (1) and (2), respectively. The rate-limiting step in the pathway to cholesterol synthesis (and a major site of regulation) is the conversion of hydroxymethylglutaryl-CoA to mevalonate, a reaction catalyzed by HMG-CoA reductase (HMDH). Metabolites are depicted as hexagons.

The enrichment analysis helped to understand mechanisms by which increased doses of dietary cholesterol affected liver homeostasis and evoked hepatic inflammation. It also showed that the liver responds to a low load of dietary cholesterol mainly by adapting its metabolic program, whereas at a high cholesterol load, the liver is much more extensively reprogrammed, and, in addition to metabolic adaptations, expresses genes involved in inflammatory stress. 3. ANALYZING CONDITION-SPECIFIC NETWORKS The cell is comprised of interconnected signaling, regulatory, and biochemical networks, with proteins and genes representing the nodes and protein–protein and protein–DNA interactions representing the edges of the network. The complete set of interactions permits

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0

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1. Tangier Disease

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2. Hypolipoproteinemia

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3. Lipid Metabolism, Inborn Errors

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4. Metabolism, Inborn Errors

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5. Hypercholesterolemia

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6. Polyneuropathies

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7. Genetic Diseases, Inborn

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8. Leukemia, Myelomonocytic, Chronic 9. Coffin-Lowry Syndrome

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11. Hypobetalipoproteinemia

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12. Coronary Disease

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5. Metabolisim, Inborn Erros 6. Neoplasms by Site 7. Cerebrovascular Disorders

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11. Hepatitis, Autoimmune 12. Carcinoma, Hepatocellular 13. Liver Diseases

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14. Liver Neoplasms

15

15. Nutritional and Metabolic Diseases 16. Adenocarcinoma

12

16 17 18 19 20

17. Ovarian Neoplasms 18. Memory Disorders 19. Neoplasms, Glandular and Epithelial 20. Ovarian Diseases

Figure 2 Global analysis of changes in gene expression in HC and LC using disease categories based on over 500 human diseases with gene content annotated in MetaCore software (GeneGo). Top 20 diseases are shown for LC and HC including their p-values.

a very large number of physically possible multistep combinations, although only a small fraction of genes are expressed and a small fraction of interactions are activated in a given time and condition. High-content data captures the subset of activated genes and proteins as a “snapshot” unique for the biological condition. Analyzing high-content data combined with biological networks allows the identification of subnetworks which are most likely activated under the conditions of interest (e.g., drug treatment or disease conditions). We show two different examples of analyzing networks in the context of gene expression. In the first approach, subnetworks were generated from an input list of genes identified as differentially expressed under the condition of interest. In the second approach, small network modules were generated; unfiltered gene expression data was mapped on them; and, by applying different statistical techniques, we identified the modules which were the most altered under different conditions. In the last part of the chapter, we review

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the most common network topological measures and show the unique topological characteristics of some condition-specific sets. 3.1. Subnetworks Generated from a List of Differential Genes It is often assumed that groups of proteins responsible for performing certain biological functions should be closely located in terms of “network distance”. The simplest procedure to generate a network from an input list of differential genes is to connect them through direct interactions. However, most times they will not form a large interconnected cluster because high-content experiments are inherently noisy and sparse. Some critically involved proteins may not change their gene expressions significantly, or the changes may be undetectable with current techniques. To identify the most affected biological processes, “shortest path” (SP) algorithm can be applied to reconstruct network modules by bringing in additional nodes connecting affected network components. In recent work (26), we analyzed networks generated from differentially expressed genes under low and high cholesterol diets (as discussed in the previous section). Biological networks were assembled from manually curated protein–protein, protein–DNA, and protein–ligand (metabolite) interactions, which are accumulated in the MetaCore™ database. Each edge or link on the network is based on small experiment data referenced in the corresponding scientific literature. Networks were generated using the SP algorithm, which links the nodes from experimental datasets by the shortest directed graphs, allowing up to two additional steps using interactions and nodes from MetaCore. A set of small networks was generated from the input list by SP algorithm, and the networks were scored (p-values) based on relative saturation of input nodes. The most relevant and highly scored networks were merged together using the Merge Network feature. The resulting networks provide links based on the known interaction data not only between the nodes from the query data set(s), but also between the nodes that regulate the given genes or metabolites. Red (blue) dots in right corner of a gene indicate upregulation (downregulation) of a particular gene. The networks shown in Fig. 3 are representative networks used to identify transcriptional master regulators that control the gene expression changes under HC conditions (threshold of significance for networks p < 0.01). Network analysis of HC-modulated genes allowed the identification of transcription factors that mediate the evolution of hepatic inflammation and that are ultimately responsible for the effects on the process level. HC-evoked changes require specific transcriptional master regulators, some of which are known players in this process (nuclear factor-kappa B, NF-κB; activator protein-1, AP-1; CAAT/enhancer-binding protein beta, C/EBPβ; p53), and others that are newly identified (CREB-binding protein, CBP; hepatocyte nuclear factor 4-α, HNF4α; specificity protein 1, SP1; signal transducer and activator of transcription-3/-5, (STAT-3/-5); Yin Yang 1, YY1; Fig. 3A–C). To verify whether the switch from metabolic adaptation (with LC treatment) to hepatic inflammatory stress (with HC treatment) is also reflected at the metabolite level, we performed a comprehensive high performance liquid chromatography mass spectroscopy (HPLC/MS) -based lipidome analysis (measuring a total of about 300 di- and triglycerides, phosphatidylcholines, lysophosphatidylcholines, and cholesterol esters) in liver homogenates of Con, LC, and HC groups, and corresponding plasma samples. Combined network analysis of genome and metabolite datasets revealed that the intrahepatic level of eicosapentaenoic acid—a suppressor of sterol regulatory element binding protein 1 (SREBP1) (31)—was increased, providing a molecular explanation for the observed downregulation of genes involved in cholesterol biosynthesis (Fig. 3D).

Figure 3 (see color insert) Biological networks of differentially expressed genes for HC allowing the identification of transcriptional (master) regulators. (A–C) Red dots in the right corner of a gene indicate upregulation, while blue dots indicate downregulation of a particular gene. The networks shown are representative networks used to identify transcriptional (master) regulators that control the gene expression changes under HC conditions (threshold of significance for networks p < 0.01). Metabolites are indicated as hexagons. (D) Network analysis of metabolomic and expression data reveals consistency between elevated level of eicosapentaenoic acid (red circle), an suppressor of SREBP1, and underexpression of genes whose transcription is activated by SREBP1 (blue circles).

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The identified transcription factors (confirmed by data mining) control the expression of genes encoding acute phase response proteins, complement factors, growth factors, proteases, chemokine receptors, and factors stimulating cell adhesion. Most importantly, HC induced genes whose products can act extracellularly and which possess reportedly proatherogenic properties. Examples include complement components (C1qb, C1qR, C3aR1, C9), chemoattractant factors (ccl6, ccl12, ccl19), chemoattractant receptors (CCR2, CCR5), cytokines inducing impaired endothelial barrier function (IFN-γ), adhesion regulators (integrin β2, integrin β5, CD164 antigen/sialomucin, junction adhesion molecule-2), growth factors (PDGF, VEGF-C, TGF-β), proteases involved in matrix remodeling during atherogenesis (cathepsin B, L, S, and Z; matrix metalloprotease-12), and cardiovascular risk factors/inflammation markers (haptoglobin, orosomucoid 2, fibrinogen-like protein 2, α1-microglobulin). This upregulation of proatherogenic candidate genes in the HC group is consistent with the observed enhanced early atherosclerosis found in this group. Expansion of the lipid and inflammatory networks revealed that hepatic lipid metabolism is linked to the hepatic inflammatory response via specific transcriptional regulators that control both processes. Among these dual regulators were CBP, C/EBPs, PPARα, and SP-1. This network analysis helped us to identify new specific transcriptional regulators—some having a dual role controlling both hepatic lipid metabolism and hepatic inflammation, indicating that the same regulatory mechanisms underlie these processes and thereby link the two processes. 3.2. Identifying Differential Response Subnetworks from Gene Expression In the previous section, we analyzed a set of differentially expressed genes; the result of a statistical analysis based on individual gene expression patterns. Such preselection based on fold-change and p-values leaves the bulk of the expression profile beyond the scope of further functional analysis and, depending on the stringency of the cutoffs applied, may substantially limit the analysis. Newer statistical methods incorporate network topology information into the analysis of high-content datasets (10–13). Here, we present a statistical approach combining gene expression and systems level network information to identify the most affected subnetworks in an experiment. First, we generated a large number of network modules, and then we mapped gene expression data and applied statistical methods to select modules most likely related to the condition of interest. After describing the method, we show how it can be applied to gene expression data generated for the response to several long-term drug treatments in livers of rats exposed to phenobarbital, mestranol, and two doses of tamoxifen (32,33). We generated a large set of pathway modules from the collection of “canonical” pathway maps manually annotated by GeneGo. The pathway maps display well-understood functional blocks in cell signaling and metabolism, and therefore are referred to as “canonical” pathways. The pathway modules represent linear sequences of protein interactions or metabolic reactions that originate and terminate at biologically meaningful start-points (membrane receptors or their ligands) and end-points (transcriptional factors or their immediate targets). All possible pathway modules were generated from each map by an exhaustive depth-first search algorithm. The result was a total of 14,582 modules from 145 “canonical” maps containing about 1,500 genes. The pathways on average had 4.5 steps and had a high degree of redundancy with every gene participating on average in 10 pathways. High redundancy was needed for “fine-tuning” the selection of network modules with correlated patterns of activity. Indeed, if every gene is included in multiple pathways, most patterns of network activity can be closely approximated as a certain combination of these pathways. Pathways that were used represent cellular cascades that were described experimentally as working units, and therefore are more likely to occur in their entirety, at least in some cell types and processes

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than other types of network modules. The objective of the next step was to test whether we can consistently select network modules that differentiate between conditions/phenotypes. Our approach to selecting pathway modules that are specific for a particular condition and/ or differentiate between conditions is based on calculating and comparing relative distances between samples in the space of gene expression in individual pathways (Fig. 4). After mapping gene expression data onto precomputed pathway modules, the matrix of sample–sample distances was calculated. Distances were calculated in the gene expression space of every individual pathway module. The most common distance metrics are the Euclidian distance and the Pearson correlation distance. The Pearson distance between samples x and y for a pathway of n genes is calculated as:

, where xi and yi are the gene expression log-ratios for genes i in samples x and y, and , , , are the mean values and respective standard deviations. Similarly, the Euclidian distance is calculated as:

.

Figure 4 Calculating distances between samples in the pathway’s gene expression space. Hypothetical pathway is shown, consisting of three genes A, B, and C. (A) Samples are represented as points in three-dimensional space of gene expression. After defining appropriate distance metrics, the relative distance between the pathways can be evaluated. (B) The pathways are defined in the gene expression space by their corresponding gene expression values represented by arrows.

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Euclidian distance is sensitive to changes in amplitude of gene expression, while Pearson distance should also identify pathways with small but strongly correlated differences. The overlap between the results produced with two metrics should be substantial; however, we also expect to find pathways uniquely identified by each technique. After calculating all distances, the procedure generates two matrices of sample–sample distances. Each row of these matrices corresponds to a pathway module, and each column represents a sample pair. The distances can be grouped according to arbitrarily defined types of samples. In the following, we apply the method on selected toxicogenomic datasets of microarray gene expression profiles in rat liver following treatment with mestranol, phenobarbital, and two concentrations of tamoxifen. For each treatment, there were five samples (biological repeats). Female rats were administered an AIN-76A basal diet, or a diet containing the drugs mixed into the basal diet at 500 mg phenobarbital per kg diet, 2 mg mestranol per kg diet, and tamoxifen at either 250 mg per kg diet (designated as low) or 500 mg per kg diet (designated as high). Overall, five treatment groups were used in the experimental design: control, phenobarbital, mestranol, and two doses of tamoxifen. We have computed log-ratio for treated vs. untreated animals. The gene expression data for the four treatments was mapped onto our set of pathway modules. We compared the Pearson correlation distances between repeat samples for the same treatment vs. samples from different treatments. For every pathway, we tested the statistical hypothesis that the average distance between repeats of the same treatment is significantly different from the average distance between different treatments. The criterion for success was identification of distinctive pathways or groups of pathways that distinguish between treatment responses, e.g., mestranol from phenobarbital. First, all distances were arranged into groups representing either within-treatment or inter-treatment distances, and ANOVA analysis was applied to identify the pathways with at least one pair of groups with different means. False discovery rate (FDR) filtering (34) was applied to the pathways, with a significance level of 0.01. For the pathways selected by ANOVA and subsequent FDR correction, the pair-wise t-test was performed (p = 0.01) to determine which specific groups of distances were significantly different. We selected pathways which clearly distinguish between two treatments. For instance, the pathways that distinguish mestranol from phenobarbital treatments had to satisfy the condition that both distances between mestranol repeats and distances between phenobarbital repeats are statistically smaller than mestranol–phenobarbital distances. Our analysis identified 112 pathway modules differentiating between phenobarbital and mestranol, 256 pathways differentiating between phenobarbital and tamoxifen low concentration, and 431 pathways differentiating between phenobarbital and tamoxifen high concentration. Four pathways were differentiating between mestranol and tamoxifen low concentration, and one pathway between mestranol and tamoxifen high concentration. These results are consistent with biological differences and similarities of these drugs. A large number of pathways differentiate between phenobarbital and mestranol treatments. Indeed, phenobarbital is known to induce cell proliferation and tumor growth, while tamoxifen is a tissue-specific estrogen receptor agonist with no known proliferative effect in liver. The most similar treatments, those for the two doses of the same drug (tamoxifen), show no significant differences in affected pathways. Furthermore, there were only four descriptor pathways for the mestranol–tamoxifen pair. Indeed, mestranol is a synthetic analog of estradiol and similar to tamoxifen in its ability to interact with estrogen receptor. A large fraction of the genes from the differentiating pathways (55% for p < 0.1 threshold) would have been excluded from the analysis

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by any conventional microarray analysis procedure based on statistical significance of individual genes. Unlike statistical methods based on changes in gene expression of individual genes, this method identifies pathways characterized by small but coordinated changes in expression. We have demonstrated two different approaches for generating subnetworks, both methods having advantages and disadvantages. In the first approach, reconstructing networks from a list of differentially expressed genes limits the analysis to a small number of genes and ignores the gene expression values for the rest of the genes. The second approach is applied to unfiltered gene expression data, but approximates the global network as a combination of a large number of smaller modules identified as functional building blocks of the global network. Because the network module “space” is very large, we limit our analysis to known pathway modules and extract possible linear sequences of protein interactions. 4. NETWORK MEASURES The development of network theory provided many advances in our understanding of the system-level organization of the cell. Network theory revealed that many biological networks of different origins (metabolic, regulatory, protein interaction) and of different organisms are scale-free (35–38), being characterized by power-law degree distribution. The scale-free nature of these networks results in important properties, such as the presence of nodes with large numbers of connections (hubs) and the robustness of these networks (39,40). Other important properties include modularity, which refers to the fact that biological networks are composed of functionally separable subnetworks corresponding to specific biological functions (41). Cellular networks are also characterized by disassortivity (42), highly connected nodes interacting with proteins with only a few connections, high clustering, and the presence of motifs (43,44). There have been many studies relating the topological properties of nodes and important biological functions. For example, studies showed that essential and toxicity-modulated proteins in yeast are characterized by high degree (see below) and centrality of the nodes (45,46). Network theory tools have been proven successful in the analysis of global interaction networks, and they can also be applied to characterize the properties of subnetworks. We will now review some of the most common network measures and show how these measures can be applied in the context of condition-specific subnetworks. 4.1. Degree of Nodes The number of interactions of a node gives the node’s degree. Since many biological networks are directed, nodes are characterized by in- and outdegree, giving the number of outgoing and incoming interactions. The average degree of a network can be calculated as an average for the nodes in the network. For an experiment (such as a set of differentially expressed genes in a certain condition), the average degree is calculated over the degree of the nodes of interest, considering all interactions they have in the global network. Comparing the average degree of nodes identified in a particular experimental condition with the average degree of the global network reveals if the nodes of interest have any special role in the global “interactome”. Average degree can also be calculated as the average of the number of interactions within the experimental set, providing tools to compare networks built under different experimental conditions. The degree of nodes has been shown to correlate with important biological functions (46).

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4.2. Average Clustering Coefficient The clustering coefficient is a measure that captures to what degree a node’s neighbors are connected. It is defined as:

, where ni is the number of links among the ki neighbors of node i. As ki(ki–1)/2 is the maximum number of such links, the clustering coefficient is a number between 0 and 1. The average clustering coefficient is obtained by averaging over the clustering coefficient of individual nodes. A network with high clustering coefficient is characterized by highly connected subgraphs. Clustering coefficient can be averaged over a subset of nodes of interest and compared with clustering coefficient of the global network.

4.3. Average Shortest Paths The shortest distance between two nodes is the number of links along the shortest path. The average shortest path is the average over the shortest paths for all node pairs in the network. In directed networks, shortest paths also have directionality; thus, average shortest paths can be calculated as the average of directed shortest paths, and, depending on the directionality of the paths, two different shortest path distances can be assigned to node pairs. We calculated shortest distance for a subset of nodes by considering all undirected paths in the global network for all pairs in the set. It is expected, for example, that functionally related nodes will be closer in terms of network distances, resulting in a smaller average shortest path than that of the global network.

4.4. Centrality of Nodes Centrality of a node is the number of shortest paths going through that node when we consider the shortest paths between all node pairs. When there is more than one shortest path between two nodes, the centrality is divided by the number of shortest paths between them. The centrality of a node (k) can be calculated as:

, where σij is the number of shortest paths between the nodes i and j, σij(k) is the number of shortest paths between i and j which passes through k. The centrality of nodes has been shown to reflect important biological functions in yeast (45). 4.5. Statistical Test for Topological Quantities If we consider a list of nodes corresponding to experimentally altered genes and proteins, this preselected list is often used to calculate network measures. The statistical significance of the resulting quantities can be defined by generating a random subset from the same list (e.g., for gene expression, the set of genes on the microarray). The procedure is repeated

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many times (e.g., 1,000 or 10,000 trials), and network measures are calculated and compared with the quantities measured for the subset of interest. 4.6. Over- and Underconnected Nodes in Subnetworks We can calculate the interactions of proteins within a set of interest and compare that with the number of connections in the global protein “interactome”. The goal of the analysis is to identify proteins with a significantly large number of interactions within the set of interest. We assign statistical significance by using the cumulative hypergeometric distribution as follows:

and

,

where N is the number of proteins in the global “interactome”, n is the number of proteins derived from the sets of genes of interest, D is the degree of a given protein in the global “interactome” database, and k is the degree of a given protein within the set of interest. The p-value gives the probability of observing k or more interactions of a given protein (with degree D in global network) by random chance within the set of interest [of size (n)]. The probability of observing “underconnected” proteins can be calculated by 1 – p(k). We applied the above defined quantities to a set of genes mutated in human cancer. With the determination of the human genome sequence and improvements in sequencing and bioinformatic technologies, systematic analyses of genetic alterations in human cancers have become possible (47–49). We recently studied the genomes of breast and colorectal cancers by examining all of the Reference Sequence (RefSeq) genes (50). The RefSeq database is a comprehensive, nonredundant collection of annotated gene sequences that represents a consolidation of gene information from all major gene databases (51). Combining the data from two recent studies (50,51), it was found that 1,718 genes (9.4% of the 18,191 genes analyzed) had at least one nonsilent mutation in either a breast or a colorectal cancer. We identified candidate cancer genes (CAN-genes) that are most likely to be drivers and are therefore most worthy of further investigation. A gene was considered to be a CAN-gene if it harbored at least one nonsynonymous mutation in both the discovery and the validation screens, and if the total number of mutations per nucleotide sequenced exceeded a minimum threshold. Using these criteria, we identified a total of 280 CANgenes, equally distributed between colorectal and breast cancers. We analyzed the network topological properties of these genes in the database of MetaCore, which includes human protein–protein interactions, signal transduction and metabolic pathways, and a variety of cellular functions and processes.

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The subset of breast and colorectal CAN-genes were both characterized by significantly larger average degree than expected from the set of experimentally studied genes (Table 3). Gene products were also closer than expected in terms of network distances, reflected by the significantly smaller average shortest paths than the one of the global networks, possibly a consequence of the functional relatedness of these genes. Indeed, it was suggested that a large number of genetic alteration in tumors reflect changes in a limited number of pathways (52), which would further account for small distances between gene products. Clustering coefficient was slightly smaller for both sets of mutated genes compared to the set of experimentally studied list of genes, although the differences were not significant at a p < 0.05 statistical significance level. This could be the consequence of the scaling of the clustering coefficient with the average degree of nodes observed in some biological networks (53)—higher degree nodes tend to have less connected neighbors— reflected in smaller average clustering coefficient. This example shows that the set of CANgenes have unique topological properties; high degree of these genes likely reflects the fact that the mutation of these genes has a high impact on multiple pathways. The CAN-genes were not densely connected among themselves—the largest interconnected clusters being 4 for breast and 13 for colorectal genes. We uploaded the lists to MetaCore and applied the included “interactome” tool to identify significantly overconnected transcriptional factors (Table 3). Network analysis of condition-specific sets of genes identified from high-content experiments provides network characteristics unique to the condition. These network measures capture the structure of the condition-specific “interactome” and provide insight into important biological functions. Network analysis allowed us to characterize the unique topological properties of genes mutated in cancer and to identify genes that deserve further attention, thus helping to prioritize research efforts on the outcomes of cancer genomesequencing efforts. Table 3

Topological Properties of Cancer Genes (CAN-genes) in Breast and Colorectal Cancers

Degree Indegree Out degree Clustering coefficient Shortest paths

Global network

Experimentally analyzed genes

Breast CAN

Colorectal CAN

10.95 6.21 7.25 0.152 3.67

11.13 6.26 7.34 0.152 3.63

24.42 (p = 0.01) 11.69 18.25 0.12 3.46 (p = 0.12)

31.01 (p = 0.006) 17.39 18.23 0.128 3.35 (p = 0.007)

Colorectal cancer Object name GSC Mixl1 TGIF ATF-2 PAX8 FAST-1/2 SOX5 c-Jun SMAD3 TBX3

Breast cancer p-value

Object name

p-value

3.58E − 05 5.08E − 05 9.21E − 05 0.000113 0.000119 0.000151 0.00039 0.000932 0.001025 0.001195

PAX8 NFIC HMX2 Staf-50 ZNF215 E2F5 AIRE NFIA FOXE1 HIC1/2

2.29E − 05 0.00124 0.004418 0.004418 0.004418 0.004586 0.005407 0.008238 0.008816 0.008816

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Toxicogenomics Data and Databases Jennifer Fostel Laboratory of Respiratory Biology, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, U.S.A.

1. INTRODUCTION 2. PUBLIC TOXICOGENOMICS DATABASES 2.1. ArrayExpress and Tox/MIAMExpress 2.2. ArrayTrack 2.3. Chemical Effects in Biological Systems (CEBS) and Biomedical Investigation Database (BID) 2.4. Comparative Toxicogenomics Database (CTD) 2.5. dbZach 2.6. Extraction of Differential Gene Expression (EDGE) 2.7. Toxicogenomic Samples and Procedures (TSP) Database 2.8. Proprietary Toxicogenomics Databases 3. TYPES OF DATA 3.1. Toxicology and Pathology 3.2. Transcriptomics 3.3. Metabolomics and Metabonomics 3.4. Proteomics 3.5. Other ’Omics Disciplines 4. ARCHIVING ’OMICS DATA 5. CHEMINFORMATICS DATA 6. SHARING DATA 6.1. Public/Private Consortia 6.2. Standardized Protocols Versus the Use of Descriptors 6.3. Formats for Sharing ’Omics Data 6.4. Formats for Sharing Non-’Omics Data 6.5. Data Quality 7. CHARACTERISTICS OF “IDEAL” DATABASE REFERENCES

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1. INTRODUCTION The science of toxicogenomics has been described as the marriage between traditional toxicology (the study of the effects of toxicants on an organism) and transcriptomics and The opinions expressed in this chapter are the authors’ personal opinions and they do not necessarily reflect those of NIEHS, DHHS, or the Federal Government.

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proteomics (a measure of the molecular state of an organism or a cell) (1). A further extension of toxicogenomics will be its predicted evolution into systems toxicology. Systems toxicology can be described as the study of perturbation by chemicals and stressors, monitoring changes in molecular expression and conventional toxicological parameters, and iteratively integrating biological response data to describe the functioning organism (2). Toxicogenomics studies are expected to lead to the identification of novel biomarkers of stress and adverse responses and to an increased understanding of the mechanisms of toxicity and adverse reactions. This chapter will describe four aspects of toxicogenomics data, databases, and their usage: (i) current public databases with applications in toxicogenomics, (ii) types of toxicogenomics data, (iii) issues relating to sharing of toxicogenomics data, and (iv) some characteristics of an ideal toxicogenomics database. 2. PUBLIC TOXICOGENOMICS DATABASES Databases serve a variety of different needs, and one size does not fit all. A user with a question, such as “what gene products interact with this chemical?” would go to curated data extracted from the literature and available in the Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org/) or in the Environment, Drugs and Gene Expression Database (EDGE2; http://edge.oncology.wisc.edu/). A user wishing to analyze proprietary microarray data behind their firewall might download the ArrayTrack toolset (http://www.fda.gov/nctr/science/ centers/toxicoinformatics/ArrayTrack/index.htm) or license the dbZach database and tools (http://dbzach.fst.msu.edu/). A user wishing to integrate assay data with microarray data, or private data with public data from toxicogenomics studies would load into Biomedical Investigation Database (BID) and the Chemical Effects in Biological Systems (CEBS) knowledgebase (www.cebs.niehs.nih.gov). A user wishing to publish their microarray or toxicogenomics data would load into Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih. gov/geo/), ArrayExpress using Tox/MIAMExpress (http://www.ebi.ac.uk/tox-miamexpress/), or into CEBS. A user wishing to monitor toxicology samples might install the Toxicology Samples and Protocols (TSP) and Laboratory Information Management System (LIMS). Each of these databases is described in more detail below. Databases can be classified as LIMS systems, repositories, warehouses, or knowledgebases depending on their capabilities. A LIMS system is used to track samples, reagents, protocols, and data, generally within a single lab. A data repository is essentially a storage facility; the term “data warehouse” refers to a data repository with analytical and query functionality. A data warehouse can provide analytical tools for the user to apply to selected data or a web-based query interface to permit the user to retrieve precomputed data. A knowledgebase is a system containing facts and rules that can be applied to solve problems and generate new knowledge. The database can be closed (data can only be added by curators), controlled (curators and depositors work collaboratively to format and add data), or downloadable so that any user can modify and use as they need. Data in the data system can be microarray data, assay data, study design, sample tracking, or various combinations and variations of these. 2.1. ArrayExpress and Tox/MIAMExpress The European Bioinformatics Institute (EBI; http://www.ebi.ac.uk/) has developed the ArrayExpress public data repository to house microarray data (http://www.ebi.ac.uk/ arrayexpress/) (3,4) and the Expression Profiler data warehouse (http://www.ebi.ac.uk/ expressionprofiler/) (5,6) to permit analysis of normalized data sets extracted from

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ArrayExpress. The Minimal Information About a Microarray Experiment (MIAME) standard is widely used to ensure that microarray data are described in such detail that they can be interpreted (7). ArrayExpress is one public repository for MIAME-compliant microarray data. Additionally, members of EBI worked in collaboration with a consortium of toxicologists organized by the International Life Sciences Institute–Health and Environmental Science Institute (ILSI–HESI) and the ILSI–HESI Technical Committee on the Application of Genomic Technologies to Mechanism-Based Risk Assessment (http://www. hesiglobal.org/Committees/TechnicalCommittees/Genomics/). Hereafter, this committee will be referred to as the HESI Genomics Committee. Microarray data from the HESI Genomics Committee are housed in ArrayExpress and Tox/MIAMExpress (http://www. ebi.ac.uk/tox-miamexpress/). Tox/MIAMExpress allows the user to link biological endpoint data (e.g., clinical observation, pathology, and histopathology) with gene expression data, such that the biological endpoint data for the same specimens used for microarray analysis can be captured. In addition to the public repository housed at the EBI, the ArrayExpress database structure can be installed locally. Alternatively, user data can be uploaded to ArrayExpress using either validated Microarray Gene Expression-Markup Language (MAGE-ML)formatted files or via the Tox/MIAMExpress tool. 2.2. ArrayTrack The National Center for Toxicological Research (NCTR; http://www.fda.gov/nctr/) has developed the ArrayTrack data warehouse (8,9) (http://www.fda.gov/nctr/science/centers/ toxicoinformatics/ArrayTrack/index.htm). ArrayTrack has advanced analytical capabilities and houses data generated by NCTR researchers, some of which are available to the public. ArrayTrack is in use at the Food and Drug Administration (FDA) and Environmental Protection Agency (EPA), and at a number of private institutions and academic labs. Data from a recent comparative study of microarray platforms, such as the Microarray Quality Control Project (MAQC), are also housed in ArrayTrack (10–14). The ArrayTrack client interface, written in java, can be downloaded and used in client–server mode, in which the tools are run on the user’s desktop. Additionally, a copy of the ArrayTrack database in Oracle can be requested from NCTR and installed at the user’s site. This permits local management and storage of microarray data. Currently, ArrayTrack is not a public data repository. 2.3. Chemical Effects in Biological Systems (CEBS) and Biomedical Investigation Database (BID) The CEBS knowledgebase is a public data warehouse which has been developed by the National Center for Toxicogenomics, within the National Institute for Environmental Health Sciences (NIEHS) (15). CEBS is designed to capture, manage, and integrate data from ’omics technologies (transcriptomics, proteomics, and metabonomics) and also from traditional toxicology studies (study organization, protocols and timeline, clinical pathology measures, and histopathology findings). CEBS permits the user to select subsets of data based on study or subject characteristics (e.g., test agent, subject response, or phenotype) and select associated ’omics data for analysis. Users can combine, integrate, and reanalyze data in CEBS to produce new information. Data can be analyzed in private mode prior to publication and then data can be made public as desired. CEBS provides a web image

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diagram of the experimental groups and the experimental timeline so that users can assess each study for suitability for their analysis, and identify samples taken at separate time points following treatment. CEBS is written in J2EE and is based on two object models, SysBio-OM and SysTox-OM. SysBio-OM models microarray, proteomics, and metabonomics data, and is based on the MIAME-compliant Microarray Gene Expression Object Model (MAGE-OM; http://www.mged.org/Workgroups/MAGE/mage-om.html) and the Pedro proteomics standard. SysTox-OM models the study design, organization and timeline, and captures associated assay data such as clinical pathology, histopathology, and observations of study subjects. Both object models van be viewed or downloaded from http://cebs.niehs.nih.gov/. CEBS has controlled data entry; new data flow to CEBS after loading and curation in BID (see below). The BID was also developed by the NIEHS (16). BID is based on the CEBS Data Dictionary and is a prototyping tool for CEBS functionality as well as serving as a loading and curation vehicle for CEBS. The BID database has been shared with Health Canada. BID is written in Oracle and has a Cold Fusion interface. Data are loaded into BID using either customized text files or via a wizard-like interface capturing the information needed to describe the study. 2.4. Comparative Toxicogenomics Database (CTD) Mount Desert Island Biological Laboratories (MDIBL) have developed the Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org/) (17–20). CTD is a web-based query reference built on curated information derived from the scientific literature. Using CTD, the user can look up specific information about gene products and toxicants of interest, and recover information derived from the literature about their interactions. Additionally, CTD integrates information across species, and recently, data about human disease interactions has been added to CTD. CTD supports in silico research in the relationship between genes and the chemical or environmental components, and links this to human diseases such as asthma and cancer. CTD uses expression data from EDGE (see below) to derive additional information about gene expression following treatment with a toxicant. CTD also supports queries on the chemicals used to perturb the systems and links to annotation for the compounds in the database. Data in CTD are derived from analysis of the literature and curated and entered by CTD staff members. 2.5. dbZach Tim Zacharewski’s laboratory at the Michigan State University has developed dbZach (http://dbzach.fst.msu.edu/), which is a MIAME-compliant enterprise solution for the capture, management, and analysis of toxicogenomics data (21). dbZach has web-based query interfaces to share information from the laboratory about clones, PCR primers, and protocols and to share comments about the software. It is possible to license the structure for use. The dbZach database has four core subsystems: Clones, Protocols, Genes, and Sample Annotations. These interact with experimental subsystems with modules for assays including cDNA microarray, Affymetrix microarray, proteomics, metabonomics, RT-PCR, and toxicology. Analytical subsystems include pathways, orthology, and gene regulation. dbZach consists of an Oracle or a DB2 database, web interface, and java analysis tools, and is a data structure more than a public toxicogenomics database.

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2.6. Extraction of Differential Gene Expression (EDGE) The EDGE (22) and EDGE2 (http://edge.oncology.wisc.edu/) (23) databases have been developed and maintained by Chris Bradfield’s laboratory at the University of Wisconsin. EDGE2 is a web-based query resource for treatment–gene interactions, derived from microarray studies carried out in mice. The data in EDGE2 are derived by the Bradfield lab, using in-house or submitted RNA samples, and is another example of a closed data system. EDGE2 users select the treatment(s) of interest and threshold information (such as minimal fold change) and can retrieve a heat map of the genes with differential expression exceeding the threshold for the conditions of interest. Additional tools available via EDGE2 include classification methods. 2.7. Toxicogenomic Samples and Procedures (TSP) Database The TSP database was developed by researchers at the US Environmental Protection Agency (EPA) and North Carolina State University, and is an example of a LIMS system. LIMS systems are used to track reagents, experimental samples, and standard operating procedures (SOPs). TSP is also aligned with the BID/CEBS data flow, making it reasonably straightforward to move data from TSP to a public data warehouse following publication. TSP is written in Microsoft Access and is available on request (24). TSP has been extended to proteomics and metabonomics samples and procedures as well as those relating to microarray experiments (25). 2.8. Proprietary Toxicogenomics Databases In addition to public databases, biotechnology companies such as GeneLogic (26,27), Iconix (28–31), and Curagen (32–35) have created proprietary databases of data compiled from exposure studies. These are available by license to companies. In addition, some data have been shared by Iconix with public data repositories such as the CEBS knowledgebase (www.cebs.niehs.nih.gov). The data in these proprietary databases are very extensive, and reports of predictive use of the data and derived signatures have been published (27,29,33,36–40). GeneLogic and Iconix use Affymetrix GeneChip microarrays, whereas Curagen uses a proprietary open technology termed “Gene Calling” (http://curatools. curagen.com/genecalling_portal/). The Critical Path Initiative to New Medical Products came out of efforts by the US Food and Drug Administration (FDA) to stimulate and facilitate a national effort to modernize the scientific process through which a potential human drug, biological product, or medical device is transformed from a discovery or “proof of concept” into a medical product (http://www.fda.gov/oc/initiatives/criticalpath/). Recently, the Critical Path Institute (C-Path; http://www.c-path.org) was founded as an independent, publicly funded, nonprofit organization to work toward implementing the Critical Path Initiative. Members can contribute toxicogenomics data behind the C-Path firewall with the aim of identifying novel molecular biomarkers of adverse or therapeutic events. Such biomarkers can include a gene expression profile, peptide, altered metabolite, or metabonomic profile. Biomarkers will be validated and used to improve safety and efficiency of drug development. 3. TYPES OF DATA The data generated during a toxicogenomics study can be of several different types. Initially, the toxicogenomics study itself generates data, such as the laboratory study, its design and

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execution, and observations made during the course of the study. Generally, additional specimens are taken during the study, which are sent to other labs where data from assays include enzyme activity, histopathology, cell population descriptions, and microarray data or other ’omics scale data. The entire spectrum of data provides the basis for subsequent analysis and interpretation of the results. 3.1. Toxicology and Pathology Toxicological assessment is an important component of the development and manufacture of novel materials such as medicines, pesticides, plastics, and silicon microchips, to name just a few. As a novel agent is developed, its effect on laboratory animals is used as a first predictor of its ultimate risk to the humans and animals exposed to it. A common use of toxicology is in safety assessment during drug development or industrial chemical development. The analysis involves systemic exposure of animals with known (and often invariant) genetic backgrounds, gender, and age to the chemical in question. Post-treatment analysis involves examination of behavioral, physiological, and physical effects of the exposure. Hematology, urinalysis, and histopathogical examinations of various organs are routinely done in these types of toxicology studies. Most institutions use standard internal protocols for short-term (8–14 days), intermediate (30–90 days), or chronic (6 months to 2 years) study design. Additional specialty study designs are used for investigation of immunotoxicology, neurotoxicology, and developmental and reproductive toxicology. The methods used within an institution tend to remain fixed, in part to facilitate comparison of vast amounts of historical data. Pathologists describe the microscopic changes associated with treatments, using a lexicon such as the Path Code Tables used by the National Toxicology Program (NTP; http://ntp.niehs.nih.gov/). Frequently, each pathology report is reviewed by other pathologists within the department in a peer review process, ensuring that the terms are accurate and understandable. While it is straightforward for a trained pathologist to comprehend the description of another pathologist, often the descriptions by two pathologists are sufficiently different that they cannot be matched computationally. An image from the micrograph is often included with the written description. Similarly, both the descriptions and micrograph images need to be incorporated into toxicogenomics databases; however, current databases described above have not sufficiently addressed this challenge. 3.2. Transcriptomics Transcriptomics experiments, the measure of the abundance of RNA levels across thousands of genetic loci simultaneously, became possible with the advent of microarray technology. Microarrays generally have a grid of features of nanometer scale, each containing nucleic acid sequence fragments corresponding to a particular DNA sequence. Transcripts are isolated from the cell or tissue of interest and allowed to anneal to the nucleic acid fragments on the microarray platform. Transcripts bound nonspecifically are washed off using increasingly higher-stringency wash conditions, and then the level of hybridization at each feature is measured. With the development of microarray, it was quickly recognized that the resulting data would be sufficiently valuable that they could be reused in subsequent analyses. In order to make the most out of microarray data, various meta-data, or data about the data, need to be included with the microarray data themselves. Because of the enormous volume of data produced in transcriptomics experiments, relatively minor changes in laboratory

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procedures can affect the results and their interpretations. For this reason, it is crucial that all relevant protocols and parameters used in an experiment are recorded along with the final transcriptomics data output. These protocols and parameters about the data constitute the meta-data. For example, part of the genomic meta-data will include information about the microarray platform, labeling and hybridization, probe sequences, serial order and location of the probes on the chip, location of each probe sequence in the gene sequence, molecular function of genes, scanner setting, and so forth. The Microarray Gene Expression Data (MGED) Society was formed by community volunteers, who recommended the use of the MIAME standard to report these meta-data. The MIAME standard of data submission has since been adopted as a submission standard requirement by numerous journals. 3.3. Metabolomics and Metabonomics Metabolomics aims to provide a comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties, and they occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry (41). Metabolomics studies are designed to measure the abundance of hundreds of known metabolites and, from this information, deduce the metabolic changes that likely gave rise to the observed metabolite levels. In contrast, metabonomics analysis “bins” individual small molecules into discrete “buckets” based on nuclear magnetic resonance (NMR) shifts, which creates a profile of the metabonomics response of a tissue under the original assay conditions. By comparing profiles associated with stressed and unstressed subjects, metabonomics researchers aim to produce profiles predictive of adverse events. 3.4. Proteomics Proteomics experiments are surveys of the abundance of proteins found within a cell or tissue. Similar to microarray experiments, the total protein content of cells or tissues of interest is isolated and separated on the basis of molecular properties, and the abundance of each protein “spot” is measured. In contrast to microarray experiments, in most cases the identity of the protein must then be determined by microsequencing or mass spectroscopy methods. This is time-consuming and often yields incomplete matches, increasing the difficulty of interpreting proteomics data. Another variant of proteomics is the use of antibody or other affinity arrays, in which a grid of reagents of known properties is used to capture and separate proteins for analysis (42–44). Other proteomics methods such as Surface-Enhanced Laser Desorption and Ionization (SELDI) follow the philosophy of metabonomics and aim to produce a profile predictive of adverse events. In these methods, the proteins are fragmented using enzymes or specific chemical agents and separated by physical properties to produce a profile of peptides in which changes associated with treatment conditions can be detected. 3.5. Other ’Omics Disciplines A number of ’omics sciences (including economics) are listed at http://www.genomicglossaries.com/content/omes.asp. Examples of ’omics disciplines related to toxicogenomics include eco-genomics or ecological genomics, nutrigenomics, and pharmacogenomics (or pharmacogenetics). These areas of science describe, respectively: studies of the effects

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of environmental toxicants on molecular populations from individual members selected at random from a population at large; the effects of diet and nutrition on the molecular response of subjects; and, the impact of the genetic make-up of an individual patient on the response (adverse or beneficial) to a particular medicine. 4. ARCHIVING ’OMICS DATA When microarray data were first created, the MGED Society recognized that having both the raw data and the summary results following analysis would be of value for public databases. This was one of the motivating factors of the MIAME Checklist, which called for a complete description of the array elements, manufacture, and as close to the raw data or image as could reasonably be deposited. This approach has been followed in other ’omics Standard Initiative checklists, such as the PSI/HUPO proteomics initiative and MSI metabolomics initiative. Raw data are often generated by proprietary equipment, so the settings from the equipment and model or version number are included to permit reanalysis. Since the microarray platform, labeling, and hybridization protocols, as well as approaches to data analysis, are important contributors to variability (45), these should also be included. Importantly, the quality of the materials with respect to degradation and storage impact the results and are not often described with the data. All these issues will apply equally to proteomics and metabolomics/metabonomics data. Once a data set is produced, the investigator processes it for analysis—including steps such as truncation of low points or removal of technical outliers and normalization of the data on either a gene or array basis or both—and then carries out statistical analysis to address specific experimental questions. The resulting data set is of great interest to investigators interested in repeating or extending the original analysis. Data sets are available in microarray databases such as GEO and ArrayExpress/Array Profiler. There is currently no adopted standard for describing the steps in the process, although community efforts are underway (see http://obi.sourceforge.net/index.php and http://genome-www5.stanford. edu/mged/normalization.html). Finally, once a list of genes with changed expression is generated, it must be linked to other known attributes of the genes under study. There are numerous sources of annotation about the sequence, the regulatory regions, the likely functions and interactions of gene products, and so forth. This information is evolving at a rapid pace as new experiments are reported. Thus, not only must annotation be constantly updated, but also different versions and sources must be carried along with the annotation. 5. CHEMINFORMATICS DATA Somewhat related to toxicogenomics is the field of cheminformatics. Toxicogenomics collects vast amounts of data about a single chemical agent, whereas cheminformatics studies collect an assay endpoint about each of a vast number of different agents, and then use computational methods to discern relationships between the structures of the agents and the corresponding biological endpoint. Examples of public cheminformatics databases include PubChem (http://pubchem.ncbi.nlm.nih.gov/) at the NCBI, the Distributed Structure Searchable Toxicology (DSSTox; http://www.epa.gov/ncct/ dsstox/) project at the USEPA, and the semi-public Lhasa (http://www.lhasalimited. org/index.php).

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6. SHARING DATA It was realized early on that genomics would be rapidly changing the face of biomedical research from the traditional hypothesis-driven experimentation paradigm to a datadriven hypothesis formulation paradigm. In this new paradigm of research, data sharing among researchers is crucial. Data sharing and analyses would lead to an increased prognostication capability including pathway annotation, regulatory gene network prediction, biomarker discovery, etc. In other words, data sharing would tremendously help in the development of the science of toxicogenomics itself. A number of consortia have been formed in response to such a need for data sharing.

6.1. Public/Private Consortia Toxicogenomics is a new field that grew out of the development of microarrays. In the beginning, there were neither any accepted standardized methodology nor a clear understanding of the use of toxicogenomics data in regulatory decision-making. As knowledge of protocol standardization and comparability of data from different laboratories grew, the HESI Genomics Committee was formed (46). Other international consortia working in fields related to toxicology include the NERC Environmental Bioinformatics Centre (http://www.nerc.ac.uk/research/programmes/genomics/), the Netherlands Toxicogenomics Centre (http://www.toxicogenomics-centre.nl/content/view/20/40/), and the Toxicogenomics Research Consortium (TRC; http://www.niehs.nih.gov/dert/trc/home.htm). The HESI Genomics Committee consists of advisors from the Government, academic institutions, and over 30 corporate members, including pharmaceutical, chemical, agrochemical, and consumer products companies. The HESI Genomics Committee was formed at the start of the microarray revolution and has produced a number of important publications covering various aspects of toxicogenomics. Many of these articles were published in the journal Environmental Health Perspectives in a March 2004 special issue on Toxicogenomics (Volume 112; e.g., see 1,46). This work encompassed several thousand microarrays collected on reference compounds in the areas of genetic toxicology, hepatotoxicity, and nephrotoxicity. The Genomics Committee worked in collaboration with the EBI to create Tox/ArrayExpress, a repository and loading tool for the Committee’s data. Specific toxicology terms were added to the MAGE ontology to describe toxicology measures such as specific blood enzymes. Recently, the data have been deposited in CEBS which includes data taken prior to sacrifice as well as the data that can also be found in ArrayExpress.

6.2. Standardized Protocols Versus the Use of Descriptors There are (at least) two schools of thought when it comes to sharing and reuse of toxicogenomics data. While everyone agrees to the importance of controlling and describing technical variables as much as possible, one group argues that this is critical to data integration and others point to the current success of public repositories, which describe but do not constrain experimental variables. Owners of proprietary databases such as those at GeneLogic, Curagen, Iconix, and EDGE control the experimental variables to the point of using a very tightly regulated study design or microarray technology. These databases are well suited to the purpose for which they were designed, but may not be flexible in the face of new technology or studies requiring a tailored design. In contrast, public repositories

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such as GEO, ArrayExpress, and CEBS aim to collect data from the public at large without imposing constraints on study design or technology used. These groups favor high levels of annotation associated with the study data so that other users can fully appreciate the experimental design and execution. These repositories depend on the user’s skill in understanding the data and extracting data for accurate analysis. In their studies, the HESI Genomics Committee (and others) has identified numerous interinstitutional technical variables which could impact data analysis. In spite of the technical variables, the committee members were able to apply methods such as the use of informatics and gene-to-pathway annotation to elucidate the mechanism of toxicity associated with various toxicants. The TRC has carried out an extensive comparison of microarray data using shared RNA samples across the member laboratories. They reported that the most significant contributors to variability were the microarray platform, labeling, and hybridization protocols as well as varying approaches to data analysis (45). Recently, the Microarray Quality Control project (MAQC; http://www.fda.gov/nctr/science/centers/ toxicoinformatics/maqc/) found good inter-platform and intra-platform reproducibility using microarrays and other transcriptomics technologies from 10 different manufacturers. The September 2006 issue of the journal Nature Biotechnology published a FOCUS on the MAQC project and published related articles in this issue (e.g., see 11,12). This is an encouraging result as it suggests that microarray data can be combined from different sources and reanalyzed informatively in a database. As consortia formed, it became apparent that parameters that had tended to be standardized within an institution, such as housing, strains, feed, husbandry, and observations and euthanasia protocols, varied between different laboratories. Initially, members of the consortia agreed on a common protocol for subject care and handling; however, recently work has been done to understand the effects of diet, husbandry, and other potential variables on the subsequent gene expression analysis (47). These parameters should be included with any description of the data to permit informed use of the data. 6.3. Formats for Sharing ’Omics Data The MGED Society has pioneered the formatting for sharing data, creating the Microarray Gene Expression-Markup Language (MAGE-ML) as a means to encode microarray data for exchange between laboratories. MAGE-ML is a format, not a standard, and initial implementations of MAGE-ML loaders did not expect all potential variations. Now that the MAGE-ML Best Practices recommendations are available, it is more straightforward to implement a general solution (https://www.cbil.upenn.edu/magewiki/index.php/BestPractices). In parallel, GEO created the Simple Omnibus Format in Text (SOFT) tabular format and recently the MIAME Notation in Markup Language (MINiML) XML-based format to permit MAGE-ML formatted data to be shared with the GEO database. Recognizing the utility of tabular data exchange formats such as SOFT, MGED has recently developed MAGE-Tab (48). From the point of view of most biologists, the SOFT and MAGE-Tab formats are ideal, since the data remain closer to their native state, with additional information added to describe the columns and rows. This approach was also taken by the CEBS database which encodes microarray data and descriptions in binary NetCDF files. Raw microarray data, in particular CEL files from Affymetrix, are generally shared in their native state. Of interest to many industries are the uses of microarray and other ’omics data in regulatory submissions. The FDA has written two important documents: Guidance for Industry Pharmacogenomics Data Submissions (http://www.fda.gov/cder/guidance/6400fnl.pdf)

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and Recommendations for the Generation and Submission of Microarray Data Concept Paper (http://www.fda.gov/cder/genomics/conceptpaper_20061107.pdf). Proteomics and metabonomics data will eventually also be shared with regulators, and it is anticipated that a parallel development will occur for formatting these data types. 6.4. Formats for Sharing Non-’Omics Data Endpoint toxicology data such as genetic toxicology, carcinogenicity, and other qualitative or quantitative values are well suited to an exchange format such as Tox-ML (https://www. leadscope.com/toxml.php), a format being developed by Leadscope and the LIST (Leadscope In Silico Toxicology) Consortium. For many years toxicologists and pathologists in industry have created study reports describing the design, execution, and interpretation of toxicology studies. These study data and reports were often housed in proprietary databases compliant with the 21 CFR Part 58 section of the Code of Federal Regulations, which describe Good Laboratory Practices (GLP) (49). Data submitted to a regulatory agency must be GLP-compliant. In order to be GLP-compliant, data must be collected and stored using validated methods. GLP-compliant databases undergo an extensive validation process to ensure compliance. Most industries use commercial products such as those provided by Provantis (http://www.instem-lss.com/ software/index.html) or Xybion (http://www.xybion.com/) which have been certified and validated. Data are then extracted from the database to share with the FDA and other regulators. The Clinical Data Interchange Standards Consortium (CDISC; http://www.cdisc. org/) was formed to develop standards for the exchange of data from clinical trials between drug sponsors and the FDA. The Standards for Exchange of Non-clinical Data (SEND) Consortium was formed to carry out the same task for nonclinical data, using the CDISC Study Data Tabulation Model (SDTM). Pharmquest (http://www.pharmquest.com/) has written a parser to generate SEND-formatted files, and the NCTR has recently added a module to ArrayTrack to receive SEND-formatted data. As CEBS was being developed, the NIEHS created the CEBS Data Dictionary (CEBS-DD) in collaboration with scientists from pharmaceutical, academic, and government laboratories (50). The CEBS-DD was developed to include “maximal information about a study”, which was accomplished by aligning data fields from public and proprietary data exchange formats including SEND, Xybion, the Data model used at Eli Lilly, the schema of TDMSU used by the NTP, TSP, and Tox-ML, with the aim of ensuring complete coverage and eventually facilitating electronic data exchange. The CEBS-DD was used to develop the study and assay domains of BID, and the information has been shared with groups developing standards such as the Ontology for Biomedical Investigations (OBI; http://obi.sourceforge.net/) and the Reporting Structures for Biological Investigations (RSBI) Working Group (http://www.mged.org/Workgroups/rsbi/rsbi.html). In contrast to the “maximal information about a study” approach, a checklist of the minimal information required to interpret a toxicology or toxicogenomics study is not well defined at the present time, although work toward this is underway. An early draft, termed “miame-tox”, created by the NIEHS and the EBI, can be found at http://www.ebi.ac.uk/microarray/doc/standards.html. A more comprehensive checklist for toxicology studies and other biology studies has recently been published (51) and has been deposited with the MICheck Minimum Information Checklist Resource (http://micheck.sourceforge.net/). Once this is completed, work toward an exchange format can be undertaken.

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6.5. Data Quality Missing from most standards and databases today is a consistent description of data quality. There are several reasons, such as the continuous improvement of experimental methods, making today’s “good” experiment seem of low quality after a few years. CEBS is taking the approach of permitting the depositor to add annotation describing the quality of reagents and specimens used in their experiments. As these annotated data are accumulated, it will be possible for the database interface to show users what information about data quality is available for each study selected. This will apply to both microarray and other assay data such as clinical chemistry (Did the lab share historical ranges for the data?) and histopathology (Was the pathologist board certified? Was the finding peer reviewed? Was a standard lexicon used?). Another component of data quality is the idea of using standardized terms and descriptors. This must wait until such controlled vocabularies and ontological structures are developed. There is a great need for standardized lexicons in the area of histopathology, for example. 7. CHARACTERISTICS OF “IDEAL” DATABASE A toxicogenomics database should not exist, or be developed, in a vacuum, but rather should be continuous with data derived from studies in genetics, human disease, environmental toxicology, nutrigenomics, pharmacogenomics, and other pertinent scientific disciplines. The ultimate goal of databases is to generate enough information that will help map changes (transcripts, proteins, metabolites, systems) and develop fingerprints of such changes to predict toxicity, and also provide valuable insights into the underlying molecular mechanisms responsible for such changes. With good annotation guidelines and consensus on the appropriate minimal information needed to include with study data, data entered in one database can be easily shared with users of other data systems. Thus, the ideal toxicogenomics database is a database for biomedical (encompassing biological, medical, and environmental) investigations and associated data. Ideally, the user of the database should see the use of the database as a benefit and not a requirement for publication. Too often the data are deposited in a public database as the manuscript is going out the door, rather than when the data are being analyzed. An ideal database should allow the user to upload easily, in private mode, and analyze the data either alone or in combination with data from other studies in the database. Additionally, the database should allow the user to easily integrate assay data from their study, quickly excluding nonresponding animals identified in one assay from analysis in another. Once loaded, at the depositor’s discretion, the data can be moved to the public domain or exchanged with another database. While in private mode, the data can be used for anonymous data mining routines designed to survey the data in the database and identify associations of potential interest to the user (e.g., additional genes that vary in concert with a list identified by the user). To make loading of the data straightforward, the exchange format should be flexible enough to permit the author to format the data easily without significant constraint, yet with sufficient rigidity to permit comparable data to be identified for combined analysis. Ideally, the author’s LIMS system should do this without help. A vocabulary that follows normal usage customs (e.g., a common, controlled vocabulary) is important for quick adoption and ease and clarity of use. A tabular exchange format such as SOFT, or MAGE-TAB is ideal for both assay and microarray data. Having text formatting and parsing tools in a toolset associated with the database will also make loading more pleasant. Following loading, the database should make curation, editing, and validation tools available to the depositor so that errors can be quickly identified and corrected. Use of

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specific rules to guide and structure the data annotation, coupled with an intelligent system in the backend of the database to ensure consistency, will speed verification and facilitate accuracy of the data. With the maturation of the semantic web, it should become possible to share and extract data among databases; this will be greatly facilitated through the use of consistent annotation built on ontologies. Once the data are loaded and validated, the depositor must see an immediate benefit, such as ease of analysis, access to other relevant data and annotation, integration of data from different assay types, or acknowledgement for publication. In order for a database to be useful in this way, it must have also earned public support and trust; support from scientists willing to deposit data, and trust that the database is secure, valid, resourced sufficiently to respond to user needs, and kept up to date. Finally, the database should permit the user to easily download integrated data, to access various public tools, and to link to data in other public databases such as model organism databases, genetic and sequence repositories, the scientific literature, and the World Wide Web.

REFERENCES 1. Mattes WB, Pettit SD, Sansone SA, et al. Database development in toxicogenomics: issues and efforts. Environ Health Perspect 2004; 112:495–505. 2. Waters M, Boorman G, Bushel P, et al. Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base. EHP Toxicogenomics 2003; 111:15–28. 3. Brazma A, Parkinson H, Sarkans U, et al. ArrayExpress – a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 2003; 31:68–71. 4. Parkinson H, Kapushesky M, Shojatalab M, et al. ArrayExpress – a public database of microarray experime