Biotechnology Annual Review, Volume 14

Biotechnology Annual Review Volume 14

Chief Editor M. Raafat El-Gewely Department of Biotechnology University of Tromsø Tromsø, Norway

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Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, UK First edition 2008 Copyright r 2008 Elsevier B.V. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://www.elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-444-53226-8 ISSN: 1387-2656 For information on all Elsevier publications visit our website at elsevierdirect.com Printed and bound in Hungary 08 09 10 11 12 10 9 8 7 6 5 4 3 2 1

v

Preface

The Biotechnology Annual Review Series is now fourteen volumes strong. A great part of the success of this publication is due to the ever-expanding awareness of what biotechnology can do in the different areas of applications from agriculture to therapeutics and from industrial utilizations to vaccines and by frequently covering of the new tools of the art. We aim in this series to cover comprehensive chapters dealing with current topics of interest with all needed illustrations and references. This forum makes it easier for scholars, investigators, students and educators as well as decision makers to go in depth about a given topic. Starting from Volume 15 this series will be linked to ‘‘New Biotechnology’’ by Elsevier and will be published as a special issue (http://www.elsevier.com/ wps/find/journaldescription.cws_home/713354/description#description). Moreover, we will be open to thematic volumes focusing on one theme for a given volume. We welcome new contributions that would meet such criteria. Please contact me or any member of the Editorial Board for any suggestion. M. Raafat El-Gewely Chief Editor

vii

Editorial board Chief Editor M. Raafat El-Gewely, Department of Molecular Biotechnology, Gene Systems Group, Institute of Medical Biology, University of Tromsø, 9037 Tromsø, Norway, E-mail: [email protected]

Editors Marin Berovic, Faculty of Chemistry and Chemical Engineering, Department of Chemical and Biochemical Engineering, University of Ljubljana, Askercˇeva 5, 1001 Ljubljana, Slovenia, E-mail: [email protected] Thomas M.S. Chang, Artificial Cells & Organs Research Centre, McGill University, 3655 Drummond St., Room 1005, Montreal, Quebec, Canada H3G 1Y6, E-mail: [email protected] Thomas T. Chen, University of Connecticut, Molecular & Cell Biology, TLS 415, 75 North Eagleville Road, Unit 3125, Storrs, CT 06269-3125, USA, E-mail: [email protected] Frank Desiere, F. Hoffmann–La Roche Ltd, Diagnostics Division, Bldg. 52/1607, CH-4070 Basel, Switzerland, E-mail: [email protected] Franco Felici, Department of Microbiological, Genetic and Molecular Science, University of Messina, Salita Sperone 31, 98166 Messina, Italy, E-mail: franco. [email protected] MaryAnn Foote, MaryAnn Foote Associates, 4284 Par Five Court, Westlake Village, California 91362, USA, E-mail: [email protected] Leodevico (Vic) L. Ilag, Cryptome Pharmceuticals Ltd, Level 1, Baker Heart Research Institute Bldg, Commercial Road, Melbourne, VIC 3004, Australia, and PO Box 6492, St Kilda Road Central, Melbourne, VIC 8008, Australia, E-mail: [email protected] Kuniyo Inouye, Laboratory of Enzyme Chemistry, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto 606-8502, Japan, E-mail: [email protected] Guido Krupp, AmpTec GmbH, Koenigstr. 4A, D-22767 Hamburg, Germany, E-mail: [email protected]

viii Alfons Lawen, Monash University, Clayton Campus, Department of Biochemistry and Molecular Biology, Room 312, Building 13D, Clayton, Victoria 3800, Australia, E-mail: [email protected] K. John Morrow Jr., 625 Washington Avenue, Newport, KY 41071, USA, E-mail: [email protected] Jocelyn H. Ng, 2/6 Jurang Street, Balwyn VIC 3103, Australia, E-mail: j.ng@ bigpond.net.au Eric Olson, Vertex Pharmaceuticals, 130 Waverly Street, Cambridge, MA 02139, USA, E-mail: [email protected] Vincenzo Romano-Spica, University Institute of Motor Science, IUSM, P.zza Lauro e Bosis 15, 00194 Rome, Italy, E-mail: [email protected]

ix

List of contributors John M. Aletta, CH3 BioSystems, LLC, University Commons (Suite 8), 1416 Sweet Home Road, Amherst, New York 14228, USA, and New York State Center of Excellence in Bioinformatics and Life Sciences Buffalo, New York, USA W. Matthijs Blankesteijn, Department of Pharmacology and Toxicology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht, The Netherlands Marion Blomenro¨hr, Molecular Pharmacology Unit, Organon BioSciences, Oss, The Netherlands Jose´ Ramo´n Botella, School of Integrative Biology, Faculty of Biological and Chemical Sciences, University of Queensland, Brisbane, Queensland 4072, Australia Sebastian Charbonnier, EMBL, Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany Debprasad Chattopadhyay, ICMR Virus Unit, I.D. & B.G. Hospital Campus, General Block: 4, First floor, 57, Dr. Suresh C. Banerjee Road, Beliaghata, Kolkata 700 010, India MaryAnn Foote, MA Foote Associates, Westlake Village, CA 91362, USA Oriol Gallego, EMBL, Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany Anne-Claude Gavin, EMBL, Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany B.A. Goodman, Department of Environmental Research, Austrian Research Centers GmbH – ARC, 2444 Seibersdorf, Austria, and Department of Chemistry & Chemical Engineering, University of Guangxi, Nanning 530005, Guangxi, People’s Republic of China Tomonori Gotoh, Department of Information Science, Kanagawa University, Hiratsuka, Kanagawa 259-1293, Japan John C. Hu, Biomedical Sciences Program, University at Buffalo, State University of New York Buffalo, New York, USA

x Kuniyo Inouye, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan Tomoyuki Ishikawa, Fukuoka Industrial Technology Centre, Kurume, Fukuoka 839-0861, Japan Donna Karolchik, UCSC Genome Bioinformatics Group: Center for Biomolecular Science & Engineering, CBSE/ITI, 501D Engineering II Building, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA Mahmud Tareq Hassan Khan, Department of Pharmacology, Institute of Medical Biology, University of Tromsø, 9037 Tromsø, Norway Warren C. Lathe III, OpenHelix, LLC, 12600 SE 38th Street, Suite 230, Bellevue, WA 98006, USA Scott M. Lathe, OpenHelix, LLC, 12600 SE 38th Street, Suite 230, Bellevue, WA 98006, USA Antonio C. Laurena, Institute of Plant Breeding, Crop Science Cluster, College of Agriculture, University of the Philippines Los Ban˜os, College, Laguna, Philippines Li Li, U.S. Department of Agriculture-Agricultural Research Service, Plant, Soil and Nutrition Laboratory, Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA Mary E. Mangan, OpenHelix, LLC, 12600 SE 38th Street, Suite 230, Bellevue, WA 98006, USA Evelyn Mae Tecson Mendoza, Institute of Plant Breeding, Crop Science Cluster, College of Agriculture, University of the Philippines Los Ban˜os, College, Laguna, Philippines Eiichi Mizuki, Fukuoka Industrial Technology Centre, Kurume, Fukuoka 839-0861, Japan Stephen Navran, Synthecon, Inc., 8042 El Rio, Houston, Texas 77054, USA Shiro Okumura, Fukuoka Industrial Technology Centre, Kurume, Fukuoka 839-0861, Japan Joji M. Otaki, The BCPH Unit, Laboratory of Cell and Functional Biology, Department of Chemistry, Biology and Marine Science, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan

xi K.F. Pirker, Department of Environmental Research, Austrian Research Centers GmbH-ARC, 2444 Seibersdorf, Austria T.G. Reichenauer, Department of Environmental Research, Austrian Research Centers GmbH – ARC, 2444 Seibersdorf, Austria Terri D. Richmond, School of Pharmacy, Regulatory Science Program, University of Southern California, 1540 Alcazar St. CHP G32, Los Angeles, CA 90033, USA Peter C. Roberts, VizX Labs, 200 West Mercer Street, Suite 500, Seattle, WA 98119, USA Hiroyuki Saitoh, Fukuoka Industrial Technology Centre, Kurume, Fukuoka 839-0861, Japan Huub Schellekens, Utrecht University, Faculty of Science, Department of Pharmaceutical Sciences, P.O. Box 80.082, 3508 TB Utrecht, The Netherlands J. Ferreira Severino, Department of Environmental Research, Austrian Research Centers GmbH – ARC, 2444 Seibersdorf, Austria Jos F.M. Smits, Department of Pharmacology and Toxicology Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht, The Netherlands Joyce Van Eck, Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY 14853, USA Chris J. van Koppen, Molecular Pharmacology Unit, Organon BioSciences, Oss, The Netherlands Jos W.G. van Rosmalen, Molecular Pharmacology Unit, Organon BioSciences, Oss, The Netherlands, and Department of Pharmacology and Toxicology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht, The Netherlands Folkert Verkaar, Molecular Pharmacology Unit, Organon BioSciences, Oss, The Netherlands, and Department of Pharmacology and Toxicology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht, The Netherlands Jennifer M. Williams, OpenHelix, LLC, 12600 SE 38th Street, Suite 230, Bellevue, WA 98006, USA David A. Winkler, CSIRO Molecular and Health Technologies, Clayton 3168, Australia

xii Haruhiko Yamamoto, Professor Emeritus, Kanagawa University, Yokohama, Kanagawa 221-8790, Japan Guido J.R. Zaman, Molecular Pharmacology Unit, Organon BioSciences, Oss, The Netherlands Xiangjun Zhou, U.S. Department of Agriculture-Agricultural Research Service, Plant, Soil and Nutrition Laboratory, Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA

1

The social network of a cell: Recent advances in interactome mapping Sebastian Charbonnier, Oriol Gallego and Anne-Claude Gavin EMBL, Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany Abstract. Proteins very rarely act in isolation. Biomolecular interactions are central to all biological functions. In human, for example, interference with biomolecular networks often lead to disease. Protein–protein and protein–metabolite interactions have traditionally been studied one by one. Recently, significant progresses have been made in adapting suitable tools for the global analysis of biomolecular interactions. Here we review this suite of powerful technologies that enable an exponentially growing number of large-scale interaction datasets. These new technologies have already contributed to a more comprehensive cartography of several pathways relevant to human pathologies, offering a broader choice for therapeutic targets. Genome-wide scale analyses in model organisms reveal general organizational principles of eukaryotic proteomes. We also review the biochemical approaches that have been used in the past on a smaller scale for the quantification of the binding constant and the thermodynamics parameters governing biomolecular interaction. The adaptation of these technologies to the large-scale measurement of biomolecular interactions in (semi-)quantitative terms represents an important challenge. Keywords: yeast-two hybrids, TAP/MS, protein complexes, affinity constant, affinity purification, pulldown, phage display, ITC, SPR, metabolites, small-molecules, interaction.

Introduction Since the sequencing of the first eukaryotic genome, Saccharomyces cerevisiae, some 10 years ago [1], our understanding of the basic building blocks that make up a cell has spectacularly improved. The explosion of new analytical tools in the fields of genomics, proteomics and metabolomics contributes ever-growing molecular repertoires of a cell. However, biology does not rely on biomolecules acting in isolation. Biological function usually depends on the concerted action of molecules acting in protein complexes, metabolic or signaling pathways or networks. Traditionally, assays were designed to study a few selected gene products and their interactions in a defined number of chosen biological contexts. These decades of one-by-one studies have contributed a wealth of knowledge on how cells sense, store and transduce information through a defined number of signaling routes or pathways [2]. In human, impaired or deregulated protein–protein or protein–metabolite interaction often leads Corresponding author: Tel.: +49-0-6221-387-8816.

E-mail: [email protected] (A.-C. Gavin). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00001-X

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

2 to disease. Also, the majority of targets of current therapeutics are part of a limited number of cellular pathways [3]. A precise cartography of these pathways is believed to contribute to the identification of drug targets, and to help understanding the mechanism of action and side effects of therapeutic compounds. One of the goals of this review is to exemplify and illustrate the importance of protein–protein, but also protein–small-molecule (metabolite) interactions for human biology and pathology. Recently, the study of protein–protein, and more broadly biomolecular interactions, has taken center stage. New strategies have been designed that allow the study of interactions more globally at the level of entire biological systems. A section of this review is dedicated to the description of the technologies adapted to chart biomolecular interactions on a systems-wide scale. Their respective advantages, shortcomings and limitations are presented. These methods have already contributed molecular maps of several pathways involved in human pathologies. More global, genome-scale protein–protein interaction screens performed in a model organism, S. cerevisiae, have provided a molecular network that serves as basis for the interpretation of simple genetic data such as gene essentiality. There is a greater tendency for proteins central in networks to be lethal when deleted [4]. Biomolecular interactions in human diseases The spatial and temporal coordination of the many cellular enzyme activities through extensive and highly regulated protein–protein interaction networks bears remarkable functional relevance. The specificity of protein–protein recognition is believed to essentially rely on two distinct mechanisms. In some cases, specialized domains or binding sites accommodate smaller determinants or peptides present on the interaction partner [2]. For example, Src homology 2 (SH2) domains specifically interact with small peptides containing a phosphotyrosyl residue. PSD95-DLG-ZO1 (PDZ) domains target small (4 amino acid long) consensus binding motifs located at the C-terminus of the interaction partners. These short linear motifs are critical to many biological processes. They often show medium to low affinities (0.5–10 mM) and thus tend to be mediators in transient interactions, especially in signaling networks. The derived plasticity from these weak affinities and the often low conservations might be an advantage for fast adaptation of networks according to changing environment [5–7]. In other cases, protein–protein interaction involves much larger interfaces. This modality of recognition requires mostly folded domains and occurs with higher affinities in the low nanomolar or even picomolar range. A famous example are proteins containing a leucine zipper, in which a-helices interact tightly and fit together [2,8]. Very similar to the associations taking place between proteins, interactions involving small molecule metabolites and proteins play key biological

3 functions. The relationship between metabolomes and proteomes is not limited to enzyme–substrate or –product interactions. Many metabolites have well-known signaling roles, as second messengers. Others, such as succinate and a-ketoglutarate, two intermediates of the citric acid cycle, are ligands of mammalian G-coupled receptors, GPR91 and GPR99 [9]. Many metabolic enzymes and many signaling proteins are allosterically modulated by metabolites. Binding to small molecules is often mediated by a variety of more than thousand specific domains (Protein Data Bank: www.rcsb.org/pdb/). Among the 2,000 monogenic syndromes with a known molecular basis (Online Mendelian Inheritance in Man, 2000), mutations that affect biomolecular interactions are not uncommon (Table 1). Noteworthy examples are mutations in receptors that affect their ability to interact with the cognate ligands. The Apert syndrome, characterized by skull malformation, syndactyly and often mental deficiency, is caused by mutations in the fibroblast growth factor receptor 2 (FGFR2) that selectively increase the affinity for FGF2 [10]. Enzymes are also extensively involved in protein– protein interactions. For instance, DNMT3B is a DNA methyltransferase implicated in the immunodeficiency, centromeric instability, facial anomalies (ICF) syndrome, a rare autosomal recessive disorder. Missense mutations characterized in ICF patients map not only within the catalytic site but also affect an N-terminal PWWP domain, involved in protein–protein interactions [11]. Also, mutations have been characterized that prevent the assembly of a functional multiprotein complex. A good example is a RFXANK gene mutant that fails to assemble a regulatory factor X (RFX) complex required for the expression of MHC class II genes. This leads to the bare lymphocyte syndrome [12]. Finally, a variety of abnormal or erroneous interactions between brain proteins can result in the formation of toxic aggregates of proteinacious fibrils. These ‘‘fatal attractions’’ [13] are the apparent cause of a variety of neurodegenerative disorders, such as sporadic and familial Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and prion encephalopathies. Similarly to protein–protein interactions, deregulation of protein–metabolite interactions leads to many pathological status in human (Table 1). For example, the Bannayan-Riley-Ruvalcaba syndrome, characterized by macrocephaly, multiple lipomas and hemangiomas [14], is caused by mutation in the phosphatase PTEN. Different mutations map in a protein kinase C conserved region 2 (C2) domain that has relatively broad specificities for phospholipids. Pleckstrin-homology domains (PH) (http://smart.embl-heidelberg.de/), the 11th most frequent domain in human, mediate the specific recruitment of key signaling proteins to the membrane pools of phosphatidylinositol phosphates (PIPs). PH domains share an extremely conserved fold, despite divergent primary sequences. X-linked a-gamma-globulinaemia (XLA), characterized by the absence of mature B-lymphocyte and all immunoglobulin

4 Table 1. Altered biomolecular interaction in human diseases. Disease and syndrome Protein–protein interaction Apert syndrome Familial melanoma CADASIL Bare lymphocyte syndrome Branchio-oto-renal/ Branchio-otic syndromes Adrenoleukodystrophy

Holt-Oram syndrome ICF syndrome

Mutated gene product

Interacting partner

References

FGF Fibroblast growth factor receptor 2: FGFR2 Tumor suppressor Cyclin-dependent gene: p16(INK4) kinases (CDK4, CDK6) NOTCH3 NOTCH3, Fringe RFXANK RFX complex

[112,113] [12]

SIX1

EYA1

[114]

ATP-binding cassette transporter: ABCD1 Tbx5 Methytransferase gene: DNMT3B Gigaxonin Mismatch repair gene: MLH1

ABCD1

[115]

NKX2.5 Unknown

[116] [117]

MAP1B-LC PMS2

[118] [119]

Phospholipids

[14]

PI3,4,5P

[15,16]

ATP

[120]

1,25-dihydroxyvitamin D3 Broad variety of ligand

[121] [122]

PI3,4,5P, PI3,4P

[123]

Giant axonal neuropathy Hereditary nonpolyposis colorectal cancer Protein-metabolite interaction Bannayan-RileyPTEN, C2 Ruvalcaba syndrome X-linked BTK, PH agammaglobulinemia Pseudoxanthoma ABCC6, NBF elasticum (nucleotide binding fold) Vitamin D-dependent VDR, Steroid Rickets, Type II hormon-binding Sporadic colon cancers, PPARG, LigandFamilial Partial binding Lipodystrophy Type 3 Human breast, colorectal AKT1, PH and ovarian cancers

[10] [110,111]

isotypes, is caused by mutations in the Bruton’s protein tyrosine kinase (Btk) (Table 1) [15]. Many mutations have been reported that cluster within the tyrosine kinase domain and also in the amino terminal PH domain of Btk [15,16]. The examples listed in Table 1 do not represent a comprehensive inventory. They illustrate that the spatial and temporal orchestration of the many enzyme activities through extensive and highly regulated biomolecular interaction networks bears remarkable functional relevance. Mutational

5 lesions or environmental factors impairing the pathway flow or deregulating connections lead to pathology, as surely as interferences with the catalytically active sites do. Charting biomolecular interactions The sequencing of the full repertoire of genes in several organisms and significant breakthrough in the fields of proteomics opened the way to more holistic protein–protein interaction analyses. Mainly two types of approaches have been adapted: the yeast two-hybrid system that allows the mapping of pairwise associations and affinity purification methods coupled to mass spectrometry, MS-based protein identification, designed for the characterization of protein complexes (Table 2). Alternative strategies are emerging, such as pulldown coupled to MS or microarrays that will also be presented. These screening methods are generally limited to qualitative measurements. Table 2. Overview of large-scale protein–protein interaction studies. Method Yeast two-hybrid Y2H

Organism

Interactions

References

H. pylori S. cerevisiae S. cerevisiae C. elegans D. melanogaster D. melanogaster H. sapiens H. sapiens Kaposi sarcomaassociated herpes virus Varicella-zoster virus Epstein-Barr virus C. jejuni

B1,520 B4,500 B1,000 B5,000 B20,400 B2,300 B2,800 B3,200 B120

[47] [124] [48] [51] [49] [50] [52] [53] [102]

B170 B40 B11,600

[102] [101] [125]

B4,100

[28]

Affinity chromatography TAP/mass S. cerevisiae spectrometry S. cerevisiae S. cerevisiae E. coli H. sapiens O. sativae Immuno-affinity S. cerevisiae purification/mass spectrometry Others Protein microarrays S. cerevisiae

B220 B3,620

[29] [30] [27] [32] [126] [24]

B40

[77]

B5,250

6 Biomolecular Networks Biochemical: AP/TAP-MS, pulldown Genetic: yeast two-hybrid, phage display Arrays

Molecular recognition Structures and modeling approaches

Koff [P]+[B] Designer: Petra Riedinger

Kon

[PB]

Dynamics, quantitative measurements SPR, ITC, equilibrium dialysis, hold-up & stopped flow

Fig. 1. Main methodologies in the study of biomolecular interactions. The appro-

aches are grouped according to their aim. (The color version of this figure is hosted on Science Direct.)

We also review the arsenal of quantitative approaches that are currently amenable to smaller scale work (Fig. 1). Finally, a number of databases have been developed that integrate biomolecular interaction data from various origins (large-scale physical or genetic interactions, literature mining) and provide a very rich source of information (Table 3). The necessity to integrate the exponentially growing number of protein interaction datasets being generated, recently lead to the development of ‘‘The Minimum Information required for reporting a Molecular Interaction experiment (MIMIx),’’ a community standard for the representation of protein interaction data [17]. Large-scale approaches: Interactome mapping Biochemical approaches Analysis of protein complexes: Affinity purification-mass spectrometry The emergence of sensitive and high-throughput MS methods has fuelled the development of methods employing the biochemical purification of whole cellular assemblies. Pioneering work used specific antibodies directed against epitopes present on endogenous protein complexes or recombinant specific interaction domains combined with MS to identify protein complexes. This

7 Table 3. Protein–protein interaction databases available on the Internet and their URL addresses. See also The Jena Center for Bioinformatics Protein–Protein Interaction Website (http://www.imb-jena.de/jcb/ppi/). Database

Internet URL address (http://)

Species

Experimental datasets for protein–protein interactions: C. elegans, D. melanogaster, S. cerevisiae SGD www.yeastgenome.org/ S. cerevisiae CYGD mips.gsf.de/genre/proj/yeast/ S. cerevisiae BOND bond.unleashedinformatics.com/ A. thaliana, B. taurus, Action? C. elegans, D. melanogaster, G. gallus, H. pylori, HIV1, H. sapiens, R. norvegicus, S. cerevisiae, M. musculus HPRD www.hprd.org/ H. sapiens DIP dip.doe-mbi.ucla.edu/ C. elegans, D. melanogaster, E. coli, H. pylori, H. sapiens, M. musculus, R. norvegicus, S. cerevisiae MINT mint.bio.uniroma2.it/mint/ B. taurus, C. elegans, Welcome.do D. melanogaster, E. coli, H. pylori, H. sapiens, M. musculus, R. norvegicus, S. cerevisiae IntAct www.ebi.ac.uk/intact/index.jsp C. elegans, D. melanogaster, E. coli, H. sapiens, M. musculus, S. cerevisiae Biocarta www.biocarta.com/ H. sapiens, M. musculus MPPI mips.gsf.de/proj/ppi/ Mammals www.rcsb.org/pdb/ Archae, Bacteria, Eucaryota, PDB Viruses Experimental datasets for protein–small-molecule interactions: The GRID

www.thebiogrid.org

KDBI

xin.cz3.nus.edu.sg/group/kdbi/ kdbi.asp www.agklebe.de/affinity lpdb.chem.lsa.umich.edu

AffinDB LPDB

has been successfully applied to the protein assembled around neurotransmitter receptors N-methyl-D-aspartate (NMDA) and 5-hydroxytryptamine 2c (5-HT2C) [18,19]. The main advantage is that endogenously expressed and native proteins are retrieved from cells or even tissues, which is closest to physiological conditions. Availability of specific antibodies or other affinity capturing agents remains a major limitation. More generic approaches exploiting known high-affinity interactions have been broadly developed where the proteins of interest ‘‘baits’’ are fused to an affinity-tag that is eventually captured on a suitable affinity chromatography

8 resin. Many different genes can be fused to the same tag in parallel, expressed in the appropriate cell type and isolated using the same affinity resins. The components of the purified protein complexes are identified by MS either directly (shot-gun sequencing approaches) or after one-dimensional gel electrophoresis. The tag is often an epitope-tag. An antibody directed against the tag, instead of the bait protein itself, is used for protein complex purification. A broad variety of epitope-tags are available such as Myc, HA, Flag and KT3. Other tagging systems have also been developed that exploit enzyme–substrate interaction, for example, between glutathioneS-transferase (GST) and glutathione (GSH) or the use of strep tags [20]. A variety of tag-affinity resin pairs are currently available. Their respective performances in a variety of expression systems have been extensively reviewed [21–23]. In the past, mono-affinity approaches have been adapted to the large-scale purification of yeast protein complexes assembled around more than 700 baits involved in cell signaling and in the DNA damage response [24] (Table 2). Further improvements aimed at a higher discrimination against unspecific protein background while retaining the essential components of the protein complex. More stringent washing steps in ‘‘traditional’’ mono-affinity purification schemes are not always compatible with the preservation of the protein complex integrity. The tandem affinity purification (TAP) protocol utilizes sequentially two epitope-tags, Protein A and calmodulin binding peptide (CBP), instead of one and addresses some of the signal-to-noise issues [25,26]. The TAP-fusion protein is expressed in cells and a protein complex can assemble under physiological conditions with the endogenous components. The tagged protein along with associated partners is retrieved by two steps of affinity purifications (Fig. 2A). First, the protein A tag is immobilized on immunoglobulin resin. The protein complex is specifically eluted by protease cleavage, using Tobacco Etch Virus (TEV) protease. The TEV protease very specifically cleaves a seven amino-acid sequence that has been introduced between the Protein A and the CBP tags. The TEV cleavage sequence is only found in a few human, mouse or yeast proteins, ensuring that the retrieved complexes are not digested. In a second affinity step, the complex is immobilized to calmodulin-coated beads via the CBP tag. This step removes the TEV and further contaminants. As the CBP–calmodulin interaction is calcium dependent, a second specific elution step is achieved through the removal of calcium with a chelating agent (EGTA). The TAP/MS protocol has been rapidly adapted to high-throughput analyses of protein complexes in a variety of organisms, including the bacteria Escherichia coli [27], S. cerevisiae [28–30], plants [31] and human [32] (Table 2). Proteome-wide screens in yeast, including more than 2,000 baits, provided the largest repertoire of eukaryotic protein complexes so far [29,33]. The TAP–MS approach is not limited to one cell type, it is possible to monitor and quantify changes in complex compositions in different cell lines,

9 during development or following various cell stimulations [34–36]. Only one protein is cloned and tagged, all other components of the complex are native proteins and reflect the natural diversity of protein isoforms, such as alternative splicing and post-translational modifications. Because of the two steps of purification, it generally efficiently reduces the unspecific protein background and most importantly, stringent purification condition can be avoided. The protein complexes can be kept under ‘‘native’’ conditions throughout the purification procedure. For example, TAP-purified complexes have been successfully used for electron microscopy studies [37,38]. A diversity of different tag combinations are also now available that have been optimized for expression in mammalian or insect cells [39,40]. The affinity purification/MS methods are not generally designed to monitor very labile or transient interactions (generally Kd p mid nM; unpublished data). In addition, the fusion with an epitope-tag may sometimes interfere with the biological function of the tagged protein, as it impairs its folding, its recruitment within a protein complex or its sub-cellular localization. These risks can be significantly reduced by creating and analyzing both N-terminal and C-terminal fusions in parallel. Finally, over-expression can also lead to aberrant localization, protein aggregation or toxicity. Generally, tight controls over the expression levels and the baitprotein localization should be included in any systematic screen setting [32]. Protein or small molecule pulldown The pulldown assay is probably one of the oldest and widest spread techniques to identify biomolecular interactions [41]. The assay monitors the ability of a ligand (bait), for example, a recombinant protein, a domain, a peptide or a metabolite bound to a matrix, to specifically capture proteins from a complex cell extract (Fig. 2B). The binding of the bait to the matrix can be achieved by chemical cross-linking. Alternatively, the bait can be expressed as a tag-fusion, for example, GST-fusion, with specificity for a particular affinity resin, for example, glutathione sepharose (GST-pulldown). Analyte proteins (preys), typically a cellular extract, are incubated with the bound bait and non-interacting proteins are washed away under mild washing conditions. Protein interactors can then be eluted by high-salt conditions, cofactors, competitors, chaotropic agents or sodium dodecyl sulfate (SDS) and are identified with specific antibodies (western blot) or by MS. In contrast to classical affinity chromatography methods (see aforementioned), pulldown is less physiological, as the binding happens in vitro on a solid support. The assay, however, is generally very sensitive. With appropriate concentrations of the immobilized ligand, i.e., well above the Kd of the interaction, interactions with binding constants as weak as 10 5 M can be detected. This high sensitivity, however, comes at the cost of a relatively low specificity and a high rate of false positives. Generally,

10 Purification step 1 TEV Y Protein A

Glu

Purification step 2

Y

Y Z

X

CBP Ca2+

Ca2+ GST

Glu 1D SDS-PAGE

X

Z

Incubation

Ca2+ Calmodulin

GST

Z

X

CBP

X

Y

Mass spectrometry

Wash Elute * *

Analyze: SDS-PAGE, western blot,

(a)

(b)

Y

3-4 rounds of panning

AD

X X

DBD

Z

AD

Wash prom.

X

reporter

Y

Specific elution, titration, amplification in E.coli

AD

DBD

Sequencing of phage DNA identification of binders

reporter

prom.

(c)

(d)

Tag

(e)

Tag

11 adequate controls, for example, inactive mutants or analogs, must be carefully designed to discriminate artifactual binding. Mann’s group used synthetic peptides from the four members of the ErbB-receptor family either tyrosine phosphorylated or non-phosphorylated in pulldown experiments. By quantitative MS they characterized the phosphotyrosine-dependent interactions induced by growth factors stimulation. The analysis recapitulates almost all previously known epidermal growth factor receptor substrates as well as 31 novel effectors [42]. Another interesting area of pulldown application is the monitoring of small molecule- or metabolite-binding profiles in complex proteomes. Such approaches have been broadly used to study protein–lipid interactions using, for example, biotinylated liposome of varying lipid composition or

Fig. 2. Technologies for the large-scale charting of biomolecular interactions. (A) Tandem Affinity Purification (TAP): A bait protein is fused to a TAP-tag built of a protein A coupled via a TEV protease sensitive linker to a calmodulin-binding peptide. The TAP-bait fusion is expressed at endogenous levels and can form a functional complex in native like conditions. The protein complex is isolated via two subsequent chromatographic purification steps, the first involving purification on IgG beads followed by elution via TEV cleavage and a second step on calmodulin beads followed by EGTA elution. Individual protein complex subunits can be visualized on SDS PAGE and identified by mass spectrometry. X, bait protein; Y and Z, natural complex subunits (preys); CBP, Calmodulin-binding peptide. (B) Pulldown: GST pulldown is shown here as an example for chromatographic affinity protein or small molecule pulldown. The bait protein is fused to an affinity tag (GST) which can bind an affinity chromatography resin (Glu, gutathione sepharose beads). It is incubated with prey proteins and unspecific proteins are washed away, while interacting proteins remain bound to the bait. Binders can be visualized by SDS PAGE, western blot or any other suitable method. (The color version of this figure is hosted on Science Direct.) (C) Yeast two hybrid: A bait protein is fused to a DNA-binding domain of a specific promoter, which is coupled to a reporter gene. A bank of prey proteins fused to an activation domain is cloned into the yeast cells, where only one prey protein is expressed in one yeast clone. If interaction between bait and prey takes place the reporter gene is activated and an expression phenotype can be visualized. DBD, DNA binding domain; AD, activation domain; X, bait protein; Y and Z, prey proteins. (D) Phage display: A phage library with individual phages expressing different prey proteins are incubated with a bait protein, which is immobilized on a solid surface. Non-specific phages are washed away and specific interacting phages are titrated and amplified. Specifically binding phages are amplified in 4–5 rounds of panning and the binding preys identified by sequencing of the phage DNA. (E) Protein or small molecule microarray: A bank of prey proteins or small molecules is spotted on a solid surface. The array is then incubated with labeled or tagged bait. Labels can be visualized either directly (GFP, Radioactivity) or indirectly via antibodies.

12 concentration [43]. More recently, pulldown approaches coupled to MS have elegantly been applied to the proteome-wide charting of cAMP/cGMP- [44] and purine-binding proteomes [45]. Genetic approaches Monitoring binary interaction; the yeast two-hybrid system The yeast two-hybrid system is a genetic, ex vivo assay that allows the charting of binary interactions. Its principle relies on the modular nature of transcription factors that contain both a site-specific DNA-binding domain (DBD) and a transcriptional activation domain (AD) that recruits the transcriptional machinery to the promoters. The interaction between a ‘‘bait’’ fusion (protein X-DBD hybrid protein) and a ‘‘prey’’ fusion (protein Y-AD hybrid protein) reconstitutes a functional transcription factor which turns on the expression of reporter genes or selection markers [46] (Fig. 2C). The system is readily scalable and has very rapidly evolved to genome-wide strategies that have been broadly applied to the charting of protein–protein interactions in a variety of organisms (Table 2), including Helicobacter pylori (Rain et al. [47]), budding yeast [48], Drosophila melanogaster [49,50], Caenorhabditis elegans [51] and Homo sapiens [52,53]. The two-hybrid system is a sensitive assay suitable for the detection of weak and/or very transient interactions. Dissociation constants down to 10 6 M, corresponding to the range of the weakest interaction occurring in the cell, can be detected this way. The system also has drawbacks mainly related to its ex vivo nature. Expressed fusion proteins are forced to the nucleus, which may not be their natural location. For example, membrane proteins are usually not compatible with such a nuclear-based assay. Ectopically expressed proteins may not undergo the appropriate post-translational modifications. Similarly, interaction often involves cooperative, allosteric events or chaperone-assisted assembly that may not occur in the nucleus. Finally, transcription factors, as well as other proteins (about 5–10% of gene products) can auto-activate transcription of the reporter genes making them unsuitable for this approach. Several modified versions of the yeast two-hybrid system have been developed that address some of these limitations. They involve the reconstitution of modular proteins other than transcription factors that enable the analyses of proteins not amenable to the ‘‘classical’’ two-hybrid assay (essentially membrane proteins and transcription factors). It includes the SOS [54] or the Ras recruitment systems [55], the G-protein-based screening assay [56], the split-ubiquitin system [57] and the mammalian protein–protein interaction trap (MAPPIT) based on the complementation of signaling-deficient type I cytokine receptors [58]. Although some of these assays are apparently robust [59], none of them has yet been used in highthroughput proteome-wide screens.

13 Reverse versions of the two-hybrid system have been developed, where the disruption of a given protein–protein interaction generates a signal. These reverse approaches, initially developed in yeast [60], have matured in an arsenal of assays that allow the screening for small molecules disrupting selected protein–protein interactions [61,62]. Finally, the most recent application of the two-hybrid principle is MASPIT, a three-hybrid trap for quantitative screening of small molecule–protein interactions in mammalian cells [63]. Using MASPIT, Caligiuri et al. [63] could show that besides its well-known inhibitory action on the SRC kinase, the pyrido[2,3-d]pyrimidine PD173955 is also a potent inhibitor of several ephrin receptor tyrosine kinases. Phage display Smith, Scott and colleagues first proposed a way of displaying polypeptides on the surface of filamentous M13-derived bacteriophages [64]. Polypeptides (preys) are expressed as fusions with the phage coat protein pIII [64,65]. During the phage assembly process, the resulting fusion proteins are transported to the bacterial cell membrane and are incorporated into the phage particle along with the single-stranded DNA (ssDNA) encoding the displayed fusion protein [65]. Phage libraries displaying large diversity of preys (109 unique sequences) can be created, amplified and screened for the specific binding to an immobilized target protein (bait). Usually three to five rounds of panning are sufficient to enrich for phages expressing a peptide sequence interacting with the bait. The identity of the polypeptide binder is deduced by sequencing the corresponding phage DNA (Fig. 2D). When necessary, affinity maturation for individual clones can be performed through generation of secondary libraries of mutated peptides [66–68]. Phage display is suitable for the charting of interactions with affinity constants in the micromolar to nanomolar range. To fine-tune the sensitivity of the assay to a particular bait, alternative systems have been engineered. They use different viral coat proteins that are expressed and displayed at the phage surface with different stoichiometry [69]. The system has clear limitations as many proteins do not fold properly in bacterial periplasm. To circumvent this problem, new strategies use lytic bacteriophages, such as T4 [70], T7 [71] or P4 [72,73], that assemble their capsids in the cytoplasm. Phage display has proven particularly powerful to the selection of relatively small peptides. Using phage display, Robinson et al. [74] screened a peptide library for the selection of nonameric sequences that specifically bind and target human papilloma viruses (HPVs) transformed cells. They identified three different consensus tumor-targeting sequences that could be employed for the selected delivery of therapeutics. Other illustrative examples are recent epitomics screens where peptide libraries were selected for the specific binding to antibodies from serum of cancer patients. The selected

14 peptides were used to develop a peptide array used for the diagnostic of cancer [75,76]. Protein and small molecule microarrays It has become routine to use DNA microarrays to probe the expression of thousands of genes in parallel. Similarly, protein or small molecule microarrays have been developed. They provide a mean to the rapid and parallel screening of large numbers of proteins for biochemical activities, protein–protein, protein–lipid, protein–nucleic acid and protein–small molecule interactions (Fig. 2E). The first high-density proteome microarray consisted of 5,800 GST-HisX6 tag-fusion yeast proteins [77]. Such protein microarrays are currently commercially available. Details on the procedure have been extensively reviewed [78,79]. Protein arrays are in principle amenable to proteome-wide screens for protein–protein interactions. So far though, they have been only used on limited scale to identify new calmodulinand phospholipid-binding proteins as well as to monitor domain–domain and antigen–antibody interactions [77,80–82]. Similarly, a variety of small molecule arrays have been developed, that involve the covalent coupling of entire chemical collections of synthetic small molecules [83,84] or natural products [85] to a solid surface. Small molecule micorarrays have been used in a broad range of applications such as the determination of protease activity profile on cell lysates [86], the study of ligand binding specificity of proteins and domains [87] and the identification of protein modulators [88]. For instance, using a small molecule microarray containing a selection of 3,800 compounds, Kuruvilla et al. [88] identified 8 small molecules that selectively bind the yeast transcriptional regulator Ure2. Measurement of the dynamics of biomolecular interactions: The quantitative methods Generally, the information contributed by large-scale studies of biomolecular interactions is static. Indeed, the methods designed to the charting of networks on a large-scale fail to capture the dynamic aspect of recognition that is central to the whole cell functioning. Many of the large-scale methods imply the expression of proteins under non-physiological conditions and in these ex vivo or in vitro systems, the regulation and the fine-tuning of the molecular interactions at cellular and physiological levels are usually lost. To fill this gap, approaches based on the affinity purification of molecular assemblies formed inside the cell and the measurement, by quantitative MS, of changes in complex stoichiometry following various cell stimulations have emerged [34,36]. Also, in silico strategies imply the integration of interaction

15 with global expression data [89–91]. Pseudo-affinity scores have been developed that approximate the tendency of protein pairs to associate and form direct physical contact [29]. In this paragraph, we review several biochemical approaches that have been used in the past on a smaller scale to quantify the binding constants and the thermodynamic parameters governing biomolecular interactions. Some of the methods might be amenable to more global strategies (Fig. 3). Fluorescence-based assays such as fluorescence resonance energy transfer (FRET) also hold great promises. They allow the monitoring and quantification of protein–protein interactions inside a living cell. These technologies are outside the scope of this review and will not be discussed; they have been extensively reviewed elsewhere [92,93].

µM Strong binding

Weak binding

Kd measurement

Weak binding

nM

Strong binding

Low throughput Equilibrium dialysis Stopped flow ITC Qualitative

Surface plasmon resonance

Holdup Quantitative Phage display Yeast two hybrid Microarray Pulldown AP/MS or TAP/MS High throughput

Fig. 3. Recapitulative plot of the described methods. The methods cited in the paper

are plotted according to their features. Qualitative methods are depicted on the left side, quantitative on the right side. The Y-axis codes compatibility for high throughput. The X-axis codes for the capacity to detect and analyze weak or strong interactions. In case Kd can be quantitatively described, the working range is indicated as a double-headed arrow, ranging from sub-micromolar to nanomolar. Black boxes are indicative for techniques which allow working in solution; grey boxes for techniques, where one partner is bound to a solid matrix.

16 Surface plasmon resonance: Equilibrium and competition in solution Almost 15 years after the development of the first biosensor relying on the principle of surface plasmon resonance (SPR) [94], SPR-based strategies have gained popularity in the field of interactomics, because of the high accuracy, reliability and sensitivity in reporting binding rates. An electromagnetic wave is measured, which propagates in a sensor surface between a dielectric and a metal (usually gold), the so-called surface plasma wave (SPW). Biacore and other SPR-based instruments use an optical method to measure the refractive index near the sensor surface (within 300 nm). A sensor surface is integrated in a flow cell (IFC) through which an aqueous solution passes under continuous flow (1–100 ml/min). The ligand (bait) is immobilized by chemical cross-linking onto the sensor surface either directly or on different carboxymethylated dextran matrices. The binding of the analyte (prey) results in an increase in local density at the surface of the sensor. This change is measured in real time and expressed in resonance units (RU). One RU represents the binding of approximately 1 pg protein/mm2. SPR has been broadly used to study the binding behavior of macromolecules such as recombinant proteins with their natural ligands. It generates real-time data and is well designed for the analysis of binding kinetics. An interesting extension is the possibility to design experiments, where measurements are made at different temperatures. This allows the monitoring of thermodynamic parameters (entropy, etc.). SPR performs for the measurement of interactions that cover broad ranges of Kd, from nM to the high mM range. An important limitation to kinetic analysis is due to the effect of mass transport that affects interactions with fast kon values. At kon faster than about 106–107 M 1s 1, the measurements significantly lose accuracy. Stopped flow Stopped flow experiments [95,96] are designed to measure interactions that have a very fast kinetic (rapid kon and koff ) and that are usually not amenable to SPR or other methods. The principle relies on the very fast mixing of bait and prey solutions. The time required for mixing the two solutions, the ‘‘dead time,’’ is the shortest measurable time point in a stopped flow experiment. It ranges from 500 to 40 ms for the latest generations of stopped flow instruments. Fast reactions also require fast measurement methods. The mixing chamber is usually coupled to an external device for measuring the binding reaction, such as UV or visible spectroscopy, circular dichroism spectrometer, fluorescence spectrometer or electrical conductivity. Besides the possibility to monitor fast kinetics other advantages rely on the fact that measurement happens in solution. In consequence, there is no need of coupling the bait or the prey to a solid surface. Finally, only low amounts of biological material are needed.

17 Calorimetry – isothermal titration calorimetry (ITC) Isothermal titration calorimetry (ITC) is a quantitative technique that is designed to measure the binding affinity (Ka), enthalpy changes (dH ) and binding stoichiometry (n) between two or more molecules interacting in solution. An isothermal titration calorimeter is composed of two identical cells made of a highly efficient thermal conducting material surrounded by an adiabatic jacket. Sensitive thermocouple circuits are used to detect temperature differences between a buffer-filled reference cell and a sample cell containing the interacting macromolecule. During the experiment, the ligand (bait) is injected into the sample cell containing a prey. An exothermic reaction produces heat; the opposite occurs in case of an endothermic reaction. A power is applied to the reference cell and is coupled to a feedback circuit, activating a heater, located on the sample cell. The calorimeter measures the power needed over time to maintain the reference and the sample cell at an identical temperature. ITC measures interaction directly in solution and does not require any modification or immobilization of bait or prey. ITC directly measures the heat change during the complex formation and, in addition to the binding constants, it also measures the thermodynamic parameters governing the interaction [97,98]. The main limitation remains the need of relatively high amount of sample (in the order of milligrams). Equilibrium dialysis Equilibrium dialysis is probably one of the simplest and nevertheless an effective assay for the study of interactions between molecules. The experimental setup is based on two chambers which are separated by a dialysis membrane. The molecular weight cut off of this membrane is chosen such that it will retain the ligand (bait) while the analyte (prey) diffuses freely. A known concentration and volume of prey is placed into one of the chambers. Its diffusion across the membrane and binding to the bait takes place until equilibrium has been reached. At equilibrium, the concentration of prey in free solution is the same in both chambers. In the bait chamber, however, the overall concentration is higher due to the presence of bait–prey complexes. The equilibrium binding of various concentrations of the prey and bait can be used to determine the Kd as well as number of binding sites on the bait. Equilibrium dialysis also offers the ability to study low affinity interactions that are undetectable using other methods. It has been used to the detailed study of antigen–antibody interactions [99]. Hold up The hold up assay, also called ‘‘comparative chromatography retention assay,’’ is based, like the pulldown assay, on the reversible binding of a bait

18 to affinity resin. The main difference is that the hold up assay does not include washing steps and directly measures the amounts of prey remaining in solution upon exposure to the resin-bound bait. Therefore, in contrast to pulldown experiments, the hold up assay gives access to visualization of complexes at equilibrium conditions. Because it measures interaction at equilibrium, it allows detection of fast-exchanging protein complexes. Equilibrium dissociation constants (Kd) are measured that are comparable to the ones measured using SPR [100]. The hold up assay is specifically adapted to monitor weak protein interactions, where high concentrations of bait are necessary. The method is extremely simple and the general experimental setup is prone to automation. Work on this aspect is actually in progress. Conclusions and perspectives Nowadays, a growing choice of technologies is available to the scientists for the global charting of biomolecular interactions. These large-scale approaches have already contributed comprehensive cartographies of the proteins functionally involved in various human pathways that underlie pathologies. For instance, in human, the systematic mapping, by TAP/MS, of the protein interaction network around 32 components of the proinflammatory TNF-alpha/NF-kappa B signaling cascade led to the identification of 221 molecular associations. The analysis of the network and directed functional perturbation studies using RNA interference highlighted 10 new functional modulators that provided significant insight into the logic of the pathway as well as new candidate targets for pharmacological intervention [32]. Recently, global analysis of the interaction between a variety of viruses and their hosts provided new hypotheses on viral strategies for replication and persistence [101–103]. Generally, the elucidation of pathways or cellular processes important to human diseases is expected to contribute alternative therapeutic targets with better chemical tractability and also to provide a molecular frame for the interpretation of genetic links. In the simple eukaryote, S. cerevisiae, more global, genome-wide screens for protein complexes raised the interesting view that protein multifunctionality may be a more general attribute than initially anticipated [28,104]. Different protein complexes very often use the same protein to exert their different biological functions. About 37% of the proteins were found to be part of more than one protein complex [105]. Proteins, similar to the globular domains they are made of, are used in a combinatorial manner and contribute to the assembly of a variety of ‘‘molecular machines.’’ Protein modularity or multifunctionality has been proposed to support parsimonious increases in organismal complexity, i.e., with a relatively constant number of genes. The understanding of protein modularity in higher eukaryotes may

19 not only provide a molecular frame for the explanation of genetic traits such as genetic pleiotropy, but is also expected to contribute to the selection of more specific and ‘‘safer’’ drug targets. The systematic and unbiased charting of biomolecular networks in a variety of organisms contributes to our understanding of the sequences, motifs and structural folds involved in the processes of molecular recognition [5]. These recent advances opened new avenues for the identification of leads that specifically abrogate or modulate disease-relevant interactions. Promising successes include FTY720 (fingolimod; 2-amino-2[2-(4-octylphenyl) ethyl]-1,3-propanediol, Novartis) a sphingosine-one phosphate (S1P) analog that binds four of the S1P receptors [106], disruptors of the interaction between p53 and murine double minute 2 (MDM2) [107], compounds that interfere with the interaction between Bcl-2 and Bak [108] and small molecules inhibitors of SH3-mediated interactions [109]. Finally, the adaptation of existing technology to the large-scale measurement of biomolecular interactions in (semi-)quantitative terms represents an important challenge. Acknowledgments Our work is supported by the EMBL and the EU-grant ‘‘3D repertoire.’’ We would like to thank Petra Riedinger for help with the figures. We are grateful to Christoph Mu¨ller, Vladimir Rybin, Gilles Trave´ and Yves Nomine´ for critical reading of the manuscript. References 1. Goffeau A, Barrell BG, Bussey H, Davis RW, Dujon B, Feldmann H, Galibert F, Hoheisel JD, Jacq C, Johnston M, Louis EJ, Mewes HW, Murakami Y, Philippsen P, Tettelin H and Oliver SG. Life with 6000 genes. Science 1996;274:546, 563–567. 2. Pawson T, Raina M and Nash P. Interaction domains: from simple binding events to complex cellular behavior. FEBS Lett 2002;513:2–10. 3. Brown D and Superti-Furga G. Rediscovering the sweet spot in drug discovery. Drug Discov Today 2003;8:1067–1077. 4. Jeong H, Mason SP, Barabasi AL and Oltvai ZN. Lethality and centrality in protein networks. Nature 2001;411:41–42. 5. Neduva V, Linding R, Su-Angrand I, Stark A, de Masi F, Gibson TJ, Lewis J, Serrano L and Russell RB. Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol 2005;3:e405. 6. Neduva V and Russell RB. DILIMOT: discovery of linear motifs in proteins. Nucleic Acids Res 2006;34:W350–W355. 7. Neduva V and Russell RB. Peptides mediating interaction networks: new leads at last. Curr Opin Biotechnol 2006;17:465–471. 8. Landschulz WH, Johnson PF and McKnight SL. The leucine zipper: a hypothetical structure common to a new class of DNA binding proteins. Science 1988;240:1759–1764.

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Gene expression microarray data analysis demystified Peter C. Roberts VizX Labs, 200 West Mercer Street, Suite 500, Seattle, WA 98119, USA Abstract. The increasing use of gene expression microarrays, and depositing of the resulting data into public repositories, means that more investigators are interested in using the technology either directly or through meta analysis of the publicly available data. The tools available for data analysis have generally been developed for use by experts in the field, making them difficult to use by the general research community. For those interested in entering the field, especially those without a background in statistics, it is difficult to understand why experimental results can be so variable. The purpose of this review is to go through the workflow of a typical microarray experiment, to show that decisions made at each step, from choice of platform through statistical analysis methods to biological interpretation, are all sources of this variability. Keywords: microarray, microarray data analysis, gene expression, normalization, preprocessing, statistical analysis, clustering, cross-platform comparison, correction for multiple testing, pathway, gene ontology.

Introduction Since the original description of the use of cDNA microarrays in gene expression analysis in 1995 [1], followed a year later by oligonucleotide arrays [2], the technology has rapidly moved from the domain of specialists to being available to the whole research community, through core facilities at most academic research institutions. The arrays themselves have evolved from representing less than 50 genes, to now addressing over 50,000 transcripts on whole genome arrays for complex mammalian genomes, in some cases represented by over 1 million individual features. These arrays have traditionally measured the differential expression of known and putative protein-coding genes. The gene expression microarray data analysis process can be broken down into three main parts: preprocessing, the conversion of the signal from an array scanner to a normalized value appropriate for comparison of expression across the arrays in a study; comparative statistical analysis, to identify significantly differentially expressed genes or co-expressed genes; and biological interpretation, preferably with a statistical measure of significance. Early microarray experiments only considered the difference in expression of a gene between two sets of samples, producing highly variable results; thus, it was soon realized that statistical analysis was required to obtain meaningful Tel.: +1-206-283-4363. Fax: +1-206-283-1606.

E-mail: [email protected] (P.C. Roberts). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00002-1

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

30 results. Initially standard statistical methods were used to identify significantly differentially expressed genes and these are still widely employed. However, the low number of replicates used in a typical microarray study; the variability introduced by the many technical steps in preparing samples; and large number of individual tests being performed on a single array; are inappropriate for standard statistical methods, so new statistical analysis methods have been developed. Many of these have been implemented in the opensource statistical language R [3], usually as part of the Bioconductor project [4]. Unfortunately these different approaches can result in quite different results; consequently, no ‘best method’ can be identified. Recently it has been realized that there are common themes in these approaches [5], helping to identify the most appropriate methods. As microarrays have become more widely accessible to the broader research community, they are increasingly being utilized by investigators with limited knowledge of statistics. These researchers tend to be more interested in biological insights, whereas the emphasis in terms of analysis tools has been on generating statistically robust gene lists, often at the expense of biological interpretation [6]. Also, as gene expression data is now required to be deposited in publicly accessible repositories, researchers are performing meta analysis of this data. Again, they often lack the knowledge to assess the quality of the data sets of interest to them and what is the appropriate analysis approach. Little assistance is readily available to these ‘unsophisticated’ users, and most of the analysis tools available to them are designed for statisticians and bioinformaticians, which assume a level of a priori knowledge to be able to use them effectively. In this review I will endeavor to provide a general overview of the whole gene expression microarray data generation and analysis process. The focus will be on commercial gene expression microarray platforms, as custom inhouse spotted microarrays are increasingly being supplanted by commercial microarrays. In particular, the emphasis will be on the whole genome microarrays that have been designed to address the transcriptome of a given organism. Other applications for microarrays have been developed for genotyping, microRNA measurement, chromosomal region copy number and other areas, which will not be addressed in this review. Gene expression microarray platforms Microarray designs have utilized spotted cDNAs [1] or oligonucleotide probes [2]. Depending on the platform, oligonucleotides are synthesized in situ on glass slides or synthesized, purified and attached to substrates. In the case of the Illumina platform, the purified oligonucleotides are attached to beads that are randomly dispensed into wells on a slide. In addition there is the choice of one-color or two-color experimental designs. For one-color systems a single sample is hybridized to the array. For two-color experiments, two

31 RNA samples, labeled with different dyes, are mixed and hybridized on the same microarray. Two-color systems can provide experimental design flexibility, since the second sample can either be an experimental sample or a standard RNA sample applied to all the arrays [7,8]. Although the original idea behind the development of two-color systems was that the competitive hybridization would reduce errors due to slide variation, it has been found, from well-controlled experiments, that analyzing the two samples independently, rather than generating ratios, can increase experimental accuracy [9]. The commercial microarray platforms are predominantly one-color oligonucleotide microarrays. The Agilent platform is one exception, initially designed as a two-color system [10], that has been modified for the analysis of one-color or two-color experiments. Most companies offer focused arrays for specific research areas and well characterized genes and transcripts. They also offer design and manufacturing of custom arrays. Over time the array manufacturers have increased the number of features that can be placed on one slide. Now several manufacturers offer multiplexed whole genome arrays. Table 1 compares the technologies of the major commercial whole genome microarray platforms. The key difference between platforms lies in the number and length of oligonucleotide probes on the microarrays: either short-oligonucleotide (25–30 bases) or long-oligonucleotide (50–70 bases). Long oligonucleotides have greater sensitivity and are better for analyzing low copy number mRNAs [11–13]; short oligonucleotides have better specificity, being less likely to cross-hybridize with other RNAs [14]. Most platforms have a single probe for each target, each probe occurring once at a fixed location on the array. Two platforms have multiple probes per target: Illumina BeadChips have over 20 randomly located technical replicates for each probe; Affymetrix 3u expression GeneChips have 11 probe pairs per probe set and the recently introduced GeneChip Gene ST arrays have 26 probes per gene. In addition to the differences in technology, the commercial companies differ in the number of species-specific arrays they offer. Affymetrix, through their GeneChip Consortia Program, offers a large number of microarrays to support different eukaryotic genomics projects. Nimblegen has focused on arrays for prokaryotic genomics. All manufacturers have whole genome arrays for human, mouse and rat. Gene content Most commercial microarrays for human, mouse and rat whole genomes share common targets, primarily transcripts from the National Center for Biotechnology Information (NCBI) Reference Sequence collection (RefSeq) [15]. Each manufacturer has tried to expand beyond this basic set of welldefined targets, in an endeavor to address all the protein-coding genes on the

Nimblegen: HG18 4plex Phalanx Biotech: Human OneArray

22,000 47,633

In-situ ink-jet

Spotted

Spotted

Beads

Beads

In-situ photolithography In-situ photolithography Spotted

60

30

50

50

60

60

60

30,968

24,000

48,000

57,347

34,000

42,000

28,869

60

25

54,000

In-situ photolithography In-situ photolithography

25

Affymetrix: U133 plus 2 Affymetrix: Human gene ST 1.0 Agilent: Human 4  44k Applied Biosystems: Human genome survey v2 Applied Microarrays: CodeLink human whole genome Illumina: Human-6 v2 Illumina: HumanRef-8 v2 Nimblegen: HG18

Targets

Oligonucleotide deposition

Oligo length

Manufacturer: array

1

3

8

1  W20

1  W20

1

1

1

26

11 pairs

Probes per target

32,050

72,000

3,85,000

70,00,000

100,00,000

54,841

35,000

44,000

764,885

13,00,000

Total features

1

4

1

8

6

1

1

4

1

1

Arrays per slide

Table 1. Comparison of human whole genome microarray platforms. Data obtained from manufacturers websites.

3u end

3u end

3u end

3u end

3u end

3u end

3u end

3u end

Exons

3u end

Probe location

33 genome. In most cases this predated the completion of the sequencing of the human genome [16,17], when it was assumed that there would be a lot more protein-coding genes than were ultimately found in complex mammalian genomes. UniGene clusters [18] with a limited number of expressed sequence tags (EST), proprietary sequences not available publicly, and predicted genes were all sources of additional target sequences. In most cases, probes were designed to target the 3u end of individual transcripts. The ability to map transcripts to the genome, an important modification to UniGene cluster generation, has led to consolidation into fewer transcriptional loci. The public sequence databases are continually being updated and UniGene clusters are revised on a regular basis, resulting in reassignment of some transcripts. One result of this is that probes, previously thought to map to different genes, have been shown to map to the same gene, in some cases to the same transcript. This redundancy needs to be taken into consideration during biological interpretation. Also, probes previously mapped to a gene can be disassociated from that gene, which can cause confusion when gene lists are reanalyzed. Consequently, care should be taken in the conclusions drawn from microarray experiments that the probe target is an authentic transcript of the gene it is mapped to. The microarray manufacturers do update their probe mapping but it is usually not on a consistent basis and not in sync with the public databases. This can lead to ambiguities between public information and the probe annotation supplied by the array manufacturers. Another source of ambiguity, documented for Affymetrix 3u expression arrays in particular, is probe sequence inaccuracies [19–25], so the probes do not match their stated target sequence. There are also probes that map to more than one gene, therefore are not specific. Several groups have created modified CDF files to eliminate these ambiguous probes from analysis, leading to more reliable results [21,22,25]. Gene expression microarray experiment process The standard workflow for a microarray experiment is shown in Fig. 1. The key to successful gene expression experiments is good experimental design and attention to detail. There are many technical steps between sample preparation and microarray scanning (Fig. 2), each of which can introduce new sources of error and bias, which can have a profound impact on data analysis and interpretation. To minimize the impact of technical error, it is advised that a single technician process all the samples at the same time and run them on microarrays from the same manufacturing batch [5]. If this is not possible, random samples for each condition should be distributed between either technicians or days to avoid bias [26]. The principle of randomization should also be used with multiplex arrays; in most cases more than one slide will be used and samples should be randomly assigned to the slides.

34 Experimental design

Sample preparation RNA extraction Reverse transcription In vitro transcription

Microarray processing Hybridization Washing Scanning

Data preprocessing Non-specific signal correction Normalization Filtering

Differential expression Comparative statistics Multiple test correction Clustering

Biological interpretation Gene annotation Gene ontologies Pathway analysis

Fig. 1. Gene expression microarray experiment workflow. The goal of a typical

microarray experiment is to identify genes that are statistically significantly differentially expressed and identify the underlying biological processes. The actual methods used at any point will be defined by the experimental design and the microarray platform.

Experimental design Gene expression microarray experiments are designed for one of two purposes: evaluation of differential gene expression between groups, referred to as class comparison; or for classification studies, referred to as class discovery and class prediction [27]. These experiments are expensive and time

35 Random priming

3’ in vitro transcription Purified total RNA

External RNA controls Affymetrix; tERC: Agilent; Applied Biosystems

rRNA reduction Reverse transcription cDNA

2nd cycle reverse transcription cDNA

In vitro transcription cRNA

Fragmentation Fragmented cDNA

Fragmentation Fragmented cRNA

Terminal labeling Fragmented biotinylated cDNA

Hybridization

cERC: CodeLink

cERC: Affymetrix; Applied Biosystems

cERC, control oligo: Affymetrix

Fig. 2. RNA sample processing. The traditional method for labeling RNA samples

for hybridizing to microarrays has used 3u in vitro transcription to generate labeled cRNA for hybridizing to the microarray. The microarray manufacturers usually provide kits for this process that contain external RNA controls (ERC). These are added either to the total RNA (tERC), as controls for the reverse transcription and in vitro transcription reactions; or to the cRNA (cERC) prior to or after fragmentation, as controls for the microarray processing steps. Affymetrix whole transcript arrays use a different protocol, using random hexamer priming to generate labeled cDNA that is hybridized to the microarray. This may include an rRNA reduction step, depending on the amount of starting RNA. An additional control oligo, added at the same time as the cERC, is an additional control for the microarray processing.

consuming and a good experimental design is essential, to maximize the return in terms of usable information [26–30]. Experimental design involves a clear scientific hypothesis, an appreciation of the number of factors to be compared and the confidence that can be assigned to the observations: the simplest design is usually the best. Confidence in the results, the power of the analysis, is derived from using the appropriate number of replicates [31–35]. Microarray studies utilize two types of replicates, technical or biological. Biological replicates, samples from individual subjects, are the only choice for good biological inference [26,36]. Technical replicates, a single or pooled sample applied to multiple

36 microarrays, only measure the consistency of the experimental system and provide limited biological information [26]. In some cases, such as two-color systems when controlling for dye-dependant effects, technical replicates are combined with biological replicates; the average intensity of the technical replicates should be used in subsequent statistical tests. Due to the amount of data generated from microarray experiments, classic power calculation methods for estimating the number of replicates required in an experiment are inadequate, so microarray-specific methods have been proposed [31–35]. In general a minimum of five replicates per group is recommended. This number will change based on whether the samples come from inbred or outbred animals, which will increase the biological variance [34]. The reality of microarray experiments is that investigators tend to run a limited number of replicates due to sample or cost constraints; even so, no less than three replicates should be used [6]. Pooling of samples can be used, when the cost of samples is much less than the cost of microarrays or when insufficient RNA is available to run on an array and amplification methods are not desirable [33,37,38]. The same number of samples should be used for each pool and multiple pools should be used for each group. Pooling may introduce biases and does not provide the same statistical power as analyzing individual samples, but it is better than comparing a limited number of samples [5]. Pooling of samples is not appropriate for classification studies, as these rely on inter-individual variation and co-variation [5]. For investigators with limited statistical knowledge considering running microarray experiments, it cannot be overemphasized that time spent in experimental design with a statistician will ensure that valid conclusions can be drawn from the results, especially for more complex experimental designs. Sample preparation The steps involved in sample preparation are shown in Fig. 2. There are several different commercial reagents and kits in the market suitable for RNA isolation. The key factor is to make sure that the input total RNA sample is of high integrity. The standard method for assessing this is to use an Agilent 2100 Bioanalyzer, which can be used to generate an RNA integrity number (RIN) [39], to assess RNA quality. The microarray manufacturers generally provide or recommend reagent kits for labeling samples and kits are also available from other commercial sources. These kits usually use a modification of the Eberwine method to produce complementary RNA (cRNA) labeled with a dye or other functional group [40]. The kits for specific platforms often contain external RNA controls (ERC), that are complementary in sequence to control probes on the microarrays and are often present as a concentration series, so can be used to assess concentration response [41,42]. The ERC are used to monitor both the RNA labeling reactions, by being added to the total RNA sample prior to cDNA synthesis,

37 and the hybridization, washing and scanning process, by being added to the cRNA immediately prior to hybridization. In addition to this standard approach, there are several different methods for amplifying RNA from samples with low RNA concentrations [43]. As with pooling of samples, RNA amplification can introduce biases that should be taken into consideration when designing experiments and analyzing data. Data preprocessing The purpose of data preprocessing is to convert the raw signal for labeled RNA hybridized to a probe to a normalized value, an adjustment to account for variance from technical rather than biological sources [36]. Many papers have been published on preprocessing of microarray data, especially for twocolor systems and Affymetrix arrays. Usually the process involves quantification of the signal from the microarray, background corrections and normalization within and across arrays. In most cases the microarray manufacturers provide software that adequately performs most of these functions. There are also preprocessing packages available in Bioconductor, developed by researchers not satisfied with the methods provided by the array manufacturers. A logarithmic transformation followed by quantile normalization has become the preferred method of preprocessing for onecolor microarrays [44]. Quantile normalization assumes that the arrays have a similar signal distribution, which is typical for most experiments. However, quantile normalization should not be used when comparing tissues with markedly different expression profiles. The purpose of the logarithmic transformation is to stabilize the variance inherent in the microarray data, changing calculations from multiplicative to additive [36]. However, as the intensity values approach zero, this transformation is less effective. To offset this, some algorithms add a constant to the intensity values, so computations of low-intensity signals have improved variance [45]. A large number of methods have been developed for preprocessing Affymetrix data [45,46]. This was because the standard Affymetrix software, Microarray Suite 5 (MAS5) [47] and earlier versions use the difference in signal between paired perfect match (PM) and mismatched (MM) probes, to account for nonspecific binding. In some cases the MM probe has a stronger signal than the PM probe, resulting in negative signal values, and on occasion false-negative expression. MAS5 also does not normalize across arrays, instead scaling within arrays to a defined intensity value. The alternate methods generally ignore the MM probes, using global or model-based background correction, and normalizing across arrays. The most significant source of difference between the preprocessing methods is how they perform background corrections [45]. Of the alternative protocols that have been developed, RMA [48] and GCRMA [49] are those most commonly utilized. Affymetrix has also developed a new algorithm, PLIER [50], that uses an

38 improved PM–MM background correction and quantile normalization. These methods are all available in Bioconductor, mostly in the affy package. There is some concern that they have been optimized using a limited data set from a single human array version, so may have different performance with other arrays [5,51]. It is also likely that the preprocessing method of choice will vary with experimental design. For two-color systems preprocessing involves other factors. It is well known that dyes have individual biases that need to be adjusted for. In particular, the dye cyanine 5 (Cy5), commonly used in two-color microarray experiments, is rapidly degraded by ozone [52], whereas the other commonly used dye, Cy3, is not. Consequently, air quality can become a major factor in two-color analysis. As with the Affymetrix arrays, background correction methods have a marked effect on preprocessing [53]. Local background subtraction can result in negative intensities and should be avoided in favor of model-based methods, where only positive intensity values are returned. There have been arguments for not performing any background correction but this can also result in problems with downstream analysis methods with cDNA microarrays [53]. For Agilent microarrays, the feature extraction software corrects for both dye biases and background. It has been shown that this results in an increase in the variability in low-intensity data [54], that may be due to the background correction used. In general, logarithmic transformation followed by loess smoothing, is the normalization method of choice [55]. These methods are available in the limma Bioconductor package. The large number of technical replicates for probes on Illumina arrays, allows for more robust variance stabilization and normalization, utilizing both quantile and loess normalization [56]. This can be implemented using the lumi Bioconductor package. Differential expression analysis The main goal of microarray experiments is to identify genes that are significantly differentially expressed between two or more experimental conditions, usually by comparing the average intensity values of the replicate samples. Microarray analysis methods attempt to minimize two types of error in measures of differential expression: type 1 or false-positive errors and type 2 or false-negative errors [5]. Controlling type 1 errors is the major goal of many of the statistical methods developed specifically for microarray analysis. Type 2 errors are more likely to be due to properties of the platform being used; a gene may not be detected due to limited sensitivity. Filtering data using quality values and fold change cutoffs Because some genes are not expressed in any sample and not all expressed genes are differentially expressed between samples, it makes sense to remove

39 these uninformative data points from the analysis process using filters. These are usually based on either quality flags or fold change cutoffs, though more sophisticated methods have been described [57]. The simplest measure of differential expression is fold change, the magnitude of the difference in the expression of a gene between the conditions, usually reported as a ratio or log-ratio. Using fold change alone as a measure of significant differential expression is not appropriate, as it does not assess the reproducibility of the measurement or confidence in the observation [5,58]. For low-intensity genes, a relatively small change in signal can have a marked effect on fold change, resulting in type 1 errors. A fold change cutoff value can be set for filtering genes, above which genes are considered differentially expressed; however, the cutoff value is arbitrary, and setting it too high can result in type 2 errors. During the preprocessing of the array data there is usually an assessment of whether a gene is expressed: does the gene have signal significantly above nonspecific signals? This is usually reported as a quality value, which is different for each platform: the Affymetrix MAS5 algorithm flags genes as being present (P), absent (A) or marginal (M) but RMA and GCRMA do not provide a quality metric; the Codelink platform provides similar flags as measures of signal quality; Illumina BeadStudio software provides a detection p value based on the signal from the replicate beads, which is usually reported as the inverse of the p value; Agilent feature extraction software provides several values but the IsWellAboveBG flag is generally used; and Applied Biosystems uses a signal-to-noise ratio for expression and a flag value for signal quality. Quality values can be used to set quality filters for the data. Some software allows for different filtering options: only analyzing genes with perfect quality scores in all samples; allowing genes to be analyzed where the majority of samples have a perfect quality value; or analyzing all genes regardless of quality flags. In cases where the gene is not expressed in one sample group but is in another sample group, either a nominal positive expression value needs to be used for the unexpressed samples, to avoid division by zero, or the gene should be excluded from analysis, which is usually undesirable. Comparative statistics The most common microarray experiment compares expression between just two groups of samples. This means that standard t tests are used to assess the statistical significance of the observed change in expression at an individual gene level [58]. The actual t test to be used is dependent on the experimental design. Usually an unpaired Student t test is used, as the samples are considered to have equal variance, being randomly assigned to each group. In some cases, such as before and after drug treatment, a paired t test

40 provides more power. When the samples are not equally variable a Welch’s t test is appropriate. The standard t tests report a p value for each comparison at an individual probe level. The p value is the confidence that there is a true difference in expression, and p values that fall below a nominal level, usually 0.05, are considered significant. The number of replicates in a typical microarray experiment are not usually sufficient to make standard t tests robust, as they are sensitive to the effects of outlier values [58]. In addition, the large number of individual tests being run in parallel means that there will be a large number of type 1 errors. Consequently, modified t tests have been developed specifically for microarray analysis, utilizing an approach referred to as variance shrinkage [5]. They are nonparametric, using the variance of all the genes on the array to improve the power of the test. In particular Bayesian statistical approaches have been used, as they have been found to improve analysis of microarray experiments with a limited number of replicates [59,60]. The significance analysis of microarrays (SAM) is another popular approach [61]. Though these approaches improve on the classic t tests in terms of controlling the false-positive rate, there is still no ‘best method’ [62]. When samples from more than two conditions are being compared, an analysis of variance (ANOVA) is often used to estimate the relative expression of each gene in each sample [58]. Depending on the experimental design there are different types of ANOVA that can be used. As with the t test, attention needs to be paid to the variance of the samples in a group and between the groups. When only a single factor is present, the one-way ANOVA is used to compare expression at the individual gene level. When two or more factors are being compared, a two-way ANOVA should be used [6]. For more complex designs, for instance, multiple conditions with biological and technical replicates, multi-way ANOVAs should be used [58,63]. Also, modifications of the standard ANOVA have been described that use global gene variance instead of, or combined with, gene-specific variance, to control type 1 errors [58]. Corrections for multiple testing The standard statistical approach for controlling the false-positive rate when using multiple comparisons is to use corrections for multiple testing. These adjust the p values based on the total number of tests being performed. There are two approaches that are taken: a family wise error rate (FWER) control, the simplest being the Bonferroni correction [5,58]; or a false discovery rate (FDR) correction, usually that of Benjamini and Hochberg [64]. Figure 3 shows an example of the effect of different correction methods on the number of significant genes identified. The FWER corrections adjust the p value to reflect the probability that one or more false-positive errors occur in a list. The Bonferroni correction is

41

5% PCER 12,143 probes

5% FDR 8,053 probes

5% FWER 87 probes

Fig. 3. Effect of different multiple testing approaches on significantly differentially expressed gene lists. The numbers are from analysis of the CodeLink data set kidney control and aristolochic acid treated kidney sample from the MAQC rat toxicology study [116] (GEO accession: GSE5350) without any additional preprocessing or filtering. The CodeLink rat whole genome array has 33,790 probes. The per comparison error rate (PCER) was from an unpaired Student t test with a p value cutoff of 0.05. The Benjamini and Hochberg correction was used for FDR. The Bonferroni correction was used for the FWER. Data was generated using GeneSifter.

very stringent, dramatically reducing or even eliminating lists of differentially expressed genes and therefore increasing the chance of type 2 errors. There are step-down modifications of the Bonferroni correction that are less conservative, including those developed by Holm [65] and Westfall and Young [66]. However, the FWER corrections are a poor choice in discovery research, where a limited number of false-positive results are acceptable compared to eliminating true positives. The FDR correction is less stringent and adjusts the p value to control the frequency of type 1 errors in the list of significantly differentially expressed genes [67]. Positive FDR (pFDR) applies a factor to the FDR, equivalent to the proportion of nondifferentially expressed genes to the total number of genes, which reduces the correction [68]. This increases the power of the analysis, while not eliminating all false-positive errors [58]. Rather than controlling FDR below a threshold, it has been suggested that FDR estimating procedures are preferable for microarray analysis [5]. These assign a false-positive probability value to each differentially expressed gene. The control and estimation of FDR is an active area of investigation [69–71].

42 Cluster analysis Another commonly used method for statistically analyzing microarray data from multiple conditions is to use clustering. This can be used on normalized data without any other analysis being performed or on a statistically significant list of differentially expressed genes from an ANOVA. Clustering algorithms recognize patterns in the data [72]. Usually the clustering algorithms are unsupervised, the raw data being analyzed with no assumption of underlying structure. Two basic approaches are taken, either the visualization of overall expression patterns or the partitioning of genes into discrete groups. In either case, genes with similar expression profiles are grouped together. It is often assumed that genes that cluster together are coexpressed; however, unsupervised clustering algorithms will always produce clusters based on the parameters that are set; the quality and relevance of the clusters is not a factor. For classification applications, where the quality and relevance of the groups are very important, additional information about the relationship of the samples is used for supervised clustering algorithms. Hierarchical clustering is the original method used, and is still widely utilized [73]. This is a simple agglomerative clustering method, where genes are sequentially added to the cluster based on the similarity of their expression profile. There are also hierarchical clustering algorithms that take a divisive approach, starting with a single cluster and finishing with the individual genes, but this is more computationally intensive. It is a useful tool for visualizing expression patterns in microarray data, the typical output being a dendrogram of the genes, from which clusters of closely matched genes can be identified. Usually a graphic visualization, referred to as a heatmap, is also generated, where the log ratios of the intensity are usually represented as a color scale, from intense red for the highest positive values, through black for the mean intensity, to intense green for the most negative values. Figure 4A shows an example of the typical output. Many different algorithms have been used for partitioning microarray data. These include K-means algorithms [74], self-organizing maps (SOM) [75] and partitioning around medoids (PAM) [76]. There is no good method of knowing what is the best algorithm to use for a particular dataset [77,78]. These approaches require that the number of clusters to partition the data into be specified at the outset; however, it is difficult to assess what is the appropriate number of clusters for a particular dataset. One approach to assessing the appropriate number of clusters is to use silhouette widths [79]. These are a measure of how closely the genes in a cluster match the mean expression profile for the cluster; the larger the overall mean silhouette width, the better the clustering of the data. This requires repeated clustering using different numbers of initial nodes. Figure 4B shows an example of a PAM output with silhouette values. Other methods have also been developed to

B

Fig. 4. Examples of hierarchical and partition clustering. The data is from a study of male germ cell tumor samples analyzed using Affymetrix human U133A GeneChips (GEO accession: GSE3218) and preprocessed with GCRMA in GeneSifter. (A) Heatmap and dendrogram from hierarchical clustering of genes of the Wnt signaling pathway between the sample groups. (B) Partitioning around medoids (PAM) silhouettes, four clusters from 4,975 significantly differentially expressed genes, identified using a one-way ANOVA and Benjamini and Hochberg FDR correction, with an adjusted p value o0.0001 and at least a four-fold change in expression compared to a normal testis control. (The color version of this figure is hosted on Science Direct.)

NT S CC E1 E2 T YS

A

44 address the question [80–82]. There is a similar issue with validation of the quality of the clusters [77,82]. Intuitively, genes in the same cluster are co-expressed and therefore share a biological function; however, this assumption is often not borne out by the functional analysis of the genes in a cluster [72]. The reasons for this are: the complexity of the biology underlying a given gene list; the limited number of samples relative to the number of genes on a typical microarray; and the strict assignment of genes to clusters. Standard clustering techniques assign a gene to a single cluster and, once assigned, it cannot be reassigned to a different cluster. Fuzzy clustering has been used to address this problem, by assigning probabilities to genes in clusters; ultimately, the gene is assigned to the cluster where it has the highest probability score [83]. Other approaches, for instance principal component analysis (PCA) [84] and independent component analysis (ICA) [85,86], have used linear models, which allow genes to belong to more than one cluster. Supervised classification To use microarray data as a phenotype classification tool, it is necessary to identify a set of discriminative genes that can be used to assign samples to pre-defined categories. The original description of the use of gene expression microarray data for cancer classification was that of Golub [87]. Since then there have been many publications describing different approaches to the problem [5,88], utilizing several standard data sets for cancer classifier testing [88]. There are several inherent problems in the development of classifiers, including the presence of redundant transcripts in gene expression data; and introducing bias by not using completely separate data sets to create and then validate the predictive algorithm. This leads to models that are susceptible to overfitting, performing well with test data but not with new data [5]. It is widely accepted that a smaller set of non-redundant informative genes will provide the most accurate classifiers; however, there is no current method of choice to identify such genes. Biological interpretation Once a list of significantly differentially expressed genes has been obtained, the next consideration is the identification of the biological processes represented in the list. The information associated with a particular gene, the annotation, is available from many online sources [89–91]. The NCBI has many annotation resources [92], including Entrez Gene, which integrates much of the gene information for a large number of organisms. There are similar resources at the European Bioinformatics Institute (EBI) [93]. Two sources of extensive gene and genome level annotation for multiple species are Ensembl [94] and the UCSC Genome Browser [95], that has tracks

45 showing the location of Affymetrix probe sets. For organisms other than human, mouse and rat annotation is generally sparse. Intensively studied organisms, such as yeast and Drosophila, have rich data resources. Many of the organisms that have had their genomes sequenced have very limited annotation, usually in a dedicated database that is difficult to query. Commonly used sources of functional information associated with genes are the Gene Ontology (GO) database [96] and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database [97]. Useful functional information can also be found at PANTHER [98], which has pathway and ontology information. It is relatively easy to collect information for a single gene, if somewhat time consuming, but it is not easy to identify broad biological themes in this way. Microarray gene lists can have thousands of entries and it can be difficult to query databases to obtain all the related information and mine that information for common themes. For GO several tools have been developed that can take a list of genes and return lists of ontologies, with statistical measures of their significance [99–101]. Similar reports can be generated for pathways [99]. Statistical significance is assessed using either z scores or p values and FDR corrections. The z score represents whether the number of genes associated with an ontological term or pathway is significantly overrepresented, a score W2, or underrepresented, a score o 2, compared to the normal distribution. The DAVID system integrates a lot of different data sources and provides rich functional reports for microarray gene lists [102]. Gene expression microarray data analysis software Software is absolutely essential to the analysis of microarray data. However, there are very few software packages that cover all the steps in microarray analysis. This means that data tends to go through a series of individual software applications that mirror the steps in the workflow in Fig. 1. There is also a limited selection of software resources that not only provide analysis tools but also associated data storage and management capabilities. Open-source software There are several papers published each month on some aspect of microarray analysis and these papers generally have a link to access the associated software. Some are set up as websites, some software is available as Microsoft Excel plug-ins [61], but most of the statistical approaches appear as packages in Bioconductor [4]. Bioconductor is a software resource for genomics data analysis for biostatisticians and bioinformatics experts, who appreciate its power and flexibility, and do not mind the difficult interface. As mentioned earlier, there are specific packages in Bioconductor for the

46 different commercial microarray platforms. Other packages can be more general in nature and gather tools for specific analysis approaches, for instance Bayesian methods. For a casual user the learning curve is steep, requiring the learning of the R statistical scripting language [3], on which Bioconductor is based. Some graphical user interfaces have been developed to make Bioconductor more accessible. As previously mentioned, the DAVID Knowledgebase and associated tools are a popular application for biological interpretation [102]. Finding other sources of microarray analysis and interpretation applications is a daunting task [103], due to the sheer number available, though attempts to catalog them have been made [104]. Commercial software The commercial software available for microarray analysis integrates much of the functionality available in separate Bioconductor packages. The applications often support only one or two microarray platforms. Most have preprocessing, differential expression analysis and clustering tools and allow the integration of R or other scripting languages. There is often an emphasis on data visualization. However, biological interpretation tools are usually not well integrated, unless that is the focus of the application. Table 2 summarizes the capabilities of the most popular commercial applications. Though the user interfaces make them easier to use than most open-source software, they are generally designed for sophisticated users and so can be difficult to learn for nonexperts. The exception is GeneSifter, the only webbased application, which was designed with the philosophy that non expert research scientists should be able to perform their own microarray data analysis for common experimental designs. This system has broad microarray platform support, built-in data management, preprocessing, differential expression analysis, clustering tools and integrated biological significance analysis. A common criticism of commercial software is that it is a ‘black box’; the actual code being run for a statistical analysis being inaccessible to the user. GeneSifter only uses algorithms from R and Bioconductor. Cross-platform comparisons In spite of attempts to standardize microarray data reporting, led by the Microarray Gene Expression Database Society (MGED) [105] with the Minimum Information About a Microarray Experiment (MIAME) standard [106], most attempts to compare results from different microarray platforms tended to show poor concordance [12,23,107–111]. The sources of the inconsistencies were identified as: the difficulty of comparing platforms at a gene level [12]; employing different protocols for sample preparation [108,110]; and different statistical tests in data analysis [111]. When

Integromics: ArrayHub

TIBCO: Spotfire Decision Site VizX Labs: GeneSifter Biodiscovery: GeneDirector Genologics: Geneus X

X X

X X X X

All

All

Affymetrix, Illumina Affymetrix, ABI, twocolor

X

X

X

Affymetrix, Illumina All

X

X

All

X

X

X

X

X

X

X X

X

X

X

X

X

Differential expression

X

All

X

X

X

Affymetrix, Nimblegen Affymetrix

Affymetrix, two-color Genepix, other Affymetrix, cDNA

X

Affymetrix

Genomatix: ChipInspector Insightful: S+ArrayAnalyzer Molecular Devices: Acuity Ocimum Biosolutions: Genowiz Partek: Genomics Suite Rosetta Biosoftware: Resolver SAS: JMP Genomics

X

All

Preprocessing

Agilent: Genespring GX Biotique Systems: X-ray DNAStar: ArrayStar

Data storage

Microarray platforms

Company: product

X

X

X

X

X

X

X

X

X

X

X

X

Gene annotation

X

X

X

X

X

Functional analysis

Table 2. Comparison of commercial gene expression microarray data analysis software capabilities.

Win

Win, Mac, Linux Client/server

Web browser

Win

Win

Client/server

Win, Linux

Win, Mac, Linux

Win, Mac, Linux Win, Linux, Solaris Client/server

Win

Win

Win, Mac

Computer platform

R

S-Plus

SAS

Proprietary

Proprietary

Proprietary

Proprietary

S-Plus

Proprietary

Proprietary

Excel

Proprietary

Statistics

48 comparing cDNA and short-oligonucleotide platforms, similar trends in direction of differential expression, but not magnitude, were observed [109]. It was also found that results were more variable between laboratories than between platforms when common samples were run at several sites [108,110]. When common procedures were implemented, and good consistency between replicate samples was achieved, the consistency between laboratories and platforms improved. Microarrays were identified as being key technologies in future submissions to the U.S. Food and Drug Administration (FDA) and the lack of uniformity between studies and platforms was of major concern. This led to two initiatives, the MicroArray Quality Control Consortium (MAQC) [111] from the FDA and External RNA Control Consortium (ERCC) [112] from the National Institute of Standards and Technology (NIST). Both represented government, commercial and academic interests. The MAQC Consortium published a group of papers in September 2006 addressing many of the issues that had been raised, to try and identify the sources of discordance observed in other studies [9,41,113–116]. All the data generated is publicly available and acts as a superb resource for comparing analysis methods. The main study [115] utilized a pooled titration series using different ratios of two reference RNA samples: a Universal Human Reference RNA and a Human Brain Reference RNA. These were assayed on six different commercial microarray platforms and the NCI provided an in-house spotted oligonucleotide microarrays. Each platform was used at multiple test sites. Each test site ran five replicate assays for each RNA pool. The microarray providers used their own software for intensity signal quantification and a quality measure for each probe on the array. However, this made the resulting analysis more complicated because of the differences in data preprocessing and quality filtering. To make sure that, as far as possible, each platform was actually measuring the same target, a common set of 12,091 probes, mapping to a non-redundant list of genes and transcripts, was identified. The generation of this list was aided by the actual probe sequences being made available by the companies involved, allowing more rigorous comparisons than had previously been possible. In general, the inter-platform detection of these genes varied more than intra-platform variation between test sites; however, the different platforms appeared to detect similar changes in gene abundance. The optimum method for generating overlapping gene lists between platforms was to use a ranked fold change analysis, which ignored the absolute degree of observed change. Using standard statistical tests reduced the concordance between platforms. Perhaps not surprisingly, the simple statistical methods chosen have been challenged [117]. The two reference RNAs were also used to compare the concordance of results from one-color and two-color microarray studies [9]. This comparison used three different platforms, hybridizing both one-color and two-color

49 samples to each. This eliminated problems due to different platforms being used for each experimental design. Within each platform there were high correlation coefficients and good concordance between the differentially expressed gene lists for the two approaches. Two-color designs appeared to have slightly better sensitivity but one-color designs had lower compression of the expression values. Using individual intensity values rather than ratios for the two-color design appeared to have the greatest sensitivity. As well as the artificial RNA sample comparison of the main MAQC study, four platforms were compared using a biologically relevant toxicogenomics data set [116]. This data was derived from four groups of six rats treated with a range of plant-derived toxins [31]. Liver samples were collected for all groups, as well as kidney samples from control animals and those treated with aristolochic acid, a nephrotoxic compound. These six groups of six replicate samples were assayed on five sets of rat whole genome microarrays from four commercial sources. The results validated the approach used in the main study that rank fold change was the best method of maximizing gene list overlap between platforms. The fold change ranking also resulted in much better concordance when comparing platforms, based on the biological functions significantly represented in gene lists. The MAQC study also included validation of the microarray data using quantitative measurement of gene expression [113]. Generally, good concordance was seen between the quantitative assays and the microarray results for genes that were detectable on both platforms. Where discordance was observed it could be explained by difference in the location of probes, meaning that alternate splice variants could be detected. Genes found to have low expression by the quantitative assays were those that showed the most variable concordance between both the quantitative assays and microarray platforms and between the different microarray platforms in the main MAQC study. This reflected the sensitivity range of the different microarray platforms. Overall, the MAQC study showed that data was consistent at individual test sites, reproducible between test sites and comparable between platforms. However, to ensure the reliability of microarray-based studies, there is still a need for unified metrics and standards, to identify poor quality arrays and monitor performance at microarray facilities.

Public gene expression data repositories A condition of many journals for publishing papers in which gene expression microarray data is described is that the data has to be publicly accessible. This is also a condition of federal grant funding agencies. Two major data repositories are the Gene Expression Omnibus (GEO) [118] at the NCBI, and ArrayExpress [119] at the EBI. The data submitted to the repositories has to

50 be MIAME-compliant, so all the experimental details are available. Many submitters also include the original raw data files. The two repositories currently contain nearly 300,000 individual samples in approximately 10,000 studies, most of which are from gene expression microarray experiments. This huge volume of data is available for meta analysis, either to extend or confirm researchers own results, or for data mining, for instance to identify previously missed gene and disease relationships [120,121]. However, when performing meta analysis it is important to remember that analysis across microarray platforms is not straightforward.

The future The original design philosophy for gene expression microarrays was to measure the expression of all protein-coding genes. However, with the refinement of whole genome annotation, it has become clear that the estimates of the number of these genes have been over optimistic. The latest estimate for humans is approximately 20,500 [122]. This will lead to further consolidation of gene expression microarray platform content, ultimately leading to the same set of genes and transcripts being represented on each platform. As the content goes down and feature densities increase, multiplexing of arrays will increase. At the same time the number of potential non-coding RNA transcripts has dramatically increased after the publication of the results of the ENCODE pilot project [123]. Tiling microarrays were extensively used in this and the follow-up project. It is likely that new microarrays will appear to measure the diverse RNA species that are being identified and also to study transcriptional control elements. This is already occurring with microarrays for micro RNAs and recent announcements of the release of microarrays for several platforms containing CpG elements. It is likely that microarray platforms will face increasing competition from high-density RT-PCR based platforms. Another technology that will have a profound effect on microarrays is digital gene expression (DGE) [124], using the new massively parallel sequencing systems. DGE is basically an extension of serial analysis of gene expression (SAGE) [125], a tag counting method of gene expression analysis, first described in the same issue of Science as the original cDNA microarray paper. The problem with SAGE was that it was expensive to sequence enough tags to get true quantitation of gene expression. The new sequencing technologies mean that deep sequencing is much cheaper and much faster and millions, rather than thousands, of tags are sequenced. This technology has been touted as the end of microarrays, and has resulted in Applied Biosystems Inc. abandoning their microarray platform in favor of their new sequencing platform. It is more likely that

51 the two technologies will be complementary; DGE results leading to new microarray designs. As well as new technologies for measuring gene expression, the use of microfluidic devices to study gene expression at a single cell level, and laser microdissection to analyze select sets of cells from tissues, will lead to a greater appreciation of localized gene expression and potentially better classification. Gene expression microarray data analysis has generally reached a state of consensus. However, developments in microarray technology, new competing technologies and sample preparation will require new analysis methods or refining of existing methods. Improvement in classification methods and more biologically meaningful clustering approaches are required. Increased interest in meta analysis of co-variance of genes across studies and across platforms should see improvements in data analysis methods for this purpose. Also, gene expression microarray data is increasingly being used with other types of data to obtain a larger biological picture, referred to as systems biology. This is a rapidly expanding field and integration of these disparate data types will be a challenge. Acknowledgements The author would like to thank Leah Klein for helpful discussions and critical review of the manuscript. References 1. Schena M, Shalon D, Davis RW and Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467–470. 2. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H and Brown EL. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996;14:1675–1680. 3. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2007. 4. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH and Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004;5:R80. 5. Allison DB, Cui X, Page GP and Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006;7:55–65. 6. Olson NE. The microarray data analysis process: from raw data to biological significance. NeuroRx 2006;3:373–383. 7. Kerr KF, Serikawa KA, Wei C, Peters MA and Bumgarner RE. What is the best reference RNA? And other questions regarding the design and analysis of two-color microarray experiments. OMICS 2007;11:152–165.

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63

UCSC Genome Browser: Deep support for molecular biomedical research Mary E. Mangan1,, Jennifer M. Williams1, Scott M. Lathe1, Donna Karolchik2 and Warren C. Lathe III1 1

OpenHelix, LLC, 12600 SE 38th Street, Suite 230, Bellevue, WA 98006, USA UCSC Genome Bioinformatics Group: Center for Biomolecular Science & Engineering, CBSE/ITI, 501D Engineering II Building, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA 2

Abstract. The volume and complexity of genomic sequence data, and the additional experimental data required for annotation of the genomic context, pose a major challenge for display and access for biomedical researchers. Genome browsers organize this data and make it available in various ways to extract useful information to advance research projects. The UCSC Genome Browser is one of these resources. The official sequence data for a given species forms the framework to display many other types of data such as expression, variation, cross-species comparisons, and more. Visual representations of the data are available for exploration. Data can be queried with sequences. Complex database queries are also easily achieved with the Table Browser interface. Associated tools permit additional query types or access to additional data sources such as images of in situ localizations. Support for solving researcher’s issues is provided with active discussion mailing lists and by providing updated training materials. The UCSC Genome Browser provides a source of deep support for a wide range of biomedical molecular research (http://genome.ucsc.edu). Keywords: UCSC Genome Browser, annotation, genomics, SNP, transcription factor, conservation, sequence analysis, ENCODE, human genome, training, bioinformatics, education.

Introduction In the last few years the amount of genomic sequence data has grown exponentially. The number of databases and tools that organize and analyze this data has grown in conjunction with the growth in the raw data. Since 1996, Nucleic Acids Research (NAR) has had an annual ‘‘database issue’’ that lists prominent or new databases and tools. In the first annual issue in 1996, 57 databases were included [1]. Just over 4 years later, 230 were listed in their online list. Now, more than 4 times the number of databases and tools are listed [2]. And that is only the tip of the iceberg. Few scientists are aware of, or make full use of, all the open-source and public resources available to them through the Internet. Tools and databases continue to grow in number. Among them are ‘‘genome browsers’’ which help organize all this sequence data, and add other Corresponding author: Tel.: (425) 401-1400.

E-mail: [email protected] (M.E. Mangan). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00003-3

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

64 data types and analysis tools. Genome browsers bring together genome data and analysis tools for draft and completed genomes into one accessible browser of the genome. They add additional data types such as single nucleotide polymorphisms (SNPs), domains, repeat elements, and more, to provide context for understanding genomic regions. All genome browsers organize data based on chromosomal locations in order to give their users the ability to search, zoom, or walk to different parts of the genome and chromosomes. Once a user arrives at a region of interest in the browser, the graphic chromosomal display offers different types of annotated data as ‘‘tracks’’ of information layered along by chromosomal location. Some examples of these ‘‘annotations’’ of the chromosome include genes and gene predictions, variation and repeats data, cross-species comparative data, and many more. Additionally, these graphical tracks usually link out to pages of more information. For example, a gene track page might have information about the gene structure, transcripts, expressed sequence tags (ESTs), SNPs, protein sequence, domains, and more. These data detail pages then usually link out to outside databases and data. For example, the protein information on a gene details page might link out to a Protein Data Bank (PDB) crystal structure [3]. In summary, genome browsers present the genome data and organize many other data types to provide context to understand genomic regions of interest. One of the oldest and most widely accepted genome browsers is the UCSC Genome Browser, which has been providing context for sequence information since the earliest days of the computational displays of Human Genome Project (HGP) data [4]. As more and more genomes are sequenced, the same framework has been used to display many other species data as well. Additionally, each new type of genome-based data brings additional insights into the genomic regions: the UCSC Genome Browser software has been enhanced and extended to provide new and informative views of the data. This tremendous resource is freely available on the web for anyone to use (http://genome.ucsc.edu/). The UCSC Genome Browser is developed and maintained by the Genome Bioinformatics Group, a cross-departmental team within the Center for Biomolecular Science and Engineering (CBSE) at the University of California Santa Cruz (UCSC). This team continues to implement new data and features every day. In this chapter, we present the basic features of the UCSC Genome Browser and introduce you to obtaining the data and views that can advance your molecular biomedical research projects, and deepen your understanding of genomic regions of interest. We will examine the ways to view various data types already available in the browser, and show you how to display your own data as well. We will show you associated tools to move beyond the default displays. We will describe new data types and features that you can

65 expect to see soon. Opportunities to expand your knowledge with additional training will be provided. Our goal is to help you to visualize and analyze molecular data that is crucial for your work, improving the effectiveness and efficiency of your research. UCSC Genome Browser: The basics – an overview There are various ways that different bioinformatics teams have chosen to represent genome data. Some are horizontal and some vertical. Different types of data can be selected for display. To provide you with an overview of the structure that the UCSC team has chosen, we show you this conceptual diagram of the organization of the viewer in Fig. 1A. The specific data groups will vary by species. For any species that you examine in the UCSC Genome Browser, you will find a similar organization. The official sequence from the team in charge of the species sequencing project is obtained from the public repository GenBank [5]. This sequence forms the framework on which all of the other data will be displayed. The official sequence is displayed as the uppermost track in the viewer. At low magnification this will appear as a series of periodic tick marks and nucleotide position numbers – but you can zoom in all the way to the base level and see the individual nucleotides. Beneath the official sequence – aligned at the appropriate nucleotides – you can find dozens of different data types that provide context for the genomic sequence regions in which you may be interested. These additional data displays are referred to as ‘‘annotation tracks’’ and offer insights to other features of the region. You can find the genes that are known to be present in a region. You will find variations such as SNPs, evolutionary relationships across multiple species, and many other data types of interest to biomedical researchers as well. The data types that you can find will vary by species, but all of the species will be organized in this manner: the official sequence at the top, annotation tracks below. Any item that you want to learn more about is clickable, and you can go deeper into any area you like (Fig. 1B). Basic searches in the browser To navigate to the genome locations you want to see in the UCSC Genome Browser, you start at the homepage (http://genome.ucsc.edu). You access the tools we will discuss in this chapter from the blue navigation bars at the top or at the side of the homepage. To get started with a basic search, click the links for Genome or Genome Browser from the homepage to access the Gateway. A sample of the Gateway search form is illustrated (Fig. 2A). On the Gateway page you need to make a number of choices. First you choose a clade and then a species (genome). You can search one species at a

66 A) Official sequence from sequence consortium teams: base positions indicated Mapping and Sequencing

Annotation Tracks

Phenotype and Disease Associations Genes and Gene Predictions mRNAs and ESTs Expression and Regulation

links out to more data

Comparative Genomics Variations and Repeats ENCODE

B)

Fig. 1. The UCSC Genome Browser organizes the official genome sequence and

many other data types to provide context for any genomic region of interest. Nucleotide sequence data at the top provides the framework on which all the other data is organized. (A) Conceptual diagram of the browser organization. (B) Sample image of the actual browser with organizational features indicated. Pages with more detailed information are one click away.

time. Next you choose an ‘‘assembly.’’ Assembly refers to the official backbone genomic sequence that is used to create the framework on which to hang all the other data. UCSC obtains the official assembly and then generates the annotation tracks for that genome. The date you see in the assembly menu refers to when the assembly sequence was frozen by the

67

Fig. 2. The Gateway search interface provides quick and easy access to the basic

genomic query functions. (A) The Gateway interface for searching the genome requires you to select: the genome to search; which official sequence assembly release you want to examine; the region of the genome that you wish to view; and the image width of the resulting view, in pixels. (B) Results of a search in the human genome for the text TP53 show all the records containing the text TP53. Some are probably the gene of interest (as indicated by the gene symbols on the left), others may be interacting or associated proteins (as indicated by the descriptive text on the right). Selecting the link for the appropriate item will take you to the genomic region indicated in the center.

assembly team. Usually you will want the most current assembly, but sometimes you may want to look back at older data and you will see that it is still available in the menu. Sometimes research papers are years in the making and may rely on coordinates from older assemblies that you need to evaluate. Even older data are still available in the UCSC archives if you need it, but may not be shown in the menu choices. You proceed to specify the position in the genome that you would like to examine. You can enter nucleotide ranges, gene symbols, or keywords. Finally you can set the image width. This is the size (in pixels) of the image that you will see when you examine the genomic region in the viewer. A ‘‘configure’’ button below lets you alter the image in many other ways as well, including changing the font and features sizes. For training, talks, or publications it may help to adjust these features for the images you will use.

68 Once you have made your choices in the Gateway, you submit your search to the database. For this chapter, we will illustrate the results of a search (with default settings) for TP53 – a well-characterized gene of biomedical interest (Fig. 2B). The result of this search is a large list of items in the database that match the text TP53. The list will be a mix of items that contain matching text somewhere in their record. Some of them will be the likely gene as you can see from the gene symbol on the left side, and some of them may have names that say ‘‘interact with TP53,’’ or have various related information in their gene details pages. In this case, we will have to select one of the links to go to the viewer. Usually if they share the gene symbol, the different items represent splice variants or other known variations. As a rule of thumb you may want to try selecting a long one. All of them will be visible when we get to the viewer – we just have to pick one at this point to be taken to the right genomic region. You can move around the region – upstream, downstream, zooming in or out – once you get there. The basic displays: An overview When you get to a region of the genome to examine, you will find that UCSC presents a set of default tracks for your view on the long browser page. We will spend a few moments now to understand the organization of this page. The Genome Viewer image is at the top of the page, and the controls for all the tracks are provided at the bottom of the page. An overview of an entire page is provided in (Fig. 3). The data in the image part of the viewer is organized into the same sections as the controls. If you are interested in the Mapping and Sequencing groups of tracks, you will find them in the uppermost portion of the viewer. If you are interested in Variation and Repeats, those correspond to the lower areas in the viewer. Each of the available tracks has a link to more information about the data that track contains and a menu to set the display type, which we will describe later. Displaying data types: Visual cues We will now take a closer look at the viewer. There are a number of helpful features of the display that we would like to illustrate. Some items are simple displays of a single location or short segment. These are illustrated as bars or tick marks. A sample of this is the SNP track shown in Fig. 4A. In this case all the SNPs are on one line. Later we will talk about how to change that for a different view of the SNPs. Another type of display will provide information about the gene structure, as we show in Fig. 4B. We focus on the UCSC Genes track here, with those likely isoforms from our original search adjacent to each other. The particular one that we clicked to get here has highlighting around the gene symbol. UCSC Genes represent

69

Fig. 3. Overview of the UCSC Genome Browser viewer, showing the human TP53

region. The viewer at the top shows graphical displays of the data. Controls for groups of similar data are collected in collapsible sections. Display data is layered in the same layout as the controls area, with ‘‘Mapping and Sequencing Tracks’’ near the top of the viewer, and ‘‘Variation and Repeats’’ further down, for example. The specific data types available will vary by species.

predictions based on data from well known and respected repositories of sequence information, such as RefSeq, GenBank, and UniProt. A moderately conservative set of predictions, this track includes protein-coding and putative non-coding transcripts. A series of thresholds of evidence, and

70

Fig. 4. Genome viewer graphics indicate important features with the shape and

height of the components. Color can provide important cues as well. (A) SNPs are indicted as tick marks, representing single nucleotides or small insertion–deletion (indel) items. Color code is the default setting from the SNP description page. (B) Gene structure is indicated using boxes and lines to represent coding and noncoding regions. Arrowheads on the lines show the direction of transcription of this gene: here we see the 5u end of the gene is to the right, and transcription would proceed to the left. (C) The conservation track displays the alignment scores across many species as a histogram, with the tallest peaks indicating the most conserved areas. Individual species can be seen below the group comparison. (The color version of this figure is hosted in Science Direct.)

quality control checks, have been applied to produce the set of predictions of the likely transcripts that you will see. A detailed explanation of this can be found in a paper from the UCSC team [6]. In this graphical representation of gene structures, full-height boxes represent protein-coding pieces, half-height boxes represent UTRs, and lines represent intron segments. Arrowheads on the gene structure lines indicate the direction of transcription, which could be in either orientation in this viewer. Be sure to watch for that for the correct interpretation of coding sequences and promoter regions. In Fig. 4B we specifically chose to show a gene that runs from right to left, so that you will be aware of that possibility. If you are looking at gene structure diagrams, you will find introns, exons, untranslated regions (UTRs), and the direction of transcription of the gene. If you are looking at the Conservation track (Mammal Cons) you will see a special type of display, which UCSC refers to as a wiggle track (Fig. 4C). The wiggle track is a nice way to display continuous data as a histogram. In this case it displays the likelihood of an evolutionary relationship as a bar with a height that represents the score from a Multiz comparison of many species [7]. Another way to display genomewide data is a new feature called Genome Graphs, which will help in the display of data from genome-wide association studies.

71 Track colors may have meanings. In the case of the UCSC Genes we see in Fig. 4B, colors offer some indication of the confidence in the quality of the sequence information. For example, in the UCSC Genes track, the color black indicates that there is a PDB structure entry for this transcript. Shades of blue indicate status – which may be reviewed or provisional, for example. However, sometimes colors are configurable. A good example of color coding is the situation with SNPs. It is often crucial to know if the variation at a site could cause a change in the amino acid sequence of the resulting protein, which is known as a coding non-synonymous change. We could choose to color those important SNPs differently from others. On the SNP information page you can do this with the filter shown (Fig. 5A). Here we illustrate that SNPs in the display should be colored by function, and non-synonymous SNPs should be blue and everything else should be black.

Fig. 5. Filters are available for some data types. These filters can be used to

customize the color keys to provide additional cues about the data. (A) SNPs can be filtered based on a number of functions, and different types of SNPs can subsequently be colored using a 5-color scheme. For our example we color ‘‘coding non-synonymous’’ SNPs in blue, all other SNPs are black. The resulting SNP track is shown. (B) ESTs can be color coded based on various criteria. In this case we choose to show ESTs found in brain tissue in green. This relies on the information in the GenBank record, which may or may not be present. (The color version of this figure is hosted in Science Direct.)

72 Another nice example of a way to color the items in a track is the ESTs track: you can choose to color ESTs by tissue type, and look for tissue-specific splice variants (Fig. 5B). For example, you could ask for the ESTs in the TP53 region to be colored green if they are from brain samples. However, it relies on that data being present in the original GenBank record, and tissue data is not always present in the original submission. Other tracks will have different color codes or filter options – you need to examine the track pages to find out the color keys, and determine if you can configure those colors yourself. Two ways to get to the track information pages are: (a) click on the ‘‘mini button’’ gray or blue bar on the left edge of the display area and (b) from the track controls area, click the hyperlinked name above the menu. Either action takes you to the track details description and filter options (if they are available; not everything can be filtered). Setting the layout with menu controls As you may have noticed, you can also change the layout or ‘‘visibility’’ of the display using the track menu controls: hide, dense, squish, pack, and full (Fig. 6A). Once you find a track of interest, you can set the display by using these menus. Here we will illustrate the different appearances of the menu selections, using the Human ESTs track as an example. We show the same region of human chromosome 17 as our TP53 gene in the Human ESTs section of the viewer, using the different menu options (Fig. 6B–E):  Hide: completely removes the data from your image.  Dense: all items become collapsed into a single line – it fuses all the rows of data into one line. In this case it means that you can see where there is EST coverage, but you do not know anything about individual ESTs in this view (Fig. 6B).  Squish: each item is on a separate line, but the graphics are only 50% of their regular height. Here you can see more information about individual ESTs (Fig. 6C).  Pack: each item is separate, but efficiently stacked like sardines. However, they are full-height diagrams – which makes it different from squish. Here you can see the GenBank accession numbers for the ESTs, which may be useful. In the case of ESTs you can also see arrowheads, which indicate the direction of the sequence ‘‘read’’ provided from the GenBank record (Fig. 6D).  Full: each item is on its own separate line, all the way down the browser viewer, up to approximately 1,000 rows (Fig. 6E). If you have too many items in any display you may exceed the capabilities of your Internet browser; in these instances the Genome Browser will

73

Fig. 6. The ‘‘visibility’’ or layout of the data can be adjusted with the menu for a

given track. (A) A typical menu for a data track may have a number of choices for setting the display. This menu shows a complete set of options: hide, dense, squish, pack, and full. Hide removes the track from the viewer completely. (B) Dense compresses all the data on a single line. (C) Squish displays all the data, efficiently packed and 50% of normal height. (D) Pack displays efficient packing, but has fullheight graphics. (E) Full puts every item on its own line, up to approximately 1,000 rows if your browser permits. If there are more items the display will reset to a more efficient display.

74 automatically revert to a more efficient view. If you still need to examine your data in a more distributed view you can try to zoom in to a smaller nucleotide range area that would contain fewer elements. To choose any of these options, just highlight it in the pulldown menu. To make the changes appear, you must click the ‘‘refresh’’ button that appears in the middle or at the very bottom of the Genome Browser page. Super-tracks for large-scale data Recently, a capability for a new class of ‘‘super-tracks’’ has been added. As more large-scale and genome-wide studies are performed and published there will be new challenges for the display of this data. Certain data tracks of related data may be grouped so that they can be shown or hidden by a single menu control option. Super-tracks are one way to address this scale and complexity, and the UCSC team will continue to develop new ways to view and manage such data. The best view for a given data type depends on your needs and the kind of data being presented. For example, sometimes it may be enough to know that there are ESTs in a region. Other times you may want to know more about a specific EST, its length, and which exon or intron regions it may possess. Page-wide display controls Besides the track menus, there are a number of other controls that permit you to adjust the view of the data in your viewer. Here we see the upper section of the Genome Viewer page, with several controls for adjusting your view of the genome (Fig. 7A). You can use the buttons with the arrowhead indicators to walk left or right along the chromosome in this area. You can take steps along the chromosome in large (signified by a triple arrowhead), medium (double arrowheads), or small (single arrowheads) jumps. These can be very handy if you are interested in what is going on near your search region. You can magnify the image area using the ‘‘zoom in’’ buttons – you can zoom in a little bit, or up to 10-fold. Or you can choose ‘‘base’’ to zoom all the way down to the nucleotide level right away. Similarly, you can ‘‘zoom out’’ with a different set of buttons. Alternatively, you can indicate a specific genome coordinate position in the ‘‘position/search’’ box. For example, if we wanted to see more of the possible promoter or downstream regions, we could subtract 1,000 from the 5u side, and add 1,000 to the 3u side, and get all of that extra sequence in our view. In addition, you can use this box just like the search box on the Gateway page – you can use it to search for text items if you enter text and hit ‘‘jump.’’ Another handy feature is the automatic zoom and recenter action. If you click your mouse on the nucleotide backbone track at the very top, the

75

Fig. 7. Controls to adjust the viewer magnification and layout are found on the page. (A) At the top of the page controls to walk left or right along the chromosomal region are shown. You can move in small, medium, or large hops. You can zoom to different levels, and even down to the display of the base nucleotides to see the A, T, G, and C display. Zooming out at different rates is possible as well. A position box can be used to add or subtract nucleotides for your view in the window, or you can search this genome from here by entering text and clicking ‘‘jump.’’ A configure button can be used to access many controls. (B) Mid-page control buttons can reset your view to defaults, hide everything, upload custom tracks, configure (as above), and refresh the image with any changes you specified.

browser will automatically recenter the image where you clicked, and zoom in threefold. Finally, you can change the way your viewer looks with the ‘‘configure’’ button. From this button, you will access a page that gives you some choices about how this page should look, including changing the font and graphics sizes. In the center of the page are another set of controls (Fig. 7B). First, let us focus attention on the control buttons.  The ‘‘default tracks’’ button will get you back to the default settings – it is like an escape hatch if you made a lot of changes on the image and want to start over.  The ‘‘hide all’’ button is nice if you want to set up a specific display with only those annotation tracks that you are interested in – it will let you start to build a nice customized view for yourself with only those things you care about.  The ‘‘add custom tracks’’ button will enable you to view your own queries or lab-generated data in the viewer, and will be discussed in more detail later.  The ‘‘configure’’ button – also found near the top of this page, and also on the Gateway page – gives you access to a big web page that will let you make all sorts of changes to the viewer. You will be able to change the font and graphic size here; you can also change the window width (in pixels again) from this page. You can make broad changes to all the

76 track menus, which are all together and grouped on this page for quick access to entire sections.  The ‘‘refresh’’ button must be clicked to enforce any of the changes you made to those pulldown menus in the annotation tracks. The changes in the pulldown menus are not made automatically, you have to click this button. Display choices are stored One thing that is important to know about changes you have made to the viewer: the browser remembers your locations, changes, and settings until you clear them. A cookie is stored on your browser that remembers where you were looking in the genome and if you made changes to those menus. As we have discussed, there are a number of changes you can make – to the position, the track displays, and the filter options. These parameters are all saved on the computer you are using. This may be useful if you always want to look at the data the same way. Or as you move from one tool at this site to another, you ‘‘carry’’ your position with you. But, that may not be great if you have forgotten that you filtered out something, or turned off a track. And if you use a shared computer in the lab or a library – you do not know if someone made some changes since you used the browser last. The UCSC team refers to these settings as being stored in your ‘‘cart,’’ almost like a shopping cart. There are a couple of ways to clear out your cart: you can choose the ‘‘default tracks’’ button from the Viewer controls, or the link that says: ‘‘click here to reset’’ on the Gateway starting page. If you ever find that your browser is not displaying what you expect, try to clear your cart to remove any stored parameters and start again. Going beyond the image A number of features below the surface of the viewer are also of interest. You can see a gene in the viewer display, but perhaps you want to know a lot more about that gene. Click on a specific gene to see more. A new page will open: an information page that contains a great deal of extra data, references, and sources about that gene (or predicted gene, or SNP, or other item) in the viewer (Fig. 8A). We are going to show just one sample here of the detailed information on the human TP53 UCSC Gene page. A UCSC Gene page has summary information, links to sequences, other resources, microarray experiments, mRNA secondary structure, protein domains, orthologous genes in other species, mRNAs collected from GenBank, pathways, synonyms, and more. This page is actually quite long, and you will not be able to see all the details right now. But later you should go and see for yourself. There is extensive information about this gene, and links to many other resources as well – practically one-stop shopping for well-known genes.

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Fig. 8. Clicking on items in the viewer will yield new pages with details about the

item that was clicked. (A) Clicking on a TP53 isoform in the viewer brings up a descriptive page with extensive details about this gene, and links to even more resources for supplemental information. In this image we highlight microarray data, domains, and pathways, but other types of data are available as well. (B) Clicking a SNP yields data relevant to SNP investigations, such as reference and variant alleles, functions (if known), and more.

78 One thing to note: not all genes will have this level of detail, and not every species will have all this information. We have specifically chosen a wellknown gene for our example. Some genes will not have protein structures, some will not have pathway information, and some will not have microarray data. But if the data is available, it will be available to you on these detail pages. Other pages will carry different types of data, of course. Various data types will have different details pages, with relevant types of information about that item. You only have to click on any item in the viewer to get to these details pages. Click on a SNP and you will see details important for SNP data, such as the variant sequence, function, and so on. We show here a small part of an SNP detail page – position, sequence, validation status, function, and more (Fig. 8B). Click on a segment of the conservation track to examine the specific alignments across the species in that area. What you will find when you click depends on the kind of data you are looking at. What about the sequence? So far, we have seen visual cues, and lots of text-based data, but one frequently asked question that people have at this point is ‘‘where is the sequence data?’’ We want to spend a couple of moments on that topic so that you will know that you can get to the sequence level data. From the viewer, there are two handy ways to get the sequence information. First, from your TP53 viewer section, you can simply click the DNA link in the blue navigation bar at the top of the page. The link will bring you to a new ‘‘Get DNA in Window’’ web page (Fig. 9A). The position you were looking at in the viewer is carried here, and is specified in the position box. On this page you have several options to format the sequence:  You can tweak the output by adding some bases upstream or downstream.  You can get the sequence in upper or lower case.  You can mask repeated, low complexity regions.  You can get the reverse strand. You could just click the ‘‘get DNA’’ button to get the sequence in a new web page; the output will be in FASTA format. The second button option offers even more ways to customize the output DNA sequence (Fig. 9A). If you click the ‘‘extended case/color options’’ button, you will get a new page that lets you change the case of individual items, change their colors, underline specific features, and so on. The choices that you will see in the list are based on the tracks actively shown in the Genome Viewer window you

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Fig. 9. To get the sequence of a region or an item you have several options. (A) Using

the ‘‘DNA’’ link at the top of the browser page you will be able to access the ‘‘get DNA in window’’ feature. It provides the span that was shown in your browser. You can get simple FASTA sequence output, or choose to color, underline, and change the font of outcome with the ‘‘extended case/color options’’ feature. We show a sample with some text underlined and case altered. (B) From a gene detail page you can access sequence information about that item. Genomic, mRNA, and protein sequence are available.

80 were looking at. If that is too much, go back and turn off some tracks. If you are missing data you want to view, turn it on in the browser. This is a really unique way to look at your sequence of interest. As you can see in a sample output, different features look different by color, case, or underlines. This could be copied and pasted to a word-processing document for storage as well. The two options that we just described deal with getting the whole region of DNA that is currently shown in your viewer. But you have another option – you can get just the sequence you want for any specific item from an annotation track. In this second example of how to get sequence data, we show a screen shot of the TP53 annotation track in the UCSC genes section. As before, we click on the line to get to the TP53 details page. From the details pages you can get the specific sequence for that item. We are looking at the details for this item which represents a TP53 isoform. Focus on a part of that details page – the sequence section (Fig. 9B). You can scroll down the details page to find the sequence section. Here you will find links to the genomic (the official or reference sequence for that span), mRNA sequence (which may differ from the genomic), and the protein sequence (of this isoform). You can use these links to get this specific sequence, plus additional options if you choose the genomic sequence – which is great for promoter studies, intron studies, and so on. So, the sequence data for the items in your viewer is just a couple of clicks away, using either the DNA link at the top to get the whole window, or the links from the information pages to obtain sequence for specific items. Basic sequence searches So far we have described what would happen if you used a gene symbol or keyword type of text search from the Gateway query option. But there may be times that you want to use a sequence to begin your search. In the UCSC browser, the tool you will use for sequence searching is called BLAT [8]. You may be familiar with the alignment tool called Basic Local Alignment Search Tool (BLASTs or BLAST2), found at the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/BLAST/) among other places [9]. If you have used the NCBI databases and searched for similar sequences, you have probably used BLAST. But BLAT is different – it is the BLAST-like alignment tool. It searches the database slightly differently than BLAST does. BLAT requires an index of the sequences in the database – something like the index in the back of a biochemistry textbook. The BLAT index consists of all the possible unique 11-oligomer sequences in the genome (or 4-mers for protein sequences). Just as you can quickly scan a book index to find the correct word, BLAT scans the index for matching 11-mers, and then builds the rest of the match out from there. It is a very fast way to search the sequences. The outcome will still

81 be a pair of sequences that are lined up with each other so you can compare the matches: yours compared to the official genomic reference sequence. The underlying algorithm that accomplishes it is different from BLAST. BLAT works best with sequences with high identity, and greater than 21 bases long, but do not let that scare you – you can find more distant matches as well. BLAT rapidly finds nucleotide sequences that have 95% or greater similarity of length 25 bases or longer. For protein queries, the algorithm finds genomic matches to your amino acids sequence submission with 80% or better similarity for stretches of 20 amino acids or more. In general, if the sequences arose within the last 350 million years they can be detected by the BLAT tool. For many people it will be enough to know that there is a means of searching for your region of interest in the database by starting with a sequence. For the more casual BLAT user, check out the documentation at the UCSC web site for a little more detail about the way BLAT works, without tremendous amounts of mathematical equations. For a more detailed description of the algorithm, you can see the publication by Jim Kent that describes BLAT in more detail [8]. So now we know a little bit about the BLAT tool. How do we get to it? Let us start at the UCSC Genome Browser homepage. As for most UCSC tools, you use the navigation bars at the top or at the side of the UCSC homepage. Select a link called BLAT to get started. The interface for BLAT is shown in Fig. 10A. We will work our way down this page. As you can see along the top there are a few parameters you can change – some choices you have to make. First, you must choose one species to search. You search one species at a time with this tool. Then you choose an assembly – which we have seen before in the Gateway basic search section. Next, you may let the BLAT tool guess whether you have entered nucleotides or amino acids, or you can tell it which one you are using. Sort output – on default settings here – will list the best-scoring matches first, but a few other sorting options are also available. Output type specifies whether you want the output to be in the browser form or in files you can use later. Hyperlink is the default which displays in the browser, and that is what we will be using for this example. The other type, pat space layout (PSL) output styles, is useful for people who want a differently structured, text-based output that can be used for a variety of purposes. For this example, we will demonstrate the default ‘‘hyperlink’’ choice. There is a big text box where you paste your sequence. You can paste one or more sequences, but there are limits to how much BLAT you can do, as it is a large burden on the servers. You can submit up to 25,000 bases or 10,000 amino acids, up to a total of 25 sequences. If you need to do more BLAT, UCSC asks that you download it and run a local copy. Instructions for this can be found in the documentation.

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Fig. 10. Another way to access information in the UCSC Genome Browser is to

begin with a sequence. (A) The BLAT tool is used to find matching sequences. Choose the search features, paste sequences, and submit the query to the database. The results will have statistics about the genomic region of the match, the length, the identity, and hyperlinks to view the match in the Browser or as Details. (B) BLAT results from the Browser link take you to the region of the match in the viewer. The Details link offers a page with the sequence alignment itself. (The color version of this figure is hosted in Science Direct.)

Shown here are two short mRNA sequences that we will use in our example. These are in the common FASTA format, which you have to use if you are going to use multiple sequences. There is also an option to upload your sequence (or sequences), if you keep a file of them. There are two ways to send your query to the database. A ‘‘submit’’ button is one choice. There is another special button – the ‘‘I’m feeling lucky’’

83 button. If you click that – just like in a Google search – you will be taken to the position of your best match right away, in the Genome Viewer. But we will be demonstrating the plain ‘‘submit’’ button right now. We see the results of a BLAT search against the human genome, using the sample human mRNA sequences we showed (Fig. 10A). We have sorted the list by the query and score: the results for the first sequence are together, and the results for the second sequence are together. Best matches for each are at the top of that group. You will see we have a really high-scoring matchup at the top in each case. After that they appear to be less good matches – pretty small regions, probably. The percentage identity is available, genomic location of the match, and the span of the match in the genomic sequence is provided. Now, you will remember that we asked for hyperlinked results in our setup. You can see that there are two columns of links for us (Fig. 10A). One says ‘‘browser,’’ the other ‘‘details.’’ Here we demonstrate a click of the ‘‘browser’’ link for the matches (Fig. 10B). This will link to the position of this match in the Genome Viewer. When you link from the BLAT results to the browser, you get a special track appearing in the Viewer. Just down from the top there is a new line on the browser – it says ‘‘Your Sequence from Blat Search.’’ And the name of your query sequence is listed over on the left. Here you can examine the genomic context of this sequence match. On the UCSC genes, because we are zoomed in to a small region for this match, you will be able to see the methionines indicated in green (stop codons would be indicated in red, if present). Also, note the direction of this gene – it is on the negative strand and therefore runs from right to left in this case as you can determine from the arrowheads on the gene structure diagram. We also show the outcome if you clicked the ‘‘details’’ link from the BLAT results page (Fig. 10B). It is impossible to see the whole alignment page clearly – even with our short query sequence this is a large web page. You can see that the page is divided into several parts. The top part shows the query sequence you put in (in this case our human CXCL5 mRNA sequence). The middle part of the page shows the matchup of your sequence (in blue capital letters) to the genomic sequence. This gives you a quick look at the possible exon/intron structure if you have used an mRNA as we have. It is a nice way to see which parts are the likely exons in an mRNA, and the likely introns in black text. The bottom part shows you the actual nucleotide-for-nucleotide matches – this may be more like the BLAST results you are used to seeing. In the side-by-side alignment at the bottom you can see where the query sequence on the upper row (starts with number y001) lines up with the genomic sequence. You may judge the quality of the match yourself in this section. So you can start to search the UCSC Genome Browser data with a sequence, and view the results in either the Genome Viewer browser, or at the level of alignment details, depending on your needs.

84 Advanced exploration with the Table Browser The UCSC Genome Browser is a large database with multiple ways to access the data. Underlying the site is a MySQL database that stores all the information (Fig. 11A) [10]. The Genome Viewer we have been discussing is one way to access this data. Another way to access the data is with the Table Browser interface (Fig. 11B). The Genome Browser database is comprised of ‘‘tables’’ of data. Some tables are primary and contain positional information – linked back to the genome position. One example is ‘‘knownGene’’ table which includes data such as chromosomal position, gene name, exon and intron sizes, and the like. Other tables include ‘‘auxiliary’’ types of data. Some have positional information and others do not, such as one that relates knownGene IDs to the corresponding Ensembl gene IDs. Or the place where author or submitter names are stored – which do not hold a genomic position of their own, for example. These myriad tables of data comprise the database. You can examine the data visually with the Viewer, which performs your query and creates the display for you, or you can reach into the database and pull out large swathes of information using the Table Browser in a customized manner. You are taking the same data out – just querying and displaying it in different ways. The Table Browser allows you to access these same tables of data directly – filtering, manipulating, and downloading the data in a very customized and flexible manner which is not possible with the Genome Browser Gateway query. For example, you may want a list of all the SNPs in a given gene, or maybe you want to output all the known genes on Chromosome 21. You may even ask questions genome-wide. You can also do a narrow search of only those SNPs that are non-synonymous coding variations found in disease genes. The possibilities of customized searching and data retrieval are nearly endless with the Table Browser.

Sample search for all the SNPs in a gene To start an example advanced search, go to the Table Browser by clicking on either of those buttons found on the homepage for ‘‘Tables’’ or ‘‘Table Browser.’’ To introduce you to advanced searching we are going to illustrate a few tasks, each building on the other and allowing us to use and learn different features of the Table Browser. Our first task will be to download the sequences of all the SNPs in a given gene. For this example we will use the human dystrophin gene, or DMD – a huge gene spanning many exons and a large genomic segment. You could look across this region in the viewer, and click all the SNPs (Fig. 12A). But for a gene of this size (with over 2,000 SNPs in this span) this would take a

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Fig. 11. The UCSC Genome Browser data can be queried in advanced ways. (A)

The mySQL database underlies the UCSC Genome Browser system. The same data can be used to generate the informative displays, or used to extract large swathes of data in text or sequence form following complex and creative queries. (B) The Table Browser interface provides user-friendly access to the database. Making choices about the types of data you want to examine are simplified with menus and filter options.

86

Fig. 12. SNPs in a region can be examined visually in the Genome Browser or

downloaded in bulk from the Table Browser. (A) The human DMD gene region can be set to display the gene and the SNPs in the region. (B) The same exact region can be queried for the SNPs, and the resulting list can contain all the important details about the SNPs (location, sequence, variant, validation status, function, etc.) as a table. The tabular data can be copied and pasted for more analysis in other tools.

really long time. Instead, we will use the Table Browser to extract the text output of the same information in a few seconds (Fig. 12B). As in the other interfaces in the Genome Browser and related tools, you must choose the genome and assembly that you wish to search in. The first row of choices will set the clade, genome, and assembly – which we are familiar with from the Gateway. Here we will choose ‘‘Vertebrate’’ from the clade menu, ‘‘Human’’ from the genome menu, and ‘‘March 2006’’ from the assembly menu. As before, you can search only one genome at a time. Next you need to choose a type of data to examine. The second row instructs us to choose the Group and Track in which we are interested. The menu for Group will remind you of the organization of the Genome Viewer pages – with groups of data such as ‘‘Mapping and Sequencing’’ or ‘‘Gene and Gene Prediction’’ tracks. In this case we want SNPs, so we will choose the ‘‘Variation and Repeats’’ group. The tracks in that group will then be displayed in the tracks menu. For our example, we will choose SNPs (126). This represents the dbSNP database release number 126 [11]. The third row then offers us the tables from which we can extract the data. You will see that multiple tables contribute to this track. Some names will be clearer than others. In this case the primary or main table is snp126.

87 But others may contain information you will want eventually. If you have any questions about the type of data in the selected table, you can click the ‘‘describe table schema’’ button which leads to a new page that shows the field names, an example value, data type, and definition of each field of the database table. If the database contains other tables which are related to the selected table by a field with shared values, those related tables are listed. If the selected table is the main table for a track, then the track description text is included. A textual description of the data and source is provided. The fourth row asks you to specify the scope of the query. Do you want to look at the whole genome? Are you interested only in the ENCODE data subset [12]? Or is there a given gene or genomic region that you want? For our example we want to have SNPs in a given gene – but as you can see you have various choices for how broad or narrow your query can be. We will type DMD into the position box. We do not know the exact nucleotide numbers, but there is an easy way to get that. If you click the ‘‘lookup’’ button, the database will run a query for your gene. You will get a list of results that match, and click an appropriate link to use as your query range. For this case we will choose the top result (DMD (uc004ddf.1) at chrX:32444634-33267647 - Dystrophin (Muscular dystrophy, Duchenne and Becker types) (DMD protein)). But there may be other times where you will want to choose a specific isoform, perhaps. When you click the link, you will find that the position range is automatically inserted back into the Table Browser interface. At this time you may also notice the ‘‘define regions’’ button on the fifth row which offers you the chance to paste or upload a list of identifiers. You may find that you want to query across multiple chromosomes, or genes, or positions – or you may want to start with a list of IDs you have from some other purpose. You can override the region choices by using these flexible alternatives. We will come back to the other options later – let us first pull that list of SNPs out and have a look. If you move to the ‘‘output format’’ section you will see there are a number of choices. You can get a look at all the fields in this table. You can choose a subset of them. You can get the sequence of your items. You can get a couple of useful formats for programming tools. You can create a special ‘‘Custom Track’’ of this query, which we will discuss later in more detail. Or you can simply look at the list with links to the browser for easy viewing. For our purpose, let us just look at the full table by selecting ‘‘all fields from selected table.’’ Make that choice, and then click the ‘‘get output’’ button near the bottom of the form. We will re-write the full query here to summarize: we want to see in the human sequence from March 2006, all the SNPs in the position of the DMD gene, output as a table with all the fields (Fig. 11B). Click ‘‘get output’’ to see the results. You will be presented with a lot of text on a new web page (Fig. 12B). This tab-delimited page could be saved in a text editor, uploaded

88 into a spreadsheet, and used for any number of purposes. All of the SNPs in the genomic span that comprise the DMD gene are listed, in just a few seconds. Sample search for filtered SNPs Maybe, though, the only SNPs of interest to you are those which change the amino acid sequence of the protein. The others are not important for you at this point. There is a way to get just those instead. If you return to your Table Browser form your choices should still be in place. Now we can decide to ‘‘filter’’ that set for just the coding, non-synonymous SNPs. In the sixth row you will find the filter ‘‘create’’ button. Click that to see your filtering options (Fig. 13A). A new page will open that lets you specify the kind of data that you will pull from the underlying database. Essentially this is a form that creates an SQL query for you. (At the bottom of the form you can enter a free-form SQL query if you know SQL, but you do not have to know it to use this form.) You can make menu choices and enter your own text in the individual boxes instead. For this example we will focus on the function data parameters, but as you can see you can be really precise about exactly which snp126 data you want to examine. We will choose func: does include coding non-synon, and store that by clicking the submit button. Once you submit, the buttons for filter will change to ‘‘edit’’ and ‘‘clear’’ back on the Table Browser main form, reminding you that you have made filter choices. Like the Genome Browser, changes and choices you make in the Table Browser will be ‘‘remembered’’ until further changes are made. If we now get the output of ‘‘all fields from selected table’’ like we did before, you will get a new list of items (Fig. 13B). At this time we get a much reduced set of SNPs – only those which change the amino acid sequence, according to the dbSNP database. Sample search for SNPs in transcription factor binding sites So we have easily downloaded large swathes of data, or chosen to extract a subset with a filter, in a few simple steps. Let us clear that filter (by clicking the clear button) and try something else. We can create even more complex queries – we can ask which SNPs in DMD overlap with other data in the Genome Browser database. For example, one of the data types stored in the database is for predicted Transcription Factor Binding Sites (TFBS). If you click the seventh row ‘‘intersection’’ create button, you can combine your SNP query with the data from the TFBS dataset (Fig. 14A). In the new interface, select Expression and Regulation group and then TFBS Conserved track. The appropriate table will be selected. You can choose the default of all overlap or specify other combinations. For our purpose we will

89

Fig. 13. Filters can be used to refine the query and focus on important features.

(A) The filter for the SNP table allows refinement on many aspects. We highlight the function choice, which will allow us to select only SNPs which are coding nonsynonymous changes to the amino acid sequence of the protein. (B) The results of the filtered query show only SNPs that meet our criteria.

simply choose the defaults – but be aware that you can create the overlap in various ways. When you submit these choices, the Table Browser main interface will reflect this by changing the intersection button to ‘‘edit’’ and ‘‘clear’’ to remind you that changes have been made. This time, let us select the output format as ‘‘hyperlinks to Genome Browser’’ and get the output. To summarize the query: we want to see in the human sequence from March 2006, the SNPs in the position of the DMD gene, but only those that intersect with predicted TFBS, output as hyperlinks to the browser. Click ‘‘get output’’ to see the results. As you can see in Fig. 14B, you can get the list of SNPs that meet our criteria, and use the links to have a look at them in the Genome Browser viewer.

90

91 Another feature in the Table Browser is the correlation tool which we will only briefly introduce here. It is available for data tables which contain genomic positions and computes a simple linear regression on the scores in two datasets. If a dataset does not contain a score for each base position, then the Table Browser assigns a score of 1 for each position covered by an item in the table, and 0 otherwise. The Table Browser computes the linear regression quickly and then displays several graphs for visualizing the correlation, as well as summary statistics including the correlation coefficient ‘‘r.’’ When datasets and parameters are chosen with some forethought, the correlation feature is a powerful tool for quickly gauging the relationship between two datasets. This correlation tool allows you to see what, if any, correlation there is between two datasets. For example, you might want to determine if there is any correlation between GC content and chromosome structure or between certain types of genes and repeats. As you can see there are many ways to create and run queries that could be very intricate and informative. We hope this introduction gives you an idea of where you can go with this tool. There are other complexities and details about the Table Browser which we cannot cover here – but we hope this taste will whet your appetite for more. More details on the Table Browser If you would like to know more about how the Table Browser was developed, its features and technical aspects, the link on the left navigation bar will take you to a list of UCSC Genome Bioinformatics Group publications. One in particular pertinent to our discussion is the paper from the 2004 database issue of Nucleic Acids Research Journal on the Table Browser [10]. The list of publications will also include more recent publications about changes and enhancements to the database. For superusers who know SQL, you can even access the underlying data directly from this URL http://genome.ucsc.edu/FAQ/FAQdownloads# download29 and build your own SQL queries. This is beyond the scope of our introduction, but we wanted you to be aware that it is possible. Another way to take your queries further is to use Galaxy. Galaxy is a separate resource provided by a team at Penn State [13]. Queries created on the UCSC Genome Browser site can be sent directly to Galaxy where users Fig. 14. Combining datasets is possible with the ‘‘intersection’’ button of the Table Browser. This can increase the complexity of the query, as well as refine the search space. (A) The intersection interface allows you to select which type of data you want to combine with the previous part of your query. It is also possible to submit how much overlap should occur between the sets, and whether the query should be ‘‘and’’ or ‘‘or’’ between the items. (B) Output as ‘‘hyperlink’’ lists your results with direct links back to the browser for viewing.

92 can perform additional types of analyses. These features are beyond our discussion here, but we encourage you to explore the Galaxy tools when you are ready. Custom tracks: Your query as a new track Once you have created some complex Table Browser queries, you may find that you want to view the output back in the Viewer. Or you may want to query on that even more with the Table Browser itself. If you choose the ‘‘custom track’’ output format from the Table Browser, you can save the work in a number of ways (Fig. 15). If you have spent time building a complex query and may want to revisit that later, we suggest that you ‘‘get custom track in file’’ which will be saved to your desktop, and can be uploaded at any time. The other options are only temporary – you can look at the data for 48 hours in the viewer or the Table Browser. But in a file you can view it again, or send it to others for them to see. All you will need to do is access an ‘‘add custom tracks’’ button from the Gateway or a Genome Browser mid-page control button.

Fig. 15. Complex query results from the Table Browser can be saved as custom tracks. These can be stored to use again later, or viewed in the Genome Browser. They can be further queried in the Table Browser. Setup features are available, including the place for a URL that could link to a page you create describing your data.

93 Custom tracks with your own data Besides the ability to create displays on complex query results that you have generated, you can also create custom tracks from your own data that you have obtained in a lab or in other software tools. These are also called ‘‘custom tracks’’ and offer a terrific way to visualize new data types that are important for your research, in the context of the other known genomic information. Custom tracks are also becoming a useful way to present data that is submitted to publications. To access the instructions and documentation for custom tracks, click on the homepage navigation for ‘‘Custom Tracks’’ (Fig. 16A). There is a link taking you to directions of how to create a custom track from your data, ‘‘Displaying Your Own Annotations in the Genome Browser.’’ We are going to briefly look at these instructions for a quick overview on how to get and view your own data in the browser. There is a lot that you can do with this custom track file, but the basics are quite straightforward – essentially you need to specify a genomic location and use a bit of structure, but it does not require programming skills. Generating custom tracks is a four-step process. In this process, you can type the information into any text editor or spreadsheet program. The four steps are (Fig. 16B):  Define how you want the annotation track to show up by default (colors, visibility, etc.).  Define the characteristics you want the Genome Browser to open up to (chromosomal location, pixel size, etc.).  Format your data.  Upload the file you created and view your track. Your first step is to set up the track characteristics. A track attribute line must go first in your file. Track attributes are on one line with nothing separated by a carriage return. If a file contains only one custom track, the track line is optional, if there are multiple custom tracks in one file, each must start with a track line. First the line starts with the word ‘‘track’’; this is followed by an attribute name and equal sign and the attribute characteristics. We show you a few here, but there are many others to choose from; you will need to look at the help section to see what those are. The second characteristics are the browser attributes – how do you want the viewer to set itself up to hold and view your track? Every browser attribute begins with the word ‘‘browser,’’ a space, and then an attribute name. This is followed by a space and then the attribute feature. Each browser attribute must be on a separate line of the file (unlike track characteristics which must be on one line). You can set the default image width of the browser, and you are able to set up the default visibility of the

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Fig. 16. Custom tracks can be used to display your own data in the browser, and

become available for queries in the table browser. (A) Information about the process of creating custom tracks is accessible from the home page. A new page with a link to the formatting instructions as well as examples of community-contributed tracks is at hand. (B) A sample of the formatting of a custom track. It does not require programming – just a little bit of structure to put things in the right rows and columns. This sample code generates a diagram like the one shown below it. This same data also becomes available in the Table Browser for more query options.

other annotation tracks. For example, ‘‘hide’’ followed by a space and ‘‘all’’ will hide all other annotation tracks, while ‘‘full’’ followed by the name of a data table will put the annotation track on full by default. The ‘‘all’’ will override other tracks you might put in. You can add several tracks to each visibility attribute by separating them by a space. One thing to note though: both the browser and the track lines are optional, you are not required to set those up to view a quick track. The next step is to format your data. There are several different types of data formats you can use and the help section on custom tracks has explanations of several, including how to set them up. We are going to

95 format our data as a Browser Extensible Data (BED) record as an example since BED is relatively straightforward and a format used by UCSC. Here you see an example of what a BED record format looks like (Fig. 16B). The BED record is simply tab delineated text with the following columns: the chromosome number the item resides on and the chromosomal location start and end points of your individual items. Optionally, you can name your item, give it a ‘‘score’’ of 0–1,000, strand, box features to be drawn, color, and more. That is an overview of how a BED record is set up and the fields and types of data you can include. Other formats will have different fields and types of data you might be able to include. See the help section for more explanation of those. Once you have a custom track file, you can upload it to the browser for viewing and querying. Go to the Genome Browser Gateway by clicking ‘‘genomes’’ from the top navigation bar found on all pages, choose the genome and assembly you wish to add your track to, and then click the button that says ‘‘add your own custom tracks’’ (Fig. 17). This will take you to the form to upload and manage your new track. This is also the place to upload a custom track file you might have saved from a Table Browser search earlier. This can either be a custom track text file you have created of your data or a previously downloaded custom track file of a table search you saved. Alternatively, you can copy and paste the custom track file you have created as we have done here using the custom track we just created in previous examples. Once you have uploaded or pasted in your custom track file, you

Fig. 17. Uploading and managing custom tracks is straightforward. Clicking a

‘‘manage custom tracks’’ button from a Gateway page yields a ‘‘Manage Custom Tracks’’ interface where these tracks for that species can be uploaded, deleted, and updated.

96 then click submit and you will be taken to the Genome Browser. Additionally, if you scroll down the page, you will find the new track is now displayed in the controls and you are able to change its visibility from the default we had set. The track will also now appear in the Table Browser, allowing you to filter and intersect with other tracks and do further analysis. Custom tracks offer a very flexible and simple way to view and analyze your own data along with any other annotated data in the Genome Browser. Together with the Genome Browser Viewer and the Table Browser, custom tracks create a very powerful and flexible tool for analysis and discovery. Community tracks We have shown you how you can create your own track. Members of the scientific community have created tracks like this and have made them available for others’ use. Sometimes they will be linked from a publication, or from a research team’s web site. You can find a list of some of these from the UCSC Genome homepage. Clicking on the custom tracks link in the left menu of the homepage will take you to the same Custom Tracks page we described earlier. If you scroll down a bit, you will see that many labs have submitted data in the form of customized annotation tracks for the community to view. Clicking on the link of any of these submitted tracks will open up a browser page to the Genome Browser with that track now included for your viewing – or for querying in the Table Browser. Saving your views as sessions After you have been working around the UCSC Genome Browser, and have done creative and informative queries and custom tracks, setting up your view exactly the way you want it – you can save this to replicate it exactly in the future, or even to share this setup with others. The UCSC team has included session management functionality in the Genome Browser, which allows users to save and share browser sessions. Users are able to configure their browsers with specific track combinations, including custom tracks, and save the configuration options. Multiple sessions may be saved for future reference, for comparison of scenarios or for sharing with colleagues. Saved sessions persist for one year after the last access, unless deleted. Custom tracks persist for at least 48 hours after the last time they are viewed. The feature is accessed via the ‘‘Sessions’’ link in the top blue bar in any assembly. To ensure privacy and security, users must login to the genomewiki site and create a username and password. Individual sessions may be designated by the user as either ‘‘shared’’ or ‘‘non-shared’’ to protect the privacy of confidential data. A URL will be generated that you can save or send to others to view that ‘‘session’’ with your settings.

97 Associated tools at the UCSC site In addition to the standard visualization and querying that you can perform with the UCSC Genome Browser, there are other tasks, analyses, and data types that you might want to examine around your molecular data. We will briefly introduce you to a few of these handy additional tools here.

Gene Sorter Have you ever wanted to quickly see if there are genes similar to your gene of interest – similar expression pattern, protein sequence, domains, functions, etc.? The Gene Sorter can be a powerful search tool for analysis of your gene in question or to find genes that might be of interest to your research. The Gene Sorter takes a gene of interest and lists other genes in the genome sorted by a similarity type to the reference gene you chose. Each gene listed also has columns of data about that gene, and the genes can be filtered to limit the search. A sample of the Gene Sorter interface is shown in Fig. 18A. When you first approach the Gene Sorter, you are given several choices. First, you must choose which species you want to search in. At this time, several popular species are available for your queries. Next you will choose which assembly you wish to search; the default will most often be the recent one. Then you will add the gene name or accession number of the gene you want to sort by. If we enter a gene name or keyword, the results will give us a list of all gene records that contain that name or keyword for you to choose from. Your next major choice is by what criteria do you wish to seek similarity to the gene in question. You have several choices of pre-calculated similarity sorting options in the menu, but the specific choices and datasets will vary by species and assembly. For example, some of these include similarity in expression patterns, protein homology, Pfam domain structure, gene distance, name, Gene Ontology (GO) similarity, and others. Your next option is to configure the display to add, subtract, and change data columns. Click the ‘‘configure’’ button, and the configuration page will appear (Fig. 18B). Some columns of data will be checked and shown by default, or you may change the configuration of the resulting data, adding more columns of data. For example, you can add a column of GO terms for each gene in the family, or the best mouse ortholog. You can also change the order in the table that the column appears with the up and down arrows. Some data columns can be configured further. For example, the GNF Atlas 2 expression data column can be configured to show all tissues or a selected set, or have the values to be absolute or a ratio to the mean level of expression. There are a few other things you can change such as the colors

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Fig. 18. The Gene Sorter tool can be used to find genes related to a gene of interest by

a number of useful metrics, including expression pattern, protein similarity, domain families, and more. (A) The Gene Sorter interface can be used to compare genes in a variety of ways. The specific choices vary by species. The microarray data is available in color in the interface. (The color version of this figure is hosted in Science Direct.) (B) Gene Sorter displays can be configured to output the display to suit your needs. (C) Filtering the data can be a remarkable way to obtain insights – such as looking only for genes highly expressed in a given tissue, or which contain certain domains.

that signify the level of expression in expression data columns and a toggle to show all splice variants. You can hide all data columns, which would be useful if you wanted to choose specific columns and eliminate default or other previous choices. You can choose to show all data columns, or if you need to, return to the default columns. A handy feature that has been added is the ability to add columns of data that are user-generated with ‘‘custom column.’’ This button will take you to a

99 page to upload your custom column and a link to find out how to format your column. This is beyond the scope of this introduction, but if you are interested, the help section is straightforward and should be simple to follow. The next option in your search is to filter the resulting genes that are sorted. This is a powerful feature that allows you to find exactly those genes that might be of interest in your research. It is wise to do some filtering with this feature because you could find yourself with a sorted list of every gene in the database. Clicking the ‘‘filter’’ button will take you to the filter page (Fig. 18C). Here you will find that you can filter any column of data. Shown here are only the first few displayed columns. If we were able to show the entire list, you would see that there are filter boxes for every column of data including those that you have chosen to display in the results and those you have not. For example, you could filter for only genes with gene names that have a specific text string or genes that have a certain minimum or maximum level of expression. For many data column filters, you can paste in or upload a list of filters if you have a large filter list. You are also able to save your filters for later use. To load your saved filters, just click the active ‘‘load’’ button and a list of your saved filters will appear. Once you choose your filters, you can list the resulting genes by name. This will be a text list of all the gene names your search will find listed alphabetically. This can be helpful to get an idea of how large your results list will be, decide if you need to tighten or loosen your filters, or make sure you are getting the genes you are expecting. We can see sample results based on our reference gene, sorted by protein similarity using BLASTP, configured with the data display we chose and filtered based on our choices in the filter interface (Fig. 18A). In-Silico PCR Another tool available from the UCSC bioinformatics team is called In-Silico PCR. Clicking the PCR link from the homepage will provide the interface shown in Fig. 19A. In-Silico PCR searches a genomic sequence database with a pair of PCR primers. It uses an indexing strategy of the BLAT algorithm to do this quickly. When you enter primer sequences, which must be at least 15 bases long, you can obtain the corresponding genomic stretch between these two primers. It is not a complicated tool – the first step is to select one genome and assembly. As we saw with Gene Sorter, a subset of the genomes is available at this time. Next, enter a forward primer and enter a reverse primer. These must be at least 15 bases in length. You have the option to flip the reverse primer if you need to.

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Fig. 19. In-Silico PCR performs a search for amplification products on genomic

DNA, using primers of your design. (A) The simple interface permits the choice of species and assembly, and requires your primers. Some parameters can be adjusted. (B) Results of an In-silico PCR query with a primer pair that matches two genomic regions. The location of the amplicon, the size, and the sequence are provided, with links back to the genome browser.

The results of a sample In-Silico PCR query using a pair of primer sequences are shown (Fig. 19B). You can see that there is an indication of where your match occurs at the top – which is linked back into the browser so you can see where the match is located. The length of the amplified sequence is provided. The primers that were used are displayed (and the reverse one is flipped if you asked for that). And, you see the sequence you should expect from PCR on genomic DNA samples. Your primers are displayed in the uppercase letters. You will find the melting temperatures of the primers displayed as well. This data is based on code from the Primer3 tool, with some standard settings for salt and oligo concentrations [14]. You can use the In-Silico PCR tool to identify the expected amplification product on genomic DNA between two primers of interest.

101 Proteome Browser Another tool available from UCSC is the Proteome Browser [15]. From the homepage, click the Proteome or Proteome Browser links from the navigation bars to access the Proteome Browser interface (Fig. 20A). You start with a gene symbol or protein ID from the Proteome Browser Gateway, and you will get access to many important characteristics of the protein from the results (Fig. 20B). You can see the intron/exon structure for the protein; see polarity and hydrophobicity; and you can find the molecular weight and isoelectric point, domains, structural features, pathways, and more. This is a handy summary of many of the features of a protein that you might want to know. You can also access these pages from the details page of a known gene. A link to the Proteome Browser is provided on gene details pages. VisiGene Another tool associated with the UCSC Genome Browser is the VisiGene Image Browser of in situ image data. The browser serves as a virtual microscope, allowing users to retrieve images that meet specific search criteria, then interactively zoom and scroll across the collection. VisiGene gathers images from a number of sources: The Gene Expression Database at the Jackson Laboratory, the GenSat collection at NCBI, the Mahoney Center for Neuro-Oncology mouse transcription factors, the National Institute for Basic Biology in Japan’s collection of Xenopus embryos, and the Allen Brain Atlas collection. The simple search form – where you can enter a gene symbol, author, body parts or stages of development, and several types of IDs – is shown in Fig. 21A. The data is returned as a page with the images – which can be zoomed in or out – and are all linked to the publications and the appropriate genomic data resources. Shown here is an in situ hybridization for mRNA detection, but there are also other types of localization as well – such as antibodies for proteins, and so on (Fig. 21B). For some more spatial and temporal information about your genes of interest, and perhaps identify some reagents that might help you in your research, please be sure to check out the VisiGene Image Browser. Additional species browsers The software that is used to create the UCSC Genome Browser can be used by other groups to display their species of interest. An excellent example of this is a browser devoted to Archaeal species (http://archaea.ucsc.edu/; Fig. 22A) [16]. A malaria browser has been developed by the Ares lab and recently released (Fig. 22B; http://areslab.ucsc.edu/) [17]. A version of the

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103 UCSC Genome Browser with Arabidopsis sequence data is available (http:// epigenomics.mcdb.ucla.edu/) [18]. A browser that contains data for the fission yeast Schizosaccharomyces pombe is available (http://pombe.nci.nih. gov/genome/) [19]. Mirror sites for the UCSC Genome Browser have been set up and installed in various locations. A mailing list with a great deal of useful advice has been set up specifically for teams who set up local copies or who would like to use the browser for alternative species. It is also possible for companies to take a copy of the browser behind a firewall for internal corporate use, with the existing species of interest or to add their own. Licensing information for this sort of use is available from the homepage. Coming attractions UCSC plans to continue to extend the Genome Browser in the years ahead to include more species and assembly updates, particularly for primates, model organisms, and species of critical importance to evolutionary studies. The sparse ENCODE datasets will be expanded to genome-wide annotations, and many new ENCODE tracks will be added. UCSC may also provide browsers for the low-coverage (2x) assemblies currently included in the Conservation tracks for higher-coverage organisms. The human and mouse UCSC Genes annotations will be updated approximately 3–4 times per year. There are plans to expand the browser’s collection of human variation, disease-related, expression, and genome-wide association data, and to explore the incorporation of federated data into the browser. Among the enhancements planned to the display and data-mining tools are improved use of custom tracks in the Table Browser, extending display features such as the next/ previous item navigation to a larger range of tracks, and the addition of a user-annotated wiki track. Next steps Once you have begun to delve in to the UCSC Genome Browser, you may find you have additional questions. Or you may find you want to do something more complex than we have illustrated in this introduction. Perhaps you will be curious about what other people are doing with this powerful tool. Maybe you will find you would like others in your lab or Fig. 20. The Proteome Browser presents protein-level data about a given isoform. (A) A simple query box offers access to the protein data. (B) A sample Proteome Browser page illustrates exon structure, biochemical properties, domains, pathway links, and sequence data.

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Fig. 21. The VisiGene browser is a virtual microscope of spatio-temporal gene

expression data. (A) VisiGene can be queried by genes, authors, developmental stages, tissues, and more. (B) Images are displayed for browsing (left) and for closer examination of your selections (right). The browser also provides links back to the genome browser and to other relevant resources relating to the image data, such as clones or probes, literature, and more.

105

Fig. 22. Other genome data can be displayed in the framework of the UCSC Genome

Browser tools. Other groups may use the UCSC Genome Browser framework to display their species of interest. (A) The homepage for the Archaeal Browser, http:// archaea.ucsc.edu/. Shown is a sample of the browser display of Escherichia coli K12 from the ‘‘Archaeal Browser.’’ The layout and software is similar to that of the main UCSC Genome Browser. (B) The Ares Lab Malaria Browser, http://areslab.ucsc. edu/, also has a familiar layout, but with different species of focus.

106 department to become more familiar with the resources. There are several ways to move forward and to go deeper.

Get help from the mailing list and documentation The ‘‘Contact Us’’ link of the UCSC Genome Browser homepage will take you to the page with contact information, address, phone, and email for the browser team. But the best and most active source of information is actually the mailing lists – a discussion group of people who are using the Genome Browser for all sorts of research. You can search the mailing list archives to see if your question has already been answered; if it has not, you can ask the Browser team yourself. There are actually two lists. One is a ‘‘low traffic list’’ of occasional general browser announcements, such as a new software tool or species genome update. The other is an interactive list where you can ask questions about the browser (simple or complex) and see what questions others are asking. The people at the UCSC Genome Browser answer quickly and in necessary detail. It is highly suggested that you sign up for the mailing list if you use the Genome or Table Browsers on a regular basis. The UCSC Genome Browser provides many data types, tools, and features that can assist you in searches for information that will enhance your research. However, a single chapter can only provide a snapshot of the tools at a given time. New features will be added frequently. Keeping track of the activities around this resource is made easier with the mailing lists. There is also documentation for each tool from the Help and FAQ navigation links on the homepage, and the genomewiki (http://genomewiki.ucsc.edu/). Citations and publications are accessible from the left navigation area on the homepage, with lists of numerous papers that describe the features.

Get training, or train others UCSC Genome Browser contracts with OpenHelix to perform outreach activities at conferences and in training workshops. OpenHelix also delivers regularly updated training materials, including online audio-visual tutorials, slides with full script, and exercises to reinforce the concepts developed in the tutorial materials. The materials are modular, so you can review the sections you want to remember, keep up-to-date, or use the material for training others. You can also receive a Quick Reference Cards with handy tips on using the UCSC Genome Browser (Fig. 23). You can also apply to bring a workshop to your institution (http://www.openhelix.com/ucsc). The goal of all these resources is to help researchers become effective and efficient users of the UCSC Genome Browser.

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Fig. 23. OpenHelix provides training materials on the UCSC Genome Browser

features. These can be used to learn more or refresh your knowledge, or to train your staff and students. Tutorial movies, slides with full script, exercises, and Quick Reference Cards are available free of charge for non-commercial use. http:// www.openhelix.com/ucsc. (The color version of this figure is hosted in Science Direct.)

Acknowledgements We acknowledge support from a grant from the NIH (1R43GM073145-01) for early work to develop effective training materials. We would like to thank the entire Genome Bioinformatics Group team members who have developed and maintained this excellent resource (http://genome.ucsc.edu/staff.html). We appreciate the encouragement and contractual support provided by the team leaders Dr. Jim Kent and Dr. David Haussler for training and outreach work performed by OpenHelix. References 1. Multiple Authors. Nucleic Acids Res 1996;24:1–252. 2. Galperin MY. The molecular biology database collection: 2007 update. Nucleic Acids Res 2007;35:D3–D4. 3. Berman HM, Westbrook J, Feng Z, Gillil G, Bhat TN, Weissig H, Shindyalov IN and Bourne PE. The protein data bank. Nucleic Acids Res 2000;28:235–242. 4. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM and Haussler D. The Human Genome Browser at UCSC. Genome Res 2002;12(6):996–1006.

108 5. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001;409(6822):860–921. 6. Hsu F, Kent WJ, Clawson H, Kuhn RM, Diekhans M and Haussler D. The UCSC known genes. Bioinformatics 2006;22(9):1036–1046. 7. Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, Haussler D and Miller W. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res 2004;14(4):708–715. 8. Kent WJ. BLAT-the BLAST-like alignment tool. Genome Res 2002;12(4):656–664. 9. Altschul SF, Gish W, Miller W, Myers EW and Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215(3):403–410. 10. Karolchik D, Hinrichs AS, Furey TS, Roskin KM, Sugnet CW, Haussler D and Kent WJ. The UCSC Table Browser data retrieval tool. Nucleic Acids Res 2004;32(Database issue):D493–D496. 11. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM and Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 2001;29(1):308–311. 12. The ENCODE Project Consortium et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007;447(7146):799–816. 13. Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ and Nekrutenko A. Galaxy: a platform for interactive large-scale genome analysis. Genome Res 2005;15(10):1451–1455. 14. Rozen S and Skaletsky H. Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol 2000;132:365–386. 15. Hsu F, Pringle TH, Kuhn RM, Karolchik D, Diekhans M, Haussler D and Kent WJ. The UCSC Proteome Browser. Nucleic Acids Res 2005;33(Database issue):D454–D458. 16. Schneider KL, Pollard KS, Baertsch R, Pohl A and Lowe TM. The UCSC Archaeal Genome Browser. Nucleic Acids Res 2006;34:D407–D410. 17. Chakrabarti K, Pearson M, Grate L, Sterne-Weiler T, Deans J, Donohue JP and Ares M, Jr. Structural RNAs of known and unknown function identified in malaria parasites by comparative genomics and RNA analysis. RNA 2007;13(11):1923–1939. 18. Zhang X, Clarenz O, Cokus S, Bernatavichute YV, Pellegrini M, Goodrich J and Jacobsen SE. Whole-genome analysis of histone H3 lysine 27 trimethylation in Arabidopsis. PLoS Biol 2007;5(5):e129. 19. Cam HP, Sugiyama T, Chen ES, Chen X, FitzGerald PC and Grewal SIS. Comprehensive analysis of heterochromatin - and RNAi-mediated epigenetic control of the fission yeast genome. Nat Genet 2005;37:809–819.

109

Potential implications of availability of short amino acid sequences in proteins: An old and new approach to protein decoding and design Joji M. Otaki1,, Tomonori Gotoh2 and Haruhiko Yamamoto3 1

The BCPH Unit, Laboratory of Cell and Functional Biology, Department of Chemistry, Biology and Marine Science, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan 2 Department of Information Science, Kanagawa University, Hiratsuka, Kanagawa 259-1293, Japan 3 Professor Emeritus, Kanagawa University, Yokohama, Kanagawa 221-8790, Japan Abstract. Three-dimensional structure of a protein molecule is primarily determined by its amino acid sequence, and thus the elucidation of general rules embedded in amino acid sequences is of great importance in protein science and engineering. To extract valuable information from sequences, we propose an analytical method in which a protein sequence is considered to be constructed by serial superimpositions of short amino acid sequences of n amino acid sets, especially triplets (3-aa sets). Using the comprehensive nonredundant protein database, we first examined ‘‘availability’’ of all possible combinatorial sets of 8,000 triplet species. Availability score was mathematically defined as an indicator for the relative ‘‘preference’’ or ‘‘avoidance’’ for a given short constituent sequence to be used in protein chain. Availability scores of real proteins were clearly biased against those of randomly generated proteins. We found many triplet species that occurred in the database more than expected or less than expected. Such bias was extended to longer sets, and we found that some species of pentats (5-aa sets) that occurred reasonably frequently in the randomly generated protein population did not occur at all in any real proteins known today. Availability score was dependent on species, potentially serving as a phylogenetic indicator. Furthermore, we suggest possibilities of various biotechnological applications of characteristic short sequences such as human-specific and pathogen-specific short sequences obtained from availability analysis. Availability score was also dependent on secondary structures, potentially serving as a structural indicator. Availability analysis on triplets may be combined with a comprehensive data collection on the j and c peptide-bond angles of the amino acid at the center of each triplet, i.e., a collection of Ramachandran plots for each triplet. These triplet characters, together with other physicochemical data, will provide us with basic information between protein sequence and structure, by which structure prediction and engineering may be greatly facilitated. Availability analysis may also be useful in identifying word processing units in amino acid sequences based on an analogy to natural languages. Together with other approaches, availability analysis will elucidate general rules hidden in the primary sequences and eventually contributes to rebuilding the paradigm of protein science. Keywords: amino acid composition, amino acid triplet, availability, nonredundant protein database, secondary structure, sequence similarity search, short constituent sequence, species specificity, structure–function, structure prediction.

Corresponding author: Tel.: þ81-98-895-8557. Fax: þ81-98-895-8557.

E-mail: [email protected] (J.M. Otaki). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00004-5

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

110 Introduction Protein is an interesting and fascinating group of molecules from the viewpoints of basic biology and biotechnology. We first overview general trends of protein sequence analysis from our historical and philosophical perspectives. We then discuss our novel approach to analyze protein sequences based on short amino acid sequences using a concept of ‘‘availability’’. In this way, we attempt to make the historical and philosophical status of our approach in protein science clear. We here start off with a well-known question on protein sequences: How were real protein sequences ‘‘chosen’’ out of the astronomical number of possible combinatorial sequences? Although possible answers to this question are necessarily multifaceted, we eventually need mechanistic explanations that are sufficient for rational protein design. Importantly, this question is directly related with the classical paradigm of protein science: sequence determines structure that determines function. Our approach, an analytical method for protein amino acid sequence, is necessarily related to this paradigm. Historical and philosophical perspectives on protein sequence information Necessity and chance in natural ‘‘protein design’’ A protein molecule is composed of amino acids connected covalently via peptide bonds. A given position of amino acid in a chain can be theoretically occupied by any one of 20 amino acids available in a cell. Possible amino acid sequences that can be made combinatorially from 20 species of amino acids increase exponentially in accordance with the increase in the number of amino acids that constitute a given sequence (Table 1). The number of combinatorially possible protein species with a typical length of, say, 350 amino acids is 20350, which is roughly 2.3  10455. This is certainly the astronomical number. In reality, however, only a limited number of sequences were ‘‘chosen’’ in proteins. Why and how? And what kinds of sequences are used and not used in proteins? One aspect associated with this problem is the fact that the number of proteins on the earth are quite small, compared to the entire population of combinatorially possible sequences. Suppose an average biological species such as Homo sapiens and Drosophila melanogaster has 104 genes and their corresponding 104 proteins, ignoring splicing variants, although this may still be an overestimate. In one study, the number of insect species on the earth was estimated to be about 5 million [1]. Since insects are thought to occupy about 80% of the whole biological species, it can be estimated that about 6 million species exist on the earth. For the sake of discussion, here suppose the number of biological species is 107, although this number of species diversity

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1.1  1.3  1065 10130

50

1.0  10260

200

2.0  10390

300

2.3  10455

350

Table 1. The number of combinatorially possible sequences (20n) with respect to the number of amino acids (n) in proteins.

112 is probably an overestimate. Thus, the total number of protein species becomes 1011. To include the number of extinct proteins, suppose all 1011 proteins have been shaped by natural selection through continuous ‘‘minor revision’’ every year. Our earth has 4.6 billion years, and the overestimated 1010 years will suffice for our purpose. Then, the number of proteins invented on the earth in the past and present becomes 1021. If every protein sequence has been ‘‘revised’’ every second (which would not be true!), our outrageous estimate becomes B1028. Still, this number is not even comparable to the number of combinatorially possible protein species (Table 1). It is highly unlikely that the real protein sequences on the earth are determined after the full exploration of the entire population of combinatorially possible protein sequences. It is conceivable that only a tiny fraction of possible protein sequences have been explored in the course of protein evolution. This is probably because the seed sequences for ‘‘the tiny fraction’’ were accidentally determined at least to some extent and such seed sequences evolved to related sequences. Given an accidental aspect of biological sequence evolution, we next want a reasonable explanation on why and how such a tiny fraction of real proteins was ‘‘chosen’’ by nature, but not other tiny fractions of similar nonexistent proteins even from the identical seed sequence. It is easily conceivable that only a limited number of sequences can thermodynamically fold into ‘‘proper’’ three-dimensional structures (and hence execute specific functions according to the structure–function paradigm), which have been naturally selected in the course of protein evolution [2]. An invention of a new gene or protein appears to be carried out by gene fusion, duplication, or similar mechanisms [3–5]. Such evolutionary processes necessarily employ the same sequences repeatedly in different proteins. Thus, protein sequences on the earth are unavoidably ‘‘biased’’ from the random incorporation of amino acids in chains via natural selection based on folding structures and functionality. To a first approximation, frequently used sequences are supposed to be the ones that fold into specific structures and show specific function. In contrast, rare or nonexistent sequences are supposed to be the ones that do not fold well and do not show usable functional properties. Although it appears that this conventional answer has been widely accepted, it is tautological and unsatisfactory. Here we just point out that this is not to mention that nonexistent sequences do not fold and function at all. As discussed already, simply because combinatorially possible protein sequences are tremendously large in number and evolutionary processes are highly conservative, evolution would not have to have fully explored the entire possibility of sequences before the present-day proteins were made. Thus, what we really want is to what degrees the present real protein sequences are ‘‘necessary’’ for their functions with respect to the entire population of combinatorially possible sequences. To put it differently, how much physicochemical ‘‘necessity’’ is embedded in that sequence? And how

113 much did historical ‘‘chance’’ contribute to the determination of that sequence? We need to mechanistically understand what kinds of sequences fold how with regard to both existing and nonexistent ones. Without question, it is not a trivial matter to answer this question to the level of explanations that make most protein scientists feel convinced. If one can meaningfully compare all the real protein sequences to the entire collection of theoretically possible sequences, it would not be conceptually difficult to answer which sequences are used and which sequences are not used in organisms, through which it might be possible to obtain general rules for protein design. Practically, the astronomical number of the possible sequences prohibits such an ambitious attempt. But let us take a look at a bright side, which is the large population of combinatorially possible sequences that nobody, even the Mother Nature, has ever touched at all. It could serve as one of the theoretical grounds for a biotechnological opportunity in protein engineering in the future. And for the time being, it is important for us to put fundamental facts on the real protein sequences in order, which may hint at how to use such knowledge for biotechnological applications. Especially important is relations among sequence, structure, and function in the real protein population on the earth. Structure prediction and sequence similarity search Protein three-dimensional structure is thought to be determined solely by physicochemical nature of its amino acid sequences [6–8], despite that the actual folding process may need chaperons [9]. We still largely believe that amino acid sequence specifies its three-dimensional structure, and threedimensional structure of protein molecule specifies its function. Although some important exceptions have emerged as will be discussed in the section ‘‘Related studies on short amino acid sequences’’, this is the age-old paradigm of protein science, and our mechanistic understanding of this paradigm is still far from complete. If we thoroughly understand the physicochemical nature of amino acid sequences and decode the hidden ‘‘words’’ and ‘‘grammar’’ in them, we should be able to reconstruct the entire three-dimensional structures of proteins in silico solely from the primary sequences, which has not been achieved yet. Indeed, this entire process is so complex that it cannot be dealt with at once easily. Amenable and widely appreciated research foci that can be investigated practically at this point are how to develop a prediction method for secondary structures, a-helix [10] and b-strand [11], from the primary sequences on the one hand, and how to perform sequence similarity searches and develop alignment methods to compare sequences on the other hand. Fortunately, the amount of data that have been deposited in the databases on protein amino acid sequences and three-dimensional structures have increased rapidly. These databases have been serving as a basis for a recent

114 progress of bioinformatics on structure prediction and sequence similarity searches. One of the earliest attempts to elaborate such a prediction method was made by Chou and Fasman in 1970s [12,13], which examined the frequency of each amino acid (i.e., amino acid composition) in a-helix, b-strand, and turn with a limited number of protein three-dimensional structure data available at that time. A more physicochemical approach was made by Wu and Kabat in 1970s [14–16], which tried to define the j and c peptide-bond angles of the protein main chain based on the assumption that amino acid triplet is a unit restricting the j and c peptide-bond angles because they are mainly determined by the two flanking amino acids. Other representative empirical approaches are the Garnier-Osguthorpe-Robson (GOR) method [17,18] and the stereochemical method [19]. Although these trials for developing a secondary structure prediction method may be the simplest first step to mechanistically understand the basic folding process, the prediction accuracy of these methods was generally low, which is about 50%. Since then, many types of prediction programs have been developed with the help of the accumulated three-dimensional data, and indeed the prediction accuracy has recently been improved significantly, although the accuracy remains below 80% [20,21]. Nowadays, a new protein is often ‘‘discovered’’ as an array of letters from genomic or complementary DNA sequences without any other useful information on its three-dimensional structure and function. Sequence similarity searches are the most popular and obvious first step to characterize such an unknown protein. Indeed, protein sequence databases are most often and somewhat exclusively used in this way from a structure–function point of view. In most cases, sequences with at least more than dozens of amino acids are studied. The representative sequence similarity search program is BLAST [22,23], which first searches for short sequences as similarity seeds called ‘‘words’’ and expand them to compare longer sequences. Sequence, structure, and function Extensive use of protein databases in similarity searches naturally focuses on highly conserved sequences of proteins in multiple alignments. For this reason, common patterns are often searched for based on alignments. In a context of sequence similarity searches, ‘‘sequence motif ’’ refers to relatively short patterns of amino acids repeatedly seen in many different proteins. In a more structural context, ‘‘structural motif ’’ refers to super-secondary structures composed of a few secondary structures repeatedly found in threedimensional structures of proteins. There is no doubt that structural motifs are the smallest functional units of proteins, and sequence motifs may well represent some functional units of proteins. It should be emphasized that sequence and structural similarities are strongly correlated with each

115 other [24–26] and many programs for sequence–structure analyses has been developed successfully [27–34]. However, sequence–structure relations in proteins are not so simple. Although similar sequences are highly likely to form similar threedimensional structures, it has become apparent that even advanced sequence alignment programs often fail to detect a common sequences for similar three-dimensional structures. This cannot always be attributed to technical problems on the detection sensitivity of those programs. Rather, this fact implicitly indicates that similar three-dimensional structures can be formed by highly different sequences. This low sequence similarity with similar three-dimensional structures is believed to be the common feature of G-protein-coupled receptors (GPCRs), also called seven-transmembrane (7TM) receptors [35–38]. They constitute the largest group of eukaryotic proteins, as a result of highly divergent and convergent evolution [35–38]. Their sequences are highly diverse, and their ligand structures are even more diverse, which range from small ions to proteins [35–38]. Intriguingly, nonalignment-based information can be extracted using many different methods for their functional classification [39–49], suggesting that sequence similarity information reveals only one aspect, although highly informative, of the entire protein structure–function relations. Similar situation is also apparent in the secondary structure prediction methods where the incorporation of distant effects that are not revealed by similarity searches significantly improved the prediction accuracy [21]. Furthermore, in an evolutionary view, it is not very surprising to find a case where two proteins with unrelated sequences and three-dimensional structures show similar function as a result of convergent evolution. A fascinating example can be drawn from olfactory receptors of mammals [50–54] and insects [55–59]. Both mammals and insects use olfactory receptors that span plasma membrane seven times for odorant detection. Their amino acid sequences are totally different from each other, indicating their independent origins. Furthermore, mammalian olfactory receptors are typical GPCRs, whereas insect receptors do not appear to have a typical configuration of GPCRs, whose amino termini are located inside the cell [60–62]. Nonetheless, we believe that both human and fruit fly can detect the same odorant chemicals, for example, amyl acetate, a smell of bananas, suggesting that both organisms harbor functionally similar but structurally different receptor molecules. As discussed above, the entire collection of combinatorially possible protein sequences is extremely large. It may be the case that only a fraction of the entire possible sequences of amino acids is explored in one phylogenetic group, and other fraction of the possible sequences is explored in other phylogenetic group. To put it differently, independent invention of entirely new sequences of proteins with similar structure or function might have

116 occurred several times during the course of evolution as a result of convergent evolution in nature. Likewise, independent invention of entirely new sequences of proteins for a particular function should be possible by human biotechnology. From amino acid composition to short sequences: A database characterization One of the approaches to characterize proteins is to analyze amino acid compositions as discussed already. Suppose a system containing 104 sequences, each of which is composed of 350 amino acids. The number of amino acids in this system is 3.5  106. If each amino acid is incorporated randomly in equal frequency 0.05 at a given position, each amino acid is used 175,000 times in this system and 17.5 times in a given protein. If each amino acid is not randomly incorporated, amino acid composition and hence the number of occurrence of each amino acid of the system will be biased positively or negatively, depending on how often each amino acid is incorporated. This simple thought experiment illustrates that amino acid composition can be a characteristic descriptor of a system or protein. In fact, recent bioinformatics programs often use amino acid composition as a key piece of information for predicting protein characters; for predicting parallel helix–helix interfaces [63], ligand binding sites [64], secondary structures with hydrophobicity [65], and disordered regions with reduced composition [66–69] and candidate DNA/RNA binding proteins from proteome data [70]. The amino acid composition appears to affect the folding property of proteins [71], which may partly explain such a wide range of applications. Interestingly, a proteome-wide composition comparison among species can extract species-specific information [72–75]. These studies collectively demonstrated the importance of each amino acid as the smallest unit in a protein chain. However, it is also true that the compositional analysis does not provide us with any information on sequences themselves. The amino acid composition is merely a single descriptor of a protein among others. It is understandable that, by extrapolating this line of thinking, similarity searches with relatively large pieces of sequences that provide us with sequence-specific information have been preferred as a protein characterization tool in most occasions. Our approach to protein characterization, which will be discussed below, can be recognized as an intermediate between a simple compositional analysis and similarity search, focusing on short amino acid sequences in proteins. On the one hand, from the viewpoint of the compositional analysis, the concept of amino acid composition can be safely expanded into those of ‘‘longer’’ sequences. To put it differently, a protein database can be characterized by the ‘‘compositions’’ of double, triple, and quadruple (and more) combinations of amino acids. On the other hand, from the viewpoint of the entire combinatorially possible protein population discussed at the

117 beginning of this chapter, which is too large to handle rationally, a smaller set that is composed of short sequences may be small enough to handle rationally with a modern computational power, and at the same time, may be large enough to find diverse structures that form functional units. Hence, together with a rapid increase of protein sequence data available in databases, the characterization of a large protein database in terms of short combinatorial amino acid sequences is a timely attempt and it is likely to extract useful information on the relations among sequence, structure, and function. It should be noted that studies focusing on short sequences as an important analysis unit are not entirely new. We already know that the functional sites of proteins consist of a few sequences of amino acids, although they have to be supported three-dimensionally by other parts of the chain. Key residues in ‘‘active sites’’ or ‘‘binding sites’’ are generally composed of a few amino acids [76–78]. As discussed above, sequence motifs are usually relatively short sequences of proteins. Additionally, the success of BLAST, which use short sequences as seeds for similarity searches [22,23], implicitly indicates the importance and usefulness of short sequences in proteins. Biotechnologically speaking, it has been shown that small peptides can act as functional protein modulators [79–81]. In the rest of this chapter, we discuss the biological and biotechnological significance of short amino acid sequences in proteins. We first mention rationale and significance of examining the relative frequency of short amino acid sets, together with the definition of ‘‘availability’’. We then discuss availability differences between species and between secondary structures. Information extraction from protein sequences using availability scores Rationale for protein characterization using short sequences of amino acids We approach to the age-long paradigm of protein science discussed in sections ‘‘Necessity and chance in natural ‘protein design’ ’’ and ‘‘Structure prediction and sequence similarity search’’, focusing on stretches of short amino acid sequences such as two, three, four, five, and n ‘‘constituent’’ amino acid sequences, called here ‘‘doublets’’ (2-aa sets), ‘‘triplets’’ (3-aa sets), ‘‘quartets’’ (4-aa sets), ‘‘pentats’’ (5-aa sets), hexat (6-aa sets), and ‘‘n-aa sets’’. As indicated in Table 1, there are 20 singlets, 400 (¼202) doublets, 8,000 (¼203) triplets, and so on. In other words, 3-aa sets, for example, are composed of 8,000 different triplet species. We consider these short sequences as a building unit of protein molecules. That is, protein molecules are considered to be constructed by serial and linear superimpositions of short sequences, especially triplets. More concretely, our methodology is based on a simple concept of ‘‘counting all possible short amino acid sequence sets in a database’’ to

118 elucidate the hidden words and grammars in proteins [82,83]. The number of short amino acid sequences that are made from random incorporation of n amino acids rapidly increases in accordance with the increase of n (Table 1). With the increase of n, nonexistent sequences in databases emerge and become conspicuous in the theoretically possible combinatorial sequences of at least five amino acids. Further increase of n quickly makes the proportion of nonexistent sequences dominant in the entire population of combinatorially possible sequences. For example, the number of random sequences with 8 amino acids (i.e., 8-aa sets or ‘‘octats’’) is larger than the number of all octats in the nonredundant protein database (see section ‘‘Simple concept of availability: Counting short amino acid sequences in a database’’). Therefore, a significant portion of combinatorially possible 8-aa sets does not exist in the real protein database. Then, what is the minimum number of amino acids in these n-aa sets to distinguish protein species based on its existence or number of occurrence in proteins? Again, suppose a system containing 104 sequences, each of which has 350 amino acids. Focusing on triplets, the number of triplets in this system can be calculated as: Q ¼ X  2N

(1)

where Q is the total number of triplets, X is the total number of amino acids, and N is the number of proteins in the system. This equation can be generalized as follows when we focus on n-aa sets: Qn ¼ X  ðn  1ÞN

(2)

where Qn is the total number of n-aa constituents, X is the total number of amino acids, and N is the number of proteins in the system. Based on this equation, the number of n-aa constituent sequences in our system is 350  104(n1)  104¼104  (350(n1))¼3.5  106  (1(n1)/ 350). On the other hand, the number of n-aa constituents that can be made combinatorially is 20n. For comparison, we calculated Qn and 20n where n¼1B6 in this system in Table 2. It is expected that each constituent sequence of the 5–6-aa sets is used only once or not used at all in this system. This discussion implies that the appropriate number of amino acids that can properly serve as a unique descriptor for each protein is at least five and preferably six in this system. This means that each protein in this system can be characterized and specified by a set of combinatorial sequences of five or six amino acids if they are randomly incorporated in protein sequences. In reality, whether or not these aa set constituents are randomly incorporated in the real proteins has to be examined, which itself contributes to our understanding of the real protein sequences. However, 6-aa sets are

a

3.49  106 4.0  102

3.50  106 2.0  101

3.50  106  (1(n1)/350) 20n 8.0  103

3.48  106

3

The system contains 104 proteins, each of which is composed of 350 amino acids.

Constituents in the system Possible n-aa sets

2

1

n

1.6  105

3.47  106

4

3.2  106

3.46  106

5

6.4  107

3.45  106

6

Table 2. Comparison between the number of n-aa constituent sequences in the system and the number of combinatorially possible sequences of n-aa setsa.

120 composed of 6.4  107 combinatorial sets, which is practically difficult to comprehend and manage. To circumvent this practical difficulty, smaller sets of at most 104 sequences will be desirable, from which we can expand our methodologies and findings to larger sets. In this study, we basically focused on a practically manageable set, a set of triplets (3-aa sets), which is composed of just 8,000 triplet species, as a first step to extract information on protein sequences systematically from databases. If we examine a collection of randomly generated 104 sequences that are composed of 350 amino acids per protein (in which 20 species of amino acids are used randomly at equal probability 0.05), each triplet species will occur approximately 435 times (¼3.48  106/ 8.0  103) as Table 2 suggests. We believe that this number is not very daunting and well within a manageable range for human eyes and comprehension. In addition, there is a sound reason for analyzing triplets instead of larger sets at least at this point. Protein three-dimensional structures are defined by the conformation of the protein main chain that is determined by the j and c angles of peptide bonds connected to a-carbon atoms at the center of three consecutive amino acids. To put it differently, these j and c angles of a given amino acid are surely influenced, at least partially and at most mainly, by the amino acids of immediate neighbors located in both sides. Although in reality, the influences from distant peptides play an important role in the structural determination, such influences cannot be systematically examined at least at this point. This line of thinking considers amino acid triplets as a tentative unit of restricting the rotational freedom of the j and c angles. Once these angles are listed for 8,000 triplets, this information will be used for the determination of the protein main chain, as envisioned by Wu and Kabat in 1970s [14–16] (see sections ‘‘Availability as an indicator for secondary structures’’ and ‘‘Availability-based structure prediction’’). Simple concept of availability: Counting short amino acid sequences in a database Our idea is to examine how many times a given triplet occurs in the nonredundant protein database (‘‘nr’’ database), and this is to be carried out in all possible 8,000 triplet species comprehensively. We examined the ‘‘nr’’ database of NCBI (National Center for Biotechnology Information), which is the most comprehensive and nonredundant collection of protein sequences. At the time we downloaded the database in September 2003, it contained 1,539,248 sequences and 497,523,470 amino acids. A simple attempt can be made to construct a conceptual bar graph of expected 8,000 species of triplets (in x-axis) whose counts are shown in absolute numbers (in y-axis). A protein with n amino acids contains (n2) triplets in the chain. If there are N proteins in the nr database, the number of existing triplets to be counted in this system is N(n2). Practically, this

121 extremely large bar graph cannot be made easily and it is difficult to examine the whole tendency at once at least by human eyes. More importantly, this conceptual bar graph should be compared with that of the randomized ‘‘imaginary’’ or ‘‘ideal’’ proteins that are conceptually made from a pool of amino acids whose composition is identical to that of the database. This takes into account the nonrandom nature of the amino acid composition. This consideration is necessary because in reality, 20 species of amino acids are not used equally frequently to make a chain and thus this is to nullify the effect of the amino acid composition [82,83]. After generating a pool of the ‘‘imaginary’’ proteins, we examined how many times a given triplet occurs in this pool, and this counting was carried out in all possible 8,000 triplet species. Then, a simple attempt can be made to construct another conceptual bar graph of 8,000 species (in x-axis) where triplet-counts are shown (in y-axis), although again this bar graph is also difficult to ‘‘read’’. To highlight the difference between the real counts and the probabilistically calculated counts, availability (A), or ‘‘relative count’’, for each triplet or a given constituent sequence of n-aa set in a database is defined as follows: ðR  EÞ A¼ ¼ E

  R 1 E

(3)

where R is the number of a given triplet or a given constituent sequence of n-aa set that appeared in a real protein database (also called real count), and E is the probabilistically calculated number of a given triplet or a given constituent sequence of an n-aa set (also called expected count). When R ¼ E, its availability score is zero. Thus, deviations from zero may be considered as nonrandom nature of the triplet in question. When R ¼ 0, its availability score is 1, the minimum score. Using availability as a deviation indicator from random sequence, it becomes possible to construct a histogram that is manageable to human eyes (Fig. 1A). It may be possible to decipher biological bias inherent to constructing protein chains in this way [82,83]. Availability bias and its biological and biotechnological implications As discussed above, the distribution pattern of availability scores for 8,000 triplet species can be visually presented in a histogram where x-axis indicates availability scores (i.e., relative triplet-counts) and y-axis the number of triplet species in absolute number (Fig. 1A and 1B). It is evident that the distribution pattern of the real proteins (Fig. 1A) and that if the imaginary proteins (Fig. 1B) are highly different. Triplets from the imaginary proteins are distributed normally around zero, and indeed, almost all samples are

122

Fig. 1. Availability scores of triplets in the nr protein database. (A) Distribution of

triplet availability scores (relative triplet-counts) in the nr database. This should be compared to B. x-Axis is truncated. (B) Distribution of triplet availability scores in the randomly generated imaginary protein pool whose amino acid composition is identical to that of the nr database. Inset shows the same distribution with much smaller bar width, indicating its normality. x-Axis is truncated. Note scale difference of y-axis between A and B. (C) Species-dependence of triplet availability scores. The bold line for ‘‘nr’’ indicates the availability distribution of the entire proteins in the nr database, which is identical to A. Representative five model species were tested: human (Homo sapiens), mouse (Mus musculus), fruit fly (Drosophila melanogaster), nematode (Caenorhabditis elegans), and colon bacterim (Escherichia coli). See also Fig. 2 for further comparison.

123 located within 0.0070.01, as expected from the definition of availability of the imaginary proteins. Sharp contrast is observed in the triplet distribution of the real proteins, which are diverged with a variety of availability scores, although its peak value is also around zero. Among the possible 8,000 triplets, some triplets are more ‘‘available’’ or more frequently used than expected, and others are less ‘‘available’’ or less frequently used than expected. The distribution pattern of availability scores is highly skewed to the positive direction. According to Eq. (3), positive availability scores mean RWE, and negative scores mean RoE. That is, short constituent sequences with positive availability scores are ‘‘preferred’’ and those with negative availability scores are ‘‘avoided’’ as building sequences in proteins. Only about 20% of triplet species are located in a range 0.0070.01, which means that only about 20% of triplets are used almost randomly. Including this, most triplets are distributed in a range 0.5Bþ1.0. This skewed distribution demonstrates that triplet usage is not random; there would be a biological bias in triplet availability in proteins. That is, most triplets are nonrandomly used in the real proteins, although its difference from the random usage is not extremely large. This bias could be one of the reasons that the most popular search program BLAST has been used successfully; BLAST uses triplets as a seed for similarity searches [22,23] (see sections ‘‘Structure prediction and sequence similarity search’’ and ‘‘From amino acid composition to short sequences: A database characterization’’). Is this trend simply an evolutionary and nonfunctional aspect of proteins generated by chance or is this trend functionally critical in constructing proteins generated by necessity? Among the short sequences with highly positive availability scores, repeated sequences and palindromic sequences are abundantly found. These sequences are especially abundant in eukaryotic proteins and believed to be functionally important [75,84,85]. Sequences with negative availability scores are more enigmatic. In our previous work, we showed that some of the probabilistically expected pentats (5-aa sets) do not occur at all in the database despite the fact that they are supposed to be found reasonably frequently, which we call zero-count pentats [83]. This can be considered as an extreme case of negative scores. This low availability in pentats and similar low availability in triplets may be explained by several different reasons, and each constituent sequence would have unique reasons for its low availability. One reason may be that these constituent sequences are thermodynamically too unstable to be produced because of, for example, physicochemical steric hindrance among their amino acid residues. Another reason may be that the biological protein production machinery cannot produce such sequences due to physicochemical reasons of ribosome and its related molecules, i.e., hardware incompatibility of the production machinery.

124 Against these possibilities, these pentats can be chemically synthesized relatively easily as stable peptides by a conventional method [83]. Furthermore, they can also be synthesized as a part of fusion proteins in bacteria at reasonably high yields by a conventional method [83]. Thus, the thermodynamic instability of protein products and the hardware incompatibility of the biological production machinery are unlikely to be the reasons for the negative availability. Similar discussion can be found in a different study, in which based on computer molecular modeling, the possibility of steric hindrance as an explanation for rare triplets was ruled out [75]. Third possibility is an evolutionary reason. It is likely that if the system needs to make these pentats, it can make them as indicated by the synthesis experiments mentioned above [83], but evolution never explored such possibility due to the conservative nature of evolutionary process [3–5]. In other words, there has been no need to use such ‘‘new sequences’’ with unknown risks; the limited explored range of the sequence diversity in the entire population of combinatorially possible sequences is already large enough to make any functional proteins even though unexplored range may be much larger. The negative deviation of availability scores may simply be evolutionary ‘‘remnants’’ (i.e., accidental signature without any functional significance), but even so, such ‘‘remnants’’ are not evolutionary constraints per se because it can be changed relatively easily when necessary [83]. ‘‘Unexplored vacuum’’ in the entire population may be more appropriate description for this possible situation. Forth possibility, an alternative to the third one, is that the biological system might have positively kept away from such sequences through natural selection due to their disadvantageous effects after the full exploration of the combinatorially possible constituent sequences during evolution. If so, the fact that many constituent sequences have nonrandom availability scores could mean that they have some biological ‘‘functions’’. Then, an interesting look at the protein primary structures is that almost all parts of proteins are made of constituent sequences that have unique biological bias or function. To put it differently, as a result of functional evolution of proteins, although it is conservative, most parts of protein sequences may have some functions when viewed from the short constituent sequences. This may be true for the small constituent sequences, because the number of constituent sequences is not extremely large for short ones but it is extremely large for larger sequences (Table 1). The zero-count constituent sequences will occupy the majority of the constitutional sequences in the population among the combinatorial sets with 10 or more amino acids. This could, in turn, mean that evolutionary and functional unit of sequences is not more than 10 amino acids in length. Protein sequences may have to be a collection of small units whose amino acid combinations are within a manageable range for nature to examine their functionality.

125 These third and forth possibilities are not completely mutually exclusive, and depending on a particular constituent sequence, either or both may apply. Whatever the reasons behind the existence of zero-count constituent sequences may be, their applications would be envisioned, for example, as a novel seed for biomedical regents.

Availability difference between species and its biomedical applications Thus far, we discussed availability bias found in the entire entries of the nr database. Nonexistent short sequences that are not found in the nr database are entirely new or very rare to organisms. Accordingly, their biotechnological applications in designing a new type of proteins, for example, with a new folding pattern and function, may be expected. In addition, it would be prosperous to examine availability of particular groups of proteins such as biological species, structural groups, and functional protein groups in order to identify the group-specific constituent sequences. Such identified short sequences will provide biotechnology field with potential seeds for various kinds of applications. From this perspective, we performed comparisons of availability scores between biological species and between secondary structures, although these studies are still preliminary. The finding that the triplet availability in the nr database is biologically biased readily suggests the possibility that the degrees and ways of such bias are different in different biological species. As mentioned previously, speciesspecific preference for amino acid usage has already been demonstrated [72– 75], and indeed, the availability distribution patterns appear to be different from species to species [82,83] (Fig. 1C). The availability distribution of the colon bacterium Escherichia coli is especially different from those of other eukaryotic species, with more flattened pattern. Also noteworthy is the peak value of the nematode distribution, which is positively shifted from those of other eukaryotes. We examined correlations on the availability scores between species. High correlation coefficients were obtained between similar species such as human and mouse. Its scatter plot shows that most triplets are located along the diagonal line (Fig. 2A). This indicates that availability score for a given triplet is highly similar in both species, reflecting smaller phylogenetic distance between them. Lower correlations were detected between distant eukaryotes, such as between human and fruit fly (Fig. 2B) and between human and nematode (Fig. 2C). Further lower correlation coefficients were obtained between evolutionarily distant species, such as human and colon bacterium. Its scatter plot shows almost even distribution from 0.5 to þ0.5 around (0, 0) (Fig. 2D). That is, triplet availability scores of these species are independent of each other at least in this range.

126

Fig. 2. Scatter plots of 8,000 triplets in representative biological species. Both axes are shown from 0.5 to þ1.0. (A) Human–mouse plot. Spearman correlation coefficient rs ¼ 0.880. (B) Human–fruit fly plot. rs ¼ 0.525. (C) Human–nematode plot. rs ¼ 0.483. (D) Human–bacterium plot. rs ¼ 0.285.

Thus, as in the case of the amino acid composition [72–75], the pattern of availability scores may be considered as a parameter for evolutionary status of a given species, and this may reflect the finding in molecular evolution that sequence changes is roughly proportional to evolutionary time or distance. In other words, availability can be considered as a quantitative expression of evolutionary consequences of necessity and chance that have been accumulated in a particular species over time. Accordingly, its application to molecular phylogenetics to clarify phylogenetic relations among species will be expected. This viewpoint has already been confirmed by a proteomewide species comparison of the amino acid singlet, doublet, and triplet ‘‘compositions’’ [75]. Although this availability-based methods need proteome-wide sequence information, it is highly quantitative and probably accurate to compare phylogenetically distant organisms. Moreover, it is also advantageous over the classical methods of multiple alignments, because it does not require homologous proteins to be aligned properly from all organisms of interest. This also means that the genome-wide availability analysis is free from the problem of serious difference between gene

127 phylogeny and species phylogeny that conventional molecular phylogenetic analysis often encounters. More detailed explanation on these scatter plots follows. Triplets that were located above the diagonal line are used more frequently in biological species of y-axis, and those located below the line are used more frequently in biological species of x-axis. We take the human–bacterium plot (Fig. 2D) as an example here. Triplets located distantly far from the diagonal line above have relatively high availability in E. coli and relatively low availability in H. sapiens. Similar specific short sequences will be found in pathogenic bacteria. Comprehensive identification of such species-specific sequences awaits further research, and it will open up a new avenue for biotechnological applications. We could take advantage of making such artificial proteins with the nonexistent constituent sequences as a new type of inhibitors or modulators of biological activities of proteins. To name a few, it will be used as a diagnosis marker, and here it will help developing a highly sensitive and specific detection system for pathogenic bacteria. Depending on its protein function containing species-specific short sequences, anti-pathogenic medicines and immunological preventatives for infection will be expected as an effective alternative to conventional antibiotics and immunization, respectively. A specific pathogen may be medically eliminated from an infected patient with a minimum side effect if antibodies raised against the pathogen-specific constituent sequences are administered. A specific vaccination may be possible if a small stretch of peptide that is composed of the pathogen-specific constituent sequence is injected as a preventative measure. This idea may corroborate with an attempt to use short synthetic peptides as vaccines [86] and a biotechnological application of random peptide libraries [87]. In contrast, triplets located far below the diagonal line in the human– bacterium plot (Fig. 2D) indicate that they are relatively rare in E. coli and relatively frequent in H. sapiens. Taking account of their corresponding locations in scatter plots of other biological species such as mouse, fruit fly, and nematode, if those particular points are highly deviated only in mammals, these triplets may be derived from mammal-specific proteins for signal transduction pathways such as ligands, receptors, channels, and signaling enzymes. This line of discussion suggests that such triplets or their elongated corresponding 4–6-aa sets may be useful for identifying proteins with mammal-specific sequences, which then can serve as pharmaceutical seeds for novel drug targets. How about triplets located near the diagonal line? These triplets are used equally well in both H. sapiens and E. coli. If these are equally frequently used in all species, they may be derived from proteins of high importance in all organisms such as those for energy metabolism and biosynthesis of basic cellular components. These triplets and their elongated corresponding 4–6-aa

128 sets will provide us with important information on studying these fundamental proteins and basic principle of life. Comprehensive one-by-one examinations of all possible 4–6-aa sets are daunting task: it requires looking up 1.6  105B6.4  107 sequences of amino acid sets. One way to circumvent this problem is to narrow down candidate sequences by consulting triplet information. For example, with reference to the triplets frequently used in E. coli, one, two, or three amino acids may be added to these triplets, and availability scores of their corresponding tetrats, pentats, and hexats may again be examined. In this way, a computationally intensive process to find out short sequences with more than three amino acids that are specific to E. coli and never found in H. sapiens will be greatly facilitated.

Availability as an indicator for secondary structures Availability differences could be found not only between different species but also between different secondary structures. To examine such a possibility, we accessed the protein three-dimensional structure database, PDB (Protein Data Bank), which harbors about 40,000 protein structural data entries. These entries are highly redundant in that depending on crystallization methods and study details, three-dimensional data on the same sequences are deposited several times as independent entries. To avoid the redundancy, we used public software that searches for representative nonredundant structure entries, PDB-REPRDB, available through Internet. As a preliminary study, we examined 553 sequences (the number of amino acids, 155,843; the number of triplets, 101,352) of known three-dimensional structures in terms of triplet availability. These selected ‘‘representative’’ entries showed no large deviations of amino acid composition from those of the nr database, although it contained somewhat more A, G, D, Y and somewhat less L, R, and S (data not shown). Base on these representative sequences, we calculated availability scores of triplets. Then, stretches of a-helices and b-strands in the 553 unrelated protein molecules were identified and their sequence data were manually collected to make a small ‘‘a-helix database’’ or a ‘‘b-strand database’’ containing sequences of both parallel and anti-parallel b-sheets. The former contained 87,063 amino acids and 49,873 triplets. The latter contained 55,630 amino acids and 28,977 triplets. Availability scores for 8,000 triplets were recorded and their distribution patterns were depicted in histograms (Fig. 3A). Distributions of triplets in these secondary structures show similar patterns to each other, but they are less peaked than that of triplets in full sequences from which the secondary structure sequences were taken (labeled as ‘‘Full sequence’’ in Fig. 3A). Triplets in positive and negative values are relatively frequent in the secondary structures. This means that many triplets occur nonrandomly in

129

Fig. 3. Triplet availability in secondary structures. Samples were collected from PDB

using public software that searches for representative nonredundant structure entries (http://mbs.cbrc.jp/pdbreprdb-cgi/reprdb_menuJ.pl). (A) Distribution of triplet availability scores (relative triplet-counts) in a-helices and b-strands. (B) Scatter plot of 8,000 triplets in a-helices and b-strands.

the secondary structures, although further confirmation is necessary to solidify this conclusion. The scatter plot was made where x-axis indicated a-helices and y-axis b-strands from availability score 1 to þ6 (Fig. 3B). We found about 100 triplets at (1, 1), where they do not occur in either structure. From this point along the diagonal line to the upper right, triplets with low availability were about 600 (1.0B0.3), triplets with nearly random availability were about 900 (0.3Bþ0.3), and triplets with high availability were about 600 (Wþ0.3). These more than 2,000 triplet species are located at or near the diagonal line and thus they occur in both structures almost equally well. Furthermore, we found some constituent sequences that appear to be unique in a-helices or b-strands. Triplets located in the upper left side from the diagonal line, about 2,000, are used more frequently in b-strands than in a-helices. Conversely, triplets located in the lower right side, also about 2,000, are used more frequently in a-helices than in b-strands. We identified about 700 triplets that did not occur in b-sheets. Similarly, we identified about 300 triplets that did not occur in a-helices. Although further characterization of individual triplets will be expected, the structure-specific triplets or the availability bias between secondary structures could be used to predict secondary structures from sequences. We can tentatively conclude that depending on triplet species, there are some triplets used limitedly in a given secondary structure and there are also other triplets used in both secondary structures equally well. The former could constitute a part of hard or stiff backbone of proteins forming restricted j and c angles, and the latter a part of soft or flexible one, forming various

130 angles. For the functionality of proteins, the hard parts are important in supporting structural backbones, and the soft parts are important in interacting with other molecules. This information may make us being capable of finding active sites, ligand binding sites, or target sequences for mutagenesis and designing new structures for biotechnological purposes. Also, our availability concept can be combined with other approaches to predict secondary structures more accurately, for example, with hydropathy [88,89] or amphiphilicity [90]. Information on amino acids that interrupt the formation of secondary structures, called secondary structure breakers [12,13,91,92], should also be incorporated with other information on amino acid compositions discussed already (see section ‘‘From amino acid composition to short sequences: A database characterization’’). Such hybrid approach may be especially useful to predict transmembrane domains of membrane proteins. Availability-based structure prediction As discussed before, protein main chain structures are essentially determined by the j and c angles of peptide bonds connected with a-carbon atoms. Wu and Kabat in 1970s [14–16] explored a possibility of the three-dimensional structure predictions from the j and c bond angles in triplets, using a limited number of the structural data, based on the assumption that the j and c angles are determined only by the two neighboring amino acids in a peptide chain. At that time, triplet data are only partially available among the 8,000 species due to the limited number of structural data, and some brave speculations were unavoidable. Although these angles are influenced not only by the two neighboring amino acids but also by other amino acids, their classical approach should be re-examined, because now we have thousands of known structures with defined sequences and j and c angles in the PDB. On average, each triplet of 8,000 has about 100 combinations of the j and c angles in the database at present. It is now possible to reconstruct a ‘‘dictionary’’ for the angles in triplets with more accuracy, which will complement to the availability dictionary of triplets. Since the j and c angles can be visually expressed in the classical Ramachandran plot [93], it is highly desirable to make a collection of triplet Ramachandran plots using these structure data. Many of such Ramachandran plots would indicate relatively broad ranges of possible angles, although some of them may show quite restricted angles, given the broad distribution in the scatter plot in Fig. 3B. These broad angles are discouraging for an attempt, as seen in Wu and Kabat [14–16], in which researchers try to predict whole protein structures based on the angle data. Clearly, broad angles make it difficult to determine three-dimensional structures. Nonetheless, when combined with structural predictions based on similarity searches widely and heavily used today, these Ramachandran plots

131 may be helpful in finding out appropriate structures more easily in the case of sequences with very low structural similarities to known structures. In addition, the ranges of the angles in the Ramachandran plots may be weighted against triplet availability scores. These weighted scores could indicate usage flexibility of angles or likeliness of being influenced from remote amino acids other than the immediate neighboring amino acids. If one prepares a ‘‘dictionary’’ of 8,000 triplets in which systematic classification of triplet-weighted Ramachandran plots, correlations of triplets between functionally classified protein databases (e.g., GPCR, ion channel, transporter, cytosolic enzyme, and so on), and other physicochemical properties of triplets (i.e., hydrophobicity, ionic charge, physical size, and so on; see section ‘‘Availability as an indicator for secondary structures’’) are complied, it would be useful in identifying functionally important parts of protein sequences such as active site and ligand binding site and in assisting biotechnological applications especially in protein reverse engineering. Such a dictionary should contain comparative results from several different categories in terms of triplets and other n-aa sets; from the entire proteins on the earth, from a particular species, from a particular functional group of protein, and from a particular secondary structure. Eventually, we may be able to find a clue to accurately predict relations between sequence and function in this way. Revealing biological word processing units Thus far, we have stressed the possible importance of short amino acid sequences in proteins without paying much attention to biological ‘‘word processing’’ mechanism or ‘‘grammars’’. But grammars can also be deciphered from the use of availability at least partly. For this discussion, an analogy between protein chains and English sentences (without spaces between words, without any symbols other than 26 alphabetical letters, and without case sensitive expression) may be fruitful, because the nature of combinatorial sequences discussed in section ‘‘Necessity and chance in natural ‘protein design’ ’’ reminds us of natural languages. Words are composed of 26 letters of the alphabet, and sentences are composed of the combinations of words. Words that are frequently used (or those with high availability) can be found repeatedly in a variety of sentences if all sentences of a book are statistically analyzed. This is a way to decode English sentences when one has no primary knowledge of English but has a plenty of functional sentences (corresponding to protein sequences) in a book (corresponding to a protein database). Suppose that a triplet ‘‘the’’ has quite high availability in an English book. However, also suppose that the quartet ‘‘thex’’ (where x may be any letter) shows very low availability. This is because ‘‘the’’ is a functional word unit frequently used in English literature but most ‘‘thex’’ entries are not a part of proper words in English. Some important exceptions are ‘‘them’’, ‘‘then’’,

132 and ‘‘they’’, which are also functional units of English. Like this example, a stretch of letters whose length is just beyond the proper length of words will have quite low levels of availability. And this feature can be used for ‘‘word identification’’ in protein sequences. More concretely, suppose that we first focus on 100 triplets with the highest availability. And these 100 triplets can be classified into 2 groups based on their availability of their corresponding 2,000 quartets; one with the dramatic decline and the other with high availability. The triplets with high availability but whose corresponding quartets show low availability may be units or ‘‘words’’ used in protein sequences. Based on this analogy, triplets can be considered as words, and its meaning is expressed in availability, Ramachandran plots, and other physicochemical factors in the context of a whole protein sequence. Their combinations are considered to be the final outputs of structure and function as their meanings in sentences. From this point of view, our study to characterize triplets corresponds to an effort to make a preliminary dictionary of a natural language. A dictionary is highly useful in comprehending and constructing sentences, and similarly, we believe that these triplet characters are indispensable for understanding proteins and applications in biotechnology field. It will be used not only to design artificial proteins but also to assist a process of designing inhibitors and blockers. Current status of availability analysis and future of protein science Related studies on short amino acid sequences We started out our research as a conceptual extension of a simple and fundamental analysis on amino acid compositions performed by Chou and Fasman in 1970s [12,13]. Coincidentally, research on amino acid compositions has already been expanded into several directions as mentioned already in section ‘‘From amino acid composition to short sequences: A database characterization’’, and indeed our idea of examining short sequences is not entirely new. Similar exploratory studies on the usefulness of constituent sequences have been sporadically reported as a tool to examine an aspect of proteins without much systematic effort to characterize general features of the constituent sequences. For example, ‘‘dipeptide’’ (i.e., doublet) information [94] and ‘‘peptide composition’’ (i.e., a concept similar to availability) [95] were used for the prediction of membrane protein types. Dipeptide information is also used to predict gene expression level [96]. As discussed already, compositions of singlets, doublets, and triplets were demonstrated to be useful to reveal phylogenetic relations between species [75]. One study focused on amino acid quartets (called ‘‘4-grams’’ in the original literature) for protein classification on the assumption that the same quartets would not be found randomly in unrelated proteins [97]. This study showed that quartet

133 distributions were useful markers for alignment-independent classification of proteins. Impressively, usefulness of short sequences has already been tested in the field of GPCRs. This is partly because GPCR multiple alignments are notoriously difficult, due to highly convergent evolution with few conserved sequences (see section ‘‘Sequence, structure, and function’’), which naturally encourages many researchers to explore unconventional methods of sequence analyses [38–48]. Amino acid composition and ‘‘dipeptide composition’’ have been used for the classification of GPCRs [98,99] and for the characterization of their ligand binding sites [48]. More general concept of n-aa sets (called ‘‘n-tuples’’ in the original literature) were employed for the classification and identification of GPCRs [100]. Although this characterization procedure requires pre-determined classification, such searches for n-aa sets that are specific to a certain group of proteins could be a useful tool for identifying functionally important short sequences that are not revealed in a conventional way [101]. Furthermore, similar unconventional approaches may also provide us with insightful information, as suggested in other studies. Identification of low complexity short sequences was shown to be useful to infer protein functions [102]. The simple amino acid composition was fortified by a gapped combinations of amino acids with one, two, and three amino acid gaps called ‘‘pseudo-amino acid composition’’ [103], k-spaced amino acid pairs [104], or amino acid coupling patterns [105]. This information was used for the prediction of cellular locations of proteins [103], structural classes [106–108], and transmembrane domains [109]. The concept of examining gapped pairs of amino acids is complementary to our concept of examining consecutive amino acid sets. Furthermore, a different look at amino acids as placeholders has been proposed [110], which could corroborate with the biological ‘‘function’’ of domain length in GPCRs [40,43,45,60]. These studies, together with ours, demonstrated the biological and biotechnological significance of the constituent sequences in proteins. We propose that the entire database characterization on availability of both consecutive and gapped constituent sequences will eventually provide us with biotechnologically practical information on sequence–structure and structure–function relations. Rebuilding the paradigm As noted previously, stereotypical assembly of secondary structures often observed in protein crystal structures are called structural motifs, and they are considered as a functional unit (see section ‘‘Sequence, structure, and function’’). This line of thinking reflects a strong brief that particular structures are directly linked to functions of proteins. Although this concept is well proven in many cases, it has been pointed out that a part of protein

134 that does not form a specific structure plays an important role in protein function [66–69,111–116]. These findings lead us to rethink the classical paradigm of protein science on sequence, structure, and function. Consequently, the presumption that protein amino acid sequences on the earth have been evolutionary selected to thermodynamically form a stable structure as discussed in section ‘‘Necessity and chance in natural ‘protein design’ ’’ [2] may not be entirely correct. To better understand proteins, we believe that protein amino acid sequences should be examined from several different aspects beyond the classical paradigm of protein science. Two main approaches to protein decoding, structure prediction methods and sequence similarity searches are highly informative, but they are not sufficient to understand protein functionality entirely. Analyses of availability of short amino acids are one of the approaches that are independent of sequence similarity searches and also not entirely constrained by the classical paradigm of protein science. Another different approach that is essentially free from the conventional view is to develop a method to ‘‘read’’ the entire sequence of a protein without introducing a procedure of cutting a continuous sequence into pieces and to compare it to the rest of sequences in a database. Such comparison of the entire sequences is not possible for human eyes at all, but it is certainly possible for an artificial intelligent system. Although our availability analysis is based on the presumption that nature prefers to construct proteins using short sequences as a building unit, the ‘‘whole sequence reading’’ may also be achieved somehow, simply because proteins exist as a single continuous chain. We demonstrated that even among GPCRs, which show very limited sequence similarities and sequence motifs among them, an artificial intelligence program called self-organizing map can accurately identify and classify them based on the entire sequences without any alignment process [49]. It should be pointed out that other unconventional approaches that ‘‘read’’ the entire sequence at once are also prosperous in the GPCR field as discussed already [39–49] (see section ‘‘Sequence, structure, and function’’). Conclusion Focusing on short amino acid sequences, especially triplets, we discussed in this chapter potential implications of availability in protein science and engineering. Our concept of availability is quite simple, yet it will make a significant contribution to biological sciences. It will contribute to prediction of protein structure and function and to clarification of phylogenetic status of biological species. Importantly, it may also shed light on rebuilding the paradigm of protein science. Moreover, its potential biotechnological applications are numerous in biomedical field such as pharmaceutical seeds for new diagnosis regents and drugs. Therefore, it is highly desirable to put serious efforts on this topic to make our perspectives more realistic.

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Network models in drug discovery and regenerative medicine David A. Winkler CSIRO Molecular and Health Technologies, Clayton 3168, Australia Abstract. Network motifs and modelling paradigms are attracting increasing attention as modelling tools in drug design and development, and in regenerative medicine. There is a gradual but inexorable convergence between these hitherto disparate disciplines. This review summarizes some very recent work in these areas, leading to an understanding of the complementary roles networks play and factors driving this convergence:  network paradigms can be excellent ways of modelling and understanding drug molecules and their action,  an understanding of the robustness and vulnerabilities of biological targets may improve the efficacy of drug design and discovery,  drug design has an increasingly large role to play in directing stem cell properties,  stem cell regulatory networks can be modelled in useful ways using network models at a suitable level of scale, and  the network tools of drug design are also very useful for the design of biomaterials used in regenerative medicine. Keywords: networks, feature selection, Bayesian methods, stem cells, cancer, QSAR.

Introduction Theory, mathematics, and computation have played an important role in the pharmaceutical industry, and have lead to the development of novel, rational approaches to the design of bioactive agents. Such methods, in concert with structural biology, informatics, and developments in high-throughput technologies, have provided useful methods for interpreting data, designing experiments, and discovering novel therapies. Theoretical and computational research has contributed to maintaining the efficiency of drug development in a research and regulatory climate where the bar is constantly being raised. Some argue that the efficiency of the pharmaceutical industry (as judged by NCEs annually) is in decline, and the current discovery methods (computational or otherwise) have not lived up to their promise. Two key reasons have been proposed for the less than optimal drug discovery efficiency: inability to account for drug-likeness or ADMET properties early in the hit or lead discovery phase of drug development and failure to understand the properties of the biological networks being perturbed by the drug [1]. Neither of these are a consequence of the failure of theoretical and computational methods to aid in drug discovery and Tel.: +61-3-9545-2477. Fax: +61-3-9545-2561.

E-mail: [email protected] (D.A. Winkler). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00005-7

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

144 development. Rather, they are shortcomings in the way people think about the discovery and development process, which are changing and require theoretical and computational methods to implement. This provides an opportunity for a paradigm shift in the way the new drugs are developed, and for concepts in theory and computation from complex systems science, and systems biology to be adapted and applied to the rapid development of in silico ADMET models, and network theory aimed at understanding systems robustness and vulnerability. More recently, regenerative biology and medicine has started to attract a similar level of attention as therapeutic or prophylactic therapies. The two main elements of regenerative medicine and biology that we will consider in this review are stem cell biology and biomaterials development. Unlike pharmaceutical research, the very large amount of experimental research in regenerative medicine has not been matched by an equivalent effort in theoretical, mathematical, or computational modelling research. This is surprising given that these methods provide a sound theoretical basis for understanding, predicting, and planning research in regenerative medicine. Although pharmaceutical and regenerative medicine approaches are currently quite disparate approaches to discover new therapies, overlap is starting to appear between the two fields, and there is increasing, albeit small, evidence of convergence in the approaches. The principal concept in which both fields can be compared and reviewed the network, either as a paradigm for understanding the behavior of systems of interacting elements, or as a robust modelling tool for design of agents and therapies. There is a rich literature on network theory as a mathematical concept, and on the behavior of biological networks systems, so the intention is not to extensively review these areas. This review will focus instead on how the concept of networks can be applied in regenerative medicine, and therapeutic and prophylactic drug development, research areas that have been much less reviewed. We aim to provide a brief overview of how networks are applied in these important areas, and how this may lead to some convergence in thinking and in computational approaches. Regenerative medicine is a nascent research area that is likely to be very significant in the future. In general, networks are an increasingly important concept in biology. This review aims to review some paradigm shifts in thinking about how networks can be used in what has until recently been relatively disparate fields of drug design and development, and regenerative medicine. There has been a convergence of thinking about the two areas, and the common feature is the role networks play. Networks in therapeutic and regenerative medicine The concept of the network plays different but complementary roles in pharmaceutical and regenerative medicine research. Modelling methods that use a network paradigm have been very useful in both fields.

145 In drug development, neural networks have significant advantages over more traditional methods of drug design and they are being used increasingly in drug discovery [2–14]. They can build robust models relating molecular structure to drug target, ADMET, or physicochemical properties, and are useful for pattern recognition and clustering of compounds according to function. More recently, the role of biological networks in modulating the efficacy of drug intervention has been discussed [15–18]. The off-target effects of drugs have also been under more scrutiny, particularly in understanding how these influence the biological networks in which the drug target is found [19]. The question of how multiple target effects influence the pharmaceutical outcome, and whether this polypharmacy of drugs can be managed or even engineered or exploited has been raised [19]. These questions are discussed in more depth in this review. In regenerative medicine, networks also have important roles to play. In stem cells it is important to understand how gene regulatory networks control stem cell fate, so that they can be controlled to yield expansion of stem cell compartments and directed differentiation. In biomaterials, the signalling networks involved in the communication between surfaces, or matrices, and cells need to be understood. This will provide a way of rationally designing biomaterials and bioactive surfaces with desirable biocompatibility or biomodulatory properties. Additionally, the first forays into design of biomaterials properties have involved the use of neural networks to model biomaterials properties [20]. Properties of networks Networks consist of entities or agents (e.g., molecules, proteins, or genes) which make up the nodes or vertices of the network, and interactions (e.g., forces, binding events, or transcription factors) that provide the connections between the nodes and are also called links or edges. Clearly almost any kind of interacting system can be described by the network paradigm. Complex systems science recognizes and exploits similarities in underlying network mechanisms, behaviours, and representations [21]. Striking similarities have been revealed between the properties of social networks, networks in ecosystems, and genetic regulatory networks for example [22]. These similarities sometimes suggest similarities in the laws governing how the interaction produce the observed system scale behavior (called an emergent property in complexity theory). Networks can adopt various architectures, which are defined by the nature of the connections in the networks. The most frequent and important architecture in biological systems is the scale-free or small world network [23–26]. In these, the number of nodes with a given number of connection obeys a power law, where a plot of the logarithm of the number of connections for each node against the logarithm of the number of nodes with

146

Fig. 1. Power law behavior. In this example, the number of ligands binding one or

more protein targets displays a power law relationship over several orders of magnitude.

a given number of connections is a straight line (Fig. 1). A small number of nodes (hubs) have many connections while a much larger number have only a small number of links. Many kinds of diverse interacting systems show power law behavior of this type. The slope of the log–log plot defines the exponent for the power law relationship. There is a tendency for the set of exponents found for diverse systems to cluster into distinct classes, called ‘‘universality classes’’ [27]. It has been suggested that systems falling into the same universality class have similar microscopic mechanisms that are responsible for the observed scale invariant behavior. Genetic regulatory networks are examples of biological scale-free networks. They are very robust against random damage to the network (a very desirable property for a biological regulatory network), although attacks targeting the hub genes can cause catastrophic collapse of the network. The number of connections, or connectedness, in a network is an important network property. As the number of links in an initially unconnected network is gradually increased, the path lengths (or equivalently, the size of the giant component) in the network undergo a kind of phase transition when a critical, threshold, number of connections is reached [22,28]. This transition defines a kind of self-organized critical point in which disturbances in the network switch from being isolated in part to the network and become system-spanning. This has implications for signal propagation (e.g., triggering cell fate decisions) in some biological systems.

147 Molecules and genomes as networks Molecules are essentially three-dimensional distributions of electron density located on a nuclear framework. In principal all physical and chemical properties can be derived from an accurate description of electron density using appropriate operators. Molecules are often described or represented as molecular graphs and the principles of graph theory used to derive properties [29]. They can also be considered as networks, where the vertices are atoms and the edges are bonds [30]. In this case the vertices have more complex properties (related to electron density between atoms) than simple linkages imply. Unlike other networks such as gene regulatory networks, social networks, or food webs, the interactions within molecules place very strong spatial restrictions on the topology of the network or graph. Thus, molecules have relatively strict geometries (allowing, however, for conformational degrees of freedom) compared with genomes and social groups. The analysis of molecules as networks is nascent, and it is not clear whether they exhibit any power law behaviour. There is extensive literature on molecules as graphs and networks, which has been reviewed recently by Garcia-Domenech et al. [31]. DNA sequences have also been shown to exhibit power law behavior by Salerno et al. [32]. There is substantial evidence that gene networks are also scale-free, exhibiting a power law distribution of connections. The master regulatory genes represent the highly connected hubs of the genetic network. Albert et al. [33] have examined the robustness of various types of network topology to random or deliberate attack or damage. They found scale-free networks to be surprisingly robust to unrealistically high random failure rates in the network compared with exponential networks where most nodes have a similar number of connections. This robustness suggests why the scale-free network has been selected for biological networks such as gene regulatory or metabolic networks. It also suggests why diseases that attack biological network hubs can often be severe. A recent example has been work on the role of biological networks in understanding ataxia. Lim and co-workers [34] showed that the hub proteins were those contributing substantially to the degeneration of the Purkinje cell and causing the clinical manifestation of ataxia. Conversely, drugs that target network hubs are likely to be more effective (for example, in killing a pathogen or pest). There are other beneficial consequences of scale-free networks in biological systems. They facilitate chemical diversity at minimal energy cost, and they recapitulate natural selection [35]. Mutations or deletions of hub genes are usually lethal, while mutations of weakly linked genes account for biological variability and natural selection. Scale-free networks can evolve rapidly toward an optimal functional state, and are robust to the consequences of many biochemical or genetic errors, unless these involve the hubs [35].

148 Networks and drug design Historically, networks have been used in drug discovery and development as tools in modelling. The field of Quantitative Structure–Activity Relationship (QSAR) has been steadily evolving toward using neural networks for classification and clustering, feature selection, and the building of robust structure–activity models. Figure 2 shows the exponential growth in papers using neural networks for QSAR and drug design. We illustrate the application of neural networks to drug design in the section on network modelling tools. Recent reviews in this also summarize the myriad of ways neural networks contribute to the design and development of drugs and other bioactives such as pesticides [2–4,36,37]. Some specific examples of how neural networks are used in drug modelling are given in later sections of this review. In terms of biological activity, interactions between molecules (e.g., drugs and proteins, proteins and DNA, etc.) are key, and these can often be conveniently described by networks. Very recently, there has been a paradigm shift in thinking in pharmaceutical research toward network ideas [16,18]. It has long been recognized that almost all drugs hit multiple targets. Figure 3 illustrates the varying degrees of polypharmacology observed in clinical drugs. This has been seen as a problem, as it led to off-target effects and toxicity. The recent change in thinking has been that this so-called polypharmacology of drugs, apart from being inevitable, is actually useful. Clearly, drugs are often designed to hit a specific target that is presumed to induce the desired phenotypic change, but it has only recently been 120

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Fig. 2. Graph of numbers of papers using neural networks in drug design vs. time.

(The color version of this figure is hosted on Science Direct.)

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Fig. 3. Cluster analysis of 2,000 compounds and drugs from the BioPrints database

across 70 pharmacological assays (pIC50). Biological assays are on the x-axis and compounds are on the y-axis. Hierarchical clustering has been performed on both axes: compounds according to their fingerprint of biological activities and targets in a chemogenomic way according to the fingerprint of the activities of the compounds for each target. The activities are shown in the form of a heat map, with red most active and blue inactive. (The color version of this figure is hosted on Science Direct.)

appreciated that the robustness of biological networks is such that they will resist such perturbations and will adapt themselves to minimize the impact. There are now a number of examples of registered drugs that hit specific targets very well but show lower efficacy than expected in vivo [15]. Conversely, there are also registered drugs that hit as many as 14 targets but show very good clinical efficacy [15,18]. The properties of the biological networks are likely to be responsible for these anomalies. If we can understand the robustness of biological networks implicated in a specific drug therapy, we could better understand how to tailor or engineer the polypharmacological spectrum to produce the best outcome. In the case

150 where death of the organism (in the case of pathogens or agrochemical pests) or specific cell population (in tumors) is the desired outcome, it would be better to target key network hubs. Damage to these essential nodes in the network causes it to collapse. In other cases the desired outcome is manipulation of the network to restore it to near-normal operation. Understanding the robustness of the network could allow us to understand how to perturbed it to produce the most efficacious outcome. The task is then to design molecules so that they hit the appropriate nodes or vertices in the network [1]. Understanding the network properties of the interactome provides a means of improving the drug design and development process, potentially increasing efficacy of drugs and minimizing undesirable effects. It may, however, require a revision in the way drugs are assessed for registration by the FDA and other regulators. Networks and stem cells Systems biology studies how the components of biological systems interact with each other to produce the phenotypes observed. Not surprisingly, network models feature very strongly in systems biology. Good progress is being made in understanding gene regulatory networks in cells and organisms. Increasingly, the importance of protein–protein interactions (the proteome) is also being recognized. At another level, interactions between cell surface signals such as carbohydrates is also under intense study. The immense complexity of these networks and interactions prompts the question as to whether higher level, coarse-grained, or mesoscale descriptions of these networks are capable of representing the most important interactions that lead to the observed phenotypes. Simplistically, it might be assumed that the fractal, or scale-free nature of many biological networks might imply that higher levels of scale may be effective at capturing the essential elements of system behavior. There is increasing evidence that such models, which are also better matched to the availability of data, can produce useful insight and predictions of system level behavior [38–41]. Clearly there is much research activity in systems biology that could be captured under the network paradigm, and it is not the intention to review this. This work will concentrate on applications in regenerative medicine solely. Surprisingly, network and systems biology approaches to understanding and predicting the behavior of stem cells has received much less attention. One of the primary properties of stem cells that we need to understand is how they make fate decisions – that is, decisions on whether to self-renew and increase the size of the stem cell pool, die, or differentiate to become an adult cell. If the decision is taken to differentiate, which differentiation pathway should be followed? An understanding of what drives fate decisions, leading

151 to a rational means of controlling stem cell fate, would allow us to expand the pool of transplantable stem cells for bone marrow transplants for example, increasing dramatically the success of this therapy. Controlling differentiation would lead to methods for replacing or regenerating tissues damaged by disease or accident [42]. There are three main modelling approaches that have been applied to stem cells. Most frequently, dynamical systems models, which employ differential equations, are used to describe the interactions of stem cell intrinsic factors. Agent-based methods have been used by a very small number of groups. Network approaches most naturally fits the intrinsic and extrinsic interactions within stem cells but, surprisingly, have not been very widely used so far. Networks and biomaterials Modelling of biomaterials is nascent, but clearly important. Limited work has been reported that uses neural network-based QSAR modelling methods and molecular dynamics calculations to describe, explain, and predict biomaterials properties. There is great scope for this important area of modelling to be expanded [43]. Thus, network paradigms have not been applied to biomaterials modelling, with the exception of neural network quantitative structure–property relationships (QSPR) modelling [125]. As understanding of how biomaterials interact with cells and tissues grows, it is clear that networks will play a role in modelling these interactions. Network modelling approaches and tools As discussed previously, the interactions within even the simplest organisms is extremely complex. The networks that these interactions generate have not been properly characterized for even the simplest model organisms. Most progress has been made with yeast, where the entire interactome has been elucidated [44,45]. Lack of data and information for biological networks and the great complexity of the resulting networks even if all components and interaction could eventually be identified and quantified, suggests that mesoscale models are required. The most important questions to answer are whether mesoscale models can provide useful descriptions and predictions of biological systems, and how the appropriate level of scale can be identified. Increasingly, stem cell biologists report that small numbers of transcription factors seem to play a major role in the fate decisions in both embryonic and adult stem cells [46–52]. For example, in mouse embryonic stem cells, the intrinsic factors Oct4, Sox2, and Nanog play that role [49,51–55]. Extrinsic regulatory factors also play key roles in maintaining pluripotency. In mouse ESCs the key factors that have been identified are LIF, BMP, and Wnt [51].

152 In human hematopoietic stem cells, the important factors include Notch 1, BMP-1, and the cell cycle inhibitors p21 and p27 [56]. These master regulatory factors usually control many genes in the regulatory networks of stem cells, and often co-regulate each other [57]. Preliminary network modelling experiments have shown that mesoscale models of genetic regulatory networks in cells can indeed make useful predictions. Hart et al. [58] showed that relatively complex neural network models using 204 transcription factors from chromatin immunoprecipitation (ChIP) experiments, can accurately recapitulate to the temporal variation of gene expression in the yeast cell cycle. Interestingly, when they progressively eliminated the transcription factors, starting from those least relevant to the model, they found as few as five transcription factors could essentially reproduce the temporal variation in the genes (see Fig. 4). Winkler et al. [59,60] similarly found that five or fewer master regulatory genes were sufficient to accurately predict gene expression profiles in embryogenesis in the model organism C. elegans, and in the mouse. Although these studies did not directly involve stem cells, they described mesoscale modelling of regulatory networks in embryogenesis pathways closely related to stem cell differentiation pathways, and it is reasonable to conclude that similar models in stem cell differentiation pathways will also yield to mesoscale modelling.

Fig. 4. Performance of a neural net models of temporal variation of genes expression in yeast as a function of the number of transcription factors used in the model. Note the collapse of the model with less than 5 transcription factors out of the original 204.

153 These examples will be described in more detail in the section on recursive neural network models. Mesoscale network models of biological systems will be useful in describing, predicting, and ultimately controlling the properties of biological systems. Of most relevance to this review is their ability to model stem cell fate decisions for regenerative medicine, and build mesoscale models of key regulatory networks for drug design exploiting polypharmacology. In both drug design for therapy, or control of stem cell fate and design of biomaterials for regenerative medicine, methods for finding the appropriate level of scale are very important. The synthesis of a mesoscale model requires that the key controllers of network function be identified. In cellular networks these are assumed to represent the highly connected hubs in the network. Feature selection methods are crucial in identifying these controllers, and additionally often allow identification of genes controlled by them. An analogous situation applies in drug modelling using QSAR. It is not widely appreciated, but QSAR models can be considered mesoscale models of very complex structure–function relationships in biological systems. The models consist of a relatively small number of key molecular descriptors that control or modulate the observed, emergent, biological response. The biological system that links the interaction of the putative drug molecule with the biological response is almost always extremely complex, nonlinear, and unknown. Many ways of identifying important features or interactions in systems have been identified, and extended discussion of these is beyond the scope of this review. The reader is directed to recent reviews of feature selection in bioinformatics and drug design for further information [61–63]. We will, however, focus on some new feature selection methods that perform this function very efficiently, objectively, and in a supervised manner. To summarize, in drug design by QSAR or biomaterials design by QSPR, an important step in the modelling process is to identify a small subset of molecular descriptors that will give the best model. It is important to do this in a way that will not generate chance correlations (that generate apparently good models with little or no predictive power) [64]. Analysis of gene expression data from microarrays also involves a selection or filtering process to identify those genes with highest expression, or largest relative expression. Often this selection is based on filters that select genes that are expressed above a certain level, or that are differentially over- or underexpressed according to some differential expression threshold. Improved feature selection methods discussed further are likely to yield more relevant genes (those that might be used in mesoscale models and which represent important interactions in the regulatory network) for the experiment under study, selected in a more objective and supervised (context dependent) manner.

154 Feature selection The aim of feature selection is to select a relatively small set of features (e.g., molecular descriptors, or genes) that is most relevant to the biological property being modelled. In drug design it is possible to generate literally thousands of molecular descriptors using methods such as Comparative Molecular Field Analysis (CoMFA) [65] or DRAGON [66], or by quantum mechanical calculations. In regenerative medicine, array technologies also generate very large data sets with tens of thousands of independent variables such as gene expression levels. Modelling problems such as these, where the number of independent variable (descriptors) greatly exceed the number of dependent variable (biological responses) are known as grossly undetermined systems [67]. It is essential to prune these large feature spaces to a small number of relevant features. There are sound reasons for carrying out feature selection. If too many features are present, the model will be overfitted, and many, equally feasible models can be obtained form the data. It is also well known in statistical theory that sparser models (models built from fewer features) are better at generalizing (predicting) than less sparse models. One intuitively obvious reason why this occurs is that extra features in the model that do not contain much information, or that contain similar information to that in other features (i.e., are correlated with them) simply add noise to the model that obscures that signal and degrades the model’s predictive power. It is also important how the small, relevant feature sets are selected from the large pool of possibilities. As alluded to earlier, if this is not done carefully it is possible to introduce statistical artifacts like chance correlations. Finally, by reducing the size of feature space, and ensuring only relevant features are present, interpretation of the resulting model is often possible. Recent developments in the mathematics of grossly underdetermined systems have yielded novel sparse Bayesian feature selection methods that hold great promise for the types of feature selection problems encountered modelling for therapeutic and regenerative medicine. Figueiredo reported that an expectation maximization (EM) algorithm using a sparse (noninformative) prior could provide a parameter-free adaptive sparseness methodology [68]. In Figueiredo’s method, irrelevant parameters are automatically set exactly to zero. Unlike other ways of obtaining sparse classifiers (e.g., support vector machines, SVM) that involve (hyper)parameters which control the degree of sparseness of the resulting classifiers. Figueiredo’s approach does not involve any (hyper)parameters to be adjusted or estimated. This is achieved by the adoption of a Jeffreys’ noninformative hyperprior. He showed that this approach yields state-of-the-art performance that outperforms SVMs, and performs competitively with the best alternative

155 techniques. The method has been applied to supervised variable selection in QSAR with very encouraging results. QSAR Variable selection in QSAR commonly employs unsupervised (context independent) clustering, nonlinear mapping, or dimensional reduction methods. Supervised methods, in which the context of the experiment is used in the clustering or selection process, are employed less frequently but is potentially more valuable. Methods such as genetic algorithms, principal components analysis, stepwise regression, unsupervised and supervised clustering are commonly employed in QSAR and drug design for this purpose [61–63]. Clustering and classification of molecules has also been achieved by Kohonen networks or self-organizing maps [69,70]. Gasteiger’s group has led the application of these novel clustering methods in drug design, and has published extensively in this area [11–13,71–74]. SOMs provide a very good, nonlinear, and unsupervised way of cluster compounds according to properties, such as mode of action of clinical use (reference NCI database). Figure 5 shows how a SOM, with suitable descriptors, is able to cluster a series of molecules according to their modes of action [5]. Similar clusters have also been visualized and analyzed as networks by Yildirim et al. [18]. They developed networks based on the connectivity of drugs and drug targets that produced clusters that were functionally similar to those produced by clustering methods like SOM. They built a drug–target (DT) network based on clinical drugs that shared a common biological target. An example of such a network is illustrated in Fig. 6. They also generated target–protein networks in which two proteins are connected if they are acted on by the same drug. Microarrays/hub genes/diagnostics and classification Microarrays are one of the most useful sources of data for network models. cDNA arrays allow the genome of biological systems to be probed, and changes in response to specific conditions to be mapped. The types of arrays available are growing, and now include proteins (proteomics), lipids (lipidomics), sugars (glycomics), other metabolites (metabolomics), and kinase chips [75]. These techniques generate very large data sets, often containing tens of thousands of data points corresponding to the levels of gene expression, proteins, metabolites, etc. Methods for analysis of these data sets are often relatively simple, amounting to the application of filters for the absolute level of gene expression, and the level of differential gene expression, for example. Features that are selected by such filters are ranked and inferences made based on this ranking.

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Fig. 5. Classification of a dataset into four different classes with a Kohonen neural network having a rectangular topology. Neurons are colored as follows: red, 5-HT1a-receptor agonists; orange, H2-receptor antagonists; yellow, MAOA inhibitors; green, thrombin inhibitors; black, conflict neurons; and white, unoccupied neurons. (The color version of this figure is hosted on Science Direct.)

Regardless of the types of interactions probed by the arrays, the problem is similar to that in QSAR where the number of independent variable (gene expression levels) is much larger than the number of dependent variable (arrays or conditions). Sparse Bayesian feature selection methods show considerable promise in identifying key, relevant genes in networks. For example, sparse feature selection methods have been used to identify classifier genes in a set of microarrays from four small, round, blue-cell tumors [76]. The method was able to find a small set of genes that could classify tissue sample in a test set accurately, and could discriminate between these tumors, other tumors not of that type, and non-tumor tissue [77]. Several of the classifier genes selected by the features selection method were known to

approved drugs and their target proteins. Circles and rectangles correspond to drugs and target proteins, respectively. A link is placed between a drug node and a target node if the protein is a known target of that drug. The area of the drug (protein) node is proportional to the number of targets that the drug has (the number of drugs targeting the protein). Color codes are given in the legend. (The color version of this figure is hosted on Science Direct.)

Fig. 6. Drug–target network (DT network) [18]. The DT network is generated by using the known associations between FDA-

158 be implicated in the tumors in the model. The method has been applied to other microarray analysis problems that are more relevant to regenerative medicine. Laslett et al. [78] reported a study of the very early stages of differentiation in embryonic stem cells. We employed the sparse feature selection methods on microarray data for four populations of embryonic stem cells with varying degrees of pluripotency. A small group of classifier genes were found and these were sufficient predict, with a high degree of confidence, the state of pluripotency of the stem cells. Several of the genes selected were known to be important for early differentiation, while other had not previously been associated with embryonic stem cell pluripotency. The key genes identified using these selection methods can be employed in mesoscale network models of regulatory networks as described below. They represent also an objective way of finding a suitable level of scale for network models. Network inference and analysis Much of systems biology is aimed at understanding how various biological interaction networks are organized in organisms. Much of this involves carefully designed experiments and models of specific parts of networks, and far fewer studies attempt to tackle larger scale modelling of these interaction networks. Microarray technologies clearly have provided methods for probing these complex interactions, providing a snapshot of a system at a particular instant of time, under specific conditions. A number of computational methods for using array data to infer networks have been devised, and a complete analysis of their advantages and disadvantages is beyond the scope of this chapter. One of the most powerful methods is the Bayesian Belief Net [79–82]. A Bayesian network is a graphical structure in which the nodes represent genes and their expression levels, the edges indicate interactions between genes (with conditional dependence relations), and which contains a suite of conditional probability distributions that jointly specify a distribution over a set of genes. A Bayesian network offers a simple and unique way of expanding the joint probability in terms of simpler conditional probabilities. An advantage of Bayesian networks is that a complex system of interacting genes can be visualized as being composed of simpler subsystems of genes, making interpretation and visualization clearer [81]. Boolean networks In its simplest form a regulatory network in a stem cell (of interest to regenerative medicine) can be represented by a Boolean network [83–86]. Boolean networks consist of nodes or vertices (that represent genes or families of genes), and edges (that represent interactions between nodes e.g.

159 transcription factors). The interactions between nodes are binary, being 1 when present and 0 when absent. The nodes contain rules that are triggered when specific Boolean expressions are triggered. In the case of a gene with three inputs, the rule may state for example, that the gene will be activated if inputs 1 and 2 are on but input 3 is off. The behavior of a Boolean network is deterministic, as the state of each gene changes according to its rule (truth table) each time the state of the network is updated. The entire state space of the network (the total of all possible states the system can adopt) can be calculated, and trajectories from any given starting point in the network result in the network reaching stationary or repeating patterns of activity called point or cyclic attractors. The regions of state space in which a given attractor is inevitably reached is called the basin of attractor for that specific attractor. Kauffman has drawn useful analogies between the behavior of Boolean networks, and the behavior of genes. He equated the attractors as corresponding to unique cell states of fates in a biological system [85,86]. Recursive networks There are two extremes in designing network models of regulatory processes. One can start with dense expression data (e.g., cDNA microarray data) and use inference methods (such as Bayesian belief nets [87–89]) to find networks consistent with the expression data. Alternatively, a large hypothetic network could be defined which is pruned or annotated using experimental data. Another alternative, that exploits mesoscale modelling, involves the use of a recursive neural network. This was first described in the context of modelling regulatory processes by Geard and Wiles [90]. They developed small network that modelled hypothetical gene regulation and expression in the model organism, the worm C Elegans. This proof of concept study showed that such models were capable of recapitulating expression levels in hypothetical genes. The method was extended and applied to real gene expression data by Winkler et al. [60], who showed that such models could explain the observed discretized gene expression levels in the same subset of cells involved in C. elegans embryogenesis with considerable fidelity. The method was able to predict whether specific genes were activated or not, with an error rate of less than 1%. The model was also capable of making predictions of gene expression in cells not used in developing the model, although the error rate in these predictions was considerably higher. Recursive neural networks were subsequently applied to quantitative and continuous experimental gene expression levels (as opposed to binary or discretized levels used in the C. elegans model) of a much larger number of genes in mouse embryogenesis pathway (Fig. 7), using the expression data from Hamatami et al. [91,92]. The recursive neural network models were mesoscale, requiring only 2 or 3 master

160

Fig. 7. Mouse embryogenesis pathway between fertilized egg (zygote) and blastocyst. (The color version of this figure is hosted on Science Direct.)

regulatory genes to accurately recapitulate the quantitative expressions of over 130 genes whose expression were determined by microarray analysis, as Fig. 8 shows. Moreover, the model was able to predict, with a surprisingly high level of fidelity, expression levels in cells in the embryogenesis pathway that were not used in developing the model [59]. The accuracy of prediction of expression of 135 mid pre-implantation genes from experiments by Hamatami et al. is illustrated in Fig. 8. The predictions of quantitative expression of these genes in the final three cells in the embryogenesis pathway based on a model trained on the expression levels in the first four cells in the pathway are shown in Fig. 9. Clearly the modelling method is capable of making accurate predictions about the level of expression of genes in the pathway, these genes effectively identifying the phenotype of the cell. It is being used to develop regulatory network models for stem cell differentiation. There is ample evidence that the properties of stem cell and other systems can often be modelled using a relatively small number of regulatory factors, thus providing a justification for using coarse-grained or mesoscale models. Hart and co-workers conducted an elegant study where they used a neural network to model the temporal variation of gene expression in yeast through two cell cycles. Their model used the levels of 204 transcription factors as input. They found the model performed well as the number of transcription factors was progressively reduced from 204 to 5, at which point the model collapsed [93].

Application of neural networks QSAR has exploited the abilities of neural networks to carry out model free, robust, nonlinear regression and classification for a considerable period of time. The use of neural networks in QSAR has been reviewed recently [2–4,7,8,36,94]. Neural networks have substantial advantages as pattern recognition and modelling tools for regenerative medicine and biomaterials, although this is only just beginning to be exploited. The most commonly used neural network, the backpropagation neural network, is capable, in principle,

161

Fig. 8. Ability of a mesoscale recursive neural network models to recapitulate the

quantitative expression levels of 135 mid pre-implantation genes. Three regulatory genes were used in the model. (The color version of this figure is hosted on Science Direct.)

of modelling any complex nonlinear relationship between molecular properties and biological properties, given sufficient data. Backpropagation neural networks provide an objective means of finding the level of nonlinearity in the relationship between structure and activity, and they can be readily trained to produce very good models. They do, however, have some disadvantages, the most prominent being that they can be overtrained (and become less able to generalize to new data), and finding the optimum network architecture and validating predictions can be very laborious. Overtraining results in an overly complex model that fits the noise as well as the underlying relationship in the data. Cross validation methods,

162

Fig. 9. Ability or mesoscale recursive neural network to predict the quantitative

expression levels of 135 genes in the 8-cell stage, morulla, and blastocysts using a models derived from the first four cell divisions only. (The color version of this figure is hosted on Science Direct.)

in which each compound is left out of the model in turn, and has its value predicted by the model, has a computational effort becomes increasingly onerous when larger data sets are modelled. For example, Ford et al. [95] used a consensus of 1,000 backpropagation neural network models to predict the activity of kinase ligands. In cancer diagnosis using microarray data, Kahn et al. [76] used a consensus of predictions of 3,750 backpropagation neural networks to produce a model that could distinguish between four types of small round blue-cell tumors based on microarray expression data. The shortcomings of standard backpropagation neural networks can be almost completely overcome by using a Bayesian regularized neural

163 network [96,97]. Employing a Bayesian regularizer allows the network to find the best balance between bias (model is too simple to explain the data) and variance (model is too complex and fits the noise, compromising its ability to predict new data). Such networks are trained until a maximum in the statistical evidence for the model is reached. These models are also substantially independent of the architecture of the network and do not need validation sets to stop the training, or to ensure maximum predictivity. Drug design Backpropagation neural networks have been applied widely in drug design and development, and it is beyond the scope of this review to discuss these. The reader is directed to recent reviews of the application of neural networks to drug design [4,7,10,98–105]. Bayesian regularized neural networks have been applied to a diverse range of modelling problems, such as drug efficacy modelling, blood–brain barrier partitioning, intestinal absorption, MHC class II binding [4,37,97,106–118]. Their use in drug discovery and development has been reviewed recently [2,119]. Neural networks are being adopted in drug discovery at an exponentially increasing rate, as the number of publications using this techniques illustrates (Fig. 2). Regenerative medicine There has been essentially no work published that describes the application of neural networks to stem cell research. There is clear scope for these methods to be used to build models describing the relationships between experimental variables and measured outcomes. Our group is starting to work in this space, applying Bayesian regularized neural networks to modelling stem cell behavior. The application of neural network models to biomaterials is nascent, with only a handful of publications describing simple application, with the majority of work being carried out at Rutgers University [20,120–126]. Most of the methods used in biomaterials modelling have been taken directly for neural network QSAR studies in drug design. Convergence It is clear that the network paradigm and network modelling methods are likely to be very important in regenerative medicine and biology. The ways in which networks have been employed in therapeutic and regenerative medicine have historically been distinct from those in therapeutic medicine. However, there overlap is starting to appear. With the interest in understanding the robustness and vulnerabilities of biological networks to better design and target drugs, there is a suggestion of convergence between these fields. Similarly, the emergence of work on applying neural networks and

164 graph theory to problems in regenerative materials and biomaterials design suggests a driving force for convergence from the opposite direction. If the fields of regenerative and therapeutic medicine/biology can learn from each other, substantial progress can be made in the very interesting, and little populated research overlap area that network paradigms occupy.

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171

Use of the cauliflower Or gene for improving crop nutritional quality Xiangjun Zhou1, Joyce Van Eck2 and Li Li1, 1

U.S. Department of Agriculture-Agricultural Research Service, Plant, Soil and Nutrition Laboratory, Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA 2 Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY 14853, USA Abstract. Carotenoids are a group of pigments that are essential to human diets. An increasing interest in carotenoids as a nutritional source of vitamin A and health-promoting compounds has prompted the recent progress in metabolic engineering of carotenogenesis in food crops. Current strategies have been mainly focused on manipulating genes encoding carotenogeic enzymes. In many cases, it is difficult to reach the desired levels of carotenoid enhancement. In this chapter, we briefly summarize the recent progress on our understanding of carotenoid biosynthesis. We describe the isolation of a novel gene, the Or gene, from a highb-carotene orange cauliflower mutant. The Or gene encodes a plastid-targeted protein containing a cysteine-rich zinc finger domain and appears to be plant-specific. The insertion of a copia-like LTR retrotransponson in the Or gene confers high levels of carotenoid accumulation in the normally low-pigmented tissues. Rather than directly regulating carotenoid biosynthesis, the Or gene controls carotenoid accumulation by inducing the formation of chromoplasts, which provide a metabolic sink to sequester and deposit carotenoids. Examination of the Or transgenic potato tubers confirms that the Or-induced carotenoid accumulation is associated with the formation of a metabolic sink. Thus, the Or gene offers a new molecular tool to complement current approaches for nutritional enhancement in agriculturally important crops. Keywords: carotenoids, cauliflower, Or gene, b-carotene, chromoplast, metabolic sink, crop nutrition enhancement.

Introduction Carotenoids are a large group of natural pigments produced by all chlorophyll-containing photosynthetic organisms, some bacteria, and many species of fungi. Carotenoids are indispensable to animals and humans in providing precursors for vitamin A synthesis. Vitamin A fulfills many physiological functions in humans such as vision, reproduction, and cell proliferation [1]. Its deficiency, which affects over 140–250 million children under five years of age (World Health Organization), remains one of the most noticeable nutritional problems in many parts of the world. In addition, carotenoids as antioxidant compounds provide additional health benefits. Dietary intake of carotenoid-rich foods has been implicated in reducing the Corresponding author: Tel.: +1-607-2555708. Fax: +1-607-2551132.

E-mail: [email protected] (L. Li). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00006-9

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

172 incidence of cancer and cardiovascular diseases [2,3]. Some carotenoids also offer protection against age-related eye diseases, such as macular degeneration, the leading cause of age-related blindness. Apparently, developing carotenoid-enriched food plants will be the most effective approach to maximize their nutritional and health potential. Therefore, elucidation of the mechanisms underlying carotenoid biosynthesis and metabolic manipulation of carotenogenesis in food crops has become one of the most interesting research areas in current plant science and biotechnology. Most of our knowledge on carotenoid biosynthesis in plants has been derived from studies of Arabidopsis thaliana, cyanobacteria, tomato, and a few other plant species. In the past decades, a complete set of genes and enzymes of carotenoid biosynthesis has been isolated and characterized by a combination of molecular, genetic, biochemical, and genomic approaches [4–6]. The availability of a large number of carotenogenic genes from bacteria and plants along with the information on carotenoid metabolism has opened the door to genetic engineering of carotenoid content and composition in food plants to benefit humans [5,7–9]. So far, the popular strategy is to overexpress plant or bacterial genes that control the committed steps of carotenoid biosynthesis in food crops. Golden Rice is one of the best-known examples in using this strategy for improving crop nutritional value. In the late generation of Golden Rice 2, overexpression of genes in a minicarotenoid biosynthetic pathway leads to the accumulation of up to 37 mg/g total carotenoids in rice endosperm, a level adequate to provide most of the recommended dietary allowance of vitamin A for children in an average daily consumption of rice [10]. However, in many other cases, alteration of expression of carotenoid biosynthetic genes is insufficient to drastically enhance carotenoid levels in transgenic plants [5], or even leads to unexpected phenotypic changes [11]. This makes it essential to search for novel genes controlling plant carotenogenesis and to gain a better understanding of the mechanisms underlying carotenoid biosynthesis and accumulation in plants. Cauliflower Or gene represents a novel gene mutation. It causes many lowpigmented tissues of the plant, most noticeably the edible curd and the shoot meristem to accumulate high levels of b-carotene and turns these tissues orange [12–14]. The Or gene has been isolated by a map-based cloning strategy [15]. This gene appears to represent a regulatory gene in controlling carotenoid accumulation. It functions in increasing the sink capacity rather than altering the expression of genes involved in carotenoid biosynthesis [14–16]. In this chapter, we briefly summarize the progress on carotenoid biosynthesis and metabolic engineering of carotenoids in food crops for improving their nutritional value. We describe the isolation of the Or gene from the orange cauliflower mutant and elucidation of its functional role in controlling carotenoid accumulation. The successful use of the Or gene to

173 enhance carotenoid levels in transgenic potato tubers demonstrates that manipulation of sink formation offers an alternative strategy to increase carotenoid content in food crops.

Carotenoid biosynthesis in plants Carotenoids are comprised of carotenes and their oxidized form, xanthophylls. The carotenoid biosynthetic pathway (Fig. 1) has been long established by labeling and inhibition studies and mutant analysis, but isolation of carotenogenic genes has only been achieved during the past two decades [4]. Carotenoids are mainly C40 isoprenoids synthesized de novo essentially in all kinds of plastids except proplastids in plants [17]. Like other plastidic isoprenoids, carotenoid biosynthesis starts with the synthesis of isopentenyl pyrophosphate (IPP) and its allylic isomer dimethylallyl pyrophosphate (DMAPP) in plastids. Three IPP molecules are added to DMAPP to produce geranylgeranyl pyrophosphate (GGPP), the immediate precursor for biosynthesis of carotenoids, as well as tocopherols, gibberellins, phytol, taxol, etc. The first committed step in carotenoid biosynthesis is the condensation of two molecules of GGPP to produce phytoene catalyzed by phytoene synthase. Subsequently, two related enzymes phytoene desaturase (PDS) and z-carotene desaturase (ZDS) catalyze phytoene into lycopene. Two cis-trans isomerases of Z-ISO and CRTISO are needed to convert poly-cis-compounds into alltrans forms in plants. A single enzyme carotene desaturase (crtI) in bacteria confers the same reactions. Lycopene is the branching point of this pathway and cyclized to yield a- and b-carotene by lycopene e-cyclase and/or lycopene b-cyclase. These carotenes can undergo hydroxylation and epoxidation reactions to form xanthophylls, such as lutein, zeaxanthin, violaxanthin, and neoxanthin. Xanthoxin, the product that results from violaxanthin/neoxanthin cleavage by 9-cis-epoxycarotenoid dioxygenase, provides the direct substrate for phytohomone ABA synthesis [18,19].

Regulation of carotenoid biosynthesis in plants The composition and relative abundance of various carotenoids in plants are remarkably conserved in green tissues, but vary in a broad range in nongreen tissues or organs, indicating that plants have developed complex regulatory mechanisms controlling carotenogenesis. Several processes affect carotenoid accumulation, including those that determine the supply of isoprenoid precursors, the catalytic activity of the pathway, and metabolic turnover or conversion. In addition, carotenoid sequestration is also known to play an important role.

174 Pyruvate + Glyceraldehyde-3-phosphate Deoxyxylulose-5-phosphate synthase

1-Deoxy-D-xylulose 5-phosphate (DXP)

MEP pathway

Deoxylulose-5-phosphate reductoisomerase

2-C-Methyl-D-erythritol 4-phosphate (MEP) 1-hydroxy-2-methyl-2-(E)-butenyl 4-phosphate reductase

Isopetenylpyrophosphate (IPP)

Dimethylallyl diphosphate (DMAPP)

GGPP synthase

Condensation

Geranylgeranyl diphosphate (GGPP) Phytoene synthase

Phytoene Phytoene desaturase 15-cis ζ-Carotene isomerase

Desaturation Isomerization

ζ-Carotene

ζ-Carotene desaturase Carotene isomerase

Lycopene Lycopene β-cyclase Lycopene ε-cyclase

Cyclization

α-Carotene

Lycopene β-cyclase

β-Carotene

β-Carotene hydroxylase ε-carotene hydroxylase

Lutein

Xanthophyll formation

β-Carotene hydroxylases

Zeaxanthin

Violaxanthin deepoxidase

Zeaxanthin epoxidase

Antheraxanthin Violaxanthin deepoxidase

Zeaxanthin epoxidase

violaxanthin Neoxanthin synthase

Neoxanthin Fig. 1. Carotenoid biosynthetic pathway in plants.

Supply of isoprenoid precursors GGPP serves as a common substrate for biosynthesis of carotenoids, gibberellins, phytol, and other isoprenoid compounds. Alteration of the precursor pool size into carotenogenic pathway is likely to affect carotenoid

175 levels [20]. For example, the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway synthesizes IPP and DMAPP for the production of plastidic isoprenoids. The null mutation of CLA1, which encodes the first enzyme in the pathway, produces less precursors, leading to lack of chlorophylls and carotenoid pigments with a retardation of chloroplast development and an albino phenotype [21,22]. Alteration of the expression of this gene results in different levels of carotenoids as well as other isoprenoids such as chlorophylls, tocopherols, abscisic acid, and gibberellins [23]. Hydroxymethylbutenyl diphosphate reductase (HDR) catalyzes the simultaneous synthesis of IPP and DMAPP in the last step of the MEP pathway. During tomato fruit ripening and Arabidopsis seedling de-etiolation, increased carotenoid production is associated with enhanced expression of the HDR gene [24]. These studies show the effect of availability of isoprenoid precursors for carotenoid accumulation. Regulation of carotenoid biosynthetic genes Among the carotenoid biosynthetic enzymes, PSY, which catalyzes the first committed step of carotenoid biosynthesis, is believed to control isoprenoid flux into the pathway [25]. Therefore, PSY is a highly regulated point in carotenoid biosynthesis. Light has been shown to have a strong effect on carotenogenesis in photosynthetic tissue while developmental cues affect carotenoid content in flowers and fruits. Studies show that both light and development cues regulate PSY expression. Light affects carotenoid biosynthesis by increasing expression of PSY in green pepper leaves, mustard, and Arabidopsis seedlings [26,27]. Comparison of Arabidopsis wild-type and phytochrome mutants reveals that up-regulation of PSY expression is mediated by phytochrome [27]. Indeed, the full-length promoter of Arabidopsis PSY mediates positive responses towards different light qualities [28]. On the other hand, PSY is strongly induced during flower and fruit development in tomato plants, implying unlike that in the leaves, PSY is mainly regulated by developmental signals in those tissues [29]. PDS, the second enzyme in carotenoid biosynthetic pathway, is also strongly induced during flower and fruit development. In tomato, high levels of PDS expression are found in organs and tissues where chromoplasts are formed [30]. More importantly, carotenogenesis requires coordinated expression of multiple carotenoid biosynthetic genes as demonstrated during citrus fruit maturation [31]. Metabolic turnover or conversion Metabolic turnover of carotenoids not only helps to maintain the steady level of carotenoids in plants, but also produces important signaling and accessory apocarotenoid molecules, such as phytohormone ABA and flavor volatiles

176 beta-ionone, pseudoionone, and geranylacetone for a variety of biological processes [32]. A family of carotenoid cleavage dioxygenases (CCDs) catabolizes oxidative cleavage of carotenoids. The first CCD gene was cloned through analysis of a maize ABA deficient mutant viviparous 14 [19]. Subsequently, more CCD genes were cloned and identified from other plant species, including Arabidopsis, tomato, petunia, cowpea, avocado, and citrus [33]. Analysis of enzymatic activities reveals that CCDs display complicated substrate and cleavage site specificity. In Arabidopsis, there are nine members of CCDs, of which AtCCD1 cleaves a variety of carotenoids symmetrically at the 9,10 or 9u,10u double bond to form a dialdehyde and one or two C13 products, whereas AtCCD7 protein catalyzes a specific 9,10 cleavage of betacarotene to produce apo-beta-carotenal and beta-ionone [34]. Similarly, CitCCD1 protein cleaved b-cryptoxanthin, zeaxanthin, and all-transviolaxanthin at the 9,10 and 9u,10u positions and 9-cis-violaxanthin at the 9u,10u positions [35]. Another class of CCDs is 9-cis-epoxycarotenoid dioxygenases (NCEDs), including VP14, which specifically cleaves 11,12 double bonds [18,35,36]. The third class of CCDs cleaves at the 7,8 double bonds of zeaxanthin [37]. In addition, lycopene cleavage dioxygenase, which is involved in sequential conversion of lycopene into bixin, cleaves lycopene at the 5,6 and 5u,6u positions [38]. A body of evidence shows that oxidative cleavage of carotenoids is induced under environmental stresses [18,36,39]. Additionally, circadian rhythm has been shown to affect carotenoid catabolism [40]. Moreover, developmental cues also play an important role. The transcript of LeCCD1 gene, which could confer the formation of the important flavor volatiles in tomato fruit, is accumulated during the ripening process [32]. Carotenoid sequestration One of the major carotenoid storage organelles within plant cells is chromoplasts. High levels of carotenoid accumulation in chromoplasts contribute the red, orange, and yellow color found in many flowers, fruits, and vegetables. While chromoplasts frequently derive from fully developed chloroplasts, as seen during fruit ripening of tomato and pepper, they also arise from other non-photosynthetic plastids, such as in carrot and squash [41]. Chromoplasts develop lipoprotein-sequestering substructures to sequester and retain large quantity of carotenoids [42]. Based on their ultrastructures, the carotenoid-sequestering structures within chromoplasts fall into globular, crystalline, membranous, fibrillar, and tubular types [43]. The various compositions of carotenoids, polar lipids, and proteins may determine the type of these structures. Most of what we have learned about the carotenoid sequestration comes from studies of globular, fibrilar, or tubular-type chromoplasts. Several genes encoding carotenoid-associated proteins in chromoplasts have been cloned

177 and identified [42]. The first fibrillin (fib) gene was isolated from pepper fruits [44]. Shortly after, another cDNA encoded the chromoplast-specific carotenoid-associated protein (CHRC) was cloned from Cucumis sativus corollas [45]. Both proteins carry a hydrophobic hairpin domain, which might be involved in interactions with carotenoids. More homologs have continually been characterized from different types of plastids and species [43,46], suggesting that they are involved in sequestration of not only carotenoids but also other hydrophobic compounds. The expression of genes encoding carotenoid-associated proteins is well associated with chromoplast development and carotenoid accumulation. The fib gene was shown to be strongly induced during fruit ripening [44]. The ChrC message was detected only in cucumber corollas, where its level increased in parallel to flower development [45]. Overexpession of the pepper fib gene in tomato fruits leads to a twofold increase in carotenoid content [47], while down-regulation of LeCHRC causes transgenic tomato flowers to accumulate 30% less carotenoids [48]. Metabolic engineering of carotenoids in crops Significant progress has been achieved in quantitative and qualitative manipulation of carotenoids in food crops for enhancing their nutritional quality and health benefits [5,7–9]. The strategy commonly used is to alter expression of the key enzymes or several enzymes of a mini-biosynthetic pathway in specific tissues such as in seeds, fruits, or other storage organs. Phytoene synthase is one of the key metabolic enzymes involved in carotenogenesis, and thus a prominent target for genetic manipulation of carotenoid content in food plants. Overexpression of the phytoene synthase gene from either bacteria or plants results in significant increase in total carotenoid levels in tomato fruit [49], potato tuber [50], and canola seed [51]. Expression of multiple biosynthetic genes in the carotenoid biosynthetic pathway leads to a profound increase of b-carotene in Golden Rice [10,52] and ‘‘golden’’ potato [53], as well as the accumulation of zeaxanthin in tomato fruit [54]. Qualitative modification of carotenoid composition in food crops by tissue-specific regulation of endogenous genes in the pathway is achieved for b-carotene or zeaxanthin enhancement in potato [55–57] and in tomato [58]. Expression of b-carotene ketolase gene in ketocarotenoid biosynthesis confers the accumulation of astaxanthin, a new and higheconomic value carotenoid, in potato tubers [59] and in model plants [60–62]. In a few cases, alteration of the expression of genes involved in light signal pathway also leads to enhanced levels of carotenoid accumulation. Cryptochromes are blue light photoreceptors found in plants, bacteria, and animals. Overexpression of the tomato CRY2 gene results in overproduction of anthocyanins and chlorophyll in leaves and of flavonoids and lycopene in fruits along with other phenotypic changes [63]. In tomato, fruit-specific

178 suppression of DET1 which encodes a nuclear regulatory protein in photomorphogenesis significantly increases carotenoid and flavonoid contents [64]. These results undoubtedly show that carotenoid biosynthesis is affected by other signal modules, indicating an alternative approach in carotenoid enhancement. The basic concept of metabolic engineering of carotenogenesis is to increase the metabolic flux towards carotenoid biosynthesis. However, in some cases, the increased flux into carotenogenesis can alter or reduce flux in other competing pathways, leading to unexpected phenotypic changes. For example, overexpression of PSY in canola and Arabidopsis seeds causes not only a high level of accumulation of carotenoids, but also leads to a delayed germination phenotype due to an increased content of the carotenoid-derived ABA [51,65]. Constitutive expression of tomato PSY-1 results in a stunted growth due to an insufficient synthesis of gibberellins and producing pleiotrophic effects such as premature pigmentation of seed coats and cotyledons [11]. Although it is possible to produce transgenic crops with significant increased levels of carotenoids via alteration of the expression of genes involved with carotenogenesis, the extent is limited due to a lack of fully understanding of the mechanisms underlying carotenogenesis and its interaction with other metabolic processes [7]. Novel carotenoid gene mutations will provide not only the new insights into the control mechanisms, but also possible genetic tools to manipulate carotenogenesis in food crops. Recent studies on the Or gene from an orange cauliflower mutant has offered such an endeavor for carotenoid enhancement. Functional analysis of the Or gene from cauliflower Characteristics of the orange cauliflower (Brassica oleracea var. botrytis) mutant The eye-appealing orange cauliflower (Fig. 2A) was first discovered in Bradford Marsh in Canada in 1970. Through many years of work by Professor Emeritus Michael Dickson at Cornell University and by other breeders, the orange cauliflower is now commercially available. The orange cauliflower results from a spontaneous mutation of a single gene, designated as Or for Orange gene [12]. The Or gene mutation causes not only the normally white curd to turn orange, but also imparts visible orange coloration in other parts of the plant, such as in the shoot meristems, t