Advances in Agronomy, Volume 82

VOLUME

82

Advisory Board John S. Boyer University of Delaware

Paul Bertsch University of Georgia

Ronald Phillips University of Minnesota

Kate M. Scow University of California, Davis

Larry P. Wilding Texas A&M University

Emeritus Advisory Board Members Kenneth J. Frey Iowa State University

Eugene J. Kamprath North Carolina State University

Martin Alexander Cornell University

Prepared in cooperation with the American Society of Agronomy Monographs Committee Diane E. Stott, Chair Lisa K. Al-Almoodi David D. Baltensperger Warren A. Dick Jerry L. Hatfield John L. Kovar

David M. Kral Jennifer W. MacAdam Matthew J. Morra Gary A. Pederson John E. Recheigl

Diane H. Rickerl Wayne F. Robarge Richard Shibles Jeffrey Volenec Richard E. Zartman

Edited by

Donald L. Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware

Amsterdam · Boston · Heidelberg · London · New York · Oxford · Paris · San Diego San Francisco · Singapore · Sydney · Tokyo

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Contents CONTRIBUTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii xv

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES IN AQUEOUS SOLUTION Treavor A. Kendall and Steven K. Lower I. Introduction — Forces in Nature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Fundamental Forces at the Interface of Biological Particles and Inorganic Surfaces. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The van der Waals Force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. The Electrostatic Force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. The Solvation Force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. The Steric Force and Bridging Polymers . . . . . . . . . . . . . . . . . . . . . . . . III. Force Curve Theory and Collecting Force Data. . . . . . . . . . . . . . . . . . . . . A. Force – Distance Curves, Capturing a Potential Force Versus Separation Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Hysteresis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Tip Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Spring Constant Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Artifacts in Force Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Data Processing and Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Advanced Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Relating Bond Chemistry and Energies to Force Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Relevance of Dynamic Force Spectroscopy to Biological – Inorganic Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Forces at the Biomolecule – Mineral Interface . . . . . . . . . . . . . . . . . . . . . . A. Ligand Linkage Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Siderophores and Oxide Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Forces at the Bacterium – Mineral Interface . . . . . . . . . . . . . . . . . . . . . . . . A. Force Microscopy Technique Using Whole Cells . . . . . . . . . . . . . . . . B. Forces Between Escherichia coli and Muscovite . . . . . . . . . . . . . . . . . C. Forces Between Shewanella oneidensis and Goethite or Diaspore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v

2 4 4 5 7 8 11 12 15 16 16 17 18 19 19 25 27 28 30 35 35 39 41 44 45 46

vi

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RICE FUNCTIONAL GENOMICS : LARGE -SCALE GENE DISCOVERY AND APPLICATIONS TO CROP IMPROVEMENT Hei Leung and Gynleung An I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Rice as a Model Genetic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Diversity of Oryza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Grass as a Single Genetic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Rice Genome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Genome Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Salient Features of the Rice Genome . . . . . . . . . . . . . . . . . . . . . . . . . C. Comparison Between Indica and Japonica Rice . . . . . . . . . . . . . . . . . IV. Key Ingredients for Gene Discovery in Rice . . . . . . . . . . . . . . . . . . . . . . . A. Genetic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. High-throughput Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Biological Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Forward and Reverse Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Insertional Mutants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Chemical- and Irradiation-induced Mutants . . . . . . . . . . . . . . . . . . . . . C. Natural Genetic Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Functional Validation of Rice Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Gene Expression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Gene Silencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Heterologous Bioassays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Gene Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Allelic Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Applications to Crop Improvement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Candidate Gene Approach and Allele Mining . . . . . . . . . . . . . . . . . . . B. Pathways and Genetic Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Cross-species Inference of Gene Function . . . . . . . . . . . . . . . . . . . . . . VIII. International Collaboration and the Role of Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

56 59 59 62 64 65 66 68 69 70 70 71 72 73 73 83 88 89 89 92 92 94 95 95 96 97 97 98 100 101 102

THE NATURE , PROPERTIES AND MANAGEMENT OF VOLCANIC SOILS R. A. Dahlgren, M. Saigusa and F. C. Ugolini I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Volcanic Soil Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

114 116

CONTENTS III. Soil Genesis in Volcanic Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Allophanic and Nonallophanic Andisols . . . . . . . . . . . . . . . . . . . . . . B. General Trends in Soil Development on Volcanic Materials. . . . . . . . IV. Soil Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. World Reference Base for Soil Resources — Andosols . . . . . . . . . . . B. Soil Taxonomy — Andisols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Chemical Weathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Mineralogical Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Common Colloidal Constituents of Volcanic Soils. . . . . . . . . . . . . . . B. Formation and Transformation of Colloids in Volcanic Soils . . . . . . . VII. Selected Chemical Characteristics of Volcanic Soils . . . . . . . . . . . . . . . . A. Organic Matter Accumulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Aluminum Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Productivity and Management of Volcanic Soils . . . . . . . . . . . . . . . . . . . A. Charge Characteristics and Chemical Fertility . . . . . . . . . . . . . . . . . . B. Physical Properties and Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Soil Management and Conservation of Volcanic Soils . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii 118 119 122 123 124 125 127 134 135 141 145 145 147 150 150 163 166 167

MICROARRAY TECHNOLOGY AND APPLICATIONS IN ENVIRONMENTAL MICROBIOLOGY Jizhong Zhou and Dorothea K. Thompson I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Microarray Types and Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Types of Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Advantages of Microarrays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Microarray Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Microarray Substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Surface Modification for the Attachment of Nucleic Acids. . . . . . . . . C. Arraying Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Critical Issues for Microarray Fabrication . . . . . . . . . . . . . . . . . . . . . IV. Microarray Hybridization and Detection . . . . . . . . . . . . . . . . . . . . . . . . . A. Probe Design and Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Target Labeling and Quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Hybridization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Critical Issues in Hybridization and Detection . . . . . . . . . . . . . . . . . . V. Microarray Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Assessment of Spot Quality and Background Subtraction. . . . . . . . . . VI. Microarray Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Data Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

184 187 187 188 190 190 191 197 201 205 205 206 210 210 211 214 214 215 219 219

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B. Data Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Methods for Identifying Differentially Expressed Genes . . . . . . . . . . D. Microarray Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Using Microarrays to Monitor Genomic Expression . . . . . . . . . . . . . . . . A. General Approaches to Revealing Differences in Gene Expression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Experimental Design for Microarray-based Monitoring of Gene Expression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Microarray-based Functional Analysis of Environmental Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Application of Microarrays to Environmental Studies . . . . . . . . . . . . . . . A. Functional Gene Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Phylogenetic Oligonucleotide Arrays . . . . . . . . . . . . . . . . . . . . . . . . C. Community Genome Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Whole-genome Open Reading Frame Arrays for Revealing Genome Differences and Relatedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Other Types of Microarrays for Microbial Detection and Characterization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

225 225 227 231 231 234 236 242 243 250 253 256 258 258 259

THE AGRONOMY AND ECONOMY OF BLACK PEPPER (PIPER NIGRUM L.) — THE “KING OF SPICES ” K. P. Prabhakaran Nair I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. The Pepper Plant — Its Botany and Chemistry . . . . . . . . . . . . . . . . . . . . A. Pepper Botany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Pepper Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Pepper Agronomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The Pepper Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Nutrition of Black Pepper. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Evolution of Pepper Manuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Response of Pepper to Mineral Nutrients . . . . . . . . . . . . . . . . . . . . . IV. The Role of “The Nutrient Buffer Power Concept” in Pepper Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The Buffer Power and Effect on Nutrient Availability . . . . . . . . . . . B. Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Measuring the Nutrient Buffer Power and its Importance in Affecting Nutrient Concentration on Root Surfaces . . . . . . . . . . . . . . . . . . . . . D. Background Informaton on the Importance of Measuring Zn Buffer Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Quantifying Zn Buffer Power of Pepper Growing Soils . . . . . . . . . . .

273 279 279 285 288 289 290 291 291 296 296 296 298 301 303

CONTENTS V. Establishing a Pepper Plantation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The Indian Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. The Indonesian Experience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Pepper Pests and Their Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. The Processing of Black Pepper On-Farm. . . . . . . . . . . . . . . . . . . . . . . . A. Sun Drying of Pepper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Solar Drying of Pepper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Garbling, Cleaning, and Fractionation . . . . . . . . . . . . . . . . . . . . . . . . D. Packaging and Storing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. An Account of Indonesian Pepper Processing . . . . . . . . . . . . . . . . . . . . . IX. Industrial Processing of Black Pepper. . . . . . . . . . . . . . . . . . . . . . . . . . . A. White Pepper. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Cryoground Pepper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Pepper Oil and Oleoresin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. The Future of the Global Pepper Economy . . . . . . . . . . . . . . . . . . . . . . . A. The Supply Side of the Pepper Economy . . . . . . . . . . . . . . . . . . . . . B. The Demand Side of the Pepper Economy . . . . . . . . . . . . . . . . . . . . C. Prices and World Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. The Pepper Price Outlook by 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . E. The Pepper Supply Outlook by 2020 . . . . . . . . . . . . . . . . . . . . . . . . F. The Pepper Demand Outlook by 2020 . . . . . . . . . . . . . . . . . . . . . . . G. Country-wise Economic Growth Impacting Production and Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XI. Pepper Economy in India. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Pepper Production Scenario in India . . . . . . . . . . . . . . . . . . . . . . . . . B. Bush Pepper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Economics of Pepper Production in the State of Kerala . . . . . . . . . . . D. Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Pepper Futures Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XII. Pepper Pharmacopoeia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Antiinflammatory and Central Nervous System (CNS) Depressant Activity of Pepper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Effect on Hepatic Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Carcinogenic and Mutagenic Effects of Black Pepper . . . . . . . . . . . . D. Pepper as an Antioxidant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Pepper as an Antimicrobial Agent . . . . . . . . . . . . . . . . . . . . . . . . . . F. The Pharmacological Effect of Pepper on Human Health . . . . . . . . . . G. Clinical Applications of Pepper . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Toxicological Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. The Insecticidal Activity of Pepper. . . . . . . . . . . . . . . . . . . . . . . . . . XIII. Consumer Products Out of Black Pepper . . . . . . . . . . . . . . . . . . . . . . . . XIV. Value Addition in Pepper. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV. Conclusions and a Peep Into Pepper’s Future . . . . . . . . . . . . . . . . . . . . . Acknowledgment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix 306 306 310 311 322 324 324 325 327 328 330 333 335 335 339 339 343 345 345 346 346 346 347 349 351 352 352 353 355 357 358 359 360 361 362 363 363 364 365 365 369 372 373

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SOIL MINERAL – ORGANIC MATTER –MICROORGANISM INTERACTIONS : FUNDAMENTALS AND IMPACTS P. M. Huang I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Mechanisms of Binding of Nonhumic Organics and Humic Substances by Soil Mineral Colloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Nature of Mineral Colloid Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . B. Binding of Nonhumic Organics . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Binding of Humic Substances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Influence of Organic Substances on the Formation, Transformation, and Surface Properties of Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . . . A. Aluminum Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Iron Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Role of Soil Minerals in Abiotic Catalysis of the Formation of Humic Substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Polyphenol Pathway. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Maillard Reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Interactions of Soil Mineral Colloids with Microorganisms . . . . . . . . . . . A. Surface Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Influence on Microbial Activity and Survival . . . . . . . . . . . . . . . . . . VI. Interactions of Soil Colloids with Enzymes. . . . . . . . . . . . . . . . . . . . . . . A. Mineral Colloid – and Humic– enzyme Complexes . . . . . . . . . . . . . . B. Effects on Enzymatic Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Microbial Mediation of Soil Mineral Weathering and Transformation . . . A. Mineral Weathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Fine-grained Mineral Development. . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Interactions of Soil Mineral Colloids with Organic Substances and Microorganisms in Relation to Soil Structure Stability . . . . . . . . . . . . . . A. Organo-mineral Complexes in Relation to Soil Structure . . . . . . . . . . B. Dynamics of Aggregate Turnover . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Influence of Mineral Colloids on Biogeochemical Cycling of C, N, P, and S in Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Role of Mineral Colloids in Soil Organic Matter Storage and Turnover B. Decomposition and Stabilization of C, N, P, and S in Relation to Primary and Secondary Particles. . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Effects of Interactions between Microorganisms and Soil Colloids on the Transformation of Organic Pollutants . . . . . . . . . . . . . . . . . . . . . . A. Catalytic Transformations of Organic Pollutants . . . . . . . . . . . . . . . . B. Binding of Organic Pollutants, Enzymes, and Microorganisms on Mineral and Humic Surfaces and the Effects on Pollutant Bioavailability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XI. Impact of Interactions of Physicochemical, Biochemical, and Biological Processes on Metal Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Redox Reactions of Metals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

393 394 394 395 397 400 400 404 406 406 410 410 410 413 415 415 417 418 418 419 421 421 423 426 426 429 431 431

436 440 440

CONTENTS B. C. D. E. F.

Complexation Reactions of Metals . . . . . . . . . . . . . . . . . . . . . . . . . . Adsorption –Desorption Reactions of Metals . . . . . . . . . . . . . . . . . . . Precipitation – Dissolution Reactions of Metals . . . . . . . . . . . . . . . . . Metal Sorption and Uptake by Microorganisms and Biomineralization Metal Transformation by Microbial Excretions and Mycorrhizal Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XII. Foreseeable Impacts of Soil Mineral – Organic Matter – Microorganism Interactions on the Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Global Ion Cycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Global Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Biodiversity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Biological Productivity and Human Nutrition . . . . . . . . . . . . . . . . . . E. Geomedicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Ecotoxicology and Human Health. . . . . . . . . . . . . . . . . . . . . . . . . . . G. Biotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Risk Management, Remediation, and Restoration . . . . . . . . . . . . . . . XIII. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi 441 442 444 445 447 447 448 449 451 451 452 453 454 456 457 459 459 460

LOW EXTERNAL INPUT TECHNOLOGIES FOR LIVELIHOOD IMPROVEMENT IN SUBSISTENCE AGRICULTURE Anil Graves, Robin Matthews and Kevin Waldie I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. The Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Intercropping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Alley Cropping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Cover Crops and Green Manures . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Biomass Transfer Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Compost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Animal Manure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Improved Fallows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Generic Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Soil Fertility Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Socio-economic Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Integrated Nutrient Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . B. A Systems Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

473 476 476 479 484 490 493 496 499 504 504 516 526 526 530 534 539 541 541

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AMMONIA EMISSION FROM MINERAL FERTILIZERS AND FERTILIZED CROPS Sven G. Sommer, Jan K. Schjoerring and O. T. Denmead I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Mineral Fertilizer Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Ammonia Volatilization from Mineral Fertilizers . . . . . . . . . . . . . . . . . . A. Production and Transport of NH3 in the Soil– Fertilizer – Atmosphere Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Temporal NH3 Loss Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Hydrolysis of Urea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Soil Hþ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Soil CEC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Solid Phase Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Climate and Infiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Microbial Processes (Nitrification/Immobilization) . . . . . . . . . . . . . . IV. Ammonia Emission from Crop Foliage. . . . . . . . . . . . . . . . . . . . . . . . . . A. Transport of NH3 Between Leaves and the Atmosphere. . . . . . . . . . . B. Magnitude of NH3 Losses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Physiological Processes Involved in NH3 Emission from Crops . . . . . V. Management Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Techniques for Reduction of NH3 Emission . . . . . . . . . . . . . . . . . . . B. Fertilizer Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Flooded Fields (Rice Paddies) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Injection of Anhydrous Ammonia. . . . . . . . . . . . . . . . . . . . . . . . . . . E. Crop NH3 Emissions as Affected by Fertilizer Application . . . . . . . . F. Ammonia Emission from Decomposing Plant Material . . . . . . . . . . . G. Absorption by Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Measurement Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Tracer Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Enclosures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Micrometeorological Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Gradient Diffusion Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Eddy Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Relaxed Eddy Accumulation or Conditional Sampling. . . . . . . . . . . . G. Lagrangian Dispersion Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Conclusions and Perspectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

558 560 561 561 565 568 570 574 575 576 578 581 581 582 584 586 587 589 591 594 594 596 598 598 598 599 601 603 605 605 606 607 609 611 611

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

623

Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

G. AN (55), Department of Life Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang, Korea 790-784 R. A. DAHLGREN (113), Land, Air and Water Resources, University of California, Davis, California 95616, USA O. T. DENMEAD (557), CSIRO Land and Water, GPO Box 1666, Canberra ACT 2601, Australia A. GRAVES (473), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK P. M. HUANG (391), Department of Soil Science, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8 Canada T. A. KENDALL (1), Division of Engineering and Applied Sciences, Harvard University, 40 Oxford St., Cambridge, MA 02138, USA H. LEUNG (55), International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines S. K. LOWER (1), Department of Geological Sciences & School of Natural Resources, The Ohio State University, 275 Mendenhall Laboratory, 125 South Oval Mall, Columbus, Ohio 43210, USA R. MATTHEWS (473), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK K. P. PRABHAKARAN NAIR (271), Akshaya, East Hill, Calicut 673 005, Kerala State, India M. SAIGUSA (113), Experimental Farm of Tohoku University, Kawatabi, Naruko, Tamatsukuri, Miyagi 989-6711, Japan J. K. SCHJOERRING (557), Plant Nutrition Laboratory, Department of Agricultural Sciences, The Royal Veterinary and Agricultural University, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark S. G. SOMMER (557), Department of Agricultural Engineering, Danish Institute of Agricultural Sciences, Research Centre Bygholm, PO Box 536, DK-8700 Horsens, Denmark D. K. THOMPSON (183), Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA F. C. UGOLINI (113), Dipartimento di Scienza del Suolo e Nutrizione della Pianta, Universita` Degli Studi, Piazzale delle Cascine 15, 50144 Firenze, Italy K. WALDIE (473), International and Rural Development Department, University of Reading, Reading RG6 6AH, UK J. ZHOU (183), Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA

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Preface Volume 82 continues the tradition and excellence of “Advances in Agronomy” by containing eight state-of-the-art reviews on topics of interest in the plant and soil sciences. Three of the reviews present cutting-edge molecular scale techniques and approaches that directly impact food production, crop improvement, and environmental quality and sustainability. Chapter 1 is an excellent review on the use of force microscopy to investigate reactions at the mineral/microbe interface. Chapter 2 is a definitive review on rice functional genomics including large-scale gene discovery and applications to crop improvement. Chapter 3 presents a comprehensive overview of volcanic soils including their genesis, chemical, mineralogical, and physical properties and the productivity and management of volcanic soils. Chapter 4 is a seminal review on microarray technology and applications to environmental microbiology. Microarray types, fabrication, hybridization and detection, image processing, data analysis, the use of microarrays to monitor genomic expression, and applications of microarrays to environmental studies are covered. Chapter 5 is a definitive overview on the agronomy and economy of black pepper. Topics that are discussed include: the botany and chemistry of black pepper, pepper agronomy, production and management, and economic aspects of global black pepper production. Chapter 6 is an outstanding overview of soil mineral-organic matter-microorganism interactions. Topics that are discussed include: mechanisms of binding of nonhumic organics and humic substances by soil mineral colloids, the influence of organic substances on the surface chemistry and transformations of metal oxides, interactions of minerals with microorganisms and enzymes, the role of microbes on mineral weathering, and impacts of mineral-humic-microbe interactions on nutrient cycling and transport of pollutants and ecosystems. Chapter 7 reviews low external input technologies for livelihood improvement in subsistence agriculture. The technologies are discussed along with economic and nutrient management considerations. Chapter 8 is a comprehensive review on ammonia emission from mineral fertilizers and fertilized crops. Topics that are covered include: ammonia volatilization from mineral fertilizers, ammonia emission from crop foliage, and management strategies and techniques. Many thanks to all the authors for their excellent reviews. DONALD L. SPARKS University of Delaware

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FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES IN AQUEOUS SOLUTION Treavor A. Kendall1 and Steven K. Lower2 1

Harvard University, Division of Engineering and Applied Sciences, 40 Oxford St., Cambridge, MA 02138, USA 2 Department of Geological Sciences & School of Natural Resources, The Ohio State University, 275 Mendenhall Laboratory, 125 South Oval Mall, Columbus, Ohio 43210, USA, Email: [email protected]

I. Introduction — Forces in Nature II. Fundamental Forces at the Interface of Biological Particles and Inorganic Surfaces A. The van der Waals Force B. The Electrostatic Force C. The Solvation Force D. The Steric Force and Bridging Polymers III. Force Curve Theory and Collecting Force Data A. Force– distance Curves, Capturing a Potential Force Versus Separation Plot B. Hysteresis C. Tip Shape D. Spring Constant Determination E. Artifacts in Force Measurements F. Data Processing and Statistics G. Advanced Algorithms H. Relating Bond Chemistry and Energies to Force Measurements I. Relevance of Dynamic Force Spectroscopy to Biological – Inorganic Interface IV. Forces at the Biomolecule– Mineral Interface A. Ligand Linkage Schemes B. Siderophores and Oxide Surfaces V. Forces at the Bacterium– Mineral Interface A. Force Microscopy Technique Using Whole Cells B. Forces Between Escherichia coli and Muscovite C. Forces Between Shewanella oneidensis and Goethite or Diaspore VI. Future Work Acknowledgments References

1 Advances in Agronomy, Volume 82 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. 0065-2113/03 $35.00

2

T. A. KENDALL AND S. K. LOWER

At the most fundamental level, intermolecular forces (e.g., van der Waals, electrostatic, solvation, steric) control interactions between biological molecules and mineral surfaces. These are forces with magnitudes of piconewtons to nanonewtons, which operate in a space that is on the order of nanometers. We have used force microscopy to quantitatively probe forces, energies, and distances between crystal surfaces and living microbial cells or biological molecules in their native state. The systems we have studied include those involving: Escherichia coli, Shewanella oneidensis, siderophores, muscovite, goethite, and/or diaspore, in aqueous solutions of varying composition. Direct force measurements at the organic – inorganic interface have been interpreted with theoretical models describing interfacial forces, adhesion, and molecular dynamic calculations. A new perspective on bacterium – mineral interactions is emerging from these studies. We have discovered a world that operates under a very different set of principles than macroscopic bodies. A world where the intermolecular force, rather than gravitational attraction, is the preeminent force controlling the evolution of processes at the bacterium – mineral interface. q 2004 Academic Press.

I. INTRODUCTION — FORCES IN NATURE The bacterium – mineral interface is ubiquitous near the surface of the Earth. As many as 97% of the , 1030 prokaryotes on Earth live in close proximity to minerals in soil, marine, and terrestrial subsurface environments (Whitman et al., 1998). As we will show in this manuscript, the fundamental forces at this interface are very small, seemingly insignificant. This review will provide evidence that forces on the order of nanonewtons (1029 N) to piconewtons (10212 N) dominate the properties/processes at bacterium –mineral and biomolecule – mineral interfaces. For comparison, there is , 0.2 nN of gravitational attraction between a person (50 kg) and the paper (5 g) upon which these words are written. Despite their small magnitude, these forces are at the heart of all interactions between biologically produced polymers and mineral surfaces in nature. It is now well established that there are four fundamental forces in nature: the strong and weak nuclear forces, the gravitational interaction, and electromagnetic forces, which are the source of all intermolecular forces (Israelachvili, 1992). Because the first two (i.e., nuclear forces) have a range of action that is less than 1025 nm (Israelachvili, 1992), we need not consider these for interactions between biological molecules, microbial cells, and/or mineral surfaces. The question then becomes, under what conditions do gravitational forces or electromagnetic forces (more specifically, intermolecular forces) dominate bacteria – mineral or biomolecule – mineral interactions?

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In nature, living organisms exist in communities that are in contact with one another, in contact with mineral surfaces, and they are also in contact with the surface of the Earth (i.e., the upper crust). For simplicity, let us define a particular species of organism as a spherical particle (having a density of water) with a unique size or radius. Each species may interact with one another and/or the Earth. In both instances, there is a force of gravitational attraction at each interface. Figure 1 reveals that the gravitational attraction is much greater between the Earth and a particle of a given size (e.g., , 4 £ 1025 N for a 1 mm particle) relative to the gravitational attraction between two particles of the same given size (e.g., , 3 £ 10216 N between two 1 mm particles). Also shown on this figure is a theoretical prediction for another type of attractive force, the so-called van der Waals force. This intermolecular force was determined using Eq. (1) (see below) to describe the attraction between two similar objects of equal size in contact with one another. For example, two identical 1 mm (radius) particles are

Figure 1 Log–log plot of the theoretical forces describing (1) gravitational attraction between a particle and the Earth (solid “Earth-particle” line), (2) gravitational attraction between two particles of the same size (dashed “particle-particle” line), and (3) van der Waals attraction between two particles of the same size (dashed “vdw” lines). In all instances the particles are assumed to be in “contact” with the Earth (for 1) or another particle (for 2 and 3). For gravitational attraction, mass was determined by assuming each particle was a solid homogeneous sphere with a density of 1 g cm23, and contact was defined as the radius of the Earth (~6.4 £ 106 m radius; “Earth-particle” interaction) or the sum of the radii of two interacting particles (“particle-particle” interaction). The shaded region outlines the boundaries of the expected van der Waals force using values for Hamaker constant of 10220 to 10221 J, which is appropriate for biological and inorganic phases (Israelachvili, 1992; Leckband and Israelachvili, 2001; Vigeant et al., 2002), and defining “contact” as an effective separation between particles of ~0.2 (for one hydration layer) to 2 nm, according to Israelachvili (1992) and Leckband and Israelachvili (2001). Only the magnitudes of the forces are shown. By convention, attractive forces (shown here) are negative. For reference, the three diamond symbols represent gravitational forces between the Earth (~1024 kg) and each of three bodies (from left to right): a bacterium (10215 kg), a human (50 kg), or the moon (1022 kg).

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expected to have an attractive, adhesion force at contact (due solely to the van der Waals force) equal to , 3 £ 1025 N (Hamaker constant ¼ 10220 J; effective separation ¼ 0.165 nm, i.e., the “universal” cut-off separation, Israelachvili, 1992). This force magnitude is approximately the same as the gravitational force between the Earth’s surface and one of these 1 mm particles. While it is debatable whether the van der Waals force applies in the same manner to both a particle of the size of an atom and an object of the size of the moon, the predictions shown in Fig. 1 for objects smaller than , 1 cm are in agreement with others (e.g., Israelachvili, 1992). Consequently, the force of gravity may dominate the interactions between macroscopic bodies (e.g., plants and animals), but intermolecular forces (e.g., van der Waals and others, see below) are the prevailing forces with which microscopic bacteria must contend. This is particularly true when one considers that the van der Waals force is significantly weaker and shorter range than other intermolecular forces, such as electrostatic and hydrophobic interactions as discussed below.

II. FUNDAMENTAL FORCES AT THE INTERFACE OF BIOLOGICAL PARTICLES AND INORGANIC SURFACES “All intermolecular forces are essentially electrostatic in origin” (page 11, Israelachvili, 1992). In theory, classical electrostatics could be used to calculate intermolecular forces if one could determine the spatial distribution of the electron cloud by solving the Schro¨dinger equation (Israelachvili, 1992). Unfortunately this is challenging for even simple atomic interactions in vacuum, never mind molecular or organism scale interactions between different functional groups on bacteria and minerals in water. For this reason, it is useful to classify four types of intermolecular forces that are expected to dominate the bacterium – mineral and biomolecule –mineral interfaces. These include the van der Waals force, electrostatic forces, solvation interactions, and steric or entropic forces (Israelachvili and McGuiggan, 1988). The reader is referred to a number of excellent reviews on these types of forces (e.g., Israelachvili and McGuiggan, 1988; Butt et al., 1995; Leckband and Israelachvili, 2001). This review will touch on all four types of intermolecular forces, although the van der Waals and electrostatic forces will be explored in more detail.

A. THE

VAN DER

WAALS FORCE

The van der Waals force, like the force of gravity, acts between all particles (Israelachvili, 1992). It is quantum mechanical in origin and arises because of

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the time dependent fluctuations in the electric dipole moment of a particle as it comes into contact with other particles nearby. Even nonpolar particles, which have a time averaged dipole moment of zero, have instantaneous dipoles due to the movement of electrons relative to protons in a nucleus. Dipoles generate an electric field that polarizes adjacent particles and gives rise to an instantaneous force between neighboring particles. Two terms describe the van der Waals force: the first polarization potential, which represents the energy necessary to ionize an atom (i.e., a dipole moment due to interactions between electrons and protons within a single particle); and the so-called dispersion term, which describes the dipole induced interactions between two or more atoms (Israelachvili, 1992). Because the dispersion term dominates the van der Waals force, it is sometimes referred to as (London) dispersion forces (Butt et al., 1995). The van der Waals force has an inverse power law dependence on the separation between two particles. For atoms and small molecules the van der Waals force is , D 27, where D is the separation distance between particles. It can be attractive or repulsive (e.g., it is always attractive between two similar particles immersed in a third liquid) and is described in terms of the Hamaker constant (Ha), which depends upon the refractive indices and dielectric constants of the interacting particles and intervening media (see Israelachvili, 1992). Hamaker constants are in the order of 10220 to 10221 J, for biological cells or molecules interacting with themselves or minerals across an aqueous solution (Ducker et al., 1991; Butt et al., 1995; Ong et al., 1999; Bhattacharjee et al., 2000; Leckband and Israelachvili, 2001; Vigeant et al., 2002). For simple geometries, the forces between atoms or molecules can be assumed to be additive (Israelachvili, 1992; Butt et al., 1995) such that equations can be derived for larger particles (e.g., organic and inorganic surfaces). Two commonly encountered geometric configurations include interactions between two spheres or a sphere and a flat surface, both of which are given by Israelachvili (1992), Butt et al. (1995) and Leckband and Israelachvili (2001): FðDÞ ¼

2Ha Rx 6D2

ð1Þ

where Ha is the Hamaker constant (J), D is the separation distance (m) between the two spheres or a sphere and a plane, and Rx (m) equals the radius of the sphere for the sphere– plane configuration, or it is equal to (R1R2/(R1þ R2)) for the interaction between two spheres of radius R1 and R2. A positive Hamaker constant indicates attraction (negative force sign).

B. THE ELECTROSTATIC FORCE The electrostatic force arises through a variety of mechanisms leading to the development of surface charge (e.g., see Sposito, 1989). Water, which has a high

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T. A. KENDALL AND S. K. LOWER

dielectric constant, causes the dissociation of surface functional groups. These functional groups display protonation/deprotonation reactions that are dependent upon pH. For example, in water, silanol groups on a silica surface undergo the following reaction: . Si OH ¼ . Si O2 þ Hþ. Similar acid – base reactions take place on carboxylic groups, amine groups, and other reactive moieties on biological molecules and inorganic surfaces. Hence, many inorganic and biological surfaces develop a charge that is dependent upon pH. Other factors such as the adsorption of charged ions and presence of permanent structural charge (e.g., for clays) are additional contributors to surface charge. The overall charge on a surface is balanced by the dissolved counterions in solution, which are attracted to the surface by its electric field and dispersed such that they (i.e., the counterions) increase entropy (Butt et al., 1995). This creates the so-called electric double-layer around surfaces immersed in aqueous solution (Stumm, 1992). When two charged surfaces approach one another, the electric double-layers are perturbed resulting in an electrostatic interaction. This interaction may be attractive (if surfaces are of opposite charge) or repulsive (if surfaces are similarly charged). The electrostatic force varies exponentially with the distance between particles. It depends strongly upon the surface charge densities of the interacting particles and the ionic strength of the intervening solution. Similar to the van der Waals force (see above) equations can be derived to describe the electrostatic force for various geometric configurations. The model for electrostatic forces between two spheres or a sphere and flat surface is (Butt et al., 1995; Muller and Engel, 1997; Leckband and Israelachvili, 2001): FðDÞ ¼

4ps1 s2 Rx 2kD e 110 k

ð2Þ

where s is the surface charge density (C m22) of particles 1 and 2, 1 is the dielectric constant of water (78.54 at 298 K), 10 is the permittivity of free space (8.854 £ 10212 C2 J21 m21), Rx and D are defined as above. The Debye length (1/k) describes the thickness of the diffuse double-layer of counterions that surrounds charged particles in solution. The Debye length depends upon the valence and concentration (c, mol L21) of the electrolyte. For monovalent electrolytes (e.g., NaCl) at a temperature of 298 K, the Debye length (in nm) ¼ 0.304/ (c)1/2; for 1:2 or 2:1 electrolytes (e.g., CaCl2) it is 0.174/(c)1/2; for 2:2 electrolytes it is 0.152/(c)1/2 (Muller and Engel, 1997). In many instances, it is easier to determine a particle’s surface potential as opposed to surface charge. The Graham equation can be used to relate these two parameters according to (Stumm, 1992),   qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi z cF ð3Þ s ¼ 8RT110 c £ 103 £ sinh 2RT where R is the gas constant (8.314 J mol21 K21), T is the temperature (K), z is the valence of ions in solution, c is the surface potential (V), and F is the Faraday constant (96,490 C mol21). A potential measured across an interface

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7

contains contributions from at least two layers, the so-called Stern layer and the “diffuse” layer (see Stumm, 1992). Techniques such as streaming potential and electrophoresis are commonly used to determine a particle’s zeta potential, which is used as a proxy for surface potential. However, the zeta potential probably represents only the “diffuse” double-layer, which is lower than the true surface potential (Stumm, 1992). Leckband and Israelachvili (2001) describe the differences for surfaces that are assumed to have a constant surface charge versus those that are assumed to have a constant surface potential. Interactions at constant surface charge are expected to occur when surface ionizable groups are fully dissociated and remain as such for all separations (D). This may be true when the pH of a solution is much greater than the pK value(s) of a particular protonation/deprotonation reaction(s). In instances where surface functional groups are not fully ionized but in equilibrium with solution ions, interactions at constant potential are expected to occur. In this latter case, as two surfaces come together (i.e., very small D) the intervening concentration of solution ions increases locally such that some solution ions bind to the surface thereby reducing that surface’s density of charged sites (Leckband and Israelachvili, 2001). For many instances, this distinction influences the interaction only at small separations where these two conditions define the boundaries of the expected electrostatic force.

C. THE SOLVATION FORCE The origin, theory, and force – distance relationships of the remaining two force classes — solvation and steric — are indefinite compared to the forces discussed above. Much work remains to be done before solvation and steric forces can be appreciated to the same extent as the van der Waals and electrostatic forces. However, it is well established that the models developed for the van der Waals and electrostatic forces, which treat the intervening solution as a continuum, break down when two particles or surfaces are within a few nanometers (Butt et al., 1995; Leckband and Israelachvili, 2001). At such close separations, solvation forces may dominate because the solvent (e.g., water) takes on a more ordered structure. Steric forces may also come into play for surfaces with polymers (e.g., biological cells or particles). Our discussion of solvation and steric forces will be more qualitative however, because general force laws (such as those described above) are relatively sparse for these latter two force classes. Solvation forces (also called hydration or structural forces when the solvent is water) seem to be the result of interactions of solvent molecules with themselves (e.g., in a confined space between two surfaces) or interactions between solvent molecules and a surface (e.g., the orientation of water molecules at the interface of a strongly hydrophilic surface). As two surfaces approach one another the

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intervening liquid ceases to behave as a structureless media resulting in a force that can be attractive, repulsive, or oscillatory (Butt et al., 1995). These forces can be further subdivided into those that result from solvent –solvent, solvent – surface, and surface – surface interactions (Israelachvili and McGuiggan, 1988). For two rigid crystalline surfaces at short-range (, 2 nm for water), water molecules interact with themselves such that they take on a semi-discrete layering or structure, which causes the “structural” forces between the interacting surfaces to oscillate between attraction and repulsion with a periodicity equal to the molecular dimension of water (Leckband and Israelachvili, 2001). Between surfaces with polymers, water cannot form well-defined layers because headgroups on lipids, for example, are “rough” on the scale of a water molecule (Israelachvili, 1992), and macromolecules in surfaces are thermally mobile (Beveridge, 1999). Consequently, any repulsion is smeared out and takes on a monotonic component (Israelachvili, 1992). For strongly hydrophilic surfaces in aqueous solution, there is a strong solvent –surface interaction that leads to the formation of hydration shells. These ordered water molecules within the “shell” generate an electric field that impinges upon another surface as two particles approach to within a few nanometers of one another (Israelachvili and McGuiggan, 1988). For example, water molecules may associate with two, adjacent hydrophillic surfaces such that the water’s hydrogens are oriented towards each surface (attracted via hydrogen bonds) and the water oxygens are exposed to the solution. This confers a negative character (from the lone pairs of the water’s oxygens) to each surface, thereby generating a repulsive force. Conversely, the dipoles may complement one another forming an attractive force if water molecules are staggered on the two surfaces. This hydration force may extend outwards more than the oscillatory force discussed previously (Leckband and Israelachvili, 2001). Finally, for nonpolar surfaces that cannot bind to water molecules — the so-called hydrophobic surfaces (defined as those surfaces having a contact angle of 75– 1158 with water) — there is often a strong attractive force that extends to separations of tens of nanometers or greater (Leckband and Israelachvili, 2001). Hydrophobic forces can be significantly greater than the van der Waals force and may play an important role in interactions involving hydrophobic molecules and/or surfaces (Israelachvili and McGuiggan, 1988; Israelachvili, 1992).

D. THE STERIC FORCE

AND

BRIDGING POLYMERS

The steric force affects surfaces that have flexible polymers extending out into solution (e.g., polysaccharides on biological cells). As two surfaces approach one another, the polymer chains become confined such that they are not free to move at random. This entropic confinement results in a repulsive force whose length scale is approximately equal to the radius of gyration of the polymer

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9

(Butt et al., 1995), where the radius of gyration is proportional to the number of monomer segments raised to some power between 0.33 (for poor solvents) and 0.6 (for good solvents) (Leckband and Israelachvili, 2001). Approximations derived for the interaction between two flat surfaces reveal that this force depends on the surface coverage of the polymer and may take an exponential form (Israelachvili, 1992; Leckband and Israelachvili, 2001). At close separation, the magnitude of the steric force can be similar to that of the electrostatic force (Leckband and Israelachvili, 2001). In some instances, surface-bound polymers may form an attractive interaction at close separation as the polymer forms a “bridge” between two particles or surfaces (Leckband and Israelachvili, 2001). The resulting adhesive bond may be very long range (i.e., extend well beyond the radius of gyration of the polymer) and resist separation when the surfaces are pulled apart (Jeppesen et al., 2001). While there is no general description for attractive bridging forces by polymers, the linkage of surfaces via a polymeric tether has been described by the so-called freely jointed chain (see e.g., Leckband and Israelachvili, 2001), or worm-like chain models (see e.g., Flory, 1989; Bustamante et al., 1994). In the case of the latter, the polymer is viewed as an elastic element and the force (F) needed to stretch the tethered polymer to a length x is: FðxÞ ¼ ðkB T=bÞ½0:25ð1 2 x=LÞ22 2 0:25 þ x=L

ð4Þ

where kB is the Boltzmann’s constant, T is the temperature, b is the persistence length (i.e., length of the stiff segment or monomer of the chain), and L is the contour length (i.e., length of the completely stretched chain). Polymer bridging is a phenomenon that crosses between the disciplines of colloidal science — which has historically tended to investigate intermolecular forces that dominate the interface between two rigid surfaces that are approaching one another — and adhesion science — which is interested in describing the contact between two surfaces and the forces necessary to pull them apart. While attractive intermolecular and intersurface forces (i.e., the four force classes discussed above) are responsible for adhesion events, real particles (e.g., bacteria and minerals) that make contact will also adhere to one another due to elastic or fluid-like deformation, which is an intrinsic and natural part of contact. There is a wealth of information on adhesion processes and theories including the Johnson – Kendall –Roberts (Johnson et al., 1971) and Derjaguin – Muller – Toporov (Derjaguin et al., 1975) theories, which relate the force required to pull two surfaces apart (i.e., the “pull off” force) to the surface energy, surface tension, or work of adhesion. Suffice it to say that surface energy (or tension or work) is determined from intermolecular forces between surfaces. For particles or surfaces that are incapable of forming hydrogen bonding (e.g., nonmetallic compounds), the surface energy can be related directly to the van der Waals force, where surface tension < Ha/2.1 £ 10221 (Israelachvili, 1992). The surface

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T. A. KENDALL AND S. K. LOWER

energies of more polar surfaces, which tend to be larger, are dependent upon van der Waals interactions, as well as an additional electrostatic-like term that relates surface energy to Lewis acid/base reactions (van Oss, 1993). These four forces — van der Waals, electrostatic, solvation (hydration and hydrophobic), and steric — operate concurrently at the interfaces between microorganisms, biological molecules, and/or mineral surfaces (see Table 1). In some instances, one force may dominate at all separations. In other instances, there is a delicate balance such that each force dominates at its own length scale. These four force classes are often invoked to describe interactions as two surfaces approach one another. Two particles that are pulled apart may experience the same sign, magnitude, and range of forces that existed upon approach. However, there is often a notable hysteresis between the forces measured upon approach versus those that are observed upon retraction for soft biological particles and surfaces. This is due to the formation of adhesive bonds (e.g., see discussion of polymer bridges and adhesion, above) once contact has been established between surfaces. This review will provide examples that illustrate the various forces and force models discussed above as they pertain to interactions between biological and inorganic particles. Further, we will discuss the differences between those forces measured as surfaces

Table I Summary of Physical Forces of Interaction Between Particles and/or Surfacesp Type of interaction van der Waals Electrostatic

Solvation

Steric

Description Force between all particles due to polarization; usually attractive; short-range Force between charged particles; attractive (for particles of opposite sign) or repulsive (for particles of similar sign); depends upon ionic strength of solution; short to long range Structural or hydration forces are typically repulsive due to sorbed water layers; short-range Hydrophobic force is attractive between nonpolar surfaces; short to long range Typically a short-range, repulsive force associated with polymers; may be longer range, attractive force for “bridging” polymers.

p This review has followed the force characterization of Israelachvili (1988). Other force classes, such as hydrogen bonding or thermal fluctuations, may dominate when two particles or surfaces are very close. However, these other classes can often be described as a subset of electrostatic (for hydrogen bonding) or steric (for thermal fluctuations) interactions, according to Leckband and Israelachvili (2001). So-called specific interactions (e.g., ligand–receptor interactions) are typically a result of unique combinations of these four “non-specific” physical forces (Israelachvili, 1992).

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come together relative to surfaces that are pulled apart. As a final point to this section, it should be noted that a force of interaction is related to energy (E) according to F ¼ 2 dE/dD.

III. FORCE CURVE THEORY AND COLLECTING FORCE DATA Force measurements attempt to capture interactions representing the electrostatic interplay between single molecules and atoms that are bound to a solid surface or exist as components of a solvated environment. Given the extraordinarily small dimensional (nanometer to angstrom) scale over which they operate, many challenges exist in capturing molecular level forces. This section reviews how force microscopy (or atomic force microscopy, AFM; also know as scanning probe microscopy) addresses these challenges, describes its operation and assesses how accurately the interactions are captured. Highlighted are some of the basic assumptions associated with force microscopy, while noting some of its advantages and limitations. An AFM force probe consists of a tip attached to a flexible cantilever, which is modeled mechanically as a single harmonic oscillator. Forces exerted on the tip are registered as a spring-like deflection in the cantilever, which may be recorded with various detection systems, including electron tunneling (Binnig et al., 1986), interferomotery (Erlandsson et al., 1988; Rugar et al., 1989) and capacitance (Goddenhenrich et al., 1990). The following summarizes an optical lever collection system (Meyer and Amer, 1990) that is most commonly found in commercially available AFMs, including the widely used Veeco/Digital Instrument system. Here the deflection is typically recorded as a change in voltage resulting from the displacement of a laser spot that is reflected off the top of the cantilever and into a photodiode. Voltage (V) is translated into cantilever deflection (nm) using a detector sensitivity value (V nm21) that is equal to the slope of the line when the tip and sample are in contact (see region of contact in Fig. 2). Provided the sample stiffness is significantly higher than the cantilever (which is the case when probing mineral surfaces), there should be a 1:1 correlation between piezo movement and cantilever deflection once the (V to nm) conversion is made. Small deviations from an absolute slope equal to one may be an indication of detector drift, and can be corrected by dividing the deflection values by the slope (H. Skulason, personal communication). A slope less than one may also be an indication of a sample compliance that is less than the cantilever (which may be the case when making measurements on a cell), in which case alternative sensitivity determinations, such as the photodiode shift voltage method may be employed (D’Costa and Hoh, 1995; Lower et al., 2001b).

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Figure 2 A typical force versus piezo movement plot showing three general regions — contact, interaction, and no contact. For clarity a single trace is shown (e.g. an approach curve); however, force plots with both approach and retraction traces are also common. In the region of no contact the tip and sample are separated at distances large enough that no interaction occurs. Hysteresis between the approach and retraction curve in the region of no contact may be a function of solution viscosity, or inelastic deformation of the cantilever. As the piezo advances the sample closer, the tip begins to “feel” the surface. In the example plot we see an initial repulsion followed by an attraction recorded as a sharp jump to contact that generates a minimum in the curve. Once in contact, the slope trace is typically constant as the cantilever is moving with the piezo. Information from this region may be used to determine detector sensitivity or elastic properties of the sample or tip.

Hooke’s law, F ¼ 2 ksd, then allows conversion of cantilever deflection, d, into force, F, using the spring constant of the cantilever, ks. Note that sign convention dictates that negative forces reflect attractive interactions and positive forces are repulsive.

A. FORCE – DISTANCE CURVES, CAPTURING A POTENTIAL FORCE VERSUS SEPARATION PLOT Figure 2 shows a typical plot of force versus piezo movement. Note the x-axis represents relative piezo movement or an indexing of a sample’s position relative to the cantilever (tip). It does not reflect tip-sample separation (discussed below). Three main components of the plot are identified: the regions of no contact, interaction and contact. Several sub-features are contained within each region including oscillations, subtle slope changes, linear and non-linear extensions, jumps to and from contact (Ducker et al., 1992; Cappella and Dietler, 1999; Gergely et al., 2001), which, in addition to providing reference points to register the force curve to an origin (discussed below), contain valuable information on the interaction between the tip and the surface, the nature of the intervening solution, tribology, adhesion, and elastic properties of the system.

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The focus is now turned to the region of interaction, which is of primary interest when studying intermolecular forces at cell or biomolecule –mineral interfaces. Here a wealth of information on the charge character of a mineral surface or biomolecule; the nature and contour length of a polymer extending from a bacterium; DLVO forces (see below) and Debye lengths associated with a colloidal particle or cell; and the energy landscape and activation barriers of a bond are found. But first, to draw both qualitative and quantitative conclusions from forces of interaction, it is imperative to have an understanding of the mechanical constraints of what is recorded in this region using force microscopy using an AFM. To illustrate this we show a simple, short-range interaction potential for atomic scale particles (note, Part II concentrated mainly on larger particles and/or surfaces) described by the Lennard – Jones equation: EðDÞ ¼ 2A=D6 þ B=D12

ð5Þ

Energy, E, has an inverse power law dependence on distance, D, with the 2 1/D 6 term representing the attractive component of the van der Waals force. The absolute value of this term is maximized at a distance De where the fluctuations in charge density coincide to result in a potential well. At separations less than De, the potential rises rapidly with distance, 1/D 12, as the interaction is repulsive in nature due to electronic overlap and nuclear interaction (Israelachvili, 1992; Cygan, 2001). Force microscopy (or AFM), however, does not record energy values directly, but instead measures force. To compare the Lennard– Jones potential with an AFM data set, we take its derivative, such that graphing the relationship dE=dD ¼ FðDÞ ¼ 26A=D7 þ 12B=D13

ð6Þ

produces a theoretical force –separation distance curve similar to the one in Fig. 3a. To further facilitate comparison with the theoretical curve, an origin is defined for the force microscopy data set as follows. A force equal to zero can be defined as the average force value within the region of no contact, while the point at which the tip and sample come into (for approach) and out (for retraction) of contact can be defined as the zero point on the x-axis. Determining the point of contact is clear when a distinct attractive or adhesive component (e.g., a jump to contact) is present, but ambiguous when such features are absent. In the latter case, the intersection between the slope of the region of no contact and constant compliance can be used as a guide (Cappella and Dietler, 1999). In a final important step, the xaxis in the force microscopy data set is adjusted to reflect tip sample separation instead of piezo movement, by adding the cantilever deflection values to the piezo movement distances (Ducker et al., 1992; Butt et al., 1995). Here the selection of the sign convention for the forces becomes intuitive. Addition of positive repulsive deflections to the piezo movement results in larger tip-sample separation, while adding negative attractive deflections result in a decrease in the separation. Unlike the Lennard – Jones curve, note that the values in Fig. 3b, left of the point of contact

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T. A. KENDALL AND S. K. LOWER

are essentially meaningless in terms of interaction force because the tip and sample are in direct contact. The end result is a force versus tip-sample separation plot with a region of interaction that can be compared to the theoretical curve (see Fig. 3a and b; also see Section V, below). Two main differences exist between the force microscopy data and the potential: (1) the slope of the attractive component of each curve, and (2) the hysteresis that exists between the approach and retraction forces in the force microscopy plot. With the force microscope, it is not uncommon to record

Figure 3 (a) Differentiated Lennard– Jones potential provided as an example interaction to be captured with force microscopy or AFM. During AFM operation the forces associated with the potential are recorded as deflections in the cantilever. If the force gradient (tangent to the solid trace) exceeds the spring constant, ks, the cantilever becomes mechanically unstable and will jump along a slope equal to kg (dashed line). (b) Force– tip sample separation curve showing jumps to and from contact along slope ¼ kg. Unlike Figure 2, the x-axis represents separation distance between the tip and the sample. Here, both the approach (open circles) and the retraction (closed circles) traces are shown. Note that the hysteresis between the two traces is absent in the Lennard–Jones curve where the solid line represents both approach and retraction forces. Points to the left of zero separation (i.e., lowest most point on the approach or retraction curves) represent movement of the piezo while the tip and sample are in contact. (c) Increasing the spring constant (e.g., using a stiffer cantilever) from ksA to ksB will capture more of the potential (region A2 –B2), however force resolution is lost and smaller magnitude forces will go undetected.

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an attractive force as a characteristic jump to contact on approach. These “jumps” represent mechanical instability in the cantilever due to a force gradient that exceeds its spring constant, ks. Clearly, interaction information is lost as the cantilever encounters a force gradient (tangent to the theoretical curve) at point A1 that is greater than its stiffness and consequently jumps to point A2 along a slope equal to ksA (see Fig. 3c). More of the attractive potential can be sampled with a stiffer cantilever (e.g., ksB), however, force resolution is lost, and the region along the theoretical curve between B2 and A1/B1 remains unsampled. A similar situation may be encountered upon retraction, which, in part, contributes to the hysteresis observed in the force data. Specifically, cantilevers with smaller spring constants generate larger amounts of hysteresis. However, hysteresis between approach and retraction curves is also due to the formation of adhesive bonds once surfaces are in contact. This is common for soft samples such as biological cells (see below). For some investigations excessive hysteresis is undesirable and several techniques have been developed to reduce it thereby recovering the “lost” information (i.e., region A2 – B2). These methods employ an opposing force that is external to the system in an attempt to increase the effective stiffness of the cantilever, while retaining force resolution. Electrostatic force (Joyce and Houston, 1991), magnetic feedback (Jarvis et al., 1996; Yamamoto et al., 1997; Jarvis et al., 1998; Ashby et al., 2000) and radiation pressure from a laser (Aoki et al., 1997; Tokunaga et al., 1997) have all been used to supply the steadying force to the cantilever.

B. HYSTERESIS Certainly other sources besides the instability of the cantilever contribute to approach –retraction hysteresis. In the theoretical Lennard –Jones relationship given as a potential example, no adhesive reaction between the tip and sample is modeled and the retraction curve retraces the approach curve (Fig. 3a). However, this is not an appropriate model for soft biological cells, which have biopolymers, designed over millions of years of evolutionary selection, for the express purpose of adhesion. When making force microscopy measurements, the tip comes into contact with the surface allowing for reaction and deformation between the two. The bonds and coordinations that result can then be explored and characterized using the associated adhesion forces and approach-retraction hysteresis (Burnham et al., 1990; Cappella et al., 1997). In some systems, the number of bonds that form (and, thus the level of hysteresis) is correlated with the amount of pressure that is applied on the sample by the tip (Weisenhorn et al., 1992). Specifically, increased pressure leads to sample and tip deformation resulting in increased contact area (Israelachvili, 1992; Cappella et al., 1997), and in the case of functionalized tips (e.g., those coated with self-assembling organic monolayers), a possible rearrangement of functional groups terminating the monolayer.

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Both phenomena facilitate additional bonding, larger hysteresis and higher adhesion values, as documented by several workers (Weisenhorn et al., 1992; Hutter and Bechhoefer, 1993; Toikka et al., 1996; Ashby et al., 2000). Therefore, the amount of indentation must be carefully documented to facilitate the comparison of adhesion data from one study to another. One way of controlling the amount of indentation using commercially available AFMs is by adjusting the scan start position, setpoint or the trigger settings. Varying these parameters can be especially useful when probing many biological systems, where pressure and/ or contact time may be a natural mechanism of inducing adhesion (e.g., see Leckband and Israelachvili, 2001; Lower et al., 2001a).

C. TIP SHAPE Tip shape is a critical AFM parameter that can dictate the force values and contact geometry between the sample and force probe (Hartmann, 1991; Butt et al., 1995). Constraining this value is essential if experimental force traces are going to be compared theoretical models such as “DLVO” (see Section V below). Yet, tip shape can be difficult to determine, in part due to the surface roughness, irregularities and asperities that are associated with traditional silicon or silicon nitride tips (Cappella et al., 1997). Moreover, tip shape can change over time as continued use promotes wear (Cappella et al., 1997). Solutions to this problem include careful, periodic characterization of the tip with electron microscopy, better constraint of tip geometry by attaching a spherical colloidal probe (Ducker et al., 1991; Butt et al., 1995), or, as described in more recent work, by attaching a carbon nanotube (Wong et al., 1998a; Hafner et al., 1999; Cheung et al., 2000).

D. SPRING CONSTANT DETERMINATION If a quantitative analysis of absolute force values is desired, determination of the spring constant (ks) is critical and nominal values provided by the manufacturer generally cannot be relied upon (Lower et al., 2001b). Many factors affect the spring constant including primary characteristics such as cantilever dimensions, geometry and substrate material; as well as, additional modifications common in force spectroscopy such as gold coating, the addition of organic monolayers, the attachment of colloidal spheres or cells, and even ion adsorption (Sader et al., 1995; Craig and Neto, 2001; Cherian and Thundat, 2002). As a result, a large body of literature detailing several methods of directly determining ks exists. A procedure commonly used because of its simplicity, non-destructive nature, and applicability to common cantilever geometries (e.g., V-shaped, rectangular) is provided by Cleveland et al. (1993). This method

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derives the spring constant by measuring changes in the cantilever’s resonance frequency after small masses (e.g. W microspheres) are loaded onto the end of the tip. The Cleveland method is further optimized when corrected for errors introduced by off-end loading of the mass (Sader et al., 1995). More recent methods measure hydrodynamic drag of the cantilever through a fluid of known viscosity to determine ks for bare rectangular cantilevers (Sader, 1998; Sader et al., 1999; Maeda and Senden, 2000). This concept has also been applied to determine ks for cantilevers activated with a colloidal probe (i.e., a silica or polystyrene microsphere) (Craig and Neto, 2001). In the latter method, it is useful to directly measure ks for a cantilever with an attached sphere because it accounts for changes in the spring constant due to the shifts in the point of load associated with the position of the colloid sphere and the change in the stiffness associated with the adhesive used for microsphere attachment. Other methods measure ks using thermal oscillations and the equipartition theorem (Hutter and Bechhoefer, 1993; Butt and Jaschke, 1995), a finite element analysis of the static deflection of a cantilever for which the Young’s modulus is known (Sader and White, 1993); unloaded resonant frequency of a cantilever of known mass (which is commonly not the case) (Sader et al., 1995), radiation pressure from an acoustic transducer (Degertekin et al., 2001); microscopic and macroscopic reference cantilevers of known stiffness (Rabinovich and Yoon, 1994; Torii et al., 1996; Jericho, 2002); and the change in resonant frequency due to gold coating (Gibson et al., 2001).

E. ARTIFACTS

IN

FORCE MEASUREMENTS

Several artifacts can arise during force measurements with the AFM. The inverse path effect represented as an upward, hysteretic shift in the retraction trace in the region of contact arises from nonlinearities of the piezoelectric actuator that positions the sample (or tip) (Cappella et al., 1997; Cappella and Dietler, 1999; Heinz and Hoh, 1999). A shift in the contact portion of the retraction trace such that it is parallel with the extension trace reflects friction as the tip plows or slides along the surface (Heinz and Hoh, 1999). A sinusoidal oscillation in the region of no contact may also be present, representing the interference of stray laser light bouncing off the sample and interfering with the laser light reflected off the top of the cantilever (Weisenhorn et al., 1992; Cappella et al., 1997). This oscillation can be distinguished from other artifacts, such as noise due to mechanical vibrations, because its wavelength should roughly be equal to , l/2n, where l is the wavelength of the laser source and n is the index of refraction of the fluid between the tip and sample (Weisenhorn et al., 1992; Craig and Neto, 2001). Thermal oscillation in the region of contact can be recognized by deflection fluctuations whose standard deviation is roughly equal to (kskBT)0.5; ks, the spring constant; kB, Boltzmann’s constant and T, temperature (Gergely et al., 2001). Operational artifacts may include a large slope that is

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present in the region of no contact, common when making measurements in a fluid cell. The origin of this slope is unclear but can often be remedied by eliminating air bubbles in the system or insuring a flat, even orientation of the gasket used to seal the system. Large plateaus at the extremities of both traces often represent a saturation of the detector, requiring an adjustment of the scale of the plot, the deflection limit of the detector or the starting position of the scan.

F. DATA PROCESSING

AND

STATISTICS

With standard AFMs, the one-click ease with which a single force curve is collected allows hundreds of curves to be recorded at a sample point in only a few minutes. Considering the fact that a typical curve can contain 2048 data points, a single experiment can produce an enormous volume of data. Further, the variability between force curves collected at a single location can often be quite high. This raises several questions regarding data processing and interpretation that are often neglected. What is the most efficient way to process these data? What is the minimum number of curves necessary to characterize each sample point or a particular interaction? What level of error and variability is associated with the force measurements? How is force data distributed about its mean? What measurable parameters or features of a force curve are the most important in characterizing the interactions (e.g., adhesion force)? What is the best way to identify trends or correlations in these parameters? Clearly, answering these questions requires statistical techniques, tests and models that determine appropriate, significant average values of force curve parameters and facilitate the identification of meaningful force curve features. This process begins by collecting summary statistics for each data set, including calculation of means, standard deviations, error values (e.g. confidence limits) and by plotting histograms for multiple parameters derived from the curves, including adhesion force and distance of jump to contact. When comparing parameters from the curves, statistical tests (e.g. ANOVA, t-tests; correlative tests) may be performed using a standard statistical and data processing package (e.g., Igo Pro, Wavemetrics, Inc.). Simple regression models may also be employed to determine important variables that contribute to the shape of a force curve. To this end, a routine has been written (Kendall and Hochella, 2003) that rapidly processes force curve data to produce plots of force versus piezo movement and force versus tip sample separation using the procedures discussed above. Automated parameter determination, statistical calculations, whole force curve averaging, autocorrelation calculations (for identifying quantized force values) and histogram generation are incorporated in this customized routine written using Igor Pro’s internal programming environment. The simple parameter extraction module quickly and consistently identifies features and selects the values using basic, objective criteria such as

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maxima thresholds in the differentiated force data and tolerance limits for specific changes in slope (see Fig. 4).

G. ADVANCED ALGORITHMS These criteria are appropriate when the bond ruptures and snaps to contact are large and/or distinct. However, other more advanced algorithms (Baumgartner et al., 2000; Kasas et al., 2000; Gergely et al., 2001) are required when features are small, numerous, less distinct (e.g., multiple ligand –receptor interactions) and/or have the potential to be masked by vibrational and thermal noise. For example Kasas et al. (2000) employ a fuzzy logic algorithm that enables automated discrimination of specific, significant adhesions in a retraction curve that might otherwise be overlooked. The routine assigns a grade to each potential rupture event, ranking it somewhere between non-specific (0) and specific (1). Assignment of the grade relies on a priori knowledge of the interaction event morphology, and uses criteria such as the angle between the jump and the background trace, or whether or not the jump is U-shaped or V-shaped. This means that the procedure is operationally defined and first has to be “taught” what the features of interest look like in order to calibrate it to the system/features being studied. Gergely et al. (2001) present an algorithm that identifies ruptures based on a comparison of the minima with neighboring peaks. Selection is controlled by adjusting an appropriate noise level, m, such that the difference between a feature and its nearest neighbors must be greater than 2m times the standard deviation of the force values. Additional smoothing of the force curve is also achieved by fitting a second order polynomial to a designated amount, p, of consecutive points. Using this routine, forces measured between human fibrinogen interacting with a silica surface were processed. By monitoring histograms of inter-rupture distances selected with successively more rigorous (higher) m values, the authors were able to detect a significant peak at 20 – 25 nm, a value that corresponds nicely to the known spacing between two domains in the protein.

H. RELATING BOND CHEMISTRY AND ENERGIES TO FORCE MEASUREMENTS Force microscopy measurements intuitively have the potential to describe energies, E(D), associated with an interaction at a small separation, D, by R integrating force over distance, E(D) ¼ F dD. As discussed above, however, differences in spring constants can produce variable hysteresis and, therefore, can lead to drastically different energy values. Without fine control of the effective spring constant, it is difficult to accurately capture a potential in a quantitative fashion, which is critical for single molecular work. Moreover, if reaction occurs

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21

upon contact (and provided single interactions can be identified in the force spectra); simple prediction of bond/interaction energies based on rupture forces is non-trivial. Specifically, it might be postulated that the maximum gradient in the potential, [dE/dD ]max, is equal to the adhesion or rupture force from the retraction trace; however, in a seminar paper, Evans and Ritchie (1997) showed that such a simple correlation is not valid for single molecule interactions, and more sophisticated theory is required for quantitative comment on the absolute energetics of a bond using force data. Before continuing, it must be noted that these findings do not preclude valuable quantitative and qualitative comparison of force measurements and bond ruptures to energy parameters. Indeed, early force experiments with various ligand receptors (e.g., biotin, iminobiotin, avidin, streptavidin combinations) revealed a correlation between the rupture forces and enthalpy values associated with each complex (Moy et al., 1994a). This information together with the lack of correlation between the rupture forces and total free energy suggested the unbinding was adiabatic and that any entropic contributions to the system (e.g., solvation forces) occurred outside of the binding pocket, and were not recorded with the AFM. Other studies followed, relating thermodynamic parameters to interaction forces (Chilkoti et al., 1995), as well as many force experiments that employed “elementary” or averaged rupture forces to compare two or more systems in a relative fashion (Florin et al., 1994; Frisbie et al., 1994; Dammer et al., 1996; Noy et al., 1997; Ito et al., 1999; Schmitt et al., 2000; Fiorini et al., 2001; Lower et al., 2001a; Kreller et al., 2002; Kendall and Hochella, 2003;). The true value of these studies is their relative quantitative and qualitative comparisons of force data. These characterize the nature of forces at an interface, demonstrate surface and molecule recognition, and define relative affinity between two molecules or between a molecule and a surface. However, Evans demonstrated that these rupture forces, as absolute values, represent one point in a continuum of bond strengths (Merkel et al., 1999); and that the detachment force recorded with the AFM (and other force measuring techniques) is not a singular fundamental

Figure 4 Screen shot of one module (Sensitivity Tweaks) in the force curve processing routine AFM 4.4 written in Igor Pro, 4.04, WaveMetrics, Inc. (Kendall and Hochella, 2003; some of the base code was provided by H. Skulason). It is designed primarily for handling force data produced Digital Instrument’s Nanoscope IIIa MultiMode system. The Sensitivity Tweaks module is designed to rapidly review and assess how well the normalizaiton routine automatically registers and normalizes force data to an origin. The normalization procedure includes the identification of a baseline in the region of no contact, calculating the detector sensitivity from the region of constant compliance and detecting when the tip and the sample are in contact. The latter is determined using peaks in the differentiated force wave that are selected based on threshold/sensitivity settings shown in the panel in the lower right. If initial normalization is unsatisfactory, these settings may be optimized and an autonormalization may be run again; or features can be identified manually.

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property of the molecular interaction being probed (Evans and Ritchie, 1997; Evans, 1998; Merkel et al., 1999). Instead, apparent bond strength as estimated by rupture force is a function of the loading rate (Evans and Ritchie, 1997). This relationship represents a refinement of a model proposed by Bell (1978) that predicts an exponential amplification of dissociation kinetics in the presence of an external force (Merkel et al., 1999). Dissociation of relatively weak associations can be conceptualized as a particle moving out of a potential well (bond), over single (simple interaction) or multiple (complex interaction) activation barriers representing transition states (Fig. 5). Under a zero force condition the particle will migrate out of the well, through the transition states, and ultimately to complete dissociation on a time scale that is dictated by thermal agitation (kBT). A constant external force on the bond, however, expedites the thermally mediated kinetics and decreases the lifetime of the bond by lowering the activation barriers in the energy landscape along a projection that is proportional to the amount of force (Fig. 5) (Evans and Ritchie, 1997). Under a dynamic load (e.g. a retracting cantilever) where the force, F, increases over time, t, as loading rate, Rf ¼ dF/dt ¼ ksvc (ks is the spring constant of the system and vc is the velocity of the cantilever), inner activation barriers are revealed as outer activation barriers are progressively lowered by the accumulating force. This

Figure 5 The effect of an external force on the energy landscape of a bond. (Modified from Evans and Ritchie, 1997; Merkel et al., 1999). The minimum of the traces represents a bond or potential well found along a reaction coordinate, x. Two activation barriers (local maxima) exist representing transition states that a system must go through during dissociation of the bond. External force, F, is represented as a mechanical potential, 2(F cos q)x oriented at an angle u to the reaction coordinate. Increasing force lowers outer activation barriers to reveal the inner maxima. Eventually all barriers are lowered allowing free diffusion from the initial minimum/bond (Evans and Ritchie, 1997).

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phenomenon leads to an intriguing positive correlation between the rupture force, Fr, and loge(Rf), that is best conceptualized in terms of thermally mediated nature of the bond rupture kinetics. At small loading rates, activation barriers are lowered at a rate slow enough for thermal contributions from the medium to be effective in helping the molecule diffuse out of the well and over the barrier before higher forces are reached (Gergely et al., 2002). Thus, with an effective thermal contribution, a lower rupture force is recorded at the time of dissociation. Under large loading rates (e.g., those typical of many AFM experiments), activation barriers are lowered fast enough such that dissociation proceeds with minimal thermal contribution, resulting in a higher rupture force (Gergely et al., 2002). At ultrafast loading rates on the time scale typical of molecular dynamic (MD) simulations (1012 s), the entire bonding potential is compromised quick enough that only frictional drag is recorded as the molecule traverses a completely “stretched” and coarsened energy landscape (Evans and Ritchie, 1997). Here, loading rates commonly exceed the time scale of unencumbered, diffusive passage of a molecule from its bonded state, leaving the complex kinetically trapped as force continues to rise. This was observed during molecular dynamic simulations of the biotin – streptavidin complex (Grubmuller et al., 1996). For the biotin system, the time for diffusive passage, tD (e.g., the lifetime of the bond) is estimated to be 500 ps under a constant force of 280 pN (Evans and Ritchie, 1997). All activation barriers are lowered at this force such that the initial minimum (e.g., the original potential well representing the bond) is exposed allowing direct diffusion out of the well. However, the ultrafast molecular dynamic simulation loading rate (1.3 £ 1012 pN s21) exceeds 280 pN/ tD, therefore, leaving the complex kinetically trapped in the bound state as rupture force rises well above 280 pN (Evans and Ritchie, 1997). With these observations and extensive theory development, Evans recognized that measurements of rupture forces over a large range of loading rates effectively probes the lifetime of the interaction under different levels of force while mapping out energy barrier position and heights in a technique now known as dynamic force spectroscopy (Evans, 1998). A dynamic force spectra is constructed by plotting the most probable rupture force of a single interaction, Fr, versus loge[Rf], where Rf values span several orders of magnitude. Regions of constant slope defined as fb ¼ kBT/xb represent activation barriers at a distance of xb from the potential well. Barrier heights Eb can be derived from the intercept of the slopes at zero force defined as (Evans and Ritchie, 1997): loge ðRof Þ ¼ 2Eb =kB T þ loge ð fb =tD Þ

ð7Þ

where, again tD is the time of diffusive passage, and thus 1/tD represents an attempt frequency. The attempt frequency is generally not known, but can be estimated from the damping phenomenon (Evans, 1998). Activation barrier positions derived from experimental dynamic force spectra of the biotin –avidin

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interaction compared well with barriers predicted by molecular dynamic simulations (Evans and Ritchie, 1997; Izrailev et al., 1997), further emphasizing the value of coupling force measurements with computer simulations. However, it must be noted that bridging the gap between the orders of magnitude in the loading rate of experimental systems versus loading rates used in computer simulations is not straightforward and extrapolation of calculated rupture forces to experimentally determined forces must be done with caution (Grubmuller et al., 1996; Izrailev et al., 1997; Wong et al., 1999). While this was attempted by Grubmuller (1996), Izrailev (1997) and Evans (1997, 1998) indicate that the real value of molecular dynamic simulations, in this context, is their potential to provide clues as to which structural determinants of the interactions contribute to the activation barriers, thereby providing a qualitative mechanism to account for dynamic force spectra features. For example, Izrailev et al. (1997) used a collection of molecular dynamic simulation packages to demonstrate a ground state avidin – biotin complex that was stabilized by hydrogen bonds between the biotin head group and polar amino acids (e.g., Tyr33) within the binding pocket. With the application of an external force, two intermediate states stabilized by H-bonds with amino acids at different positions in and near the pocket (e.g., Ser16 and Arg114) are revealed as the ligand is removed — an observation that was consistent with dynamic force spectroscopy experiments. Lo et al. (2002) also used a variation of the Bell model to explore the relationship between rupture force and ambient temperature in the biotin – avidin system. The experiments were conducted on an AFM with a constant, millisecond time scale loading rate that was slow enough, compared to the nano to picosecond time scale of molecular dynamic simulations, to neglect any frictional energy loss due to viscous drag. The slow loading rates also allowed the key assumption that thermal equilibrium is achieved at any moment during the unbinding process. This validated the use of a Maxwell –Boltzmann energy distribution to describe the thermal energy being supplied to the complex. The end result is a relationship (Lo et al., 2002):   t Fi2 ¼ 2DEC kbond 2 2kB Tkbond loge R ð8Þ tD that can be fitted to an experimental AFM data set of adhesion forces (Fi), to derive bond stiffness (kbond), and critical binding energy (DEC). Both the derived values reflect a summation of the different types of forces that make up the biotin – avidin interaction (e.g., H-bonds, van der Waals and polar interactions). Other variables include thermal energy (kBT), and the ratio of the rupture time (tR, determined from the AFM data) and time required for the ligand to diffuse out of the binding pocket (tD, estimated independently). Critical binding energy, DEC, may be related to a dissociation energy (De) by defining a potential to describe the interaction — in this case a Morse potential was used. Their De value based on force measurements, 28.4 kcal mol21, compared favorably with

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

25

the enthalpy change of the dissociation determined by independent means (DH ¼ 23.4 kcal/mol) (Swamy, 1995; Lo et al., 2002). Moreover, the enthalpy value was combined with bond stiffness in additional calculations to determine a critical displacement magnitude (0.1 nm) that was close to inner barrier position, xb, value determined with dynamic force spectroscopy (xb ¼ 0.12 nm). Both temperature dependent and load dependent dynamic force spectroscopy rely on force measurements of the interaction of a single ligand – receptor pair. This is accomplished by reducing the density of the sites that are present and available for bonding (similar to protocols outlined by Florin et al. (1994), Rief et al. (1997a, b), Marszalek et al. (1998) and Grandbois et al. (1999)), such that 1 in 7 –10 touches results in attachment (Evans, 1998). Governed by Poisson statistics (Evans and Ipsen, 1991; Williams et al., 1996), 90– 95% of the attachments are predicted to be single bonds. In addition to the biotin –avidin linkage, Poisson distributions are common in force measurements associated with several other systems (Han et al., 1995; Williams et al., 1996; Wenzler et al., 1997; Lo et al., 1999; Stevens et al., 1999). The probability, P(n), of an attachment representing n linkages follows a defined, Poisson distribution, making it possible to extract the n ¼ 1 case from a large number of rupture force measurements (Lo et al., 2002). Feedback mechanisms are also employed to control impingement on the sample thereby insuring each approach and retraction cycle has the same magnitude and history of contact force (Evans, 2001). This is especially important when making measurements with biomolecules secured to monolayers that can easily deform on contact to produce various contact areas and configurations, and ultimately different numbers of attachment (Evans, 2001). Although successful dynamic force spectroscopy experiments have been carried out on a single force measuring instrument (AFM) with a range of loading rates from 100 –5000 pN s21 (Yuan, 2000), due to the exponential relationship between kinetic rates and barrier energies (discussed above), dynamic force spectroscopy is optimized when collecting force measurements over a range of loading rates that are different by orders of magnitude. This can require the use of several force measuring techniques including laser/optical tweezers for slow loading rates 1– 10 pN s21, a biomembrane force probe (BFP) for intermediate rates (10 – 1000 pN s21) and AFM for fast loading rates (104 –106 pN s21) (Evans and Ritchie, 1997).

I. RELEVANCE OF DYNAMIC FORCE SPECTROSCOPY TO BIOLOGICAL – INORGANIC INTERFACE Techniques to extract energy information from force spectroscopy were developed primarily using the biotin –(strept)avidin system (Moy et al., 1994a; Evans and Ritchie, 1997) due to its relevance to biological systems, non-covalent nature, high affinity and extensive history of experimentation and study. Since

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then, dynamic force spectroscopy has been applied to other systems, primarily non-covalent and biological in nature. These include DNA base pair interactions (Strunz et al., 1999), unfolding of muscle protein domains (Rief et al., 1997a), antibody – antigen interactions (Schwesinger et al., 2000) and lipid anchoring in membranes (Evans and Ludwig, 2000) to name a few. Grandbois et al. (1999) also made an attempt at using the dynamic force spectroscopy concept to measure the strength of a single covalent bond. Although they produced values for only one loading rate due to the difficulty of collecting individual covalent interactions (H. Gaub, personal communication). Their calculation of rupture force probabilities based on dynamic force spectroscopy methods allowed them to identify the covalent attachment being terminated as a Si –C bond. Applying dynamic force spectroscopy concepts to AFM data collected on environmental systems has great potential to provide new insight on the interaction energetics and bond chemistry associated with biogeochemical interfaces. This is, in part, because experimental data collected at the molecular level to describe surface reactions between single biomolecules and mineral surfaces is lacking. Traditionally confined to computer simulation (Cygan, 2001), dynamic force spectroscopy now affords a unique, direct examination of energy landscapes associated with some of the non-covalent mechanisms (e.g., H-bonding) assumed to initiate sorption reactions between minerals and ligands (Holmen and Casey, 1996), the possible ionic or covalent binding of a metal in a mineral surface associated with dissolution (Stumm, 1992), reversible and non-reversible adhesion states of colloids or cells to a surface (Absolom et al., 1983; Ryan and Elimelech, 1996; Ryan and Gschwend, 1994), mineral and or metal recognition of a mineral structure by membrane bound proteins (Lower et al., 2001a). Perhaps, the true advantage of using dynamic force spectroscopy is realized when used in a comparative framework, for example, dynamic force spectra of cell or biomolecule– mineral interaction before and after structural and functional changes in either (1) the cell surface (e.g., via altered gene expression due to imposed environmental conditions) or the biomolecule (e.g., via point mutations in proteins, functional group substitutions/inactivation in ligands) or (2) the mineral via metal substitution or by comparing isostructural mineral equivalents or different crystal growth faces. Changes in slopes of the dynamic force spectra resulting from structural modifications can provide clues as to which proteins, functional groups or even crystallographic constraints contribute to surface complex stability or specific activation barriers to binding or detachment. Concomitant correlation of force values with independently determined thermodynamic parameters can also provide insight as to whether a surface attachment or detachment or metal extraction is enthalpy driven or entropically dominated. And as seen above, a common theme when using dynamic force spectroscopy is to supplement and validate characteristics of a particular energy landscape with mechanistic information derived from computer simulations.

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The role of these simulations is anticipated to be just as important when applying these techniques to biogeochemical systems.

IV. FORCES AT THE BIOMOLECULE–MINERAL INTERFACE Organic ligands produced by microorganisms such as low molecular weight organic acids and siderophores have the potential to greatly impact the geochemistry and ecology of soil environments (Stone, 1997; Hersman, 2000). These ligands interact with mineral surfaces to form a critical interface that has implications on biological nutrient/metals acquisition, control of metal toxicity or even ecological competition (Bossier et al., 1988; Stone, 1997; Neubauer et al., 2000; Brantley et al., 2001; Kraemer et al., 1999, 2002). Ligands enter into sorption and desorption reactions with minerals that enhance dissolution or surface passivation, mediate contaminant mobility, or alter the charge character of the mineral surface (Barker et al., 1997; Stumm, 1992). As a result this interface has been studied extensively with bulk experiments and sorption studies (e.g., Kummert and Stumm, 1980; Ludwig et al., 1995; Yao and Yeh, 1996), and with surface sensitive techniques such as XPS (Kalinowski et al., 2000), and Fourier Transform Infrared (FTIR) Spectroscopy (Hansen et al., 1995; Holmen et al., 1997). Key to sorption and desorption reactions between ligands and minerals, however, are the forces that bring the ligand into and out of contact with the surface. Such forces are dependent on the charge character, structure and reactivity of the ligand, the mineral surface and the intervening solution. Characterization of these forces using force microscopy holds great potential to complement information from the existing methodologies listed above in addition to providing new insight on how ligands interact and coordinate with mineral bound metals. In pioneering work, activation of an AFM tip with a specified chemistry was carried out to examine the biotin –avidin interaction (Florin et al., 1994; Lee et al., 1994b). Florin et al. (1994) sorbed biotin (an organic ligand) to an AFM tip, and probed a surface coated with the protein receptor avidin. Force measurements of this high affinity ligand – receptor system showed a positive correlation between the elementary quantized adhesion forces detected with an autocorrelation analysis and the thermodynamic binding affinities. Specifically, biotin adhesion to the avidin substrate measured 160 pN, while iminobiotin, which contains a nitrogen substitution in place of an oxygen and has a lower binding affinity, exhibited adhesions closer to 85 pN. Several force spectroscopy studies of the biotin system followed (Moy et al., 1994a, b; Chilkoti et al., 1995; Wong et al., 1999; Lo et al., 2002), along with other force investigations of biomolecules, including examination of: interactions

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T. A. KENDALL AND S. K. LOWER

between antibodies and antigens (Dammer et al., 1996; Hinterdorfer et al., 1996; Schwesinger et al., 2000); enzyme activity (Fiorini et al., 2001); proteoglycans (Dammer et al., 1995); observations on the stretching of polysaccharides (Rief et al., 1997b; Marszalek et al., 1998) and muscle proteins (Rief et al., 1997a); and the hybridization of oligonucleotides (Lee et al., 1994a; Mazzola et al., 1999). Simple functional groups have also been covalently attached to AFM tips in order to explore more fundamental interactions, such as the forces between methyl, carboxyl or methyl –carboxyl pairs (Frisbie et al., 1994; Noy et al., 1997). Specifically, this technique, termed chemical force microscopy (CFM), was used to identify the nature of the interacting force (H bond, van der Waals, electrostatic), characterize surface energies and charge distributions, and generate force maps that showed the spatial arrangement of simple functional groups or hydrophobic regions on a monolayer or surface, sometimes with nanometer resolution (Noy et al., 1997). Collectively, these studies provide a foundation, which allows the application of force spectroscopy to additional, more complex, natural systems, such as the ligand/biomolecule– mineral interface that is characteristic of soil environments. Indeed the same forces (e.g., H-bonding, hydrophobic/hydrophilic forces, the van der Waals force, steric forces, non-specific and specific interactions) that allow molecular recognition between biomolecules are also present in ligand mineral interaction (Israelachvili, 1992; Stumm, 1992). However, to our knowledge, only two studies, one of which is summarized below, have probed ligand interaction with a mineral surface using force microscopy (Kendall and Hochella, 2003; Kreller et al., 2002). A discussion of this burgeoning application begins with a description of protocols enabling linkage of a ligand to an AFM tip.

A. LIGAND LINKAGE SCHEMES Devising a suitable linkage scheme to attach the ligand of interest to the AFM probe can present a significant challenge. Each scheme should be appropriately tailored to the relevant experimental goal; however, the following summarizes general considerations. Successful linkage will provide a strong (e.g., covalent or stronger than the interaction of interest), reproducible bond between the ligand and the tip while avoiding non-specific interactions associated with the cantilever material, tip or linker molecule (Wagner, 1998; Fiorini et al., 2001). Simple ligands such as carboxylate and phosphate groups are commonly linked as terminations of alkylthiol monolayers that coat the tip (Noy et al., 1997; Kreller et al., 2002). The ampiphilic molecules of the monolayer not only provides an anchor for the ligand but also serves as a spacer, providing separation between the ligand and the tip material thereby reducing non-specific interactions. Larger ligands and proteins that contain either a free amino or carboxyl group may be attached using an active ester technique commonly used

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29

to couple two proteins (Cheung et al., 2000; Fiorini et al., 2001; Hinterdorfer et al., 2002; Kendall and Hochella, 2003). In the presence of a carboxyl group, 1-Ethyl-3-(3-Dimethylaminopropyl) carbodiimide (EDC) together with N-hydroyxsuccinimide (NHS) will form a stable, hydrolysis resistant, active succinimidyl ester that readily forms a peptide bond with an available amino group (Grabarek and Gergely, 1990). Note that the position of the amino and carboxyl groups can vary with one being supplied as a self-assembling monolayer (SAM) terminal group on the tip and the other contributed by the molecule to be attached, or vice versa. Other linkage protocols employ polyethylene glycol (PEG) as a crosslinking spacer that is terminated with various functional groups such as pyridildithiopropionate (PDP). PDP coordinates with thiol groups and nitrilotriacetic acid (NTA) which, in combination with various divalent metals, binds to consecutive histidine residues (Kienberger et al., 2000; Schmitt et al., 2000; Hinterdorfer et al., 2002). One advantage in using PEG-NTA-Me2þ-His linkage system is that selection of the divalent metal (Cu2þ, Co2þ, Ni2þ) permits control of the binding force, and, to a certain extent, the probability of the linkage. In addition, the NTA-Me2þ-His bond is easily reversible, such that it can be terminated with the use of EDTA, and then regenerated with the reintroduction of the free metal (Schmitt et al., 2000). Other workers propose attaching ligands or molecules via carbon nanotubes that extend from the AFM tip (Wong et al., 1998a, b, Hafner et al., 2001). This provides ideal spacing between the molecule and the tip, but more importantly, drastically increases the resolution of the force spectroscopy (and imaging) due to the nanotube’s extremely small radius of curvature compared to a traditional Si3N4 tip. Because nanotubes can only be functionalized at the end termination of the carbon lattice this also places an important constraint on the orientation and localization of the molecules being linked. As a result, the probability of capturing a single molecule interaction is increased, especially when working with lower molecular weight molecules. It is important that the linkage must not directly interfere with the activity of the ligand (Fiorini et al., 2001), and thus, electron donor functional groups should be protected during the linkage reaction. Kendall and Hochella (2003) accomplished this by inserting a metal (Al3þ) into the ligand (azotobactin) structure to occupy and protect the chelating groups, while carrying out the linkage reaction. Once attached to the tip, the azotobactin was reactivated by removing the Al with high concentrations of a competing ligand (EDTA), a process that was monitored in a test solution with UV – vis spectroscopy (Fig. 6). Unfortunately, inherent to fixing a molecule to a surface is a reduction in the degree of freedom afforded to the molecule’s conformation. This can result in an alteration or loss of chelation or ligand activity and should be considered. To this end, control activations are often run in parallel to tip activations, where monolayers, linker molecules and the biomolecule of interest are reacted with a flat, Si3N4 or SiOH substrate (Fiorini et al., 2001; Hinterdorfer et al., 2002). Similar in composition to the tip, the flat test substrates serve as a proxy for

30

T. A. KENDALL AND S. K. LOWER

Figure 6 UV –vis spectra showing the transition of Al into and out of the azotobactin (Azb) structure; corrected for dilution. Upon the addition of Al to the system a characteristic shoulder appears in the spectra. This shoulder could be eliminated with high concentrations of EDTA. A similar process was employed to protect and then regenerate the azotobactin chelating groups during linkage of the siderophore to a hydrazide terminated AFM tip (see Kendall and Hochella, 2003).

tip that are readily probed with AFM imaging, fluorescence and confocal microscopy, surface plasmon resonance (SPR) and various enzyme and ligand assays in an effort to assess the success of the linkage reaction; estimate coverage, density and footprint area of the monolayer-biomolecule construct; and evaluate activity retention in the immobilized biomolecule (Fiorini et al., 2001).

B. SIDEROPHORES

AND

OXIDE SURFACES

Kendall and Hochella (2003) collected force signatures between a ligand (siderophore) and two mineral bound metals (Fe(III) and Al(III)) in an attempt to examine the mechanism of siderophore-mediated dissolution of oxide surfaces. Siderophores are ligands produced by microorganisms to assimilate the essential nutrient ferric iron, in spite of its extreme insolubility in near surface, circumneutral environments. The aqueous chemistry of siderophores has long been studied (Winkelmann, 1991), and it is recognized that their effectiveness in acquiring iron, can, in part, be attributed to a thermodynamic binding affinity for Fe(III) (aq) that has a magnitude above that for other metals, including Al(III). Only recently, however, it was recognized that, in addition to the formation of stable, aqueous iron complexes, siderophores can release iron from minerals (Seaman et al., 1992; Watteau and Berthelin, 1994; Hersman et al., 1995; Holmen and Casey, 1996; Liermann et al., 2000; Maurice et al., 2000). The mechanism of this release, however, is not clearly defined. Force microscopy of the pyoverdin type siderophore azotobactin interacting with iron and aluminum oxide surfaces showed a unique relationship between

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

31

ligand – metal affinity and adhesion forces (Kendall and Hochella, 2003). Average adhesion forces between azotobactin and goethite (a-FeOOH) at pH 7 were 2 –3 times the value between azotobactin and goethite’s isostructural Al-equivalent, diaspore (a-AlOOH) (Fig. 7a). A similar force relationship was also observed between the trihydroxamate siderophore deferoxamine (DFO) and each oxide surface (Fig. 7b). Control experiments where each mineral surface was probed with a SAM coated tip lacking the azotobactin molecule produced force signatures that were almost identical, indicating the distinction in the force signature between diaspore and goethite could be attributed to the presence of the azotobactin on the tip. Force measurements collected under various solution conditions (e.g., pH, ionic strength and soluble iron concentrations) and at different sample locations on the mineral extended the characterization of the ligand –mineral interaction and helped identify the source of discrepancy in adhesion values associated with each oxide. As a first guess, it could be hypothesized that the forces of interaction

Figure 7 Force spectra showing the interaction of two siderophores (a) azotobactin and (b) deferoxamine (DFO) with goethite (FeOOH) and diaspore (AlOOH) surfaces. Note the large increase in the adhesion force between each siderophore and goethite and versus the adhesion value for diaspore.

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T. A. KENDALL AND S. K. LOWER

are dominated by an electrostatic component; and that the difference in the adhesion values between diaspore and goethite, simply reflects variation in the charge character of each mineral. Although point-of-zero-charge (pzc) literature values for goethite (pzc 7 –9) and diaspore (pzc 7 –8) are similar, suggesting both should be neutral or slightly positively charged (Cornell and Schwertmann, 1996; Kosmulski, 2001), it is possible that our model system deviates from pristine charge conditions, such that the goethite is positive and the diaspore is slightly negative. With a net negative charge predicted for azotobactin at pH 7 (pKa hydroxycarboxylate ¼ 4 –5; Telford and Raymond, 1996), this could result in a larger adhesion force for goethite presumably due to a stronger electrostatic interaction. Measurements at lower pH and different sample locations, however, suggest otherwise. At pH 3.5, far from the pzc value of each mineral, and where the azotobactin is anticipated to be neutral, the same 2 –3-fold increase in adhesion values is observed. Moreover, the azotobactin-goethite/azotobactindiaspore force relationship remained intact when comparing adhesion distributions representative of different sample locations on each mineral surface, where anomalous charge distributions and changes in microtopography are expected. Overall, similar to observations made with the biotin ligand system (Moy et al., 1994a; Chilkoti et al., 1995; Izrailev et al., 1997), adhesion values upon retraction appear to be relatively independent of protonation equilibria, and may reflect a specific interaction between the siderophore oxygens and the metal contained in each mineral. The discrepancy in adhesion for goethite versus diaspore can then be explained by differences in the electronic character of each metal (e.g., Fe(III) versus Al(III)), where the more electronegative ferric iron will behave as a harder acid with a higher affinity for the oxygens. In additional experiments with goethite only, this surface affinity was readily disrupted with the addition of soluble iron (Fig. 8). Here, increased [FeCl3(6H2O] (pH 3.5) led to a saturation of the ligand as the soluble iron out competed the mineral iron for the siderophore oxygens, resulting in lowered adhesion values. This does not discount an electrostatic component to the azotobactin –oxide interaction. Indeed, decrease in the jump to contact distance with increasing ionic strength thought to reflect a collapse in the double-layer associated with the mineral surface (Noy et al., 1997; Lower et al., 2000), confirms the effect of charge, especially upon approach. Instead, force evidence suggests a balance between electrostatics dominating the approach and more specific interactions directing surface adhesion; a scheme that is embodied in the following observation — goethite force signatures at pH 7 often show a long range, electrostatic repulsion on approach that was equal to or significantly lower in the diaspore signatures; yet, the goethite adhesion force averaged 3.81 nN compared to 1.38 nN for diaspore. Force data also provided information on which azotobactin functional groups might be important in the interaction. Distinctive plateaus were commonly observed after retracting the tip ~6 – 7 nm from the surface (Fig. 9). These features

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

Figure 8 Plot showing decrease in azotobactin– geothite adhesion forces with increasing concentrations of added soluble iron (FeCl3 6H2O). Measurements were collected at pH 3.5 to minimize the precipitation of solid iron phases upon the addition of the iron chloride. Taken from Kendall and Hochella (2003).

33

34 T. A. KENDALL AND S. K. LOWER Figure 9 Plateau feature common in many retraction curves while probing oxide surfaces with an azotobactin activated AFM. It is suggested that this feature may represents the extension of the azotobactin and linker molecule during separation from the mineral surface as shown in the inset (not to scale). Also shown in the inset is the geometry of the linkage of the siderophore to the tip. Modified from Kendall and Hochella (2003).

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

35

are thought to represent the energy absorption associated with the combined extension and stretching of azotobactin’s polypeptide chain and the molecule used to link the ligand to the tip. Using an approximation of 0.38 nm amino acid, a quick calculation shows that azotobactin’s fully outstretched length of , 3.8 nm, together with an additional 3 nm from the linker molecule gives a value that is close to the 6 – 7 nm observed in the force signatures. This distance, then, requires that azotobactin’s terminal homoserine group serves as an anchor to the surface, providing a strong, persistent link in the interaction. This coincides with other reports that, in aqueous systems, the adjacent hydroxamate group initiates chelation (Telford and Raymond, 1996; Albrecht-Gary and Crumbliss, 1998). Additionally, considering its terminal position on the molecule, it is feasible that the homoserine group is a dominant component during surface interaction. Finally, these force microscopy results give cause to reassess the role of large ligands, such as azotobactin, in dissolving minerals. Instead of serving as an Fe shuttle between smaller ligands that interact with the surface and the cell, the force evidence demonstrating azotobactin’s strong surface affinity presents a distinct possibility of the relatively large molecule entering into a strong, stable complex with the mineral. As seen above, the force data also allow comment on the nature of the association with the surface. Steric constraints imposed by ligand size, structure and conformation, together with the limited access to an iron atom contained on a mineral surface, would certainly preclude the hexadentate coordination characteristic of the siderophore-aqueous complex (Holmen and Casey, 1996; Hersman, 2000; Cocozza et al., 2002). Instead, plateau features in the retraction curves suggest a strong coordination formed by a single oxygen pair that terminates the azotobactin molecule as one possibility. Recent, ongoing MD simulations, in collaboration with U. Becker (University of Michigan), confirm this possibility, as well as the extended dimensions of the azotobactin-linker construct. Interestingly enough, however, simulations reveal that the spacing between the two, chelating hydroxamate oxygens is sufficient to allow individual coordination with neighboring irons in the goethite structure (Fig. 10). Siderophore– oxide interaction continues to be examined with molecular dynamic simulation as well as dynamic force spectroscopy.

V.

FORCES AT THE BACTERIUM–MINERAL INTERFACE

A. FORCE MICROSCOPY TECHNIQUE USING WHOLE CELLS The fundamental forces between a bacterium and mineral surface are central to understanding the intricacies of interfacial phenomena such as bacterial

36

T. A. KENDALL AND S. K. LOWER

Figure 10 Molecular model of azotobactin (with linker molecule) interacting with a goethite surface. Simulations were completed using Cerius2, Accelrys, Inc. Arrows point to terminal hydroxamate group oxygens interacting and coordinating with irons (balls) in the lattice. Note the spacing of the siderophore oxygens allow for “bonds” (i.e., Fe–O distances ,2.1 Angstroms) with neighboring irons. With this coordination, the cross-distance between a siderophore oxygen and an iron diagonally across is over 3 Angstroms.

adhesion to minerals and dispersal in the environment (van Loosdrccht et al., 1989; Fletcher, 1996), mineral growth and dissolution (Myers and Nealson, 1988; Hiebert and Bennett, 1992; Schultze-Lam et al., 1992; Roden and Zachara, 1996; Fortin et al., 1997), biofilm formation (Lawrence et al., 1991; Davies et al., 1998),

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

37

and bacterial affinity for or recognition of specific mineral surfaces (Ohmura et al., 1993; Fleminger and Shabtai, 1995; Bhosle et al., 1998; Dziurla et al., 1998; Edwards et al., 1998). A myriad of physicochemical interactions occur at biological – mineral interfaces in nature, due to (1) the mosaic of spatially discrete macromolecular cell wall structures on bacteria, (2) the dynamic nature of these structures, and (3) the diversity of mineral surface functionality, topography, and crystallography (Lower et al., 2000). As discussed above in section II, these interactions are expected to be governed by the cumulative effects of intermolecular forces (Israelachvili and McGuiggan, 1988; Israelachvili, 1992; Kendall, 1994; Butt et al., 1995; Fletcher, 1996; Gay and Leibler, 1999). However, acquiring even an elementary appreciation of these forces presents a daunting challenge, primarily due to the minute scale at which these interfaces must be probed, and the difficulty in developing a technique that preserves the natural intricacies of the bacterial surface (Lower et al., 2000). Measurement of fundamental forces between whole bacterial cells and inorganic phases can be conducted in one of two ways with force microscopy. The first involves “fixing” cells to a solid substrate (e.g., a glass slide) and probing these cells with a force-sensing cantilever. The simplest setup makes use of the sharp tip that is integrated into most force microscopy cantilevers (see above). In many instances, however, this is not ideal because these tips are not well constrained with respect to their geometry and/or area of contact. As shown in section II, this greatly influences force measurements thereby making it difficult to compare measured data to theoretical force models, and impedes the comparison of data collected with different tips. To overcome the limitations imposed by using a sharp tip, Ducker et al. (1991) devised a simple yet ingenious solution. They created a “colloidal tip” by attaching a glass bead to the end of a force-sensing cantilever. This bead was then used to probe a flat silicon surface (Ducker et al., 1991, 1992), although such a “colloidal tip” could also be used to probe microbial cells on a glass surface. A number of companies, such as Duke Scientific Corporation, Polysciences Incorporated, and Bangs Laboratories Incorporated, sell spheres ranging in size from nanometers to micrometers. A major drawback to this scenario, however, is that it limits the inorganic phases that can be utilized to those materials commonly used to make tips (e.g., silicon and silicon nitride) or beads (e.g., plastic and glass). With the exception of silica (e.g., glass beads), minerals or other inorganic phases cannot be attached to a force-sensing cantilever. Therefore, interactions between bacteria and minerals much employ another technique. That is, the cells must be linked to the forcesensing cantilever, which is then used to probe a particular face on a mineral crystal or other surface. The first cell to be linked to a force-sensing cantilever was a large mammalian cell (Antonik et al., 1997). This cell was not actually “attached”, rather it was induced to grow on the cantilever. The researchers conducting this experiment were not interested in measuring forces, which was fortunate because cells grew

38

T. A. KENDALL AND S. K. LOWER

on both the top and bottom surfaces of the cantilever. Hence, the cell growth would have affected not only the spring constant of the lever, but it would also alter the optical lever detection system. Nonetheless, this opened the door to a number of other protocols of linking cells to a force microscope cantilever. It is a difficult challenge to link microbial cells on the order of 1 mm to the end of a cantilever. Early attempts relied on the attachment of cells that had been chemically fixed or treated with harsh chemicals (e.g., gluteraldehyde) (Razatos et al., 1998a, b). While these investigations produced some very intriguing force measurements, this type of linkage protocol is often undesirable because the cells are killed in the process. Further, chemicals such as gluteraldehyde are known to denature proteins and other macromolecules. Another method was developed that allowed the force-sensing cantilever to support bacterial cells in a living, native, fully functional state — thereby creating “biologically active force probes” (Lower et al., 2000, 2001b). A polycationic linker molecule (e.g., aminopropyltriethoxysilane or polylysine) can be used to link living bacteria to a small glass bead that is then attached to the cantilever, or the bacteria can be linked directly onto the cantilever itself (Lower et al., 2001b). Polycationic linkers work well because many bacteria are negatively charged over a wide range of pH conditions. Hydrophobic molecules (e.g., octadecyltrichlorosilane) are also attractive linkers because many microorganisms have hydrophobic surfaces. Techniques similar to affinity chromatography (e.g., see Pleuddemann, 1991; Egger et al., 1992; Rezania et al., 1999) may be employed to design tailor-made linker molecules (e.g., ligand –receptor or antibody – antigen) that work on virtually any bacterial species. The use of polycationic linkers, or similar molecules, preserves the natural conformation, structure, and function of the macromolecules on the microbial surface. When live cells are used (i.e., a biologically active force probe), force measurements may be collected under different physiological or environmental conditions in real time (Lower et al., 2000, 2001a, b). Finally, for larger microbial cells such as yeast or fungal cells, the “colloidal tip” technique (see above) can be used to glue a single cell to the end of a cantilever (Bowen et al., 1998b). Using these techniques, a number of groups have used force microscopy to measure intermolecular forces at the bacterium – mineral interface (Ong et al., 1999; Bowen et al., 1998a; Razatos et al., 1998a, b; Bowen et al., 2000a, b; Camesano and Logan, 2000; Lower et al., 2000, 2001a, b). In our laboratories, we have used biological force microscopy (Lower et al., 2000) to measure intermolecular forces between living bacteria (e.g., E. coli, Burkholderia cepecia, and S. oneidensis) and inorganic phases (e.g., muscovite, goethite, diaspore, graphite, and glass) in solutions of varying ionic strength, pH, and oxygen concentration (Lower et al., 2000, 2001a, b). Below we will examine the force– distance relationships at the E. coli – muscovite and S. oneidensis– goethite interfaces. We will concentrate on the forces measured upon approach of a bacterium towards a mineral in the case of the former. For the latter system

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

39

(i.e., S. oneidensis – goethite), we will explore forces measured as the two surfaces are pulled apart or retracted from one another.

B. FORCES

BETWEEN

ESCHERICHIA

COLI AND

MUSCOVITE

Figure 11 shows the interaction between E. coli and the (001) surface of muscovite as the sodium chloride (NaCl) solution was exchanged five times between low (, 1025 M) and high (~102l M) ionic strength. While both approach and retraction forces were measured, shown in Fig. 11 are only the forces detected upon approach of the mineral towards living cells on a biologically active force probe. At low ionic strength, repulsive (positive sign) forces were detected at a separation of approximately 100 run. This repulsive interaction increased exponentially (see below) to a maximum value of , 30 – 35 nN at contact. At high ionic strength, the magnitude of repulsion was significantly less as was the range of separation over which force interactions took place. The two surfaces did not “feel” one another until they were within 15– 20 nm of separation. As with the measurements at low ionic strength, an exponential force appears to dominate at high ionic strength. It is important to note that these data shown in Fig. 11 span the entire range of measurements for literally hundreds of force – distance curves taken as a solution was exchanged

Figure 11 Force–distance relationship between the basal plane surface of muscovite and E. coli in solutions of high (open symbols across lower portion of figure) or low (closed symbols across upper portion of figure) ionic strength. Shown for each solution condition are five data curves that span the entire range of measurements for literally hundreds of force-distance curves. The lines correspond to the DLVO model prediction at high (dotted) or low (solid) ionic strength. Repulsive forces have a positive sign; whereas attractive forces have a negative sign. Only those forces measured upon approach of the mineral towards the bacteria are shown. See text for discussion.

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T. A. KENDALL AND S. K. LOWER

several times between high and low ionic strength. Hence, the measurements are reproducible. Results can be interpreted with the so-called DLVO theory (Derjaguin and Landau, 1941; Verwey and Overbeek, 1948). This theory describes forces (F) as a function of the distance (D) (e.g., between a bacterium, treated as a sphere and a mineral, treated as a flat plane) as the sum of the electrostatic and van der Waals forces (Ducker et al., 1991; Israelachvili, 1992; Butt et al., 1995; Muller and Engel, 1997): FDLVO ðDÞ ¼ Felectrostatic ðDÞ 2 Fvdw ðDÞ ¼

4psbacteria smineral R 2kD HR e 2 a2 110 k 6D

where s is the surface charge density (C m22), R is the radius of a cell (or in this case the radius of the bacteria coated bead attached to the cantilever), 1 is the dielectric constant of water (78.54 at 298 K), 10 is the permittivity of free space (8.854 £ 10212 C2 J21 m2l), k is the inverse Debye length (Debye length , 1 nm at 102l M and , 100 nm at 1025 M; see above), and Ha is the Hamaker constant. For the model results plotted in Fig. 11, Hamaker’s constant was 10221 J (Vigeant et al., 2002); surface charge density of the bacterium was estimated using Eq. (3) as 2 0.001 or 2 0.04 C m22 at low or high IS, respectively (surface potential measurements were taken from Camesano and Logan (2000), Ong et al. (1999) and Vigeant et al. (2002), and the surface charge density of the mineral was estimated using Eq. (3) as 2 0.004 or 2 0.2 C m22 at low or high ionic strength, respectively (surface potential measurements were taken from Ducker et al. (1992) and Ong et al. (1999)). Figure 11 compares the measured forces with those predicted by DLVO theory. Ionic strength (approx. 102l versus 1025 M) appears to have a strong effect on the interactions between E. coli and muscovite. This is because higher salt concentrations cause the electrostatic double-layer to become thinner (i.e., surfaces cannot “feel” one another until they get very close). Further, the increased concentration of counter ions at high ionic strength effectively screens the negative charges on both surfaces, thereby resulting in smaller magnitude forces of repulsion. These particular measurements are fairly consistent with DLVO theory. However, there are some important discrepancies. For example, at low ionic strength the attractive van der Waals force is expected to dominate the interaction at separations less than 5 nm. However, measurements reveal that E. coli and muscovite do not exhibit attraction even at the closest approach. Indeed, adhesion forces were not detected when E. coli and muscovite were forced together and subsequently pulled apart at low ionic strength (Lower et al., 2000). This suggests that electrostatic and/or other repulsive forces (e.g., solvation interactions) dominate this particular interaction. Many other force measurements conducted in our laboratories, suggest that electrostatic and van der Waals forces are not the only intermolecular forces at the bacterium – mineral interface (S. Lower, unpublished results). Others have

FORCES BETWEEN MINERALS AND BIOLOGICAL SURFACES

41

attempted to invoke extended-DLVO models to explain deviations from purely van der Waals and electrostatic forces and fit model predictions to measurements (see e.g., Ong et al., 1999; Camesano and Logan, 2000). While these investigations may be valid, one needs to remember that force models are sensitive to the geometric shapes of interacting particles as well as the roughness of surfaces and contact area (Israelachvili, 1992) all of which are difficult to rigorously define or control for minerals and cells with biopolymers. Further, DLVO was developed to describe the phenomena between inanimate particles rather than living cells that have exquisite control over the expression of surface macromolecules. Seemingly simple concepts such as “contact” become difficult to define for cells having polymers of varying length, which extend for some distance beyond the cell wall. Further, living cells and/or surfaces with polymers are expected to show a time dependent adhesion (measured by Lower et al., 2001a) as biopolymers diffuse into the cell wall and reorient with respect to another surface (Beveridge, 1999; Leckband and Israelachvili, 2001). The true value may not be in whether a model perfectly fits data, but the most definite answer comes when the measurements contradict the theory, thereby disproving a particular construct and suggesting that other forces are responsible for a particular bacteria –mineral interaction. As stated by Oreskes et al. (1994) scientific investigations are at their best when one combines experimental measurements and model predictions to challenge existing formulations. Hence, there is a great need to test such models by comparing theories to precise force measurements using “model” microorganisms and minerals. Only then will we be able to understand how all of the various intermolecular forces (e.g., electrostatic, van der Waals, hydration, hydrophobic, and steric interactions) govern interactions at the bacterium –mineral interface.

C. FORCES

BETWEEN

SHEWANELLA ONEIDENSIS OR DIASPORE

AND

GOETHITE

The forces required to pull the mineral and bacteria apart (i.e., retraction data) are not shown in Fig. 11. In fact, a very strong adhesion force was detected between E. coli and muscovite, but only at high ionic strength (Lower et al., 2000). Aside from being a notable example of a situation that DLVO theory often cannot explain, retraction data provide an immense amount of information about the adhesive strength and structural properties of specific biopolymers on a cell’s surface. Recently, we interpreted these retraction data for studies of bacterial adhesion and electron transfer reactions between S. oneidensis (a dissimilatoryiron-reducing-bacteria) and the minerals goethite (FeOOH) or diaspore (AIOOH) under aerobic and anaerobic solution conditions (Lower et al., 2001a; Lower et al., 2002). S. oneidensis is capable of using either oxygen or ferric iron in the crystal structure of iron oxyhydroxides as a terminal electron

42

T. A. KENDALL AND S. K. LOWER

acceptor (Nealson and Saffarini, 1994). We used force microscopy to determine whether a microorganism could discriminate between two very similar minerals (diaspore and goethite) under anaerobic conditions, when electron transfer is expected to occur between S. oneidensis and iron containing minerals. A mineral crystal, mounted on a piezoelectric scanner, approached live bacteria on a biologically active force probe at a rate that was comparable to the natural velocity of motile bacteria. Once contact was established, the two surfaces were pulled apart resulting in retraction data. Figure 12 illustrates the retraction profile for S. oneidensis and goethite versus diaspore under anaerobic or aerobic conditions (Lower et al., 2001a). The intricate details of these curves and the entire data set provide valuable information about intermolecular forces and structures at the bacterium –mineral interface. Initially the entire retraction data were characterized by integrating the force with respect to distance. This provided quantitative energy values associated with adhesion. The retraction curves were further analyzed by the worm-like chain model (see above) to establish a correlation between specific bridging polymers and unique signatures in the retraction curves. Energy values determined from retraction curves similar to those shown in Lower et al. (2001a) revealed that S. oneidensis had a higher affinity for diaspore (versus goethite) under aerobic conditions (Lower et al., 2001a). However, under anaerobic conditions the bacteria exhibited a significant increase in affinity for goethite (see Lower et al., 2001a); whereas the adhesion energy for diaspore was indifferent to oxygen concentrations (Lower et al., 2001a). The attractive energy between S. oneidensis and goethite was 30 aJ (aJ ¼ 10218 J) and 130 aJ under aerobic and anaerobic conditions, respectively. Further, the energetic affinity between goethite and S. oneidensis also increased with contact time under

Figure 12 Measured force-distance (or force-extension) relationship between living cells of S. oneidensis and diaspore (AlOOH) under anaerobic conditions (solid circles). Interactions with goethite (FeOOH) under aerobic (“X” symbols), or anaerobic conditions (open squares). Also shown is the theoretical prediction for the unravelling of a 150 kDa protein that may tether the bacteria to the surface of goethite (see text). Attractive forces, shown here, have a negative sign. Shown are only those forces measured as the mineral is pulled away from the bacteria (retraction data). Modified from Lower et al. (2001a).

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anaerobic conditions. This provided quantitative evidence suggesting that this microorganism recognized a particular inorganic surface such that it localized and/or produced biopolymers to mediate contact with goethite under anaerobic conditions. This idea was further tested by using a “bridging polymer” model to interrogate the intricate details (e.g., the saw-tooth pattern) in the retraction data. As shown above, for the worm-like chain model, linear polymers such as proteins are expected to unravel according to Eq. (4). According to this model, one needs only the persistence length and the contour length to describe the force as a function of the distance a polymer is extended. This model describes a physical process similar to that which is recorded in the retraction data of force microscopy. A protein, for example, in the cell wall of a bacterium makes contact with a mineral. The mineral is then pulled away from the bacterium causing the protein to unravel until it is completely extended at which point the protein is either ripped from the cell wall or it breaks free of the mineral surface and recoils into the cell surface. The outer surface proteins of Shewanella are well characterized. Shewanella is known to have proteins on its outer membrane that mediate contact with iron oxyhydroxides (Caccavo, 1999; Caccavo et al., 1997; Das and Caccavo, 2000). Several of these proteins are putative iron reductases, which are expected to make physical contact with goethite such that they can transfer electrons across the organic – inorganic interface (Arnold et al., 1990; Myers and Nealson, 1990; Myers and Myers, 1992, 1993, 1997; DiChristina and Delong, 1994; Nealson and Saffarini, 1994; Roden and Zachara, 1996; Myers and Myers, 1998, 2000, 2001). In fact, four putative iron reductase proteins have been characterized according to their mass and/or genetic sequence (Myers and Myers, 1997, 1998, 2000, 2001, 2002). The worm-like chain model was used to predict the way in which each of these four proteins (ranging in size from , 50 to 150 kDa; (Myers and Myers, 1997; Myers and Myers, 1998) would unfold. The molecular mass of each protein was used to estimate its overall length according to the following conversion, , 110 Da per amino acid residue (Voet and Voet, 1995). The persistence length of each amino acid, defined as the distance between two adjacent Ca, is equal to 0.38 nm (Karlsson et al., 1996; Muller et al., 1999; Myers and Myers, 2001). As shown in Fig. 12, the saw-tooth pattern at approximately 500 nm corresponds to the force-extension profile of one of the four putative iron reductase proteins. This profile was reproducible suggesting that the cell wall protein was not ripped from the bacterium, but rather retained its native conformation after multiple extensions. This unique signature was present only for goethite under anaerobic conditions where it was detected in , 80% of the retraction curves, but only when the bacterium was given some period of time to make contact with the surface of goethite (Lower et al., 2001a). This suggests that the bacterium required time to “recognize” the mineral surface and subsequently create and/or mobilize a specific protein to the area of contact with goethite.

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VI. FUTURE WORK On Earth, literally millions of different species of prokaryotes may interact with any of the thousands of different minerals. An interface is formed at the junction of a bacterium and a mineral surface that is complex, dynamic, and by its very nature, nanoscale in size. This is because bacteria are living cells that have mastered the art of synthesizing fully functional structures (e.g., lipids, proteins, polysaccharides) and utilizing properties that exist only at the nanometer scale. The study of the interface between minerals and microorganisms requires a unique fusion of geomicrobiology and nanoscale science. What are the fundamental forces that control the binding of a silanol group on a mineral surface to a carboxylic group in a bacterium’s cell wall? How does the density and distribution of functional groups on a crystal face influence the way microorganisms sense mineral surfaces? Do bacteria express specific outer surface proteins to interact with certain minerals? How do bacteria modulate forces of interaction between themselves and minerals (or other bacteria) to either enhance or inhibit adhesion and subsequent biofilm formation? Researchers must be able to thoroughly explore both sides of the interface (i.e., the bacterium and the mineral) and the fundamental nanoscale forces in the intervening region to discover phenomena that exist only in the nanospace between a microorganism (or microbially produced polymers) and a mineral surface. As mentioned earlier, application of force microscopy to the biogeosciences is in its infancy, and there exists many other uses and unexplored possibilities of force experiments with ligands, microorganisms, and minerals. Structural elements within a particular biomolecule/ligand may contribute to its ability to bind to a surface or promote dissolution, or chelate dissolved or mineral bound metals (Stumm, 1992; Ludwig et al., 1995; Nubel et al., 1996). One can envision collecting a force signature for a large ligand interacting with a mineral, followed by collection of spectra associated with several, individual cognate functional groups associated with the ligand. Comparison of the whole ligand, baseline spectrum with the individual component spectra could reveal which functional groups are dominating the interaction with the mineral. Or, a similar process could be achieved by making force measurements after successive chemical modification of the original ligand structure. Such modification might include inactivation of a specific functional group with a residue-specific reactive reagent (Voet and Voet, 1995), or an amino acid substitution resulting from alteration of the genes associated with the biosynthesis of the molecule. Again changes in the force signature with each modification might help determine the critical moieties contributing to the interaction. Force maps (Noy et al., 1997) are also possible using ligand activated tips. Here, the contrast in the map may be supplied by the differential adhesion between the ligand and various metals that are associated with a surface.

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For example, a map constructed with a siderophore activated tip might show large adhesions in areas of high concentrations of trivalent metals such as Fe(III) or Al(III) and lower adhesions for divalent metals such as Cu(II), Zn(II) or Fe(II). Given the spatial resolution of the AFM, such images could be useful for identifying contaminant distribution on a surface or pinpointing impurity concentrations on a mineral growth face, both on a nanometer scale. CFM is traditionally carried out in a fluid cell (Digital Instruments) that allows direct observation of ligand –surface interaction under environmentally relevant conditions with pico- to nanonewton force resolution and a spatial resolution of tens of nanometers down to potentially the atomic level. Changes in the forces of interaction with solution composition provide important information about the structure and charge character of the ligand and mineral surface, and the nature of the interaction between the two. While the effect of solution composition (e.g. pH) on ligand sorption can be monitored with force measurements using a force titration (Kreller et al., 2002). The sensitivity of this technique also allows small changes in mineral solubility and associated metal concentrations, pH, and ionic strength to be detected (Kendall and Hochella, 2003). Given the spatial resolution mentioned above, this opens up the possibility of using this technique to detect localized solution micro- or even nanoenvironments associated with a surface. Finally, force investigations with living microorganisms are rich with possibilities. For example, one could measure forces of adhesion using wildtype stains versus mutants that are incapable of producing specific cell wall macromolecules. These data may result in unique force signatures characteristic of particular biomolecules. Force measurements could also be coupled to other techniques such as confocal scanning laser microscopy. This provides the potential to collect force measurements concurrent with fluorescence observations of the distribution and localization of cell wall macromolecules.

ACKNOWLEDGMENTS SKL acknowledges the support of the National Science Foundation, the Department of Energy, the American Chemical Society, and the General Research Board of the University of Maryland. SKL would also like to thank J. Tak for support. Funding was provided to TAK by a GAAN Fellowship (U.S. Dept. of Education), the NSF’s Nanoscale Science and Engineering (NSE) Program (EAR 01-03053), and the Department of Energy’s OBES Geosciences Program (DE-FG02-99ER 15002). TAK acknowledges the support of Michael F. Hochella, Jr. (Virginia Tech).

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RICE FUNCTIONAL GENOMICS: LARGE-SCALE GENE DISCOVERY AND APPLICATIONS TO CROP IMPROVEMENT Hei Leung1 and Gynleung An2 1

International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines 2 Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang, Korea 790-784 I. Introduction II. Rice as a Model Genetic System A. Diversity of Oryza B. Grass as a Single Genetic System III. Rice Genome A. Genome Sequencing B. Salient Features of the Rice Genome C. Comparison Between Indica and Japonica Rice IV. Key Ingredients for Gene Discovery in Rice A. Genetic Resources B. High-throughput Technologies C. Biological Evaluation D. Bioinformatics V. Forward and Reverse Genetics A. Insertional Mutants B. Chemical- and Irradiation-induced Mutants C. Natural Genetic Variation VI. Functional Validation of Rice Genes A. Gene Expression Analysis B. Gene Silencing C. Heterologous Bioassays D. Gene Replacement E. Allelic Series VII. Applications to Crop Improvement A. Candidate Gene Approach and Allele Mining B. Pathways and Genetic Regulation C. Cross-species Inference of Gene Function VIII. International Collaboration and the Role of Developing Countries IX. Concluding Remarks Acknowledgments References

55 Advances in Agronomy, Volume 82 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. 0065-2113/03 $35.00

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I. INTRODUCTION Rice has an old and rich cultivation history. Archeological ruins found in China and India suggest that domestication of rice might date back 7000 years (Watanabe, 1997). Several regions in Asia, including Assam in India, Myanmar, northern Thailand, and southwest and southern China, have been hypothesized as the origins of cultivated rice. Also, rice cultivation probably originated independently in several locations (Matsuo, 1997). Rice is more than just a food crop. As a key sustenance for people since ancient times, rice has become part of the social and religious fabric of many cultures (see White 1994 for an interesting popular article about rice culture in National Geographic). Today, rice remains the staple food of more than half the world population and supplies up to 60% of the calories in the diet of people living in Asia. In many developing countries, consumers spend more than 20% of disposable income just to buy sufficient rice to sustain daily food needs. Rice cultivation is also a major source of employment in rural areas. The rice economy — production, distribution, and consumption — therefore plays an important role in the livelihood of many people. Rice is cultivated in diverse ways. Rice production is often classified according to the ecosystem in which it is grown. In terms of area, a majority (58%) of rice is grown in the irrigated environment, commonly called paddy rice. This is a highly intensive rice production environment, which accounts for about 75% of the rice produced worldwide. Most investment in production technologies has been applied to this environment. The remaining portion of rice is grown in the rainfed environment, that is, on land that relies on natural rainfall. About half of the rainfed rice is grown in areas with a reasonably reliable water supply (the favorable rainfed area), but the other half is grown in very difficult conditions with too little or too much water and poor soil conditions. Figure 1 summarizes yield trends of rice over the course of the Green Revolution that started more than four decades ago in response to the food crisis in many parts of the developing world (Hossain et al., 2000). As a result of concentrated efforts devoted to improving rice germplasm and agronomy, a dramatic and steady increase in rice productivity has occurred across Asia (Hossain, 1998). Rice yield in irrigated areas nearly doubled from 1960 to 1980. This has resulted in lower rice prices that benefit consumers. The yield increase, however, is low in unfavorable environments where the water supply is unreliable and the soil is often infertile or problematic for rice production. Recognizing this historical trend is relevant in the context of applying genomics to crop improvement. The trend enables us to identify the key areas where advances in science can help solve intractable problems. In the highly intensive system, technologies are needed to increase production efficiency with less cost. The system also needs improved resilience against pests and diseases to reduce

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Figure 1 Yield trend of rice from 1967 to 1997 in three rice production environments (modified from Hossain et al., 2000).

reliance on chemical inputs. In the unfavorable rainfed environment, there is plenty of room to improve productivity and the nutritional value of germplasm to meet the need of the rural poor. Although rice is such an important food crop, it has only a relatively short history as a model organism for biological investigation. Before the 1980s, rice research — from basic to applied — was mostly done by countries where rice is the staple crop and where sufficient infrastructure existed to conduct research. This limited the majority of rice research to a few countries or international agricultural research centers where varietal improvement is a mandate or a national necessity. There was hardly any private interest in rice research. In many respects, rice was an “orphan” crop in terms of research investment. This picture changed in the mid-80s when advances made in plant molecular biology and gene transfer techniques were seen as promising avenues to convert biological discoveries to applications. The Rockefeller Foundation, a long-time supporter of international agricultural development, made a strategic decision to support the application of molecular biology to solve rice production problems. The beginning of the International Rice Biotechnology Program in 1985 launched the “molecular revolution” in rice science. For the next 16 years (1985 – 2001), we witnessed the blossoming of basic research in rice. The sustained support of rice biotechnology produced a large cadre of scientists in the developing world, well trained and ready to apply useful genes in production (O’Toole et al., 2001).

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While gene identification and cloning were advanced toward the end of the Rockefeller Biotechnology Program, there was a growing sense that many biological problems limiting rice production required a comprehensive understanding of multiple genes and their interactions with the environment. In the last Rice Biotechnology meeting held in Phuket, Thailand, in 1998, questions were raised about the next quantum leap in rice productivity. What would the investment in research capacity and infrastructure in the rice-growing world do to future rice production? Around this time, the genome of the first eukaryote — Saccharomyces cerevisiae — was completed (Williams, 1996), followed by several initiatives to sequence other model organisms — Drosophila, nematode, Arabidopsis, and human. These events marked a genomics decade that has put rice at the center of plant science research and provided a logical extension of the Rockefeller Foundation Rice Biotechnology Program. Genomics can be broadly defined as the understanding of all genes and their encoded biological function in an organism in totality. It has emerged as an integrative discipline that provides the means to understand biological complexity and to solve difficult practical problems. Rice, with the smallest genome size among major cereals and crop plants, is seen as the obvious target for genomics research. The Rockefeller Foundation sponsored a meeting in 1997 at the International Congress of Plant Molecular Biology in Singapore to discuss the merits and feasibility of sequencing the entire rice genome. At that time, physical mapping using YAC and BAC libraries was progressing rapidly, producing the critical resources for sequencing. However, with the available sequencing technologies at that time, completing the rice genome would cost about $200 million over 10 years, a formidable undertaking for any individual institute or program. Thus, a sensible way to achieve the task was to launch an international collaboration. This led to the formation of an International Rice Genome Sequencing Project (IRGSP) in 1998 under the coordination of the Rice Genome Program of Japan. The IRGSP represents a consortium of multiple countries collaborating to sequence the rice genome. In the initial stage, over 10 countries pledged to take part to sequence individual chromosomes. The IRGSP was a unique undertaking that has galvanized government commitment and it subscribed to the principle of releasing all sequence information to the public. Since then, rice genome sequencing has been the topic of scientific drama and excitement involving the private and public sector (Normile and Pennisi, 2002). To a large extent, the public initiative has been a key factor to leverage the contribution of rice genome data from the private sector. Together, this has led to the availability of genome sequence of two rice subspecies ( japonica and indica), a favorable situation for public researchers working not only on rice but on other plant species. With a genetic blueprint in place, we ask, what are the new opportunities to understand gene functions and their interactions? What are the most efficient approaches to find genes of agronomic importance? Why is international

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collaboration necessary for gene discovery in rice? How can we turn gene discovery into practical products? This review is organized with these questions in mind. In particular, we hope to lay out the framework for large-scale gene discovery that will benefit the developing world, where access to knowledge and tools is often limited. For more discussion of rice gene function analyses and comparative genomics, readers are referred to a recent review by Shimamoto and Kyozuka (2002). In this chapter, we will begin with an introduction of the rice plant as a model species. We will discuss the ingredients needed for large-scale gene discovery and the approaches that will benefit the improvement of traits needed in rice production. We will discuss the importance of producing public resources and the need for international collaboration, and how gene discovery can be integrated into plant improvement programs.

II. RICE AS A MODEL GENETIC SYSTEM Rice has emerged as a model in plant science research because of its many positive attributes for genetic studies. Rice has a simple genetic system (diploid and disomic inheritance) and one of the most amenable transformation systems among grasses (Hiei et al., 1994; Christou, 1997; Cheng et al., 1998). The small genome size and relatively low amount of repetitive sequences make rice a tractable system for whole-genome sequencing. Other than these technical advantages, an important, but perhaps less appreciated attribute is the great store of genetic diversity in the genus Oryza. With a long history of cultivation and selection, and a large and heterogeneous geographical niche, the wealth of phenotypic and genetic variation in all aspects of plant life, particularly genes controlling agronomically important traits, is enormous in rice. Below, we highlight the genetic diversity available in rice that is relevant for functional genomics research.

A. DIVERSITY

OF

ORYZA

Taxonomically, rice is in the subfamily Ehrhartoideae (former Oryzoideae) of the grass family Poaceae (Gramineae). Figure 2 shows the overall relationship of rice to other cereals and the recognized genomes within the genus Oryza (Katayama, 1997; Ge et al., 1999; Lu, 1999; Ge et al., 2001). The most widely cultivated rice, O. sativa, has the AA genome. Another cultivated rice species, O. glaberrima Steud., with the AA genome is grown only in West Africa (for an interesting anthropological perspective of African rice, see Linares, 2002). Both O. sativa and O. glaberrima have 12 chromosomes as their basic complement

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Figure 2 Diversity of rice as part of the broad gene pool of the grass families. Dendrograms depict the relationships among cereal crops in the grass family (Kellogg, 1998), domesticated and wild rice with different genomes (from Ge et al. 1999), and cultivated rice species. Dendrograms are not drawn to scale.

(2n ¼ 24). F1 hybrids between the two species can be produced, but advancing beyond F2 generation is difficult because of sterility. Nonetheless, the small amount of fertility has allowed the successful transfer of useful genes from O. glaberrima into O. sativa (Jones et al., 1997). The cultivated O. sativa has two subspecies — indica and japonica — that provide the main gene pool for rice breeding. There is a third minor group called “tropical japonica” (also called javanica) that is common in Southeast Asia. Japonica rice is generally adapted to the temperate area, whereas indica rice is planted in the tropics. Indica rice is the most widely grown type, constituting about 80% of world rice production. Chang et al. (1991) summarized the main agronomic characteristics of the main rice types (Table I). There is much overlap in agronomic characteristics between the two subspecies. Whether indica and japonica are monophyletic or diphyletic remains a research question (Morishima, 2001). Although divergence of these subspecies has occurred over time, sufficient fertility exists to allow routine cross hybridization and gene flow between the two subspecies. Also, because of their divergence, a high level of hybrid heterosis can be harnessed from indica £ japonica crosses. Such extra hybrid vigor is being exploited in hybrid rice production (Virmani and Ilyas-Ahmed, 2001). Now that the genome sequence of these two subspecies is available, this will open up opportunities to understand the molecular basis of japonica £ indica sterility as well as hybrid heterosis. There are more than 30 named Oryza species (Watanabe, 1997) with a broad geographic distribution. Table II lists some of the species that have been used in rice improvement programs or genetic studies (Brar and Khush, 2002). This is by no means a comprehensive list of all known wild rice species but it illustrates the diversity of the domesticated and wild germplasm that forms the basis for gene

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Table I Diverse Morphological and Physiological Features of indica, japonica, and Tropical japonica ( javanica) Rice Reflect a Broad Genetic Base for Gene Discovery Rice type

Characteristics Leaf morphology Grain Tillering Plant stature Awn Lemma and palea Shattering Plant tissue Photosensitivity Amylose Gelatinization temperature

Indica

Japonica

Tropical japonica (javanica)

Broad to narrow, light green Long to short, slender, flat High Tall to intermediate Mostly awnless Thin, short hair Easy Soft Varying 23–31% Varying

Narrow, dark green Short, roundish Medium Short to intermediate Awnless to long-awned Dense, long hair Low Hard Zero to low sensitivity 10 –24% Low

Broad, stiff, light green Long, broad, thick Low Tall Long-awned or awnless Long hair Low Hard Low sensitivity 20–25% Low

Adapted from Chang et al. (1991).

discovery. The wild species that have evolved in diverse, heterogeneous, and often harsh environments are potentially a rich pool of useful genes for breeding. Wild rice with a non-AA genome is not interferile with cultivated rice, thus making gene transfer between these wild species difficult, though not impossible, through intervention with embryo rescue and tissue culture techniques (Brar and Khush, 1997). There are examples of useful genes that have been extracted from wild germplasm for breeding (Brar and Khush, 2002). One well-known example is the grassy stunt resistance gene obtained from O. nivara. This resistance gene has been used in nearly all rice cultivars developed for the tropical lowland and it remains effective in conditioning resistance to the dominant strain of the grassy stunt virus until now (Khush, 1977). The first cloned disease resistance gene, Xa21, was also originally introgressed from the wild rice O. longistaminata from an accession in Mali, Africa. After many years of genetic and breeding work, Xa21 was put into an agronomically acceptable background that has facilitated its molecular cloning and use in rice production (Song et al., 1995). Yet, one may also argue that the number of genes extracted for use represents only a handful of the potential useful genes in the wild species. Tanksley and McCouch (1999) popularized the use of unadaptive wild germplasm through systematic introgression into elite backgrounds. However, the large-scale introgression of wild species alleles into domesticated rice is limited by the laborious process of embryo rescue. This picture may change if we have a way to predetermine

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H. LEUNG AND G. AN Table II Reservoir of Useful Genes and Alleles in Domesticated and Wild Rice Germplasm

Genome

Speciesa

AA AA AA AA AA

O. sativa O. glaberrima O. barthii O. nivara O. rufipogon

AA AA AA BB, BBCC BBCC CC

O. longistaminata O. glumaepatula O. meridionalis O. punctata O. minuta O. officinalis

CC CC CCDD CCDD CCDD EE FF HHKK HHJJ

O. rhizomatis O. eichingeri O. alta O. latifolia O. grandiglumis O. australiensis O. brachyantha O. schlechteri O. longiglumis

HHJJ GG GG GG

O. ridleyi O. granulata O. meyeriana O. neocaledonica

Distributionb

Number of accessions at IRRI Gene Bankc

Worldwide West Africa Africa Tropical, Subtropical Asia Tropical, Subtropical Asia, Tropical Australia Africa South, Central America Tropical Australia Africa Philippines, Papua New Guinea Tropical, Subtropical Asia, Tropical Australia Sri Lanka South Asia, East Africa South, Central America South, Central America South, Central America Tropical Australia Africa Papua New Guinea Indonesia (Irian Jaya), Papua New Guinea South Asia South, Southeast Asia Southeast Asia New Caledonia

91,963 1543 218 1258 1048 206 54 56 71 64 279 19 30 6 58 10 36 19 1 6 15 24 11 1

a

Lu (1999). Brar and Khush (2002). c Information from IRGCIS database as of February 7, 2003, http://grcsvr4/irgc/main.htm. b

the potential value of specific alleles in useful donors. The application of reverse genetics to determine the allelic state of candidate genes can be an attractive strategy to select wild germplasm for introgression. How to tap this genetic potential is the core agenda of functional genomics.

B. GRASS

AS A

SINGLE GENETIC SYSTEM

The grass family diverged from the dicotyledon (dicot) lineage about 200 million years ago (Wolfe et al., 1989; Crane et al., 1995). Within the grass family,

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an estimated . 10,000 species diverged from each other about 70 million years ago (Kellogg, 2001). During this period of evolution, there has been an expansion and contraction of the ancestral genome estimated to be about 3.5 pg/2C nucleus. Kellogg (1998) examined the DNA contents of representative species in relation to the phylogenetic tree of four subfamilies of grasses that contain the cereals and showed that the genome size had expanded in some but contracted in others (see Fig. 4 in Kellogg 1998). The Pooideae, which contains wheat, barley, rye, and oats, has a large genome (10 pg DNA per 2C nucleus), whereas members of the Ehrhartoideae have a generally smaller genome (3.3 pg). The large difference in genome size is attributed, to a large extent, to the expansion of repetitive sequences. The large genome size has been an impediment to the systematic sequencing of certain cereals, although progress has been made in identifying gene-rich regions in large genomes (Gill et al., 1996; Farris et al., 2000). That the grasses belong to a monophyletic group and all share a set of common “grass alleles” argues favorably for using a few reference species to avoid the difficulties (and expenditure involved) in sequencing individual species (Freeling, 2001). The concept of grass as a single genetic system rests on two main premises. First, the gene order on a chromosome is conserved in different related genomes. Second, there is sufficient conservation of gene content across species, and that function can be inferred by sequence structure. In dicots, Bonierbale et al. (1988) first demonstrated that tomato cDNA probes could be used to establish an RFLP linkage map in potato that is colinear with that in tomato. In monocots, Ahn and Tanksley (1992) used a common set of probes to show conserved linkage in maize and rice. Since then, comparative mapping in both dicots and monocots has led to the general conclusion that the gene order of related plant species is sufficiently conserved even after millions of years of evolution, making it possible to infer the location of a particular gene based on linkage information from another species (Devos and Gale, 2000). In most cases, colinearity between plant species can be demonstrated at the macro chromosomal level. There are, however, exceptions to colinearity due to the accumulation of chromosomal changes over millions of years of evolution. Such exceptions are particularly common at the micro level because of inversion, translocation, insertion, gene duplications, and deletions. A distinction between macro- and micro-colinearity is important because success in finding a gene through a syntenic relationship depends on the maintenance of micro-colinearity between related species. Freeling (2001) offers a useful clarification of the terms “colinearity” and “synteny”. Macro-colinearity is inferred when the same marker orders are observed in a typical mapping experiment with 10 — 20 cM resolution. Occasional chromosomal rearrangements may break up this gene order, but the pair of chromosomes in question is considered to be syntenic to each other. Thus, even for related species that do not have perfect macro-colinearity, they can still be syntenous.

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However, macro-colinearity may or may not enable one to predict gene position at a fine scale. For a syntenic relationship to be useful for finding a gene, we need markers with a high probability of finding the same adjacent markers in another species. Currently, the number of successful cases of gene isolation strictly based on positional relationships is still too few. The predictive power of the syntenic relationship has not yet been vigorously tested (Gaut, 2002). The exceptions in micro-colinearity, however, by no means argue against the use of grass as a single genetic system (Bennetzen and Freeling, 1997; Freeling, 2001). With the whole-genome sequence available, the benefit of using a model species to find a gene will be based more on gene sequence conservation rather than on map locations. For example, a candidate gene for disease resistance in barley can be used to find its orthologs in rice or vice versa without knowledge of map location. Thus, map locations are useful data, but they are not a prerequisite for finding orthologous genes to evaluate function. In the future, the syntenic relationship will be more relevant for mapping unique traits in a species with little sequence information. For example, drought tolerant traits are common in many millets which do not have detailed sequence information. In such a case, moderate investment in mapping in the “trait-bearing” crops can lead to identification of candidate genes in a species with a detailed sequence map. In summary, the original discovery of a colinear relationship between markers in different species triggered a large number of studies on the extent of gene order conservation. While macrosynteny (10 – 20 cM range) has usually been supported, evidence of microsynteny (within 1 cM, or in the 100-kb range) is more difficult to obtain. Rearrangements, insertions, and deletions appear to have disrupted the syntenic relationship, which potentially leads to futile pursuit (Feuillet and Keller, 2002; Li and Gill, 2002). Notwithstanding these exceptions, comparative analyses of genome organization and gene function within the grasses will elevate the studies of genome evolution and practical applications to a new level. With the completion of the rice genome sequence, a comparative genomic approach can now be pursued with even greater intensity.

III. RICE GENOME Not long ago, sequencing the genome of an organism was a daunting proposition. However, with advances in sequencing technologies, the cost of sequencing a genome has dropped dramatically. For the past decade, genome sequencing projects, either completed or begun, have produced such excitement in biological sciences that many young students have been attracted to the discipline of genomics. The complete genome sequence represents a public resource that opens up opportunities for new understanding of biological systems.

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With sequence available, identification and isolation of a gene can be largely done electronically or by simple PCR amplification, making gene cloning in the conventional sense something of the past. This may prove to be one of the biggest returns from the investment in sequencing projects.

A. GENOME SEQUENCING As of January 2003, four versions of rice genome sequences exist: three draft versions produced by Monsanto, Syngenta, and the Beijing Genomics Institute (BGI) and a highly accurate version produced by the IRGSP (for a summary, see Buell, 2002). The Monsanto and Syngenta sequences, originally produced by independent efforts, have now been integrated with the IRGSP sequence. The BGI sequence is from indica rice and has now been improved from the initial 4X draft to a higher-quality 6X draft (Yu Jun, personal communication). Different versions of the genome sequence serve different purposes and require different levels of investment. The Syngenta and BGI genome sequences were produced by a “shot gun” approach, whereas the IRGSP sequence used a map-based approach. In shot-gun sequencing, the whole genome is sheared into small pieces (1 – 2 kb), subcloned in plasmids, and sequenced. On average, each sequence is analyzed 4 to 6 times. Then the sequences are assembled by a computer program. In high eukaryotes, the high proportion of repetitive sequences can obscure the alignment of truly contiguous sequences. Computationally demanding programs are therefore needed to solve this problem to obtain a reliable assembly. The shotgun sequencing approach has been successfully applied to the complex genomes of human and Drosophila. Depending on the objectives, the shot-gun approach has the advantage of producing a draft sequence quickly and allowing researchers to identify similar sequences in different species. On the other hand, there are compelling reasons for developing a high-quality sequence using a map-based approach. Here, each stretch of sequence is physically anchored to a chromosome. At the outset, the IRGSP committed to sequencing the rice genome at an accuracy of , 1 error per 10,000 bases to provide the gold standard for accurate sequence annotation and for detecting natural variation. On December 18, 2002, the IRGSP announced the completion of the “phase 2” sequence data with all contigs anchored on the 12 rice chromosomes. As of February 2003, 94% of the genome is covered, spanning a genetic length of 1530 cM. By definition, finishing the rice genome means that: (1) all sequences are physically aligned to the chromosome map, (2) there are no gaps, and (3) all sequences are annotated. Currently, chromosomes 1, 3, 4, 10 are considered to be finished (phase 3). It is expected that a completely finished genome sequence will be accomplished by the end of 2004.

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B. SALIENT FEATURES

OF THE

RICE GENOME

Although sequencing of some chromosomes remains to be finished, the existing sequence information together with the completed Arabidopsis genome (The Arabidopsis Genome Initiative, 2000) give us a good picture of the structure and organization of the rice genome (Table III). For details, readers are referred to the original papers by Goff et al. (2002), Feng et al. (2002), Sasaki et al. (2002), and Yu et al. (2002). The rice genome is predicted to contain a large number of genes with an upper estimate as high as 62,000. On average, a gene can be found every 5– 7 kb, a density similar to that observed in Arabidopsis. The functional classification of rice genes is similar to the Arabidopsis sequence [see Fig. 9 in Yu et al. (2002) and Fig. 1 in Goff et al. (2002)]. About 30% of the rice genes are plant-specific. There are signs that rice experienced many duplication events at the level of genes or chromosomal segments. To determine the extent of gene duplication, Goff et al. (2002) examined homology among all predicted genes (longer than 300 bp) using the Syngenta draft sequence and found that 77% were homologous to at least another gene, yielding about 15,000 gene families, a picture similar to that observed in Arabidopsis and other eukaryotes. Local duplications (i.e., homologous genes mapped to a single BAC contig) range from 15.4 to 30.4% depending on the chromosomes. The authors concluded that a genome duplication event might have occurred in rice 40– 50 million years ago and that chromosomes 11 and 12 were probably a result of duplication about 25 million years ago. The extent of gene or chromosomal duplication has implications for the strategy for assigning function to each gene. If gene redundancy is widespread, simple knockouts at individual genes would not produce detectable phenotypes. However, it is too early to tell the extent to which structural duplication is translated into functional redundancy. Genes that are structurally similar may have diverged to provide developmental and tissue specific expression. We have yet to determine what proportion of the duplicated genes is indeed functionally equivalent. Also, the number of gene families in tight clusters will affect the way we confirm the function of a candidate gene. When we encounter a cluster of genes with similar sequences, how can we determine whether the QTL effect is due to a single member of the cluster or a collective effect of multiple members? For these reasons, better gene annotation and understanding of the functional relationship of paralogous and orthologous genes and members within gene families are important. While Arabidopsis and rice share common characteristics of plant genomes, there are significant differences. The average size of rice genes is larger than that of Arabidopsis. The larger gene size in rice is apparently due to larger introns. Thus, the transcript size of a rice gene is not much larger than that of Arabidopsis (Sasaki et al., 2002). The rice genome also has a high CG content.

Table III Features and Composition of the Rice Genome Revealed by Different Versions of the Rice Genome Sequencea Finished genome sequence Chromosome 1 of Nipponbare

Features

Chromosome 4 of Nipponbare

Draft Sequence Nipponbareb

Indica 93.11c

Arabidopsis thaliana

3.4 6.4 4.8 230 3.8 605 58.2

2.7 7.4 4.4 340 – 370 47.2

2.4 5.7 4.2 296 3.2 371 54.9

4.5 4.5 3.3 201 2.3 356 51.4

2.4 4.5 5.2 250 4.2 170 44.1

40.7

42.3

38.9

37.0

32.7

43.8 214 kb/cM for short arm; 288 kb/cM for long arm

44.0 253 kb/cMd

43.3 –

34.9 213 kb/cMd

Estimated genome size (Mb) Estimated gene number for whole genome

– 62,500

44.2 208 kb/cM for euchromatic region; 636 kb/cM for heterochromatic region – 57,000

420 32,000– 50,000

466 46,000– 55,000

125 25,498

RICE FUNCTIONAL GENOMICS

Gene size (kb) Gene density (kb/gene) No. exon/gene Exon size (bp) No. intron/gene Intron size (bp) CG content in coding region (%) CG content in non-coding region (%) CG content overall (%) Physical distance/genetic distance

a Compiled from published data in Yu et al., 2002, Goff et al., 2002, Feng et al., 2002, Sasaki et al., 2002, and The Arabidopsis Genome Initiative (2000). – ¼ no information provided in these papers. b TIGR IRGSP annotation statistics as of 23 Jan 2003 (www.tigr.org/tdb/e2k1/osa1/riceInfo/info.shtml#Genes) c Beijing Genomics Institute published draft (4X genome coverage), Wong et al. (2002). d For rice, assuming a genetic length of 1699 cM and a genome size of 430 Mb; for Arabidopsis, assuming a genetic length of 586 cM and genome size of 125 Mb.

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Wong et al. (2002) observed a negative gradient of CG content starting from the 50 end to 30 end of the coding region. This CG gradient may reflect codon usage, and hence may influence the accuracy of gene prediction as most gene prediction programs do not take into account such a bias. It has been suggested that the large difference between the number of predicted genes in rice and Arabidopsis may in part be caused by artifacts of gene prediction. Nearly all Arabidopsis genes can be found in rice but not vice versa. Based on the draft sequences of indica and japonica and the completed chromosomes 1 and 4, nearly 50% of the predicted rice genes have no known function in Arabidopsis. Yu et al. (2002) suggested that the rice genome represents a “superset” of plant genes, only part of which is present in Arabidopsis. Although part of the difference could be due to imperfect sequence annotation, rice probably has many unique genes that are diverged from Arabidopsis genes. Since a large sequence dataset is not yet available in other cereals, a pairwise genome comparison with other cereals is not yet possible. Nonetheless, the utility of rice sequences for other cereals has been demonstrated by using rice sequences as substrates for investigating gene expression and genetic variation in other species. Goff et al. (2002) reported that 90% of the rice sequences arrayed as oligos on a chip can be used to detect expression in maize, barley, and wheat.

C. COMPARISON BETWEEN INDICA

AND

JAPONICA RICE

Because genetic variation within a species represents the raw material for adaptive changes, special attention is given to examining the occurrence of single nucleotide polymorphisms (SNP), deletions and insertions between indica and japonica rice on a genome-wide scale. At the whole-chromosome level, Sasaki et al. (2002) used sequences from finished chromosome 1 of Nipponbare to query the assembled contigs of the BGI draft indica genome data. They found about 78% of the japonica sequence in the indica database but failed to identify matches to the remaining 22% of the sequences in chromosome 1. How much of this discrepancy is due to genetic differences between the indica and japonica sequences or to artifacts in the assembly of the indica sequence remains to be determined. At a finer scale, Feng et al. (2002) compared a 2.3-Mb region on chromosome 4 of japonica (Nippponbare) and indica (Guanglui 4) rice. While the overall colinearity is maintained, there were many insertions and deletions. Insertions occurred not only in the intergenic region but also in the coding regions. Interestingly, there were more transposable elements in the japonica chromosome, contrary to the common perception that the total genome size of indica rice is larger than that of japonica rice. Overall, there was one SNP per 268 bp (0.37%) between the two sequenced varieties in this region of the chromosome.

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Using genome sequence of indica variety 93-11 as a reference, Yu et al. (2002) examined the polymorphisms with three varieties — Nipponbare, PA64s, and Guanglui 4 — and found frequencies of SNP in unique sequences (relative to 93-11) at 0.50, 0.35, and 0.50%, respectively. Nasu et al. (2002) examined 417 chromosomal regions in three japonica, two indica varieties and a wild relative O. rufipogon and found one SNP in 232 bp between two randomly chosen lines. In comparison, about 1 SNP in 1000 bp was observed between Arabidopsis ecotypes Columbia and Landsberg (Cho et al., 1999). More recently, Katagiri (2003) presented preliminary data on the sequence variation between chromosome 1 of indica variety Kasalath and Nipponbare. Of 189,912 bp BAC-end sequences compared, one SNP/122 bp was observed. About 0.15% of the SNP was found in expressed genes. Monna (2003) analyzed six japnoica varieties (Nipponbare, Koshihikari, Kitaake, Akihikari, Sensho, and Itadaki) and two indica varieties (Kasalath and Guang-lu-ai 4) and found more SNP between the two indica varieties than between japonica and indica varieties. It is too early to conclude that these statistics are indicative of the degree of variation between the subspecies as these differences may simply reflect differences between varieties (rather than between two subspecies). A generalization of level of SNP can only be made after more sequence data become available for multiple varieties within japonica and indica rice. Such a determination of the within- and between-subspecies variation will be important for applying nucleotide-level variation in genetic association tests.

IV. KEY INGREDIENTS FOR GENE DISCOVERY IN RICE The genetic blueprint of rice can be viewed as a dictionary of characters, but without explanation of their function. To find the gene function encoded by each gene or set of genes requires special kinds of genetic resources and methodologies. Figure 3 depicts such a platform of resources and technologies for large-scale gene discovery. The platform has three basic components: genetic resources, phenotypic/biological evaluation, and genomic tools. These three components are integrated by bioinformatics, which provides integration of data produced from different activities. Furthermore, bioinformatics helps develop hypotheses for further experimentation. This reiteration of hypothesis testing and data production eventually leads to the discovery and validation of gene functions. The framework brings multidisciplinary teams and different expertise together. It also highlights the importance of delivering the products of gene discovery to end-users through collaboration with breeding programs that can incorporate genes into appropriate genetic backgrounds.

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Figure 3 Public research platform for rice functional genomics: genetic resources, phenotyping, high-throughput genomic tools, and bioinformatics. Linkage to breeding networks in rice growing countries represents the practical dimension of rice gene discovery.

A. GENETIC RESOURCES Genetic stocks harboring the traits of interest and associated variation are the essential materials for revealing biological function of a sequence. A range of genetic stocks can be used to identify or validate gene function. These include mutants, segregating mapping populations, backcross lines, well-characterized breeding lines and traditional germplasm. Among these genetic stocks, mutants are the “work horse” of gene discovery because they reveal phenotypic changes that correspond to a precise change in the genome. Furthermore, mutations often lead to over- or under-production of biochemical intermediates that can lead to identification of pathways. Induced mutations alone, however, are not sufficient to understand phenotypic variation caused by allelic diversity. Therefore, it is important to couple functional analysis with the diverse germplasm developed for genetic mapping and breeding. Allelic variation present in conserved or selected germplasm is particularly valuable because they represent products of natural selection under various evolutionary forces.

B. HIGH-THROUGHPUT TECHNOLOGIES A distinct feature of functional genomics is the application of high-throughput technologies that can reveal the behavior of thousands of genes at one time (Hieter and Boguski, 1999). Large-scale and genome-wide methodologies

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become an essential part of experimental design, hypothesis testing, and data analysis. We consider only briefly the current technologies that are sufficiently robust for broad applications. With available genome sequence information from the large-scale sequencing of many organisms, it is possible to produce arrays carrying thousands of genes, providing a high-throughput method for gene expression analysis (essentially reverse northern analysis) and for the detection of genetic variation (reverse Southern analysis) (DeRisi et al., 1997; Schena and Davis, 1999). Originally, there were two main technologies for producing gene arrays: (1) spotting DNA [cDNA clones, PCR products, oligonucleotides (oligos)] and (2) in situ synthesis of oligos on slides by photolithography (Fodor et al., 1991). New and “hybrid” methodologies have been developed: ink-jet deposition (Hughes et al., 2001) and maskless array synthesis (Nuwaysir et al., 2002), which improve the flexibility and utility of the arrays. Furthermore, some arrays (e.g., Diversity Array) assay the genetic diversity of any organism without depending on available sequence information (Jaccoud et al., 2001). While spotted cDNA arrays are currently the most commonly used, increasing attention is given to oligo arrays (50 or 60 mers) because of their increased specificity, quality control, and portability (Kane et al., 2000). Depending on the design of the oligos (choice of gene sequence, oligo size and number), oligo arrays can be used effectively to detect nucleotide polymorphism or deletions in the target genomes. Currently, rice gene arrayscDNA or oligos- of varying sizes and coverage are produced by individual groups or national programs. Progress is being made to develop large-scale oligo arrays for rice that are publicly accessible (S. Kikuchi, personal communication; Beijing Genomics Institute, personal communication). Although gene arrays provide a picture of the transcription profile, they do not reveal post-transcriptional regulation of the genes. As in transcript analysis, proteomics provides a high-throughput platform to survey many proteins simultaneously under different experimental conditions (Zivy and de Vienne, 2000). Proteins can be separated in two-dimensional polyacrylamine gel electrophoresis (2D-PAGE), first by isoelectric focusing in the first dimension followed by molecular weight in the second dimension. Protein spots can then be extracted for microsequencing. In general, 2000 –3000 proteins can be resolved and analyzed using the 2D gel technique coupled with mass spectrometry. New technologies for proteomic analyses are being developed (Koller et al., 2002). Readers are referred to an overview of proteomic technology and its applications for gene discovery by Salekdeh et al. (2002a).

C. BIOLOGICAL EVALUATION High-quality phenotypic data are central to a gene discovery program yet infrastructure and expertise for phenotyping are often taken for granted. The lack

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of adequate phenotyping capacity can be a bottleneck for gene discovery. High-quality phenotyping means a precise, reproducible biological assay of the trait in question. Phenotyping may entail different levels of sophistication from simple visual evaluation to the measure of a detailed metabolic response under highly controlled environmental conditions. Overall, in a chemical- or irradiation-induced mutant population, 6 – 10% of the mutant lines has visible phenotypes in either the vegetative or reproductive stage. While the obvious phenotype (e.g., plant size and color) can be discerned by inexperienced observers, subtle morphological changes often require trained eyes. Plant breeders, for instance, are particularly keen to observe overall plant architecture, panicle shape, and grain filling as a means to improving plant type and yield. To maximize the use of any large collection of mutants, it would be beneficial to have regular “grow-out” field days for expert observers to select phenotypes of interest. For a lot of agronomic traits, the target phenotypes must be observed through conditional screens in which mutants, isogenic lines and breeding lines are subject to specific conditions to express the phenotypes. For examples, in screening for drought-tolerance, the developmental stages (vegetative or reproductive) at which the plants are subject to water stress may determine the kinds of genes that can be identified. The nature and parameters of conditional screens are therefore vital for successful detection of the desired traits. Mutants with a uniform genetic background have the advantage that any deviation in phenotype can be identified readily. As we search for specific variants, innovative screening techniques using reporter gene constructs will prove useful (Page and Grossniklaus, 2002). Furthermore, screening for secondary mutations will be increasingly important for defining the effect of the first mutations and revealing interaction between genes and pathways.

D. BIOINFORMATICS Bioinformatics brings together biological and computational tools that can integrate data from structural and functional analyses and present new models and hypotheses for further testing or validation. Bioinformatics helps us integrate information to formulate new questions to be examined, hence bringing new insights into the process of gene discovery. Because of the increasingly large amount of information produced, it is no longer adequate to examine data in an isolated manner. A key function of bioinformatics is to provide a system by which information can be retrieved, analyzed, and distributed to a broad community of researchers and users. In summary, the four components — genetic resources, biological evaluation, genomics tools, and bioinformatics — require diverse skills in biology and modern technologies. They require infrastructure in modern science and

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engineering (nanotechnology, computer science, etc.) and conventional biological studies (phenotyping and innovative screening procedures). While individual laboratories or institutions may be well suited to pursue some of these activities, a single laboratory will unlikely have all the resources and expertise to examine all questions. Thus, functional genomics is necessarily a collaborative process and a public resource platform provides a framework for the division of labor and formation of partnerships.

V. FORWARD AND REVERSE GENETICS Forward genetics refers to finding gene function by starting with a phenotype and then identifying the genes responsible through inheritance studies, followed by genetic mapping to locate the target gene on a chromosomal region. By “walking” down the chromosome using overlapping markers, the DNA sequence responsible for the trait can eventually be identified. The final step of gene isolation can be hastened considerably with the whole-genome sequence available. Reverse genetics, in contrast, begins with a known DNA sequence and works backward to identify the biological function ascribed to that sequence. Typically, a sequence with a predictive function is used to search for knockout lines in a pool of DNA or electronically from databases of insertion mutants (see below). Once a mutant is found, it can be assayed for phenotypes based on the functional prediction. For organisms with whole-genome sequences available, this provides a new paradigm for gene discovery. With forward and reverse genetics, it is within our reach to systematically assign a function to every gene of interest in rice. For this reason, there have been many initiatives around the world to produce rice mutants. Below, we will use several representative mutant stocks to demonstrate the various strategies.

A. INSERTIONAL MUTANTS Insertional mutagenesis has been the favored approach for tagging genes of interest. The most advanced systems have been developed in maize and Arabidopsis using both transposons and integrative plasmids as the mutagenic agents. Although the initial investment in producing insertion mutants is high, the pay off is great because the mutated genes are potentially tagged. Using a variety of vectors originally designed and tested in Arabidopsis, rapid progress has been made in the production and characterization of rice insertion mutants over the past several years.

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1.

T-DNA Tagging

The Arabidopsis knockout facility at the University of Wisconsin recently established a population of 60,480 T-DNA-tagged lines (Krysan et al., 1999). This is a significant step toward the production of genome-wide mutations. T-DNA insertion in Arabidopsis is probably a random event, with the inserted sequences being stable through multiple generations (Azpiroz-Leehan and Feldmann, 1997). Recently, this T-DNA tagging strategy was employed in rice (Jeon et al., 2000). An’s laboratory at Pohang University of Science and Technology in Korea has produced approximately 100,000 fertile rice lines that have been tagged by T-DNA using binary vectors of Agrobacterium tumefaciens (Jeon et al., 2000). Figure 4 illustrates the basic principle of insertional mutagenesis using known DNA elements. Analysis of randomly selected transgenic plants has indicated an average of 1.4 loci of T-DNA inserts per line. Therefore, the T-DNA knockout stocks would provide a 63% probability of finding an insertion within a given gene if T-DNA inserts randomly. It appears that T-DNA prefers transcriptionally active regions in rice (Barakat et al., 2000). Therefore, the chance of finding a knockout gene in the rice T-DNA insertional stocks would be higher than the estimated value. One difficulty in establishing a population of T-DNA-tagged lines is obtaining enough seeds from the first generation. In experiments with 10,743 primary transgenic plants, 39% bore fewer than 50 seeds and only 50% yielded more than 100 (Jeon et al., 2000). Yin and Wang (2000) observed similar results from their analysis of 2633 transgenic rice plants. Although the majority of the transgenic plants became fertile in the T2 generation, 22% of the lines showed

Figure 4 Production of insertion lines using introduced T-DNA and endogenous retrotransposon Tos17. T-DNA can be inserted into either a gene (line 1) or an intergenic region (line 2). During the transformation procedure, the endogenous retrotransposon Tos17 can be activated and inserted into a gene (line 3).

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less than 50% fertility. Reduced fertility is probably due, in part, to the lethal effects of T-DNA insertion. However, a considerable number of lethal mutations are likely induced during tissue culture. Those lines that show low fertility must be individually amplified before being used for further analysis.

2.

Transposon Tagging

Transposon tagging has been a powerful tool for isolating new genes since the controlling element was first recognized by McClintock (Fedoroff et al., 1983). The first successful cloning of a plant gene was achieved via the Ac/Ds (Activator – Dissociation) transposon system (Fedoroff et al., 1984). Other transposon systems, such as En/Spm (Enhancer/Suppressor – mutator) and Mu (Mutator), have been used for cloning several genes of maize (Walbot, 2000). These elements are active not only in their natural hosts but also in other plants, including Arabidopsis. The maize Ac/Ds system has been tested for gene tagging in rice. First, the autonomous Ac element was cloned between a promoter and the hygromycin phosphotransferase coding region. The construct was then introduced into rice chromosomes by direct transformation. Transposition of the Ac element was proven by recovering hygromycin-resistant plants (Izawa et al., 1991; Murai et al., 1991). Enoki et al. (1999) analyzed the behavior of 559 plants from four transgenic rice families through three successive generations; 18.9% of the plants contained newly transposed Ac insertions. DNA pools from 6000 Ac-inserted plants were organized in a 3D matrix (row — column –plate pools) and subjected to PCR screening. Of the 14 randomly selected genes, two knockouts were identified, one of which encodes the rice cytochrome P450 (CYP86) gene. Molecular analysis of Ac transmission to the progeny revealed that its germinal transmission was detected in 9 of the 12 insertions, indicating that some Ac transmissions occurred in somatic tissues that would not transmit to the progeny. Sequence analysis of 99 flanking regions over the 50 region of Ac indicated that it preferentially transposed into protein-coding regions. The non-autonomous Ds element has been transposed in the presence of Ac transposase via the direct gene transfer method (Shimamoto et al., 1993; Sugimoto et al., 1994). Germinal transposition of Ds was observed at a high frequency in the R2 progeny when a transgenic plant containing the Ds element was crossed with a transgenic plant carrying Ac transposase under the control of the cauliflower mosaic virus (CaMV) 35S promoter. A wide spectrum of mutations, affecting growth, morphogenesis, flowering time, and disease resistance, was observed in the Ds population (Izawa et al., 1997). Whether these mutations are due to Ds must still be determined. The frequency of Ds transposition declined significantly in subsequent generations, even in

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the presence of Ac transposase. However, transposition could be recovered in certain lines through protoplast regeneration. Similar Ac/Ds gene tagging systems have been developed for rice by employing the Agrobacterium-mediated gene delivery method (Chin et al., 1999; Nakagawa et al., 2000). This method mediates transfer of one to a few copies of T-DNA into plant cell chromosomes, thereby making the transgene more stable compared with the direct gene transfer method (Dai et al., 2001). As many as 80% of the Ds elements have been excised from the original integration sites in the presence of Ac transposase activity provided in trans. The germinal excision frequency was no more than 40% (Nakagawa et al., 2000). Repetitive ratoon culturing caused new transposition, at about 30% frequency, thereby demonstrating that the procedure is capable of producing a large population of mutants (Chin et al., 1999). Linkage analysis of the empty donor site and the transposed Ds insertion site revealed that four of five insertion sites were linked to the donor site (Nakagawa et al., 2000). This indicates that most of the transposition events occurred at a tightly linked site, as had been reported for Arabidopsis (Machida et al., 1997; Sundaresan, 1996). In that species, the physical map positions of 356 Ds insertions showed significant preference for transposition to the regions adjacent to the nucleolus organizer regions (Parinov et al., 1999). For any given gene, insertions preferentially occurred at the 50 end of the gene. Therefore, the shortrange and highly preferential transposition system can be effectively used for the targeted mutagenesis of closely linked genes. In contrast, transpositions of another mobile element, En/spm, were well distributed over different chromosomes and were not clustered at a few genomic locations in Arabidopsis (Wisman et al., 1998). However, the tendency of the En/Spm element to amplify can potentially complicate the interpretation of phenotypes and molecular analyses. Ac/Ds in conjunction with Spm/dSpm has been proposed for use in developing a four-element system for directed tagging of crop-specific alleles (Phogat et al., 2000). In the first step, stocks carrying Ds within dSpm would be crossed to a line with Spm to facilitate random transposition of the transposon. In the second step, individual lines would be crossed to a line with Ac to induce local transposition of the Ds element. This four-element system would therefore exploit the natural tendency of unlinked jumps of dSpm and linked jumps of Ds. Unfortunately, the Ac element in this four-element system would undergo frequent excision from the target gene, causing variegation. To overcome this, a self-stabilizing Ac derivative has been developed that undergoes autonomous transposition but is stable after integration (Schmitz and Theres, 1994). Charng et al. (2000) have designed an inducible transposon system using an element that contains a PR-1a promoter::transposase fusion (PR-1a::Tpase). Treatment with salicylic acid (SA) induces the expression of transposase, resulting in the transposition of Ds in somatic tissues. Germinal transposition events also have

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been observed after SA treatment of flowers. The development of inducible transposable element systems will also be valuable for rice. Recently, Qu et al. (2003) explored the use of a GVG chemically inducible system to control transposition of Ds. Preliminary data suggested that transposition of Ds could be induced by dexamethasone (DEX).

3. Retrotransposon Tagging Another effort toward gene tagging in rice involves introducing the tobacco retrotransposon Tto1 and demonstrating its autonomous transposition through reverse transcription (Hirochika et al., 1996). A transposable element, Tag1 from Arabidopsis, has also been analyzed in rice (Liu et al., 1999b). The transcription and excision behaviors of Tag1 were similar in both rice and Arabidopsis. However, the excision was tissue-specific only in rice. A survey of 73,000 sequence-tagged connectors (STC), corresponding to nearly 50 Mb of the rice genome, showed that retroelements are randomly distributed with respect to potential genes (Mao et al., 2002). Retrotransposons are both functionally and structurally different from well-characterized transposable elements such as Ac and Spm. These retrotransposons are believed to be involved in gene duplication as well as in the regulation of gene expression. Transposition of some retroelements can be induced by stresses caused by pathogen infection, cell culture, and wounding (Takeda et al., 1998; Parinov et al., 1999). Such endogenous elements in the rice genome have provided excellent tools for gene tagging. One such element is Tos17, which is activated during tissue culture and amplified up to 30 copies (Parinov et al., 1999). A gene knockout system using Tos17 has been developed for identifying insertional mutations in several genes. After screening 550 plants that were mutagenized by Tos17, Sato et al, (1999) identified a mutation in the homeobox gene OSH15. Rice phytochrome A ( phyA) mutant lines have also been isolated from a Tos17induced mutant population using a 3D DNA-pooling system (Takano et al., 2001). By screening for viviparous mutants, Agrawal et al. (2001) identified Tos17 insertions in the rice zeaxanthin epoxidase gene (OsABA1) and in a novel OsTATC gene, which shows a weak homology with bacterial Sec-independent translocase TATC. More than 32,000 Tos17 insertion lines have been produced and more than 8600 independent sequence-flanking insertion sites have been determined (Miyao et al., 2001). The mutant stocks are available upon request (see http://tos.nias.affrc.go.jp/~miyao/pub/tos17). Analysis of insertion points in the rice genome sequence has revealed that insertions in the genic regions (exon and intron) were 2-fold higher than in the intergenic region, suggesting that Tos17 prefers the genic regions for target sites. In addition, hot spots of Tos17 insertions

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were observed, with some of them being clustered. This may limit the efficiency of genome-wide gene tagging using Tos17 alone.

4.

Activation Tagging

Conventional strategies, such as T-DNA mutagenesis or transposon tagging, are not efficient for analyzing the function of redundant genes. Likewise, neither T-DNA tagging nor transposon tagging will identify the genes that are required during multiple stages of a life cycle and whose loss-of-function results in early embryonic or gametophytic mortality (Weigel et al., 2000). Fewer than 10% of the genes tagged in the Arabidopsis genome are likely to produce a visible phenotypic change. Therefore, complementing technologies are needed to assess the function of the remaining genes. One such technique is the activation-tagging system developed in Arabidopsis and successfully used for cloning several genes (Kardailsky et al., 1999; Ito and Meyerowitz, 2000; Lee et al., 2000; Weigel et al., 2000). This system uses T-DNA vectors that contain multimerized CaMV 35S transcriptional enhancers positioned near the right T-DNA border (Hayashi et al., 1992). Gain-of-function mutagenesis in rice by activation tagging requires a strong enhancer element. Since the CaMV 35S enhancer element has enhancer activity in rice cells, they can be used to produce activation tagging lines in rice (Fig. 5). A transposon-mediated activation-tagging system has also been developed using the Ds element that carries the tetramerized CaMV 35S enhancer (Mori et al., 2000). From the T-DNA activation-tagging pools of Arabidopsis, Weigel et al. (2000) characterized over 30 dominant mutants with various phenotypes.

Figure 5 Activation tagging using an insertion construct carrying a 4X 35S promoter to drive the expression of endogenous genes. The multimerized 35S enhancer located at one end of the T-DNA can enhance the expression of a nearby gene from upstream (top) or downstream (bottom) of the gene.

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Analysis of a subset of the mutants has shown that the tagging vector causes overexpression of the gene immediately adjacent to the inserted enhancer. In rice, T-DNA activation-tagging vector pGA2715 was developed for both trapping and activation tagging of rice genes (Jeong et al., 2002). The binary vector pGA2715 contains the promoterless glucuronidase (GUS) reporter gene next to the right border and the multimerized transcriptional enhancers from the CaMV 35S promoter next to the left border. A total of 13,450 T-DNA insertional lines were produced using the binary vector pGA2715. Histochemical GUS assay revealed that the GUS-staining frequency from the pGA2715 lines was about two times higher than that from the lines transformed with binary vector pGA2707, which lacks the enhancer element. This result suggests that the enhancer sequence present in the T-DNA enhanced GUS tagging efficiency. RT-PCR analysis of a subset of randomly selected pGA2715 lines has shown that expression of the genes immediately adjacent to the inserted enhancer was increased significantly. These results indicated that the large population of T-DNA-tagged lines transformed with pGA2715 could be used for trapping a gene using the GUS reporter as well as for isolation of gain-offunction mutants in rice.

5.

Entrapment tagging

An entrapment-tagging system allows for monitoring gene activity by creating fusions between tagged genes and a reporter gene, such as GUS and green fluorescent protein (GFP). Insertion of the promoterless reporter will not only destroy normal gene function but also activate expression of the reporter gene. Three entrapment systems are available: enhancer trap, promoter trap, and gene trap (Springer, 2000). The devices for entrapment can be transferred into plant cells as a part of T-DNA or transposons (Sundaresan, 1996; Martienssen, 1998; Springer, 2000). This approach has been successfully applied to genes that are difficult to identify by traditional methods. Some examples are regulatory sequences that drive reporter gene expression in nematode-feeding structures (Barthels et al., 1997), molecular markers for embryogenesis (Topping and Lindsey, 1997) or regulatory sequences that mediate guard-cell-specific expression (Plesch et al., 2000). Activation of a reporter gene in a promoter trap vector can be as high as 30% (Sundaresan, 1996). In rice, at least 5% of the T-DNAs and 10% of the transposed Ds elements become activated in various tissues, for examples, roots, leaves, flowers, and seeds (Chin et al., 1999; Jeon et al., 2000). If reporter gene activation by certain environmental conditions or by chemicals such as growth substances is included, total tagging efficiency can be higher. Some of the tags have displayed tissue- or organ-specific reporter expression, while others have exhibited

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ubiquitous expression patterns. One must study whether these lines will manifest any mutant phenotypes in organs where the reporter has been activated, and whether the phenotypes co-segregate with the inserted reporter gene. The GUS gene has frequently been used for gene trapping in plants because of the accurate detection of its gene product and tolerance for the N-terminal translational fusions in its enzyme activity. However, one problem with the GUS assay is the destructive nature of its staining and destaining procedures (Jefferson et al., 1987). Non-invasive and non-destructive reporter genes, such as GFP or luciferase, have not yet been widely used for gene trapping in plants. Recently, a visualization system with a charge-coupled device camera, band-pass filters, and a light source was used to demonstrate that green fluorescence emitted from GFP could be visualized in the calli, dry seeds, roots, and seedlings of transgenic rice plants (Chung et al., 2000). Such an efficient visualization system should facilitate the use of a non-destructive visual selection marker for entrapment tagging in rice. We are currently investigating the efficiency of GFP expression in rice plants transformed with the vector pGA2717 that carries the promoterless GFP next to the left T-DNA border (Gyn An, unpublished data). A new genomic DNA-based signal sequence trap method, the signal-exon trap (SET), may help identify genes that encode secreted and membrane-bound proteins (Peterfy et al., 2000). SET is based on the coupling of an exon trap to the translation of captured exons, thus allowing the screening of exon-encoded polypeptides for signal peptide function. This system may be helpful in the discovery of novel members of known secretory gene clusters as well as for other positional cloning approaches.

6.

Forward Screening

Insertional mutant pools are useful resources for studying gene function. First, they can be used to identify mutants whose function is altered by the insertion. Because most mutations are recessive, their phenotypes are easily detected in a segregating population. However, because rice plants are much larger than Arabidopsis and need to be grown in specific conditions, large-scale phenotypic screening is often limited by space and time. Systematic efforts by collaboration with several research groups are therefore needed to screen a large number of mutants. Because mutations could be induced by unknown endogenous transposons during tissue culture, one must determine whether the phenotypes induced by the rice transformation are due to the insertion. Once the mutant phenotype has been shown to co-segregate with the insertional element, the sequence flanking the insert can then be isolated by PCR-based methods, such as thermal asymmetric interlaced (TAIL) PCR (Liu and Whittier, 1995),

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adapter-ligated PCR (Balzergue et al., 2001), inverse PCR (Triglia et al., 1988), a universal biotinylated adapter amplification procedure (Hanley et al., 2000), a panhandle PCR (Walbot, 2000), or a plasmid rescue system (Weigel et al., 2000).

7.

DNA Pool Screening

Insertional mutants can be identified in a given gene via PCR-based screening (Krysan et al., 1999; Sato et al., 1999). Using a gene-specific primer and a primer located near the end of the insert, the fragment flanking the insert can be amplified and its sequence then determined. Because screening of individual lines requires great effort, DNA pools of a large number of lines are commonly used for identifying a knockout. In Arabidopsis, it was reported that the maximum useful pool size is about 2350 lines per pool (Krysan et al., 1999). Because the rice genome is about four times larger than the Arabidopsis genome, a pool of 500 lines (less complexity) would be needed for PCR-based screening of the knockout mutations in rice. DNA pool screening has been performed from the Tos17 insertional pools. An’s laboratory has prepared DNA pools from 40,000 T-DNA lines and provided the screening service for the isolation of mutants from the T-DNA insertional lines. Figure 6 shows the overall scheme of detecting insertional rice mutants from the screening of DNA pool from T-DNA tagged lines. 8.

Insertion Sequence Database

Random sequence analysis of the fragments flanking the insertional element can help identify gene function. Flanking sequences from Ds insertions of 931 independent transgenic lines in Arabidopsis have been characterized (Parinov et al., 1999). In a similar study, Ito et al. (2002) determined the genomic sequences flanking the Ds elements and mapped the elements’ insertion sites in 1173 transposed lines by comparison with the published genomic sequences. One-half of the lines contained Ds on the same chromosome, in particular, the hot spots near the three starting loci. In the other lines, the Ds elements were transposed across chromosomes. The Ds elements tended to transpose near the chromosome ends and rarely transposed near the centromeres. Likewise, directsequence characterization of 761 Mutator-tagged fragments in maize has shown that random sequencing of transposon-tagged fragments can produce significant numbers of interesting transposon-tagged genes and mutant lines (Hanley et al., 2000). In rice, the flanking sequences of Tos17 were determined by TAIL- and suppression-PCR (Hirochika, 2001). As of October 2002, 23,709 flanking sequences from 2424 insertion lines had been determined (A Miyao et al.,

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Figure 6 PCR screening of DNA pool obtained from T-DNA insertion lines. a) DNA from individual plants pooled in different levels of complexity. Each pool contains DNA from 50 to 100 lines. A super pool consists of 500 lines. b) A rice gene OsMADS6 is used to illustrate PCR screening to detect the tagged mutant. Four screens were conducted using two primers from the gene and two

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unpublished data). These are classified into 8653 independent flanking sequences, about 39% of which showed homology to known genes. The flanking sequences of T-DNA insertional lines and Ds-tagged lines of rice are being determined. This strategy should be useful for both the identification of mutants and the discovery of new genes. Since the rice genome sequence is almost completed, disrupted genes can be readily identified and it will be possible to map the insertion sites on the chromosomes by comparing the flanking sequences with the genomic sequence. An insertion mutant line of any gene can be identified in silico and, thus, flanking-sequence databases will become the basis for reverse genetics studies. Construction of such databases through collaboration with laboratories producing rice insertional mutant lines will be valuable for determining gene function on a large scale.

B. CHEMICAL-

AND IRRADIATION-INDUCED

MUTANTS

Although insertional mutagenesis represents the most direct approach for tagging a particular gene, there are technical and logistical problems in achieving complete genome coverage based on insertional mutagenesis alone. First, the investment of producing a large collection of insertion mutants is high and not necessarily feasible for many laboratories. Second, insertional mutagenesis based on the introduction of exogenous transponsons and T-DNA is often genotypedependent. Currently, only certain japonica rice genotypes are amenable to efficient large-scale transformation. Activation of retrotransposons such as Tos17 depends on the ability to perform tissue culture and the level of transposition also varies among genotypes. Third, the transgenic nature of the mutants often prevents large-scale field cultivation in certain parts of the world. Finally, parts of the genome may not be accessible to insertional inactivation, thus preventing complete genome coverage. In contrast, chemical and irradiation mutagenesis offer several advantages. Production of the mutants is relatively inexpensive. Any genotype can be mutagenized and the distribution of mutations is probably random in the genome. The main shortcoming of the chemical- and irradiationinduced mutants is that they are not tagged, making subsequent gene isolation difficult. However, with the new high-throughput genotyping techniques, it is now possible to detect mutations rapidly, hence increasing the utility of these mutants. primers located near the right or left border of the T-DNA. In the first step, super pool 7 was detected to carry a T-DNA insertion in the OsMADS6 gene. To increase sensitivity and fidelity, a DNA-gel blot analysis was performed with the radioactively labeled OsMADS6 probe. In step 2, pool 65 was identified to carry the insertion. An individual line that carried the insertion was identified in step 3. The exact insertion position can be determined by sequencing the PCR product from step 3.

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1.

Choice of Mutagens

To produce a comprehensive mutant library useful for forward and reverse genetics, we need to consider the efficiency and specificity of the mutagens. Efficiency means how much potency the mutagen has for producing mutations. It also means that the mutagenesis procedure should be straightforward such that a sufficient number of mutations in the genome can be produced in a few largescale experiments. In this respect, chemical mutagenesis is more convenient than irradiation experiments as special radiation facilities are not needed. Specificity refers to the predominant kinds of mutations (small or large deletions, point mutations) produced by a particular mutagen (Koornneef et al., 1982). Depending on the approaches to be used to identify mutations, specific mutagens can be selected to produce the desired type and size of genetic lesions. Ethyl methanesulfonate (EMS) is perhaps the most commonly used mutagen in plants because of its potency and ease of use. The mutagen causes mostly transition point mutations. In rice, EMS mutagenesis involves soaking the seeds in an aqueous solution at a chosen concentration (from 0.2 – 2.0%) for a given time. It should be emphasized that rice genotypes have a different sensitivity to mutagens. It is therefore important to produce a kill curve to determine the desirable level of survival. An advantage of EMS mutagenesis is that a relatively small mutant population (, 10,000) is needed to provide near-genome coverage because the genome can tolerate a large number of point mutations without showing deleterious effects. In Arabidopsis, point mutation density can be as high as four mutations per Mb (L. Comai, personal communication). Deletion can be induced by a variety of chemical and irradiation treatments. Ionizing radiation has been widely used to induce mutations for plant breeding and classical genetic analysis, but the consequences of ionizing radiation have been examined at the molecular level only in a few organisms. Shirley et al. (1992) examined the effects of fast neutron (FN) and X-ray irradiation at two genetic loci encoding chalcone flavonone isomerase and dihydroflavonol 4-reductase in Arabidopsis. They found not only deletions at the loci but also distant chromosomal rearrangements. From a population of FN-induced M2 seedlings, Bruggemann et al. (1996) isolated 20 mutations at the HY4 locus in Arabidopsis; 15 of these were deletions detectable by RFLP analysis. Okubara et al. (1994) used FN to produce lettuce mutants with increased susceptibility to downy mildew. Of 14 mutations at four Dm genes isolated from 2211 FN-treated M2 lines, 10 were shown to be deletions by analysis of RAPD markers linked to the Dm genes. Besides ionizing irradiation, several chemicals have been shown to induce predominantly small to kilobase-size deletions. Using diepoxybutane (DEB) to induce mutations at the rosy locus in Drosophila, Reardon et al. (1987) found that 43% of the mutations were deletions ranging from 50 to 8 kb. Yandell et al. (1994) found that trimethylpsoralen was effective in inducing deletions, ranging

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from 0.1 to 15 kb in Caenorhabditis elegans. Aguirrezabalaga et al. (1995) showed that hexamethylphosphoramide (HMPA), a cross-linking agent, caused deletions in 96% of all mutants recovered in Drosophila. How many mutants do we need to have sufficient coverage of the genome? In Arabidopsis, Krysan et al. (1996) estimated that the probability of any gene being tagged by T-DNA insertion depends on the size of the gene and the total genome size. Dividing the total genome by an average gene size will give the number of targets in the genome to be tagged or mutated. In Arabidopsis, to have a 95% chance of finding an insertion in a 3.2-kb gene, one would need 95,000 T-DNA lines (assuming 1.5 insertions per line). Extending the same reasoning to rice (with approximately 4 £ the genome size of Arabidopsis), the population size needed for genome coverage would be . 400,000 lines, assuming one deletion per line. Since multiple mutations are induced by chemical or irradiation mutagenesis, fewer independent lines are needed. For example, assuming an average of 10 deletions per line, one would need approximately 40,000 lines to have a 95% chance of mutating any gene in the genome. These are obviously rough estimates as we do not have an accurate estimate of the number of induced mutations per genome. Nonetheless, it appears that a population of 50,000 mutant lines should provide reasonable genome coverage.

2. Deletion and Point Mutation Stocks To our knowledge, there are currently two large collections of chemical- and irradiation-induced rice mutants. The first one is an FN-induced population originally developed by Pamela Ronald at the University of California-Davis using japonica variety M202 and subsequently acquired by a private company. The collection consists of 24,660 M2 lines that have been used for highthroughput PCR screening (Li et al., 2001). The second collection is produced and maintained at IRRI. Four mutagenic agents — fast neutron, gamma ray, DEB, and EMS — were used to produce mutants with different sizes of genetic lesions. Variety IR64 was chosen as the standard genotype for producing a comprehensive mutant population in indica rice. IR64 is the most widely grown rice variety in the tropics and it carries many valuable agronomic traits related to yield, plant architecture, grain quality, and tolerance for biotic and abiotic stresses. Creating mutations in such an elite genetic background can facilitate the detection of phenotypic changes in important agronomic traits (Leung et al., 2001). Currently, the population consists of approximately 30,000 lines at the M4 stage. The goal is to produce . 40,000 independent mutant lines toward the end of 2003 to give a high probability of detecting a mutation in most genes. By producing a population of mutants with an identical genotype using different mutagens, we aim to produce

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a large allelic series for any locus as a tool for gene validation. Furthermore, the population is advanced to individual M4 lines such that sufficient seeds are available for distribution for the screening and evaluation of quantitative traits. Deletions. The concept of using deletion mutations for forward and reverse genetics is straightforward though it could be technically challenging. If sizable DNA regions (e.g., 100 –1000 base pairs) are deleted in a genome, it should be possible to detect such lesions by various genotyping approaches. As a proof of concept, Chang et al. (2003) evaluated the utility of a genome wide chip to detect deletions in known mutations. DNA from a gamma ray induced d1 mutation with a known deletion in the locus encoding a G-protein (Fujisawa et al., 1999) was used to hybridize to 21,000 predicted genes (each represented by a 16 25-mer probe set) on a Rice GeneChip. A hybridization signal five times below the wildtype was used as a criterion to indicate deletion. A stretch of deletions encompassing four genes was detected on chromosome 5 precisely at the map location containing the d1 gene. Results from experiments with a deletion at the Xa21 locus, however, were less definitive due to cross hybridization between similar sequences of the disease resistance gene family. Nonetheless, these experiments open up a powerful approach to apply genome-wide oligo chip to identify deletions in mutants that have been phenotypically well characterized. A successful reverse genetic screen using deletion mutants was first demonstrated in C. elegans (Jansen et al,. 1997; Westlund et al., 1999; Liu et al., 1999a). Unlike screening for insertion lines where the sequence of one end of the T-DNA or inserted transposon is known, screening for deletion requires sequence information flanking the target region on both sides. In C. elegans, a high-throughput approach was developed by growing the worms and extracting genomic DNA in a 96-well microtiter plate format. DNA is pooled at different levels of complexity (pooling of mutant lines, plates, and multiple plates) and PCR primers are then designed to bracket a region of the gene for deletion detection. The PCR amplicon of a deletion mutant, despite its low frequency, would be preferentially amplified in a complex DNA pool as a small amplicon (due to deletion). Differential amplification can be further enhanced by adjusting the extension time in the PCR procedure. Liu et al. (1999a) described a systematic PCR screen using C. elegans mutant libraries produced by four mutagens: EMS, ethylnitrosourea, diepoxyoctane, and ultraviolet-activated trimethylpsoralen. These mutagens produce deletion sizes from 700 to 2900 bp (average 1400 bp). From screening a 3-kb window for more than 100 genes, this method detected a deleted target gene per 600,000 mutagenized genomes. The approach was successfully extended to plants by Li et al. (2001). Using a population of 51,840 FN-induced Arabidopsis lines, they succeeded in detecting deletion mutants in a DNA pool containing 2592 lines. The scanning window chosen was from 3 to 17 kb, somewhat wider than that used in C. elegans. Twenty-one out of 25 (84%) genes attempted were successful. The detected deletions ranged from 0.8 to 12 kb. The authors emphasized the need to use PCR

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conditions that favored the amplification and detection of the smaller amplicons. The screening technique was applied to a rice mutant library of 24,660 FN-induced lines and a 2.5-kb deletion in a target gene, RG1, was found. We are developing a modified PCR screening and detection protocol for the IR64 mutants (Fig. 7). More than 8000 M3 lines derived from FN and DEB were pooled (10 lines per pool, with 10 plants per line) and amplicons were observed on polyacrylamide gel to increase the sensitivity of detection. In a pilot experiment, a deletion at the PAL1 gene (phenylammonia lyase) was identified (P. Manosalva, unpublished data). By using a DNA pool of lesser complexity (1/100 dilution), the need for preferential amplification of the small amplicons is less, but the pooling efficiency is lower than that reported by Li et al. (2001). Point mutations. In genetic analysis, it is often useful to have many point mutations that give a range of allelic variants. The value of a point mutation stock becomes even greater with the development of the high-throughput detection system called Targeting Induced Local Lesions IN Genome (TILLING) (McCallum et al., 2000a, 2000b). TILLING is a reverse genetics strategy based on high-throughput detection of point mutations in targeted genetic loci. This approach makes use of DNA strand mismatches formed between mutant and wild-type DNA. EMS is the preferred mutagen because it is known to produce predominantly A/T to G/C transitions, allowing the prediction of nucleotide positions that would yield missense,

Figure 7 Reverse genetics in rice using populations of plants mutagenized with fast neutron and diepoxybutane that create sizable deletions. An approach similar to that in screening insertion lines but this protocol requires primer sequences on both side of the target gene. DNA is extracted from mutagenized plants and grouped into pools. PCR reactions are performed on DNA pools using oligonucleotide primer combinations from the gene of interest. PCR products are then loaded on an agarose gel, transferred to a membrane, and hybridized to probes from the gene of interest. Detection of a smaller amplicon suggests the presence of a deletion in the mutant DNA pool. Once the deletion has been confirmed by PCR, the pools can be deconvoluted into individual lines.

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nonsense, or splice site mutations. DNA from individual M2 plants is isolated, pooled, and arrayed in 96-well plates. Primers are designed using CODDLE (www.proweb.org/input/) to bracket a 1-kb region that most likely contains a deleterious mutation in a target gene. The primers are then used to amplify the gene of interest and, by denaturing and re-annealing DNA, homoduplexes and heteroduplexes will be formed in the DNA pool. Originally, denatured HPLC was used to detect the presence of a DNA mismatch; however, a high-throughput system making use of enzymatic detection of heteroduplexes has proved to be more efficient (Colbert et al., 2001). For TILLING to be cost-effective, mutation density (number of mutations per Mb) has to be high. It is therefore necessary to evaluate the relationship between the mutagen dose and mutation density. We first produced a mutant population using 0.8% EMS. DNA was isolated individually from approximately 2000 M2 plants, pooled (8 genotypes per pool), and subject to screening. Of 10 genes screened, independent mutations were detected in two genes — pp2A4 encoding the serine/threonine protein phosphatase catalytic subunit and a gene encoding callose synthase. Sequencing of the mutated loci confirmed that they were A/T to G/C transition mutations (B. Till, personal communication). Although the frequency of the mutations is low, the preliminary data suggest that TILLING can be a robust strategy for identifying an allelic series for any gene of interest. High-dose mutagenesis is being evaluated to produce a high mutation density in the mutant population.

C. NATURAL GENETIC VARIATION Natural variants have been selected purposefully to produce favorable agronomic traits in plant breeding. The widely used semidwarf gene sd1 in rice is a mutation in a gene encoding gibberellic acid (GA)-20 oxidase, which controls a step in gibberellin biosynthesis (Spielmeyer et al., 2002; Sasaki et al., 2002a). Mutations in Os20ox2 (280-bp deletion and a point mutation in the conserved motif of the dioxygenase) result in a semidwarf phenotype. Another example is the use of allelic variation to adjust flowering time and maturity for local adaptation. The gene Hd1 controlling heading date in rice encodes a zinc finger transcription factor (homolog of CONSTANS (CO) of Arabidopsis). The “wildtype” Hd1 allele in variety Nipponbare inhibits heading under long days (natural field condition) but promotes heading under short days. Sequence analysis showed that the Nipponbare allele has a 36-bp deletion in the zinc finger domain, which probably affects the regulatory role of the transcription factor (Yano et al., 2000). These examples suggest that many useful functional variants used in plant breeding are in fact mutant forms of ancestral “wild-type” alleles. Therefore, the natural variation present in traditional rice germplasm represents a reservoir of useful alleles awaiting discovery.

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The conventional way of investigating genes controlling quantitative variation is to first map the QTL to a chromosomal region. For genes with large phenotypic effects, one can produce a large mapping population with sufficient resolution to fine-map the locus. For traits that require quantitative measurement, inbred families (such as recombinant inbred lines or advanced backcross lines) are needed for replicated trait measurement. With sequence information in hand, it is now possible to begin with a gene sequence and look for sequence variation that is likely to affect function. To some extent, genetic variation observed in traditional or modern improved varieties can be viewed as a large collection of mutations under the influence of natural or artificial selection. Many highthroughput techniques developed for forward and reverse genetics are therefore well suited for characterizing germplasm. For example, the same principle of TILLING can be applied to detecting spontaneous mutations present in the natural population (L. Comai and S. Henikoff, personal communication). In this case, instead of pooling DNA from mutants, DNA of individual germplasm accessions is annealed with a reference template DNA to detect mismatches. Once a variant is detected, the PCR fragment can be sequenced to determine the precise location of the variant.

VI. FUNCTIONAL VALIDATION OF RICE GENES So far, we have discussed the kinds of genetic materials available for discovering gene function. Without these genetic stocks, it is not possible to assign biological function. However, the utility of these genetic materials can be maximized when used with genome-wide analytical tools. It is important to note that genome-wide approaches can help identify candidate genes and pathways in a broad sense, but the data produced are often correlative in nature. Thus, standard genetic and biochemical techniques are necessary to pinpoint the functions and to dissect the pathways. Ultimately, the convergence of multiple lines of experimental evidence holds promise in understanding gene function and gene interactions. We highlight below several approaches that will probably be common tools for gene function validation in the next few years.

A. GENE EXPRESSION ANALYSIS 1.

Transcription Profiling

Transcription profiling refers to the display of a large set of genes that are expressed under specified conditions. Genetic messages from genetic stocks with different characteristics (e.g., salinity-tolerant versus sensitive) can be used to

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assay which genes are expressed and the quantity of messages produced. Gene expression profiling also reveals what genes are regulated in coordination. If genome sequence information is available, one can look for a common sequence upstream of co-regulated genes to find important motifs involved in gene regulation. A parallel analysis of many genes at one time can reveal which genes are collectively responsible for complex traits. While the literature on the use of microarrays and gene chips is growing, few published papers are available on genome-wide expression analysis in rice. The first analysis was conducted by Kawasaki et al. (2000) who applied a set of 1728 cDNA libraries of salt-stressed roots obtained from salt-tolerant variety Pokkali. The expression patterns of Pokkali and sensitive variety IR29 under salt-induced conditions were compared in this set of genes. The data suggested that the most significant difference in gene expression occurred within the initial phase of salt stress. Within 15 min after salt stress (NaCl, 150 mM), about 10% of the genes were either up-regulated or down-regulated in Pokkali. This expression pattern was delayed in sensitive variety IR29. The results suggest that the salttolerant property observed in Pokkali may in part be attributed to the rapid expression of a small set of genes, transmitted as signals leading to a downstream response. Failure to mount this rapid response, as in the case of IR29, could be responsible for the down-regulation of transcription and eventual death in 24 h. A second example of gene expression analysis focuses on nutrient partitioning during rice grain filling (Zhu et al., 2003). The objective of this study was to identify sets of co-regulated genes involved in grain filling. In this case, the array platform is an oligo chip designed to contain 16 probes (25-mer per probe) covering the 30 end of each of 21,000 predicted genes. Thirty-three samples of rice RNA representing different stages of grain filling were used as targets. Based on sequence annotation, the authors narrowed down from 21,000 genes to 491 candidate genes that are potentially involved in the synthesis and transport of carbohydrates, proteins, and fatty acids. Expression analysis led to the identification of 269 “grain-filling genes” potentially involved in grain development. By examining the upstream regions of these genes, Zhu et al. (2003) identified a cis-element AACA that is over-represented in the promoters of 103 genes presumably involved in grain filling. The AACA element is hypothesized to interact with certain transcription factors to regulate nutrient partitioning in the grain. Both the stress tolerance and nutrient partitioning studies illustrate the power of using gene arrays to identify a core set of genes that are potentially important in regulating or being regulated in the traits of interest. The power of microarray analysis will be much enhanced when expression data are compiled and made available for data mining by the public. Efforts are underway to make available the expression data of a variety of experiments from more than 60 laboratories in Japan (http://red.dna.affrc.go.jp/RED; S. Kikuchi, personal communication).

RICE FUNCTIONAL GENOMICS

2.

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Protein Profiles

Proteomic analysis of rice is still in its infancy but the few studies published so far have suggested its potential to gain a deeper understanding of gene expression in rice under various environmental and developmental conditions. Koller et al. (2002) reported a comprehensive proteomic analysis of rice using tissues from leaf, root, and seed. Complementary protein separation and analytical techniques were applied (2D PAGE followed by HPLC-tandem mass spectrometry and multidimensional protein identification technology). A total of 2528 unique proteins was identified. About 32% of all the proteins identified in leaves, roots, and seeds could not be functionally classified based on known databases. Salekdeh et al. (2002a,b) investigated the protein profiles of rice plants in response to drought and salt stresses. In their studies, the 2D PAGE system can reproducibly display about 2000 proteins. Salt-tolerant variety Pokkali and sensitive IR29 were subjected to 50 and 100 mM NaCl at 14 and 21 days, respectively, and root proteins were compared. Two proteins — ascorbate peroxidase and caffeoyl-CoA O-methyltransferase — showed particular