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Systems Biology And Biotechnology Of Escherichia ColiSung Ho Yoon, Haeyoung Jeong (auth.), Prof.Dr. Sang Yup Lee (eds.)
Systems biology is changing the way biological systems are studied by allowing us to examine the cell and organism as a whole. Systems biotechnology allows optimal design and development of upstream to downstream bioprocesses by taking a systems-approach. E. coli has been a model organism for almost all biological and biotechnological studies. This book brings together for the first time the state-of-the-art reviews by the world-leading experts on systems biology and biotechnological applications of E. coli. The topics covered include genomics and functional genomics, resources for systems biology, network analysis, genome-scale metabolic reconstruction, modelling and simulation, dynamic modelling and simulation, systems-level analysis of evolution, plasmids and expression systems, protein synthesis, production and export, engineering the central metabolism, synthetic biology, and systems metabolic engineering of E. coli. This book provides readers with guidance on how a complex biological system can be studied using E. coli as a model organism. It also presents how to perform synthetic biology and systems metabolic engineering studies on E. coli with successful examples, the approaches of which can be extended to other organisms. This book will be a complete resource for anyone interested in systems biology and biotechnology.
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Sang Yup Lee Editor Systems Biology and Biotechnology of Escherichia coli 123 Editor Prof. Dr. Sang Yup Lee Korea Advanced Institute of Science and Technology (KAIST) Dept. of Chemical and Biomolecular Engineering 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea (South Korea) email@example.com ISBN 978-1-4020-9393-7 e-ISBN 978-1-4020-9394-4 DOI 10.1007/978-1-4020-9394-4 Library of Congress Control Number: 2008942030 c Springer Science+Business Media B.V. 2009 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com To Hyejean and Gina, with love Preface Escherichia coli has been a workhorse not only in fundamental biological and microbiological studies, but also in various biotechnological applications. Recent advances in systems biology have been changing the way biological studies are performed – we are hoping to move towards system-wide understanding of the cell and organism; however, we are still far from truly doing so due to the lack of thorough understanding on complex metabolic, gene regulatory and signaling networks and their interactions. E. coli, being a much simpler organism compared with higher organisms, can be a good model system in systems biological studies. The strategies, methods, and tools developed through the systems biological studies on E. coli can be extended to other organisms, just like recombinant DNA techniques and other molecular biological tools did. Also, biotechnological applications developed using E. coli can be extended to other organisms, in particular other microorganisms, by taking similar metabolic and cellular engineering strategies. Significant advances have been made during the last several years on the systems biology and biotechnology of E. coli. In this book, the worldwide experts in the field provide us with the state-of-the-art reviews on the systems-level analyses and applications of E. coli. In Chapter 1, Dr. Kim and his colleagues present the genome project of E. coli B. They performed comprehensive functional genomic studies after finishing the genome sequencing. This will be an important addition to the literature as we now have the complete picture of another E. coli workhorse, B strain, in addition to the K12 strains. In Chapter 2, Professor Kim and his colleagues present the stateof-the-art strategies and applications of genome minimization. Impressive genome engineering strategies are presented with the practical applications of E. coli strains with reduced genomes. In Chapter 3, Professor Tomita and his colleagues review the systems biology of E. coli based on multi-omics analyses. Strategies for the combined analyses of transcriptome, proteome, metabolome, and fluxome are presented. Cellular robustness observed based on this systems biological studies is discussed. Structural proteomics is, as we all know, as equally important as quantitative proteomics for better biological and biochemical understanding and many applications. Professor Cygler and his colleagues review the current status of structural proteomics of E. coli and methods and techniques available to perform the studies. In Chapters 5 and 6, resources available for systems biological studies on E. coli are reviewed. Professor Mori and Professor Wanner review the important vii viii Preface techniques, tools, and libraries available, including the famous gene knock-out system and the complete KEIO single gene knock-out E. coli library. Then, Dr. Karp presents an updated status of EcoCyc, the very important database for E. coli community and beyond. Through this chapter, he proves that EcoCyc is not just a database for genes and proteins, but is a comprehensive platform for performing systems biological studies. In Chapter 7, Professor Jeong gives his insights on how to investigate the complex metabolic network of E. coli by applying the methods developed in other studies on networks. He applied these methods to metabolic network as well as proteinprotein interaction network of E. coli. The global and local characteristics of the network structures along with the recent studies on the dynamic aspects of these networks are discussed. In addition to Professor Jeong's chapter on the physical aspects of E. coli networks, Dr. Galperin provides an in-depth review on signal transduction network of E. coli in Chapter 8. He reviews 6 classes of sensor proteins and 32 response regulators, chemotaxis proteins and several others. Several levels of responses ranging from gene expression to chemotaxis to biofilm formation are discussed. Chapters 9 to 12 deal with the computational analyses of the genome-scale E. coli metabolic network. In Chapter 9, Professor Palsson, one of the pioneers in genomescale metabolic modeling and simulation, together with his colleagues describe foundational concepts central to the reconstruction process and model formulation and the history of reconstruction of the E. coli metabolic network. They also describe the development of reconstruction technology, constraints-based modeling and simulation, and future insights. In Chapters 10 and 11, Professor Goryanin's group and Professor Reuss's group describe kinetic modeling and simulation of E. coli metabolism. Starting by describing the basic principles of kinetic description of enzymatic reactions using in vitro enzyme assays, Goryanin and his colleagues report detailed kinetic modeling of key enzymes in E. coli metabolism. They emphasize that these kinetic models are important to understand key regulatory properties of enzymes. Professor Reuss and his colleagues describe integration of the different networks of E. coli exposed to an increasing carbon limitation during the fed-batch process with constant feeding of glucose. They report the analysis and dynamic modeling of regulation phenomena in the catabolism based on the global observation of flux distribution and gene expression in the central metabolism. In Chapter 12, Palsson and Applebee describe the use of genome-scale metabolic model in the analysis of the functions of acquired adaptive mutations for understanding their system-wide effect on phenotype. The constraints-based flux analysis proves to be a powerful tool to study genome-wide characteristics of metabolism. In Chapter 13, Professor Busby and his colleagues describe the promoters in E. coli and molecular characteristics of RNA polymerase recruitment. In addition, transcription factors and their roles in regulation are described. Importance of plasmids in basic biological and applied biotechnological studies does not need to be emphasized. Ow et al. report in Chapter 14 our current understanding on plasmid replication and the effects of plasmid presence on host cell physiology at systemslevel including the results of in silico analysis. Preface ix In Chapter 15, the research team of Dr. Rinas and Professor Villaverde review various factors affecting recombinant protein production, especially in the form of inclusion bodies. Protein folding, the machinery of protein quality control, structure and composition of inclusion bodies, and how to control inclusion body formation are described. In Chapter 16, Professor Georgiou's group covers the protein secretion system in E. coli. Starting with the general protein transport in E. coli, expression and secretion of proteins as well as folding of exported proteins are described. They also discuss on the display of proteins using phage display system and E. coli-based cell surface display system. Chapters 17 and 18 cover the key aspects of E. coli central metabolism. Professor Bennett and Professor San's group review a systems view of the central metabolic network and strategies for engineering the metabolic network for the production of various primary and secondary metabolites. Emphasis was also given to cofactor balance and cofactor engineering issues during the metabolic engineering of E. coli. Then, Dr. Shiloach and Dr. Rinas further describe the characteristics of central carbon metabolism with a focus on acetate production in E. coli. They describe the results of comparative analysis of acetate metabolism in E. coli K12 and B strains, and the effects of recombinant protein production on glucose catabolism. The bottlenecks in the primary metabolism and how to overcome these by metabolic engineering are also described. Chapters 19 and 20 concern with synthetic biology and systems metabolic engineering of E. coli. Professor Voigt and his colleagues review the reprogramming of E. coli metabolic and regulatory circuits. They describe in detail three classes of genetic parts: sensors, circuits and actuators. Construction of genetic sensors and circuits, and genetic methods to provide perturbation are described. The final chapter contributed by my own team deals with metabolic engineering of E. coli, in particular a new paradigm shift towards systems metabolic engineering. Systems metabolic engineering allows genome-wide metabolic engineering based on the findings of systems biological studies including omics and computational analyses. Detailed strategies for systems metabolic engineering are described using E. coli as a model organism. For many decades, E. coli has been the organism of choice in studying basic microbiology, genetics and molecular biology as well as in developing important biotechnological applications. As compiled in this book, E. coli is now serving as a platform system for systems biological studies as well. It is hoped that this book comprised of the state-of-the-art reviews provided by the worldwide experts would be a good starting point for the new comers in the field and also an important update for those who have been around in this field. It is hoped that this book will be of a long lasting value to the scientists and engineers working in the field. I wish to thank all the contributing authors who made this book possible. Special thanks go to the members of my lab, led by Dr. Tae Yong Kim, who exerted much effort to uniformly format the book. Last but not least, I want to thank Springer people for their help in the production of this book. KAIST Daejeon, Republic of Korea Sang Yup Lee Contents 1 Genomics, Biological Features, and Biotechnological Applications of Escherichia coli B: "Is B for better?!" . . . . . . . . . . . . . . . . . . . . . . . . . . Sung Ho Yoon, Haeyoung Jeong, Soon-Kyeong Kwon, and Jihyun F. Kim 1 2 Escherichia coli Genome Engineering and Minimization for the Construction of a Bioengine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Bong Hyun Sung, Jun Hyoung Lee, and Sun Chang Kim 3 Multi-Omics Data-Driven Systems Biology of E. coli . . . . . . . . . . . . . . . 41 Nobuyoshi Ishii and Masaru Tomita 4 A Medium-Throughput Structural Proteomics Approach Applied to the Genome of E. coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Allan Matte, Irena Ekiel, Zongchao Jia, Kalle Gehring, and Miroslaw Cygler 5 Resources for Escherichia coli Systems Biology . . . . . . . . . . . . . . . . . . . . 87 Hirotada Mori, Natsuko Yamamoto, Hitomi Dose, Kenji Nakahigashi, Kirill A. Datsenko, and Barry L. Wanner 6 The Multiple Scientific Disciplines Served by EcoCyc . . . . . . . . . . . . . . 99 Peter D. Karp 7 Analysis of E. coli Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Hawoong Jeong 8 Sensory Transduction Network of E. coli . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Michael Y. Galperin xi xii Contents 9 Genome-Scale Reconstruction, Modeling, and Simulation of E. coli's Metabolic Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Adam M. Feist, Ines Thiele, and Bernhard Ø. Palsson 10 Kinetic Modeling of E. coli Enzymes: Integration of in vitro Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Ekaterina A. Mogilevskaya, Kirill V. Peskov, Eugeniy A. Metelkin, Galina V. Lebedeva, Tatiana Y. Plyusnina, Igor I. Goryanin, and Oleg V. Demin 11 Dynamic Modeling of the Central Metabolism of E. coli – Linking Metabolite and Regulatory Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Timo Hardiman, Karin Lemuth, Martin Siemann-Herzberg, and Matthias Reuss 12 Genome-Scale Models and the Genetic Basis for E. coli Adaptation . . 237 M. Kenyon Applebee and Bernhard Ø. Palsson 13 Organisation of Complex Escherichia coli Promoters . . . . . . . . . . . . . . . 257 Douglas F. Browning, David C. Grainger, and Stephen J. W. Busby 14 Plasmid Regulation and Systems-Level Effects on Escherichia coli Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Dave Siak-Wei Ow, Dong-Yup Lee, Hsiu-Hui Tung, and Sue Lin-Chao 15 Systems-Level Analysis of Protein Quality in Inclusion Body-Forming Escherichia coli Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Elena Garcı́a-Fruitós, Nuria González-Montalbán, Mónica Martı́nez-Alonso, Ursula Rinas, and Antonio Villaverde 16 Mechanistic Challenges and Engineering Applications of Protein Export in E. coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Eva-Maria Strauch and George Georgiou 17 Engineering E. coli Central Metabolism for Enhanced Primary Metabolite Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 George N. Bennett and Ka-Yiu San 18 Glucose and Acetate Metabolism in E. coli – System Level Analysis and Biotechnological Applications in Protein Production Processes . . 377 Joseph Shiloach and Ursula Rinas Contents xiii 19 Performance Characteristics for Sensors and Circuits Used to Program E. coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Jeffrey J. Tabor, Eli S. Groban, and Christopher A. Voigt 20 Systems Metabolic Engineering of E. coli . . . . . . . . . . . . . . . . . . . . . . . . . 441 Sang Yup Lee and Jin Hwan Park Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Contributors M. Kenyon Applebee Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA 92093-0412, USA, firstname.lastname@example.com George N. Bennett Department of Biochemistry and Cell Biology, Rice University, Houston, TX 77005-1892, USA, email@example.com Douglas F. Browning School of Biosciences, University of Birmingham, Birmingham, B15 2TT UK Stephen J. W. Busby School of Biosciences, University of Birmingham, Birmingham, B15 2TT UK, email@example.com Sue Lin-Chao Institute of Molecular Biology, Academia Sinica, 128 Sec. 2, Academia Rd, Nankang, Taipei 115, Taiwan, R.O.C. Miroslaw Cygler Biotechnology Research Institute, National Research Council Canada, Department of Biochemistry, McGill University, 6100 Royalmount Ave., Montreal, QC H4P2R2 Canada, firstname.lastname@example.com Kirill A. Datsenko Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA Oleg V. Demin A.N. Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia; Institute for Systems Biology SPb, SanktPetersburg, Russia, email@example.com Hitomi Dose Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma, Nara 630-0101, Japan Irena Ekiel Biotechnology Research Institute, National Research Council Canada, Department of Chemistry and Biochemistry, Concordia University, 6100 Royalmount Ave., Montreal, QC H4P2R2, Canada Adam M. Feist Department of Bioengineering, University of California San Diego, La Jolla, CA, USA, email@example.com xv xvi Contributors Michael Y. Galperin National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA, email@example.com Elena Garcı́a-Fruitós Institute for Biotechnology and Biomedicine and Department of Genetics and Microbiology, Autonomous University of Barcelona, and CIBER-BBN Network in Bioengineering, Biomaterials and Nanomedicine, Bellaterra, 08193 Barcelona, Spain Kalle Gehring Department of Biochemistry, McGill University, Montreal, QC, Canada, email@example.com George Georgiou Department of Chemical Engineering, Biomedical Engineering, Molecular Genetics and Microbiology and the Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX USA, firstname.lastname@example.com Nuria González-Montalbán Institute for Biotechnology and Biomedicine and Department of Genetics and Microbiology, Autonomous University of Barcelona, and CIBER-BBN Network in Bioengineering, Biomaterials and Nanomedicine, Bellaterra, 08193 Barcelona, Spain Igor I. Goryanin Centre for Systems Biology, University of Edinburgh, Darwin Building, The Kings Buildings, Mayfield Road, Edinburgh EH9 3JU, 0131 6519063, Scotland UK; Informatics Forum, University of Edinburgh, Crichton Street, Edinburgh EH8 9LE Scotland, UK, firstname.lastname@example.com David C. Grainger School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK Eli S. Groban Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143-2280 USA; Biophysics Program, San Francisco, CA 94158 USA Timo Hardiman Institute of Biochemical Engineering and Centre Systems Biology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany Nobuyoshi Ishii Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan Haeyoung Jeong Systems Microbiology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 111 Gwahangno, Yuseong-gu, Daejeon 305-806, Republic of Korea Hawoong Jeong Department of Physics, Institute for the BioCentury, KAIST, Daejeon 305-701, Korea, email@example.com Zongchao Jia Department of Biochemistry, Queen's University, Kingston, ON, Canada Peter D. Karp SRI International, Menlo Park, CA 94025, USA, email@example.com Contributors xvii Jihyun F. Kim Industrial Biotechnology and Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 111 Gwahangno, Yuseong-gu, Daejeon 305-806, Republic of Korea; Field of Functional Genomics, School of Science, University of Science and Technology (UST), 113 Gwahangno, Yuseong, Daejeon 305-333, Republic of Korea, email@example.com Sun Chang Kim Department of Biological Sciences, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea, email@example.com Soon-Kyeong Kwon Systems Microbiology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 111 Gwahangno, Yuseong-gu, Daejeon 305-806 Republic of Korea; Field of Functional Genomics, School of Science, University of Science and Technology (UST), 113 Gwahangno, Yuseong, Daejeon 305-333, Republic of Korea Galina V. Lebedeva Centre for Systems Biology, University of Edinburgh, Darwin Building, The Kings Buildings, Mayfield Road, Edinburgh, EH9 3JU, 0131 6519063, UK, firstname.lastname@example.com Dong-Yup Lee Bioprocessing Technology Institute, Agency for Science, Technology and Research (A∗ STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576 Jun Hyoung Lee Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea Sang Yup Lee Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Department of Bio and Brain Engineering, Department of Biological Sciences, and Bioinformatics Research Center Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, BioProcess Engineering Research Center, KAIST, 335 Gwahangno, Yuseong–gu, Daejeon 305-701, Korea, email@example.com Karin Lemuth Institute of Biochemical Engineering and Centre Systems Biology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany Allan Matte Biotechnology Research Institute, National Research Council Canada, 6100 Royalmount Ave., Montreal, Quebec H4P2R2, Canada Mónica Martı́nez-Alonso Institute for Biotechnology and Biomedicine and Department of Genetics and Microbiology, Autonomous University of Barcelona, and CIBER-BBN Network in Bioengineering, Biomaterials and Nanomedicine, Bellaterra, 08193 Barcelona, Spain Eugeniy A. Metelkin A.N. Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia; Institute for Systems Biology SPb, Sankt-Petersburg, Russia, firstname.lastname@example.com xviii Contributors Ekaterina A. Mogilevskaya A.N. Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia; Institute for Systems Biology SPb, Sankt-Petersburg, Russia, firstname.lastname@example.com Hirotada Mori Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma, Nara 630-0101, Japan; Advanced Institute of Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan, firstname.lastname@example.com Kenji Nakahigashi Advanced Institute of Biosciences, Keio University, Tsuruoka, Yamagata, Japan, email@example.com Dave Siak-Wei Ow Bioprocessing Technology Institute, Agency for Science, Technology and Research (A∗ STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, dave firstname.lastname@example.com Bernhard Ø. Palsson Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA, email@example.com Jin Hwan Park Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program) Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, BioProcess Engineering Research Center, KAIST, 335 Gwahangno, Yuseong–gu, Daejeon 305-701, Korea Kirill V. Peskov A.N. Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia, email@example.com Tatiana Y. Plyusnina Institute for Systems Biology SPb, Sankt-Petersburg, Russia; Biophysics Department, Biological Faculty, Moscow State University, Moscow, Russia, email@example.com Matthias Reuss Institute of Biochemical Engineering and Centre Systems Biology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany, email@example.com Ursula Rinas Helmholtz Center for Infection Research, Inhoffenstr. 7, 38124, Braunschweig, Germany, email@example.com Ka-Yiu San Department of Bioengineering, Rice University, Houston, TX 770051892, USA, firstname.lastname@example.com Joseph Shiloach Biotechnology Unit, NIDDK, NIH, Bethesda, MD 20892, USA, firstname.lastname@example.com Martin Siemann-Herzberg Institute of Biochemical Engineering and Centre Systems Biology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany Eva-Maria Strauch Department of Chemistry and Biochemistry, The Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX; Department of Biochemistry, University of Washington, Seattle, Washington, USA Contributors xix Bong Hyun Sung Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea Jeffrey J. Tabor Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143-2280, USA Ines Thiele Program in Bioinformatics, Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA, email@example.com Masaru Tomita Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan, firstname.lastname@example.com Hsiu-Hui Tung Institute of Molecular Biology, Academia Sinica, 128 Sec. 2, Academia Rd, Nankang, Taipei 115, Taiwan, R.O.C. Antonio Villaverde Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193 Spain, email@example.com Christopher A. Voigt Department of Pharmaceutical Chemistry, University of California San Francisco, CA 94143-2280 USA; Biophysics Program, San Francisco, CA 94158, USA, email@example.com Barry L. Wanner Department of Biological Sciences, Purdue University, West Lafayette, IN, USA, email@example.com Natsuko Yamamoto Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma, Nara 630-0101, Japan Sung Ho Yoon Systems Microbiology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 111 Gwahangno, Yuseong-gu, Daejeon 305-806, Republic of Korea About the Editor Dr. Sang Yup Lee is a Distinguished Professor and LG Chem Chair Professor at the Department of Chemical and Biomolecular Engineering at KAIST, Daejeon, Korea. He is currently Dean of the College of Life Science and Bioengineering, Director of BioProcess Engineering Research Center, Director of Bioinformatics Research Center, and Director of Center for Systems and Synthetic Biotechnology. His major research interests are on metabolic engineering of microorganisms for industrial and medical biotechnology by integrating systems biology and synthetic biology approaches. He has published more than 270 journal papers, 40 books/book chapters, and is holding 300 patents registered/applied. He received numerous awards including National Order of Merit from Korean government, the First Young Scientist's Award from the President of Korea, the Elmer Gaden Award from Biotechnology and Bioengineering, and many others, including the most recent Merck Metabolic Engineering Award. He is a Fellow of Korean Academy of Science and Technology, and Junior Fellow of National Academy of Engineering of Korea, Fellow of AAAS, and Fellow of American Academy of Microbiology. He is currently serving Editor-in-Chief of Biotechnology Journal, and Associate Editor and editorial board member of many journals in the field of biotechnology and biochemical engineering. xxi Chapter 1 Genomics, Biological Features, and Biotechnological Applications of Escherichia coli B: "Is B for better?!" Sung Ho Yoon, Haeyoung Jeong, Soon-Kyeong Kwon, and Jihyun F. Kim Contents 1.1 1.2 1.3 1.4 1.5 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genomics of E. coli B Strains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Genomic Comparison of E. coli REL606 and MG1655 . . . . . . . . . . . . . . . . . . . . 1.2.2 Comparative Genomics of E. coli BL21(DE3) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Comparative Omics Analysis of E. coli B and K-12 . . . . . . . . . . . . . . . . . . . . . . Biological Features of E. coli B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Catabolism and Acetate Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Anabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Utilization of Carbon Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 Cell Surface Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.5 Heat Shock Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.6 Cell Cycle and Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.7 Secretion Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Usage of B Strains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 As an Academic Lab Rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 As an Industrial Workhorse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2 4 6 6 7 7 8 9 9 10 10 11 11 12 13 14 Abstract Strains of Escherichia coli B, especially BL21, have been widely used for overproducing recombinant proteins, ethanol, and other biomolecules. Almost all laboratory strains of E. coli are derivatives of non-pathogenic K-12 or B strains. While most genetic and metabolic studies have been performed with K-12 strains, little has been done on B strains. Recently, genome sequences of two E. coli strains of the B lineage, REL606 and BL21(DE3), have been determined, and results of multi-omics analyses were compared between B and K-12. As compared to K-12, J.F. Kim (B) Industrial Biotechnology and Bioenergy Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 111 Gwahangno, Yuseong, Daejeon 305-806, Republic of Korea; Field of Functional Genomics, School of Science, University of Science and Technology (UST), 113 Gwahangno, Yuseong, Daejeon 305-333, Republic of Korea e-mail: firstname.lastname@example.com S.Y. Lee (ed.), Systems Biology and Biotechnology of Escherichia coli, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9394-4 1, 1 2 S.H. Yoon et al. B strains show a number of phenotypes such as faster growth in minimal media, lower acetate production, higher expression levels of recombinant proteins, and less degradation of such proteins during purification. In this review, we summarize the unique biological features of the B strains and overview their academic and industrial applications. 1.1 Introduction Escherichia coli, a common inhabitant of the mammalian intestines, undoubtedly has been one of the best studied organisms and plays important roles in biological sciences, medicine, and industry. E. coli strain B was named by Delbrück and Luria in 1942 (Delbruck and Luria 1942), but the early history of B is less well established whereas the origin of E. coli K-12 is clear. In any case, derivatives of E. coli B have been serving not only as a research model for the study of phage sensitivity, restriction systems, and bacterial evolution in the laboratories, but as a major workhorse for protein expression in the biotechnological industry. However, genetic bases of the apparent superiority of B in many industrial setups have been restricted to a limited number of topics and the rest have been left largely undetermined. The genomes of two derivative strains of B recently have been deciphered through an international collaboration among scientists in Korea (KRIBB; our group), the United States (Michigan State University and Brookhaven National Laboratory), and France (Genoscope). This has opened a new possibility of examining B through various omics technologies including DNA microarray for gene expression profiling and two dimensional gel electrophoresis followed by MALDI-TOF identification of proteins. In this chapter, we review the current understanding of the biological features of B strains in the context of genomic information and results of the multi-omics analyses together with their utility in scientific studies and biotechnical applications. 1.2 Genomics of E. coli B Strains 1.2.1 Genomic Comparison of E. coli REL606 and MG1655 The first complete genome sequence of a B strain was determined by an international consortium (Jeong et al. submitted). The strain of choice was REL606, an Ara− clone derived from chemical mutation of Bc251 (F− mal + λs ) (Lederberg 1966). REL606 has been long used as a founder strain for long-term evolution experiments by Richard E. Lenski at Michigan State University (Cooper and Lenski 2000, Lenski et al. 1991). A Sanger chemistry-based, standard shotgun approach was exploited for the genome sequencing of REL606. KRIBB participated in the initial shotgun sequencing and final process for genome annotation, while Genoscope led genome sequencing to completion and automatic annotation based on MaGe (Vallenet 2006). 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 3 4500000 4000000 3500000 EB03 3000000 2500000 2000000 1500000 1000000 500000 0 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000 4500000 X12 Fig. 1.1 Whole-genome alignment of E. coli K-12 MG1655 (x-axis) and B REL606 (y-axis). NUCMER script in MUMMER 3.0 was used for the generation of alignment with default parameters (http://mummer.sourcefourge.net/) E. coli B REL606 has a single circular chromosome of 4,612,812 bp with no plasmid, which makes it the most compact genome among the completely sequenced strains of E. coli. Its chromosome size is most similar to that of K-12 MG1655 (4,639,675 bp), and as shown by the whole-genome alignment plot no chromosomal rearrangement was observed other than some insertions or deletions (Fig. 1.1). When MUMMER was used as an alignment generator, the total length of aligned regions between REL606 and MG1655 amounted to more than 96%. Average percent identity of the aligned regions is 97.5%, and it further increases up to 99.09% if it is length-weighted average. Though overall genome organization is very similar between two strains, several prominent factors contribute to shaping peculiarities of each genome (Fig. 1.2, see below). First, highly divergent regions, occupying the equivalent positions on each genome, are readily identified by the broken lines appearing on the whole-genome alignment plot (Fig. 1.3). Most of them are related to genes involved in surface characteristics (e.g., LPS core oligosaccharide biosynthesis), which are probably ones under strong positive selection. Second, there are several horizontally transferred genomic segments that represent genome-specific regions. Fitness island encoding gene sets for the metabolism of aromatic hydrocarbon is a good example. Lastly, distribution of mobile genetic elements such as prophages and insertion sequence (IS) elements are significantly different between the two strains. Specifically, IS seems to exert most dramatic effect to its host genome, since it can deacti- 4 S.H. Yoon et al. Fig. 1.2 Genomic regions unique to E. coli B REL606 or K-12 MG1655. The horizontal axis represents the map coordinates of backbone regions common to REL606 and MG1655; vertical bars denote locations and lengths of strain-specific regions larger than 2 kb. A H denotes the region overlapping predicted genomic islands (Yoon et al. 2005). Genomic regions containing prophage genes are marked by asterisks. Abbreviations: LPS, lipopolysaccharide; HPA, 3-hydroxyphenylacetic acid and 4-hydroxyphenylacetic acid; PA, phenylacetic acid; VSP, very short-patch vate functional genes by insertion events, and can result in genomic rearrangement by homologous recombination as well. For example, REL606 has a 41-kb deletion (∼ 0.8% of the entire chromosome), probably mediated by IS1 (the most abundant IS in B), from yecF to yedS. The deleted segment, that includes sdiA, amyA, rcsA, vsr, dcm, and the fli cluster, produce relevant B-specific phenotypes that were previously known. Functions affected by IS transpositional inactivation include porin expression, restriction-modification, and Lon protease (saiSree et al. 2001, Schneider et al. 2002). 1.2.2 Comparative Genomics of E. coli BL21(DE3) BL21(DE3) is a specifically engineered B descendant harboring the T7 RNA polymerase gene for high-level expression of recombinant proteins (Studier and Moffatt 1986). With other B-specific traits such as deficiency in proteases and amenability to high-density culture as mentioned above, BL21(DE3) has become the most widely used strain for biotechnological applications. Since BL21(DE3) and REL606 are very close to each other being diverged just dozens of years ago from their common ancestor, we applied a hybrid approach that combines hybridization-based genome resequencing by NimbleGen's CGS (Comparative Genome Sequencing) technology and 454 pyrosequencing by Roche GS 20 at about 10X coverage to assemble genome sequence of BL21(DE3) using the REL606 genome (Jeong et al. submitted). All potential mutagenic sites were confirmed by Sanger sequencing of PCR-amplified products, and genomic rearrangement that could be only identified by pairwise comparison between the reference sequence and de novo-assembled 454 contigs were also verified by the same method. Over 98% of the final BL21(DE3) sequence could be covered by 454 pyrosequencing contigs, and ∼ 166 kb were confirmed by conventional sequencing. We confirmed 415 SNPs, 16 insertions, and 28 deletions in the genome of BL21(DE3) with respect to REL606. 79% of SNPs (326 out of 415) occur in a nar- Prophage O−antigen biosynthesis Prophage Putative fimbrial protein Prophage Prophage Prophage yedK−lsrD Prophage Prophage gltF−yhcF Putative hexuronate metabolism Prophage Prophage LPS core biosynthesis 4.75 Mb >99.5% 99.0~99.5% 98.5~99.0% 98.0~98.5% < 98.5% Percent identity LPS core biosynthesis 4−hydroxyphenylacetic acid degradation Prophage Xanthosine uptake Putative adhesin biosynthesis Phenylacetic acid degradation Prophage 4.0 Mb 3.0 Mb 2.0 Mb 1.0 Mb Genomics, Biological Features, and Biotechnological Applications of E. coli B Fig. 1.3 A schematic diagram showing alignment of the blocks of homologous sequences (thick lines) between E. coli B REL606 (top lines) and K-12 MG1655 (bottom lines). Percent identity for each block is indicated as a grey parallelogram of a different shade. Texts denote genes or functions encoded by specific genomic segments K−12 B K−12 B O−antigen biosynthesis yegF−yedS ompC−rcsB Prophage/capsular polysaccharide synthesis Secondary type II secretion system K−12 B K−12 B K−12 B 1 5 6 S.H. Yoon et al. row region around 4.2 Mb-position, which occupies only 1.4% of the chromosome. Chemical mutagenesis applied to Bc251 to produce the progenitor to REL606 only cannot accounts for such a big differences. Close inspection revealed the highly divergent region was indeed transferred from W3110 by P1 transduction to produce Bc251. Another P1 transduction to create BL21 replaced again most of the W3110 DNA with B DNA, which resulted in K-12-derived sequence remaining only in REL606. To obtain a complete genome sequence of BL21(DE3) based on 454 contigs, we recently constructed a fosmid library and produced paired-end sequences. Scaffolds have been made by a mixed assembly approach using fosmid end reads and verified 454 contigs, and gap closure is in progress (Jeong and Kim, unpublished data). 1.2.3 Comparative Omics Analysis of E. coli B and K-12 Recent advances in high-throughput omics technology are providing us with the possibility of deciphering an organism's genotype-to-phenotype relationships. Recently, we have carried out comparative and integrated analysis of the genome, transcriptome, proteome, and phenome data of B and K-12 strains that are closely related (Yoon et al. submitted). We also reconstructed an in silico metabolic network of B that accommodates the multidimensional omics data. From the study, we identified many important differences in cellular metabolism and physiology between B and K-12. Lack of flagellar biosynthetic genes and low expression of motility-related genes make B non-motile. This is an important property of B when used as a cell factory because flagella biosynthesis is energy-intensive and is not necessary under an industrial setup of constant agitation and generous supply of nutrients (Posfai et al. 2006, Yu et al. 2002). Differences in the composition of the LPS core and expression of outer membrane proteins may influence the permeability and integrity of the cell envelope, which presumably result in alterations to screening barriers that control import and export of materials such as antibiotics, nutrients, and proteins. Importantly, the existence of the second T2S system and enhanced capability for protein release qualify B strains as the first choice for extracellular production of recombinant proteins. Information on naturally exported proteins can be useful in developing a strategy of excretory protein production, as exemplified by the use of OmpF fusion approach for the extracellular production of human proteins (Jeong and Lee 2002). B strains exhibited up-regulation of many amino acid biosynthetic genes, and showed lower expression of proteases. These characteristics are desirable for the enhanced production of recombinant proteins. 1.3 Biological Features of E. coli B Elucidation of biological features of a strain is always the starting point for its biotechnological applications. B and its derivatives have been widely used for the 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 7 production of recombinant proteins and biomolecules. B strains have also served as research models for studies of phage sensitivity, restriction systems, mutagenic assays, and bacterial evolution (Cooper and Lenski 2000, Herrera et al. 2002, Swartz 1996). Although the genome sequences of B and K-12 are highly similar based on comparison of IS elements (Schneider et al. 2002), B often shows phenotypes distinct from those of K-12 (Swartz 1996). 1.3.1 Catabolism and Acetate Metabolism B strains have been widely used for overproduction of recombinant proteins because they offer faster cell growth in minimal media and lower production of acetate than K-12 derivatives. Such differences between B and K-12 strains can be attributed to their genetic backgrounds. However, from the analysis of genome sequences of two B strains, REL606 and BL21(DE3) (Jeong et al. submitted), we found that there was no genetic difference in genes involved in glycolysis, TCA cycle, pentose phosphate pathway, glyoxylate pathway, gluconeogenesis, and acetate production among genomes of B and K-12. Thus, it is possible that the metabolic genes are under different regulation of in these two groups of E. coli. Transcriptome analysis of BL21 and JM109 (B derivative) during batch fermentation with high initial glucose concentration (Phue et al. 2007) demonstrated that genes involved in glyoxylate shunt, TCA cycle, fatty acid, gluconeogenesis and anaplerotic pathways were expressed differently between the two strains, while no apparent differences were detected for those in glycolysis and pentose phosphate pathway. Acetate accumulation is one of the major problems encountered during high cell density cultivation of E. coli, because it inhibits cell growth and production of foreign proteins (Eiteman and Altman. 2006). Generally, B strains accumulate less acetate than K-12 strains during high cell density cultivation with glucose as a carbon source. A common explanation for low acetate accumulation by B is the active glyoxylate shunt which is the main pathway for acetate utilization due to the high expression of acetate operon (aceBAK). Analyses of DNA microarray and Northern blot demonstrated that BL21 showed high activity in glyoxylate shunt, TCA cycle, gluconeogenesis pathway, conversion of acetate to acetyl CoA, and fatty acid degradation irrespective of glucose concentration in culture media (Phue et al. 2005). In case of JM109, a K-12 derivative, the trend was similar at low glucose culture conditions, while it was lowered at high glucose conditions. Phue et al. (2005) suggested that insensitivity of BL21 to glucose concentration can be attributed to absence of a regulatory mechanism and possibility of altered activity of FruR, a transcriptional regulator of the control of carbon and energy metabolism, in BL21. Much effort should be made to fully understand the metabolism of glucose and acetate of the B and K-12. 1.3.2 Anabolism Sequence differences in amino acids biosynthetic pathways have been found in genes for L-arginine and branched-chain amino acids biosyntheses. In K-12, for- 8 S.H. Yoon et al. mation of the enzymes involved in arginine biosynthetic pathway is under feedback inhibition by arginine, while those enzyme levels are not affected by arginine in B. The different regulation mode is due to the differences in a single amino acid in the arginine repressor protein (ArgR), changing from the proline residue in K-12 to leucine in B (Tian et al. 1994). K-12 contains two genes encoding ornithine carbamoyltransferase in arginine biosynthesis, argI and argF, while B, E. coli W, and other species in Enterobacteriaceae have only argI or its equivalents (Legrain et al. 1976). B is insensitive to extracellular valine while K-12 cannot grow in the presence of valine (Yoon et al. submitted). The first reaction in the biosynthesis of branchedchain amino acids (leucine, isoleucine and valine) is catalysed by three isozymes, acetohydroxy acid synthase (AHAS) I, II, and III encoded by ilvBN, ilvGM, and ilvIH respectively. It is known that valine exerts feedback inhibition on isozymes I and III (Umbarger 1996). Thus, exogenous valine can inhibit cell growth because the valine makes the cell unable to synthesize leucine and isoleucine, which is known as valine toxicity. It is thus essential that the functional isozyme II is expressed for cell growth in the presence of exogeneous valine. K-12 has a frameshift mutation in the ilvG gene, but B has an intact ilvG mediating valine resistance. 1.3.3 Utilization of Carbon Sources Ability to utilize a variety of substrates is quite different between B and K-12, which can be attributed largely to genetic discrepancy in nutrient uptake systems. Most enteric bacteria cannot grow on D-arabinose which is uncommon in the natural environments. As enzymes for L-fucose utilization can degrade D-arabinose to dihydroxyacetone phosphate and glycoaldehyde, regulatory mutations of the Lfucose pathway in K-12 led to growth on D-arabinose (LeBlanc and Mortlock 1971). In contrast to K-12, B strains cannot grow on L-fucose because of the lack of L-fuculose-1-phosphate aldolase (Boulter et al. 1974). Interestingly, B strains can degrade D-arabinose without mutation. This is due to the possession of gene cluster for converting D-arabinose to D-xylulose 5-phosphate, which appears to have acquired through horizontal gene transfer (Elsinghorst and Mortlock 1994). Eliminating environmental pollutants such as aromatic compounds by microorganisms is a competitive alternative to the commonly used chemical processes (Pieper and Reineke 2000). Aromatic compounds are highly abundant in soil and water, and Pseudomonas strains and other soil bacteria can catabolize a wide range of aromatic compounds. Unexpectedly, some E. coli strains and other enteric bacteria are reported to be able to degrade aromatic amino acids (Diaz et al. 2001). E. coli B and C can grow on 3- and 4-hydroxyphenylacetic acid (HPA) but not on phenylacetic acid (PA), while K-12 grow on PA but not on 3-HPA and 4-HPA (Diaz et al. 2001). Recent studies that compared the genomes of B and K-12 (Yoon et al. submitted) demonstrated that each has a different gene cluster for the catabolism of aromatic compounds – the paa cluster for the catabolism of phenylacetic acid in K-12 and the hpa cluster for the degradation of 3- and 4-HPA in B. 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 9 1.3.4 Cell Surface Features Cell envelope is the principal stress-bearing and shape-maintaining element in E. coli, and its integrity is of critical importance to cell viability. B strains have been widely used for mutagenic assays and toxicological studies because they show higher membrane permeability than does K-12 (Herrera et al. 2002). Structural studies on the LPS core oligosaccharides have revealed that K-12 is devoid of the O antigen while B lacks the O antigen plus the distal part of the polysaccharide core of the outer membrane (Jansson et al. 1981). Sequence comparison has revealed that the outer membrane structure of B is quite different from that of K-12 (Jeong et al. submitted). IS elements were found to be inserted at the gene clusters for O antigen biosynthesis: at wbbL for K-12 and between manC and wbbD for B strain. In the B genome, the core part of LPS was further disrupted by the insertion of IS1 at waaT encoding the UDP-galatose:(glucosyl) LPS ␣1,2-galactosyltransferase. Importantly, flagellar biosynthesis genes are missing in B (Jeong et al. submitted). A 38-kb region of K-12 from yecF to yedS, containing fliYZACDSTEFGHIJKLMNOPQR genes was deleted in the genome of B. Thus, B cannot form the flagella and thus is non-motile. Porin proteins control the permeability of polar solutes across the outer membrane and play important roles in the nutrient uptake process (Nikaido 1996). In K-12, though the total amount OmpC and OmpF is constant, their relative proportion changes depending on the culture medium osmolality, which is controlled by the EnvZ-OmpR regulatory system. By contrast, B strains express only OmpF in large quantity (Pugsley and Rosenbusch 1983). This is attributed to the fact that IS insertion in the B genome results in the deletion of the first 114 bp of ompC and the upstream region containing micF which posttranscriptionally prevents the production of OmpF (Schneider et al. 2002). Noxious agents such as antibiotics and bile acids diffuse far better through OmpF because OmpF produces a larger channel than OmpC (Nikaido 2003). Thus, ompF mutants became highly resistant to β-lactam compounds (Harder et al. 1981). In the phenotype microarray test, we discovered that B displayed sensitivity to various stress conditions of osmolarity, pH stress, and antibiotics much higher than K-12 (Yoon et al. submitted). 1.3.5 Heat Shock Proteins Heat shock proteins (HSPs) including molecular chaperones and proteases make sure cellular proteins being in the right shape and in the right place at the right time (Gross 1996). Thus, they are required both during stress and normal growth conditions. Among the ATP-dependent proteases, B strains are naturally deficient in the major protease Lon which degrades abnormally folded proteins. This is due to the insertion of IS186 in the promoter region of lon (saiSree et al. 2001). Additionally, the BL21 cells lack the OmpT outer membrane protease. Besides its major role in protein quality control, Lon is involved in many biological processes such as cell differentiation, pathogenicity, motility, stringent response to amino acid starvation, 10 S.H. Yoon et al. and regulation of the toxin-antitoxin module (Tsilibaris et al. 2006). Lon mutants are viable, but display sensitivity to ultraviolet light and overproduce capsular polysaccharide, which are the result of the elevated levels of regulatory proteins (SulA and RcsA) that are normally degraded by Lon. As HSPs are up-regulated by the heat-shock sigma factor σ 32 encoded by rpoH when cells are exposed to stress condition such as temperature upshift and production of recombinant proteins, they can be used as a stress probe for monitoring cellular stress (Cha et al. 1999, Vostiar et al. 2004). When cellular stress levels of BL21 and K-12 strains (JM105, HB101, and TOP10) were measured by fusing promoters of heat-shock genes (rpoH, dnaK or clpB) to the reporter gene (gfp), BL21 exhibited the lowest cellular stress level and expressed the highest foreign protein (Seo et al. 2003). Possibly, lower cellular stress level of B strain is one of the reasons for high capacity in foreign protein production. 1.3.6 Cell Cycle and Growth Bacterial cell growth is closely coordinated with DNA replication and chromosome segregation (Haeusser and Levin 2008). The cell cycle of slowly growing bacteria can be divided into three time periods: (i) period B, cell division to the initiation of chromosome replication, (ii) period C, chromosome replication, and (iii) period D, termination of replication to cell division. From the cytometry data that measured periods C and D of E. coli, the D period in B/r is much shorter than in K-12 strains (Michelsen et al. 2003). Normally, B strains grow faster than K-12 in minimal media. The widely used K12 strains, MG1655 and W3110, grow slowly in a pyrimidine-free medium than in a medium containing uracil. In the rph-pyrE operon involved in de novo pyrimidine biosynthesis of these strains, rph is frame-shifted to produce truncated RNase PH, and the premature translation stop leads to decreased expression of pyrE encoding orotate phosphoribosyltransferase (Jensen 1993). However, the rph gene is intact in the B strains (Yoon et al. submitted). 1.3.7 Secretion Capacity Bacterial extracellular proteins perform important biological processes such as assembly of flagella and fimbriae, nutrient acquisition, cell-to-cell communication, and pathogenesis. In Gram-negative bacteria, excretory proteins are much less than in Gram-positive species because they should cross the two membranes of the cell envelope. Laboratory E. coli strains normally does not secrete extracellular proteins because the genes encoding type II secretion (T2S) pathway operon (gsp) are silenced by H-NS (Francetic et al. 2000). B strains released more proteins according to analyses of the extracellular proteomes of B and K-12 during flask culture (Yoon et al. submitted) and high cell density cultivation (Xia et al. 2008). This could be at 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 11 least partly due to an additional gene cluster for T2S in the B strains (Yoon et al. submitted). Phylogenetic analysis revealed that the T2S system commonly found in REL606 and MG1655 were clustered into the clade of E. coli and other genera in the Enterobacteriaceae family, whereas the sequence of the additional T2S system in REL606 was grouped into the branches of E. coli strains having multiple T2S systems and families other than Enterobacteriaceae. This implies that the two T2S systems of the B strains have evolved independently or more specifically the latter might have been introduced. 1.4 Usage of B Strains 1.4.1 As an Academic Lab Rat Since 1940s, K-12 and B strains have been widely used as a laboratory strain and have had significant impact on biological sciences, medicine, and industry (Daegelen et al. submitted, Lederberg 2004). While K-12 strains have been mainly used for developing recombinant DNA techniques, B strains have been the subject of physiological studies (Swartz 1996). B strains also served as hosts for the historical studies of T1-T7 bacteriophages (Delbruck 1946), which led to the construction of BL21(DE3) (Studier and Moffatt 1986). Due to the rapid growth in minimal media and enhanced membrane permeability, B strains were favored by physiologists. A radiation-resistant mutant, E. coli B/r, was isolated after UV-irradiation (Witkin 1946), and has been used for determining cell cycle-related parameters (Helmstetter 1968, Michelsen et al. 2003). Chemical composition measurements of B/r was measured (Neidhardt and Umbarger 1996), which is essential to estimate growth requirements such as energy distribution, reducing power (Neijssel et al. 1996) and metabolic fluxes in a genome-scale metabolic model (Feist et al. 2007). Due to the increased membrane permeability, B strains have been used widely for mutagenic assays and toxicological studies. Mutants of B strain WP2 (e.g. WP2 uvrA and WP2 uvrA/pKM101) have been used as a tester strain in mutagenic assays (Gatehouse et al. 1994) and officially included in the OECD guideline for bacterial reverse mutation test (OECD guideline for testing of chemicals: bacterial reverse mutation test, 1998). These mutants are sensitive to oxidizing mutagens, crosslinking agents and hydrazines (Blanco et al. 1998, Herrera et al. 1993, Wilcox et al. 1990). The higher permeability can make B a primary choice for functional studies by flow cytometry and fluorescence microscopy. When B and K-12 strains were stained with several fluorochromes, B strain showed higher uptake of fluorescent dyes and higher fluorescent intensity (Herrera et al. 2002). Evolution experiments with microorganisms are of a great interest because they allow one to investigate genetic and phenotypic evolution in action under the controlled environment (Elena and Lenski 2003, Philippe et al. 2007). For decades, B has served as a research model for long-term bacterial evolution. Twelve popu- 12 S.H. Yoon et al. lations derived from a common ancestor have propagated by daily serial transfer in a glucose-limited minimal medium for more than 40,000 generations (Cooper et al. 2003). In the experiment, all the populations have adapted to the growth environment via beneficial mutations. Critical issues in evolution are being addressed using laboratory populations of bacteria, e.g. the dynamics of evolutionary adaptation, the genetic bases of adaptation, interactions between different genotypes in a population, and between interacting microbial species (Elena and Lenski 2003). Application of evolutionary principle to strain development and process optimization, which is so called evolutionary engineering, is becoming an important strategy in the field of metabolic engineering (Sauer 2001). Recently, the efficiency of natural selection using long-term evolution experiment is successfully exploited to improve industrial strains (de Crecy et al. 2007, Fong et al. 2005). 1.4.2 As an Industrial Workhorse A primary goal of the bioprocess development is the cost-effective production of desired products such as therapeutic and industrial proteins on a large scale. B and K-12 are preferred production hosts because of fast growth, facility in genetic modification and cultivation, and high yields for many recombinant proteins. As mentioned above, B and its derivatives have salient features desirable for high cell density culture such as low acetate production even when grown on excess glucose, faster growth in minimal media, protease deficiency, and simple cell surfaces that enhance permeability. Thus, they have been widely used for the overproduction of recombinant proteins, ethanol, and other biomolecules on a large scale (Choi et al. 2006). The most popular strains, BL21 and its derivatives (Studier and Moffatt 1986), are derived from B, thus are naturally deficient in the major protease Lon. Additionally, their chromosomes are deleted from the gene for the outer membrane protease OmpT. The absence of these proteases can lead to higher expression levels of recombinant proteins and less degradation of such proteins during purification. A derivative of BL21, BL21(DE3), was constructed to harbor a recombinant phage carrying the T7 RNA polymerase gene under the control of the lacUV5 promoter (Studier and Moffatt 1986). Addition of isopropyl ␤-D-thiogalactopyranoside (IPTG) into growth media is required to express the T7 RNA polymerase, which then transcribes target genes located in a plasmid under the control of the T7 promoter. Due to its high selectivity and activity, BL21(DE3) is extremely popular for mass-production of recombinant proteins which are toxic to the host cells (Choi et al. 2006). Various versions of the T7 RNA polymerase-based expression system have been developed to use cheap and nontoxic inducers instead of IPTG or to minimize basal expression of the cloned gene (Sorensen and Mortensen 2005). Membrane proteins (MPs) account for more than 50% of all drug targets and are of major pharmaceutical and biotechnological interests. Generally, a large amount of MPs is required for their functional and structural studies. However, in many 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 13 cases, over-expression of MPs is lethal to host cells. To overcome this difficulty, mutant hosts, C41(DE3) and C43(DE3) were derived from BL21(DE3) over-producing some membrane proteins (Miroux and Walker 1996). These two mutant hosts, especially C43(DE3), showed reduced toxicity of expressed MPs and are widely used for a variety of MPs. Although the genetic mutation(s) responsible for the changes have not yet been identified, comparative genome sequence analyses of C41(DE3), C43(DE3), and their parental BL21(DE3) revealed several interesting genetic changes (Kwon and Kim, unpublished data). Comparative analysis revealed that there are six SNPs in C41(DE3) and seven in C43(DE3) as compared to BL21(DE3). Interestingly and perplexingly, only two of them overlaps between the two strains. Also, there are two IS-mediated deletions that have been observed – one in both C41(DE3) and C43(DE3) and the other only in C43(DE3). It is reported that C41(DE3) and C43(DE3) are also superior to BL21(DE3) in the production of some cytoplasmic proteins and in the stability of their cloning plasmid (DumonSeignovert et al. 2004). However, it is hard to predict an expression host and system working best for a target protein, and so, screening process is required to some extent. 1.5 Future Prospects Until now, most genetic and metabolic studies of E. coli have been performed with K-12 or its derivatives. Also, a variety of omics analyses (Choi et al. 2003, Franchini and Egli 2006, Han and Lee 2006, Ishii et al. 2007, Nandakumar et al. 2006, Yoon et al. 2003) and in silico metabolic modeling of K-12 (Covert et al. 2004, Feist et al. 2007) have been accelerated by the availability of the complete genome sequences of the MG1655 and W3110 strains (Blattner et al. 1997, Hayashi et al. 2006). In contrast to K-12, little studies have been performed for B strains. This can be attributed to the fact that K-12 strains are the best fit in the current recombinant DNA techniques and a wealth of safety information makes them preferred recombinant organisms by the biosafety communities (Swartz 1996). Additionally, many B derivatives as industrial hosts have been developed in private companies, which can make some difficulties in academic and public research. However, various features of B strains are beneficial for the overexpression of foreign proteins and studies of E. coli physiology. B strain is now in its early stages of global omics studies and systems biology (Xia et al. 2008, Yoon et al. submitted). With the availability of genome sequences of B strains (Jeong et al. submitted), the omics information on the cellular metabolism and physiology should be pivotal in better understanding the underlying biological networks and is invaluable for designing strains having customized genomes as well as establishing rational fermentation strategies. Acknowledgments This work was supported by the 21C Frontier Microbial Genomics and Applications Center Program, Ministry of Science and Technology, Republic of Korea. 14 S.H. Yoon et al. References Blanco, M., A. Urios, and A. Martinez. 1998. New Escherichia coli WP2 tester strains highly sensitive to reversion by oxidative mutagens. Mutat. Res. 413:95–101. Blattner, F. R., G. Plunkett, 3rd, C. A. Bloch, N. T. Perna, V. Burland, M. Riley, J. Collado-Vides, J. D. Glasner, C. K. Rode, G. F. Mayhew, J. Gregor, N. W. Davis, H. A. Kirkpatrick, M. A. Goeden, D. J. Rose, B. Mau, and Y. Shao. 1997. The complete genome sequence of Escherichia coli K-12. Science 277:1453–74. Boulter, J., B. Gielow, M. McFarland, and N. Lee. 1974. Metabolism of D-arabinose by Escherichia coli B/r. J. Bacteriol. 117:920–3. Cha, H. J., R. Srivastava, V. N. Vakharia, G. Rao, and W. E. Bentley. 1999. Green fluorescent protein as a noninvasive stress probe in resting Escherichia coli cells. Appl. Environ. Microbiol. 65:409–14. Choi, J. H., K. C. Keum, and S. Y. Lee. 2006. Production of recombinant proteins by high cell density culture of Escherichia coli. Chem. Eng. Sci. 61:876–85. Choi, J. H., S. J. Lee, and S. Y. Lee. 2003. Enhanced production of insulin-like growth factor I fusion protein in Escherichia coli by coexpression of the down-regulated genes identified by transcriptome profiling. Appl. Environ. Microbiol. 69:4737–42. Cooper, T. F., D. E. Rozen, and R. E. Lenski. 2003. Parallel changes in gene expression after 20,000 generations of evolution in Escherichia coli. Proc. Natl. Acad. Sci. USA 100:1072–7. Cooper, V. S., and R. E. Lenski. 2000. The population genetics of ecological specialization in evolving Escherichia coli populations. Nature 407:736–9. Covert, M. W., E. M. Knight, J. L. Reed, M. J. Herrgard, and B. O. Palsson. 2004. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429:92–6. Daegelen, P., F. W. Studier, R. E. Lenski, S. Cure, and J. F. Kim. Tracing ancestors and relatives of Escherichia coli B, and the derivation of B strains REL606 and BL21(DE3). submitted. de Crecy, E., D. Metzgar, C. Allen, M. Penicaud, B. Lyons, C. J. Hansen, and V. de Crecy-Lagard. 2007. Development of a novel continuous culture device for experimental evolution of bacterial populations. Appl. Microbiol. Biotechnol. 77:489–96. Delbruck, M. 1946. Bacterial viruses or bacteriophages. Biol. Rev. 21:30–40. Delbruck, M., and S. E. Luria. 1942. Interference between bacterial viruses. I. Interference between two bacterial viruses acting upon the same host, and the mechanism of virus growth. Arch. Biochem. 1:111–41. Diaz, E., A. Ferrandez, M. A. Prieto, and J. L. Garcia. 2001. Biodegradation of aromatic compounds by Escherichia coli. Microbiol. Mol. Biol. Rev. 65:523–69. Dumon-Seignovert, L., G. Cariot, and L. Vuillard. 2004. The toxicity of recombinant proteins in Escherichia coli: a comparison of overexpression in BL21(DE3), C41(DE3), and C43(DE3). Protein Expr. Purif. 37:203–6. Eiteman, M. A., and E. Altman. 2006. Overcoming acetate in Escherichia coli recombinant protein fermentations. Trends Biotechnol. 24:530–6. Elena, S. F., and R. E. Lenski. 2003. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4:457–69. Elsinghorst, E. A., and R. P. Mortlock. 1994. Molecular cloning of the Escherichia coli B L-fucoseD-arabinose gene cluster. J. Bacteriol. 176:7223–32. Feist, A. M., C. S. Henry, J. L. Reed, M. Krummenacker, A. R. Joyce, P. D. Karp, L. J. Broadbelt, V. Hatzimanikatis, and B. O. Palsson. 2007. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3:121. Fong, S. S., A. P. Burgard, C. D. Herring, E. M. Knight, F. R. Blattner, C. D. Maranas, and B. O. Palsson. 2005. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91:643–8. Francetic, O., D. Belin, C. Badaut, and A. P. Pugsley. 2000. Expression of the endogenous type II secretion pathway in Escherichia coli leads to chitinase secretion. Embo J. 19: 6697–703. 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 15 Franchini, A. G., and T. Egli. 2006. Global gene expression in Escherichia coli K-12 during shortterm and long-term adaptation to glucose-limited continuous culture conditions. Microbiology 152:2111–27. Gatehouse, D., S. Haworth, T. Cebula, E. Gocke, L. Kier, T. Matsushima, C. Melcion, T. Nohmi, T. Ohta, S. Venitt, et al. 1994. Recommendations for the performance of bacterial mutation assays. Mutat. Res. 312:217–33. Gross, C. A. 1996. Function and regulation of the heat shock proteins. In F. C. Neidhardt, R. Curtiss III, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, D. Schneider, and H. E. Umbarger (eds.), Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology, 2nd ed. ASM Press, Washington, DC, pp. 1382–99. Haeusser, D. P., and P. A. Levin. 2008. The great divide: coordinating cell cycle events during bacterial growth and division. Curr. Opin. Microbiol. 11:94–9. Han, M. J., and S. Y. Lee. 2006. The Escherichia coli proteome: past, present, and future prospects. Microbiol. Mol. Biol. Rev. 70:362–439. Harder, K. J., H. Nikaido, and M. Matsuhashi. 1981. Mutants of Escherichia coli that are resistant to certain beta-lactam compounds lack the ompF porin. Antimicrob. Agents Chemother. 20:549–52. Hayashi, K., N. Morooka, Y. Yamamoto, K. Fujita, K. Isono, S. Choi, E. Ohtsubo, T. Baba, B. L. Wanner, H. Mori, and T. Horiuchi. 2006. Highly accurate genome sequences of Escherichia coli K-12 strains MG1655 and W3110. Mol. Syst. Biol. 2:2006 0007. Helmstetter, C. E. 1968. DNA synthesis during the division cycle of rapidly growing Escherichia coli B/r. J. Mol. Biol. 31:507–18. Herrera, G., A. Martinez, M. Blanco, and J. E. O'Connor. 2002. Assessment of Escherichia coli B with enhanced permeability to fluorochromes for flow cytometric assays of bacterial cell function. Cytometry 49:62–9. Herrera, G., A. Urios, V. Aleixandre, and M. Blanco. 1993. Mutability by polycyclic hydrocarbons is improved in derivatives of Escherichia coli WP2 uvrA with increased permeability. Mutat. Res. 301:1–5. Ishii, N., K. Nakahigashi, T. Baba, M. Robert, T. Soga, A. Kanai, T. Hirasawa, M. Naba, K. Hirai, A. Hoque, P. Y. Ho, Y. Kakazu, K. Sugawara, S. Igarashi, S. Harada, T. Masuda, N. Sugiyama, T. Togashi, M. Hasegawa, Y. Takai, K. Yugi, K. Arakawa, N. Iwata, Y. Toya, Y. Nakayama, T. Nishioka, K. Shimizu, H. Mori, and M. Tomita. 2007. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316:593–7. Jansson, P. E., A. A. Lindberg, B. Lindberg, and R. Wollin. 1981. Structural studies on the hexose region of the core in lipopolysaccharides from Enterobacteriaceae. Eur. J. Biochem. 115:571–7. Jensen, K. F. 1993. The Escherichia coli K-12 "wild types" W3110 and MG1655 have an rph frameshift mutation that leads to pyrimidine starvation due to low pyrE expression levels. J. Bacteriol. 175:3401–7. Jeong, H., V. Barbe, D. Vallenet, S.-H. Choi, C. H. Lee, S.-W. Lee, B. Vacherie, S. H. Yoon, D.-S. Yu, L. Cattolico, C.-G. Hur, H.-S. Park, B. Ségurens, M. Blot, D. Schneider, F. W. Studier, S. C. Kim, T. K. Oh, R. E. Lenski, P. Daegelen, and J. F. Kim. Genome sequencing and comparative analysis of Escherichia coli B REL606 and BL21(DE3). submitted. Jeong, K. J., and S. Y. Lee. 2002. Excretion of human beta-endorphin into culture medium by using outer membrane protein F as a fusion partner in recombinant Escherichia coli. Appl. Environ. Microbiol. 68:4979–85. LeBlanc, D. J., and R. P. Mortlock. 1971. Metabolism of D-arabinose: a new pathway in Escherichia coli. J. Bacteriol. 106:90–6. Lederberg, J. 2004. E. coli K-12. Microbiol. Today 31:116. Lederberg, S. 1966. Genetics of host-controlled restriction and modification of deoxyribonucleic acid in Escherichia coli. J. Bacteriol. 91:1029–36. Legrain, C., V. Stalon, and N. Glansdorff. 1976. Escherichia coli ornithine carbamolytransferase isoenzymes: evolutionary significance and the isolation of arg F and arg I transducing bacteriophages. J. Bacteriol. 128:35–8. 16 S.H. Yoon et al. Lenski, R. E., M. R. Rose, S. C. Simpson, and S. C. Tadler. 1991. Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. Am. Nat. 138. Michelsen, O., M. J. Teixeira de Mattos, P. R. Jensen, and F. G. Hansen. 2003. Precise determinations of C and D periods by flow cytometry in Escherichia coli K-12 and B/r. Microbiology 149:1001–10. Miroux, B., and J. E. Walker. 1996. Over-production of proteins in Escherichia coli: mutant hosts that allow synthesis of some membrane proteins and globular proteins at high levels. J. Mol. Biol. 260:289–98. Nandakumar, M. P., A. Cheung, and M. R. Marten. 2006. Proteomic analysis of extracellular proteins from Escherichia coli W3110. J. Proteome Res. 5:1155–61. Neidhardt, F. C., and H. E. Umbarger. 1996. Chemical composition of Escherichia coli. In F. C. Neidhardt, R. Curtiss III, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, D. Schneider, and H. E. Umbarger (eds.), Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology, 2nd ed. ASM Press, Washington, DC, pp. 13–16. Neijssel, O. M., M. J. Teixeira de Mattos, and D. W. Tempest. 1996. Growth yield and energy distribution. In F. C. Neidhardt, R. Curtiss III, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, D. Schneider, and H. E. Umbarger (eds.), Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology, 2nd ed. ASM Press, Washington, DC, pp. 1683–92. Nikaido, H. 2003. Molecular basis of bacterial outer membrane permeability revisited. Microbiol. Mol. Biol. Rev. 67:593–656. Nikaido, H. 1996. Outer membrane. In F. C. Neidhardt, R. Curtiss III, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, D. Schneider, and H. E. Umbarger (eds.), Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology, 2nd ed. ASM Press, Washington, DC, pp. 29–47. Philippe, N., E. Crozat, R. E. Lenski, and D. Schneider. 2007. Evolution of global regulatory networks during a long-term experiment with Escherichia coli. Bioessays 29:846–60. Phue, J. N., B. Kedem, P. Jaluria, and J. Shiloach. 2007. Evaluating microarrays using a semiparametric approach: application to the central carbon metabolism of Escherichia coli BL21 and JM109. Genomics 89:300–5. Phue, J. N., S. B. Noronha, R. Hattacharyya, A. J. Wolfe, and J. Shiloach. 2005. Glucose metabolism at high density growth of E. coli B and E. coli K: differences in metabolic pathways are responsible for efficient glucose utilization in E. coli B as determined by microarrays and Northern blot analyses. Biotechnol. Bioeng. 90:805–20. Pieper, D. H., and W. Reineke. 2000. Engineering bacteria for bioremediation. Curr. Opin. Biotechnol. 11:262–70. Posfai, G., G. Plunkett, 3rd, T. Feher, D. Frisch, G. M. Keil, K. Umenhoffer, V. Kolisnychenko, B. Stahl, S. S. Sharma, M. de Arruda, V. Burland, S. W. Harcum, and F. R. Blattner. 2006. Emergent properties of reduced-genome Escherichia coli. Science 312:1044–6. Pugsley, A. P., and J. P. Rosenbusch. 1983. OmpF porin synthesis in Escherichia coli strains B and K-12 carrying heterologous ompB and/or ompF loci. FEMS Microbiol. Lett. 16:143–47. saiSree, L., M. Reddy, and J. Gowrishankar. 2001. IS186 insertion at a hot spot in the lon promoter as a basis for Lon protease deficiency of Escherichia coli B: identification of a consensus target sequence for IS186 transposition. J. Bacteriol. 183:6943–6. Sauer, U. 2001. Evolutionary engineering of industrially important microbial phenotypes. Adv. Biochem. Eng. Biotechnol. 73:129–69. Schneider, D., E. Duperchy, J. Depeyrot, E. Coursange, R. Lenski, and M. Blot. 2002. Genomic comparisons among Escherichia coli strains B, K-12, and O157:H7 using IS elements as molecular markers. BMC Microbiol. 2:18. Seo, J. H., D. G. Kang, and H. J. Cha. 2003. Comparison of cellular stress levels and greenfluorescent-protein expression in several Escherichia coli strains. Biotechnol. Appl. Biochem. 37:103–7. 1 Genomics, Biological Features, and Biotechnological Applications of E. coli B 17 Sorensen, H. P., and K. K. Mortensen. 2005. Advanced genetic strategies for recombinant protein expression in Escherichia coli. J. Biotechnol. 115:113–28. Studier, F. W., and B. A. Moffatt. 1986. Use of bacteriophage T7 RNA polymerase to direct selective high-level expression of cloned genes. J. Mol. Biol. 189:113–30. Swartz, J. R. 1996. Escherichia coli recombinant DNA technology. In F. C. Neidhardt, R. Curtiss III, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, M. Shaechter, and H. E. Umbarger (eds.), Escherichia coli and Salmonella: cellular and molecular biology, 2nd ed. ASM Press, Washington, DC, pp. 1693–1712. Tian, G., D. Lim, J. D. Oppenheim, and W. K. Maas. 1994. Explanation for different types of regulation of arginine biosynthesis in Escherichia coli B and Escherichia coli K12 caused by a difference between their arginine repressors. J. Mol. Biol. 235:221–30. Tsilibaris, V., G. Maenhaut-Michel, and L. Van Melderen. 2006. Biological roles of the Lon ATPdependent protease. Res. Microbiol. 157:701–13. Umbarger, H. E. 1996. Biosynthesis of the branched-chain amino acids. In F. C. Neidhardt, R. Curtiss III, J. L. Ingraham, E. C. C. Lin, K. B. Low, B. Magasanik, W. S. Reznikoff, M. Riley, D. Schneider, and H. E. Umbarger (eds.), Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology, 2nd ed. ASM Press, Washington, DC, pp. 442–57. Vallenet, D., L. Labarre, Z. Rouy, V. Barbe, S. Bocs, S. Cruveiller, A. Lajus, G. Pascal, C. Scarpelli, and C. Medigue. 2006. MaGe: a microbial genome annotation system supported by synteny results. Nucl. Acids Res. 34:53–65. Vostiar, I., J. Tkac, and C. F. Mandenius. 2004. Off-line monitoring of bacterial stress response during recombinant protein production using an optical biosensor. J. Biotechnol. 111:191–201. Wilcox, P., A. Naidoo, D. J. Wedd, and D. G. Gatehouse. 1990. Comparison of Salmonella typhimurium TA102 with Escherichia coli WP2 tester strains. Mutagenesis 5:285–91. Witkin, E. M. 1946. Inherited differences in sensitivity to radiation in Escherichia coli. Proc. Natl. Acad. Sci. USA 32:59–68. Xia, X. X., M. J. Han, S. Y. Lee, and J. S. Yoo. 2008. Comparison of the extracellular proteomes of Escherichia coli B and K-12 strains during high cell density cultivation. Proteomics 8:2089– 103. Yoon, S. H., M. J. Han, H. Jeong, C. H. Lee, X. X. Xia, J. H. Shim, S. Y. Lee, T. K. Oh, and J. F. Kim. Comparative multi-omics systems analysis of closely related Escherichia coli strains. submitted. Yoon, S. H., M. J. Han, S. Y. Lee, K. J. Jeong, and J. S. Yoo. 2003. Combined transcriptome and proteome analysis of Escherichia coli during high cell density culture. Biotechnol. Bioeng. 81:753–67. Yoon, S. H., C. G. Hur, H. Y. Kang, Y. H. Kim, T. K. Oh, and J. F. Kim. 2005. A computational approach for identifying pathogenicity islands in prokaryotic genomes. BMC Bioinformatics 6:184. Yu, B. J., B. H. Sung, M. D. Koob, C. H. Lee, J. H. Lee, W. S. Lee, M. S. Kim, and S. C. Kim. 2002. Minimization of the Escherichia coli genome using a Tn5-targeted Cre/loxP excision system. Nat. Biotechnol. 20:1018–23. Chapter 2 Escherichia coli Genome Engineering and Minimization for the Construction of a Bioengine Bong Hyun Sung, Jun Hyoung Lee, and Sun Chang Kim Contents 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Estimating the Size and Gene Content of Minimal Genomes . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Identification of Minimal Essential Gene Sets by Sequence Comparison in silico 2.2.2 Identification of Essential Genes by Large–Scale Gene Inactivation . . . . . . . . . . . . 2.3 Techniques for Experimental Genome Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Suicide Plasmid–Mediated Genomic Deletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Linear DNA–Mediated Genomic Deletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Site-Specific Recombination–Mediated Genomic Deletion . . . . . . . . . . . . . . . . . . . 2.3.4 Transposon–Mediated Random Deletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Genome Minimization of Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 E. coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Other Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Conclusions and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 21 22 23 24 24 26 28 30 30 31 34 36 36 Abstract A profusion of diverse genome-related information has been obtained by the sequencing of genomes from many microorganisms, functional analyses of these genomes, and the application of bioinformatics techniques to genomics, proteomics, and systems biology. The resulting barrage of data coupled with large-scale gene inactivation studies have allowed researchers to produce a genetic blueprint for a streamline, custom-designed microbe that carries the minimal gene set required for the organism to replicate in a given environment. On the basis of this minimal genome information, several research groups have generated minimalgenome Escherichia coli strains using sophisticated genome engineering techniques, such as the dual transposition, site-specific recombinations, and markerless genome recombination. These minimal genomes display various desirable traits for biological researches, such as improved genome stability, increased transformation efficacy, and higher production of biological materials. Therefore, the generation of S.C. Kim (B) Department of Biological Sciences, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea e-mail: email@example.com S.Y. Lee (ed.), Systems Biology and Biotechnology of Escherichia coli, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9394-4 2, 19 20 B.H. Sung et al. a large number of deletion mutants of the minimal E. coli genomes coupled with restructuring of regulatory circuits may lead to facilitate the construction of a variety of custom-designed bacterial strains (also called a "bioengine") with myriad practical and commercial applications. 2.1 Introduction Nucleotide sequencing and comparative analysis of multiple diverse genomes is revolutionizing contemporary biology by providing a framework for interpreting and predicting the physiological properties of an organism. A variety of emerging postgenomic techniques, such as genome-wide gene expression profiling and monitoring of interactions among macromolecules, are helping to define the molecular compositions of cells. Scientists have developed, and continue to refine, sophisticated new computational approaches that allow one to explore the inherent organization of cellular networks as well as the mode and dynamics of interactions among cellular constituents (Hasty et al. 2001, Herring et al. 2006, Ishii et al. 2007, Kitano 2002, Koonin et al. 2002). These intricate tools and techniques have introduced a new paradigm in cell biology research: the construction of custom-designed, minimal-genome microbes (bioengines) that perform functions that raise the quality of life for human beings. A minimal genome can be defined as a one that contains the smallest set of genes that allows the organism to replicate in a given environment (Mushegian 1999). The creation and study of minimal microbial genomes can help to increase our understanding of complex genetic material and provide a basis for the design of custom bacterial strains. Drawing on the complete genome sequences of more than 800 microorganisms (June 2008, Genomes OnLine Database http://www.genomesonline. org/) as well as extensive functional analyses of their gene products, researchers have proposed two different approaches for the construction of minimal genomes (Cho et al. 1999, Luisi 2002, Maniloff 1996). The first is a "top-down" approach, which involves trimming the genome of sequences that appear to be unnecessary on the basis of functional genomic studies of microorganisms. The second is a "bottom-up" approach, which entails synthesizing the proposed minimal genome from basic chemical building blocks and inserting it into an environment that allows metabolic activity and replication (Forster and Church 2006, 2007, Szostak et al. 2001). Although simple biological constructs can be synthesized artificially (Cello et al. 2002, Gibson et al. 2008, Itaya et al. 2008, Smith et al. 2003, Tumpey et al. 2005), the bottom-up approach is technically challenging and the actual synthesis of an artificial minimal genome from chemical building blocks is not possible if one lacks a complete functional analysis of the genes needed for life. However, the top-down approach, which starts with the intact genome of a well-characterized microorganism, is more technically feasible; this is because the top-down approach can be initiated in parallel with rapidly progressing functional genomics research in microorganisms. 2 Escherichia coli Genome Engineering and Minimization 21 Back in 1984, H. Morowitz first raised the idea of using mycoplasmas as models for the construction of a minimal genome in a living cell (Razin 1997a, b). The complete genomic sequence of the human pathogen Mycoplasma genitalium (Fraser et al. 1995) revealed that the genome is only 580 kb in length and contains only about 470 predicted protein coding genes, as compared with 1,703 and 4,288 in the Haemophilus influenzae (Fleischmann et al. 1995) and Escherichia coli (Blattner et al. 1997) genomes, respectively. However, mycoplasmas are parasites that evolved, by degenerative or reductive evolution, from Gram-positive bacteria and they have not been well characterized, because they are hard to grow and their genomes are difficult to manipulate. Therefore, scientists chose to attempt minimal genome construction in free-living microorganisms (Koob et al. 1994). Among the various free-living microorganisms, E. coli is the most logical choice for minimal genome construction experiments, as it is more fully defined at the molecular level than any other microorganism. Furthermore, because of its nearly ubiquitous use as a research tool and its favorable growth characteristics, this bacterium has been the organism of choice in the development of tools for sophisticated genetic engineering. Finally, E. coli is one of the best commercially applicable hosts for the pharmaceutical and fermentation industries (Blattner et al. 1997, Riley et al. 2006). Functional analysis of the E. coli genome has revealed that bacteria that are grown under a given condition use only a fraction of their genes for growth, replication, and production of important biological materials (Richmond et al. 1999, Tao et al. 1999). This is because the E. coli genome contains nonessential genes, transposable elements, bacteriophage DNA, cryptic prophages, pseudogenes, gene remnants, damaged operons, and virulence factors, some of which yield unnecessary or unwanted products that interfere with rational strain improvement and the production of desired biological substances (Blattner et al. 1997, Hayashi et al. 2006). Therefore, the identification and deletion of nonessential genes and other dispensable sequences in the microbial genome is necessary for the construction of a custom-designed bioengine, in which cellular metabolites and energy sources are efficiently optimized and directed toward the production of both essential and desired gene products (Edwards and Palsson 2000, Park et al. 2007). Concomitantly, the bioengine's metabolic waste and bio-pollution can be minimized, and the quality and stability of its products can be maximized (Cho et al. 1999, Kolisnychenko et al. 2002, Westers et al. 2003). In this chapter, we describe approaches for minimizing the E. coli genome by eliminating unnecessary genes, to create a self-sustaining, self-replicating, artificial bioengine. 2.2 Estimating the Size and Gene Content of Minimal Genomes Identification of the regulatory and protein-coding DNA sequences that are most essential for maintaining and replicating a free-living cellular organism is a logical first step in the construction of a custom-designed bioengine. Several approaches 22 B.H. Sung et al. have been used to identify essential and nonessential genes in microorganisms under given conditions, with the goal of defining the minimal set of genes necessary for cell survival and self-replication. 2.2.1 Identification of Minimal Essential Gene Sets by Sequence Comparison in silico The sequenced genome of the parasitic bacterium M. genitalium contains only about 470 identified protein-coding genes, and these have been dubbed a minimal gene complement (Fraser et al. 1995). Although the complete gene complement of M. genitalium is nearly the smallest one among cellular life forms with sequenced genomes, there is no evidence that this collection of 470 genes represents a minimal self-sufficient gene set. To derive such a set, Mushegian and Koonin (1996) compared the 468 predicted M. genitalium protein sequences with the 1,703 predicted protein sequences encoded by the other completely sequenced microbial genome, that of H. influenzae (Fleischmann et al. 1995). Because these microorganisms belong to two distinct ancient bacterial lineages [that is, Gram-positive (M. genitalium) and Gram-negative (H. influenzae) bacteria], genes that are conserved in these two organisms are almost certainly essential for growth and replication. This genomic comparison suggested that the minimal number of genes necessary and sufficient to sustain the existence of a modern-type cell is closer to 256 (Koonin et al. 2002, Mushegian 1999). On the basis of these 256 genes, Mushegian and Koonin suggested the following key features that must be specified by a minimal gene set: rudimentary systems for gene transcription, protein translation, DNA replication, recombination, and repair; chaperone-like proteins and machinery for protein export and metabolite transport; and nucleotide salvage pathways. Since then, comparisons of protein-coding regions in complete genome sequences from diverse organisms have revealed clusters of orthologous groups (COGs), (Tatusov et al. 1997); because orthologous proteins likely have similar functions, the COGs have been used to define a minimal gene set of life (Mushegian 1999). Using a similar approach, researchers compared genome sequences from uropathogenic E. coli CFT073, enterohemorrhagic DEL933, and the E. coli laboratory strain MG1655 and defined a combined set of nonredundant protein-encoding genes. Of these genes, only 39.2% (2,996 genes) are common to all three strains (Welch et al. 2002). However, when such an analysis was carried out with about 100 sequenced genomes, only 63 genes were found to be ubiquitous; most of these genes encode translational components, and a few specify basic components of the transcription machinery (Koonin 2003). The challenge of this comparative genomic approach to the identification of a minimal gene set is that certain function-related complexities are not taken into account. For example, the minimal number of essential functions may be larger than that predicted by genome sequence comparison, because not all proteins that perform the same function share detectable sequence similarity (Riley and Serres 2000). Therefore, because it may substantially underestimate the size of the min- 2 Escherichia coli Genome Engineering and Minimization 23 imal gene set, one cannot rely exclusively on the comparative approach (Feher et al. 2007). 2.2.2 Identification of Essential Genes by Large–Scale Gene Inactivation Global transposition mutagenesis has been used to identify nonessential genes in an effort to learn whether the naturally occurring gene complements behave as true minimal genomes under laboratory conditions. The positions of 2,209 transposon insertions in the completely sequenced genomes of M. genitalium and its close relative Mycoplasma peumoniae were determined by sequencing across the junctions of the transposons and the genomic DNA (Hutchison et al. 1999). These junctions defined 1,354 distinct sites in which transposon insertion did not lead to lethality. This analysis suggests that 265–350 of the 480 protein-coding genes of M. genitalium are essential under laboratory conditions, including about 100 genes of unknown function (Hutchison et al. 1999). In Bacillus subtilis, Itaya (1995) introduced mutations in a small set of randomly selected genetic loci, examined the growth properties of the mutants, and determined the percentage of genes that could be disrupted without loss of viability. This led to the hypothesis that the minimal B. subtilis genome may comprise about 318– 562 kb which, given the average size of ∼1 kb for a bacterial protein-coding gene, corresponds to 300–500 genes (Kunst et al. 1997). And with a systematic approach that employed single-gene disruptions that covered the complete Bacillus genome, Kobayashi et al. (2003) identified about 270 genes that are indispensable for growth of B. subtilis in a rich medium at 37 ◦ C. In Pseudomonas aeruginosa, H. influenzae, Corynebacterium glutamicum, and E. coli, global transposition mutagenesis has identified 678, 670, 658, and 620 genes, respectively, those essential for growth under laboratory conditions (Akerley et al. 2002, Gerdes et al. 2003, Jacobs et al. 2003, Suzuki et al. 2006). However, when Baba et al. (2006) used Red recombination to generate a set of precisely defined, single-gene deletions of all putative protein-coding genes in E. coli K-12, of the 4,288 genes targeted, only 303 genes, including 37 of unknown function, were unable to be disrupted in Lulia-Bertani (LB) medium. When these 303 essential genes were divided into functional groups, the major subsets contained members of COGs that play roles in protein translation, ribosomal structure, cell division, lipid metabolism, transcription, and cell envelope biogenesis. And only 67% (205 genes) of the 303 essential genes overlap with those in the essential gene set predicted by global transposition (Baba et al. 2006, Gerdes et al. 2003). These differences can be attributed to the use of different mutagenesis strategies and different growth conditions. However, because the global transposition system measures the effect of mutations on cell populations, a mutation that causes very slow growth can appear to be lethal and hence be falsely classified as essential. Furthermore, of the 3,988 single-gene deletion mutants made by Baba et al., 119 gave rise to mutant E. coli strains that were unable to grow on glycerol-supplemented M9 minimal medium 24 B.H. Sung et al. Table 2.1 Number of estimated essential genes in various microorganisms Method Strain Comparative genomics M. genitalium and H. influenzae M. genitalium H. influenzae RD E. coli K12 P. aeruginosa H. pyroli C. glutamicum R S. cerevisiae B. subtilis 168 S. typhimurium LT2 E. coli K12 Global transposition Single-gene deletion or knockout No. of essential genes/total ORFs (%) 256 265–350/468 (79%) 670/1703 (38%) 620/4296 (14%) 678/5500 (12%) 255/1590 (16%) 658/2990 (22%) 1105/5916 (19%) 271/4099 (6.8%) 490/4597 (11%) 303/4296 (7%) Ref. Mushegian and Koonin 1999 Hutchison et al. 1999 Akerley et al. 2002 Gerdes et al. 2003 Jacobs et al. 2003 Salama et al. 2004 Suzuki et al. 2006 Giaever et al. 2002 Kobayashi et al. 2003 Knuth et al. 2004 Baba et al. 2006 (Joyce et al. 2006). Information about the variously defined minimal gene sets is summarized in Table 2.1. 2.3 Techniques for Experimental Genome Minimization For some bacteria, strains that carry spontaneous deletion mutations can be obtained by long-term serial passage of the cells under laboratory culture conditions (Cooper et al. 2001). However, the spontaneous deleterious mutation rate for the E. coli genome is too low (∼2 × 10−4 per generation) to allow deletion mutants to be efficiently created using this technique (Elena and Lenski 2003, Kibota and Lynch 1996). Therefore, for the rapid construction of minimal genomes by removing protein-coding genes and other genomic sequences that previously were shown to be nonessential, researchers have developed a variety of deletion methods, including homologous recombination using suicide plasmids, linear DNA recombination using the phage Red system, site-specific recombination system, and random deletion by double transposition. 2.3.1 Suicide Plasmid–Mediated Genomic Deletion Suicide plasmids are convenient vehicles for the delivery of DNA into the E. coli chromosome. Link et al. (1997) have described a method for gene replacement in E. coli that uses a homologous recombination between the bacterial chromosome and a suicide plasmid, carrying cloned chromosomal fragments (homologous arms), whose replication ability is temperature sensitive (Fig. 2.1A). At the non-permissive temperature (42 ◦ C), cells maintain drug resistance only if the plasmid integrates into the chromosome by homologous recombination between the cloned fragment and the bacterial chromosome (Hamilton et al. 1989, Posfai et al. 1999). Alternative suicide plasmids that contain plasmid R6K-origin of replication (ori) can be made in an E. coli cell that supports synthesis of the replication initiation protein and 2 Escherichia coli Genome Engineering and Minimization 25 Fig. 2.1 General schemes of the deletion procedures. In all schemes, the chromosomal DNA is shown at the top of the figure, and the targeted plasmid or DNA fragment is shown below the chromosomal DNA schematic. The sequences of the A and B regions in the chromosome and the targeted plasmid or DNA fragment are the same thus can undergo homologous recombination. (A) Protocol for use of the suicide plasmid-mediated deletion system. The plasmid, which carries two PCR-amplified DNA fragments (A and B, >500 bp) that flank a target genomic region to be deleted, is transformed into E. coli, and cells are plated at the nonpermissive temperature (42 ◦ C) of the plasmid replicon. When shifted to the permissive temperature (30 ◦ C), the plasmid is excised from the chromosome by recombination at either A or B. The resistance colonies against the counterselection marker are screened for deletion of the target region (Link et al. 1997, Posfai et al. 1999). (B) Overview of the Red-mediated linear DNA deletion method. An FRT-flanked antibiotic resistance gene is amplified by PCR and transformed into E. coli expressing the Red recombinase. After selection of the antibiotic-resistant transformants, the inserted antibiotic resistance gene is eliminated by the Flp/FRT recombination system (Datsenko and Wanner 2000). (C) The scarless deletion system. The targeted DNA fragment, which carries the actual post-deletion sequence joints, was amplified by PCR and integrated into the genome by Red recombination. Integration creates a duplication of the segment that flanks the deletion target region. Cleavage of the inserted DNA by meganuclease I-SceI introduces a DSB between the duplicated segments and stimulates their intramolecular recombination, resulting in a scarless genome deletion (Kolisnychenko et al. 2002). (D) Deletion of a genomic region by two serial linear DNA recombination events. A DNA 26 B.H. Sung et al. then transferred to a target cell that lacks the replication protein, which permits the plasmid to integrate into the E. coli genome (Koob et al. 1994, Posfai et al. 1994, Yoon et al. 1998). After the integration of a suicide plasmid that contains a homologous arm into the chromosome, the suicide plasmid can be excised out by the second homologous recombination. Depending on the position of the second recombination event that excises the plasmid, the chromosome either retains the wild-type sequence or has a deletion between the target sites (Fig. 2.1A). For easy identification of the resolved products, a counterselection marker, such as sacB (sucrose sensitivity) (Dedonder 1966, Gay et al. 1985, Link et al. 1997) or rpsL (streptomycin sensitivity) (Hashimoto et al. 2005, Russell et al. 1989, Wang et al. 1993), is integrated in the suicide plasmids. The resolution of the plasmids is thus screened on media supplemented with sucrose or streptomycin, respectively. Another efficient mode of enhancing the plasmid excision step is the introduction of an 18-base pair (bp) meganuclease I-SceI cleavage site in the suicide plasmid (Posfai et al. 1999). Cleavage of the genome at this unique site creates a double-strand break, which simultaneously stimulates recombination and selects for resolution of the integrated plasmid. Although methods that employ suicide plasmids can be used to delete genomic segments, for each deletion experiment, these procedures require the creation of specific targeting vectors before recombining them into the chromosome. 2.3.2 Linear DNA–Mediated Genomic Deletion To avoid the inconvenience of constructing the targeting vectors in the suicide plasmid-mediated genomic deletion, linear DNA-mediated genomic deletion approach has been introduced. In Saccharomyces cerevisiae and a few naturally competent bacteria, genes or genomic regions can be directly disrupted by transformation with double-stranded DNA (dsDNA) fragments, created with the polymerase chain reaction (PCR), that encode a selectable marker and have only about 50 bp of flanking DNA (called homology arms) that are homologous to the chromosome region of interest. Through homology arm-directed homologous recombination between the DNA fragments and the chromosome, this procedure facilitates the generation of specific chromosomal mutations and thus functional analysis of the genome (Baudin et al. 1993, Oliver et al. 1998, Wilson et al. 1999). In E. coli, however, intracellular exonucleases, such as RecBCD, degrade linear DNA (Lorenz and Wackernagel Fig. 2.1 (continued) fragment that contains an antibiotic resistance gene and a counterselection marker cassette flanked by DNA segments that are homologous to the chromosomal target region was amplified by PCR and inserted into the chromosomal DNA by Red–mediated recombina