METABOLOMICS Edited by Ute Roessner Metabolomics Edited by Ute Roessner Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Igor Babic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published February, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from [email protected] Metabolomics, Edited by Ute Roessner p. cm. 978-953-51-0046-1 Contents Preface IX Part 1 Metabolomics of Microbes and Cell Cultures 1 Chapter 1 Metabolomics and Mammalian Cell Culture 3 Kathya De la Luz-Hdez Chapter 2 Quantitative Metabolomics and Its Application in Metabolic Engineering of Microbial Cell Factories Exemplified by the Baker’s Yeast 19 Mario Klimacek Part 2 Data Analysis and Integration 45 Chapter 3 Online Metabolomics Databases and Pipelines 47 Adam J. Carroll Chapter 4 Generic Software Frameworks for GC-MS Based Metabolomics 73 Nils Hoffmann and Jens Stoye Chapter 5 Computational Methods to Interpret and Integrate Metabolomic Data 99 Feng Li, Jiangxin Wang, Lei Nie and Weiwen Zhang Chapter 6 Metabotype Concept: Flexibility, Usefulness and Meaning in Different Biological Populations 131 Nabil Semmar Chapter 7 Software Techniques for Enabling High-Throughput Analysis of Metabolomic Datasets 167 Corey D. DeHaven, Anne M. Evans, Hongping Dai and Kay A. Lawton VI Contents Part 3 Metabolomics to Identify Health Promoting Factors and New Bioactives 193 Chapter 8 Metabolic Pathways as Targets for Drug Screening 195 Wai-Nang Paul Lee, Laszlo G. Boros and Vay-Liang W. Go Part 4 Metabolomics in Plant Research 211 Chapter 9 New Opportunities in Metabolomics and Biochemical Phenotyping for Plant Systems Biology 213 Gibon Yves, Rolin Dominique, Deborde Catherine, Bernillon Stéphane and Moing Annick Chapter 10 Metabolomics of Endophytic Fungi Producing Associated Plant Secondary Metabolites: Progress, Challenges and Opportunities 241 Souvik Kusari and Michael Spiteller Part 5 Metabolomics in Human Disease Research 267 Chapter 11 Metabolomics in the Analysis of Inflammatory Diseases 269 Sabrina Kapoor, Martin Fitzpatrick, Elizabeth Clay, Rachel Bayley, Graham R. Wallace and Stephen P. Young Chapter 12 Clinical Implementation of Metabolomics 289 Akira Imaizumi, Natsumi Nishikata, Hiroo Yoshida, Junya Yoneda, Shunji Takahena, Mitsuo Takahashi, Toshihiko Ando, Hiroshi Miyano, Kenji Nagao, Yasushi Noguchi, Nobuhisa Shimba and Takeshi Kimura Part 6 Improving Analytics 315 Chapter 13 Improvement in the Number of Analytic Features Detected by Non-Targeted Metabolomic Analysis: Influence of the Chromatographic System and the Ionization Technique 317 R. Pandher, E. Naegele, S.M. Fischer and F.I Raynaud Part 7 Metabolomics in Safety Assessments 329 Chapter 14 Challenges for Metabolomics as a Tool in Safety Assessments 331 George G. Harrigan and Bruce Chassy Chapter 15 Metabolomics Approach for Hazard Identification in Human Health Assessment of Environmental Chemicals 349 Suryanarayana V. Vulimiri, Brian Pachkowski, Ambuja S. Bale and Babasaheb Sonawane Preface Metabolomics is a new scientific field which has developed with an accelerating speed over the last decade as demonstrated through the increasing numbers of publications in scientific journals of any biological research field. These developments are mainly driven by increasingly robust and sensitive analytical instrumentations allowing the analysis and quantification of thousands of metabolites from any biological system. Together with the application of sophisticated computational methodology and statistical approaches the vast amount of data generated from instrumentation can be analysed and mined aiding biological and biochemical interpretation. Especially, once experimental metabolomics data can be integrated with other ‘omics type data such as from genomics and proteomics analyses the path is paved for a better holistic understanding of the biological system under investigation. This book will provide the reader with summaries of the state-of-the-art of the technologies and methodologies, especially in the data analysis and interpretation approaches as well as gives insights into exciting applications of metabolomics in human health studies, safety assessments and plant and microbial research. Dr. Ute Roessner School of Botany, The University of Melbourne, Victoria, Australia Part 1 Metabolomics of Microbes and Cell Cultures 1 Metabolomics and Mammalian Cell Culture Kathya De la Luz-Hdez Center of Molecular Immunology Cuba 1. Introduction Since the mid-1950s, when pioneering work of Earle and colleagues (1954) enable routine cell culture, mammalian cell culture has been used in the large-scale production of recombinant protein and monoclonal antibodies. Mammalian cell lines are preferred as production host for many pharmaceuticals, since complex post-translational modifications of the produced proteins (especially glycosylation) are generally not properly performed by microbial systems (Lake-Ee Quek et al., 2010). Wagburg described that under batch conditions, mammalian cells display an inefficient metabolic phenotype characterized by high rates of glucose to lactate conversion (Warburg, 1956) together with partial oxidation of glutamine to ammonia and non-essential amino acids (Fitzpatrick et al., 1993; Jenkins et al., 1992; Ljunggren and Haggstrom, 1992; Ozturk and Palsson, 1991). The accumulation of fermentation by-products causes a reduction of the culture density and product titer that can be realized (Martinelle et al., 1998). In order to increase the cell productivity a common optimization approach is to grow cells to moderately high density in fed-batch and the deliberately induce a prolonged, productive stationary phase. While optimization of this perturbed batch strategy is responsible for the increase of monoclonal antibodies titer seen over the past decades it has a number of short- comings, including: a. the strategy has to be refined for each new cell line, b. the ultimate metabolic phenotype during prolonged stationary phase varies between cell lines and it is not always possible to achieve the most productive phenotypes for a given strain c. volumetric productivity remains relatively low due to moderate cell density (Lake Ee Quek et al., 2010). Other approaches are related with the changes of cell phenotypes through metabolic engineering or change of culture media conditions in order to manipulate the cellular metabolic behavior. Although transcriptomics and proteomics have been explored extensively for mammalian cell engineering (Korke et al., 2002; Seow et al., 2001; Seth et al., 2007; Smales et al., 2004; de la Luz et al., 2007, 2008) these tools fall short of generating direct measurements of the physiological state of the cell. It is essential to combine these techniques with metabolic flux 4 Metabolomics analysis (MFA) a powerful method to quantify the manifestation of a phenotype: the intracellular reaction rates or the fluxome. One of the relatively new “omic” sciences is the field of metabolomics. The metabolome was first described by Oliver and collagues (1998) as being the set of all of low-molecular-mass compounds synthesized by an organism. Metabolomics is therefore the analysis of small molecules that constitute the metabolism, and it offers the closest direct measurements of a cell´s physiological activity (Beecher, 2002; Khoo and Al-Rubeai, 2007). The metabolomic analysis can be considered as “the measurement of the change in the relative concentrations of metabolites as the result of the deletion or overexpression of a gene, should allow the target of a novel gene product to be located on the metabolic map”. Another definition of the metabolome states that it consists of “only those native small molecules that are participant’s in general metabolic reactions and that are required for the maintenance, growth and normal function of a cell” (Khoo and Al-Rubeai, 2007). The metabolomic as a new powerful tool to understand the complex processes of large scale mammalian cell cultures for biopharmaceutical production has not been yet embraced during process development and scale-up. This is mostly because