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Concepts in plant metabolomics : [derived from presentations made at the 3rd International Congress of Plant Metabolomics, which was held in 2004 at Iowa State University, Ames, Iowa] PDF

305 Pages·2007·8.15 MB·English
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CONCEPTS IN PLANT METABOLOMICS Concepts in Plant Metabolomics Edited by BASIL J. NIKOLAU Iowa State University, Ames, Iowa, U.S.A. and EVE SYRKIN WURTELE Iowa State University, Ames, Iowa, U.S.A. AC.I.P. Catalogue record for this book is available from the Library of Congress. ISBN 978-1-4020-5607-9 (HB) ISBN 978-1-4020-5608-6 (e-book) Published by Springer, P.O. Box 17, 3300 AADordrecht, The Netherlands. www.springer.com Printed on acid-free paper All Rights Reserved © 2007 Springer 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. PREFACE Metabolomics is a word that progress in science forces linguists to invent in order to keep up with emerging technologies. The word is a hybridization of two words, metabolites and genomics, and it reflects a shift in biological research that is now possible in an era in which the entire genetic blueprint of an organism is available for scientific research. Although the concepts of metabolomics are in the scientific literature since the 1970s, the word “metabolomics” was first used in the title of a scientific publication in 2001. Since then, the field of metabolomics has expanded and is becoming an integral sector of post-genomic research in biology. Analogous to genomics, which defines all genes in a genome irrespective of their functionality, metabolomics seeks to profile “all” metabolites in a biological sample irrespective of the chemical and physical properties of these molecules. Despite the fact that this is probably an unachievable goal, the ability to profile an ever- increasing proportion of the metabolome (the set of all metabolites of a sample) has many applications is solving biological problems. These range from the expansion of the tradition of natural products chemistry, to the finding of metabolic markers of disease states in humans and animals. In the field of plant biology, metabolomics has a key role as a fundamental tool in basic research for elucidating gene functions that are currently undefined. Thus, metabolomics has the potential of defining cellular processes as it provides a measure of the ultimate phenotype of an organism, as defined by the collage of small molecules, whose levels of accumulation is altered in response to genetic and environmentally induced changes in gene expression. As an emerging field of science, new developments will greatly change the practice of metabolomics; these will likely occur in the area of improvement in analytical technologies and computational integration and interpretation of data. We hope that this book will present a guide for new practitioners of metabolomics, providing insights as to its current use and applications. These chapters are derived from presentations made at the 3rd International Congress of Plant Metabolomics, which was held in 2004 at Iowa State University, Ames, Iowa. We are grateful to the National Science Foundation, the National Research Initiative program of the US Department of Agriculture, and the Office of Basic Science of the Department of Energy, for financial support of this meeting. Finally, we would like to acknowledge the contributors to this volume, for their patience and efforts to ensure a high scientific quality. Specifically, we acknowledge the professional editing provided by Ms. Julie Lelonek, her help was invaluable in getting this volume completed. November 2006 Basil J. Nikolau Eve Syrkin Wurtele CONTENTS Preface v Chapter 1. Validated High Quality Automated Metabolome Analysis of Arabidopsis Thaliana Leaf Disks Quality Control Charts and Standard Operating Procedures 1 Oliver Fiehn Chapter 2. GC-MS Peak Labeling Under ArMet 19 Helen Jenkins, Manfred Beckmann, John Draper, and Nigel Hardy Chapter 3. Metabolomics and Plant Quantitative Trait Locus Analysis – The Optimum Genetical Genomics Platform? 29 Daniel J. Kliebenstein Chapter 4. Design of Metabolite Recovery by Variations of the Metabolite Profiling Protocol 45 Claudia Birkemeyer and Joachim Kopka Chapter 5. Uncovering the Plant Metabolome: Current and Future Challenges 71 Ute Roessner-Tunali Chapter 6. Lipidomics: ESI-MS/MS-Based Profiling to Determine the Function of Genes Involved in Metabolism of Complex Lipids 87 Ruth Welti, Mary R. Roth, Youping Deng, Jyoti Shah, and Xuemin Wang Chapter 7. Time-Series Integrated Metabolomic and Transcriptional Profiling Analyses Short-Term Response of Arabidopsis Thaliana Primary Metabolism to Elevated CO - Case Study 93 2 H. Kanani, B. Dutta, J. Quackenbush, and M.I. Klapa viii Contents Chapter 8. Metabolomics of Cuticular Waxes: A System for Metabolomics Analysis of a Single Tissue-Type in a Multicellular Organism 111 M. Ann D.N. Perera and Basil J. Nikolau Chapter 9. Metabolic Flux Maps of Central Carbon Metabolism in Plant Systems Isotope Labeling Analysis 125 V.V. Iyer, G. Sriram, and J.V. Shanks Chapter 10. MetNet: Systems Biology Tools for Arabidopsis 145 Eve Syrkin Wurtele, Ling Li, Dan Berleant, Dianne Cook, Julie A. Dickerson, Jing Ding, Heike Hofmann, Michael Lawrence, Eun-kyung Lee, Jie Li, Wieslawa Mentzen, Leslie Miller, Basil J. Nikolau, Nick Ransom, and Yingjun Wang Chapter 11. Identification of Genes Involved in Anthocyanin Accumulation by Integrated Analysis of Metabolome and Transcriptome in Pap1-Overexpressing Arabidopsis Plants 159 Takayuki Tohge, Yasutaka Nishiyama, Masami Yokota Hirai, Mitsuru Yano, Jun-ichiro Nakajima, Motoko Awazuhara, Eri Inoue, Hideki Takahashi, Dayan B. Goodenowe, Masahiko Kitayama, Masaaki Noji, Mami Yamazaki, and Kazuki Saito Chapter 12. Identifying Substrates and Products of Enzymes of Plant Volatile Biosynthesis with the Help of Metabolic Profiling 169 Dorothea Tholl, Feng Chen, Yoko Iijima, Eyal Fridman, David R. Gang, Efraim Lewinsohn, and Eran Pichersky Chapter 13. Profiling Diurnal Changes in Metabolite and Transcript Levels in Potato Leaves 183 Ewa Urbanczyk-Wochniak, Charles Baxter, Lee J. Sweetlove, and Alisdair R. Fernie Chapter 14. Gene Expression and Metabolic Analysis Reveal that the Phytotoxin Coronatine Impacts Multiple Phytohormone Pathways in Tomato 193 Srinivasa Rao Uppalapati and Carol L. Bender Contents ix Chapter 15. Profiling of Metabolites and Volatile Flavour Compounds from Solanum Species Using Gas Chromatography-Mass Spectrometry 209 Tom Shepherd, Gary Dobson, Rhoda Marshall, Susan R. Verrall, Sean Conner, D. Wynne Griffiths, Derek Stewart, and Howard V. Davies Chapter 16. Metabolomic Analysis of Low Phytic Acid Maize Kernels 221 Jan Hazebroek, Teresa Harp, Jinrui Shi, and Hongyu Wang Chapter 17. The Low Temperature Metabolome of Arabidopsis 239 Gordon R. Gray and Doug Heath Chapter 18. Cloning, Expression and Characterization of a Putative Flavonoid Glucosyltransferase from Grapefruit (Citrus Paradisi) Leaves 247 Tapasree Roy Sarkar, Christy L. Strong, Mebrahtu B. Sibhatu, Lee M. Pike, and Cecilia A. McIntosh Chapter 19. Application of Metabolite and Flavour Volatile Profiling to Studies of Biodiversity in Solanum Species 259 Gary Dobson, Tom Shepherd, Rhoda Marshall, Susan R. Verrall, Sean Conner, D. Wynne Griffiths, James W. McNicol, Derek Stewart, and Howard V. Davies Chapter 20. Metabolic Profiling Horizontal Resistance in Potato Leaves (cvs. Caesar and AC Novachip) Against Phytophthora Infestans 269 Y. Abu-Nada, A.C. Kushalappa, W.D. Marshall, S.O. Prasher, and K. Al-Mughrabi Chapter 21. In Vivo 15N-Enrichment of Metabolites in Arabidopsis Cultured Cell T87 and Its Application to Metabolomics 287 Kazuo Harada, Ei-ichiro Fukusaki, Takeshi Bamba, and Akio Kobayashi Chapter 1 VALIDATED HIGH QUALITY AUTOMATED METABOLOME ANALYSIS OF ARABIDOPSIS THALIANA LEAF DISKS Quality Control Charts and Standard Operating Procedures Oliver Fiehn UC Davis Genome Center, Health Sci. Drive, Davis, CA 95616, USA Abstract: Plants readily respond to changes in environmental conditions by alterations in metabolism. In addition, breeding processes as well as modern molecular tools often target at or result in constitutive changes in metabolite levels or metabolic pathways. These properties render metabolomics an ideal tool to characterize the degree of impact of genetic or environmental perturbation. In agronomic and agrobiotechnology, but also in some areas of fundamental plant biology research, this leads to experimental designs of genotype × environment (G×E) plots, which results in huge numbers of individual plants to be grown, harvested, processed, and analyzed. The benefit to add metabolomics is then to utilize analyses of metabolic events to better understand biochemical or regulatory mechanisms by which the plant responded to the G×E perturbations. However, technical challenges are still imminent regarding the complexity of plant metabolism and the need for high quality control in large projects. This chapter details how even larger projects with thousands of analyses can be managed in an academic laboratory while still keeping control over the total process by use of Standard Operating Procedures (SOP) and continuous Quality Control (QC) measures. This process is exemplified by SOP and QC implementations used for a larger study on effects of abiotic treatments on select Arabidopsis ecotype accessions. 1 INTRODUCTION 1.1 Theoretical considerations Metabolomics aims at achieving qualitative and quantitative metabolite data from biological samples grown under a specific set of experimental conditions (Fiehn et al., 2000; Bino et al., 2004). In order to interpret and 1 B.J. Nikolau and E. Syrkin Wurtele (eds.), Concepts in Plant Metabolomics, 1–18. © 2007 Springer. 2 O. Fiehn reuse data (via metabolomic databases), the sources of quantitative variability of data must be accurately described. Technical errors of the analytical process must be controlled and minimized in order to distinguish such variance in data (noise) from the inherent biological variability within and between the populations that are subjected to a certain experimental design. This chapter describes why and how Standard Operating Procedures and Quality Control charts are needed for larger metabolomic projects. 1.2 No data without metadata The process of metabolomic analysis involves many steps from the actual experimental design of the biological trial to the conditions of plant growth and potential treatments by external factors such as changes in abiotic or biotic stressors, and following plant responses within temporal or spatial patterns, e.g., over plant organ development or within diurnal cycles. Reproducibility and reusability of metabolomic data sets therefore necessitate capturing this underlying information about the details of the total experimental design: without this, no data set can be understood and interpreted in a correct way. Such “data about the data” are called “metadata” in computing sciences and are at least in parts described and collated in the “materials and methods” sections of plant journals. However, in such sections plant biologists tend to focus on the novel parts of their experimental setup and do not give fully precise descriptions on more standard growth specifications. For example, unless researchers carry out specific light treatment studies, the light qualities (emission spectra) within green houses or climate chambers are usually not detailed out. The same is most often true for the type and dimensions of the climate chamber used, although it is known that each climate chamber has its own specifics with respect to air circulation conditions, which will ultimately affect water evaporation rates from the soil and by this, plant metabolic rates. One might argue that such description is overly detailed, but on the other hand, for each institution such information would only need be recorded once and then deposited as an object number in a database for future experiments. The need to accompany metabolic data with exact experimental metadata is also given by the fact that each plant species and even each organ comprises a wealth of unannotated or unknown metabolites which will only reveal their specific importance when tracing back their relative levels under a multitude of conditions. Unlike other cellular components such as primary and secondary gene products (transcripts and proteins), most metabolites do not carry annotated biological functions which relate to well-described unique biological roles. For a few secondary metabolites like auxins or glucosinolates such roles are known for controlling plant growth or

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