Table Of ContentCONCEPTS 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)
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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
