The plant metabolome is highly complex, being composed of over 200,000 metabolites. The characterization of these small molecules has been crucial to study plant growth and development as well as their response to environmental changes. The potential of metabolomics in plant research, particularly if applied to crop plants, is also extremely valuable in the discovery of biomarkers and in the improvement of crop yield and quality. This Frontiers Research Topic addresses many applications of metabolomics to crop research, based on different analytical platforms, including mass spectrometry, and nuclear magnetic resonance. It comprises 13 articles from 109 authors that show the importance and the contribution of metabolomics in the analysis of crop’s traceability and genetic variation, in the study of fruit development, and in the understanding of the plant’s response to the environment and to different biotic and abiotic stresses.

Metabolomics – which deals with all metabolites of an organism – is a rapidly-emerging sector of post-genome research fields. It plays significant roles in a variety of fields from medicine to agriculture and holds a fundamental position in functional genomics studies and their application in plant biotechnology. This volume comprehensively covers plant metabolomics for the first time. The chapters offer cutting-edge information on analytical technology, bioinformatics and applications. They were all written by leading researchers who have been directly involved in plant metabolomics research throughout the world. Up-to-date information and future developments are described, thereby producing a volume which is a landmark of plant metabolomics research and a beneficial guideline to graduate students and researchers in academia, industry, and technology transfer organizations in all plant science fields.

Metabolomics is the identification and quantification of all metabolites in a system under a given set of conditions. In past decades, metabolomics approaches have been increasingly applied in agricultural research. The goals of this research were to explore new metabolomics applications in agricultural research, and to develop tools for volatile compound capture and tracking in large-scale studies. Several different projects were involved in achieving these goals. The first project (Chapter 1) sought to assess genetic and environmental impacts on the metabolite composition of maize grain. Gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) measured 116 identified metabolites including free amino acids, free fatty acids, sugars, organic acids and other small molecules (i.e., molecular weight below 550 Da) in a range of hybrids derived from 48 inbred lines crossed against two different tester lines and grown at three locations in Iowa, USA. It was reasoned that expanded metabolite coverage would contribute to a comprehensive evaluation of the grain metabolome, its degree of variability and, in principle, its relationship to other compositional and agronomic features. The metabolic profiling results established that the small molecule metabolite pool is highly dependent on genotypic variation and that levels of certain metabolite classes may have an inverse genotypic relationship to each other. Different metabolic phenotypes were clearly associated with the two distinct tester populations. In the second project (Chapter 2) metabolite profiles of white wines, including Chardonnay, Pinot gris, Riesling, Sauvignon blanc, and Viognier varieties, were determined using both gas chromatography-coupled time-of-flight mass spectrometry (GC-TOF-MS) and proton nuclear magnetic resonance spectroscopy (1H NMR). A total of 108 metabolites were identified by GC-TOF-MS, and 51 metabolites were identified by 1H NMR; the majority of metabolites identified include the most abundant compounds found in wine (ethanol, glycerol, sugars, organic acids, and amino acids). Compositional differences in these wines correlating to the wine sensory property "body", or viscous mouthfeel, as scored by a trained panel were identified using partial least-squares (PLS) regression. Independently calculated GC-TOF-MS and NMR-based PLS models demonstrate potential for predictive models to replace expensive, time-consuming sensory panels. At the modeling stage, correlations between the measured and predicted values have coefficients of determination of 0.83 and 0.75 for GC-TOFMS and 1H NMR, respectively. Additionally, the MS- and NMR-based models present new insights into the chemical basis for wine mouthfeel properties. The focus of the dissertation work then shifted to volatile metabolites, and the objective of the next project (Chapter 3) was the development of tools for the automated tracking and identification of compounds in complex volatile mixtures. Previous work in our lab established BinBase, an automated peak annotation algorithm for GC-TOF-MS analysis of metabolite mixtures that incorporates database capabilities. Extension of the BinBase database to volatile compounds involved multiple steps. First, standard methods for volatile compound capture and GC-TOF-MS detection were developed, and a protocol for retention index marker addition was devised. Next, the existing BinBase algorithms were modified and extended to allow for annotation of volatile compounds. Finally, a commercial library containing over 2000 plant volatiles (mass spectra plus retention index) was integrated into the system to assist compound identification. The operational database is currently comprised of 1537 unique mass spectra generated from 3200 samples (18 species) and 1.6 million spectra, and is continuously expanding. This novel volatile compound annotation and tracking database is capable of annotating large datasets (hundreds to thousands of samples) and is well-suited to cross-study comparisons (e.g. source, species, season, etc.). In the final project (Chapter 4) vineyard volatiles were sampled in three Cabernet Sauvignon blocks during the 2008 and 2009 growing seasons using head-space stir-bar sorptive extraction sampling methods. Automated annotation of the 1318 sample chromatograms by the new VOC BinBase database yielded 900 reliably detected compounds. Partial least squares (PLS) regression analysis was employed to construct models relating a subset of canopy volatiles to traditional measures of grape berry development including soluble solids (Brix) and sugar/TA ratios. PLS models constructed from the 2008 data were used to predict 2009 data, and composite 2008-09 models were also built to compare performance. At the modeling stage, correlations between the measured and predicted values for the 2008 models were at least 0.94 for Brix and 0.93 for sugar/TA values, and for the composite 2008-2009 models were at least 0.90 for Brix and 0.80 for sugar/TA values. Prediction errors (root mean square error of prediction, RMSEP) for the validation sample set were as low as 1.19 degrees Brix (R2=0.81) and 9.05 sugar/TA units (R2=0.80) for the 2008 models, and as low as 1.11 degrees Brix (R2=0.85) and 8.79 sugar/TA units (R2=0.79) for the 2008-09 composite models. This preliminary work demonstrates the feasibility of monitoring grape maturity by vine volatile organic compound (VOC) emissions. In addition, we have demonstrated use of SBSE-based passive sampling methods for large-scale field studies, and the utility and capability of the VOC BinBase annotation and database software for large-scale studies.

Metabolomics is the scientific study of the chemical processes in a living system, environment and nutrition. It is a relatively new omics science, but the potential applications are wide, including medicine, personalized medicine and intervention studies, food and nutrition, plants, agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field, from masters to postgraduate students, PhDs, postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies, but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather, the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples, computing and exercises. Key Features: Provides an overview of qualitative /quantitative methods in metabolomics Offers an introduction to the key concepts of metabolomics, including experimental design and technology Covers data handling, processing, analysis, data standards and sharing Contains lots of examples to illustrate the topics Includes contributions from some of the leading researchers in the field of metabolomics with extensive teaching experiences

Metabolomics: Fundamentals and Applications authoritatively presents the basic principles and applications of metabolomics. Topics covered in this book range from the analysis of metabolites from different biological sources and their data processing and statistical analysis. This book serves as a basic guide for a wide range of audiences from less familiar with metabolomics techniques to more experienced researchers seeking to understand complex biological systems from the systems biology approach.

Mass Spectrometry-Based Metabolomics: A Practical Guide is a simple, step-by-step reference for profiling metabolites in a target organism. It discusses optimization of sample preparation for urine, serum, blood, tissue, food, and plant and animal cell samples. Encompassing three different technical fields-biology, analytical chemistry, and informa

Computational Phytochemistry, Second Edition, explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance many of their operations, refocusing and expanding the possibilities of phytochemical studies. By applying computational aids and mathematical models to extraction, isolation, structure determination, and bioactivity testing, researchers can obtain highly detailed information about phytochemicals and optimize working approaches. This book aims to support and encourage researchers currently working with or looking to incorporate computational methods into their phytochemical work. Topics in this book include computational methods for predicting medicinal properties, optimizing extraction, isolating plant secondary metabolites, and building dereplicated phytochemical libraries. The roles of high-throughput screening, spectral data for structural prediction, plant metabolomics, and biosynthesis are all reviewed before the application of computational aids for assessing bioactivities and virtual screening is discussed. Illustrated with detailed figures and supported by practical examples, this book is an indispensable guide for all those involved with the identification, extraction, and application of active agents from natural products. This new edition captures remarkable advancements in mathematical modeling and computational methods that have been incorporated in phytochemical research, addressing, e.g., extraction, isolation, structure determination, and bioactivity testing of phytochemicals. - Includes step-by-step protocols for various computational and mathematical approaches applied to phytochemical research - Features clearly illustrated chapters contributed by highly reputable researchers - Covers all key areas in phytochemical research, including virtual screening and metabolomics