A novel integrated method for large-scale detection, identification, and quantification of widely targeted metabolites: application in the study of rice metabolomics.
TL;DR: Evaluation of the dehydration responses and natural variations of these metabolites in rice leaf not only suggested the coordinated regulation of abscisic acid with metabolites such as serotonin derivative(s), polyamine conjugates under drought stress, but also revealed some C-glycosylated flavones as the potential markers for the discrimination of indica and japonica rice subspecies.
About: This article is published in Molecular Plant.The article was published on 2013-11-01 and is currently open access. It has received 912 citations till now. The article focuses on the topics: Metabolomics.
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TL;DR: This work focuses on the application of mass spectrometry to the treatment of metabolomics and Lipidomics with a focus on the characterization of the role of phosphorous in the biosynthetic pathway.
Abstract: in Mass Spectrometry-Based Metabolomics and Lipidomics Tomas Cajka† and Oliver Fiehn*,†,‡ †UC Davis Genome Center−Metabolomics, University of California Davis, 451 Health Sciences Drive, Davis, California 95616, United States ‡King Abdulaziz University, Faculty of Science, Biochemistry Department, P.O. Box 80203, Jeddah 21589, Saudi Arabia ■ CONTENTS Sample Extraction 525 Extraction of Polar Metabolites (Metabolomics) 525 Extraction of Lipids (Lipidomics) 527 Combined Extraction of Amphiphilic and Lipophilic Metabolites 527 Mass Spectrometry-Based Metabolomics and Lipidomics 528 Direct Infusion MS 528 Ion Mobility-Mass Spectrometry (IM-MS) 529 Liquid Chromatography−Mass Spectrometry (LC−MS) 533 Reversed-Phase Liquid Chromatography (RPLC) 533 Hydrophilic Interaction Chromatography (HILIC) 534 Normal-Phase Liquid Chromatography (NPLC) 535 Supercritical Fluid Chromatography (SFC) 535 Two-Dimensional Liquid Chromatography (2D-LC) 535 Mass Spectrometric Detection 536 Data Processing 540 Quality Control 541 Conclusions 541 Author Information 542 Corresponding Author 542 Notes 542 Biographies 542 Acknowledgments 542 References 542
549 citations
TL;DR: The study reveals a multi-omics view of the metabolic breeding history of tomato, as well as provides insights into metabolome-assisted breeding and plant biology.
519 citations
Cites methods from "A novel integrated method for large..."
...To produce maximal signal, collision energy (CE) and de-clustering potential (DP) were optimized for each precursor–product ion (Q1–Q3) transition (Chen et al., 2013)....
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TL;DR: This study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.
Abstract: Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on ∼6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.
511 citations
TL;DR: A comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels, finds metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.
Abstract: Plants produce a variety of metabolites that have a critical role in growth and development. Here we present a comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels. We quantify 983 metabolite features in 702 maize genotypes planted at multiple locations. We identify 1,459 significant locus-trait associations (P≤1.8 × 10(-6)) across three environments through metabolite-based genome-wide association mapping. Most (58.5%) of the identified loci are supported by expression QTLs, and some (14.7%) are validated through linkage mapping. Re-sequencing and candidate gene association analysis identifies potential causal variants for five candidate genes involved in metabolic traits. Two of these genes were further validated by mutant and transgenic analysis. Metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.
376 citations
TL;DR: Both technical and functional aspects focusing on the influence that various modifications have on biosynthesis, degradation, transport, and storage of metabolites, as well as their bioactivity and toxicity are covered.
195 citations
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TL;DR: The use of metabolite profiling is described as a new tool for a comparative display of gene function and has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly.
Abstract: Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of “metabolic phenotypes” using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.
2,036 citations
TL;DR: This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes.
Abstract: This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.
1,954 citations
TL;DR: The most recent release of HMDB has been significantly expanded and enhanced over the previous release, with the number of fully annotated metabolite entries growing from 2180 to more than 6800, a 300% increase.
Abstract: The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.
1,748 citations
TL;DR: This study identifies ∼3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method, demonstrating that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.
Abstract: Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified ∼ 3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained ∼ 36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.
1,718 citations
TL;DR: Recently, a major transcription system that controls abscisic-acid-independent gene expression in response to dehydration and low temperature has been identified and it includes the DRE/CRT (dehydration-responsive element/C-repeat) cis-acting element and its DNA-bindingprotein, DREB/CBF (DRE-binding protein/ C-repeat binding factor).
1,689 citations