Integrated Analysis Of Metabolomics Research Articles

Metabolomics technology and its applications in agricultural animal and plant research.

Metabolomics is a discipline that uses Chromatography-Mass Spectrometry and Nuclear Magnetic Resonance techniques to identify and quantify all small molecule metabolites in living organisms and biological samples, which relies on sensitive, stable analytical procedures and improving databases. Metabolomics has been widely used in medicine, food science, crop and farm animal research, and other fields. Metabolomics can establish a more direct relationship between changes in the type and content of metabolites and phenotypes. Metabolomics has gradually become a new research method for the analysis of genetic mechanisms of complex traits following genomics, transcriptomics, and proteomics with the advances in omics technology. In this review, we firstly introduce common analytical strategies, metabolomics platforms, and metabolomics databases. Then, we review the application of metabolomics in metabolic molecular identification of important economic traits in agricultural animals, disease diagnosis, meat quality and safety detection of animal products. We also introduce the latest achievements in the development, formation and analysis of important traits of animals and plants by using metabolomics, transcriptome, and genomics. Overall, the integrated analysis of metabolomics and other omics can comprehensively explain the genetic mechanism of all kinds of complex traits and help to improve the complete biological process of "mutation-gene-expression-metabolism-phenotype". Metabolomics provides a new method for the mechanism analysis of complex characters and a novel idea for new agricultural breeding.

Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs.

Electroacupuncture Inhibits Atherosclerosis through Regulating Intestinal Flora and Host Metabolites in Rabbit.

Methods In this study, general rabbit conditions, vascular histology, metabolites, and intestinal flora structures were analyzed. Integrated analysis of metabolomics and 16S rRNA sequencing were performed. All the rabbits were randomly divided into four groups. The rabbit model of atherosclerosis was established. The histopathological change in the common carotid artery was assessed by HE staining and the structural change in the flora by 16S rRNA sequencing. HPLC-TOF-MS and Agilent MPP 12.1 were integrated to identify and screen out differential metabolites. Correlational analyses of every differential metabolite with intestinal flora were integrated on Omicshare platform. Results Atherosclerotic rabbits showed obvious changes in general conditions, significant fibrous cap and necrotic center on carotid artery, abnormal intestinal bacteria structure, and metabolites levels. Electroacupuncture improved the conditions, reduced lipid deposition on the carotid artery wall, diversified intestinal flora, and normalized host metabolism. Integrated analysis showed that 149 altered metabolites were related to 22 intestinal flora, among which eight intestinal floras and 21 metabolites have relationships with atherosclerosis. Conclusion Electroacupuncture can effectively reverse atherosclerosis through manipulating the structural feature of intestinal flora to influence the host metabolites. The possible mechanisms involved activating signal pathways through host metabolites or affecting the activity of cardiovascular-related enzymes, or regulating host lipid metabolism directly.

Transcriptomic analysis reveals flavonoid biosynthesis of Syringa oblata Lindl. in response to different light intensity

BackgroundHazy weather significantly increase air pollution and affect light intensity which may also affect medicinal plants growth. Syringa oblata Lindl. (S. oblata), an effective anti-biofilm medicinal plants, is also vulnerable to changes in plant photoperiods and other abiotic stress responses. Rutin, one of the flavonoids, is the main bioactive ingredient in S. oblata that inhibits Streptococcus suis biofilm formation. Thus, the present study aims to explore the biosynthesis and molecular basis of flavonoids in S. oblata in response to different light intensity.ResultsIn this study, it was shown that compared with natural (Z0) and 25% ~ 35% (Z2) light intensities, the rutin content of S. oblata under 50% ~ 60% (Z1) light intensity increased significantly. In addition, an integrated analysis of metabolome and transcriptome was performed using light intensity stress conditions from two kinds of light intensities which S. oblata was subjected to: Z0 and Z1. The results revealed that differential metabolites and genes were mainly related to the flavonoid biosynthetic pathway. We found out that 13 putative structural genes and a transcription factor bHLH were significantly up-regulated in Z1. Among them, integration analysis showed that 3 putative structural genes including 4CL1, CYP73A and CYP75B1 significantly up-regulated the rutin biosynthesis, suggesting that these putative genes may be involved in regulating the flavonoid biosynthetic pathway, thereby making them key target genes in the whole metabolic process.ConclusionsThe present study provided helpful information to search for the novel putative genes that are potential targets for S. oblata in response to light intensity.

Integrative analysis of metabolome and transcriptome reveals the mechanism of color formation in pepper fruit (Capsicum annuum L.)

To understand the mechanism of the color formation of pepper fruit, integrative analysis of the metabolome and transcriptome profiles was performed in pepper varieties with 4 different fruit colors. A total of 188 flavonoids were identified, and most of the anthocyanins, flavonols and flavones showed markedly higher abundances in purple variety than in other varieties, which was linked to the high expression of flavonoid synthesis and regulatory genes. Using weighted gene co-expression network analyses, modules related to flavonoid synthesis and candidate genes that regulate flavonoid synthesis and transport were identified. Furthermore, the analysis of 12 carotenoids showed that the content of xanthophylls at 50 days after anthesis was significantly different between the four pepper varieties, which was resulted from the differential expressions of genes downstream of the carotenoid pathway. Our results provide new insights into the understanding of the synthesis and accumulation of flavonoids and carotenoids in pepper fruit.

Integrated Metabolomics-DNA Methylation Analysis Reveals Significant Long-Term Tissue-Dependent Directional Alterations in Aminoacyl-tRNA Biosynthesis in the Left Ventricle of the Heart and Hippocampus Following Proton Irradiation.

In this study, an untargeted metabolomics approach was used to assess the effects of proton irradiation (1 Gy of 150 MeV) on the metabolome and DNA methylation pattern in the murine hippocampus and left ventricle of the heart 22 weeks following exposure using an integrated metabolomics-DNA methylation analysis. The integrated metabolomics-DNA methylation analysis in both tissues revealed significant alterations in aminoacyl-tRNA biosynthesis, but the direction of change was tissue-dependent. Individual and total amino acid synthesis were downregulated in the left ventricle of proton-irradiated mice but were upregulated in the hippocampus of proton-irradiated mice. Amino acid tRNA synthetase methylation was mostly downregulated in the hippocampus of proton-irradiated mice, whereas no consistent methylation pattern was observed for amino acid tRNA synthetases in the left ventricle of proton-irradiated mice. Thus, proton irradiation causes long-term changes in the left ventricle and hippocampus in part through methylation-based epigenetic modifications. Integrated analysis of metabolomics and DNA methylation is a powerful approach to obtain converging evidence of pathways significantly affected. This in turn might identify biomarkers of the radiation response, help identify therapeutic targets, and assess the efficacy of mitigators directed at those targets to minimize, or even prevent detrimental long-term effects of proton irradiation on the heart and the brain.

Metabolomics Analyses in High-Low Feed Efficient Dairy Cows Reveal Novel Biochemical Mechanisms and Predictive Biomarkers.

Residual feed intake (RFI) is designed to estimate net efficiency of feed use, so low RFI animals are considered for selection to reduce feeding costs. However, metabolic profiling of cows and availability of predictive metabolic biomarkers for RFI are scarce. Therefore, this study aims to generate a better understanding of metabolic mechanisms behind low and high RFI in Jerseys and Holsteins and identify potential predictive metabolic biomarkers. Each metabolite was analyzed to reveal their associations with two RFIs in two breeds by a linear regression model. An integrative analysis of metabolomics and transcriptomics was performed to explore interactions between functionally related metabolites and genes in the created metabolite networks. We found that three main clusters were detected in the heat map and all identified fatty acids (palmitoleic, hexadecanoic, octadecanoic, heptadecanoic, and tetradecanoic acid) were grouped in a cluster. The lower cluster were all from fatty acids, including palmitoleic acid, hexadecanoic acid, octadecanoic acid, heptadecanoic acid, and tetradecanoic acid. The first component of the partial least squares-discriminant analysis (PLS-DA) explained a majority (61.5%) of variations of all metabolites. A good division between two breeds was also observed. Significant differences between low and high RFIs existed in the fatty acid group (P < 0.001). Statistical results revealed clearly significant differences between breeds; however, the association of individual metabolites (leucine, ornithine, pentadecanoic acid, and valine) with the RFI status was only marginally significant or not significant due to a lower sample size. The integrated gene-metabolite pathway analysis showed that pathway impact values were higher than those of a single metabolic pathway. Both types of pathway analyses revealed three important pathways, which were aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate metabolism, and the citrate cycle (TCA cycle). Finally, one gene (2-hydroxyacyl-CoA lyase 1 (+HACL1)) associated with two metabolites (-α-ketoglutarate and succinic acid) were identified in the gene-metabolite interaction network. This study provided novel metabolic pathways and integrated metabolic-gene expression networks in high and low RFI Holstein and Jersey cattle, thereby providing a better understanding of novel biochemical mechanisms underlying variation in feed efficiency.

Integrative analysis of metabolome and transcriptome reveals anthocyanins biosynthesis regulation in grass species Pennisetum purpureum

Genes or metabolites involved in the same biological process will have same or similar change rules, so association analysis of transcriptome and metabolome is an effective way to find key metabolic pathways, key genes and explain the molecular mechanisms. Anthocyanin-rich forage is rare but a powerful antioxidant, which is good for both humans and animals. Pennisetum purpureum cv. Purple is abundant in anthocyanins and has higher biomass than most forage grasses. To study the mechanisms underlying the purple color of P. purpureum, transcriptomic and metabolomics analyses were performed with the purple-leaved cultivar ‘Purple’ and the green-leaved cultivar ‘Mott’. Metabolites, including luteolin, kaempferol, quercetin, malvidin, apigenin, pelargonidin and cyanidin were present at different levels between the cultivars. High levels of malvidin correlated with purple color, suggesting that this compound probably plays a dominant role in making the purple leaves in P. purpureum considering its high content and strong correlation with the color expression. A total of 16 differentially expressed genes (DEGs) in the flavonoid biosynthesis pathway were discovered between the purple and green varieties, and these were selected as candidate genes possibly involved in purple color formation and accumulation. Moreover, 47 differentially expressed transcription factors (TFs) were identified, including 15 up-regulated and 12 down-regulated TFs between ‘Purple’ and ‘Mott’. Furthermore, 55 SNPs in 11 DEGs and 231 SNPs in 41 differentially expressed TFs were found between ‘Purple’ and ‘Mott’, suggesting that SNPs may play an important role in regulating gene expression. This study provides crucial information on leaf color development and anthocyanins production of P. purpureum, and will facilitate the development of breeding forage varieties with high-anthocyanins contents.

Mitochondria in precision medicine; linking bioenergetics and metabolomics in platelets

Mitochondria possess reserve bioenergetic capacity, supporting protection and resilience in the face of disease. Approaches are limited to understand factors that impact mitochondrial functional reserve in humans. We applied the mitochondrial stress test (MST) to platelets from healthy subjects and found correlations between energetic parameters and mitochondrial function. These parameters were not correlated with mitochondrial complex I-IV activities, however, suggesting that other factors affect mitochondrial bioenergetics and metabolism. Platelets from African American patients with sickle cell disease also differed from controls, further showing that other factors impact mitochondrial bioenergetics and metabolism. To test for correlations of platelet metabolites with energetic parameters, we performed an integrated analysis of metabolomics and MST parameters. Subsets of metabolites, including fatty acids and xenobiotics correlated with mitochondrial parameters. The results establish platelets as a platform to integrate bioenergetics and metabolism for analysis of mitochondrial function in precision medicine.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

Computational Approaches for Integrative Analysis of the Metabolome and Microbiome.

The study of the microbiome, the totality of all microbes inhabiting the host or an environmental niche, has experienced exponential growth over the past few years. The microbiome contributes functional genes and metabolites, and is an important factor for maintaining health. In this context, metabolomics is increasingly applied to complement sequencing-based approaches (marker genes or shotgun metagenomics) to enable resolution of microbiome-conferred functionalities associated with health. However, analyzing the resulting multi-omics data remains a significant challenge in current microbiome studies. In this review, we provide an overview of different computational approaches that have been used in recent years for integrative analysis of metabolome and microbiome data, ranging from statistical correlation analysis to metabolic network-based modeling approaches. Throughout the process, we strive to present a unified conceptual framework for multi-omics integration and interpretation, as well as point out potential future directions.

Metabolomic and transcriptomic profiling of hepatocellular carcinomas in Hras12V transgenic mice.

Activation of the Ras/MAPK pathway is prevalently involved in the occurrence and development of hepatocellular carcinoma (HCC). However, its effects on the deregulated cellular metabolic processes involved in HCC in vivo remain unknown. In this study, a mouse model of HCC induced by hepatocyte‐specific expression of the Hras12V oncogene was investigated using an integrative analysis of metabolomics and transcriptomics data. Consistent with the phenotype of abundant lipid droplets in HCC, the lipid biosynthesis in HCC was significantly enhanced by (1) a sufficient supply of acetyl‐CoA from enhanced glycolysis and citrate shuttle activity; (2) a sufficient supply of NADPH from enhanced pentose phosphate pathway (PPP) activity; (3) upregulation of key enzymes associated with lipid biosynthesis; and (4) downregulation of key enzymes associated with bile acid biosynthesis. In addition, glutathione (GSH) was significantly elevated, which may result from a sufficient supply of 5‐oxoproline and L‐glutamate as well as an enhanced reduction in the process of GSSG being turned into GSH by NADPH. The high level of GSH along with elevated Bcl2 and Ucp2 expression may contribute to a normal level of reactive oxygen species (ROS) in HCC. In conclusion, our results suggest that the lipid metabolism, glycolysis, PPP, tricarboxylic acid (TCA) cycle, citrate shuttle activity, bile acid synthesis, and redox homeostasis in the HCC induced by ras oncogene are significantly perturbed, and these altered metabolic processes may play crucial roles in the carcinogenesis, development, and pathological characteristics of HCC.

Integrative metabolomic analysis reveals diet supplementation with green tea alleviates UVB-damaged mouse skin correlated with ascorbate metabolism and urea cycle

While green tea is known to protect skin from ultraviolet (UV) light, underlying damage-repair mechanisms remain unclear. The major objective of this study was to investigate, using multi-omics analysis, the metabolic mechanisms associated with the effects of a diet supplemented with green tea (SGT) in UVB-damaged mice. Six to eight weeks old female Skh:HR-1 mice were randomly divided into three experimental groups i.e., non-irradiated with control diet (NOR), UVB-irradiated with control diet (UND), and UVB-irradiated with SGT (UGD), and subjected to experimental conditions over 10 weeks. The skin samples were analyzed by metabolomics, transcriptomics, and in silico modeling. Our results revealed that SGT significantly alleviated UVB-induced metabolite alterations (aspartic acid, ornithine, ascorbic acid, ethanolamine, and C20:0-lysophosphatidylethanolamine) and gene expressions (keratin sulfate biosynthesis/degradation, fatty acid oxidation, and steroid metabolism) in the skin. Among these changes, key metabolic pathways, including ascorbate metabolism and the urea cycle, were the major pathways mitigated through SGT diet among UGD group mice. Additionally, SGT treatment also affected serum and hepatic lysophospholipid levels through attenuating and intensifying UVB-induced metabolic changes, respectively. Our results suggested that the SGT diets primarily influenced ascorbate metabolism and the urea cycle in UVB-irradiated mouse skin, alleviating deleterious UVB-induced skin wrinkles, epidermal thickening, and collagen-fiber destruction.

Metformin Targets Central Carbon Metabolism and Reveals Mitochondrial Requirements in Human Cancers

Repurposing metformin for cancer therapy is attractive due to its safety profile, epidemiological evidence, and encouraging data from human clinical trials. Although it is known to systemically affect glucose metabolism in liver, muscle, gut, and other tissues, the molecular determinants that predict a patient response in cancer remain unknown. Here, we carry out an integrative metabolomics analysis of metformin action in ovarian cancer. Metformin accumulated in patient biopsies, and pathways involving nucleotide metabolism, redox, and energy status, all related to mitochondrial metabolism, were affected in treated tumors. Strikingly, a metabolic signature obtained from a patient with anexceptional clinical outcome mirrored that of aresponsive animal tumor. Mechanistically, we demonstrate with stable isotope tracing that these metabolic signatures are due to an inability to adapt nutrient utilization in the mitochondria. This analysis provides new insights into mitochondrial metabolism and may lead to more precise indications of metformin in cancer.

Integrative analysis of metabolome and gut microbiota in diet-induced hyperlipidemic rats treated with berberine compounds.

BackgroundHyperlipidemia is a major component of metabolic syndrome, and often predicts cardiovascular diseases. We developed a new therapeutic agent berberine compounds (BC), consisting of berberine, oryzanol and vitamin B6, and determined their anti-hyperlipidemia activity and underlying mechanisms.MethodsMale Wistar rats were fed a high fat diet (HFD) to induce hyperlipidemia, and then given BC orally for 4 weeks. Body weight and food intake were recorded weekly, and lipid profiles in serum were determined biochemically. Metabolites in serum, urine, liver and feces were analyzed by GC–MS, and the structure of microbiota was determined by 16S rDNA sequencing.ResultsLipid lowering was observed in the hyperlipidemic rats upon BC treatment without apparent adverse side effects. Metabolomics analysis indicated that the BC treatment resulted in increased pyruvic acid, serotonin, and ketogenic and glycogenic amino acid levels in the serum, increased pyridoxine and 4-pyridoxic acid in the urine, decreased hypotaurine and methionine in the liver, and increased putrescine and decreased deoxycholate and lithocholate in feces. The BC treatment also resulted in an enrichment of beneficial bacteria (e.g. Bacteroides, Blautia) and a decrease in Escherichia.ConclusionsThe lipid lowering effect of BC treatment in hyperlipidemic rats is associated with a global change in the metabolism of lipids, carbohydrates and amino acids, as well as the structure of microbiota.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-016-0987-5) contains supplementary material, which is available to authorized users.

Targeting One Carbon Metabolism with an Antimetabolite Disrupts Pyrimidine Homeostasis and Induces Nucleotide Overflow.

Antimetabolites that affect nucleotide metabolism are frontline chemotherapy agents in several cancers and often successfully target one carbon metabolism. However, the precise mechanisms and resulting determinants of their therapeutic value are unknown. We show that 5-fluorouracil (5-FU), a commonly used antimetabolite therapeutic with varying efficacy, induces specific alterations to nucleotide metabolism by disrupting pyrimidine homeostasis. An integrative metabolomics analysis of the cellular response to 5-FU reveals intracellular uracil accumulation, whereas deoxyuridine levels exhibited increased flux into the extracellular space, resulting in an induction of overflow metabolism. Subsequent analysis from mice bearing colorectal tumors treated with 5-FU show specific secretion of metabolites in tumor-bearing mice into serum that results from alterations in nucleotide flux and reduction in overflow metabolism. Together, these findings identify a determinant of an antimetabolite response that may be exploited to more precisely define the tumors that could respond to targeting cancer metabolism.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as nuclear magnetic resonance, gas or liquid chromatography, mass spectrometry, infrared and UV–visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks.In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment.The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Integrative Analysis of Metabolomics and Transcriptomics Data: A Unified Model Framework to Identify Underlying System Pathways

The abundance of high-dimensional measurements in the form of gene expression and mass spectroscopy calls for models to elucidate the underlying biological system. For widely studied organisms like yeast, it is possible to incorporate prior knowledge from a variety of databases, an approach used in several recent studies. However if such information is not available for a particular organism these methods fall short. In this paper we propose a statistical method that is applicable to a dataset consisting of Liquid Chromatography-Mass Spectroscopy (LC-MS) and gene expression (DNA microarray) measurements from the same samples, to identify genes controlling the production of metabolites. Due to the high dimensionality of both LC-MS and DNA microarray data, dimension reduction and variable selection are key elements of the analysis. Our proposed approach starts by identifying the basis functions (“building blocks”) that constitute the output from a mass spectrometry experiment. Subsequently, the weights of these basis functions are related to the observations from the corresponding gene expression data in order to identify which genes are associated with specific patterns seen in the metabolite data. The modeling framework is extremely flexible as well as computationally fast and can accommodate treatment effects and other variables related to the experimental design. We demonstrate that within the proposed framework, genes regulating the production of specific metabolites can be identified correctly unless the variation in the noise is more than twice that of the signal.

Metabolomics In Biomarker Discovery: Future Uses For Cancer Prevention

Metabolomics is the systematic study of small-molecular-weight substances in cells, tissues and/or whole organisms as influenced by multiple factors including genetics, diet, lifestyle and pharmaceutical interventions. These substances may directly or indirectly interact with molecular targets and thereby influence the risk and complications associated with various diseases, including cancer. Since the interaction between metabolites and specific targets is dynamic, knowledge regarding genetics, susceptibility factors, timing, and degree of exposure to an agent (drug or food component) is fundamental to understanding the metabolome and its potential use for predicting and preventing early phenotypic changes. The future of metabolomics rests with its ability to monitor subtle changes in the metabolome that occur prior to the detection of a gross phenotypic change reflecting disease. The integrated analysis of metabolomics and other 'omics' may provide more sensitive ways to detect changes related to disease and discover novel biomarkers. Knowledge regarding these multivariant characteristics is critical for establishing validated and predictive metabolomic models for cancer prevention. Understanding the metabolome will not only provide insights into the critical sites of regulation in health promotion, but will also assist in identifying intermediate or surrogate cancer biomarkers for establishing preemptive/preventative or therapeutic approaches for health. While unraveling the metabolome will not be simple, the societal implications are enormous.

Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over‐expressing an MYB transcription factor

The integration of metabolomics and transcriptomics can provide precise information on gene-to-metabolite networks for identifying the function of unknown genes unless there has been a post-transcriptional modification. Here, we report a comprehensive analysis of the metabolome and transcriptome of Arabidopsis thaliana over-expressing the PAP1 gene encoding an MYB transcription factor, for the identification of novel gene functions involved in flavonoid biosynthesis. For metabolome analysis, we performed flavonoid-targeted analysis by high-performance liquid chromatography-mass spectrometry and non-targeted analysis by Fourier-transform ion-cyclotron mass spectrometry with an ultrahigh-resolution capacity. This combined analysis revealed the specific accumulation of cyanidin and quercetin derivatives, and identified eight novel anthocyanins from an array of putative 1800 metabolites in PAP1 over-expressing plants. The transcriptome analysis of 22,810 genes on a DNA microarray revealed the induction of 38 genes by ectopic PAP1 over-expression. In addition to well-known genes involved in anthocyanin production, several genes with unidentified functions or annotated with putative functions, encoding putative glycosyltransferase, acyltransferase, glutathione S-transferase, sugar transporters and transcription factors, were induced by PAP1. Two putative glycosyltransferase genes (At5g17050 and At4g14090) induced by PAP1 expression were confirmed to encode flavonoid 3-O-glucosyltransferase and anthocyanin 5-O-glucosyltransferase, respectively, from the enzymatic activity of their recombinant proteins in vitro and results of the analysis of anthocyanins in the respective T-DNA-inserted mutants. The functional genomics approach through the integration of metabolomics and transcriptomics presented here provides an innovative means of identifying novel gene functions involved in plant metabolism.