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PubMed2025

Uncovering potential biomarkers and metabolic pathways in systemic lupus erythematosus and lupus nephritis through integrated microbiome and metabolome analysis.

OBJECTIVE: This study aims to explore the relationship between gut microbiota and fecal metabolomic profiles in patients with systemic lupus erythematosus (SLE), with and without lupus nephritis (LN), in order to identify potentially relevant biomarkers and better understand their association with disease progression. METHODS: Fecal samples from 15 healthy controls (HC) and 36 SLE patients (18 SLE-nonLN and 18 SLE-LN) were analyzed using 16S rRNA gene sequencing and untargeted metabolomics. Differential microbial taxa and metabolites were identified using Linear Discriminant Analysis Effect Size (LEfSe) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Receiver Operating Characteristic (ROC) curve analyses were used to assess the potential clinical relevance of selected metabolites. RESULTS: Beta diversity analysis demonstrated distinct microbial clustering between groups (p < 0.05). SLE-LN samples showed an increased relative abundance of Proteobacteria and decreased Firmicutes compared to SLE-nonLN. Metabolomic profiling identified multiple differentially abundant metabolites, with notable enrichment in primary bile acid biosynthesis pathways (e.g., Glycocholic acid, AUC = 0.951). In the SLE-nonLN group, increased Glycoursodeoxycholic acid levels (AUC = 0.922) were observed in pathways related to taurine and hypotaurine metabolism. Correlation analysis indicated a negative association between Escherichia-Shigella and bile acid levels (p < 0.01). CONCLUSION: This integrative analysis suggests that patients with SLE and LN harbor distinct gut microbiota and metabolomic profiles. The identified microbial taxa and metabolites may have potential as non-invasive biomarkers and could contribute to a better understanding of SLE pathogenesis and progression.

Humans↗

PubMed2026

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BACKGROUND: Cerebral infarction (CI) is a major cause of adult disability and mortality worldwide. Mounting evidence supports the critical role of the gut-brain axis in cerebrovascular disease progression. This study aimed to characterize the alterations in gut microbiota, serum metabolome, and serum proteome in patients with CI, and to identify multi-omics signatures associated with clinical symptoms. METHODS: A total of 20 CI patients and 20 healthy controls (HC) were enrolled. Fecal microbiota was profiled using 16 S rRNA gene high-throughput sequencing. Serum metabolomics and proteomics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and data-independent acquisition (DIA) proteomics, respectively. Spearman correlation and multi-omics integration were applied to explore the associations among microbiota, metabolites, proteins, and clinical indicators. RESULTS: CI patients displayed significant gut microbiota dysbiosis, with a markedly lower gut microbiota health index (GMHI) and higher microbiota disorder index (MDI) compared with HC (P < 0.001). The genera g_norank_o_RF39 and Oxalobacter were significantly enriched in CI patients, whereas Clostridium_sensu_stricto_1 and Agathobacter were enriched in HC. Metabolomic analysis identified 445 differential metabolites, mainly involved in glycerophospholipid metabolism, phenylalanine metabolism, and caffeine metabolism. Proteomic analysis revealed 140 differentially expressed proteins linked to inflammatory responses, calcium signaling, and NF-κB signaling. Multi-omics integration showed that signature gut microbiota was strongly correlated (P < 0.005) with key serum metabolites and proteins implicated in CI pathogenesis. CONCLUSIONS: This integrated multi-omics study revealed distinct gut microbiota, serum metabolomic, and proteomic alterations in CI patients. The microbiota-metabolite-protein regulatory axes provide novel insights into the gut-brain axis in CI and may serve as potential diagnostic biomarkers or therapeutic targets.

Humans↗

PubMed2026

Integrated multi-omics analysis of metabolomics and proteomics uncovers dysregulated amino acid metabolism in HCC metastasis.

BACKGROUND: Metastasis is the primary cause of treatment failure and adverse prognosis in hepatocellular carcinoma (HCC), and the molecular basis of HCC metastasis remains poorly defined. This work investigated the potential mechanisms underlying HCC metastasis through integrated multi-omics analysis of metabolomics and proteomics. METHOD: This retrospective study included 105 individuals with HCC, with comparative analysis between metastatic and non-metastatic cases. We further evaluated the effects of metastasis on serum metabolomics and proteomics in HCC patients. RESULT: Widespread disturbances in amino acid metabolism were identified via untargeted metabolomics in HCC patients with metastasis, closely governing inflammation-related metabolic remodeling and oxidative stress responses. Specifically, we identified 91 and 59 distinct differential metabolites capable of indicating HCC metastasis, with the screening criteria set as log2 fold change > 1.5, adjusted P value < 0.05, and VIP > 1.5 in positive and negative modes, respectively. The alanine, aspartate and glutamate metabolism pathway correlated with HCC-associated lung metastasis, while the gluconeogenesis pathway was linked to HCC-associated bone metastasis. Compared with HCC (non-metastatic hepatocellular carcinoma), the key molecular alterations in the multi-omics network of HCC_M (HCC with metastasis) are implicated in inflammatory metabolic reprogramming, oxidative stress response, gluconeogenesis, glycolysis, and the tricarboxylic acid (TCA) cycle. Twenty-five proteins, including PKM2, PERCK, ALDH2, CPS1, GLS1, GLUD1, GOT1, and SLC38A2, were identified as potential biomarkers for HCC metastasis. CONCLUSION: By integrating untargeted metabolomic and proteomic profiling, we identified distinct metabolic and proteomic changes linked to HCC metastasis. This work also characterized the pathological characteristics and core pathways underlying HCC metastasis, while identifying potential therapeutic candidates.

Humans↗

PubMed2026

Blood Metabolomic Signatures of 1-Hour Glucose Predict Cardiometabolic Risk.

BACKGROUND: Elevated 1-hour glucose levels during an oral glucose tolerance test strongly predict type 2 diabetes (T2D) and cardiovascular disease. We investigated whether the fasting blood metabolome predicting 1-hour glucose could be a target for improving β-cell function, long-term glycemic trajectories, and reducing the risks of T2D and coronary heart disease. We also investigated whether plasma microRNAs derived from key metabolic organs regulate changes in a metabolomic risk score (MRS) for predicting 1-hour glucose. METHODS: Untargeted blood metabolomics and a frequently sampled 75-g oral glucose tolerance test were performed in participants from the OmniCarb trial (n=162). In an independent weight-loss dietary intervention trial (POUNDS Lost [Preventing Overweight Using Novel Dietary Strategies]), temporal changes in MRS and plasma microRNAs measured by genome-wide sequencing were analyzed. In addition, associations of MRS at baseline and its 10-year changes with long-term risk of incident T2D and coronary heart disease were prospectively investigated in the NHS (Nurses' Health Study). RESULTS: We created a fasting blood MRS for predicting 1-hour glucose (Pearson r=0.8) and found significant associations with half-day (diurnal) postprandial glucose excursions and insulin secretion after 5-week controlled feeding interventions varying in carbohydrate amount and glycemic index. In the POUNDS Lost trial, diet-induced changes in MRSs were related to 2-year trajectories of glucose metabolism; circulating microRNAs regulating cardiometabolic abnormalities were pivotal factors influencing these changes. In the NHS, women in the top 20% of MRS had a multivariate-adjusted relative risk of 3.80 (95% CI, 2.22-6.51) for T2D and 1.48 (95% CI, 1.04-2.12) for coronary heart disease compared with those in the lowest 20%. In addition, 10-year increases in plasma metabolites related to 1-hour glucose were linearly associated with a higher risk of T2D. CONCLUSIONS: Our findings indicate that fasting blood metabolomic signatures predicting elevated 1-hour glucose reflect disease pathophysiology and could be targets for preventing T2D and coronary heart disease.

blood glucose↗

PubMed2025

Four-dimensional data independent acquisition proteomics and metabolomics reveal mechanisms of hydrogen-rich water at Zusanli (ST36) point against triple-negative breast cancer in mice.

OBJECTIVE: To develop a safe and effective green therapy for triple-negative breast cancer, this study combines hydrogen-rich water with acupuncture point injection, and finds that it can prevent tumor growth and minimize cancer metastasis. METHODS: After 21 d of hydrogen rich water injection treatment on 4T1 (mouse breast cancer cells) xenograft mice, in order to systematically identify differentially expressed proteins in tumor samples between the model group and the Zusanli (ST36) group injected with hydrogen rich water at acupoints, with a focus on functional proteins or signaling pathways related to tumor occurrence and development, researchers conducted four-dimensional data independent acquisition (4D-DIA) proteomic analysis on tumor tissues. In order to further investigate the dynamic changes of metabolites after therapeutic intervention, researchers conducted liquid chromatography-tandem mass spectrometry untargeted metabolomics identification and analysis on mouse serum. The results of the joint proteomics-metabolomics analysis were validated using experimental methods such as immunofluorescence, Western blotting, and quantitative reverse transcription polymerase chain reaction detection. RESULTS: Injecting hydrogen-rich water into acupoints significantly inhibited tumor growth (P < 0.05). 4D-DIA proteomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses uncovered pathways such as T helper 1 cell (Th1) and T helper 2 cell (Th2) cell differentiation. The KEGG metabolic pathways identified in the metabolomics analysis included galactose metabolism along with fructose and mannose metabolism. Based on the combined proteomics and metabolomics analysis, the key pathways included the C-type lectin receptor signaling pathway. The major cancer-related differential proteins detected in Th1 and Th2 cell differentiation [interleukin 6 signal transducer, nuclear factor of activated T cells 4, recombinant mitogen activated protein kinase 10 (MAPK10), and MAPK11] were upregulated after the injection of hydrogen-rich water into the Zusanli (ST36) acupoint, whereas Linker for activation of T cells (Lat), signal transducer and activator of transcription 1, and protein kinase C, theta were downregulated. CONCLUSION: The injection of hydrogen-rich water into the Zusanli (ST36) acupoint effectively inhibited the hyperplasia of 4T1 BC cells and enhanced their apoptosis, potentially exerting a therapeutic effect through multiple pathways and targeting various sites.

Animals↗

PubMed2026

Integrated analysis of plasma metabolomics and proteomics reveals the biological characteristics of damp-heat and stasis-toxin syndrome in colorectal cancer.

OBJECTIVE: To investigate the biological attributes of core syndromes in colorectal cancer, namely, the damp-heat and stasis-toxin syndrome (SRYD). METHODS: Between October 2021 and October 2022, a cohort comprising 40 patients with colorectal cancer (CRC) diagnosed with damp-heat and stasis-toxin syndrome (SRYD group), 40 patients with CRC without this syndrome (non-SRYD group), and 40 healthy controls (Normal group) was recruited at Jiangsu Province Hospital of Chinese Medicine. Untargeted metabolomics analysis was conducted on plasma samples from all 120 participants, while differential protein analysis using four-dimensional data-independent acquisition proteomics was performed on 20 randomly selected samples per group. A combined analysis of proteomics and metabolomics data followed, and the identified potential diagnostic biomarkers were subsequently used to train and validate multiple machine learning models. RESULTS: Proteomic analysis revealed 130 differential proteins in the colorectal cancer with damp-heat and stasis-toxin syndrome (CRC-SRYD) group, enriched in pathways including complement and coagulation cascades, as well as nuclear factor kappa-B (NF-κB) signaling. Metabolomic analysis identified 584 differential metabolites within the same group, showing enrichment in pathways such as primary bile acid biosynthesis, central carbon metabolism in cancer, and glucagon signaling. Integrated pathway analysis indicated heightened activity of the NF-κB signaling pathway in the CRC-SRYD group. A biomarker panel, comprising 6 proteins and 9 metabolites selected through the ReliefF algorithm, was used to construct a diagnostic model with random forest, achieving an accuracy of 93.33%, sensitivity of 80.00%, and specificity of 100%. CONCLUSION: This study systematically elucidates plasma metabolomic and proteomic alterations in patients with CRC, establishing a robust diagnostic model for CRC syndrome (CRC-SRYD). Further investigation is warranted to clarify the underlying molecular mechanisms and biological foundations.

Humans↗

PubMed2026

Revealing Hidden Variables in DESI-Based Spatial Metabolomics: Solvent Composition and Tissue Type as Critical Drivers.

In the development of a desorption electrospray ionization (DESI) workflow for spatial metabolomics, we investigated the impact of two commonly used solvent systems, 90% acetonitrile (ACN) and 90% methanol (MeOH), on the spatial metabolomic profiling of various murine tissues. The performance of both solvents was evaluated across several metabolite classes (central carbon metabolites, amino acids, and fatty acids). Although the ACN-based solvent system led to higher signal intensities for small polar metabolites involved in glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism, the MeOH-based solvent system provided superior signal intensities for fatty acids. These findings demonstrate that the solvent composition differentially influences metabolite extraction and ionization processes in DESI and should be carefully matched to the biological questions and metabolite classes of interest. As a proof-of-principle, the ACN solvent system was applied to a pilot study based on a rat model of renal ischemic injury, revealing region-specific metabolic changes between normoxic and ischemic conditions. Together, these results demonstrate the importance of solvent selection in DESI-based spatial metabolomics and showcase the ability of this approach to uncover spatially resolved metabolic adaptations associated with tissue injury.

Animals↗

PubMed2006

Metabolomics: current technologies and future trends.

The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30-40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so-called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non-invasive footprinting analysis is also discussed as this has many applications to in vitro cell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation.

Biomarkers↗

PubMed2006

Proteomics and metabolomics: the molecular make-up of toxic aromatic pollutant bioremediation.

Microbial-mediated attenuation of toxic aromatic pollutants offers great potential for the restoration of contaminated environments in an ecologically acceptable manner. However, incomplete biological information regarding the regulation of growth and metabolism in many microbial communities restricts progress in the site-specific mineralization process. In the postgenomic era, recent advances in MS have allowed enormous progress in proteomics and elucidated many complex biological interactions. These research forefronts are now expanding toward the analysis of low-molecular-weight primary and secondary metabolites analysis, i.e., metabolomics. The advent of 2-DE in conjunction with MS offers a promising approach to address the molecular mechanisms of bioremediation. The two fields of proteomics and metabolomics have thus far worked separately to identify proteins and primary and secondary metabolites during bioremediation. A simultaneous study combining functional proteomics and metabolomics, i.e., proteometabolomics would create a system-wide approach to studying site-specific microorganisms during active mineralization processes. This article deals with advances in environmental proteomics and metabolomics and advocates the simultaneous study of both technologies to implement cell-free bioremediation.

Biodegradation, Environmental↗

PubMed2005

Toxic actions of dinoseb in medaka (Oryzias latipes) embryos as determined by in vivo 31P NMR, HPLC-UV and 1H NMR metabolomics.

Changes in metabolism of Japanese medaka (Oryzias latipes) embryos exposed to dinoseb (2-sec-butyl-4,6-dinitrophenol), a substituted dinitrophenol herbicide, were determined by in vivo (31)P NMR, high-pressure liquid chromatography (HPLC)-UV, and (1)H NMR metabolomics. ATP and phosphocreatine (PCr) metabolism were characterized within intact embryos by in vivo (31)P NMR; concentrations of ATP, GTP, ADP, GDP, AMP and PCr were determined by HPLC-UV; and changes in numerous polar metabolites were characterized by (1)H NMR-based metabolomics. Rangefinding exposures determined two sublethal doses of dinoseb, 50 and 75 ppb, in which embryos survived from 1-day post fertilization (DPF) through the duration of embryogenesis. In vivo (31)P NMR data were acquired from 900 embryos in 0, 50, and 75 ppb dinoseb at 14, 62, and 110 h (n = 6 groups) after initiation of exposure. After 110 h, embryos were observed for normal development and hatching success, then either preserved in 10% formalin for growth analysis or flash frozen and extracted for HPLC-UV and (1)H NMR analysis. Dinoseb exposure at both concentrations resulted in significant declines in [ATP] and [PCr] at 110 h as measured by in vivo (31)P NMR (p < 0.01), HPLC-UV (p < 0.001) and NMR-based metabolomics. Reduced eye growth and diminished heart rate occurred in a concentration-dependent fashion. Metabolic effects measured by in vivo (31)P NMR showed a significant increase in orthophosphate levels (P(i); p < 0.05), and significant decreases in [ATP], [PCr] and the PCr/P(i) ratio (p < 0.05). Metabolomics revealed a dose-response relationship between dinoseb and endogenous metabolite changes, with both dinoseb concentrations producing significantly different metabolic profiles from controls (p < 0.05). Metabolic changes included decreased concentrations of ATP, PCr, alanine and tyrosine, and increased concentrations of lactate with medaka embryotoxicity. This study demonstrated that medaka embryos respond to dinoseb with significant changes in metabolism, reduced growth and heart rates, and increased abnormal development and post-exposure mortality. All three analytical methods confirmed similar trends, and utilization of PCr to compensate for ATP loss was found to be a consistent indicator of sublethal stress-one that could be used to quantify stress associated with medaka embryotoxicity.

2,4-Dinitrophenol↗

PubMed2005

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling.

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

Biochemistry↗

PubMed2003

Plant metabolomics: large-scale phytochemistry in the functional genomics era.

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.

Computational Biology↗

PubMed2005

Metabolomics: from pattern recognition to biological interpretation.

Metabolomics is a technology that aims to identify and quantify the metabolome -- the dynamic set of all small molecules present in an organism or a biological sample. In this sense, the technique is distinct from metabolic profiling, which looks for target compounds and their biochemical transformation. The combination of both approaches is an emerging technique for the characterization of biological samples and for drug treatment. Metabolomics has proven to be very rapid and superior to any other post-genomics technology for pattern-recognition analyses of biological samples. Changing steady state concentrations and fluctuations of metabolites that occur within milliseconds are a result of biochemical processes such as signalling cascades: metabolomic techniques are instrumental in measuring these changes rapidly and sensitively. Metabolite data can be complemented by protein, transcript and external (environmental) data, thereby leading to the identification of multiple physiological biomarkers embedded in correlative molecular networks that are not approachable with targeted studies.

Biomarkers↗

PubMed2005

Fusion of mass spectrometry-based metabolomics data.

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process.

Databases as Topic↗

PubMed2006

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data.

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.

Computational Biology↗

PubMed2004

A proposed framework for the description of plant metabolomics experiments and their results.

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

Database Management Systems↗

PubMed2005

Measuring the metabolome: current analytical technologies.

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.

Animals↗

PubMed2004

Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana.

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all "omics" sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis, with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such gene-to-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants.

Arabidopsis↗