Search

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies., (© 2024. The Author(s).)

The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.

Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic ( n = 1713) and metabolomic ( n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ≤ 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ≤ 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ≤ 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations.

Cyanide is a potent toxic agent, and the few available antidotes are not amenable to rapid deployment in mass exposures. As a result, there are ongoing efforts to exploit different animal models to identify novel countermeasures. We have created a pipeline that combines high-throughput screening in zebrafish with subsequent validation in two mammalian small animal models as well as a porcine large animal model. We found that zebrafish embryos in the first 3 days post fertilization (dpf) are highly resistant to cyanide, becoming progressively more sensitive thereafter. Unbiased analysis of gene expression in response to several hours of ultimately lethal doses of cyanide in both 1 and 7 dpf zebrafish revealed modest changes in iron-related proteins associated with the age-dependent cyanide resistance. Metabolomics measurements demonstrated significant age-dependent differences in energy metabolism during cyanide exposure which prompted us to test modulators of the tricarboxylic acid cycle and related metabolic processes as potential antidotes. In cyanide-sensitive 7 dpf larvae, we identified several such compounds that offer significant protection against cyanide toxicity. Modulators of the pyruvate dehydrogenase complex, as well as the small molecule sodium glyoxylate, consistently protected against cyanide toxicity in 7 dpf zebrafish larvae. Together, our results indicate that the resistance of zebrafish embryos to cyanide toxicity during early development is related to an altered regulation of cellular metabolism, which we propose may be exploited as a potential target for the development of novel antidotes against cyanide poisoning., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: a patent application has been submitted for the use of glyoxylate as a cyanide antidote. However, this does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Background: Patients with chronic kidney disease (CKD) experience high rates of atherosclerotic cardiovascular disease and death that are not fully explained by traditional risk factors. In animal studies, defective cellular cholesterol efflux pathways which are mediated by the ATP binding cassette transporters ABCA1 and ABCG1 are associated with accelerated atherosclerosis. We hypothesized that cholesterol efflux in humans would vary in terms of cellular components, with potential implications for cardiovascular disease., Methods: We recruited 120 CKD patients (eGFR<30mL/min/1.73m 2 ) and 120 control subjects (eGFR ≥60mL/min/1.73m 2 ) in order to measure cholesterol efflux using either patients' HDL and THP-1 macrophages or patients' monocytes and a flow cytometry based cholesterol efflux assay. We also measured cell-surface levels of the common β subunit of the IL-3/GM-CSF receptor (IL-3Rβ) which has been linked to defective cholesterol homeostasis and may promote monocytosis. In addition, we measured plasma inflammatory cytokines and plasma metabolite profiles., Results: There was a strong positive correlation between cell-surface IL-3Rβ levels and monocyte counts in CKD (P<0.001). ABCA1 mRNA was reduced in CKD vs. control monocytes (P<0.05), across various etiologies of CKD. Cholesterol efflux to apolipoprotein A1 was impaired in monocytes from CKD patients with diabetic nephropathy (P<0.05), but we found no evidence for a circulating HDL-mediated defect in cholesterol efflux in CKD. Profiling of plasma metabolites showed that medium-chain acylcarnitines were both independently associated with lower levels of cholesterol transporter mRNA in CKD monocytes at baseline (P<0.05), and with cardiovascular events in CKD patients after median 2.6years of follow-up., Conclusions: Cholesterol efflux in humans varies in terms of cellular components. We report a cellular defect in ABCA1-mediated cholesterol efflux in monocytes from CKD patients with diabetic nephropathy. Unlike several traditional risk factors for atherosclerotic cardiovascular disease, plasma metabolites inversely associated with endogenous cholesterol transporters predicted cardiovascular events in CKD patients. (Funded by the National Institute of Diabetes and Digestive and Kidney DiseasesK23DK097288 and others.)., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Through the measure of thousands of small-molecule metabolites in diverse biological systems, metabolomics now offers the potential for new insights into the factors that contribute to complex human diseases such as cardiovascular disease. Targeted metabolomics methods have already identified new molecular markers and metabolomic signatures of cardiovascular disease risk (including branched-chain amino acids, select unsaturated lipid species, and trimethylamine- N -oxide), thus in effect linking diverse exposures such as those from dietary intake and the microbiota with cardiometabolic traits. As technologies for metabolomics continue to evolve, the depth and breadth of small-molecule metabolite profiling in complex systems continue to advance rapidly, along with prospects for ongoing discovery. Current challenges facing the field of metabolomics include scaling throughput and technical capacity for metabolomics approaches, bioinformatic and chemoinformatic tools for handling large-scale metabolomics data, methods for elucidating the biochemical structure and function of novel metabolites, and strategies for determining the true clinical relevance of metabolites observed in association with cardiovascular disease outcomes. Progress made in addressing these challenges will allow metabolomics the potential to substantially affect diagnostics and therapeutics in cardiovascular medicine., (© 2017 American Heart Association, Inc.)

The study of rare variants may enhance our understanding of the genetic determinants of the metabolome. Here, we analyze the association between 217 plasma metabolites and exome variants on the Illumina HumanExome Beadchip in 2,076 participants in the Framingham Heart Study, with replication in 1,528 participants of the Atherosclerosis Risk in Communities Study. We identify an association between GMPS and xanthosine using single variant analysis and associations between HAL and histidine, PAH and phenylalanine, and UPB1 and ureidopropionate using gene-based tests (P<5 × 10(-8) in meta-analysis), highlighting novel coding variants that may underlie inborn errors of metabolism. Further, we show how an examination of variants across the spectrum of allele frequency highlights independent association signals at select loci and generates a more integrated view of metabolite heritability. These studies build on prior metabolomics genome wide association studies to provide a more complete picture of the genetic architecture of the plasma metabolome.

Background: Although obesity and cardiometabolic traits commonly overlap, underlying pathways remain incompletely defined. The association of metabolite profiles across multiple cardiometabolic traits may lend insights into the interaction of obesity and metabolic health. We sought to investigate metabolic signatures of obesity and related cardiometabolic traits in the community using broad-based metabolomic profiling., Methods and Results: We evaluated the association of 217 assayed metabolites and cross-sectional as well as longitudinal changes in cardiometabolic traits among 2,383 Framingham Offspring cohort participants. Body mass index (BMI) was associated with 69 of 217 metabolites (P<0.00023 for all), including aromatic (tyrosine, phenylalanine) and branched chain amino acids (valine, isoleucine, leucine). Additional metabolic pathways associated with BMI included the citric acid cycle (isocitrate, alpha-ketoglutarate, aconitate), the tryptophan pathway (kynurenine, kynurenic acid), and the urea cycle. There was considerable overlap in metabolite profiles between BMI, abdominal adiposity, insulin resistance [IR] and dyslipidemia, modest overlap of metabolite profiles between BMI and hyperglycemia, and little overlap with fasting glucose or elevated blood pressure. Metabolite profiles were associated with longitudinal changes in fasting glucose, but the involved metabolites (ornithine, 5-HIAA, aminoadipic acid, isoleucine, cotinine) were distinct from those associated with baseline glucose or other traits. Obesity status appeared to "modify" the association of 9 metabolites with IR. For example, bile acid metabolites were strongly associated with IR among obese but not lean individuals, whereas isoleucine had a stronger association with IR in lean individuals., Conclusions: In this large-scale metabolite profiling study, body mass index was associated with a broad range of metabolic alterations. Metabolite profiling highlighted considerable overlap with abdominal adiposity, insulin resistance, and dyslipidemia, but not with fasting glucose or blood pressure traits.

Experimental studies have suggested possible protective effects of dimethylglycine (DMG) on glucose metabolism. DMG is degraded to glycine through a DMG-dehydrogenase (DMGDH)-catalyzed reaction, and this is the only known pathway for the breakdown of DMG in mammals. In this study, we aimed to identify the strongest genetic determinant of circulating DMG concentration and to investigate its associations with metabolic traits and incident diabetes. In the cohort with full metabolomics data (n = 709), low plasma levels of DMG were significantly associated with higher blood glucose levels (P = 3.9E(-4)). In the genome-wide association study (GWAS) of the discovery cohort (n = 5,205), the strongest genetic signal of plasma DMG was conferred by rs2431332 at the DMGDH locus, where the major allele was associated with lower DMG levels (P = 2.5E(-15)). The same genetic variant (major allele of rs2431332) was also significantly associated with higher plasma insulin (P = 0.019), increased HOMA insulin resistance (P = 0.019), and an increased risk of incident diabetes (P = 0.001) in the pooled analysis of the discovery cohort together with the two replication cohorts (n = 20,698 and n = 7,995). These data are consistent with a possible causal role of DMG deficiency in diabetes development and encourage future studies examining if inhibition of DMGDH, or alternatively, supplementation of DMG, might prove useful for the treatment/prevention of diabetes., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Background: Although metabolomic approaches have begun to document numerous changes that arise in end stage renal disease (ESRD), how these alterations relate to established metabolic phenotypes in uremia is unknown., Methods: In 200 incident hemodialysis patients we used partial least squares discriminant analysis to identify which among 166 metabolites could best discriminate individuals with or without diabetes, and across tertiles of body mass index, serum albumin, total cholesterol, and systolic blood pressure., Results: Our data do not recapitulate metabolomic signatures of diabetes and obesity identified among individuals with normal renal function (e.g. elevations in branched chain and aromatic amino acids) and highlight several potential markers of diabetes status specific to ESRD, including xanthosine-5-phosphate and vanillylmandelic acid. Further, our data identify significant associations between elevated tryptophan and long-chain acylcarnitine levels and both decreased total cholesterol and systolic blood pressure in ESRD. Higher tryptophan levels were also associated with higher serum albumin levels, but this may reflect tryptophan's significant albumin binding. Finally, an examination of the uremic retention solutes captured by our platform in relation to 24 clinical phenotypes provides a framework for investigating mechanisms of uremic toxicity., Conclusions: In sum, these studies leveraging metabolomic and metabolic phenotype data acquired in a well-characterized ESRD cohort demonstrate striking differences from metabolomics studies in the general population, and may provide clues to novel functional pathways in the ESRD population.

Alterations in metabolism influence lifespan in experimental models, but data in humans are lacking. Here we use liquid chromatography/mass spectrometry to quantify 217 plasma metabolites and examine their relation to longevity in a large cohort of men and women followed for up to 20 years. We find that, higher concentrations of the citric acid cycle intermediate, isocitrate, and the bile acid, taurocholate, are associated with lower odds of longevity, defined as attaining 80 years of age. Higher concentrations of isocitrate, but not taurocholate, are also associated with worse cardiovascular health at baseline, as well as risk of future cardiovascular disease and death. None of the metabolites identified are associated with cancer risk. Our findings suggest that some, but not all, metabolic pathways related to human longevity are linked to the risk of common causes of death.

Objective: A genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits., Research Design and Methods: Diabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC)., Results: Two hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002)., Conclusions: Metabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors., (© 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Objective: To determine whether increases in cardiac work lead to alterations in the plasma metabolome and whether such changes arise from the heart or peripheral organs., Background: There is growing evidence that the heart influences systemic metabolism through endocrine effects and affecting pathways involved in energy homeostasis., Methods: Nineteen patients referred for cardiac catheterization were enrolled. Peripheral and selective coronary sinus (CS) blood sampling was performed at serial timepoints following the initiation of pacing, and metabolite profiling was performed by liquid chromatography-mass spectrometry (LC-MS)., Results: Pacing-stress resulted in a 225% increase in the median rate·pressure product from baseline. Increased myocardial work induced significant changes in the peripheral concentration of 43 of 125 metabolites assayed, including large changes in purine [adenosine (+99%, p = 0.006), ADP (+42%, p = 0.01), AMP (+79%, p = 0.004), GDP (+69%, p = 0.003), GMP (+58%, p = 0.01), IMP (+50%, p = 0.03), xanthine (+61%, p = 0.0006)], and several bile acid metabolites. The CS changes in metabolites qualitatively mirrored those in the peripheral blood in both timing and magnitude, suggesting the heart was not the major source of the metabolite release., Conclusions: Isolated increases in myocardial work can induce changes in the plasma metabolome, but these changes do not appear to be directly cardiac in origin. A number of these dynamic metabolites have known signaling functions. Our study provides additional evidence to a growing body of literature on metabolic 'cross-talk' between the heart and other organs.

Because metabolites are hypothesized to play key roles as markers and effectors of cardiometabolic diseases, recent studies have sought to annotate the genetic determinants of circulating metabolite levels. We report a genome-wide association study (GWAS) of 217 plasma metabolites, including >100 not measured in prior GWAS, in 2076 participants of the Framingham Heart Study (FHS). For the majority of analytes, we find that estimated heritability explains >20% of interindividual variation, and that variation attributable to heritable factors is greater than that attributable to clinical factors. Further, we identify 31 genetic loci associated with plasma metabolites, including 23 that have not previously been reported. Importantly, we include GWAS results for all surveyed metabolites and demonstrate how this information highlights a role for AGXT2 in cholesterol ester and triacylglycerol metabolism. Thus, our study outlines the relative contributions of inherited and clinical factors on the plasma metabolome and provides a resource for metabolism research., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Background: Although metabolic risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and cardiovascular disease, the underlying biological mechanisms remain poorly understood., Methods and Results: To identify pathways associated with cardiometabolic risk, we used liquid chromatography/mass spectrometry to determine the plasma concentrations of 45 distinct metabolites and to examine their relation to cardiometabolic risk in the Framingham Heart Study (FHS; n=1015) and the Malmö Diet and Cancer Study (MDC; n=746). We then interrogated significant findings in experimental models of cardiovascular and metabolic disease. We observed that metabolic risk factors (obesity, insulin resistance, high blood pressure, and dyslipidemia) were associated with multiple metabolites, including branched-chain amino acids, other hydrophobic amino acids, tryptophan breakdown products, and nucleotide metabolites. We observed strong associations of insulin resistance traits with glutamine (standardized regression coefficients, -0.04 to -0.22 per 1-SD change in log-glutamine; P<0.001), glutamate (0.05 to 0.14; P<0.001), and the glutamine-to-glutamate ratio (-0.05 to -0.20; P<0.001) in the discovery sample (FHS); similar associations were observed in the replication sample (MDC). High glutamine-to-glutamate ratio was associated with lower risk of incident diabetes mellitus in FHS (odds ratio, 0.79; adjusted P=0.03) but not in MDC. In experimental models, administration of glutamine in mice led to both increased glucose tolerance (P=0.01) and decreased blood pressure (P<0.05)., Conclusions: Biochemical profiling identified circulating metabolites not previously associated with metabolic traits. Experimentally interrogating one of these pathways demonstrated that excess glutamine relative to glutamate, resulting from exogenous administration, is associated with reduced metabolic risk in mice.

The metabolome is the terminal downstream product of the genome and consists of the total complement of all the low-molecular-weight molecules (metabolites) in a cell, tissue, or organism. Metabolomics aims to measure a wide breadth of small molecules in the context of physiological stimuli or disease states. Metabolomics methodologies fall into two distinct groups: untargeted metabolomics, an intended comprehensive analysis of all the measurable analytes in a sample including chemical unknowns, and targeted metabolomics, the measurement of defined groups of chemically characterized and biochemically annotated metabolites. The methodologies considered in this unit focus on the processes of conducting targeted metabolomics experiments, and the advantages of this general approach are highlighted herein. This unit outlines procedures for extracting nitrogenous metabolites (including amino acids), lipids, and intermediary metabolites (including TCA cycle oxoacids) from blood plasma. Specifically, protocols are described for analyzing these metabolites using targeted metabolomics experiments based on liquid chromatography-mass spectrometry., (© 2012 by John Wiley & Sons, Inc.)

Background: Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery., Content: Because no single analytical method can accommodate the chemical diversity of the entire metabolome, various methods such as nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) have been employed, with the latter coupled to an array of separation techniques including gas and liquid chromatography. Whereas NMR can provide structural information and absolute quantification for select metabolites without the use of exogenous standards, MS tends to have much higher analytical sensitivity, enabling broader surveys of the metabolome. Both NMR and MS can be used to characterize metabolite data either in a targeted manner or in a nontargeted, pattern-recognition manner. In addition to technical considerations, careful sample selection and study design are important to minimize potential confounding influences on the metabolome, including diet, medications, and comorbitidies. To this end, metabolite profiling has been applied to human biomarker discovery in small-scale interventions, in which individuals are extremely well phenotyped and able to serve as their own biological controls, as well as in larger epidemiological cohorts. Understanding how metabolites relate to each other and to established risk markers for diseases such as diabetes and renal failure will be important in evaluating the potential value of these metabolites as clinically useful biomarkers., Summary: Applied to both experimental and epidemiological study designs, metabolite profiling has begun to highlight the breadth metabolic disturbances that accompany human disease. Experimental work in model systems and integration with other functional genomic approaches will be required to establish a causal link between select biomarkers and disease pathogenesis.

Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.

BackgroundWhereas several longitudinal metabolomics studies have been conducted in individuals with normal estimated glomerular filtration rate (eGFR) at baseline, disease progression among individuals with established chronic kidney disease (CKD) has not been rigorously examined.MethodsWe performed a nested case-control study of rapid CKD progression in the Chronic Renal Insufficiency Cohort Study, profiling baseline plasma from 200 individuals each with eGFR slope

To identify genetic and environmental factors contributing to the pathogenesis of non-alcoholic fatty liver disease, we examined liver steatosis and related clinical and molecular traits in more than 100 unique inbred mouse strains, which were fed a diet rich in fat and carbohydrates. A >30-fold variation in hepatic TG accumulation was observed among the strains. Genome-wide association studies revealed three loci associated with hepatic TG accumulation. Utilizing transcriptomic data from the liver and adipose tissue, we identified several high-confidence candidate genes for hepatic steatosis, including Gde1, a glycerophosphodiester phosphodiesterase not previously implicated in triglyceride metabolism. We confirmed the role of Gde1 by in vivo hepatic over-expression and shRNA knockdown studies. We hypothesize that Gde1 expression increases TG production by contributing to the production of glycerol-3-phosphate. Our multi-level data, including transcript levels, metabolite levels, and gut microbiota composition, provide a framework for understanding genetic and environmental interactions underlying hepatic steatosis. DOI: http://dx.doi.org/10.7554/eLife.05607.001