Search

Drug transporters and drug-metabolizing enzymes are primarily known for their role in the absorption, distribution, metabolism, and excretion (ADME) of small molecule drugs, but they also play a key role in handling endogenous metabolites. Recent cross-tissue co-expression network analyses have revealed a "Remote Sensing and Signaling Network" of multispecific, oligo-specific, and monospecific transporters and enzymes involved in endogenous metabolism. This includes many proteins from families involved in ADME (e.g., SLC22, SLCO, ABCC, CYP, UGT). Focusing on the gut-liver-kidney axis, we identified the endogenous metabolites potentially regulated by this network of ~1000 proteins by associating SNPs in these genes with the circulating levels of thousands of small, polar, bioactive metabolites, including free fatty acids, eicosanoids, bile acids, and other signaling metabolites that act in part via G-protein coupled receptors (GPCRs), nuclear receptors, and kinases. We identified 77 genomic loci associated with 7236 unique metabolites. This included metabolites that were associated with multiple, distinct loci, indicating coordinated regulation between multiple genes (including drug transporters and drug-metabolizing enzymes) of specific metabolites. We analyzed existing pharmacogenomic data and noted SNPs implicated in endogenous metabolite handling (e.g., rs4149056 in SLCO1B1) also affecting drug ADME. The overall results support the existence of close relationships, via interactions with signaling metabolites, between drug transporters and drug-metabolizing enzymes that are part of the Remote Sensing and Signaling Network, and with GPCRs and nuclear receptors. These analyses highlight the potential for drug-metabolite interactions at the interfaces of the Remote Sensing and Signaling Network and the ADME protein network.

BackgroundThe prognosis and therapeutic responses are worse for pulmonary arterial hypertension associated with systemic sclerosis (SSc-PAH) compared with idiopathic pulmonary arterial hypertension (IPAH). This discrepancy could be driven by divergence in underlying metabolic determinants of disease.Research questionAre circulating bioactive metabolites differentially altered in SSc-PAH vs IPAH, and can this alteration explain clinical disparity between these PAH subgroups?Study design and methodsPlasma biosamples from 400 patients with SSc-PAH and 1,082 patients with IPAH were included in the study. Another cohort of 100 patients with scleroderma with no PH and 44 patients with scleroderma with PH was included for external validation. More than 700 bioactive lipid metabolites, representing a range of vasoactive and immune-inflammatory pathways, were assayed in plasma samples from independent discovery and validation cohorts using liquid chromatography/high-resolution mass spectrometry-based approaches. Regression analyses were used to identify metabolites that exhibited differential levels between SSc-PAH and IPAH and associated with disease severity.ResultsFrom hundreds of circulating bioactive lipid molecules, five metabolites were found to distinguish between SSc-PAH and IPAH, as well as associate with markers of disease severity. Relative to IPAH, patients with SSc-PAH carried increased levels of fatty acid metabolites, including lignoceric acid and nervonic acid, as well as eicosanoids/oxylipins and sex hormone metabolites.InterpretationPatients with SSc-PAH are characterized by an unfavorable bioactive metabolic profile that may explain the poor and limited response to therapy. These data provide important metabolic insights into the molecular heterogeneity underlying differences between subgroups of PAH.

Aims Circulating dimethylguanidino valeric acid (DMGV) was identified as a novel metabolite related to cardiorespiratory fitness and cardiometabolic abnormalities. Circulating DMGV levels are subjective to dietary modulation; however, studies on its associations with intakes of coronary heart disease (CHD)–related foods/nutrients are limited. We investigated whether plasma DMGV was related to risk of incident CHD. We tested associations of DMGV with CHD-related dietary intakes measured by 7-day dietary records and estimated corresponding disease risk. Methods and results This nested case–control study on the incidence of CHD included 1520 women (760 incident cases of fatal CHD and nonfatal myocardial infarction and 760 controls) from the Nurses' Health Study. Separately, plasma DMGV and CHD-related dietary intakes and cardiometabolic abnormalities were assessed in the Women's Lifestyle Validation Study (WLVS; n = 724). Higher plasma DMGV was related to a greater risk of CHD [relative risk (RR) per 1 SD, 1.26 (95% CI 1.13, 1.40); P-for-linearity = 0.006]. Greater intakes of sodium, energy-dense foods, and processed/red meat were related to higher DMGV levels; every 1 SD intake of sodium was associated with β 0.13 (SE 0.05; P = 0.007) for DMGV Z -scores, which corresponded to a RR of 1.031 (1.016, 1.046) for CHD. High DMGV (the top quartile, Q4) showed a significant RR of 1.60 (1.17, 2.18) after adjusting for diet and lifestyle factors; the RR further adjusting for obesity and hypertension was 1.29 (0.93, 1.79) as compared with the lowest quartile. In both cohorts, greater adiposity and adverse cardiometabolic factor status were significantly related to higher DMGV levels. Conclusion Higher levels of plasma DMGV, a metabolite reflecting unfavourable CHD-related dietary intakes, were associated with an increased risk of CHD. The unfavourable association was attenuated by cardiometabolic risk factor status. Our study underscores the potential importance of plasma DMGV as an early biomarker associated with diet and the long-term risk of CHD among women. [ABSTRACT FROM AUTHOR]

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects. [ABSTRACT FROM AUTHOR]

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease. [ABSTRACT FROM AUTHOR]