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Our understanding of how the gut microbiome interacts with its human host has been restrained by limited access to longitudinal datasets to examine stability and dynamics, and by having only a few isolates to test mechanistic hypotheses. Here, we present the Broad Institute-OpenBiome Microbiome Library (BIO-ML), a comprehensive collection of 7,758 gut bacterial isolates paired with 3,632 genome sequences and longitudinal multi-omics data. We show that microbial species maintain stable population sizes within and across humans and that commonly used ‘omics’ survey methods are more reliable when using averages over multiple days of sampling. Variation of gut metabolites within people over time is associated with amino acid levels, and differences across people are associated with differences in bile acids. Finally, we show that genomic diversification can be used to infer eco-evolutionary dynamics and in vivo selection pressures for strains within individuals. The BIO-ML is a unique resource designed to enable hypothesis-driven microbiome research. A comprehensive biobank of bacterial isolates with longitudinal and multi-omics characterization will advance understanding of the diversity and functions of human gut bacteria.

Unbiased, “nontargeted” metabolite profiling techniques hold considerable promise for biomarker and pathway discovery, in spite of the lack of successful applications to human disease. By integrating nontargeted metabolomics, genetics, and detailed human phenotyping, we identified dimethylguanidino valeric acid (DMGV) as an independent biomarker of CT-defined nonalcoholic fatty liver disease (NAFLD) in the offspring cohort of the Framingham Heart Study (FHS) participants. We verified the relationship between DMGV and early hepatic pathology. Specifically, plasma DMGV levels were correlated with biopsy-proven nonalcoholic steatohepatitis (NASH) in a hospital cohort of individuals undergoing gastric bypass surgery, and DMGV levels fell in parallel with improvements in post-procedure cardiometabolic parameters. Further, baseline DMGV levels independently predicted future diabetes up to 12 years before disease onset in 3 distinct human cohorts. Finally, we provide all metabolite peak data consisting of known and unidentified peaks, genetics, and key metabolic parameters as a publicly available resource for investigations in cardiometabolic diseases.

Background Epidemiologic studies suggest a strong link between poor habitual sleep quality and increased cardiovascular disease risk. However, the underlying mechanisms are not entirely clear. Metabolomic profiling may elucidate systemic differences associated with sleep quality that influence cardiometabolic health. Methods We explored cross-sectional associations between sleep quality and plasma metabolites in a nested case–control study of coronary heart disease (CHD) in the Women’s Health Initiative (WHI; n = 1956) and attempted to replicate the results in an independent sample from the Nurses’ Health Study II (NHSII; n = 209). A sleep-quality score (SQS) was derived from self-reported sleep problems asked in both populations. Plasma metabolomics were assayed using LC–MS with 347 known metabolites. General linear regression was used to identify individual metabolites associated with continuous SQS (false-discovery rate Results After adjusting for age, race/ethnicity, body mass index (BMI) and smoking, we identified 69 metabolites associated with SQS in the WHI (59 were lipids). Of these, 16 were replicated in NHSII (15 were lipids), including 6 triglycerides (TAGs), 4 phosphatidylethanolamines (PEs), 3 phosphatidylcholines (PCs), 1 diglyceride (DAG), 1 lysophosphatidylcholine and N6-acetyl-L-lysine (a product of histone acetylation). These metabolites were consistently higher among women with poorer sleep quality. The LASSO selection resulted in a nine-metabolite score (TAGs 45: 1, 48: 1, 50: 4; DAG 32: 1; PEs 36: 4, 38: 5; PCs 30: 1, 40: 6; N6-acetyl-L-lysine), which was positively associated with CHD risk (odds ratio per SD increase in the score: 1.16; 95% confidence interval: 1.05, 1.28; p = 0.0003) in the WHI after adjustment for matching factors and conventional CHD risk factors. Conclusions Differences in lipid metabolites may be an important pathogenic pathway linking poor habitual sleep quality and CHD risk.

Age-related macular degeneration (AMD) is the major cause of blindness in developed nations. AMD is characterized by retinal pigmented epithelial (RPE) cell dysfunction and loss of photoreceptor cells. Epidemiologic studies indicate important contributions of dietary patterns to the risk for AMD, but the mechanisms relating diet to disease remain unclear. Here we investigate the effect on AMD of isocaloric diets that differ only in the type of dietary carbohydrate in a wild-type aged-mouse model. The consumption of a high-glycemia (HG) diet resulted in many AMD features (AMDf), including RPE hypopigmentation and atrophy, lipofuscin accumulation, and photoreceptor degeneration, whereas consumption of the lower-glycemia (LG) diet did not. Critically, switching from the HG to the LG diet late in life arrested or reversed AMDf. LG diets limited the accumulation of advanced glycation end products, long-chain polyunsaturated lipids, and their peroxidation end-products and increased C3-carnitine in retina, plasma, or urine. Untargeted metabolomics revealed microbial cometabolites, particularly serotonin, as protective against AMDf. Gut microbiota were responsive to diet, and we identified microbiota in the Clostridiales order as being associated with AMDf and the HG diet, whereas protection from AMDf was associated with the Bacteroidales order and the LG diet. Network analysis revealed a nexus of metabolites and microbiota that appear to act within a gut–retina axis to protect against diet- and age-induced AMDf. The findings indicate a functional interaction between dietary carbohydrates, the metabolome, including microbial cometabolites, and AMDf. Our studies suggest a simple dietary intervention that may be useful in patients to arrest AMD.

Tumors put in a vulnerable position Cancer cells often display alterations in metabolism that help fuel their growth. Such metabolic “rewiring” may also work against the cancer cells, however, by creating new vulnerabilities that can be exploited therapeutically. A variety of human tumors show changes in methionine metabolism caused by loss of the gene coding for 5-methylthioadenosine phosphorylase (MTAP). Mavrakis et al. and Kryukov et al. found that the loss of MTAP renders cancer cell lines sensitive to growth inhibition by compounds that suppress the activity of a specific arginine methyltransferase called PRMT5. Conceivably, drugs that inhibit PRMT5 activity could be developed into a tailored therapy for MTAP-deficient tumors. Science , this issue pp. 1208 and 1214