The human gut microbiome is linked to many states of human health and disease. 1 The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase Veillonella relative abundance in marathon runners post-marathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill runtime. Veillonella utilize lactate as their sole carbon source, which prompted us to perform shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance post-exercise. Using 13 C 3 -labeled lactate in mice we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill runtime performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves runtime via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

Highlights d Cross-study meta-analysis of metagenomes covering 3,655 samples from two body sites d Meta-analysis uncovers staggering microbial gene diversity d 50% of all genes in a metagenomic sample are individualspecific or ''singletons'' d Individual's microbiomes can be fingerprinted via rare microbial strains

Loss of gut microbial diversity1–6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.

We analyzed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status, air pollution, and climate) in each phenotype. Mean heritability (h 2 = 0.311) and shared environmental variance (c 2 = 0.088) were higher than variance attributed to specific environmental factors such as zip code-level socioeconomic status (SES) (var SES = 0.002), daily air quality (var AQI = 0.0004), and average temperature (var temp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h 2 = 0.433, c 2 = 0.241) and average monthly cost (h 2 = 0.290, c 2 = 0.302). All results are available using our “Claims Analysis of Twin Correlation and Heritability (CaTCH)” web application.

We propose microbiome disease “architectures”: linking >1 million microbial features (species, pathways, and genes) to 7 host phenotypes from 13 cohorts using a pipeline designed to identify associations that are robust to analytical model choice. Here, we quantify conservation and heterogeneity in microbiome-disease associations, using gene-level analysis to identify strain-specific, cross-disease, positive and negative associations. We find coronary artery disease, inflammatory bowel diseases, and liver cirrhosis to share gene-level signatures ascribed to the Streptococcus genus. Type 2 diabetes, by comparison, has a distinct metagenomic signature not linked to any one specific species or genus. We additionally find that at the species-level, the prior-reported connection between Solobacterium moorei and colorectal cancer is not consistently identified across models—however, our gene-level analysis unveils a group of robust, strain-specific gene associations. Finally, we validate our findings regarding colorectal cancer and inflammatory bowel diseases in independent cohorts and identify that features inversely associated with disease tend to be less reproducible than features enriched in disease. Overall, our work is not only a step towards gene-based, cross-disease microbiome diagnostic indicators, but it also illuminates the nuances of the genetic architecture of the human microbiome, including tension between gene- and species-level associations.

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 4 different types of metagenomic data. Using 1,570 samples from 300 infants, we fit 7,865 models for 6 host phenotypes. We demonstrate the dependence of accuracy on algorithm choice and feature definition in microbiome data and propose a framework for building microbiomederived indicators of host phenotype. We additionally identify biological features predictive of age, sex, breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_ predictions/.

No abstract

No abstract