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Author(s): David Zeevi [sup.1] [sup.2] [sup.3] , Tal Korem [sup.1] [sup.2] [sup.4] [sup.5] , Anastasia Godneva [sup.1] [sup.2] , Noam Bar [sup.1] [sup.2] , Alexander Kurilshikov [sup.6] , Maya Lotan-Pompan [...], Differences in the presence of even a few genes between otherwise identical bacterial strains may result in critical phenotypic differences. Here we systematically identify microbial genomic structural variants (SVs) and find them to be prevalent in the human gut microbiome across phyla and to replicate in different cohorts. SVs are enriched for CRISPR-associated and antibiotic-producing functions and depleted from housekeeping genes, suggesting that they have a role in microbial adaptation. We find multiple associations between SVs and host disease risk factors, many of which replicate in an independent cohort. Exploring genes that are clustered in the same SV, we uncover several possible mechanistic links between the microbiome and its host, including a region in Anaerostipes hadrus that encodes a composite inositol catabolism-butyrate biosynthesis pathway, the presence of which is associated with lower host metabolic disease risk. Overall, our results uncover a nascent layer of variability in the microbiome that is associated with microbial adaptation and host health. The authors systematically characterize structural variation in the genomes of gut microbiota and show that they are associated with bacterial fitness and with host risk factors, and that examining genes coded in these regions facilitates investigation of mechanisms that may underlie these associations.

Author(s): Daphna Rothschild [1, 2]; Omer Weissbrod [1, 2]; Elad Barkan [1, 2]; Alexander Kurilshikov [3]; Tal Korem [1, 2]; David Zeevi [1, 2]; Paul I. Costea [1, 2]; Anastasia [...], Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment 1 . The origins of specific compounds are known, including metabolites that are highly heritable 2,3 , or those that are influenced by the gut microbiome 4 , by lifestyle choices such as smoking 5 , or by diet 6 . However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts 7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis 9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disorder, in which the clinical manifestations may be influenced by genetic and unknown environmental factors. Here we show that ALS-prone Sod1 transgenic (Sod1-Tg) mice have a pre-symptomatic, vivarium-dependent dysbiosis and altered metabolite configuration, coupled with an exacerbated disease under germ-free conditions or after treatment with broad-spectrum antibiotics. We correlate eleven distinct commensal bacteria at our vivarium with the severity of ALS in mice, and by their individual supplementation into antibiotic-treated Sod1-Tg mice we demonstrate that Akkermansia muciniphila (AM) ameliorates whereas Ruminococcus torques and Parabacteroides distasonis exacerbate the symptoms of ALS. Furthermore, Sod1-Tg mice that are administered AM are found to accumulate AM-associated nicotinamide in the central nervous system, and systemic supplementation of nicotinamide improves motor symptoms and gene expression patterns in the spinal cord of Sod1-Tg mice. In humans, we identify distinct microbiome and metabolite configurations-including reduced levels of nicotinamide systemically and in the cerebrospinal fluid-in a small preliminary study that compares patients with ALS with household controls. We suggest that environmentally driven microbiome-brain interactions may modulate ALS in mice, and we call for similar investigations in the human form of the disease.