Bacterial SNPs in the human gut microbiome associate with host BMI.

Genome-wide association studies (GWASs) have provided numerous associations between human single-nucleotide polymorphisms (SNPs) and health traits. Likewise, metagenome-wide association studies (MWASs) between bacterial SNPs and human traits can suggest mechanistic links, but very few such studies have been done thus far. In this study, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI), from which we distilled 40 independent associations. Most of these associations were unexplained by diet, medications or physical exercise, and 17 replicated in a geographically independent cohort. We uncovered BMI-associated SNPs in 27 bacterial species, and 12 of them showed no association by standard relative abundance analysis. We revealed a BMI association of an SNP in a potentially inflammatory pathway of Bilophila wadsworthia as well as of a group of SNPs in a region coding for energy metabolism functions in a Faecalibacterium prausnitzii genome. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward improved understanding of interpersonal microbiome differences and their potential health implications.

Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines.

Diabetes and associated comorbidities are a global health threat on the rise. We conducted a six-month dietary intervention in pre-diabetic individuals (NCT03222791), to mitigate the hyperglycemia and enhance metabolic health. The current work explores early diabetes markers in the 200 individuals who completed the trial. We find 166 of 2,803 measured features, including oral and gut microbial species and pathways, serum metabolites and cytokines, show significant change in response to a personalized postprandial glucose-targeting diet or the standard of care Mediterranean diet. These changes include established markers of hyperglycemia as well as novel features that can now be investigated as potential therapeutic targets. Our results indicate the microbiome mediates the effect of diet on glycemic, metabolic and immune measurements, with gut microbiome compositional change explaining 12.25% of serum metabolites variance. Although the gut microbiome displays greater compositional changes compared to the oral microbiome, the oral microbiome demonstrates more changes at the genetic level, with trends dependent on environmental richness and species prevalence in the population. In conclusion, our study shows dietary interventions can affect the microbiome, cardiometabolic profile and immune response of the host, and that these factors are well associated with each other, and can be harnessed for new therapeutic modalities.

An expanded reference map of the human gut microbiome reveals hundreds of previously unknown species.

The gut is the richest ecosystem of microbes in the human body and has great influence on our health. Despite many efforts, the set of microbes inhabiting this environment is not fully known, limiting our ability to identify microbial content and to research it. In this work, we combine new microbial metagenomic assembled genomes from 51,052 samples, with previously published genomes to produce a curated set of 241,118 genomes. Based on this set, we procure a new and improved human gut microbiome reference set of 3594 high quality species genomes, which successfully matches 83.65% validation samples' reads. This improved reference set contains 310 novel species, including one that exists in 19% of validation samples. Overall, this study provides a gut microbial genome reference set that can serve as a valuable resource for further research.

A Prediction Model to Prioritize Individuals for a SARS-CoV-2 Test Built from National Symptom Surveys.

BACKGROUND: The gold standard for COVID-19 diagnosis is detection of viral RNA through PCR. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. METHODS: We devised a model estimating the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions that have been associated with SARS-CoV-2 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel in its first 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. FINDINGS: Our model was validated on a held-out set of individuals from Israel where it achieved an auROC of 0.737 (CI: 0.712-0.759) and auPR of 0.144 (CI: 0.119-0.177) and demonstrated its applicability outside of Israel in an independently collected symptom survey dataset from the US, UK, and Sweden. Our analyses revealed interactions between several symptoms and age, suggesting variation in the clinical manifestation of the disease in different age groups. CONCLUSIONS: Our tool can be used online and without exposure to suspected patients, thus suggesting worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified. Moreover, individuals at high risk for a positive test result can be isolated prior to testing. FUNDING: E.S. is supported by the Crown Human Genome Center, Larson Charitable Foundation New Scientist Fund, Else Kroener Fresenius Foundation, White Rose International Foundation, Ben B. and Joyce E. Eisenberg Foundation, Nissenbaum Family, Marcos Pinheiro de Andrade and Vanessa Buchheim, Lady Michelle Michels, and Aliza Moussaieff and grants funded by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation. H.R. is supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and by a research grant from Madame Olga Klein - Astrachan.