Schedule

Talk title: Computational methods for mining microbiome multi-omics data

Yuzhen Ye, Indiana University, Bloomington, Indiana, USA

Advances of experimental and computational techniques have enabled the study of microbiomes (communities of microorganisms) that are related to almost every aspect of human beings. We have been developing new algorithms and computational tools for microbiome research, to address arising computational demands and challenges, and to make new use of microbiome data. In this talk, I will focus on our recently developed approaches for analyzing metaproteomic data and demonstrate their use in the inference of gut microbial signatures that are likely expressed and are predictive of host phenotypes, and for studying the proteome of human-associated bacterial species. In addition, I will share our research on the CRISPR–Cas adaptive immune systems, focusing on the tools we developed for discovery and characterization of CRISPR–Cas systems using microbiome data.

Talk title: Personalized medicine based on microbiome and clinical data

Eran Segal, Weizmann Institute of Science, Israel

Accumulating evidence supports a causal role for the human gut microbiome in obesity, diabetes, metabolic disorders, cardiovascular disease, and numerous other conditions. I will present our research on the role of the human microbiome in health and disease, ultimately aimed at developing personalized medicine approaches that combine human genetics, microbiome, and nutrition.

In one project, we tackled the subject of personalization of human nutrition, using a cohort of over 1,000 people in which we measured blood glucose response to >50,000 meals, lifestyle, medical and food frequency questionnaires, blood tests, genetics, and gut microbiome. We showed that blood glucose responses to meals greatly vary between people even when consuming identical foods; devised the first algorithm for accurately predicting personalized glucose responses to food based on clinical and microbiome data; and showed that personalized diets based on our algorithm successfully balanced blood glucose levels in prediabetic individuals.

Using the same cohort, we also studied the set of metabolites circulating in the human blood, termed the serum metabolome, which contain a plethora of biomarkers and causative agents. With the goal of identifying factors that determine levels of these metabolites, we devised machine learning algorithms that predict metabolite levels in held-out subjects. We show that a large number of these metabolites are significantly predicted by the microbiome and unravel specific bacteria that likely modulate particular metabolites. These findings pave the way towards microbiome-based therapeutics aimed at manipulating circulating metabolite levels for improved health.

Finally, I will present a novel metagenome-wide association study (MWAS) framework that we devised for analyzing the microbiome at the level of single nucleotide polymorphisms (SNPs). When applied to a large-scale cohort of ~50,000 microbiome samples, we identified ~100,000 statistically significant associations between a bacterial SNP and several traits such as BMI, diabetes, and acute coronary syndrome. MWAS benefits from the functional redundancy of orthologous genes across different bacterial species, as they can provide independent validation of SNP associations. Indeed, we found several such cases, such as 227 non-synonymous SNPs in orthologs for beta-galactosidase across dozens of bacterial species that were associated with BMI and other host phenotypes. Overall, we demonstrate that microbiome SNPs and host health have numerous robust associations with large effect sizes, paving the way towards a better understanding of the mechanisms underlying microbiome-disease associations.