Computer Science, Weizmann Institute
Tuesday 24th November 2020
Title of presentation: Personalized medicine based on gut microbiome and clinical data
Abstract: 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 an algorithm that we devised for identifying variability in microbial sub-genomic regions. We find that such Sub-Genomic Variation (SGV) are prevalent in the microbiome across multiple microbial phyla, and that they are associated with bacterial fitness and their member genes are enriched for CRISPR-associated and antibiotic producing functions and depleted from housekeeping genes. We find over 100 novel associations between SGVs and host disease risk factors and uncover possible mechanistic links between the microbiome and its host, demonstrating that SGVs constitute a new layer of metagenomic information.
Biography: Eran Segal is a Professor at the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, heading a lab with a multi-disciplinary team of computational biologists and experimental scientists in the area of Computational and Systems biology. His group has extensive experience in machine learning, computational biology, and analysis of heterogeneous high-throughput genomic data. His research focuses on Microbiome, Nutrition, Genetics, and their effect on health and disease. His aim is to develop personalized medicine based on big data from human cohorts.
Prof. Segal published over 150 publications, and received several awards and honors for his work, including the Overton prize, awarded annually by the International Society for Bioinformatics (ICSB) to one scientist for outstanding accomplishments in computational biology, and the Michael Bruno award. He was also elected as an EMBO member and as a member of the young Israeli academy of science.
Before joining the Weizmann Institute, Prof. Segal held an independent research position at Rockefeller University, New York.
Education: Prof. Segal was awarded a B.Sc. in Computer Science summa cum laude in 1998, from Tel-Aviv University, and a Ph.D. in Computer Science and Genetics in 2004, from Stanford University.