Mathematical modeling from metagenomics - minimizing risk of enteric infections

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 DESCRIPTION: Enteric infections represent a critical issue in today's healthcare. Recent analysis of DNA sequencing data has demonstrated that such infections are associated with the prophylactic treatment with broad-spectrum antibiotics. This is due to their role in killing the native intestinal microbiota, which normally antagonizes pathogens. Computational analysis of these data should facilitate the optimization of antibiotic and fecal transplantation strategies. This is not yet possible because the currently used methods are based on correlations. The main goal of this project is to combine recently developed and novel mathematical modeling tools with metabolic pathways inference and experimentation to predict the risk of enteric diseases and to prototype rationally designed fecal transplantation therapies to minimize it. Leveraging on preliminary work, the PI and collaborators propose to: predict all the stable microbiota profiles mediating colonization by clinically-relevant enteric pathogens using 16S rRNA-constrained mathematical models; combine hazard regression modeling with microbiota dynamics predictions to evaluate the risk of enteric infections in hospitalized patients; determine microbial metabolic pathways associated with the interactions between native intestinal bacteria and enteric pathogens; prototype modeling-based fecal transplantation strategies by experimental validation of modeling predictions. The design of rational therapies minimizing the incidence of enteric diseases depends on our understanding of the dynamics regulating the intestinal microbiota. For this reason, the proposed research is timely and relevant to the mission of the NIAID. The application of new predictive models to DNA sequencing data from a large population of hospitalized patients will allow identifying microbiota states with probiotic (and dysbiotic) properties to be targeted by therapies. The forecasting of microbial dynamics, combined with novel statistical models based on risk analysis, will deliver the first computational tool for monitoring the risk of enteric diseases in quasi-real time. The application of metabolic reconstruction methods to the mathematical modeling predictions will provide new insights about potential metabolic mechanisms regulating and responsible for the predicted stability of pathogen-refractory and compatible stable steady states. The experimental validation of the modeling predictions, not only will allow evaluating the predictive power of the developed mathematical frameworks, but also will provide the opportunity to test the efficacy of the proposed rationally designed fecal transplantation strategies.