Dynamics of microbial populations are critical for human health. Auto-inflammatory disease, colitis, sepsis and obesity are all dysbiotic conditions that depend on the interactions between microbes (pathogenic and beneficial), the host, and the surrounding environment. As the traditional drug-based treatment to cure these conditions is ineffective due to selection for resistance and disruption of the healthy balance in the resident intestinal flora, it is necessary to determine targeted therapies that effectively cure these diseases while minimizing side effects. The goal of my research program is to develop new therapies that, by exploiting the ecological and evolutionary processes underlying these microbes’ dynamics, cure or prevent intestinal diseases. We achieve this by combining our expertise at the interface between engineering, systems biology and microbiology, with that in immunology, medicine and infectious disease from our collaborators. Below are brief descriptions of project undergoing in the laboratory.
Mathematical modeling from Metagenomics. Minimizing risk of enteric infections.
The main goal of this NIH-NIAID (http://grantome.com/grant/NIH/R15-AI112985-01A1) funded project is to combine by us 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 our preliminary and current work (on microbiome dynamics inference and prediction, see Stein et al., 2013; Bucci and Xavier, 2014 and Bucci et al. 2016 – In review), we (1) predict all the stable microbiota profiles mediating colonization by clinically–relevant enteric pathogens using 16S rRNA-constrained mathematical models; (2) combine hazard regression modeling with microbiota dynamics predictions to evaluate the risk of enteric infections in hospitalized patients; (3) determine microbial metabolic pathways associated with the interactions between native intestinal bacteria and enteric pathogens; (4) prototype modeling-based fecal transplantation strategies by experimental validation of modeling predictions.
A new computational framework for the prediction of microbiome dynamics
The goal of this NSF-ABI funded research (http://www.nsf.gov/awardsearch/showAward?AWD_ID=1458347) is to deliver new theoretical methods and computational software to forecast environmental and host-associated microbiomes’ dynamics that are constrained on sequencing surveys. We work for the delivery of scalable, open-source, freely downloadable and upgradable MATLAB and Python-based software that can be broadly used by the microbiome research community. Method testing and validation represents a major part of the proposed studies within this project. We benchmark our framework predictions against ground-truth data obtained from in silico and in vitro microbial ecosystem of known ecological structure.
Modulation of gut microbiota by extrathymic regulatory T cells.
This project investigates the effect of extrathymically generated regulatory T cells on the composition of the commensal microbiota, specifically on bacterial species carrying key metabolic and immunological functions that allow for the maintenance of the intestinal ecosystem. Specifically, the aims of the project are: to assess the effects of pTreg depletion on the microbial community and on production of metabolites by the commensal flora, to infer causal relations underlying microbiome response to pTreg modulation and to characterize the immune-modulatory properties of bacteria affected by pTreg cell ablation and how they relate to microbial fitness within the host. In this project, my group leads the entire bioinformatics, statistical and modeling analysis in order to determine intestinal microbiome representatives and functions that are under selection in response to pTregs modulation.
Predicting post-perturbation recovery from temporal microbiome surveys.
This project investigates the development and the application of new theory inspired by statistical physics to problems in microbiome biology. This theory aims to provide us with the ability of predicting microbiome’s recovery time following perturbation as a function of microbial interactions and of the role of a stochastic external environment (Bucci et al., 2012a). To test the validity of our theoretical work we are creating minimal ecosystems of interacting microbes that lead to predictable temporal dynamics. These systems are experimentally investigated using micro-chemostats and monitored over time with time-lapse fluorescence microscopy.
Leveraging evolution to develop therapeutics against enteropathogenic E. coli
Enteropathogenic Escherichia coli (EPEC) is a highly virulent bacterial pathogen, responsible for life-threatening complications upon ingestion of contaminated food or water. Critical to the success of this pathogen is the ability to colonize the host, which includes passing through a very acidic stomach, and being able to competing against the resident flora. Antibiotic treatment to clear EPEC is under debate as they increase the risk of clinical complications. Therefore, a primary objective is to develop therapeutics alternative to antibiotics targeting pathogen’s ability to colonize the host. Direct alteration of genes involved in colonization is a possible approach. However, as this involves a large reduction in pathogen’s fitness it also increases the likelihood of fixation of compensatory mutations. Building on a set of recently-collected preliminary data from experimental evolution assays, whole-genome sequencing, microbial competition experiments and protein modeling, we propose to solve this problem by leveraging those ecological and evolutionary traits that reduce this pathogen’s host colonization ability but that are simultaneously under selection in not-host secondary environments.