Mathematical ecology models of host-microbiota interaction in auto microbiota transplants (auto-FMT)

Sloan-Kettering Inst Can Research, New York NY

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Abstract

Project Summary Our goal is to develop a mathematical model for the rational design of microbiota transplants to restore compositional diversity and function to the damaged microbiota of antibiotic-treated patients. We will focus on hospitalized cancer patients receiving allogeneic hematopoietic stem cell transplants (allo-HSCT)?a potentially curative cancer treatment that is highly immune compromising?who must receive massive antibiotic treatments to prevent and treat life-threatening infections. We will build on a vast clinical database, in vitro experiments in bioreactors and in vivo experiments with mice to develop a predictive mathematical model of microbial composition dynamics and its interactions with the host immune system. The model expands the approach pioneered by our team: the Generalized Lotka Volterra Ecological Regression (GLOVER). In aim 1 we will parameterize GLOVER using data from a unique clinical resources available at the Memorial Sloan Kettering Cancer center?a database with >1,000 allo-HSCT patient microbiota data and its associated clinical metadata; we will also use data from a first-of-its-kind controlled randomized trial of an autologous fecal microbiota transplant (auto-FMT) undergoing in allo-HSCT patients. We will use these data to parameterize a new model to investigate how the compositions of the microbiota influence the recovery of the host immune system. In aim 2 we will validate the microbial component of our mathematical model using experimental data from anaerobic laboratory reactors that recreate?in vitro?the human microbiota dynamics during antibiotic treatment and auto-FMT in the absence of a living host. In aim 3 we will develop mouse models to investigate those same microbiota dynamics experimentally but now in the context of a living host. The data coming from clinical samples, in vitro experiments and in vivo models will refine GLOVER in close cycles of mathematical modeling and quantitative experimentation. Our ultimate goal is to use GLOVER to define optimal microbial cocktails for precision-reconstitution of microbiota composition in allo-HSCT patients; in the process, we hope to uncover general principles of microbiota ecology for future therapies in other patient populations whose microbiota is damaged by antibiotic treatments.