Evaluating deterministic strain interactions and stochasticity in GUT microbiomes using a novel model system

The National Institute of Diabetes and Digestive and Kidney Diseases awarded Emory University $100,228 on April 15, 2027, under the Diabetes, Digestive, and Kidney Diseases Extramural Research program (CFDA 93.847).

The research evaluates deterministic strain interactions and stochasticity in gut microbiomes using a specialized host-microbe model system pairing squash bugs (Anasa tristis) with their mutualistic gut bacterium (Caballeronia spp.). The project assesses how bacterial competition and neutral stochasticity influence microbiome assembly during gut colonization, maintenance, and transmission. Work includes culturing defined multi-strain bacterial communities and inoculating them into Caballeronia-free hosts to evaluate competitive and stochastic colonization patterns. The research examines how bacterial motility and biofilm production mediate strain interactions and competition in both naive and perturbed microbiomes, with the hypothesis that both selective forces from strain competition and neutral forces from stochastic processes drive microbiome simplification during establishment, maintenance, and following perturbations.

Performance occurs in Atlanta, Georgia, with a period of performance from April 15, 2027, through August 31, 2029. This is a Project Grant assistance type.

Abstract

Establishing and maintaining a healthy GUT microbiome is essential for human health, and disruptions to the GUT microbiome are associated with many disease states and can exacerbate chronic ailments. Human observational studies have identified correlative patterns between GUT microbial communities and host development and health. However, there is a lack of experimental evidence to establish deterministic links between bacterial interactions and stochasticity to these observed community states. I propose using a specialized host-microbe model system, squash bugs (anasa tristis) and their mutualistic GUT bacterium (caballeronia SPP.), to assess the impacts of bacterial competition and neutral stochasticity on microbiome assembly during GUT colonization, maintenance, and transmission. This is an exceptional model system to study these dynamics since caballeronia is the only genus that can successfully colonize the host midgut, whose crypts parallel mammalian macrovilli structures. Natural caballeronia strains exhibit variation in host-relevant phenotypes like motility and biofilm production, and these strains can be modified using established genetic tools and experimental evolution in vitro. Therefore, i can culture defined, multi-strain communities and inoculate them into caballeronia-free hosts to assess competitive and stochastic colonization patterns. I hypothesize that both selective forces arising from strain competition and neutral forces due to stochastic processes contribute to microbiome simplification during GUT microbiome establishment and maintenance throughout host development and following perturbations. Aim 1 will evaluate how bacterial motility and biofilm production mediate strain interactions and competition in naïve and disturbed microbiomes. I will evaluate the outcomes of these interactions in uncolonized and pre-colonized hosts following antibiotic administration to model dynamics in infant and dysbiotic microbiomes, respectively. Aim 2 will quantify stochasticity during microbiome establishment, maintenance, and transmission. I will generate a metabarcoded caballeronia library to track microbiome richness and evenness during initial colonization and host maturation. A. Tristis juveniles undergo GUT restructuring during development, which will serve as a model for infant GUT remodeling during early life. Then, i will assess microbiome similarity between adult hosts, their feces, and recipient hosts to evaluate the fidelity of fecal transfers in shifting target communities from donors to recipients. This tractable model system will permit the rigorous and reproducible elucidation of deterministic interactions and stochastic forces that underlie patterns of GUT microbiome assembly. This fundamental knowledge will inform existing observational and correlative data of GUT microbial communities and facilitate the translation of discoveries using model systems to improve clinical efforts to monitor and treat dysbiosis-related diseases.