Dietary Fiber-Microbiome Interactions: Elucidating Mechanisms to Suppress Multi-Drug Resistant Organisms in the Human Gut

About This Grant

Asymptomatic carriage of MDROs increases the risk of infection for the carrier and members of the community to whom they may transmit the organism. Due to the increased incidence of community-acquired MDRO infections, there is a pressing need to identify factors influencing MDRO colonization. While diet has been shown to modulate the dynamics and metabolite profiles of human gut microbiota, we lack a detailed and quantitative understanding of how specific dietary factors and human gut microbiome impact MDRO carriage. By integrating a longitudinal human study coupled to a novel sequencing-based approach to elucidate dietary species, advanced computational modeling, high-throughput construction of human gut communities and germ-free mouse experiments, will revolutionize our understanding of the multi-scale fiber-dependent interaction networks shaping MDRO (Clostridioides difficile, vancomycin-resistant Enterococci, third generation cephalosporin- resistant Enterobacteriaceae, and carbapenem-resistant Enterobacterales) fitness and colonization of the mammalian gut. In Aim 1, we will perform a prospective, longitudinal study of community-based participants to elucidate the mappings between diet, human gut microbiome taxa, microbial pathways and MDRO carriage. We will go beyond food intake self-reporting which is limited by systematic biases, to track human diet using a DNA sequencing methodology referred to as FoodSeq. Leveraging the longitudinal data, dynamic computational modeling will reveal microbe-microbe interaction networks across individuals. To identify the key dietary fibers and human gut species shaping MDRO colonization, we will use a high-throughput and automated human gut community culturing pipeline. The species identities as well as the specific combinations of these species will be selected using a novel microbial genome-to-function deep machine learning model (data-driven Community Genotype-Function or dCGF) that predicts MDRO abundance as a function of dietary fibers and the genetic features of constituent community members. Using this expanded design-test-learn (E-DTL) approach, we will identify combinations of species and dietary fibers that significantly influence MDROs fitness in human gut communities. Analysis of the model using explainable artificial intelligence techniques will reveal genes/pathways within constituent community members that impact MDRO fitness, providing mechanistic insights beyond the taxonomic level. In Aim 3, we will use dCGF to design robust and maximally inhibitory or enhancing species- fiber combinations for characterization in a murine C. difficile model. We will evaluate the ability of these designed species-fiber combinations to decolonize C. difficile from the murine gut. Overall, this proposed research will provide critical data and models across multiple scales to understand the interplay between diet, the gut microbiome, and MDRO carriage. Our results will inform future interventions aimed at reducing MDRO carriage, transmission, and infections in the community. Finally, this systems biology framework will be generalizable to study other bacterial pathogens, environmental factors, and microbiome functions.