Metagenome-scale Modeling to Assess Microbiome Metabolic Complementarity for Precision Microbiota Transplantation Therapies

Abstract

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable, in part because existing computational models do not fully capture the metabolic compatibility between donor and recipient communities. Here, we present a metagenome-scale metabolic modeling framework that quantifies metabolic niche complementarity between donor and recipient microbiomes to predict transplantation outcomes. Using MICOM-derived community metabolic models, we show that donor taxa whose metabolic flux profiles are more dissimilar from the recipient community engraft at significantly higher rates in both murine and human FMT cohorts. In a human IBS trial, metabolic models accurately predicted post-FMT community composition via leave-one-out cross-validation and recapitulated disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to demonstrate a platform for personalized donor screening. This screen identified super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition, as quantified by a flux-derived ecological network balance index that strongly predicted engraftment potential. This framework provides a mechanistic, scalable tool for rational donor-recipient matching that could guide personalized microbiome-based therapies.