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Fecal Microbiota Transplantation / Bacterial Therapeutics / Irritable Bowel Syndrome · Journal article
Gut Microbes · September 2, 2026
Raises a question worth testing. It does not answer one.
This is a mechanistic, hypothesis-generating study using metagenome-scale metabolic modeling (MICOM) to propose that donor–recipient metabolic niche complementarity predicts post-FMT microbial colonization and community composition. The authors validate the concept in a murine FMT model and an observational human IBS cohort using cross-validation, then conduct 2,548 in silico simulations to identify donor characteristics associated with successful engraftment, but do not report prospective clinical trials or clinical outcome data.
Computational metagenome-scale metabolic modeling with experimental validation in murine model and observational human IBS cohort. Murine FMT model; human IBS patients (IBS-D/M subtype) in observational trial; in silico analysis used OpenBiome donor biobank samples.. Intervention: Fecal microbiota transplantation (donor microbiota transfer) guided by computational prediction of metabolic niche complementarity.. Compared with: No explicit control; murine model and human trial appear to be single-arm or observational; in silico simulations used cross-validation but no alternative prediction method was compared.. Not explicitly stated; OpenBiome is a US-based nonprofit biobank; affiliations listed are USA and Hong Kong..
Donor genomes with metabolic flux profiles more dissimilar from recipient community colonize at significantly higher rates in murine FMT model Metabolic dissimilarity metric predicted post-FMT community composition in human IBS trial via leave-one-out cross-validation and captured disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism In silico screening of 2,548 FMT simulations between IBS-D/M patients and OpenBiome donors identified super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and cross-feeding–dominated interaction networks
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If validated prospectively, this framework could enable personalized donor selection for FMT based on metabolic compatibility rather than empirical trial-and-error. However, current evidence is computational and observational; clinical utility remains unproven and requires prospective validation in randomized trials.
Mechanistic modeling study using computational predictions and limited empirical validation (murine model and observational human trial) to propose metabolic niche complementarity as a predictor of FMT outcomes; results are not yet confirmatory in clinical practice.
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If validated prospectively, this framework could enable personalized donor selection for FMT based on metabolic compatibility rather than empirical trial-and-error. However, current evidence is computational and observational; clinical utility remains unproven and requires prospective validation in randomized trials.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known 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 evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.
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