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Hydrogen sulfide (H2S) produced by human gut microbiota is highly toxic and implicated in pathogenesis of gastrointestinal tract disorders. Sulfur-containing amino acid (SAA) degradation is a major contributor to its production, but SAA degradation pathways have not been extensively characterized. In vitro model systems of fecal H2S production offer a basic method to help elucidate SAA degradation pathways, but the approach is not standardized. To address this, we used fecal microbiota separated from feces and standardized for bacterial counts to measure H2S production potential in response to different substrates in healthy controls (n = 6) with repeated sampling (three samples per participant). H2S production was highest with cysteine (mean = 16.7 ppm) compared to sodium sulfate (0.7 ppm) and taurine (0.8 ppm). Sodium-sulfate-driven H2S production negatively correlated with Ruminococcus (Spearman’s ρ = −0.5) and cysteine-driven H2S production negatively correlated with Firmicutes (Spearman’s ρ = −0.5). These findings, using a protocol controlling for confounding variables such as bacterial counts, validate previous findings of cysteine as a primary driver of H2S production. Finally, the inclusion of samples from two patients with UC allowed for the illustration of the potential of this approach to identify functional differences in specific disease states.

Abstract Recent advancements in translational gut microbiome research have revealed its crucial role in shaping predictive healthcare applications. Herein, we introduce the Gut Microbiome Wellness Index 2 (GMWI2), an enhanced version of our original GMWI prototype, designed as a standardized disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involves pooling existing 8069 stool shotgun metagenomes from 54 published studies across a global demographic landscape (spanning 26 countries and six continents) to identify gut taxonomic signals linked to disease presence or absence. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the “reject option”). This performance exceeds that of the original GMWI model and traditional species-level α-diversity indices, indicating a more robust gut microbiome signature for differentiating between healthy and non-healthy phenotypes across multiple diseases. When assessed through inter-study validation and external validation cohorts, GMWI2 maintains an average accuracy of nearly 75%. Furthermore, by reevaluating previously published datasets, GMWI2 offers new insights into the effects of diet, antibiotic exposure, and fecal microbiota transplantation on gut health. Available as an open-source command-line tool, GMWI2 represents a timely, pivotal resource for evaluating health using an individual’s unique gut microbial composition.