Dynamics and fermentation patterns of stool microbiota on simple carbon sources in in vitro batch cultures reveal dysbiosis in Crohn’s disease

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Abstract

Crohn’s disease (CD)-associated dysbiosis is characterized by reduced microbial diversity, depletion of short-chain fatty acid (SCFA) producers, especially butyrate-forming taxa, and altered metabolic profiles. This study examined whether CD-related dysbiosis is reflected in the fermentation behavior of stool-derived microbial communities cultured in vitro on simple carbon substrates (glucose or acetate+lactate). Shotgun metagenomics and metabolomics revealed that CD-derived communities produced lower levels of butyrate (mainly via lactate and acetate), valerate, caproate, and propionate, and higher levels of ethanol and certain amino acids compared to healthy controls. These metabolic alterations aligned with compositional shifts, including a loss of beneficial commensals (e.g., Coprococcus catus, Ruminococcus torques, Eubacterium rectale, Fusicatenibacter saccharivorans, and Faecalibacterium prausnitzii ) and an overrepresentation of CD-associated taxa, particularly Escherichia coli. Metabolic potential analysis revealed an enrichment of genes linked to ethanol and amino acid synthesis in CD-associated microbiotas, underscoring the metabolic adaptability of E. coli. Notably, acetate+lactate substrates supported the growth of healthy microbiota-associated bacteria, whereas glucose favored CD-associated taxa, highlighting disease-specific metabolic imbalances. These findings suggest that in vitro fermentation profiling may help distinguish CD-associated dysbiosis from a healthy microbiome and might support the development of microbiome-informed diagnostic or therapeutic approaches for CD.

A short abstract: CD-related dysbiosis alters stool microbial fermentation, reducing butyrate, propionate, valerate, caproate, and increasing ethanol/amino acids in vitro . Fermentation tests may help distinguish CD-associated patterns, suggesting potential for future microbiome-based diagnostics.

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