BMC MicrobiologyMicrobiologyPeer-reviewed journal article
Bacterial metabolic signatures in MASLD predicted through gene-centric studies in stool metagenomes
Juan Manuel Medina-MéndeziD, Paula IruzubietaiD, Raúl Fernández-LópeziD, Javier CrespoiD, Fernando de la CruziD
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a multifactorial condition influenced by the gut microbiome (GM). While previous studies have reported inconsistent associations between MASLD and key microbial clades using low-resolution 16S rRNA profiling, we employed high-resolution metagenomic sequencing and multi-marker taxonomic classification across three independent cohorts to identify robust microbial and functional signatures of MASLD. We consistently detected a depletion of Agathobacter rectalis, a known butyrate producer, in MASLD patients. Functionally, MASLD was characterized by a depletion of genes involved in butyrate and methane biosynthesis-particularly within the crotonyl-butyryl-CoA axis-alongside an enrichment of genes driving the production of endogenous alcohols such as ethanol and 1-propanol. Genes encoding these fermentative pathways, often organized in operons like pdu and tor, were more abundant in MASLD samples, indicating a potential shift toward alcohol-producing metabolism. These geno-metabolic changes were accompanied by a broader displacement of beneficial taxa and an increase in accessory gene content across the GM, underscoring the limitations of taxonomy-based disease associations. Many of the differentially abundant genes were also found on plasmids, suggesting that horizontal gene transfer contributes to strain-level metabolic variability relevant to MASLD progression. Our findings support a model in which GM-driven metabolic shifts-rather than taxonomic changes alone-play a central role in MASLD pathogenesis, highlighting the importance of functional and mobile genetic element (MGEs) profiling for uncovering mechanistic links between the microbiome and liver disease.
- organism: human
- assay: metagenomics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
- FWCI
- 1.49
- Field percentile
- top 17%
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