Authors
Selim, M. K., Panadero Soler, D., De Santis, S., Bentez-Paez, A., Flor, A., Sanz, C., Mesquita, M., Cubero, F. J., Ciccociopo, R., Pertusa, A., Sanz, Y., Canals, S.
Abstract
Alcohol use disorder (AUD) disrupts the gut-liver-brain axis, yet mechanistically grounded and therapeutically actionable targets within this network remain poorly defined. To identify microbial modulators of alcohol-induced tissue pathology, longitudinal advanced diffusion MRI and fecal 16S rRNA profiling were integrated across Marchigian Sardinian alcohol-preferring rats evaluated at baseline, after four weeks of voluntary alcohol intake, and following six weeks of abstinence. Machine learning, specifically random forest models combining neuroimaging and microbiota data, improved phase classification and identified Akkermansia as the microbial feature most strongly associated with alcohol-related white matter microstructural abnormalities. Alcohol exposure induced widespread white matter alterations alongside gut dysbiosis characterized by reduced microbial diversity. To evaluate functional relevance, Akkermansia muciniphila was administered during the abstinence phase. Supplementation with A. muciniphila restored intestinal mucus, reduced liver injury markers, and elevated myelin basic protein levels within affected white matter regions. Collectively, these findings highlight Akkermansia as a critical modulator of alcohol-induced gut-liver-brain pathology and provide experimental support for a causal contribution of specific gut bacteria to persistent white matter damage in AUD. More broadly, this work establishes a robust multimodal framework for microbiome-based target discovery with clear translational relevance for disorders characterized by dysfunction along the gut-liver-brain axis.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 08 Aug 2026.
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