# Predicted functional alterations in colonic microbiota metabolism underlie ethanol consumption and preference behavior in mice

**Authors:** Mírian Velten Mendes, Thiago Cavalcante Lima, Mariana Siqueira Amormino, Jamil Silvano de Oliveira, Fernanda Lima Alvarenga Barroso, Gaëlle Boudry, Renato Elias Moreira‐Júnior, Ana Lúcia Brunialti‐Godard

PMC · DOI: 10.1111/acer.70165 · Alcohol, Clinical & Experimental Research · 2025-10-30

## TL;DR

Switching to a healthier diet increased alcohol preference in mice, linked to changes in gut bacteria and their metabolites, suggesting new targets for treating alcohol use disorder.

## Contribution

Shows diet-induced microbiota changes drive alcohol preference and identifies microbiota-derived metabolites as potential therapeutic targets for AUD.

## Key findings

- Switching to a healthier diet increased ethanol consumption and preference in mice.
- Microbiota changes included reduced amino acid metabolism and lower SCFA production.
- Network analysis linked microbiota shifts to oxidative stress and dopamine pathways.

## Abstract

Alcohol use disorder (AUD) is a complex condition affecting several body systems. Gut microbiota alterations, intestinal‐barrier disruption, and the consequent translocation of metabolites foster chronic inflammation, lower short‐chain fatty acid (SCFA) output, and depleted beneficial bacteria may contribute to transcriptional, epigenetic, and metabolic changes that influence ethanol preference.

Two experimental phases were used. T1 (8 weeks): mice received either the American Institute of Nutrition standard diet (AING) or a high‐sugar‐butter (HSB) diet. T2 (4 weeks): HSB animals switched to AING (SWITCH), while AING mice maintained the same diet. Each diet arm was split into ethanol (EtOH; free access to 10% ethanol) or H2O, generating four groups (AING + H2O, AING + EtOH, SWITCH + H2O, and SWITCH + EtOH). Sample processing involved colonic‐content collection, 16S rRNA sequencing, microbiome profiling, functional inference, metabolic‐network analysis, and SCFA/amino acid quantification.

SWITCH + EtOH mice displayed high ethanol consumption and preference, whereas AING + EtOH mice showed ethanol aversion. Their colonic microbiota differed markedly; amino acid metabolism fell, secondary bile acid synthesis rose, and SCFA production dropped in SWITCH + EtOH animals. Direct measurements confirmed significant reductions in butyrate, acetate, propionate, and selected amino acids. Network analysis revealed enrichment of bacterial metabolism, oxidative stress, and dopamine pathway genes.

Diet‐induced dysbiosis, reflected in shifts in microbiota‐derived metabolites, was associated with excessive alcohol intake; the metabolites identified can represent potential therapeutic targets for AUD.

Using a free‐choice ethanol model, we show that mice switched from a high‐sugar/butter diet to AIN93G standard diet consume and prefer more alcohol than mice maintained on AIN93G. 16S profiling and inferred metabolome revealed reduced amino acid metabolism, increased secondary bile acid pathways, and lower microbiota‐derived SCFAs and selected amino acids. Network analyses highlighted bacterial metabolism, oxidative stress, and dopamine‐related genes. These metabolite shifts link diet‐driven microbiota changes to excessive drinking and nominate microbiota‐derived metabolites as therapeutic targets for AUD.

## Linked entities

- **Chemicals:** ethanol (PubChem CID 702), butyrate (PubChem CID 104775), acetate (PubChem CID 175), propionate (PubChem CID 104745)
- **Species:** Mus musculus (taxon 10090)

## Full-text entities

- **Diseases:** AUD (MESH:D000437), chronic (MESH:D002908), inflammation (MESH:D007249)
- **Chemicals:** EtOH (MESH:D000431), acetate (MESH:D000085), AING (-), butyrate (MESH:D002087), SCFA (MESH:D005232), alcohol (MESH:D000438), amino acid (MESH:D000596), H2O (MESH:D014867), bile acid (MESH:D001647), dopamine (MESH:D004298), propionate (MESH:D011422)
- **Species:** Mus musculus (house mouse, species) [taxon 10090]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12638287/full.md

## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12638287/full.md

## References

68 references — full list in the complete paper: https://tomesphere.com/paper/PMC12638287/full.md

---
Source: https://tomesphere.com/paper/PMC12638287