Back

Murine model of antibiotic-associated S. aureus gastrointestinal infections (SAGII) and colonization

Niamba, M.; Yang, S.; Blank, L.; Watanabe, L.; Heredia, E.; Abel-Santos, E.

2025-04-30 microbiology
10.1101/2025.04.30.651547 bioRxiv
Show abstract

BackgroundStaphylococcus aureus is an opportunistic pathogen that can both colonize the gastrointestinal tract and cause antibiotic associated diarrhea. MethodsTo develop a robust murine model for S. aureus gastrointestinal infection (SAGII) and colonization, mice were (a) treated with varying antibiotic regimes prior to infection, (b) infected with either a methicillin-sensitive S. aureus (MSSA) or a methicillin-resistant S. aureus (MRSA) strain, (c) challenged with different bacterial inocula, (d) tested for sexual dimorphism of SAGII virulence, and (e) tested for macronutrient effects on SAGII onset and virulence. ResultsWe found that antibiotic-treated male mice (but not female mice) were highly susceptible to even low inoculums of both an MSSA and an MRSA strains. Interestingly, male mice challenged with an MSSA strain showed more severe and more prolonged SAGII symptomatology than animals challenged with an MRSA strain. We also showed that for male mice a high-carbohydrate diet and a high-fat diet led to asymptomatic intestinal colonization followed by delayed SAGII sign onset. In contrast, male mice fed a high-protein diet started developing mild SAGII signs early but did not develop severe SAGII until two weeks post-challenge. Furthermore, only the high-protein diet sensitized female mice to SAGII, but their symptomatology remained less severe than in male mice. ConclusionsWe developed a robust murine model for antibiotic-associated S. aureus gastrointestinal infection and colonization. This model shows both sexual dimorphism and macronutrient preference for SAGII severity. Diet manipulation can also be used to establish S. aureus colonization of the GI tract.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.