Back

Community composition and strain identity drive metabolic competition and Staphylococcus aureus colonization resistance in Synthetic Nasal Communities

Navarro Diaz, M.; Camus, L.; Ham, S.; Angenent, L. T.; Heilbronner, S.; Stincone, P.; Rapp, J.; Petras, D.; Link, H.

2026-01-09 microbiology
10.64898/2026.01.08.698450 bioRxiv
Show abstract

The human nasal microbiome is a low-diversity ecosystem whose assembly principles and mechanisms of colonization resistance remain poorly understood. Staphylococcus aureus is a member of the nasal microbiome of some individuals with variable abundance. We hypothesized that nutritional competition, strain-level diversity, and nutrient availability shape community stability and the ability of commensal species to inhibit S. aureus. To test this, we constructed 50 defined synthetic communities composed of representative human nasal bacteria differing in strain and species composition and tracked their temporal dynamics, S. aureus growth, metabolic profiles, and nutritional interactions. The composition of synthetic communities with 5-10 species showed robust and reproducible dynamics and converged in one of three stable states. Synthetic communities dominated by a specific strain of Corynebacterium propinquum were highly stable and consistently excluded S. aureus. Growth curves and coculture assays showed that C. propinquum outcompetes S. aureus under poor nutritional conditions resembling the nasal environment, whereas S. aureus dominates in nutrient-rich conditions. Metabolomics analyses revealed that nutritional competition, including siderophores utilization and amino acid limitation, likely underlies this colonization resistance. These results establish a tractable synthetic community model for the human nasal microbiome and identify nutrient-dependent competition and microbial metabolite production as key drivers of community structure and pathogen exclusion.

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.