Transitions in lung microbiota landscape associate with distinct patterns of pneumonia progression.

Summary

Using integrated 16S, metagenomic, metatranscriptomic, and bacterial-load data from bronchoscopic samples, this study defines dynamic lung microbiota states that predict pneumonia subtypes and therapy responses. Aspiration emerges as a disruptive ecological force linked to coordinated shifts in microbial gene expression and community structure.

Key Findings

• Integrated multi-omics and bacterial-load quantification delineated dynamic lung microbiota states across pneumonia course.
• Microbiota states predicted pneumonia subtypes and showed differential stability and therapy responses.
• Aspiration was associated with cohesive shifts in microbial gene expression and community structure.

Clinical Implications

Microbiome state profiling could complement diagnostics to classify pneumonia subtypes, inform aspiration-targeted care, and anticipate therapy response, potentially optimizing antibiotics and supportive strategies.

Limitations

• Observational design limits causal inference regarding microbiota changes and outcomes.
• External validation and standardized sampling under varying antibiotic exposures are needed.

Future Directions

Prospective validation of microbiome state classifiers, interventional studies targeting aspiration or dysbiosis, and integration with host omics to build actionable decision-support tools.