Abstract
BACKGROUND: The role of the respiratory microbiome in lung diseases is increasingly recognized, with the potential migration of respiratory pathogens being a significant clinical consideration. Despite its importance, evidence elucidating this phenomenon remains scarce. METHODS: This prospective study collected clinical samples from patients with suspected lower respiratory tract infections (LRTI), including oropharyngeal swabs (OPS), sputum, and bronchoalveolar lavage fluid (BALF). Metagenomic next-generation sequencing (mNGS) was employed to analyze respiratory microbial diversity, complemented by Bayesian source tracking and sequence alignment analyses to explore pathogen migration patterns. RESULTS: A cohort of 68 patients was enrolled, with 56 diagnosed with LRTI and 12 with non-infectious respiratory conditions. A statistically significant disparity in respiratory microbiome diversity was observed between infected and non-infected groups (p < 0.05). Intriguingly, no significant variations in microbial community structure, including alpha and beta diversity, were detected across different respiratory tract sites within individuals. The Bayesian source tracking analysis revealed a pronounced migration pattern among pathogens compared to the overall microbial community, with migration ratios of 51.54% and 1.92%, respectively (p < 0.05). Sequence similarity analysis further corroborated these findings, highlighting a notable homology among specific migrating pathogens. CONCLUSION: This study represents a pioneering effort in deducing pathogen migration patterns through microbial source tracking analysis. The findings provide novel insights that could significantly advance clinical diagnostics and therapeutic strategies for respiratory infections.