AUTHOR=Fang Ping , Wen Yanhua , Deng Wenjing , Liang Ruobing , He Ping , Wang Chunya , Fan Na , Huo Kaikai , Zhao Kaikai , Li Cong , Bai Ying , Ma Yuwan , Hu Long , Guan Yuanlin , Yang Shuanying 

TITLE=Investigation of dynamic microbial migration patterns in the respiratory tract

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1542562

DOI=10.3389/fcimb.2025.1542562

ISSN=2235-2988

ABSTRACT=BackgroundThe 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.MethodsThis 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.ResultsA 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.ConclusionThis 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.