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
Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, China.
Front Cell Infect Microbiol. 2025 Apr 22;15:1542562. doi: 10.3389/fcimb.2025.1542562. eCollection 2025.
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.
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.
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 ( < 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 ( < 0.05). Sequence similarity analysis further corroborated these findings, highlighting a notable homology among specific migrating pathogens.
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.
呼吸道微生物群在肺部疾病中的作用日益受到认可，呼吸道病原体的潜在迁移是一个重要的临床考量因素。尽管其重要性，但阐明这一现象的证据仍然匮乏。
这项前瞻性研究收集了疑似下呼吸道感染（LRTI）患者的临床样本，包括口咽拭子（OPS）、痰液和支气管肺泡灌洗液（BALF）。采用宏基因组下一代测序（mNGS）分析呼吸道微生物多样性，并辅以贝叶斯源追踪和序列比对分析来探索病原体迁移模式。
共纳入68例患者，其中56例诊断为LRTI，12例患有非感染性呼吸道疾病。感染组和非感染组之间呼吸道微生物群多样性存在统计学显著差异（<0.05）。有趣的是，在个体的不同呼吸道部位未检测到微生物群落结构的显著变化，包括α和β多样性。贝叶斯源追踪分析显示，与整体微生物群落相比，病原体之间存在明显的迁移模式，迁移率分别为51.54%和1.92%（<0.05）。序列相似性分析进一步证实了这些发现，突出了特定迁移病原体之间的显著同源性。
本研究是通过微生物源追踪分析推断病原体迁移模式的开创性工作。这些发现提供了新的见解，可能显著推进呼吸道感染的临床诊断和治疗策略。
