Integrative analyses identify opportunistic pathogens of patients with lower respiratory tract infections based on metagenomic next-generation sequencing

Tingyan Dong; Yueming Liang; Junting Xie; Wentao Fan; Haitao Chen; Xiaodong Han

doi:10.1016/j.heliyon.2024.e30896

Integrative analyses identify opportunistic pathogens of patients with lower respiratory tract infections based on metagenomic next-generation sequencing

Heliyon. 2024 May 8;10(10):e30896. doi: 10.1016/j.heliyon.2024.e30896. eCollection 2024 May 30.

Authors

Tingyan Dong^{1

2}, Yueming Liang³, Junting Xie³, Wentao Fan², Haitao Chen², Xiaodong Han^{1

4}

Affiliations

¹ Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing, China.
² Integrated Diagnostic Centre for Infectious Diseases, Guangzhou Huayin Medical Laboratory Center, Guangzhou, China.
³ Department of Respiratory and Critical Care Medicine, The First People Hospital of Foshan, Foshan, China.
⁴ Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Science, Medical School, Nanjing University, Nanjing, China.

Abstract

Lower respiratory tract infections (LRTIs) represent some of the most globally prevalent and detrimental diseases. Metagenomic next-generation sequencing (mNGS) technology has effectively addressed the requirement for the diagnosis of clinical infectious diseases. This study aimed at identifying and classifying opportunistic pathogens from the respiratory tract-colonizing microflora in LRTI patients using data acquired from mNGS analyses. A retrospective study was performed employing the mNGS data pertaining to the respiratory samples derived from 394 LRTIs patients. Linear discriminant analysis effect size (LEfSe) analysis was conducted to discern the discriminant bacteria. Receiver operating characteristic curves (ROC) were established to demonstrate discriminant bacterial behavior to distinguish colonization from infection. A total of 443 discriminant bacteria were identified and segregated into three cohorts contingent upon their correlation profiles, detection frequency, and relative abundance in order to distinguish pathogens from colonizing microflora. Among them, 119 emerging opportunistic pathogens (cohort 2) occupied an average area under the curve (AUC) of 0.976 for exhibiting the most prominent predictability in distinguishing colonization from infection, 39 were colonizing bacteria (cohort 1, 0.961), and 285 were rare opportunistic pathogens (cohort 3, 0.887). The LTRIs patients appeared modular in the form of cohorts depicting complex microbial co-occurrence networks, reduced diversity, and a high degree of antagonistic interactions in the respiratory tract microbiome. The study findings indicate that therapeutic interventions should target interaction networks rather than individual microbes, providing an innovative perspective for comprehending and combating respiratory infections. Conclusively, this study reports a profile of LRTIs-associated bacterial colonization and opportunistic pathogens in a relatively large-scale cohort, which might serve as a reference panel for the interpretation of mNGS results in clinical practice.

Keywords: Identification; Lower respiratory tract infections; Metagenomic next-generation sequencing; Opportunistic pathogens.