Distribution Patterns of Pathogens Causing Lower Respiratory Tract Infection Based on Metagenomic Next-Generation Sequencing

Abstract

Purpose

Lower Respiratory Tract Infection (LRTI) is a leading cause of morbidity and mortality worldwide. In this study, the distribution patterns of causative pathogens in LRTI were evaluated within a city-level hospital by combining conventional microbiological tests (CMT) with metagenomic next-generation sequencing (mNGS).

Patients and Methods

This retrospective cohort study involved 160 patients suspected of having LRTI in a single center. Specimens, including bronchoalveolar lavage fluid (BALF), blood, tissue, sputum, and pus were utilized to identify pathogens. The seasonal prevalence of pathogens and co-pathogens involved in multiple infections was analyzed.

Results

A total of 137 patients with 156 samples were included in this study. Pseudomonas aeruginosa, Corynebacterium striatum, Klebsiella pneumoniae, Candida, and human herpesvirus were the top prevalent pathogens. We observed seasonal dynamic variation in the top prevalent bacteria (Pseudomonas aeruginosa and Klebsiella pneumoniae) and herpesvirus (Epstein-Barr virus and Human herpesvirus-7). The majority of patients had single bacterial infections, followed by instances of bacterial-viral co-infections, as well as mixed infections involving bacteria, fungi, and viruses. Notably, the spectrum of co-infecting pathogens was broader among the elderly population, and positive Spearman correlations were observed among these co-infecting pathogens.

Conclusion

Co-infections were prevalent among patients with LRTI, and the pathogens displayed distinct seasonal distribution patterns. The findings underscored the significance of comprehending pathogen distribution and epidemic patterns, which can serve as a basis for early etiological identification.

Keywords:

• metagenomic next-generation sequencing
• low respiratory tract infection
• co-pathogens
• seasonal prevalence

Acknowledgments

We would like to thank all the clinicians who contributed diagnostic data of patients to our study. Our thanks also go to Xiaojing Zhang, Yafeng Zheng and WillingMed Technology (Beijing) Co. for their technical support with mNGS.

Disclosure

The authors declare no conflicts of interest in this work.

Additional information

Funding

This work was supported by Hebei Province Medical Science Research Project plan (grant number 20231610).