Characteristics of the lung microbiota in lower respiratory tract infections with and without history of pneumonia

Abstract

Lung microbiota plays an important role in many diseases including lower respiratory tract infections (LRTI) and pneumonia. This study aimed to explore the effects of community-acquired pneumonia (CAP) on microbial diversity and identify potential biomarkers of respiratory tract in CAP LRTI patients. In the current study, a comprehensive bioinformatics analysis was performed based on metagenomic next-generation sequencing technology, followed by alpha and beta diversity, LEfSe, and co-occurrence network analysis, and random forest model construction. Our results showed that CAP dramatically influenced taxon abundance, and the significant differences in microbiota including Proteobacteria, Bacteroidetes, Euryarchaeota, Firmicutes and Spirochetes were observed at the phylum level. Co-occurrence network selected out novel modules involved in microbial proliferation-associated pathways. A random forest model screened Klebsiella pneumoniae and Bacillus cereus as potential diagnostic biomarkers with high AUC values. The microbial composition was different between CAP LRTI patients and non-CAP LRTI patients. Klebsiella pneumoniae and Bacillus cereus were strongly associated with increased severity of LRTI with a pneumonia history. Our findings provided an insight for a better understanding of community and structure of lung microbiota for future diagnosis and treatment in LRTI patients with a history of pneumonia. Moreover, these microbes were considered as potential biomarkers for predicting the risks for the treatment strategies of LRTI.

Microbial communities of the upper respiratory tract in mild and severe COVID-19 patients: a possible link with the disease course

“…The greatest difference was observed between CT4 and CT1-3 groups, where patients with СT4 had the lowest alpha diversity indices. Theoretically, this strong decrease in microbiota diversity could be related to inflammation in the airways and more frequent medication treatment of patients with severe pneumonia (Hong et al, 2021). Nevertheless, due to the restricted number of samples (n = 3) and the lack of information on drug treatment, we can neither confirm nor deny this assumption.…”

Exploring the pathogen diagnosis and prognostic factors of severe COVID-19 using metagenomic next-generation sequencing: A retrospective study

“…Incorrect preservation or handling increases the risk of decreased sensitivity and specificity. ( 18); (3) Some uncommon pathogens have only 1-3 strains, and the small sample size reduces the detection rate ( 19); (4) There is no unified standard for the sequencing process of mNGS, and different testing units use different reagent standards, and even DNA and RNA sequencing strategies may vary, which further affects the accuracy of the results (20,21); (5). The nucleic acid content of the specimen source can affect the relative abundance of pathogenic microorganisms detected by mNGS, reducing the sequencing sensitivity (22).…”

Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection

“…For example, LEfSe analysis identified the major genera in the pneumonia group as Pseudomonas, Corynebacterium, Roche, Enterococcus, and Neisseria (Woo et al, 2020). Random forest model selected pneumonia Klebsiella bacteria and Bacillus wax samples as community-acquired pneumonia patients with high AUC value potential diagnostic biomarkers (Hong et al, 2021). Here, we used LEfSe and random forest model respectively to further analyze the differentially expressed microorganisms between the two groups.…”