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

BACKGROUND: Lung infection is a global health problem associated with high morbidity and mortality and increasing rates of hospitalization. The correlation between pulmonary microecology and infection severity remains unclear. Therefore, the purpose of this study was to investigate the differences i...

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Autores principales: Zhan, Danting, Li, Dan, Yuan, Ke, Sun, Yihua, He, Lijuan, Zhong, Jiacheng, Wang, Lingwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602873/
https://www.ncbi.nlm.nih.gov/pubmed/37900322
http://dx.doi.org/10.3389/fcimb.2023.1227581
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author Zhan, Danting
Li, Dan
Yuan, Ke
Sun, Yihua
He, Lijuan
Zhong, Jiacheng
Wang, Lingwei
author_facet Zhan, Danting
Li, Dan
Yuan, Ke
Sun, Yihua
He, Lijuan
Zhong, Jiacheng
Wang, Lingwei
author_sort Zhan, Danting
collection PubMed
description BACKGROUND: Lung infection is a global health problem associated with high morbidity and mortality and increasing rates of hospitalization. The correlation between pulmonary microecology and infection severity remains unclear. Therefore, the purpose of this study was to investigate the differences in lung microecology and potential biomarkers in patients with mild and severe pulmonary infection. METHOD: Patients with pulmonary infection or suspected infection were divided into the mild group (140 cases) and the severe group (80 cases) according to pneomonia severity index (PSI) scores. Here, we used metagenomic next-generation sequencing (mNGS) to detect DNA mainly from bronchoalveolar lavage fluid (BALF) collected from patients to analyze changes in the lung microbiome of patients with different disease severity. RESULT: We used the mNGS to analyze the pulmonary microecological composition in patients with pulmonary infection. The results of alpha diversity and beta diversity analysis showed that the microbial composition between mild and severe groups was similar on the whole. The dominant bacteria were Acinetobacter, Bacillus, Mycobacterium, Staphylococcus, and Prevotella, among others. Linear discriminant analysis effect size (LEfSe) results showed that there were significant differences in virus composition between the mild and severe patients, especially Simplexvirus and Cytomegalovirus, which were prominent in the severe group. The random forest model screened 14 kinds of pulmonary infection-related pathogens including Corynebacterium, Mycobacterium, Streptococcus, Klebsiella, and Acinetobacter. In addition, it was found that Rothia was negatively correlated with Acinetobacter, Mycobacterium, Bacillus, Enterococcus, and Klebsiella in the mild group through co-occurrence network, while no significant correlation was found in the severe group. CONCLUSION: Here, we describe the composition and diversity of the pulmonary microbiome in patients with pulmonary infection. A significant increase in viral replication was found in the severe group, as well as a significant difference in microbial interactions between patients with mild and severe lung infections, particularly the association between the common pathogenic bacteria and Rothia. This suggests that both pathogen co-viral infection and microbial interactions may influence the course of disease. Of course, more research is needed to further explore the specific mechanisms by which microbial interactions influence disease severity.
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spelling pubmed-106028732023-10-28 Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection Zhan, Danting Li, Dan Yuan, Ke Sun, Yihua He, Lijuan Zhong, Jiacheng Wang, Lingwei Front Cell Infect Microbiol Cellular and Infection Microbiology BACKGROUND: Lung infection is a global health problem associated with high morbidity and mortality and increasing rates of hospitalization. The correlation between pulmonary microecology and infection severity remains unclear. Therefore, the purpose of this study was to investigate the differences in lung microecology and potential biomarkers in patients with mild and severe pulmonary infection. METHOD: Patients with pulmonary infection or suspected infection were divided into the mild group (140 cases) and the severe group (80 cases) according to pneomonia severity index (PSI) scores. Here, we used metagenomic next-generation sequencing (mNGS) to detect DNA mainly from bronchoalveolar lavage fluid (BALF) collected from patients to analyze changes in the lung microbiome of patients with different disease severity. RESULT: We used the mNGS to analyze the pulmonary microecological composition in patients with pulmonary infection. The results of alpha diversity and beta diversity analysis showed that the microbial composition between mild and severe groups was similar on the whole. The dominant bacteria were Acinetobacter, Bacillus, Mycobacterium, Staphylococcus, and Prevotella, among others. Linear discriminant analysis effect size (LEfSe) results showed that there were significant differences in virus composition between the mild and severe patients, especially Simplexvirus and Cytomegalovirus, which were prominent in the severe group. The random forest model screened 14 kinds of pulmonary infection-related pathogens including Corynebacterium, Mycobacterium, Streptococcus, Klebsiella, and Acinetobacter. In addition, it was found that Rothia was negatively correlated with Acinetobacter, Mycobacterium, Bacillus, Enterococcus, and Klebsiella in the mild group through co-occurrence network, while no significant correlation was found in the severe group. CONCLUSION: Here, we describe the composition and diversity of the pulmonary microbiome in patients with pulmonary infection. A significant increase in viral replication was found in the severe group, as well as a significant difference in microbial interactions between patients with mild and severe lung infections, particularly the association between the common pathogenic bacteria and Rothia. This suggests that both pathogen co-viral infection and microbial interactions may influence the course of disease. Of course, more research is needed to further explore the specific mechanisms by which microbial interactions influence disease severity. Frontiers Media S.A. 2023-10-12 /pmc/articles/PMC10602873/ /pubmed/37900322 http://dx.doi.org/10.3389/fcimb.2023.1227581 Text en Copyright © 2023 Zhan, Li, Yuan, Sun, He, Zhong and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Zhan, Danting
Li, Dan
Yuan, Ke
Sun, Yihua
He, Lijuan
Zhong, Jiacheng
Wang, Lingwei
Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
title Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
title_full Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
title_fullStr Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
title_full_unstemmed Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
title_short Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
title_sort characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602873/
https://www.ncbi.nlm.nih.gov/pubmed/37900322
http://dx.doi.org/10.3389/fcimb.2023.1227581
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