bioRxivPathologyPreprint
The Pulmonary Microbial Signatures of Early Stages of Severe Infection in Children
Hao WuiD, Yinghu CheniD, Dengming LaiiD, Wei LiiD, Mingming ZhouiD, Fang Shu, Yuan Chen, Ping Guo, Yu WangiD, Xiaofeng CheniD, Xuan Nie, Ran TaoiD, Lingfeng MaoiD, Qiang ShuiD, Shiqiang Shang
Abstract
The respiratory tract related diseases are the main causes of death in children. Child pulmonary severe infection is the most important inducement for Respiratory tract related diseases. Therefore, it is necessary to understand the composition of the microbial community and the species correlation in children with severe pulmonary infection. In this study, the bronchoalveolar lavage fluid (BALF), blood and cerebrospinal fluid (CSF) samples of 782 children with severe infection in the early stage were systematically analyzed by amplicon-sequencing technology to reveal the distribution of microbial community and its clinical correlation. Results reveal significantly higher bacterial abundance in BALF than in blood/cerebrospinal fluid, with community structures shaped by tissue microenvironments, geographical disparities, age, and clinical symptoms. Neonatal BALF harbors simplified microbiota dominated by probiotics, while diversity increases with age, showing marked differences between 1-3 and 6-12-year-olds. Sepsis samples exhibit reduced microbial diversity, with Staphylococcus and Moraxella enrichments in the CST-4 cluster. Respiratory symptom progression correlates with microbiota succession from oral colonizers to respiratory pathogens. Third-generation sequencing-derived co-occurrence networks illustrate synergistic interactions among opportunistic pathogens, providing a basis for ecological targeted therapy. This study provides a key basis for breaking through the limitations of traditional diagnosis, establishing a precise diagnosis and treatment system based on third-generation sequencing, and developing ecological targeted treatment strategies.
- organism: human
- assay: metagenomics
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
- Citations
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