Network analyses of upper and lower airway transcriptomes identify shared mechanisms among children with recurrent wheezing and school-age asthma

作者全名："Wang, Zhili; He, Yu; Li, Qinyuan; Zhao, Yan; Zhang, Guangli; Luo, Zhengxiu"

作者地址："[Wang, Zhili; He, Yu; Li, Qinyuan; Zhao, Yan] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Resp Med, Key Lab Child Dev & Disorders,Minist Educ,Children, Chongqing, Peoples R China; [Wang, Zhili; He, Yu; Li, Qinyuan; Zhao, Yan] Chongqing Key Lab Pediat, Chongqing, Peoples R China; [Zhang, Guangli; Luo, Zhengxiu] Chongqing Med Univ, Childrens Hosp, Dept Resp Med, Chongqing, Peoples R China"

通信作者："Luo, ZX (通讯作者)，Chongqing Med Univ, Childrens Hosp, Dept Resp Med, Chongqing, Peoples R China."

来源：FRONTIERS IN IMMUNOLOGY

ESI学科分类：IMMUNOLOGY

WOS号：WOS:000933034500001

JCR分区：Q1

影响因子：5.7

年份：2023

卷号：14

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：cell deconvolution; gene co-expression network; machine learning; recurrent wheezing; school-age asthma; scRNA-seq; RNA-seq

摘要："BackgroundPredicting which preschool children with recurrent wheezing (RW) will develop school-age asthma (SA) is difficult, highlighting the critical need to clarify the pathogenesis of RW and the mechanistic relationship between RW and SA. Despite shared environmental exposures and genetic determinants, RW and SA are usually studied in isolation. Based on network analysis of nasal and tracheal transcriptomes, we aimed to identify convergent transcriptomic mechanisms in RW and SA. MethodsRNA-sequencing data from nasal and tracheal brushing samples were acquired from the Gene Expression Omnibus. Combined with single-cell transcriptome data, cell deconvolution was used to infer the composition of 18 cellular components within the airway. Consensus weighted gene co-expression network analysis was performed to identify consensus modules closely related to both RW and SA. Shared pathways underlying consensus modules between RW and SA were explored by enrichment analysis. Hub genes between RW and SA were identified using machine learning strategies and validated using external datasets and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the potential value of hub genes in defining RW subsets was determined using nasal and tracheal transcriptome data. ResultsCo-expression network analysis revealed similarities in the transcriptional networks of RW and SA in the upper and lower airways. Cell deconvolution analysis revealed an increase in mast cell fraction but decrease in club cell fraction in both RW and SA airways compared to controls. Consensus network analysis identified two consensus modules highly associated with both RW and SA. Enrichment analysis of the two consensus modules indicated that fatty acid metabolism-related pathways were shared key signals between RW and SA. Furthermore, machine learning strategies identified five hub genes, i.e., CST1, CST2, CST4, POSTN, and NRTK2, with the up-regulated hub genes in RW and SA validated using three independent external datasets and qRT-PCR. The gene signatures of the five hub genes could potentially be used to determine type 2 (T2)-high and T2-low subsets in preschoolers with RW. ConclusionsThese findings improve our understanding of the molecular pathogenesis of RW and provide a rationale for future exploration of the mechanistic relationship between RW and SA."

基金机构："National Clinical Research Center for Child Health and Disorders (Children's Hospital of Chongqing Medical University, Chongqing, China) [NCRCCHD-2020-GP-05]; National Key Clinical Specialty Discipline Construction Program of China [2011-873]"

基金资助正文："The present study was supported by grants from National Clinical Research Center for Child Health and Disorders (Children's Hospital of Chongqing Medical University, Chongqing, China) (NCRCCHD-2020-GP-05) and National Key Clinical Specialty Discipline Construction Program of China (2011-873)."