Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, Ministry of Education, Chongqing, China.
Chongqing Key Laboratory of Pediatrics, Chongqing, China.
Front Immunol. 2023 Jan 27;14:1087551. doi: 10.3389/fimmu.2023.1087551. eCollection 2023.
Predicting 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.
RNA-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.
Co-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.
These 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.
预测反复喘息（RW）的学龄前儿童中哪些会发展为学龄期哮喘（SA）具有挑战性，这突出表明需要阐明 RW 的发病机制以及 RW 和 SA 之间的机制关系。尽管存在共同的环境暴露和遗传决定因素，但 RW 和 SA 通常是分开研究的。基于鼻腔和气管转录组的网络分析，我们旨在确定 RW 和 SA 中趋同的转录组学机制。
从基因表达综合数据库（Gene Expression Omnibus）中获取鼻腔和气管刷检样本的 RNA 测序数据。结合单细胞转录组数据，使用细胞去卷积来推断气道内 18 种细胞成分的组成。进行共识加权基因共表达网络分析，以识别与 RW 和 SA 均密切相关的共识模块。通过富集分析探讨 RW 和 SA 之间共识模块的共同潜在途径。使用机器学习策略确定 RW 和 SA 之间的枢纽基因，并使用外部数据集和定量逆转录聚合酶链反应（qRT-PCR）进行验证。最后，使用鼻腔和气管转录组数据确定枢纽基因在定义 RW 亚群中的潜在价值。
共表达网络分析显示 RW 和 SA 在上、下呼吸道的转录网络存在相似性。细胞去卷积分析显示，与对照组相比，RW 和 SA 气道中的肥大细胞分数增加，而 club 细胞分数减少。共识网络分析确定了两个与 RW 和 SA 均高度相关的共识模块。两个共识模块的富集分析表明，脂肪酸代谢相关途径是 RW 和 SA 之间的共享关键信号。此外，机器学习策略确定了五个枢纽基因，即 CST1、CST2、CST4、POSTN 和 NRTK2，RW 和 SA 中上调的枢纽基因通过三个独立的外部数据集和 qRT-PCR 进行验证。五个枢纽基因的基因特征可能可用于确定 RW 学龄前儿童中 2 型（T2）高和 T2 低亚群。
这些发现提高了我们对 RW 分子发病机制的理解，并为进一步探索 RW 和 SA 之间的机制关系提供了依据。
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