Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Health Sciences, University of Yamanashi, Chuo, Yamanashi, Japan.
NPJ Syst Biol Appl. 2024 Aug 22;10(1):93. doi: 10.1038/s41540-024-00420-x.
Bronchiolitis is the leading cause of infant hospitalization. However, the molecular networks driving bronchiolitis pathobiology remain unknown. Integrative molecular networks, including the transcriptome and metabolome, can identify functional and regulatory pathways contributing to disease severity. Here, we integrated nasopharyngeal transcriptome and metabolome data of 397 infants hospitalized with bronchiolitis in a 17-center prospective cohort study. Using an explainable deep network model, we identified an omics-cluster comprising 401 transcripts and 38 metabolites that distinguishes bronchiolitis severity (test-set AUC, 0.828). This omics-cluster derived a molecular network, where innate immunity-related metabolites (e.g., ceramides) centralized and were characterized by toll-like receptor (TLR) and NF-κB signaling pathways (both FDR < 0.001). The network analyses identified eight modules and 50 existing drug candidates for repurposing, including prostaglandin I analogs (e.g., iloprost), which promote anti-inflammatory effects through TLR signaling. Our approach facilitates not only the identification of molecular networks underlying infant bronchiolitis but the development of pioneering treatment strategies.
毛细支气管炎是婴儿住院的主要原因。然而，驱动毛细支气管炎发病机制的分子网络仍然未知。整合的分子网络，包括转录组和代谢组，可以识别有助于疾病严重程度的功能和调节途径。在这里，我们整合了 397 名在 17 个中心进行的前瞻性队列研究中患有毛细支气管炎的婴儿的鼻咽转录组和代谢组数据。使用可解释的深度网络模型，我们确定了一个包含 401 个转录本和 38 个代谢物的组学聚类，可区分毛细支气管炎的严重程度（测试集 AUC，0.828）。该组学聚类衍生出一个分子网络，其中先天免疫相关的代谢物（例如神经酰胺）集中，并以 Toll 样受体（TLR）和 NF-κB 信号通路为特征（均 FDR < 0.001）。网络分析确定了 8 个模块和 50 种现有药物候选物可重新用于治疗，包括前列腺素 I 类似物（例如伊洛前列素），它们通过 TLR 信号通路促进抗炎作用。我们的方法不仅有助于确定婴儿毛细支气管炎的分子网络，而且有助于开发开创性的治疗策略。
