Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis

Abstract

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 I2 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.