Stage-Specific Reconstruction of Genome-Wide Genetic and Epigenetic Regulatory Networks Reveals Mechanistic Insights into Asthma Progression

Asthma is a chronic respiratory disease characterized by airway hyperresponsiveness, obstruction, and persistent inflammation, arising from complex interactions among genetic, epigenetic, immune, and environmental factors. To elucidate the stage-specific molecular mechanisms underlying asthma progression, we constructed candidate genome-wide genetic and epigenetic networks (GWGENs) of human cells through large-scale biological database mining. Using a system order detection scheme, false-positive interactions were pruned to identify real GWGENs corresponding to three clinical stages of asthma: quiet, exacerbation, and follow-up. Core GWGENs were subsequently extracted from each real network using the principal network projection (PNP) method to highlight dominant regulatory structures and pathogenic pathways. Based on the inferred core networks, key stage-specific biomarkers were identified and further explored as potential drug targets. Drug–target relationships were investigated by integrating gene expression perturbation profiles from the Connectivity Map (cMap), comprising microarray data for 14,207 genes across 1327 compounds. This network-guided analysis enabled the qualitative design of multi-molecule drug combinations tailored to each disease stage. Our results suggest that asthma onset is associated with reduced innate immunity, increased disease susceptibility, and impaired endothelial barrier recovery influenced by microenvironmental factors such as cigarette smoke and lipopolysaccharides, together with genetic and epigenetic alterations. During the exacerbation stage, enhanced differentiation of T cells toward the T helper 2 lineage contributes to airway inflammation and tissue injury. In the follow-up stage, T helper 1–mediated responses are linked to mucus hypersecretion, airway obstruction, and sustained inflammation. Collectively, these findings demonstrate that a systems-level, network-based framework can uncover stage-specific pathogenic mechanisms of asthma and provide hypothesis-generating insights for network-informed drug repurposing strategies.