Abstract Motivation Understanding how signaling networks differ across molecular subgroups of Parkinson’s disease (PD) is essential for gaining further mechanistic insights and advancing therapeutic development for the disease. This study introduces an integrative, stratified computational framework to characterize subgroup-specific changes in kinase-transcription factors’ (TFs) interactions using transcriptomic profiles. Results Differential expression analysis was leveraged to identify kinases with altered expression across various PD subgroups, while transcription factor activity inferred by multi-sample Virtual Inference of Protein-activity by Enriched Regulon revealed dysregulated transcription relative to controls. Phosphorylation data from SIGNOR 4. 0 enabled the construction of kinase-TF subnetworks, which were analyzed via pathway enrichment to reveal affected biological pathways. Comparative analyses and modeling revealed both shared and distinct signaling features among PD stratified subgroups. A recurring pattern across multiple groups involved STAT family-specific activation downstream of receptor and non-receptor tyrosine kinases, consistently with a conserved inflammatory and pro-survival signaling axis. In contrast, PDLRRK2 showed selective involvement of immune-metabolic pathways, including AMPK to HNF4A and PAK5 to NF-κB, while PDGBA and prodromal cohorts were characterized by stress and apoptosis-related mechanisms involving MAPK10 (JNK3), TP53, and hormone receptor pathways (AR and ESR1). Overall, this novel stratified computational framework integrates gene expression, infers subtle TF activity, identifies differentially expressed kinases, and leverages mechanistic interaction data to unveil signaling heterogeneity in PD. Identifying regulators and subgroup-specific network features provides opportunities to inform, influence, and enable the unveiling of novel biomarkers and develop more effective and proactive precision therapeutics. Availability and Implementation Source code is available at https: //github. com/xyzhou218/KinTFₙet.
Zhou et al. (Fri,) studied this question.