ArticleBioinformatics advances2026
Stratified signaling network remodeling of kinase-transcription factors' interactions in Parkinson's disease.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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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 analysed 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, PD_LRRK2 showed selective involvement of immune-metabolic pathways, including AMPK to HNF4A and PAK5 to NF- Availability and Implementation: Source code is available at https://github.com/xyzhou218/Kin_TF_net.
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