Stratified signaling network remodeling of kinase-transcription factors' interactions in Parkinson's disease

Xiaoyan Zhou; Luca Parisi; Sicen Liu; Ziqi Cheng; Hanwen Liang; Mansour Youseffi; Farideh Javid; Renfei Ma

doi:10.1093/bioadv/vbag059

Stratified signaling network remodeling of kinase-transcription factors' interactions in Parkinson's disease

Bioinform Adv. 2026 Feb 17;6(1):vbag059. doi: 10.1093/bioadv/vbag059. eCollection 2026.

Authors

Xiaoyan Zhou¹, Luca Parisi², Sicen Liu³, Ziqi Cheng¹, Hanwen Liang¹, Mansour Youseffi⁴, Farideh Javid⁵, Renfei Ma¹

Affiliations

¹ Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen, Guangdong 518115, China.
² Department of Computer Science, Tutorantis, Edinburgh, EH2 4AN, United Kingdom.
³ SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology, Shenzhen MSU-BIT University, Shenzhen, Guangdong 518115, China.
⁴ Faculty of Management, Sciences and Engineering/School of Computing and Engineering, University of Bradford, Bradford, West Yorkshire BD7 1DP, United Kingdom.
⁵ Department of Pharmacy, University of Huddersfield, Queensgate, HD1 3DH, United Kingdom.

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- $κ$ B, while PD_GBA 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/Kin_TF_net.