# Stratified signaling network remodeling of kinase–transcription factors’ interactions in Parkinson’s disease

## TL;DR

This paper introduces a new computational framework to study how signaling networks involving kinases and transcription factors change in different subgroups of Parkinson’s disease.

## Contribution

The novel stratified framework integrates gene expression, inferred transcription factor activity, and kinase interaction data to reveal subgroup-specific signaling features in Parkinson’s disease.

## Key findings

- A recurring pattern of STAT family activation downstream of tyrosine kinases was observed across multiple PD subgroups.
- PD_LRRK2 showed unique involvement of immune-metabolic pathways like AMPK to HNF4A and PAK5 to NF-κB.
- Stress and apoptosis mechanisms involving MAPK10, TP53, and hormone receptors were prominent in PD_GBA and prodromal cohorts.

## Abstract

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.

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-κB, 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.

Source code is available at https://github.com/xyzhou218/Kin_TF_net.