Target prioritization using human genetics – anchored protein – protein interactions network for Parkinson’s Disease
Year: 2023
Type: article
Abstract: Background Advancements in genome‐wide association studies (GWAS) have provided pivotal information about genes that are genetically associated to disease conditions. For example, Parkinson’s Disease GWAS studies and GWAS meta‐analysis has provided many candidate genes representing various pathways associated with Parkinson’s Disease, e.g., Lysosomes, mitochondrial dysfunction, Syn‐aggregation, etc. Amongst them, Leucine‐rich repeat kinase 2 (LRRK2) is one of the strongest candidates for PD therapeutic and many compounds targeting LRRK2 are already under clinical trials. To optimize therapeutic effects, protein interactors of LRRK2 could be targeted. We hypothesized that instead of selecting GWAS candidate itself, e.g., LRRK2, exploring a network of interacting proteins to GWAS candidates could give us better understanding of the genetic link to disease biology and hence better targets for therapeutic success. Method We created genetically anchored protein‐protein interaction network for Parkinson’s Disease from genes having strong genetic link to PD (seed genes) and all proteins interacting with any of the seed genes. These networks were uploaded and visualized on our in‐house tool – PDNet, a network‐based visual analytics prototype to analyze the network modules and propose candidates for potential therapeutic target prioritization in PD. Published and internally generated gene expression data is used to identify differentially expressed genes that fit in pathways implicated in PD. Result Pathway crosstalk were observed amongst different pathways and hub‐genes were identified that could be potential key drivers of the network in the disease progression and help in understanding the disease pathology. Multiple parameters were computed for the prioritization of the candidate genes, that have the highest score of an indirect genetic link to PD and could be good target for consideration based on other OMICS evidence. The experimental target evaluation of these candidates has narrowed down targets for further screening for drug discovery. Conclusion Our GWAS‐based network analysis framework provides a new way of identifying targets that are genetically involved in the disease but doesn’t show strong signal in GWAS analysis.
Source: Alzheimer s & Dementia
DOI: 10.1002/alz.077486
Authors: Shilpi Chaurasia, Gyan P Srivastava, Dong Xu
Institutions: Microsoft (India), University of Missouri
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4390201236