Phenome-based gene discovery provides information about Parkinson’s disease drug targets

Abstract

BackgroundParkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs.MethodsWe propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery.ResultsIn a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.

Parkinson’s Disease: From Pathogenesis to Pharmacogenomics

“…Mutations in a series of primary genes are known to cause autosomal dominant and recessive forms of PD [28,29,30,31,32,33,34,35,36,37,38]. Mutations in some genes—e.g., α-Synuclein ( SNCA ), Parkin 2 ( PARK2 ), PTEN-induced putative kinase 1 ( PINK1 ), PARK7 , Leucine-rich repeat kinase 2 ( LRRK2 ), Bone narrow stromal cell antigen 1 ( BST1 ), Microtubule-associated protein tau ( MAPT )—might be causative in familial forms of PD whereas diverse genetic defects in other loci might represent susceptibility loci associated with sporadic PD without family history [28,34,35,36,37,38,39].…”

“…Mutations in some genes—e.g., α-Synuclein ( SNCA ), Parkin 2 ( PARK2 ), PTEN-induced putative kinase 1 ( PINK1 ), PARK7 , Leucine-rich repeat kinase 2 ( LRRK2 ), Bone narrow stromal cell antigen 1 ( BST1 ), Microtubule-associated protein tau ( MAPT )—might be causative in familial forms of PD whereas diverse genetic defects in other loci might represent susceptibility loci associated with sporadic PD without family history [28,34,35,36,37,38,39]. Mendelian variants with high penetrance (e.g., SNCA , LRRK2 , PINK1 , PARK7 genes) explain less than 10% of familial PD [40].…”

Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment

“… 5 The primary advantage of drug repositioning is that it starts from compounds with well-characterized pharmacology and safety profiles that can greatly reduce the risk of attrition in drug development in clinical phases. 6 We have recently developed novel computational algorithms that identified repurposed drug candidates to treat neuropsychiatric disorders, including schizophrenia 7 and Parkinson’s disease, 8 , 9 , 10 infectious diseases including dengue fever 11 and malaria; 12 cancers including glioblastoma; 13 , 14 and immune-mediated diseases including Crohn’s disease, 15 inflammatory bowel disease 16 and rheumatoid arthritis. 17 However, to date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for HGSOC have not been undertaken.…”

Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery

“…The input to TargetPredict is a drug and output is a ranked list of genes. We applied the standard random walker with restart network ranking algorithm ( Li and Patra, 2010 ), which we previously applied this algorithm to predict disease genes ( Chen and Xu, 2016 ; Chen et al , 2015b ), drug targets ( Zhou et al , 2019 ) and repositioned drug candidates ( Xu and Wang, 2015 , 2016 ). Given an input drug as the seed, the algorithm estimated the probability scores of each gene being reached from the seed.…”