Dataset Information

A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools.

ABSTRACT: Vitiligo is a prevalent depigmentation disorder affecting around 1% of the general population. So far, various Genome Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS) have identified several single nucleotide variants (SNVs) as a risk factor for vitiligo. Nonetheless, little has been discerned regarding their direct functional significance to the disease pathogenesis. In this study, we did extensive data mining and downstream analysis using several experimentally validated datasets like GTEx Portal and web tools like rSNPBase, RegulomeDB, HaploReg and STRING to prioritize 13 SNVs from a set of 291SNVs that have been previously reported to be associated with vitiligo. We also prioritized their underlying/target genes and tried annotating their functional contribution to vitiligo pathogenesis. Our analysis revealed genes like FGFR10P, SUOX, CDK5RAP1 and RERE that have never been implicated in vitiligo previously to have strong potentials to contribute to the disease pathogenesis. The study is the first of its kind to prioritize and functionally annotate vitiligo-associated GWAS and CGAS SNVs and their underlying/target genes, based on functional data available in the public domain database.

SUBMITTER: Dutta T

PROVIDER: S-EPMC9411560 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools.

Dutta Tithi T Mitra Sayantan S Saha Arpan A Ganguly Kausik K Pyne Tushar T Sengupta Mainak M

Scientific reports 20220825 1

Vitiligo is a prevalent depigmentation disorder affecting around 1% of the general population. So far, various Genome Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS) have identified several single nucleotide variants (SNVs) as a risk factor for vitiligo. Nonetheless, little has been discerned regarding their direct functional significance to the disease pathogenesis. In this study, we did extensive data mining and downstream analysis using several experimentally val ...[more]

PMID: 36008553

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Project description:PurposeProliferative vitreoretinopathy (PVR) is the dreaded cause of failure following retinal detachment repair; however, no cures or preventative therapies exist to date. The purpose of this study was to use bioinformatics tools to identify drugs or compounds that interact with biomarkers and pathways involved in PVR pathogenesis that could be eligible for further testing for the prevention and treatment of PVR.MethodsWe queried PubMed to compile a comprehensive list of genes described in PVR to date from human studies, animal models, and genomic studies found in the National Center for Biotechnology Information database. Gene enrichment analysis was performed using ToppGene on PVR-related genes against drug-gene interaction databases to construct a pharmacome and estimate the statistical significance of overrepresented compounds. Compounds with no clinical indications were filtered out from the resulting drug lists.ResultsOur query identified 34 unique genes associated with PVR. Out of 77,146 candidate drugs or compounds in the drug databases, our analysis revealed multiple drugs and compounds that have significant interactions with genes involved in PVR, including antiproliferatives, corticosteroids, cardiovascular agents, antioxidants, statins, and micronutrients. Top compounds, including curcumin, statins, and cardiovascular agents such as carvedilol and enalapril, have well-established safety profiles and potentially could be readily repurposed for PVR. Other significant compounds such as prednisone and methotrexate have shown promising results in ongoing clinical trials for PVR.ConclusionsThis bioinformatics approach of studying drug-gene interactions can identify drugs that may affect genes and pathways implicated in PVR. Predicted bioinformatics studies require further validation by preclinical or clinical studies; however, this unbiased approach could identify potential candidates among existing drugs and compounds that could be repurposed for PVR and guide future investigations.Translational relevanceNovel repurposable drug therapies for PVR can be found using advanced bioinformatics models.

Dataset Information

A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools.

Publications

A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools.

Similar Datasets

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