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Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis

Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to pla...

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Autores principales: Irham, Lalu Muhammad, Adikusuma, Wirawan, La’ah, Anita Silas, Chong, Rockie, Septama, Abdi Wira, Angelina, Marissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451728/
https://www.ncbi.nlm.nih.gov/pubmed/37627776
http://dx.doi.org/10.3390/bioengineering10080890
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author Irham, Lalu Muhammad
Adikusuma, Wirawan
La’ah, Anita Silas
Chong, Rockie
Septama, Abdi Wira
Angelina, Marissa
author_facet Irham, Lalu Muhammad
Adikusuma, Wirawan
La’ah, Anita Silas
Chong, Rockie
Septama, Abdi Wira
Angelina, Marissa
author_sort Irham, Lalu Muhammad
collection PubMed
description Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings.
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spelling pubmed-104517282023-08-26 Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis Irham, Lalu Muhammad Adikusuma, Wirawan La’ah, Anita Silas Chong, Rockie Septama, Abdi Wira Angelina, Marissa Bioengineering (Basel) Article Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings. MDPI 2023-07-27 /pmc/articles/PMC10451728/ /pubmed/37627776 http://dx.doi.org/10.3390/bioengineering10080890 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Irham, Lalu Muhammad
Adikusuma, Wirawan
La’ah, Anita Silas
Chong, Rockie
Septama, Abdi Wira
Angelina, Marissa
Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis
title Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis
title_full Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis
title_fullStr Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis
title_full_unstemmed Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis
title_short Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis
title_sort leveraging genomic and bioinformatic analysis to enhance drug repositioning for dermatomyositis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451728/
https://www.ncbi.nlm.nih.gov/pubmed/37627776
http://dx.doi.org/10.3390/bioengineering10080890
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