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Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis

Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new dru...

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Autores principales: Afief, Arief Rahman, Irham, Lalu Muhammad, Adikusuma, Wirawan, Perwitasari, Dyah Aryani, Brahmadhi, Ageng, Cheung, Rocky
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464879/
https://www.ncbi.nlm.nih.gov/pubmed/36105612
http://dx.doi.org/10.1016/j.bbrep.2022.101337
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author Afief, Arief Rahman
Irham, Lalu Muhammad
Adikusuma, Wirawan
Perwitasari, Dyah Aryani
Brahmadhi, Ageng
Cheung, Rocky
author_facet Afief, Arief Rahman
Irham, Lalu Muhammad
Adikusuma, Wirawan
Perwitasari, Dyah Aryani
Brahmadhi, Ageng
Cheung, Rocky
author_sort Afief, Arief Rahman
collection PubMed
description Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10(−8)), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r(2) > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease.
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spelling pubmed-94648792022-09-13 Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis Afief, Arief Rahman Irham, Lalu Muhammad Adikusuma, Wirawan Perwitasari, Dyah Aryani Brahmadhi, Ageng Cheung, Rocky Biochem Biophys Rep Research Article Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10(−8)), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r(2) > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease. Elsevier 2022-09-05 /pmc/articles/PMC9464879/ /pubmed/36105612 http://dx.doi.org/10.1016/j.bbrep.2022.101337 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Afief, Arief Rahman
Irham, Lalu Muhammad
Adikusuma, Wirawan
Perwitasari, Dyah Aryani
Brahmadhi, Ageng
Cheung, Rocky
Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
title Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
title_full Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
title_fullStr Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
title_full_unstemmed Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
title_short Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
title_sort integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464879/
https://www.ncbi.nlm.nih.gov/pubmed/36105612
http://dx.doi.org/10.1016/j.bbrep.2022.101337
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