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

Abstract

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 r² > 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.

Keywords: Autoimmune disease; Bioinformatics; Drug repurposing; Genomic variants; Multiple sclerosis.

Fig. 1

Scheme of drug repurposing using genomic database for multiple sclerosis (MS).

Fig. 2

Biological annotations prioritized for multiple sclerosis (MS) genes with score ≥2.

Fig. 3

Histogram of the number of genes (y-axis) meeting each of the six biological criteria (x-axis) for drug prioritization..

Fig. 4

Histogram of the number of genes (y-axis) meeting each of the six biological criteria (x-axis) for drug prioritization. It is shown that there were 173 genes with score 0, 110 genes with score 1, and 144 (67 + 47+21 + 7+2) genes with total score ≥2, denoted as “biological MS risk genes”.

Fig. 5

Relationship between biological MS risk genes and available drugs for MS.

Fig. 6

Relationship between biological MS genes, and drugs approved for other indications and under clinical investigation for MS.