Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies

Abstract

Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.

Keywords: Alzheimer’s disease; computational approach; drug repurposing; gene signatures; genome-wide association study; pathway enrichment; single-cell sequencing study.

FIGURE 1

Study design. The study included four major steps: (A) four risk genes lists (GWAS, Ex, Ast, and Oli) were generated from GWAS and scRNA-seq studies, respectively. Meanwhile, a preliminary functional analysis of the gene lists was performed in Enrichr; (B) Gene2drug analysis was then performed on the four risk gene lists by incorporating eight pathways databases; (C) drug lists were analyzed and filtered based on enrichment score and adjusted p-value to select top five drugs from each list; (D) the identified AD candidate drugs were validated through literature review to determine the most promising candidates.

FIGURE 2

Major pharmacological targets of the eight candidate drugs during AD progression. Candidate drugs are each associated with several major targets involved in different timepoints of AD progression. Arrows and lines show the correlation between drug and pharmacological targets based on the literature evidence, as well as the correlation between targets and disease progression timeline.

FIGURE 3

The inferred mechanism of alterpaullone in AD pathology. The drug acts through the inhibition of GSK3β, CDK5, and ERK1/2. The inhibition of GSK3β and CDK5 leads to reduced hyperphosphorylation of toxic cytoskeletal proteins and promote cell survival and proliferation. The inhibition of ERK1/2 leads to deactivation of NF-kB p65 that produces proinflammatory cytokines during inflammation. The solid dash lines show the interactions of protein kinases and signaling cascades. The long-dashed lines show the pharmacological action of drug.

FIGURE 4

The inferred mechanism of ginkgolide A in AD pathology. The drug acts through the inhibition of NMDA receptor, JNK signaling and the activation of PI3K-AKT pathway. Inhibition of NMDA receptor prevents excessive amounts of glutamate that are associated with synaptic dysfunction and tau phosphorylation. Inhibition of JNK pathway decreases the production of proinflammatory cytokines involved in neuroinflammation. Activation of PI3K-AKT pathway strengthens the inhibition of GSK3β and further prevents the hyperphosphorylation of tau proteins. The solid dash lines show the interactions of protein kinases and signaling cascades. The long-dashed lines show the pharmacological action of the drug.

FIGURE 5

The inferred mechanism of chrysin in AD pathology. The drug acts through inhibition of NF-kB p65, C/EBPs and activation of NRF2, AKT pathway. The inhibition of NF-kB p65 and C/EBPs leads to decreased transcription of iNOS and TNF genes which contributes to the production of neurotrophic factors. The activation of NRF2 leads to the binding with Maf and ARE which increases the transcription of antioxidant and cytoprotective genes HO-1, CAT, and SOD. The strengthening of AKT pathway prevents the deactivation of MEF2D by GSK3β, where MEF2D is a key regulator for cell survival, mitochondrial dysfunction and autophagy dysregulation. The solid lines show the interactions of protein kinases and translocation into the nucleus. The long-dashed lines show the pharmacological action of the drug.

FIGURE 6

The inferred mechanism of ouabain in AD pathology. The drug acts through the inhibition of Na/K⁺-ATPase, mTOR and activation of TFEB. The deactivation of Na/K⁺-ATPase prevents excessive Ca²⁺ influx that leads to production of proinflammatory cytokines. The drug inhibits mTOR while augmenting TFEB can indicate a better efficacy in toxic tau degradation leading to neuronal survival. The solid dash lines show the interactions of protein kinases. The long-dashed lines show the pharmacological action of the drug.