Table1_Computational Drug Repurposing for Alzheimer’s Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies.XLSX

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.

背景：迄今为止，针对阿尔茨海默病（Alzheimer’s Disease, AD）相关亚病理的传统治疗手段均被证实无效。药物重定位（Drug Repurposing）作为一种更为高效的策略，旨在为已上市药物发掘针对其他疾病的新适应症，可为AD药物研发带来新的契机。本研究旨在基于全基因组关联分析（Genome-Wide Association Studies, GWAS）与单细胞转录组学研究鉴定的AD相关风险基因，通过对药物诱导的通路转录谱开展富集分析，筛选潜在的抗AD活性物质。
方法：本研究从GWAS与单细胞转录组学研究中系统性构建了4组基因列表（共包含972个风险基因），并通过Enrichr工具完成功能富集与基因重叠分析。随后，我们结合药物诱导的转录应答与相关通路，使用综合性药物重定位工具Gene2Drug从每组基因列表中筛选得到候选药物。最终筛选出的8种潜在候选药物，进一步通过文献调研完成验证性评估。
结果：本研究构建的基于基因组学的基因列表涵盖晚发性AD相关基因（BIN1、ABCA7、APOE、CLU与PICALM）以及临床AD药物靶点（TREM2、CD33、CHRNA2、PRSS8、ACE、TKT、APP与GABRA1）。本分析共鉴定出8种AD候选药物（ellipticine、alsterpaullone、tomelukast、ginkgolide A、chrysin、ouabain、sulindac sulfide及lorglumide），其中4种（alsterpaullone、ginkgolide A、chrysin与ouabain）已被文献中的临床前研究证据与中度毒性特征证实具备AD治疗的重定位潜力。上述结果证实了基于GWAS与单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）鉴定的疾病风险基因开展通路优先排序策略的应用价值。
结论：本研究的分析策略成功筛选出若干AD潜在药物候选物。尽管这些药物仍需开展进一步的实验验证，但该方法可依托大规模基因组研究鉴定的基因组信息，被应用于其他神经系统疾病的药物重定位研究。