Table_7_Immune cells transcriptome-based drug repositioning for multiple sclerosis.docx

ObjectiveFinding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells.
Materials and MethodsBased on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
ResultsWe obtained 50 hub target genes for CD4+ T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-β (IFN-β) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19+ B cells and 15 target pathways for CD4+ T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-β for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways.
ConclusionWe found that applying candidate drugs that target both the “PI3K-Akt signaling pathway” and “Chemokine signaling pathway” (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

本研究旨在基于多发性硬化（multiple sclerosis, MS）免疫细胞的转录组变化，挖掘现有药物用于多发性硬化药物重定位的潜在靶基因与靶通路。
材料与方法：本研究从基因表达综合（Gene Expression Omnibus, GEO）数据库获取转录组数据，首先依据免疫细胞类型，通过生物信息学分析筛选未经治疗的多发性硬化患者的差异表达基因（differentially expressed genes, DEGs），以及多发性硬化患者给药前后的差异表达基因。通过构建蛋白质-蛋白质相互作用网络，分析多发性硬化治疗药物的核心靶基因；再通过连接图谱（connectivity map, CMap）数据库与DrugBank数据库，筛选出可靶向2个及以上核心靶基因的候选药物。随后，将未经治疗的多发性硬化患者的富集通路与患者给药前后的富集通路进行交集分析，得到多发性硬化治疗药物的靶通路；再通过京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）数据库，筛选出可靶向2个及以上靶通路的候选药物。
结果：本研究针对用于多发性硬化治疗的芬戈莫德（Fingolimod），筛选出CD4+ T细胞相关的50个核心靶基因；针对干扰素-β（interferon-β, IFN-β），筛选出浆细胞样树突状细胞（plasmacytoid dendritic cells, pDCs）相关的15个核心靶基因，以及外周血单个核细胞（peripheral blood mononuclear cells, PBMC）相关的7个核心靶基因。最终获得6种可靶向2个及以上核心靶标的候选药物，分别为福他替尼（Fostamatinib）、铜离子、青蒿素（Artenimol）、异硫氰酸苯乙酯（Phenethyl isothiocyanate）、阿司匹林与锌制剂。此外，针对芬戈莫德，筛选出CD19+ B细胞相关的4条靶通路与CD4+ T细胞相关的15条靶通路；针对干扰素-β，筛选出浆细胞样树突状细胞相关的7条靶通路与外周血单个核细胞相关的6条靶通路，其中多数靶通路隶属于免疫系统与病毒性传染病通路。本研究还筛选出69种可靶向2条靶通路的候选药物。
结论：本研究发现，同时靶向"PI3K-Akt信号通路"与"趋化因子信号通路"的候选药物（如奈拉利昔（Nemiralisib）与乌布利昔（Umbralisib）），或酪氨酸激酶抑制剂（如福他替尼），有望成为多发性硬化的潜在治疗方案。