Table_5_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.

研究目标：基于多发性硬化（multiple sclerosis, MS）免疫细胞的转录组变化，挖掘可用于多发性硬化（MS）药物重定位的现有药物靶基因与靶通路。
材料与方法：依托基因表达综合数据库（Gene Expression Omnibus, GEO）的转录组数据，按免疫细胞亚型，通过生物信息学分析筛选未接受治疗的MS患者的差异表达基因（differentially expressed genes, DEGs），以及MS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（protein-protein interaction, PPI）网络，分析MS治疗药物的核心靶基因；通过连通性图谱（connectivity map, CMap）数据库与DrugBank数据库，筛选靶向2个及以上核心靶基因的候选药物。随后，将未接受治疗的MS患者的富集通路与MS患者给药前后的富集通路进行交集分析，得到MS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，筛选靶向2个及以上靶通路的候选药物。
结果：本研究获得了用于治疗MS的芬戈莫德（Fingolimod）针对CD4+ T细胞的50个核心靶基因，干扰素-β（interferon-β, IFN-β）针对浆细胞样树突状细胞（plasmacytoid dendritic cells, pDCs）的15个核心靶基因，以及针对外周血单个核细胞（peripheral blood mononuclear cells, PBMC）的7个核心靶基因。共筛选得到6种靶向2个及以上核心靶标的候选药物，分别为福沙他替尼（Fostamatinib）、铜（Copper）、青蒿素（Artenimol）、异硫氰酸苯乙酯（Phenethyl isothiocyanate）、阿司匹林（Aspirin）与锌（Zinc）。此外，获得了芬戈莫德针对CD19+ B细胞的4个靶通路、针对CD4+ T细胞的15个靶通路；干扰素-β针对浆细胞样树突状细胞的7个靶通路，以及针对外周血单个核细胞的6个靶通路，其中多数通路隶属于免疫系统与病毒感染性疾病通路。最终筛选得到69种靶向2个靶通路的候选药物。
结论：本研究发现，同时靶向"PI3K-Akt信号通路（PI3K-Akt signaling pathway）"与"趋化因子信号通路（Chemokine signaling pathway）"的候选药物（如奈莫利昔布（Nemiralisib）与厄布利塞（Umbralisib）），或应用酪氨酸激酶抑制剂（如福沙他替尼（Fostamatinib）），或可成为治疗多发性硬化的潜在疗法。