Establishment of comorbidity target pools and prediction of drugs candidate for multiple sclerosis and autoimmune thyroid diseases based on GWAS and transcriptome data.

BACKGROUND: Clinical observation has revealed that multiple sclerosis (MS) and autoimmune thyroid disease (AITD) are strongly correlated. The aim of this study was to explore the shared molecular causes of MS and AITD, and to conduct drug rearrangement on this basis, search for comorbidity drugs and feasible drugs for mutual reference between the two diseases. METHODS: Based on genome-wide association study (GWAS) data and transcriptome data, susceptibility genes and differentially expressed genes related to MS and AITD were identified by bioinformatics analysis. Pathway enrichment, gene ontology (GO), protein-protein interaction analysis, and gene-pathway network analysis of the above genes were performed to identify a common target pool, including common genes, common hub genes, and common pathways, and to explore the specific pathogenesis of the two diseases, respectively. Drugs that target the common pathways/genes were identified through the Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. Common hub genes were compared with the target genes of drugs approved for treating MS/AITD and drugs under investigation identified by DrugBank and ClinicalTrials, respectively. RESULTS: We identified a pool of shared targets containing genes and pathways, including 46 common genetic susceptibility pathways and 9 common differentially expressed pathways, including JAK-STAT signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. In addition, a total of 29 hub genes, including TYK2, JAK1, STAT3, IL2RA, HLA-DRB1, and TLR3, were identified. Drugs approved for treating MS or AITD, such as methylprednisolone, cyclophosphamide, glatiramer, natalizumab, and methimazole, can target the shared genes and pathways, among which methylprednisolone and cyclophosphamide have been shown to be beneficial for the treatment of the two diseases, indicating that these drugs have the potential to become a priority in the treatment of comorbidities. Moreover, drugs targeting multiple common genes and pathways, including tacrolimus, deucravacitinib, and nivolumab, were identified as potential drugs for the treatment of MS, AITD, and their comorbidities. CONCLUSION: We observed that T-cell activation-related genes and pathways play a major role in the pathogenesis of both MS and AITD, which may be the molecular basis of the comorbidity. Moreover, we identified a variety of drugs which may be used as priority or potential treatments for comorbidities.

Immune cells transcriptome-based drug repositioning for multiple sclerosis.

Objective: Finding 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 Methods: Based 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. Results: We 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-ß (IFN-ß) 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-ß for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways. Conclusion: We 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.

Integrated Analysis and Identification of CSF-Derived Risk miRNAs and Pivotal Genes in Multiple Sclerosis.

Multiple sclerosis (MS) is a common chronic autoimmune disorder of the central nervous system that predominantly affects young adults. Mounting evidence indicates that deregulation of microRNAs (miRNAs) in cerebrospinal fluid (CSF) has been implicated in MS as a potential biomarker. However, comprehensive assessments of CSF miRNAs and their target genes are lacking. Here, aberrantly expressed CSF miRNAs of MS patients were obtained from numerous studies by manual search. With detailed information on these miRNAs, we utilized online databases to screen out immune-related target genes and further performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. To identify MS high-risk pathways and pivotal genes, pathway crosstalk and pathway-gene networks were constructed, followed by the establishment of a protein-protein interaction (PPI) network. The datasets collected from ArrayExpress were used to assess pivotal genes. Overall, 21 MS-related CSF miRNAs were included in this study. Subsequently, we identified 469 MS-related genes and 14 high-risk pathways. In the pathway-gene network, 27 critical MS-related genes participated in at least half of the high-risk pathways, and these genes were used to identify pivotal genes. Finally, miR-150, miR-328, and miR-34c-5p were determined to be risk miRNAs via the regulation of the pivotal risk genes MAPK1, AKT1, and VEGFA. Among them, VEGFA was validated to be significantly decreased in the CSF cells of MS patients by transcriptomic datasets. These findings may provide potential biomarkers or therapeutic targets and help elucidate the molecular mechanisms underlying the pathogenesis of MS.

The Shared Mechanism and Candidate Drugs of Multiple Sclerosis and Sjögren's Syndrome Analyzed by Bioinformatics Based on GWAS and Transcriptome Data.

Objective: This study aimed to explore the shared mechanism and candidate drugs of multiple sclerosis (MS) and Sjögren's syndrome (SS). Methods: MS- and SS-related susceptibility genes and differentially expressed genes (DEGs) were identified by bioinformatics analysis based on genome-wide association studies (GWAS) and transcriptome data from GWAS catalog and Gene Expression Omnibus (GEO) database. Pathway enrichment, Gene Ontology (GO) analysis, and protein-protein interaction analysis for susceptibility genes and DEGs were performed. The drugs targeting common pathways/genes were obtained through Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. The target genes of approved/investigational drugs for MS and SS were obtained through DrugBank and compared with the common susceptibility genes. Results: Based on GWAS data, we found 14 hub common susceptibility genes (HLA-DRB1, HLA-DRA, STAT3, JAK1, HLA-B, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB5, HLA-DPA1, HLA-DPB1, TYK2, IL2RA, and MAPK1), with 8 drugs targeting two or more than two genes, and 28 common susceptibility pathways, with 15 drugs targeting three or more than three pathways. Based on transcriptome data, we found 3 hub common DEGs (STAT1, GATA3, PIK3CA) with 3 drugs and 10 common risk pathways with 435 drugs. "JAK-STAT signaling pathway" was included in common susceptibility pathways and common risk pathways at the same time. There were 133 overlaps including JAK-STAT inhibitors between agents from GWAS and transcriptome data. Besides, we found that IL2RA and HLA-DRB1, identified as hub common susceptibility genes, were the targets of daclizumab and glatiramer that were used for MS, indicating that daclizumab and glatiramer may be therapeutic for SS. Conclusion: We observed the shared mechanism of MS and SS, in which JAK-STAT signaling pathway played a vital role, which may be the genetic and molecular bases of comorbidity of MS with SS. Moreover, JAK-STAT inhibitors were potential therapies for MS and SS, especially for their comorbidity.

Identification of potential key genes and immune infiltration in Multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is an extremely serious autoimmune disease of the nervous system. Extensive evidence indicated that immune system activation plays a crucial role in the development of MS. However, the exact mechanism of MS is still not well understood. Our objective was to identify potential key genes of Multiple sclerosis (MS) via bioinformatic analysis and apply CIBERSORT algorithms to calculate the proportion of infiltrating immune cells. METHODS: The differentially expressed genes (DEGs) were analyzed from two public datasets, which included 99 MS, 45 controls and 133 MS, 79 controls. Then the common DEGs were obtained (p < 0.05). LASSO regression analysis was performed on common DEGs of GSE17048. The receiver operating characteristic (ROC) curves were created. The key genes were screened based on area under the receiver operating characteristic curve (AUC). CIBERSORT algorithms were used to explore the immune infiltration in MS. RESULTS: 516 common DEGs were screened from two public datasets. And then 54 signature genes were obtained by constructing LASSO model. MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 with the largest AUC values were selected as the key genes. Neutrophils, Monocytes, resting memory CD4+ T cells, CD8+ T cells and resting NK cells accounted for a large proportion of infiltrating immune cells in MS. CONCLUSION: MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 may be closely related pathogenesis of MS, and may represent new candidate biomarkers. In addition, immune cell infiltration may also play an important role in the progression of MS.

Identification of multiple sclerosis-related genes regulated by EBV-encoded microRNAs in B cells.

BACKGROUND: Multiple sclerosis (MS) is driven by the interaction between genetic susceptibility and environmental triggers, particularly to Epstein-Barr virus (EBV) infection. EBV-encoded microRNAs (miRNAs) are abundantly expressed in all stages of EBV infection and latency, which can target both viral and host cellular mRNAs, allowing EBV-infected B cells to evade the host immune response. However, it remains a big gap to understand the roles of EBV miRNAs and their target genes in MS pathogenesis. METHODS: We investigated the correlation between MS-related viruses infection and MS risk quantitatively by systematic analysis. All MS-related genes in B cells were obtained by integrating MS susceptibility genes and differentially expressed genes from B cells. In comparison with differentially expressed genes from B cells after EBV infection in vitro, we confirmed EBV-regulated, MS-related genes. Subsequently, we obtained target EBV miRNAs which can regulate these genes from several online databases. By constructing pathway-pathway, pathway-gene and protein-protein interaction networks, we further screened out MS-related genes and risk pathways regulated by EBV miRNAs. Finally, we identified target EBV miRNAs which may directly regulate MS-related genes through bioinformatic prediction. RESULTS: EBV infection showed the strongest correlation with MS risk. A total of 568 MS-related genes and 80 risk pathways in B cells were obtained. We then identified 112 MS-related genes and 18 associated risk pathways that EBV was involved in. In addition, 33 human target genes regulated by 33 EBV miRNAs overlapped with EBV-regulated, MS-related genes. Finally, 15 target EBV miRNAs and their regulated, 7 MS-related genes (MALT1, BCL10, IFNGR2, STAT3, CXCR4, PTK2B and FOXP1) have been confirmed as crucial pathogenic molecules, which could promote the initiation and development of MS through NF-kappa B (MALT1 and BCL10) and PD-L1/PD-1 (IFNGR2 and STAT3) pathways. Surprisingly, ebv-miR-BHRF1-2-5p directly targeting MALT1 was confirmed by our experiments, and FOXP1 was identified as a target gene of ebv-miR-BART11. CONCLUSIONS: This work identified the target EBV miRNAs and their regulated, MS-related genes as well as risk pathways, which may provide a novel insight into discovering diagnostic biomarkers and therapeutic targets for MS.

Identification of common susceptibility genes and drug target genes in multiple sclerosis, systemic lupus erythematosus, and rheumatoid arthritis and its value to guide clinical treatment.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune-mediated demyelinating disease of the white matter in the central nervous system (CNS). In clinical practice, it was found that MS is associated with a variety of autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA). The aim of this study was to identify common susceptibility genes and drug target genes in MS, SLE, and RA and to provide new insights into treatment. METHODS: The common susceptibility genes of MS, SLE, and RA were obtained by searching the GWAS database and using microarray data to validate. The Genome Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, and the common KEGG pathways were selected. All the genes enriched in the common pathways were obtained and intersected with the susceptibility genes of MS, SLE, and RA to obtain the pathway genes of them respectively, and found the common pathogenesis-related genes of the three diseases. By reviewing the literature and the DrugBank database, the drugs and drug target genes that have been approved for the treatment of the three diseases were obtained. Finally, the DGIdb database was searched to predict potential drugs or molecular compounds that interact with susceptibility genes common to MS, SLE, and RA. RESULTS: In MS, SLE, and RA, there were 46 common susceptibility genes, of which 23 were significantly differentially expressed in the microarray expression profile. Then, 2117 genes were obtained in the 42 common pathways, among which 17 pathogenesis-related genes were common in MS, SLE, and RA. The Drugbank database was used to obtain 29 drug target genes for MS, 43 drug target genes for RA, and 20 drug target genes for SLE. DHODH is a common drug target gene for MS, SLE, and RA, and its corresponding drugs are Leflunomide and Teriflunomide. A total of 13 genes and 366 potential drugs or molecular compounds were predicted to have interaction relationships after searching the DGIdb database. CONCLUSION: The common susceptibility genes and drug target genes among MS, SLE, and RA provide a theoretical basis for the co-morbidity phenomenon of the three diseases in clinical practice and may guide the clinical treatment.

Identification and functional analysis of specific MS risk miRNAs and their target genes.

BACKGROUND: It has been widely acknowledged that abnormal expression of microRNAs (miRNAs) may lead to the occurrence and development of MS through regulating target genes. Currently, only few studies have comprehensively evaluated the function and relationship between MS-related miRNAs and their target genes. METHODS: Differentially expressed miRNAs in MS patients' serum and plasma were selected by reviewing numerous literatures manually. Then, thousands of target genes were screened by several online databases, of which 899 MS-related genes were further identified. Gene ontology, protein-protein interaction and KEGG pathway analysis were used to determine high-risk pathways and MS risk genes. Transcriptomic datasets from GEO was analyzed to evaluate these risk genes. RESULTS: 28 MS-related miRNAs were extracted. MiR-30e, miR-93, miR-155 were identified as the most crucial miRNAs through targeting hub genes: PIK3CA, PIK3R1, PIK3R2 and MAPK8. Seven immune pathways were screened out according to KEGG pathway analysis. Six transcriptomic datasets were used to evaluate results, and PIK3CA was differentially expressed in MS patients compared with healthy donors. CONCLUSIONS: According to our research, MS-related miRNAs and their target genes of MS were identified and comprehensively evaluated. This work may provide a new insight for discovering pathogenesis and possible biomarkers of MS in future studies.