Abstract Background Vogt-Koyanagi-Harada (VKH) disease is a complex disease associated with multiple molecular immunological mechanisms. As the underlying mechanism for VKH disease is unclear, we hope to utilize an integrated analysis of key pathways and drug targets to develop novel therapeutic strategies. Methods Candidate genes and proteins involved in VKH disease were identified through text-mining in the PubMed database. The GO and KEGG pathway analyses were used to examine the biological functions of the involved pathways associated with this disease. Molecule-related drugs were predicted through Drug-Gene Interaction Database (DGIdb) analysis. Results A total of 48 genes and 54 proteins were associated with VKH disease. Forty-two significantly altered pathways were identified through pathway analysis and were mainly related to immune and inflammatory responses. The top five of significantly altered pathways were termed as “inflammatory bowel disease,” “cytokine-cytokine receptor interaction,” “allograft rejection,” “antigen processing,” and “presentation and Herpes simplex infection” in the KEGG database. IFN-γ and IL-6 were identified as the key genes through network analysis. The DGIdb analysis predicted 48 medicines as possible drugs for VKH disease, among which Interferon Alfa-2B was co-associated both with IFN-γ and IL-6. Conclusions In this study, systematic analyses were utilized to detect key pathways and drug targets in VKH disease via bioinformatics analysis. IFN-γ and IL-6 were identified as the key mediators and possible drug targets in VKH disease. Interferon Alfa-2B was predicted to be a potentially effective drug for VKH disease treatment by targeting IFN-γ and IL-6, which warrants further experimental and clinical investigations. Keywords: Vogt-Koyanagi-Harada disease, pathway analysis, network analysis, drug repurposing, uveitis Introduction Vogt-Koyanagi-Harada (VKH) disease is an immune-mediated disorder characterized by chronic, bilateral granulomatous panuveitis, often associated with neurological, audiovestibular and cutaneous manifestations ([31]1). VKH disease is more commonly seen in Asians, Hispanics, Native Americans ([32]2), and rare in Africans ([33]3). Bilateral panuveitis, hearing disorder and meningitis are the main clinical features. Treatment with systemic corticosteroid is the mainstay of VKH disease therapy in the acute uveitic phase ([34]4). However, despite proper treatment with a high-dose of corticosteroid, 79% of patients will experience recurrent attacks and develop chronic disease ([35]5). Moreover, a high-dose of corticosteroid over a prolonged period may lead to side effects such as Cushing syndrome, hyperglycemia, and increased incidence of severe infections ([36]6). Currently, there remain unmet medical needs for novel therapies that can etiologically target the molecules or immune mediators involved in the disease. Although the exact biological mechanisms are still unclear, an increasing number of candidate genes and proteins have been reported to be involved in the development of VKH disease, which may be possible drug targets for the disease ([37]7, [38]8). However, it is still challenging to prioritize these drug targets among many genes and proteins. Here, we used integrated bioinformatic analysis to summarize the candidate genes and proteins associated with VKH disease and identify the potential key pathways and drug targets, which may help to develop new therapeutic agents. Materials and Methods Identifying Candidate Genes and Proteins Associated With VKH Disease We manually collected candidate genes and proteins associated with VKH disease by a thorough review of the literature in any language published from May 1981 to November 2019, using a similar approach used earlier by others ([39]9, [40]10). We used the following terms to search the PubMed database: “idiopathic uveoencephalitis” OR uveoencephalitis OR “uveomeningitic syndrome” OR Vogt-Koyanagi-Harada. Studies were eligible if they included a comparison of the expression levels of a gene or protein between VKH patients and controls. Key exclusion criteria included: (i) studies only conducted in animal models, (ii) meta-analysis of published results, and (iii) review and comment papers. We also matched eligible genes and proteins with our previously published database (UVEOGENE, [41]http://www.uvogene.com) ([42]11). Two researchers were trained in each step with pilot tests before collecting and managing data independently. Regular meetings were held to clear up any misunderstandings or disagreements. Functional Analysis Functional analysis was performed based on the DAVID online tools (version 6.8, [43]http://david.ncifcrf.gov). In the gene ontology (GO) database, the analysis of candidate genes and proteins was divided into three categories, termed as biological processes (BP), molecular functions (MF), and cellular components (CC) ([44]12). The GO database provides annotations to describe the properties of genes and gene products of different organisms and shows enriched genes’ potential function. BP is an ordered combination of molecular functions to describe a wide range of biological processes. The MF is used to describe the function of a gene or gene product, and the CC is designed for describing subcellular structures, locations, and macromolecular complexes of genes. We submitted the list of identified candidate genes to the DAVID online tools and obtained the significant enrichment of the above categories. A significant threshold of P < 0.05 was used. KEGG Pathway Enrichment Analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on candidate genes to enrich significantly altered pathways, using the DAVID online tools regarding the KEGG database. KEGG is an encyclopedia of genes and genomes for understanding high-level biological functions and utilities ([45]13). The KEGG database provides a collection of graphical diagrams (pathway maps), in which some of the known metabolic pathways and regulatory pathways are displayed to describe the linkage, interaction and function of enriched candidate genes across different cells and organisms. In this study, enriched pathways were considered significant if including at least two genes and reaching a significance level of P < 0.05. Protein-Protein Interaction Analysis Protein-protein interaction (PPI) analysis was performed based on candidate genes and proteins using the STRING database ([46]https://stringdb.org/). The STRING database aims to collect and integrate the information by integrating known and predicted protein-protein association data from many organisms ([47]14). Default parameters in STRING were used. Cytoscape software (version 3.7.2, California, USA) was used to construct and visualize the PPI network. Cytoscape is a graphical display tool used for visualizing complex biological networks ([48]15). Furthermore, we used the Cytoscape plugin molecular complex detection (MCODE) to explore significant modules in the PPI network using the following scores and parameters: k score= 2, degree cutoff= 2, node score cutoff= 0.2, and maximum depth= 100 ([49]16). Identification of Hub Genes CytoHubba plugin in Cytoscape was used to identify hub genes in the PPI network by calculating and analyzing the network structure. Three algorithms were used to generate intersecting genes, including the maximum neighborhood component (MNC), the degree, and the closeness, as described previously ([50]17). Briefly, the degree is the number of the edges of a gene in the network, representing the interaction pairs with others. The closeness could be used to evaluate the genes in the network according to the calculated centrality. The MNC represents the number of nodes in the maximum connected subgraph. The genes generated by the above algorithms’ intersection were more likely to locate in a core position and were considered as the hub genes. Drug-Gene Interactions The Drug-Gene Interaction Database (DGIdb, [51]http://dgidb.genome.wustl.edu/) is a drug prediction database, which can be used to screen drugs that potentially target certain genes of interest ([52]18). The DGIdb provides gene-drug interactions and potential druggability according to their gene category. We utilized the DGIdb analysis to obtain all possible gene-drug interactions for the top-two ranked molecules of hub genes. Cytoscape was used to visualize the acquired interactions. Results Genes and Proteins Acquisition After screening a total of 1,323 articles from PubMed, we obtained 128 eligible articles. Based on the inclusion and exclusion criteria, we identified 48 genes and 54 proteins associated with VKH disease ([53] Table 1 ). The expression of these candidate genes was reported to be significantly changed in VKH disease as compared with healthy individuals. Besides, among these candidate genes, 36 genes overlapped with genes recorded in the Uveogene database for VKH disease ([54]11). These 36 genes had been reported to have susceptible single nucleotide polymorphisms and loci for VKH disease in the Uveogene database. [55]Figure 1 illustrates the flow chart of this study. Table 1. The genes selected from eligible articles associated with VKH disease. Number Gene Symbol Description Gene ID of NCBI Described in the Uveogene database yes or not 1 DRB1 RNA-binding protein 45 129831 Not 2 C3 complement C3 718 Yes 3 FCRL3 Fc receptor like 3 115352 Yes 4 PDCD1/PD1 programmed cell death 1 5133 Yes 5 IL23R interleukin 23 receptor 149233 Yes 6 HLA-DRA major histocompatibility complex, class II, DR alpha 3122 Yes 7 HLA-DRB5 major histocompatibility complex, class II, DR beta 5 3127 Not 8 ADO 2-aminoethanethiol dioxygenase 84890 Yes 9 ZNF365 zinc finger protein 365 22891 Not 10 EGR2 early growth response 2 1959 Not 11 SUMO4 small ubiquitin like modifier 4 387082 Yes 12 CYP2R1 cytochrome P450 family 2 subfamily R member 1 120227 Not 13 KIR 3DS1 killer cell immunoglobulin like receptor, three Ig domains and short cytoplasmic tail 1 3813 Not 14 KIR 2DS1 killer cell immunoglobulin like receptor, two Ig domains and short cytoplasmic tail 1 3806 Not 15 KIR 2DS5 killer cell immunoglobulin like receptor, two Ig domains and short cytoplasmic tail 5 3810 Not 16 KIR 3DL1 killer cell immunoglobulin like receptor, three Ig domains and long cytoplasmic tail 1 3811 Not 17 KIR B killer- cell immunoglobulin- like receptor B 3805 Not 18 aKIR akirin 2 55122 Not 19 JAK1 Janus kinase 1 3716 Yes 20 JAK2 Janus kinase 2 3717 Yes 21 STAT3 signal transducer and activator of transcription 3 6774 Yes 22 MIR146A microRNA 146a 406938 Yes 23 ETS1 ETS proto-oncogene 1, transcription factor 2113 Yes 24 CFH complement factor H 3075 Yes 25 KIAA1109 KIAA1109 84162 Yes 26 IL27 interleukin 27 246778 Yes 27 TGFBR3 transforming growth factor beta receptor 3 7049 Yes 28 CD40 CD40 molecule 958 Yes 29 TLR9 toll like receptor 9 54106 Yes 30 NLRP1 NLR family pyrin domain containing 1 22861 Yes 31 CLEC16A C-type lectin domain containing 16A 23274 Yes 32 PTPN22 protein tyrosine phosphatase non-receptor type 22 26191 Yes 33 IFN-γ/IFN Gamma interferon gamma 3458 Yes 34 BACH2 BTB domain and CNC homolog 2 60468 Yes 35 C1orf141 chromosome 1 open reading frame 141 400757 Not 36 CTLA4 cytotoxic T-lymphocyte associated protein 4 1493 Yes 37 UBLCP1 ubiquitin like domain containing CTD phosphatase 1 134510 Yes 38 IL12B interleukin 12B 3593 Not 39 C2 complement C2 717 Not 40 CFB complement factor B 629 Yes 41 CFI complement factor I 3426 Yes 42 IL17F interleukin 17F 112744 Yes 43 IL12RB2 interleukin 12 receptor subunit beta 2 3595 Not 44 MCP-1/CCL2 C-C motif chemokine ligand 2 6347 Yes 45 CCR6 C-C motif chemokine receptor 6 1235 Yes 46 FGFR1OP centrosomal protein 43 11116 Yes 47 TNFAIP3 TNF alpha induced protein 3 7128 Yes 48 TRAF5 TNF receptor associated factor 5 7188 Yes 49 IL25 Interleukin-25 64806 Not 50 HLA-DRB4 HLA class II histocompatibility antigen, DR beta 4 chain 3126 Not 51 IGHD Immunoglobulin heavy constant delta 3495 Not 52 TGFBR2 TGF-beta receptor type-2 7048 Not 53 PSIP1 PC4 and SFRS1-interacting protein 11168 Not 54 HLA-B HLA class I histocompatibility antigen B alpha chain 3106 Not 55 UACA Uveal autoantigen with coiled-coil domains and ankyrin repeats 55075 Not 56 PAPSS2 Bifunctional 3'-phosphoadenosine 5'-phosphosulfate synthase 2 9060 Not 57 CXCL10 C-X-C motif chemokine 10 3627 Yes 58 CD4 T-cell surface glycoprotein CD4 920 Not 59 HLA-DPB1 HLA class II histocompatibility antigen, DP beta 1 chain 3115 Not 60 TNFSF13 Tumor necrosis factor ligand superfamily member 13 8741 Not 61 CD3E T-cell surface glycoprotein CD3 epsilon chain 916 Not 62 GPBAR1 G-protein coupled bile acid receptor 1 151306 Not 63 HLA-DRB1 major histocompatibility complex, class II, DR beta 1 3123 Yes 64 BCL2A1 Bcl-2-related protein A1 597 Not 65 CXCL9 C-X-C motif chemokine 9 4283 Not 66 LEP Leptin 3952 Not 67 AGER Advanced glycosylation end product-specific receptor 177 Not 68 FAS Tumor necrosis factor receptor superfamily member 6 355 Not 69 IL35 Interleukin-35 3592 Not 70 TYR Tyrosinase 7299 Not 71 IL23A Interleukin-23 subunit alpha 51561 Not 72 HLA-DQA1 HLA class II histocompatibility antigen, DQ alpha 1 chain 3117 Not 73 ARMC9 armadillo repeat containing 9 80210 Not 74 IL9 Interleukin-9 3578 Not 75 C4B Complement C4-B 721 Not 76 IL21 Interleukin-21 59067 Not 77 IL6 Interleukin-6 3569 Not 78 C3AR1 C3a anaphylatoxin chemotactic receptor 719 Not 79 IL2RA Interleukin-2 receptor subunit alpha 3559 Not 80 CCL8 C-C motif chemokine 8 6355 Not 81 DAB2 Disabled homolog 2 1601 Not 82 KIR2DS3 Killer cell immunoglobulin-like receptor 2DS3 3808 Not 83 SPP1 Osteopontin 6696 Yes 84 NOD1 Nucleotide-binding oligomerization domain-containing protein 1 10392 Not 85 IL37 Interleukin-37 27178 Not 86 HLA-DQB1 HLA class II histocompatibility antigen, DQ beta 1 chain 3119 Not 87 FOXP3 Forkhead box protein P3 50943 Not 88 IL7 Interleukin-7 3574 Not 89 IRAK1 Interleukin-1 receptor-associated kinase 1 3654 Not 90 HLA-A MHC class I antigen 3105 Not 91 IL4 Interleukin-4 3565 Not 92 MIF Macrophage migration inhibitory factor 4282 Not 93 TLR3 Toll-like receptor 3 7098 Not 94 KIR2DS2 Killer cell immunoglobulin-like receptor 2DS2 100132285 Not 95 VEGFA Vascular endothelial growth factor A 7422 Not 96 ESD S-formylglutathione hydrolase 2098 Not 97 CXCL1 C-X-C motif chemokine ligand 1 2919 Not 98 FCGBP Fc fragment of IgG binding protein 8857 Not 99 PAX3 Paired box 3 5077 Not 100 CXCL13 C-X-C motif chemokine 13 10563 Not 101 IL15 Interleukin-15 3600 Not 102 IL1B Interleukin-1 beta 3553 Not [56]Open in a new tab Figure 1. [57]Figure 1 [58]Open in a new tab The flowchart of this study. GO Analysis of Genes and Proteins GO enrichment analysis was performed with the DAVID online tools to examine the identified genes and proteins’ biological characteristics. The analysis of BP (biological processes) showed a total of 251 functions, 200 of which were significantly enriched (P < 0.05). The top-ranked functions included the categories “immune response,” “inflammatory response,” “positive regulation of T cell proliferation,” “positive regulation of tyrosine phosphorylation of Stat3 protein,” and “interferon-gamma-mediated signaling pathway.” The CC (cellular components) analysis included 29 functions, of which 26 were significantly enriched (P < 0.05). For the CC analysis, the identified genes and proteins were mostly enriched in the “external side of plasma membrane,” “extracellular space,” “integral component of luminal side of endoplasmic reticulum membrane,” “extracellular region,” and “MHC class II protein complex.” The MF (molecular functions) analysis included 37 functions, 26 of which were significantly enriched (P < 0.05). Changes in MF were significantly enriched in “cytokine activity,” “peptide antigen binding,” “MHC class II receptor activity,” “growth factor activity,” and “chemokine activity.” The top ten functional enrichment analyses of GO (BP, MF, CC) are shown in [59]Figure 2 , and the significant GO (BP, MF, CC) are provided in [60]Supplementary Table S1 . The corresponding genes enriched in GO analysis ([61] Figure 2 ) are listed in [62]Supplementary Table S2 . Figure 2. [63]Figure 2 [64]Open in a new tab The top ten functional enrichment analyses of gene ontology (GO). (A): biological processes (BP); (B): cellular components (CC); (C): molecular functions (MF). Each circle in [65]Figure 2 represented a pathway of GO analysis. The pathway highlighted in red represented more significant P-values, whereas the pathway highlighted in green represented less significant P-values. The size of circle represented the count of pathway, and a larger circle indicated a larger count enriched in the pathway. KEGG Analysis of Genes and Proteins The DAVID online tools were utilized for the KEGG enrichment pathway analysis to show the potential involvement of pathways related to identified candidate genes and proteins. The analysis of KEGG identified 42 significantly altered pathways (P < 0.05) ([66] Supplementary Table S3 ). The top-ranked pathways were mainly involved in categories termed as “inflammatory bowel disease (IBD),” “cytokine-cytokine receptor interaction,” “allograft rejection,” “antigen processing,” and “presentation and Herpes simplex infection” ([67] Figure 3 ). The corresponding genes enriched in the KEGG pathway ([68] Figure 3 ) are listed in [69]Supplementary Table S4 . Additionally, the IBD pathway was the most significant in the enrichment analysis with 19 genes involved ([70] Figure 4 ). The cytokine-cytokine receptor interaction had the largest number of genes, 27 genes enriched in the pathway ([71] Figure 5 ). Moreover, several genes, including IFN-γ, IL6, IL12, IL4, IL23R and IL21, were shared by the IBD pathway and the cytokine-cytokine receptor interaction pathway and were related to the signaling transductions by members of the interleukin-family indicating that these members of the interleukin-family might play an important role in the pathogenesis of VKH disease. Figure 3. [72]Figure 3 [73]Open in a new tab The top ten pathway enrichment analyses from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Figure 4. [74]Figure 4 [75]Open in a new tab The KEGG pathway schematic diagram of inflammatory bowel disease (IBD). Figure 5. [76]Figure 5 [77]Open in a new tab The KEGG pathway schematic diagram of cytokine-cytokine receptor interaction. PPI Network Analysis and Related Gene Modules The PPI network consisted of STRING and Cytoscape, which were used to determine the most important genes and proteins clusters. All the 87 nodes and 754 edges in the PPI network are shown in [78]Figure 6 . Besides, the MCODE plugin in Cytoscape identified two main modules. Cluster 1 (score = 23.28) consisted of 26 nodes and 291 edges, and cluster 2 (score = 6.889) consisted of 10 nodes and 31 edges ([79] Figure 6 ). In cluster 1, several genes, including IFN-γ, IL6, IL4, IL23R and IL21, were significantly enriched. These genes were also related to the enriched pathways, including the IBD pathway and the cytokine-cytokine receptor interaction pathway. In cluster 2, the most enriched genes were related to the human leukocyte antigen (HLA) family, including HLA-A, HLA-B, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DPB1 and HLA-DRB5. Figure 6. [80]Figure 6 [81]Open in a new tab The PPI network was constructed by STRING and Cytoscape. There were 87 nodes and 754 edges in the PPI network. Besides, two modules were recognized by MCODE in Cytoscape. Cluster 1 (score = 23.28) was the most powerful module containing 26 nodes and 291 edges. Cluster 2 (score = 6.889) consisted of 10 nodes and 31 edges. Molecules represented as a pink diamond in the circle with a blue background represents genes belonging to cluster 1, pink triangles in the circle with a purple background represent genes belonging to cluster 2, and genes in the white background do not belong to either cluster. HOXB3, KIR2DL4, GH1, EBI3 and GPR29 in pink graphics were predicted genes using interactions between submitted genes in the PPI network by Cytoscape, and other nodes were of submitted genes in the PPI network and showed interactions between multiple genes. Hub Genes Recognition CytoHubba was utilized to search for the key genes and calculated all the molecular nodes and edges. Consequently, 102 target genes were involved in the PPI network complex, forming 87 nodes and 754 edges ([82] Figure 7 ). To search for the important nodes in the PPI network, all nodes were ranked by the three algorithms, including the degree, closeness and MNC provided by cytoHubba. The cytoHubba plugin Cytoscape was used to analyze the hub genes in the PPI network, and the following genes with the top ten grades were identified as hub genes: IL6, IFN-γ, IL4, CTLA4, IL1B, STAT3, CCL2, CD40, FOXP3 and IL2RA. Among these, IFN-γ and IL-6 were the top two of the ten grades and considered to be the key genes in this model, given the fact that the products of genes were at the core of the PPI network ([83] Figure 7 ). The descriptions of gene symbols and the details shown in [84]Figure 7 are provided in [85]Supplementary Table S5 . Figure 7. [86]Figure 7 [87]Open in a new tab Using Cytohubba, a plugin Cytoscape, the top 10 genes were identified as hub genes. This analysis revealed two key genes: IFN-γ and IL-6 in the cluster. All the gene nodes and edges were calculated. Molecules represented as pink diamonds in the circle with a blue background represented genes belonging to the cluster showing the top ten genes in the network by the Cytoscape plugin Cytohubba, and pink circles without a colored background represent genes not belonging to this cluster. HOXB3, KIR2DL4, GH1, EBI3 and GPR29 in pink graphics were predicted genes using interactions between submitted genes in the PPI network by Cytoscape, and other nodes were of submitted genes in the PPI network and showed interactions between multiple genes. Drug-Gene Interactions IFN-γ and IL-6, as the identified key genes, were entered into the DGIdb to obtain potential drugs. 48 drugs were identified in the DGIdb analysis; the information about the source, scores, and interaction type of target drugs is provided in the [88]Supplementary Table S6 . Most of the target drugs were inhibitors, monoclonal antibodies and immunomodulatory agents. Among these identified drugs, Interferon Alfa-2B was predicted to act on both IFN-γ and IL-6 ([89] Figure 8 ). Figure 8. [90]Figure 8 [91]Open in a new tab Drug targeting the two genes, IFN-γ and IL-6, shown as pink circles. 48 drugs as the main therapeutic agents shown in triangles and expressed in different groups according to scores. Firstly, the triangle in green represented GLUCOSAMINE obtained five scores through the DGIdb analysis, and it was the most relevant drug. Secondly, triangles in purple, including FONTOLIZUMAB and SILTUXIMAB acquired four scores, and these were the second most relevant drugs. Thirdly, drugs with three scores were OLSALAZINE and GINSENG ASIAN as shown by dark blue triangles, and INTERFERON ALFA-2B shown as dark blue diamonds since Interferon Alfa-2B was able to target both of the two key genes. Moreover, most drugs obtained two scores and are shown as light blue triangles (the target drugs with scores can be seen in [92]Supplementary Table S6 ). Lastly, eight drugs, including FUMARIC ACID, APREMILAST, VX-702, CDP-6038, SIRUKUMAB, OLOKIZUMAB, ELSILIMOMAB and PF-04236921, obtained one score and are shown as blue-grey triangles. The scores of 48 drugs identified by the DGIdb show the importance of drugs, with a higher score indicating greater importance. Discussion In the present study, we used systematic analyses to show key pathways and potential VKH disease drugs. Two significant modules and a top ten hub genes were detected with IFN-γ and IL-6 as key genes. 48 target drugs were potentially useful drugs for the treatment of VKH disease. Interferon Alfa-2B targeted both IFN-γ and IL-6 and predicted a potentially useful drug to treat VKH, and warrants further experimental and clinical investigations. The results of GO enrichment analysis indicated that immune response and inflammatory response play a significant role in VKH disease, which confirms earlier studies in this field ([93]19–[94]23). There still are many mediators related to these pathways, which have not been reported to be associated with VKH disease. Our enrichment analysis suggests that these molecules along with their involved pathways are closely linked with the development and pathogenic processes of VKH disease, which warrants further experimental investigations. Various mediators, including HOXB3, GH1, KIR2DL4 have not been reported in VKH disease, but these were predicted to have a high relevance for VKH disease as shown by our PPI network analysis. Previous studies have suggested that HOXB3, GH1 and KIR2DL4 are involved in autoimmune disease such as Thyroid-associated orbitopathy (TAO), Type 1 diabetes (T1DM), and Systemic lupus erythematosus (SLE) ([95]24–[96]26). Their functional role in VKH disease requires further studies. We also identified a variety of pathways related to certain autoimmune diseases such as the IBD pathway, the Toll-Like receptor pathway, the CD27–CD70 pathway and the CD40–CD40L pathway which have been shown to be associated with systemic lupus erythematosus (SLE), multiple sclerosis (MS), rheumatoid arthritis (RA), systemic sclerosis (SSc), Sjögren’s syndrome (SS), psoriasis, uveitis and other autoimmune diseases ([97]11, [98]27–[99]29). These findings suggest a certain degree of shared pathogenic pathways between VKH disease and other autoimmune diseases. Two key genes, IFN-γ and IL-6, were identified in our study by cytoHubba, a plug-in Cytoscape. Lymphocytes produce IFN-γ in response to various immune stimuli. The essential role of IFN-γ in human anti-viral immunity has been illustrated earlier ([100]30, [101]31). Several studies have indicated that VKH is an autoimmune disease mediated by Th1/IFN-γ and Th17/IL-17 pathways ([102]32). Examples include studies that showed that the expression level of IFN-γ is significantly higher in peripheral blood mononuclear cell (PBMC), aqueous or serum of VKH patients as compared with those in control subjects ([103]33–[104]36). Besides IFN-γ we also identified the cytokine IL-6 as a key player in VKH disease. IL-6 is a four-helix cytokine composed of 184 amino acids ([105]37) with various physiological functions, including regulating the proliferation and differentiation of immune cells. IL-6 modulates almost all aspects of the innate immune system. It has been shown that IL-6 plays a significant role in regulating the balance between IL-17 producing Th17 cells and regulatory T cells (Treg) ([106]38, [107]39). Both T-cell subsets play an important role in the pathogenesis of VKH disease. Several studies have shown that the concentration of IL-6 in PBMC, monocyte-derived macrophages (MDMs), or aqueous humor from VKH patients is significantly higher than that observed in controls ([108]40–[109]42). This evidence support that IFN-γ and IL-6 are key mediators related to VKH disease, which suggests that they might be an attractive drug target for this disease. According to the analysis of the DGIdb, Interferon Alfa-2B is specific for both IFN-gamma and IL-6. The Drug-Gene Interaction database (DGIdb) mines available resources and predicts potentially effective therapeutic targets or prioritized drug development based on specific genes ([110]18, [111]43, [112]44). We performed drug-gene interaction networks through bioinformatics analysis to identify target drugs that may act on both IFN-γ and IL-6. Of the 48 target drugs obtained from the DGIdb, most were inhibitors, monoclonal antibodies or immunomodulators. Among these potential drugs, Interferon Alfa-2B was found to be the drug that could target both IFN-γ as well as IL-6. The DGIdb provides evidence showing that Interferon Alfa-2B affects the expression of both IFN-γ and IL-6 ([113]45, [114]46). Interferon, a class of cytokines, can interfere with virus replication, reduce cell proliferation, and alter immunity ([115]47, [116]48). In recent studies, the role of IFN-Alfa in the pathogenesis of autoimmune diseases has been recognized ([117]49, [118]50). Interferon Alfa-2B is an effective drug for treating autoimmune diseases such as idiopathic thrombocytopenic purpura (ITP) and uveitis ([119]51, [120]52). The use of Interferon Alfa-2B has also been reported in the treatment of severe chronic uveitis in patients with Behçet’s disease ([121]53) and uveitic cystoid macular edema ([122]54). VKH disease, along with Behçet’s disease and uveitic cystoid macular edema, are anatomically classified as non-infectious posterior or pan-uveitis and can be treated with the same class of immunomodulatory drugs such as cyclosporin A ([123]55, [124]56). The efficacy of Interferon Alfa-2B in the treatment of VKH disease has not yet been reported, but our analyses highlight the potential of Interferon Alfa-2B for the treatment of this disease, which necessitates further studies. This study has some major limitations. It should be noted that we were dependent on existing data by integrative bioinformatic analysis, and our analyses were based on currently available information obtained from existing research surveys, suggesting that new information from future studies may influence the results presented here. Due to the lack of available data, dynamic networks’ development is not yet based on a genetic-epigenetic association. Moreover, our analyses are largely exploratory, and these results need to be further confirmed by experimental in vitro and in vivo studies. Conclusion In this study, systematic analyses were performed to identify key pathways and drug targets in VKH disease via bioinformatics analysis. Two significant modules and a top ten hub genes in VKH disease were detected with IFN-γ and IL-6 as the top two genes. The study furthermore predicted Interferon Alfa-2B as a potentially useful drug for the treatment of VKH disease. Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Author Contributions PY and ZC conceived and designed the study. ZC and WZ did the literature review. ZZ and GS checked data. ZC and ZZ analyzed and interpreted the data. ZC wrote the first draft of the paper. PY supervised the study. All authors contributed to the article and approved the submitted version. Funding This study was supported by National Natural Science Foundation Key Program (81930023), Natural Science Foundation Major International (Regional) Joint Research Project (81720108009), Chongqing Outstanding Scientists Project (2019), Chongqing Key Laboratory of Ophthalmology (CSTC, 2008CA5003), Chongqing Science & Technology Platform and Base Construction Program (cstc2014pt-sy10002) and the Chongqing Chief Medical Scientist Project (2018). Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary Material The Supplementary Material for this article can be found online at: [125]https://www.frontiersin.org/articles/10.3389/fimmu.2020.587443/ful l#supplementary-material [126]Click here for additional data file.^ (143.3KB, zip) References