Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for Psoriasis

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for Psoriasis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 January 2025 V1 Latest version Share on Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for Psoriasis Authors : Shu-ying Zha 0009-0007-1464-4885 , Wen-jie Wang , Yi-feng Wu , and Li-yun Sun 0000-0002-6021-3927 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173761441.11419357/v1 271 views 153 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background Psoriasis is recognized as a refractory skin disease in the modern world. There are no safe and effective pharmacological treatments with few adverse effects for psoriasis. The objective of this study was to identify potential therapeutic targets for psoriasis through the integration of diverse datasets. Methods We integrated drug-available genomic data, human blood cis-eQTL/Cis-pQTL data, skin tissue cis-eQTL data, and psoriasis GWAS aggregated data, and used drug target Mendelian randomization, Steiger filter analysis, and colocalization analysis to verify the causal relationship between psoriasis and drug target genes. In addition, we perform PH-MR analyses to determine the safety of drug target genes at the genetic level. Results MR analysis identified seven drug genes that may be associated with psoriasis. All of these genes were further confirmed by Bayesian co-localization analysis. (PPH4 > 80%). The up-regulation of PSMB9 expression was associated with a lower risk of psoriasis in the blood eQTL dataset (HR:0.827, 95% CL: 0.780-0.877, P=2.491108e-10), blood pQTL dataset (HR:0.071, 95% CL: 0.036-0.141, P=3.521467e-14) and skin eQTL dataset (HR:0.764, 95% CL: 0.701-0.833, P=9.425763e-10).Increased TNF transcription levels may increase the risk of psoriasis in the blood pQTL (HR:2.590, 95%CL: 1.123-5.976, P=2.561598e-02) /eQTL (HR:1.338, 95%CL: 1.213-1.475, P=5.082644e-09). MICA, HSPA1L, AGER, COL11A2 and FOLH1 were verified in skin eQTL dataset and blood pQTL dataset. Increased transcription levels of FOLH1 may reduce the risk of psoriasis in skin eQTL dataset (HR:0.901, 95%CL: 0.849-0.956, P=5.672095e-04) and blood pQTL dataset (HR:0.813, 95%CL: 0.671-0.987, P=3.4118311e-02); Increased transcription levels of MICA may reduce the risk of psoriasis in skin eQTL dataset (HR:0.787, 95%CL: 0.691-0.896, P=1.048889e-02) and blood pQTL dataset (HR:0.864, 95%CL: 1.048889E-02). 0.762-0.981, P=2.360710e-14). Conclusions: Our study suggests that PSMB9, MICA, HSPA1L, AGER, COL11A2 and FOLH1 may be promising targets for the treatment of psoriasis. However, the role of Artenimol in psoriasis needs to be further verified. Introduction Psoriasis is an immune-mediated chronic, inflammatory, and systemic skin disorder characterized by erythema, papules, and plaques covered with white scales.(1) As one of the most common skin diseases, the main incidence rate of psoriasis in various countries ranges from 0.09% to 11.43%.(2) Among them, the prevalence and incidence of psoriasis in North America, Western Europe and other developed countries are much higher than those in other developing countries. Psoriasis is frequently associated with numerous complications, such as psoriatic arthritis, Crohn’s disease, cancer, metabolic syndrome, and cardiovascular disease.(3) These complications not only cause harm to the patient’s body, but also the stubborn skin changes of psoriasis seriously affect the patient’s mental health. One study indicated that among psoriasis patients, the prevalence of depressive symptoms was 14.9%, while the prevalence of suicidal tendencies reached 6.3%.(4) This set of issues poses a tremendous challenge to the current healthcare system. At present, traditional drugs such as glucocorticoids, vitamin D3 derivatives, and calcineurin inhibitors are mainly used in the treatment of psoriasis. (5) Although the short-term effects are significant, there are unavoidable adverse reactions, such as skin irritation and gastrointestinal reactions, and most of these drugs have shortcomings of recurrence after drug withdrawal.(6) In recent years, the rise of biologics and small-molecule targeted drugs has mitigated the issue of the challenging treatment and cure of psoriasis to a certain degree. Currently, the prevalent biologics targets for psoriasis mainly encompass TNF-α inhibitors and IL-12/23 inhibitors. However, these biological agents have high medical costs, and have certain hepatorenal toxicity, bone marrow suppression and other side effects, and may appear drug resistance, and some biological agents may also induce or aggravate the complications of psoriasis.(7, 8) Therefore, it is necessary to discover safe, effective, and inexpensive new targets to provide the optimal treatment methods for the clinical treatment of psoriasis. Fortunately, with the advancement of human genetics and genomics, scientists have succeeded in identifying and validating a considerable number of new drug targets through large-scale genetic studies based on biobanks, which has given rise to safe and effective innovative therapeutic approaches and drugs for various rare and complex diseases.(9) Genome-wide association studies (GWASs) have played a significant role in identifying single nucleotide polymorphisms (SNPS) associated with psoriasis risk. However, local correlation of multiple genetic variants caused by linkage imbalance can only be preliminatively identified at a single site, and it is difficult to identify causal variants, while most of the association signals point to non-coding regions of the genome. As a result, GWAS cannot accurately locate disease-causing genes and provide clues to drug targets.(10) Therefore, the transition from sites identified in GWAS to drug targets remains a complex undertaking that demands the combination of genomics with other omics approaches, such as proteomics, epigenomics, or transcriptomic analysis. MR is a genetic instrumental variable analysis that estimates the causal effect of exposure on outcomes by using single nucleotide polymorphisms in genome-wide association studies (GWAS) as a genetic tool.(11) Mendelian randomization, which is as persuasive as controlled trials and is less costly and time-consuming, is being widely used to identify new therapeutic targets by integrating aggregated data from disease GWAS and expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) studies, such as Alzheimer’s and ischemic stroke.(12, 13) Our study aims to identify effective drug targets for psoriasis by integrating medicable genomic data, cis-EQTL/Cis-PQTL data from human blood, cis-EQTL data from skin tissue, and aggregated GWAS data for psoriasis. The research flowchart of the study is shown in Figure 1. Figure 1 Research flow chart In the first step, we obtained all the medicinal genes from DGIdb database and Finan.C and others’ research, and merged them to obtain 6889 useful medicinal genes. In the second step, we used eQTL data of skin tissue, eQTL data of human blood and pQTL data of human blood to construct a tool, and according to different conditions, we screened out independent genetic variations (as IVs) which were significant to the expression of pharmaceutical genes, and they were located in the range of 1Mb above and below the coding sequence. In the third step, we carried out MR analysis and a series of tests to verify our results. Finally, we used Phe-MR method to evaluate the side effects of drug targeting, and looked up and evaluated its clinical development progress on related websites. Table 1 data sources of MR analysis in current research Data sources Source of drug target gene We study the use of database interaction between genes from drug – gene(14) (DGIdb v. 5.0.7, https://www.dgidb.org/downloads) and Fina. C, et al. Study (15) (table 1, table S1 and S2). The DGIdb is a publicly accessible resource that aggregates records of genes or gene products, drugs, and drug-gene interactions; (14) We downloaded the interaction data at DGIdb. In addition, we also included the pharmacizable genes reported in the Fina.C et al study,(15) because it works by linking complex disease and biomarker associated sites in genomic association studies (GWAS) to gene sets that code for pharmacizable human proteins, drugs that are biologically active against these targets, and clinical indications for licensed drugs. To uncover the resulting drug development opportunities. eQTL and pQTL data and GWAS data sources Considering that cis-regulatory elements regulate gene expression more directly than trans-regulatory elements,(16) we used cis-EQTL/Cis-PQTL data from human blood and cis-EQTL data from skin tissue (genetic variation in the 1 Mb range on both sides of the coding sequence of medicable genes) (Table 1). Blood cis-EQTL data included blood-derived expression data from 31,684 individuals.(17) Blood cis-PQTL data were derived from deCODE, a large-scale pQTL study of 35,559 Icelanders, which extracted aggregated statistics on genetic associations at levels of 4907 circulating proteins. In addition, we obtained skin tissue cis-eQTL data from the genote-Tissue expression (GTEx) Consortium, which included information from gene transcripts from 605 individuals.(18) Regarding the source of GWAS data for psoriasis, we obtained GWAS data for psoriasis from FinnGen, including data for 10,312 disease groups and 397,564 control groups. FinnGen is a public-private research project, it is a combination of biological samples from Finland library and legacy samples from new genotype data generated by the interpolation and from Finland health registry (https://www.finngen.fi/en), digital health records data, aims to provide new insight into 18 genetic disease.(19) Instrument selection MR Studies use genetic variants strongly associated with exposure factors as instrumental variables to infer causality between exposure factors and outcomes. The selection of instrumental variables for Mendelian randomization study needs to satisfy the three core hypotheses of correlation, independence and exclusivity,(20) namely: (1) there is a strong correlation between instrumental variables and exposure factors; (2) Instrumental variables were not associated with confounding factors that affected the expose-outcome association; (3) Instrumental variables affect outcomes only through exposure. We selected single nucleotide polymorphisms (SNPs) as the instrumental variable. First, we performed a cross-analysis of 6,889 potential drug-related genes derived from the drug-ready genome utilizing three datasets: the human blood eQTL/pQTL dataset and the human skin eQTL dataset. Secondly, it is crucial that instrumental variables (IVs) influence the results exclusively through their effects on exposure factors. (21) We established criteria for identifying robust IVs by applying a genomic significance threshold (P < 5*10-5) and an F statistic ≥ 10 for both skin eQTL and blood pQTL analyses; for blood eQTL analysis, we employed a more stringent genomic significance threshold (P < 5*10-8) with an F statistic ≥ 10. Finally, we set the coefficient of linkage disequilibrium R² to 0.001 and defined a window width of 10 Mb to ensure independence among selected SNPs while mitigating confounding effects due to linkage disequilibrium.(22) Subsequently, we utilized the clustering function available in the Two-Sample MR Package to identify suitable IVs. Mendelian randomization Upon identification of IVs, we extracted the effect estimates of the same variants or their proxies from the psoriasis GWAS dataset for data coordination. We used Wald ratio or inverse variance weighting (IVW) methods to estimate the relationship between exposure factors and outcomes. The P values of the medicinal genes were corrected by BH method. In addition, sensitivity analysis was performed using MR Egger, weighted model and weighted median.(23) In this study, Cochran’s Q test was used to examine heterogeneity, which was able to exclude heterogeneity due to pleiotropy and other uncertain factors. IVW and MR-Egger in Cochran’s Q statistics have been widely used to check heterogeneity.(24) Pleiotropy was evaluated by the MR-Egger intercept.(25) We use the MR-PRESSO method to detect outliers, and if there are outliers, they are removed and re-analyzed.(26) In heterogeneity and pleiotropy analyses, an uncorrected P < 0.05 was considered significant. In Mendelian randomization causality analysis, when evaluating causality between exposure A and outcome B, the instrumental variable (IV) should have A greater correlation with exposure A than with outcome B to avoid reverse causality. To verify the directionality of the association between the protein and BD, Steiger filter analysis was introduced to determine whether there was reverse causality in IVs in addition to those factors that were significantly associated with the result.(27) Steiger filter analysis was performed using the ”two sample Mr” R package. Bayesian colocalization Bayesian colocalization is used to identify whether two phenotypes are driven by the same causal variation in a certain region, thus strengthening the evidence of association between the two phenotypes. This method tests for the presence of colocalization between these signals by comparing the strength of associations between different gene expression trait loci (eQTLs) and specific diseases or biomarkers.(28) A gene-based PPH4 greater than 80% is defined as evidence that the gene has colocation.(29) Phenome-wide MR analysis The purpose of this study is to explore the side effects of seven psoriasis-related proteins by performing full phenotype MR(Phe-MR analysis). We used Zhou et al. (30) to analyze the aggregated statistical data of the British biological sample bank cohort. SNP-outcome associations were downloaded from Sai Gegwas (https://www.leelabsg.org/resource), and we excluded the result that the sample size was less than 500, leaving only 725 non-psoriasis diseases or features to ensure the statistical validity (Table 1 and Table S12). Identification of actionable drugs for candidate genes The DrugBank database (version 5.1.12, https://go.drugbank.com) is a comprehensive repository of drugs and drug targets. The ChEMBL database (version 34, https://www.ebi.ac.uk/chembl) is a bioactive molecule database. ClinicalTrials (https://www.clinicaltrials.gov) is a clinical trial information database. We search through the above-mentioned three databases to determine the feasible drug-related information of candidate genes, including information on the drug molecule type, indications, and clinical development of the relevant drug. Results Druggable genome We downloaded 5012 potential drug target genes from DGIdb v.5.0.7 (Table S1) (14) and extracted 4479 drug-capable genes from Finan et al. ’s study(15) (Table S2). We combined and intersected the available drug genes from the two sources obtained, thus obtaining 6889 available drug genes, and matched these genes with the genes with official names designated by the Human Genome Naming Committee to obtain their official gene names. Candidate drug genes for psoriasis related traits We overlapped 6889 potential drug genes with genes in the eQTL/pQTL dataset of human blood and eQTL dataset of human skin tissue, and then extracted genetic variants within 1 Mb on either side of the overlapping drug gene coding sequence. After IVs selection and quality control, we obtained 3,629 SNPS associated with 3,068 drugable genes from human skin tissue cis-eQTL and 8,291 SNPS associated with 3,776 drugable genes from human blood cis-eQTL. And 6492 SNPS associated with 1445 medicable genes were obtained from human blood CISI-PQTL as IVs, representing exposure to MR Analysis (Figure 1 and Table S4). Next, we performed an MR Analysis of exposure using a GWAS aggregate dataset for psoriasis. We found that 355, 305 and 111 drug-available genes in human blood eQTL and skin tissue eQTL had potential causal relationship with psoriasis (P two drug-capable genes in human blood pQTL (FDR < 0.05), 23 drug-capable genes in human blood eQTL (FDR < 0.05), and 62 drug-capable genes in human skin eQTL (FDR < 0.05) were causally associated with psoriasis. Protein is the basic functional unit of the human body and is the largest drug target,(31) so we selected data with a P-value less than 0.05 in the pQtl dataset. In order for genes to be causally associated with disease across multiple omics, Pval < 0.05 in at least 2 datasets was selected; To exclude correction problems with multiple tests, at least 1 dataset with Fdr less than 0.05 was selected. Finally, we found that seven drug genes (PSMB9, TNF, MICA, HSPA1L, AGER, COL11A2 and FOLH1) met the above screening criteria and had a causal relationship with psoriasis. We used Bayesian colocalization analysis to identify potential drug targets that share a genetic basis with psoriasis, thereby confirming that the causal associations revealed by MR Analysis are indeed derived from common genetic variants. The results showed that seven potential drug genes were colocalized and appeared in multiple QTL datasets (Figure 2 and Table S10). Among them, increased transcription levels of PSMB9 may reduce the risk of psoriatic disease in blood eQTL dataset (HR:0.827, 95%CL: 0.780-0.877, P=2.491108e-10), blood pQTL dataset (HR:0.071, 95%CL: 0.036-0.141, P=3.521467e-14) and skin eQTL dataset (HR:0.764, 95%CL: 0.701-0.833, P=9.425763e-10). Increased TNF transcription levels may increase the risk of psoriasis in the blood pQTL (HR:2.590, 95%CL: 1.123-5.976, P=2.561598e-02) /eQTL (HR:1.338, 95%CL: 1.213-1.475, P=5.082644e-09). MICA, HSPA1L, AGER, COL11A2 and FOLH1 were verified in skin eQTL dataset and blood pQTL dataset. Increased transcription levels of FOLH1 may reduce the risk of psoriasis in skin eQTL dataset (HR:0.901, 95%CL: 0.849-0.956, P=5.672095e-04) and blood pQTL dataset (HR:0.813, 95%CL: 0.671-0.987, P=3.4118311e-02); Increased transcription levels of MICA may reduce the risk of psoriasis in skin eQTL dataset (HR:0.787, 95%CL: 0.691-0.896, P=1.048889e-02) and blood pQTL dataset (HR:0.864, 95%CL: 1.048889E-02). 0.762-0.981, P=2.360710e-14). Our data passed the Steiger filter test, which improves the accuracy of our results by checking directionality and filtering out inappropriate SNPs. (Figure 2). (A) （B） Phe-MR analysis of psoriasis-associated candidate druggable genes We performed a PH-MR analysis of 725 diseases and characteristics in the UK biobank (Table S12). The IVs used for the PH-MR analysis were consistent with previously identified psoriasis drug-able genes (15 SNPS for 7 drug-able genes, see Table S11). At FDR < 0.05, causal effects in the PH-MR analysis were considered statistically significant. We found that increased protein richness of AGER in blood was associated with reduced risk of diverticulosis (OR:0.90), diverticulitis (OR:0.90), asthma (OR:0.81), and in skin with reduced risk of prostatic hyperplasia (OR:0.90), coercive spondylitis (OR:0.24). Increased AGER protein richness in the skin was associated with an increased risk of type 1 diabetes (OR:1.83), disc disease (OR:1.39), gastritis and duodenitis (OR:1.12). Increased COL11A2 protein richness in blood was associated with a reduced risk of bronchiectasis (OR:0.53), and increased COL11A2 protein richness in skin was associated with an increased risk of hypothyroidism (OR:1.48). Increased abundance of PSMB9 protein in blood and skin was associated with reduced risk of multiple sclerosis (OR:0.73, OR:0.64) and iron metabolism disorders (OR:0.73, OR:0.47). It was associated with type 1 diabetes (OR:1.48, OR:1.77), thyrotoxicosis (OR:1.39, OR:1.62), obstructive chronic bronchitis (OR:1.23, OR:1.35), hypothyroidism (OR:1.16, OR:1.24), anemia (OR:1.11, OR:1.16), Peripheral neuropathy was associated with increased risk of related syndromes (OR:1.10, OR:1.15), asthma (OR:1.09, OR:1.13), and interestingly, the increased protein abundance of PSMB9 affected in the same direction as in psoriasis, suggesting that drug targets for PSMB9 in psoriasis may be beneficial for these diseases. FOLH1 genes were not associated with 725 diseases and traits (Table S12), suggesting that drugs targeting that genes have no potential side effects. The rest of the PH-MR analysis is shown in Table (S13). Identification of actionable drugs for target genes We evaluated viable drugs for seven psoriasis candidate genes via three major websites, and five gene-related agents have been evaluated in clinical trials for other diseases (Table 2) but have not yet been used to treat psoriasis, of which MICA and COL11A2 gene related drugs have not been found. However, we found that Artenimol, a ligand of the HSPA1L gene, regulates the level of heat shock factor and thus becomes a potential drug for the treatment of psoriasis.(32) Carfilzomib, a proteasome inhibitor, may not be the ideal drug because it has the opposite effect on psoriasis. Table 2 Identification of Operable Drugs for Target Genes Discussion By integrating datasets, this study offers potential evidence for the genetic colocalization of seven drug target genes (PSMB9, TNF, MICA, HSPA1L, AGER, COL11A2, FOLH1) with psoriasis. Among them, the research on TNF and its predictive drugs in psoriasis is highly extensive, so this aspect will not be elaborated in this study. PSMB9 is a multicatalytic protease complex, belonging to the immune proteasome subunit. In addition to being responsible for intracellular proteolysis, PSMB9 plays an important role in cellular protein homeostasis, and is also mainly involved in MHC Class I antigen processing and CD8+ T cell activation.(33) Immunoproteasomes are constitutively expressed in various immune cells, such as T cells, B cells, and antigen-presenting cells, and their expression can be triggered by pro-inflammatory cytokines, such as interferon gamma (IFN-γ), type I IFN, and tumor necrosis factor alpha (TNF-α). Studies have confirmed that immune proteasome plays an important role in autoimmune diseases and inflammatory diseases, by targeting immune proteasome can inhibit autoimmune and inflammatory diseases, such as rheumatoid arthritis, colitis, etc.(34) Therefore, as psoriasis is an immune-mediated inflammatory disease, we have reason to speculate that there is a close relationship between immune proteasome and psoriasis. In a study by Nobuo Kanazawa et al., they found that heterozygous missense variants of PSMB9 lead to autoinflammatory/immune deficiency syndromes in human and mouse models.(35) In another study, the researchers showed through proteomic analysis that the immune proteasome subunit PSMB9 is upregated in the epidermis of dermatomyositis and systemic lupus erythematosus compared to healthy skin.(36) Few studies have been conducted on PSMB9 and psoriasis, but in a large-scale whole exome sequencing analysis,(37) a rather heterogeneous gene, CDSN, located in the Major histocompatibility Complex (MHC) Class I region on chromosome 6, encodes a protein found in the corneal connectome.It is highly polymorphic in the population, and its variation is associated with psoriasis, and PSMB9 can significantly alter the blood expression of CDSN, thereby affecting the occurrence and development of psoriasis. This finding is the first to suggest a relationship between PSMB9 and psoriasis. Our study found that upregulation of PSMB9 is associated with a reduced risk of psoriasis. Currently, compounds related to the PSMB9 gene have been approved for clinical use, such as Carfilzomib, but they are proteasome inhibitors,(38) which are mostly used to treat multiple myeloma. Therefore, the development of agonists related to the PSMB9 gene for therapeutic intervention in psoriasis is promising. Advanced glycation endproducts (AGER) receptor is a receptor for advanced glycation endproducts (AGEs), also known as RAGE. The loci where the gene is present are mainly involved in inflammation and immune responses. AGER activates multiple signal transduction mechanisms and regulates gene expression through a well-defined set of transcription factors (such as NFκB and AP1). In a study by Pan Kang et al,(39) they detected an abnormal accumulation of AGE in epidermal keratinocytes from psoriasis patients where AGEs promote the production of interleukin 36α(IL-36α) in keratinocytes, thereby enhancing the complementary Th17 (Th17) immune response. The effect of AGEs on keratinocytes is mediated by the AGE receptor (RAGE). But in another study,(38) researchers found that psoriasis patients had significantly lower serum RAGE levels compared to eczema patients or healthy individuals, and that RAGE levels were inversely associated with disease severity as assessed with the PASI score. This is consistent with our findings. It seems that RAGE is implicated in the formation of psoriatic plaques and induces the secretion of pro-inflammatory cytokines as well as the migration of T lymphocytes to the site of inflammation.(38, 40) Oxytocin is a target drug predicted to target AGER gene in this study. Previous studies have shown that oxytocin has therapeutic potential for a variety of inflammatory diseases by regulating the balance between pro-inflammation and anti-inflammatory due to its endogenous anti-inflammatory ability.(41) MICA (MHC class I chain-related genes A) belongs to the MIC gene family and is a loci gene associated with MHC Class I genes. Its receptor is NKG2D on NK cells, CD8+αβT cells and γδT cells (Natural Killer Group 2 member D).(42) The expression of MICA is notably elevated under cellular stress circumstances (such as inflammation, DNA damage, ischemia and reperfusion, etc.). On the one hand, membrane-bound MICA binds to NKG2D to form a complex, triggering NK cells and T cells to release inflammatory cytokines and produce cytotoxic effects.(43) On the other hand, under the action of ADAM protease, MICA can be shed into soluble MICA (sMICA) and secreted into the extracellular environment, which can control the immune reaction process by down-regulating the expression of NKG2D, competitively binding with NKG2D and promoting the proliferation and activity of immunosuppressive CD4+T cell subpopulation.(44) Studies have shown that MICA, located in MHC region I, is an independent psoriasis risk allele and one of the decisive factors leading to susceptibility to MHC psoriasis.(45) Individuals with a strong NKG2D binding MICA-129 Met allele are more likely than those with the Val allele to achieve an activated state and a cytotoxic response, resulting in a greater susceptibility to chronic inflammatory skin diseases such as psoriasis.(46) In a meta-analysis, the researchers found that the MICA-TM A9 allele was significantly associated with psoriasis and psoriatic arthritis in the studied population.(47) A recent study in Taiwan, China, suggests that MICA deficiency is a potential mechanism of psoriasis susceptibility, and functional MICA alleles are significantly associated with the pathogenesis of psoriasis in Taiwan.(48) All the above suggest that MICA gene plays an important role in the pathogenesis of psoriasis, providing ideas for finding new therapeutic targets for psoriasis. HSPA1L gene is A member of the human heat shock protein family A (Hsp70). HSP70 is a key target in the pathogenesis of psoriasis and has important functions in protecting cells from inflammation, apoptosis, immune regulation, and oxidative stress.(49-51) In addition, the expression of HSP70 leads to excessive proliferation of keratinocytes and the secretion of pro-inflammatory cytokines, such as tumor necrosis factor α (TNF-α), interleukin-1β (IL-1B), or IL-6. The study of Wafaa Taresh Adday et al.(52) found that the mean serum HSP70 in patients with psoriasis was significantly higher than that in the control group. Raghuwanshi et al.(53) developed an HSP70-1 inhibitor through molecular docking and molecular dynamics (MD) simulation analysis and evaluated its in vivo anti-psoriasis activity against imiquimod (IMQ) -induced skin inflammation in mice. The results showed that it was effective in reducing DAI score, serum cytokine (TNF-α, IL-22 and IL-23) levels, epidermal thickness, and reducing psoriasis parateratosis and excessive proliferation of keratinocytes. This indicates that our Hsp70 has a good prospect in the treatment of psoriasis. In previous studies, researchers found that patients with inflammatory bowel disease (IBD), which is closely related to psoriasis, had new and rare mutations in the HSPA1L gene.(54) It is not difficult for us to think that HSPA1L gene, as a member of the heat shock protein family, plays an important role in psoriasis. We concluded that two small-molecule drugs acting on HSPA1L gene were Dasatinib and Artenimol, both of which were approved by FDA. Artenimol is an anti-malarial drug, and Dihydroartemisinin (DHA Artenimol is a form of dihydroartemisinin) has been shown to reduce epidermal pathology and T cell infiltration in the skin of IMQ-induced psoriasis mice.(55) At the same time, the expression of IL-15, IL-17 and other proinflammatory cytokines in the skin was inhibited. FOLH1, namely folate hydrolase 1, is a crucial folate metabolizing enzyme. At present, the research on its role in psoriasis is very scarce. In known studies, it was found that the FOLH1 gene plays a significant role in inflammatory bowel disease, which is closely related to psoriasis. FOLH1 enzyme activity is highly expressed in intestinal biopsies of IBD patients and mouse models, and pharmacological and genetic inhibition improves IBD abnormalities.(56) Our research has several advantages. First of all, this study used MR To predict the drug target genes for psoriasis, and explored the potential side effects of the obtained genes through PH-MR analysis, which provided a certain direction for the clinical development of new drugs for psoriasis. Secondly, Mendel’s analysis method combined with colocalization can further verify whether the causal signal between exposure factors and diseases comes from the same genetic variation, which increases the reliability of the research results. However, our experiment still has some shortcomings. First, due to the limitations of the GWAS data, it is based only on the European population, which limits its generalization to other ethnic groups, especially those from Africa and Asia. In addition, our study is not an interventional study, and the results we obtained are predictive results, which need to be further verified if they are to be accurately applied to the clinic. Furthermore, the lack of comprehensive pQTL data for psoriasis in public databases limits our ability to conduct thorough pQTL analyses on this condition. We anticipate the future availability of psoriasis pQTL datasets, which will enable more detailed investigations in this field. Conclusion In conclusion, this study identified seven candidate therapeutic target genes for the treatment of psoriasis (PSMB9, TNF, MICA, HSPA1L, AGER, COL11A2, FOLH1). In this research, we aim to offer novel ideas and targets for the clinical treatment of psoriasis. However, to fully comprehend the underlying molecular mechanisms and confirm the efficacy and safety of these targets, further experimental studies and clinical trials are necessary. Disclosure The authors report no conflicts of interest in this work. Funding Capital Health Research and Development of Special Fund: A Multicenter Randomized Controlled Clinical Trial on the Treatment of Psoriasis (Bai Bi) Blood Stasis Syndrome with Chitosan Nano-Qinteng Activating Blood and Moisturizing Ointment, Project Number: Shoufa 2024-2-2237; State Administration of Traditional Chinese Medicine of the People’s Republic of China Clinical Excellent Talent Training Project (Document [2022] No. 1 from the National Administration of Traditional Chinese Medicine); Beijing Hospitals Authority ”Peak” Talent Training Program (DFL20220801). Author Contributions Professor Sun Liyun conceived and designed the study. Shuying Zha and Wenjie Wang were involved in the data collection and analysis. Shuying Zha drafted the initial manuscript, which was subsequently revised and expanded by Wenjie Wang and Yifeng Wu. The final version of the manuscript was critically reviewed by Professor Sun Liyun. 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Dihydroartemisinin Ameliorates Psoriatic Skin Inflammation and Its Relapse by Diminishing Cd8+ T-Cell Memory in Wild-Type and Humanized Mice. Theranostics (2020) 10(23):10466-82. doi: 10.7150/thno.45211.56. Rais R, Jiang W, Zhai H, Wozniak KM, Stathis M, Hollinger KR, et al. Folh1/Gcpii Is Elevated in Ibd Patients, and Its Inhibition Ameliorates Murine Ibd Abnormalities. JCI Insight (2016) 1(12). PSMB9 Small Molecule Carfilzomib inhibitor The proteasome is a multicatalytic proteinase complex with a highly ordered ring-shaped 20S core structure. TNF Fusion proteins Etanercept inhibitor; antibody This gene encodes a multifunctional proinflammatory cytokine that belongs to the tumor necrosis factor (TNF) superfamily. Monoclonal antibody (mAb) Adalimumab inhibitor; antibody Monoclonal antibody (mAb) Infliximab inhibitor Fusion proteins Certolizumab pegol neutralizer Small Molecule Pomalidomide inhibitor Monoclonal antibody (mAb) Golimumab antibody Cell transplant therapies Foreskin keratinocyte (neonatal) agonist Small Molecule Glycyrrhizic acid antagonist HSPA1L Small Molecule Dasatinib binder This gene encodes a 70kDa heat shock protein. Small Molecule Artenimol ligand AGER Hormones Oxytocin binder The advanced glycosylation end product (AGE) receptor encoded by this gene is a member of the immunoglobulin superfamily of cell surface receptors. FOLH1 Monoclonal antibody (mAb) Capromab pendetide other/unknown This gene encodes a type II transmembrane glycoprotein belonging to the M28 peptidase family. Small Molecule Piflufolastat F 18 binder Small Molecule Flotufolastat F-18 binder Small Molecule Spaglumic acid ligand Druggable genome DGIdb 4.0 Freshour.SL,et al. 2020 https://www.dgidb.org/downloads. Prior druggable gene Finan.C, et al. 2017 Finan C, et al. PMID: 28356508. QTL datasets Blood cis-eQTL eQTLGen Consortium 31684 European https://eqtlgen.org/ Skin cis-eQTL GTEx v8 605 European https://yanglab.westlake.edu.cn/software/smr/ Blood cis-pQTL deCODE 35559 European https://www.decode.com/summarydata/ GWAS summary Psoriasis GWAS Catalog 10312case: 397564con European https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_L12_PSORIASIS.gz 1419 Phenotypes UK Biobank 408961 European https://www.leelabsg.org/resources Supplementary Material File (figures.zip) Download 4.96 MB Information & Authors Information Version history V1 Version 1 23 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords diseases skin Authors Affiliations Shu-ying Zha 0009-0007-1464-4885 Beijing Hospital of Traditional Chinese Medicine View all articles by this author Wen-jie Wang Beijing Hospital of Traditional Chinese Medicine View all articles by this author Yi-feng Wu Beijing Hospital of Traditional Chinese Medicine View all articles by this author Li-yun Sun 0000-0002-6021-3927 [email protected] Beijing Hospital of Traditional Chinese Medicine View all articles by this author Metrics & Citations Metrics Article Usage 271 views 153 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shu-ying Zha, Wen-jie Wang, Yi-feng Wu, et al. Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for Psoriasis. Authorea . 23 January 2025. DOI: https://doi.org/10.22541/au.173761441.11419357/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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