Possible Linking and Treatment between Parkinson’s Disease and Inflammatory Bowel Disease: A Study of Mendelian Randomization Based on Gut–Brain Axis

Possible Linking and Treatment between Parkinson’s Disease and Inflammatory Bowel Disease: A Study of Mendelian Randomization Based on Gut–Brain Axis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Possible Linking and Treatment between Parkinson’s Disease and Inflammatory Bowel Disease: A Study of Mendelian Randomization Based on Gut–Brain Axis Beiming Wang, Xiaoyin Bai, Yingmai Yang, Hong Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4594793/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background : Mounting evidence suggests that Parkinson’s disease (PD) and inflammatory bowel disease (IBD) are closely associated and becoming global health burdens. However, the causal relationshipsand common pathogeneses between them are uncertain. Furthermore, they are uncurable. Thus, we aimedto identify the causal relationships and novel therapeutic targets shared between them based on their common pathophysiological mechanisms in gut-brain-axis (GBA). Methods : A meta-analysis on bidirectional Mendelian randomization (MR) utilizing various datasets was performed to estimate their causal relationship. Then, pleiotropicanalysis under the composite null hypothesis(PLACO) with functional mapping combined withannotation of genetic associations (FUMA) analysis were conducted to identify pleiotropic genes. Next, blood, brain and intestine expression quantitative trait locus (eQTL) were taken to perform drug-target MR finding common causal genes in two diseases. Colocalization analysis ensured the eQTLs of corresponding gene colocalized with disease. Enrichment analysis and protein‒proteininteraction (PPI) network were done to explorecommon pathogenesis pathways. Genes passed all analysis were regarded as drug targets. Results: Our MR meta-analysis revealed thebidirectional causal relationship between diseases, with combined ORs for PD on IBD, CD, UC (1.050 [95% CI: 1.014-1.086], 1.044 [95% CI: 0.995-1.095], 1.063 [95% CI: 1.016-1.120]); for IBD, CD, UC on PD (1.003 [95% CI: 0.973-1.034], 1.035 [95% CI: 1.004-1.067], 1.008 [95% CI: 0.977-1.040]). Overall, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC. Total of733 genes were classified as tier 3 (found in only one tissue) druggable targets, 57 as tier 2 (found in two tissues, 51 protein-coding genes) and 9 as tier 3 (found in three tissues). Among 60 protein-coding druggable targets over tier 2, 18 overlapped with pleiotropic genes and enriched in mitochondria, antigen presentation, processing and immune cell regulation pathways. Three druggable genes ( LRRK2 , RAB29 and HLA-DQA2 ) passed colocalization analysis. LRRK2 and RAB29 were reported to be pleiotropic genes, and RAB29 and HLA-DQA2 were reported for the first time as potential drug targets. Conclusions : This study established areliable causal relationship, possible shared drug targets and common pathogenesis pathways of two diseases, which had important implications for intervention and treatment of two diseases simultaneously. Parkinson’s disease Inflammatory bowel disease Gut-brain-axis Mendelian randomization drug target Quantitative trait loci Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Parkinson’s disease (PD) is traditionally recognized as originating from the early prominent death of dopaminergic neurons in the substantia nigra pars compacta. This neurodegenerative disorder is characterized by symptoms such as tremor, bradykinesia, rigidity, impaired postural reflexes, and balance, along with numerous other motor abnormalities(1). On the other hand, inflammatory bowel disease (IBD), which encompasses two subtypes, Crohn's disease (CD) and ulcerative colitis (UC), is a common and complex gastrointestinal condition marked by intermittent, chronic, or progressive intestinal inflammation. The pathogenesis of IBD is hypothesized to involve a broad range of processes that disrupt the balance between the intestinal mucosa, the immune system, and the microbiota(2). A notable similarity between PD and IBD is the lack of effective, specific treatments for either condition. As PD and IBD have the same prevalence of 0.3% in the general population, they contribute significantly to the global disease burden(1). Interestingly, an increasing number of studies have indicated a close association between these two diseases. From an epidemiological perspective, several comprehensive meta-analyses have reported a significantly increased risk of PD incidence among IBD patients, with risk ratios (RRs) of 1.41, 1.24, and 1.17 and a hazard ratio (HR) of 1.39(3–6). Conversely, an increased incidence of IBD has also been reported among PD patients(7). From the perspective of pathogenesis, numerous studies have highlighted the gut-brain axis (GBA) as a crucial link between these diseases. According to Braak et al.'s hypothesis, PD may originate in the gastrointestinal tract, and pathological α-synuclein—a hallmark of PD—and various cytokines are transferred to the brain via the GBA(8). Furthermore, several studies suggest that this communication could be bidirectional, indicating a complex interplay between the gut and brain in both diseases(9). In this study, we first aimed to explore the potential link between PD and IBD. To achieve this goal, we conducted a meta-analysis using a bidirectional Mendelian randomization (MR) approach to establish a credible causal relationship between the two diseases, addressing the inconsistencies in previous findings(10–12). Determining this causality will help identify risk factors common to both conditions and could enable the prevention of one disease by targeting the pathways associated with these risk factors. Furthermore, given the current lack of effective treatments for both diseases, identifying common drug targets between PD and IBD would be invaluable, regardless of their causal relationship. With the GBA emerging as a possible link between PD and IBD, targeting common drug sites within the GBA is a viable approach(1). Recent research has highlighted gene expression quantitative trait loci (eQTL) as crucial omics integration data, revealing genetic variants that explain variations in gene expression levels(13). These eQTLs serve as functional intermediates for investigating the underlying biological mechanisms of genetics in various diseases and developing potential drug targets. In this study, we used eQTLs from brain, blood, and intestinal tissues—including the small intestine, transverse colon, and sigmoid colon—to model the GBA pathway and identify shared drug targets. These targets were then tested using colocalization methods and overlapped with the pleotropic genes obtained from the PLACO and FUMA analyses. Functional enrichment analysis was performed as the final step. As a result, our study not only confirmed previous findings but also revealed new genetically supported drug targets as pleiotropic genes between the two diseases, along with their possible underlying signaling pathways and functions through enrichment analysis. Our findings contribute to the potential treatment and prevention of both diseases in the future. Method The overall study design is illustrated in Fig. 1 . Further details of the methods and materials used are provided as follows. PD GWAS dataset The GWAS summary data for PD were obtained from the International Parkinson’s Disease Genomics Consortium, which was recently the largest PD GWAS dataset. In total, 482730 discovery samples and 991367 replication samples composed of a European population were included in this GWAS [cases/controls (discovery + replication) for PD: 33674 + 22,632/449,056 + 991367]. The GWAS summary data were obtained from the GWAS Catalog and IEU OpenGWAS project databases(14). IBD, CD, UC GWAS dataset Summary statistics for IBD, UC and CD were obtained from several GWAS datasets: (1) the latest largest meta-analysis GWAS of IBD, which contained a total sample size of 59957 participants of predominantly European ancestry [cases/controls for IBD: 25042/34915; UC: 12366/33609; CD: 12194/28072](15); (2) another IBD GWAS of the European population, which contained a discovery sample size of 34652 participants and a replication sample size of 51998 [cases/controls (discovery + replication) for IBD: 12,882 + 25273/21,770 + 26,715; UC: 6968 + 10679/20464 + 26715; CD: 5,956 + 14594/14927 + 26715](16); (3) the FinnGen 9th release of Finland IBD patients registered in the KELA [cases/controls for IBD: 7625/369652; UC: 5034/371530; CD: 1665/375445](17); (4) the IBD GWAS dataset of the UK biobank, which contained only IBD GWAS summary data [cases/controls for IBD: 7045/449282]. The data were obtained from the GWAS Catalog and IEU OpenGWAS project databases (18). eQTL GWAS dataset A total of 5 eQTL datasets from different tissues were used in this study. The blood eQTL dataset was obtained from eQTLGen(19), where the fully significant cis-eQTLs (false discovery rate (FDR) < 0.05) of 16144 genes were obtained from 31684 blood samples of healthy Europeans. The brain eQTL dataset was obtained from the PsychENCODE consortia, where the fully significant cis-eQTLs (FDR 0.1 fragments per kilobase per million mapped fragments in at least 10 samples and all SNP (single nucleotide polymorphism) information) of 15188 genes were obtained from 1387 brain samples of primarily European populations(20). Finally, we obtained the cis-eQTLs of the intestine, including all three available parts (small intestine, transverse colon and sigmoid colon), from the Genotype-Tissue Expression project (GTEx) V.8, which includes 4445, 8266 and 7362 genes from 174, 368 and 318 accordant tissue samples of the European population, respectively(21). Bidirectional two-sample MR analysis and meta-analysis In this study, a bidirectional two-sample MR approach was used to evaluate the possible causal relationship between PD and IBD, which means that two diseases take turn as exposure or outcome, as showed in Fig. 1 . All the GWAS datasets we used are publicly available, and the details are described above. To obtain a plausible result from MR analysis, which aims to mimic randomized controlled trials (RCTs) in real life, three principal rules must be followed when selecting an eligible instrument variant (IV), which is the first and most important step to perform MR: (1) IV is robustly associated with the exposure; (2) no confounder is associated with IV and outcome; (3) IV is only associated with the outcome through the exposure(22). Therefore, first, to obtain IVs closely associated with exposure, we selected SNPs that reached genome-wide significance (p value < 5*10^-8); then, the intercept of MR‒Egger was calculated as pleiotropy in the post-MR analysis; finally, any IV associated with phenotype related to outcome was removed through the FastTraitR method with a p value < 5*10^-5. F-statistics were calculated with each SNP chosen from exposures as [beta/SE] 2 , which were computed to quantify the strength of instruments (over 10 was considered sufficient). After IVs were chosen, MR was performed between exposure and outcome. First, we harmonized the frequency of affected alleles according to exposure and outcome to ensure that they matched each other. Second, for the specific details of MR, we clumped SNPs based on linkage disequilibrium (LD, r2 = 0.0001) and genomic region (clump window 1,00000 kilobases) to obtain more reliable results. Palindromic genetic variants were discarded, and proxies of missing genetic variants with an LD score > 0.8 were used in further MR analysis. The final results from the inverse-variance weighted (IVW) method were used as the main MR results because they integrate all the effects of IVs with the highest reliability. All the statistically significant IVW results (p < 0.05) used in the next step of the meta-analysis are expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). For the meta-analysis, the OR values of the corresponding exposure-outcome groups were combined using a fixed-effects model with a heterogeneity score of I 2 < 50%. In sensitivity analyses, 4 other MR methods, including MR‒Egger, weighted median mode, simple mode and weighted mode, were also used to assist in IVW interpretation, and post-MR analysis, including heterogeneity tests (Cochrane’s Q value was calculated to evaluate the heterogeneity of IVs, and p < 0.05 was considered to indicate the presence of heterogeneity of IVs) and pleiotropy tests (MR‒Egger regression intercept tests with p < 0.05 were considered to indicate the presence of horizontal pleiotropy), were performed to determine whether heterogeneity and pleiotropy existed in the MR results(23, 24). Due to heterogeneity, a multiplicative random effect IVW model was adopted, and if pleiotropy existed, the whole MR result was discarded. Moreover, Steiger filtering analysis was applied to ensure that the effect of direction was from exposure to outcome but not reverse(25). Finally, the MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) method was used to detect outliers that differed from other IVs and calculate the corrected causal effect after removing the outliers(26). Meta-analysis was reperformed with corrected results to support and verify the final conclusion. PLACO and FUMA analysis PLACO was used to identify the potential pleiotropic SNPs between PD and IBD (including its subtypes)(27). This method merged the two GWAS datasets of PD and IBD patients into one. Then, the generated GWAS data were subjected to FUMA analysis ( https://fuma.ctglab.nl/snp2gene ) to characterize potential pleiotropic genes between two diseases by setting P PLACO 0.2 into a single genetic locus(28). eQTL MR analysis The fundamentals of eQTL MR analysis are identical to those of two-sample MR analysis except that the eQTLs of each genome are selected as the exposure data. To obtain IVs that can represent the expression level of accordance genes, we selected SNPs with both a genome-wide significance threshold of p < 5E-08 and an FDR < 0.05 within ± 100 kb from each gene’s coding region (except for eQTLs from GTEX, for which we selected SNPs with a genome-wide significance threshold of p < 5E-06 to obtain more SNPs to avoid only one SNP remaining to perform MR)(29). The IVs were then clumped at r2 < 0.3 within 1,00000 kilobases(29). The rest of the analysis process was exactly the same as that used for the two-sample MR analysis mentioned above, and the results with an FDR < 0.05 were considered to indicate statistical significance. In this study, we applied drug-target MR analysis to explore the novel causal genes shared by both PD and IBD with eQTL datasets from the human brain, blood and intestine, which could further reveal the links between them. Colocalization analysis Subsequent colocalization analysis was performed for drug targets over tier 2, deciding whether the eQTL SNP is significantly associated with the expression level of a gene, the disease outcome, or both via the following five hypotheses: PPH0, no association with either trait; PPH1, association with the exposure but not the outcome; PPH2, association with the outcome but not the exposure; PPH3, association with the exposure and outcome, with distinct causal variants; and PPH4, association with the exposure and outcome, with a shared causal variant. A low PPH3 + PPH4 value cannot support the null hypothesis(30). We therefore restricted our analysis to genes with a PPH3 + PPH4 value ≥ 0.8(30). Pathway enrichment analysis and protein‒protein network To investigate the biological functions and signaling pathways of the genes identified via MR analysis, enrichment analysis was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods(31). Furthermore, to explore the interactions between previously reported pleiotropic genes and genes identified in this study, a PPI network was constructed by using the Search Tool for the Retrieval of Interacting Genes (STRING) database version 12 ( https://string-db.org/ ). R package All the statistical analyses were performed in R (version 4.3.0) with the following packages: “TwoSampleMR” (version 0.5.7)(25), “FastTraitR” (version 1.0), “MRPRESSO” (version 1.0), “PLACO” (version 0.1.1), and “coloc” (version 5.2.3, default setting). Data visualization was conducted using R packages, including “forestploter” and “heatmap”. Results PD as exposure and IBD as outcome A total of 19 SNPs associated with PD were identified as IVs, but after harmonization with IBD, CD and UC, only some of the IVs remained [Figure 2 ], and all the F statistics were greater than 10 (ranging from 29.91-774.77) [Supplementary Table 1]. In the sensitivity analysis [Supplementary Table 2], no pleiotropic effect was detected by MR‒Egger intercept, and heterogeneity existed in each instrument estimation evaluated by Cochran’s Q test statistics, which was solved with a multiplicative random effects model. Potential outliers, which resulted in potential pleiotropy assessed by the global test, were identified by MR PRESSO for IBD, CD and UC outcomes, and the results remained similar after outlier correction [Figure 2 ]. Steiger filtering detected no ‘FALSE’ direction in significant results [Supplementary Table 2]. In the meta-analysis of estimates from IVW, the combined ORs for PD on IBD, CD, and UC were 1.050 [95% CI: 1.014, 1.086], 1.044 [95% CI: 0.995, 1.095] and 1.063 [95% CI: 1.016, 1.12], respectively, and no heterogeneity > 50% was found. It seems that PD had a positive causal effect on IBD and UC, nearly on CD. These results were robust to MR-PRESSO correction (IBD 1.038 [95% CI: 1.007, 1.070], CD 1.044 [95% CI: 0.995, 1.095], UC 1.046 [95% CI: 1.005, 1.089]). IBD as exposure and PD as outcome When the direction of causal analysis was opposite, the number of SNPs representing IBD, CD and UC varied [Figure 3 ], but all the F statistics were greater than 10, indicating sufficient strength [Supplementary Table 1] (ranging from 29.91–774.77). In the sensitivity analysis, similar to the PD-to-IBD analysis, no pleiotropic effect was detected, heterogeneity was resolved with multiplicative random effects, and the results remained similar after MR-PRESSO outlier correction [Figure 3 ]. Steiger filtering detected no ‘FALSE’ direction between exposure and outcome [Supplementary Table 2]. In the meta-analysis of estimates from IVW, the combined ORs for IBD, CD and UC on PD were 1.003 [95% CI: 0.973, 1.034], 1.035 [95% CI: 1.004, 1.067] and 1.008 [95% CI: 0.977, 1.040], respectively, and no heterogeneity > 50% was found. CD had a positive causal effect on PD but not IBD or UC on PD. These results were robust after MR-PRESSO correction (IBD 0.990 [95% CI: 0.964, 1.017], CD 1.029 [95% CI: 1.001, 1.058], UC 1.012 [95% CI: 0.982, 1.044]). PLACO with FUMA identified pleiotropic genes To identify more possible pleotropic genes between PD and IBD (including their subtypes), we selected the two GWAS datasets with the largest sample sizes and SNP numbers (Nalls MA and de Lang et al.). Then, the GWAS data were subjected to PLACO to obtain overlapping GWAS data. FUMA helped us to annotate our newly obtained GWAS data, and as a result, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC, respectively, which provided us with a possible range of shared drug targets [Supplementary Table 3]. Shared drug targets between PD and IBD We used eQTL datasets from the brain, blood and intestine, including the small intestine, transverse colon and sigmoid colon, to extract eQTLs of the corresponding genes. A total of 15188 genes were extracted from the brain eQTL dataset, and 7427 genes remained after clumping; 16144 genes were extracted from the blood eQTL dataset, and 15055 genes remained after clumping; 4445 genes were extracted from the small intestine eQTL dataset, and 4169 genes remained after clumping; 8266 genes were extracted from the transverse colon eQTL dataset, and 7809 genes remained after clumping; 7362 genes were extracted from the sigmoid colon eQTL dataset, and 6975 genes remained after clumping. After performing MR on both PD and IBD with these gene symbols, 733 genes were found to have a significant (FDR < 0.05) causal effect on both PD and IBD in only one type of tissue (defined as tier 3 drug target), 57 genes (51 protein coding genes) were found to have a significant (FDR < 0.05) causal effect in two types of tissues (defined as tier 2 drug target), and 9 genes were found to have a significant (FDR < 0.05) causal effect in all types of tissues, including blood, brain and intestine (defined as tier 1 drug target)[supplementary table 3]. We believe that only genes in tier 2 have the potential to link PD and IBD through the GBA, so a total of 60 protein-coding genes over tier 2 were included in the next step of the analysis, and the beta values of their effects are presented in Fig. 4 . Next, we aimed to identify genes that had same direction of effects on PD and IBD in the same tissues. In summary, 10 genes had a positive effect on all types of tissues ( GPR161 , LRRK2, MAEL , MAPT , SLC41A3 , RAB29 , NMT1 , ATP23 , ZYG11B , and ERCC3 ); 12 genes had a negative effect on all types of tissues ( CPEB1 , CTSS , HORMAD1 , MAN2B2 , SRPK1 , TBRG4 , TXNDC16 , ENTR1 , ZNF641 , DCAKD , HLA-DQA2 , and HLA-DOB ); and 2 genes ( ASB1 and GALK2 ) were found to have a consistent effect on the same tissues, but an inverse effect between different tissues and the remaining genes with antagonistic effects can be seen as separate drug targets for PD and IBD (Fig. 4 ). We then compared our results with the list of pleiotropic genes and found that 70 genes overlapped with the results from eQTL MR analysis, including 18 genes classified beyond tier 2, which showed significant overlap with our drug targets [Supplementary Table 3]. We also summarized 44 pleiotropic genes reported between PD and IBD in previous studies and found that 10 genes overlapped with our drug targets in tier 2, and 17 genes overlapped with our PLACO with FUMA-identified pleiotropic genes [Supplementary Table 3](1, 32, 33). Notably, XPO1 , RAB29 , KANSL1 , PNKD , TMBIM1 , MAPT , and LRRK2 were found in all three groups. Results of colocalization analysis Colocalization analysis was subsequently performed to further determine whether the eQTLs of 58 genes shared causal genetic variants using the SNPs within ± 100 kb of the gene coding region [Figure 5 ]. The results suggested that most identified genes likely shared a causal variant with only one disease (PD or IBD). However, several genes ( HLA-DQA2 , LRRK2 , RAB29 , NRBP1 , and PPM1G ) produced significant results in both diseases, which means that they were shared at the same time. We then focused on HLA-DQA2 , LRRK2 , and RAB29 because they had concordant effects on the same tissues. LRRK2 shares causal variants in the blood, brain, transverse colon and sigmoid colon between the two diseases. RAB29 shares causal variants in the brain, small intestine, transverse colon and sigmoid colon between the two diseases. HLA-DQA2 shares causal variants in the blood, small intestine, transverse colon and sigmoid colon between the two diseases. Notably, LRRK2 and RAB29 were also found to overlap among drug targets, our pleiotropic genes and previously identified pleiotropic genes, indicating that they may play important roles in PD and IBD. Enrichment and PPI network analysis We conducted enrichment analysis using GO and KEGG methods to explore the potential shared pathogenesis underlying PD and IBD. Interestingly, we found that all positive genes were enriched in pathways related to mitochondrial regulation, protein localization, and processing [Supplementary Fig. 1]. Conversely, genes with protective effects were mainly enriched in antigen processing and presentation via major histocompatibility complex (MHC) class II proteins and the phagosome pathway according to both GO and KEGG analyses [Supplementary Fig. 1]. We then constructed separate PPI networks for positive genes and negative genes between the two diseases [Supplementary Fig. 2]. LRRK2 was found to interact with MAPT and RAB29 , while HLA-DQA2 closely interacted with CTSS and HLA-DOB [Supplementary Fig. 2], consistent with the enriched biological pathways. Finally, we constructed a PPI network based on the combination of genes beyond tier 2 with previous identified pleiotropic genes, which were significantly connected and enriched (P = 1.05E-04) and converged on antigen processing, mitochondrial protein processing, and immune cell regulation, aligning with our enrichment analysis results and enhancing the credibility of our results [Figure 6 ]. Discussion There is now mounting evidence supporting the observational association and genetic correlation between PD and IBD. We evaluated their causality using a meta-analysis based on MR. Additionally, more than 200 genes were identified as pleiotropic genes, and a total of 799 genes were identified (733 tier 1 drug targets, 57 tier 2 drug targets, and 9 tier 3 drug targets). Among these, LRRK2 , RAB29 , and HLA-DQA2 have emerged as the most important drug targets due to their concordant effects and colocalization on both diseases. Notably, LRRK2 and RAB29 were identified as pleiotropic genes, and RAB29 and HLA-DQA2 were first reported as drug targets for both diseases. All three genes highlight a new direction for drug development targeting these two diseases. Our meta-analysis revealed that PD may have a positive causal effect on IBD [OR: 1.038, 95% CI: 1.007–1.070] and UC [OR: 1.046, 95% CI: 1.005–1.089], while CD may have a positive causal effect on PD [OR: 1.029, 95% CI: 1.001–1.058]. We believe that previous studies did not reach the same conclusion because they relied on a single GWAS dataset. To address this issue, we conducted a meta-analysis using diverse European populations, including GWAS data from the UK Biobank, FinnGen, and other sources, to minimize potential bias. Our final results suggest a possible bidirectional association between PD and IBD. Decades ago, Braak et al. considered the role of the GI system in PD and were the first to hypothesize that PD originates in the gut based on the finding of accumulated α-synuclein (α-syn) in enteric nerves(34). Braak proposed that α-syn can be transported from the intestine to the brain via the vagus nerve(34). This is supported by evidence that α-syn spreads in a caudo-rostral direction to the brain in the early stages of PD(35). This may explain why CD may have a causal effect on PD, as enteric α-syn expression is increased in CD patients but not in UC patients(36). However, the communication between the gut and brain is bidirectional, as supported by various pieces of evidence. One study from Sweden reported that the incidence of IBD significantly increased following PD diagnosis (adjusted RR = 1.40, 95% CI: 1.20–1.80)(7). Experimentally, one study revealed that damage to the nigrostriatal dopaminergic system in rats led to increased colonic levels of inflammatory and oxidative stress markers, along with activation of the proinflammatory arm of the local renin-angiotensin system(37). Other studies have suggested that α-syn might accumulate in the brain and be transported to enteric nerves via the vagus nerve or blood, causing intestinal inflammation(38–41). Although the link between PD and IBD is still debated, it is confidently assumed to be mediated through the GBA. In our study, LRRK2, RAB29, and HLA-DQA2 emerged as the three most important shared drug targets. Notably, this is the first time that RAB29 and HLA-DQA2 have been reported as druggable genes. This finding highlights new potential drug targets for both PD and IBD. The abnormal regulation of three gene-related pathways can be recognized as an indicator of disease development. LRRK2 , RAB29 , and HLA-DQA2 are the three most valuable identified drug targets in this article. LRRK2 is a well-known gene and protein associated with both PD and CD. It is a large protein (2527 amino acids, 286 kDa) with multiple domains that functions as a kinase, a GTPase, or a scaffold for protein interactions(42). The G2019S mutation in LRRK2 is the most common mutation associated with PD and may account for up to 1% of all PD cases(43). Increasing evidence suggests that LRRK2 plays a critical role in both PD and IBD. For example, the G2019S mutation, along with the N2018D and M2397T mutations in LRRK2 , can increase the risk of both PD and CD(1). At present, LRRK2-in-1 and other small molecule antagonist drugs targeting LRRK2-G2019S and inhibit LRRK2 kinase activity have been developed for PD patients and are in the clinical trial stage, but there is no evidence that they can be applied to CD patients(44). RAB29 , often reported to be closely associated with LRRK2 , has not been previously linked to IBD pathogenesis. It is believed to act as a master regulator of LRRK2 , controlling its activation, localization, and potentially biomarker phosphorylation. As a substrate of LRRK2 , RAB29 undergoes LRRK2 -mediated phosphorylation, which might act as a negative feedback loop to prevent RAB29 from activating LRRK2 kinase activity(45). This finding suggested that the kinase activity of LRRK2 plays an important role in both diseases, with LRRK2 and RAB29 harmonically controlling it. Additionally, they regulate phagocytosis and lysosome-related organelle biogenesis(45, 46). HLA-DQA2 , on the other hand, belongs to the human leukocyte antigen (HLA class II) alpha chain family. The protein it encodes is located in intracellular vesicles and plays a central role in the peptide loading of MHC class II molecules by helping release the CLIP molecule from the peptide binding site. HLA-DQA2 is involved in immune regulation in both PD and IBD, making it a critical target for understanding and potentially treating these diseases(47). We have summarized the genetic correlation between PD and IBD for comparison with our results. It is rational to find an obvious overlap. However, we went a step further by utilizing eQTLs from various GBA tissues to identify novel drug targets beyond tier 2. These newly discovered drug targets are closely associated with previously reported causal genes and are enriched in pathways related to mitochondria and antigen presentation. The risk genes were found to be enriched in pathways related to mitochondrial regulation, mitochondrial protein localization and processing. Abnormalities in these pathways are recognized as central mechanisms in multiple neurodegenerative diseases and contribute to IBD inflammation(48, 49). Antigen presentation and processing, which are related to protective genes, are essential for establishing immune tolerance and effective immune responses. Reduced antigen presentation can worsen immune conditions in both PD and IBD patients. Therefore, biological processes related to mitochondrial function and immune regulation may represent core pathogenic mechanisms shared between PD and IBD. Limitations This study has several limitations. First, the study lacked protein quantitative trait locus (pQTL) datasets. Due to the small amount of pQTL data and poor overlap between the pQTL and eQTL datasets, we opted for eQTL datasets with a broader range of genes to identify more potential drug targets. Second, while PD originates in the basal ganglia, the eQTL data we used were from the human parietal lobes due to the limited availability of eQTL data from the basal ganglia. Third, the number of genes aggregated from different eQTL datasets varies, which could result in some MR results being missed due to gene misalignment. Additionally, the sample sizes of the GTEx eQTL datasets are relatively small; however, we utilized three different GTEx eQTL datasets to cross-validate our findings. However, data from other non-European populations are lacking. Finally, incorporating an eQTL dataset from peripheral nerves is recommended, as the vagus nerve is considered a possible connection pathway between PD and IBD. Conclusion This study not only established a reliable causal relationship and identified drug targets between PD and IBD but also provided a novel approach for studying the genetic correlation between these two diseases. The causal genes identified in multiple GBA tissues were enriched in pathways related to mitochondrial protein processing and localization, as well as antigen presentation and processing. These findings indicate possible common pathogenic pathways via the GBA. LRRK2, RAB29, and HLA-DQA2 are the three most compelling drug targets for further treatment of both IBD and PD. Abbreviations PD Parkinson’s disease IBD Inflammatory bowel disease GBA Gut-brain-axis MR Mendelian randomization PLACO Pleiotropic analysis under the composite null hypothesis FUMA Functional mapping combined with annotation of genetic associations eQTL expression quantitative trait locus PPI Protein‒protein interaction CD Crohn's disease UC Ulcerative colitis HR Hazard ratio RRs Risk ratios MHC Major histocompatibility complex GTEx Genotype-Tissue Expression project RCTs Randomized controlled trials IV Instrument variant SNP Single nucleotide polymorphism LD Linkage disequilibrium IVW Inverse-variance weighted CIs Confidence intervals ORs Odds ratios MR-PRESSO MR Pleiotropy Residual Sum and Outlier GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes STRING Search Tool for the Retrieval of Interacting Genes MHC Major histocompatibility complex FDR False discover rate Declarations Ethics approval and consent to participate This study was based on previously collected and published data. In all original studies, ethical approval and consent to participate were obtained. Consent for publication Not applicable Availability of data and materials Code availability: The R code is available upon request from the corresponding author. Data availability: Most data generated or analysed during this study are included in this published article [and its supplementary information files]. The summary statistic data availability is described in supplementary table 4. The rest data is available upon request from the corresponding author. Competing interests The authors declare that they have no competing interests. Acknowledgments We want to acknowledge the participants and investigators of the FinnGen study. Funding information of the genome wide association studies is specified in the cited studies. Author Contributions Beiming Wang collected, processed the data and wrote the article. Xiaoyin Bai contributed to the literature research. Xiaoyin Bai, Yingmai Yang and Hong Yang contributed to manuscript modification. Funding This work was supported by the Capital Health Research and Development of Special Foundation (2022-2-4014), CAMS Innovation Fund for Medical Sciences (2022-I2M-C&T-B-011), National Natural Science Foundation of China (81970495), National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-0222022-PUMCH-C-018, 2022-PUMCH-A-074, 2022-PUMCH-A-179), and National Key Clinical Specialty Construction Project (ZK108000). References Lee HS, Lobbestael E, Vermeire S, Sabino J, Cleynen I. Inflammatory bowel disease and Parkinson's disease: common pathophysiological links. Gut. 2021;70(2):408-17. Keely S, Talley NJ, Hansbro PM. Pulmonary-intestinal cross-talk in mucosal inflammatory disease. Mucosal Immunol. 2012;5(1):7-18. Zhu F, Li C, Gong J, Zhu W, Gu L, Li N. The risk of Parkinson's disease in inflammatory bowel disease: A systematic review and meta-analysis. Dig Liver Dis. 2019;51(1):38-42. 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Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926-40. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-25. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693-8. Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer. PLoS Genet. 2020;16(12):e1009218. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. Xie W, Li J, Du H, Xia J. Causal relationship between PCSK9 inhibitor and autoimmune diseases: a drug target Mendelian randomization study. Arthritis Res Ther. 2023;25(1):148. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383. Chen L, Chu C, Lu J, Kong X, Huang T, Cai YD. Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. PLoS One. 2015;10(5):e0126492. Witoelar A, Jansen IE, Wang Y, Desikan RS, Gibbs JR, Blauwendraat C, et al. Genome-wide Pleiotropy Between Parkinson Disease and Autoimmune Diseases. JAMA Neurol. 2017;74(7):780-92. Kang X, Ploner A, Wang Y, Ludvigsson JF, Williams DM, Pedersen NL, et al. Genetic overlap between Parkinson's disease and inflammatory bowel disease. Brain Commun. 2023;5(1):fcad002. Braak H, Del Tredici K, Rub U, de Vos RA, Jansen Steur EN, Braak E. Staging of brain pathology related to sporadic Parkinson's disease. Neurobiol Aging. 2003;24(2):197-211. Klingelhoefer L, Reichmann H. Pathogenesis of Parkinson disease--the gut-brain axis and environmental factors. Nat Rev Neurol. 2015;11(11):625-36. Prigent A, Lionnet A, Durieu E, Chapelet G, Bourreille A, Neunlist M, et al. Enteric alpha-synuclein expression is increased in Crohn's disease. Acta Neuropathol. 2019;137(2):359-61. Garrido-Gil P, Rodriguez-Perez AI, Dominguez-Meijide A, Guerra MJ, Labandeira-Garcia JL. Bidirectional Neural Interaction Between Central Dopaminergic and Gut Lesions in Parkinson's Disease Models. Mol Neurobiol. 2018;55(9):7297-316. Arotcarena ML, Dovero S, Prigent A, Bourdenx M, Camus S, Porras G, et al. Bidirectional gut-to-brain and brain-to-gut propagation of synucleinopathy in non-human primates. Brain. 2020;143(5):1462-75. Ulusoy A, Phillips RJ, Helwig M, Klinkenberg M, Powley TL, Di Monte DA. Brain-to-stomach transfer of alpha-synuclein via vagal preganglionic projections. Acta Neuropathol. 2017;133(3):381-93. Beach TG, Adler CH, Sue LI, Shill HA, Driver-Dunckley E, Mehta SH, et al. Vagus Nerve and Stomach Synucleinopathy in Parkinson's Disease, Incidental Lewy Body Disease, and Normal Elderly Subjects: Evidence Against the "Body-First" Hypothesis. J Parkinsons Dis. 2021;11(4):1833-43. Noorian AR, Rha J, Annerino DM, Bernhard D, Taylor GM, Greene JG. Alpha-synuclein transgenic mice display age-related slowing of gastrointestinal motility associated with transgene expression in the vagal system. Neurobiol Dis. 2012;48(1):9-19. Deyaert E, Wauters L, Guaitoli G, Konijnenberg A, Leemans M, Terheyden S, et al. A homologue of the Parkinson's disease-associated protein LRRK2 undergoes a monomer-dimer transition during GTP turnover. Nat Commun. 2017;8(1):1008. Zhao Y, Perera G, Takahashi-Fujigasaki J, Mash DC, Vonsattel JPG, Uchino A, et al. Reduced LRRK2 in association with retromer dysfunction in post-mortem brain tissue from LRRK2 mutation carriers. Brain. 2018;141(2):486-95. Deng X, Dzamko N, Prescott A, Davies P, Liu Q, Yang Q, et al. Characterization of a selective inhibitor of the Parkinson's disease kinase LRRK2. Nat Chem Biol. 2011;7(4):203-5. Purlyte E, Dhekne HS, Sarhan AR, Gomez R, Lis P, Wightman M, et al. Rab29 activation of the Parkinson's disease-associated LRRK2 kinase. EMBO J. 2018;37(1):1-18. Wang S, Ma Z, Xu X, Wang Z, Sun L, Zhou Y, et al. A role of Rab29 in the integrity of the trans-Golgi network and retrograde trafficking of mannose-6-phosphate receptor. PLoS One. 2014;9(5):e96242. Chen L, Zhao Y, Li M, Lv G. Proteome-wide Mendelian randomization highlights AIF1 and HLA-DQA2 as targets for primary sclerosing cholangitis. Hepatol Int. 2024;18(2):517-28. Macdonald R, Barnes K, Hastings C, Mortiboys H. Mitochondrial abnormalities in Parkinson's disease and Alzheimer's disease: can mitochondria be targeted therapeutically? Biochem Soc Trans. 2018;46(4):891-909. Ho GT, Theiss AL. Mitochondria and Inflammatory Bowel Diseases: Toward a Stratified Therapeutic Intervention. Annu Rev Physiol. 2022;84:435-59. Supplementary Files Supplementarydata.rar Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 23 Jul, 2024 Editor assigned by journal 20 Jun, 2024 First submitted to journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4594793","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330800153,"identity":"bfb2b2ef-4cf5-4299-8e6c-d1987fd3265b","order_by":0,"name":"Beiming Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3RMQrCMBSA4ZRApmrXCFKvECl08jAvFDopOGbo0KIYwYprj+HopkXIFPeO8QjduqmdFVs3h3xzft5LgpBl/SHiZekF2Mx35Ko0IJLuZEivpTEiDnCuIma06k58FEdTo69cFvNwdF/jHoshHVK+wcGggFDwlCBP7uB7gvM2If4oN3HFT2NE9e3YZ4obTCWoimuCGF10JfM2ofx84Zvla8M+SRwx0Ixn24igfkn7yCAgcHKFKWjldt5lcsjSsmGP11ce6roRie/J/ffkjfvbccuyLOujJ3qdUAaW7ZGtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-2771-5591","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Beiming","middleName":"","lastName":"Wang","suffix":""},{"id":330800154,"identity":"fc5336d1-cb2c-4ac0-8b8e-c5f7433ef32e","order_by":1,"name":"Xiaoyin Bai","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyin","middleName":"","lastName":"Bai","suffix":""},{"id":330800155,"identity":"dfcd1646-f759-4ec5-8415-190e8e8b5266","order_by":2,"name":"Yingmai Yang","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingmai","middleName":"","lastName":"Yang","suffix":""},{"id":330800156,"identity":"8a338427-ed71-4a63-9096-999fa4222008","order_by":3,"name":"Hong Yang","email":"","orcid":"https://orcid.org/0000-0002-2986-7324","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-06-17 14:31:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4594793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4594793/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-024-06045-2","type":"published","date":"2025-01-11T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62890316,"identity":"9703951a-c920-4998-b125-af078ee2142e","added_by":"auto","created_at":"2024-08-20 17:17:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126778,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of this study.\u003c/p\u003e\n\u003cp\u003eFirst, bidirectional MR was performedbetween PD and IBD, CD, UC. Second, a meta-analysis was conducted to integrate the causal effects between PD and IBD, CD, UC. We then performed PLACO and FUMA analyses to identifypleiotropic genes related to diseases. Moreover, drug target MR was conducted to estimate the causal effects of blood, brain, and intestinal druggable eQTLs on both PD and IBD to identify shared drug targets. Colocalization analyses verified the association between genes and disease, and gene function analysis illustrated the role of possible drug targets in biological processes. In the final step, we overlapped the results from the drug-target MR and FUMA to generate credible shared drug targets. MR, Mendelian randomization;PD, Parkinson’sdisease; IBD, inflammatorybowel disease; CD, Crohn’s disease; UC, ulcerative disease; PLACO, pleiotropic analysis under composite null hypothesis; FUMA, functional mapping combining annotation of genetic associations; eQTL, expression quantitative trait locus.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/0ae48e4f3cf8f7fc566e6a4a.png"},{"id":62890584,"identity":"f4703628-fa28-4503-88db-e76ac3f011ce","added_by":"auto","created_at":"2024-08-20 17:25:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372801,"visible":true,"origin":"","legend":"\u003cp\u003eCausal relationship between exposure IBD, CD, UC and outcome PD according to MR analyses.\u003c/p\u003e\n\u003cp\u003eEstimated ORs that obtained from an inverse-variance weighted analysis were combined from the four outcome databases for IBD and three outcome databases for CD and UC using fixed-effect meta-analyses. PD: Parkinson’s disease;IBD: inflammatory bowel disease; CD: Crohn’s disease; UC: ulcerative colitis; OR: odds ratio; CI: confidence interval.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/660fee18721a8dc414574e2c.png"},{"id":62890317,"identity":"4ed91687-344f-4b04-90be-1c6e74affcf0","added_by":"auto","created_at":"2024-08-20 17:17:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":375496,"visible":true,"origin":"","legend":"\u003cp\u003eCausal relationship between exposure IBD, CD, UC and outcome PD according to MR analyses.\u003c/p\u003e\n\u003cp\u003eEstimated ORs that obtained from an inverse-variance weighted analysis were combined from the four exposure databases for IBD and three exposure databases for CD and UC using fixed-effect meta-analyses. PD: Parkinson’s disease; IBD: inflammatory bowel disease; CD: Crohn’s disease; UC: ulcerative colitis; OR: odds ratio; CI: confidence interval.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/23d25553ad4ff86b02a5db5e.png"},{"id":62890322,"identity":"1353119c-6c7d-4bcb-8cf6-0f682a91fe9f","added_by":"auto","created_at":"2024-08-20 17:17:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165857,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of drug-target MR results\u003c/p\u003e\n\u003cp\u003eHeatmap showing the beta values of causal estimates of blood, brain, and intestine (small intestine, transverse colon, and sigmoid colon) eQTLs on PD, IBD, UC, and CD (only genes over tier 2 are shown). CD, Crohn's disease; PD, Parkinson’s disease; IBD, inflammatory bowel disease; UC, ulcerative colitis\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/183ee483c60dba335438be9e.png"},{"id":62890321,"identity":"42486153-c17f-44d5-904c-79cb9778cdf7","added_by":"auto","created_at":"2024-08-20 17:17:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86121,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the colocalization results\u003c/p\u003e\n\u003cp\u003eHeatmap showing the colocalizationvalues of PPH3+PPH4 for genes expressed in the blood, brain, and intestine (small intestine, transverse colon, and sigmoid colon) beyond tier 2 in PD, IBD, UC, and CD. CD, Crohn's disease; PD, Parkinson’s disease; IBD, inflammatory bowel disease; UC, ulcerative colitis\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/2e739767be808241233ccb35.png"},{"id":62890318,"identity":"d83f8367-3d74-41d0-8eb2-6ff7a082eaf7","added_by":"auto","created_at":"2024-08-20 17:17:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":274618,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI network using drug-target MR-identified genes (over tier 2) with previously reported pleiotropic genes\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/3693e24456b31c9f3a2fcaa3.png"},{"id":73694247,"identity":"9ad32ae8-52e5-4d25-bd6b-7c2f5106162b","added_by":"auto","created_at":"2025-01-13 16:12:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2039922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/da721828-6e62-443c-b9d4-13c628ea8a51.pdf"},{"id":62890319,"identity":"965acd79-a12f-48ca-9d84-bd6b74a85258","added_by":"auto","created_at":"2024-08-20 17:17:40","extension":"rar","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1414287,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.rar","url":"https://assets-eu.researchsquare.com/files/rs-4594793/v1/e4a729987d70967a74f67470.rar"}],"financialInterests":"","formattedTitle":"Possible Linking and Treatment between Parkinson’s Disease and Inflammatory Bowel Disease: A Study of Mendelian Randomization Based on Gut–Brain Axis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is traditionally recognized as originating from the early prominent death of dopaminergic neurons in the substantia nigra pars compacta. This neurodegenerative disorder is characterized by symptoms such as tremor, bradykinesia, rigidity, impaired postural reflexes, and balance, along with numerous other motor abnormalities(1). On the other hand, inflammatory bowel disease (IBD), which encompasses two subtypes, Crohn's disease (CD) and ulcerative colitis (UC), is a common and complex gastrointestinal condition marked by intermittent, chronic, or progressive intestinal inflammation. The pathogenesis of IBD is hypothesized to involve a broad range of processes that disrupt the balance between the intestinal mucosa, the immune system, and the microbiota(2). A notable similarity between PD and IBD is the lack of effective, specific treatments for either condition.\u003c/p\u003e \u003cp\u003eAs PD and IBD have the same prevalence of 0.3% in the general population, they contribute significantly to the global disease burden(1). Interestingly, an increasing number of studies have indicated a close association between these two diseases. From an epidemiological perspective, several comprehensive meta-analyses have reported a significantly increased risk of PD incidence among IBD patients, with risk ratios (RRs) of 1.41, 1.24, and 1.17 and a hazard ratio (HR) of 1.39(3\u0026ndash;6). Conversely, an increased incidence of IBD has also been reported among PD patients(7).\u003c/p\u003e \u003cp\u003eFrom the perspective of pathogenesis, numerous studies have highlighted the gut-brain axis (GBA) as a crucial link between these diseases. According to Braak et al.'s hypothesis, PD may originate in the gastrointestinal tract, and pathological α-synuclein\u0026mdash;a hallmark of PD\u0026mdash;and various cytokines are transferred to the brain via the GBA(8). Furthermore, several studies suggest that this communication could be bidirectional, indicating a complex interplay between the gut and brain in both diseases(9).\u003c/p\u003e \u003cp\u003eIn this study, we first aimed to explore the potential link between PD and IBD. To achieve this goal, we conducted a meta-analysis using a bidirectional Mendelian randomization (MR) approach to establish a credible causal relationship between the two diseases, addressing the inconsistencies in previous findings(10\u0026ndash;12). Determining this causality will help identify risk factors common to both conditions and could enable the prevention of one disease by targeting the pathways associated with these risk factors.\u003c/p\u003e \u003cp\u003eFurthermore, given the current lack of effective treatments for both diseases, identifying common drug targets between PD and IBD would be invaluable, regardless of their causal relationship. With the GBA emerging as a possible link between PD and IBD, targeting common drug sites within the GBA is a viable approach(1). Recent research has highlighted gene expression quantitative trait loci (eQTL) as crucial omics integration data, revealing genetic variants that explain variations in gene expression levels(13). These eQTLs serve as functional intermediates for investigating the underlying biological mechanisms of genetics in various diseases and developing potential drug targets.\u003c/p\u003e \u003cp\u003eIn this study, we used eQTLs from brain, blood, and intestinal tissues\u0026mdash;including the small intestine, transverse colon, and sigmoid colon\u0026mdash;to model the GBA pathway and identify shared drug targets. These targets were then tested using colocalization methods and overlapped with the pleotropic genes obtained from the PLACO and FUMA analyses. Functional enrichment analysis was performed as the final step.\u003c/p\u003e \u003cp\u003eAs a result, our study not only confirmed previous findings but also revealed new genetically supported drug targets as pleiotropic genes between the two diseases, along with their possible underlying signaling pathways and functions through enrichment analysis. Our findings contribute to the potential treatment and prevention of both diseases in the future.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThe overall study design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Further details of the methods and materials used are provided as follows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePD GWAS dataset\u003c/h2\u003e \u003cp\u003eThe GWAS summary data for PD were obtained from the International Parkinson\u0026rsquo;s Disease Genomics Consortium, which was recently the largest PD GWAS dataset. In total, 482730 discovery samples and 991367 replication samples composed of a European population were included in this GWAS [cases/controls (discovery\u0026thinsp;+\u0026thinsp;replication) for PD: 33674\u0026thinsp;+\u0026thinsp;22,632/449,056\u0026thinsp;+\u0026thinsp;991367]. The GWAS summary data were obtained from the GWAS Catalog and IEU OpenGWAS project databases(14).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIBD, CD, UC GWAS dataset\u003c/h2\u003e \u003cp\u003eSummary statistics for IBD, UC and CD were obtained from several GWAS datasets: (1) the latest largest meta-analysis GWAS of IBD, which contained a total sample size of 59957 participants of predominantly European ancestry [cases/controls for IBD: 25042/34915; UC: 12366/33609; CD: 12194/28072](15); (2) another IBD GWAS of the European population, which contained a discovery sample size of 34652 participants and a replication sample size of 51998 [cases/controls (discovery\u0026thinsp;+\u0026thinsp;replication) for IBD: 12,882\u0026thinsp;+\u0026thinsp;25273/21,770\u0026thinsp;+\u0026thinsp;26,715; UC: 6968\u0026thinsp;+\u0026thinsp;10679/20464\u0026thinsp;+\u0026thinsp;26715; CD: 5,956\u0026thinsp;+\u0026thinsp;14594/14927\u0026thinsp;+\u0026thinsp;26715](16); (3) the FinnGen 9th release of Finland IBD patients registered in the KELA [cases/controls for IBD: 7625/369652; UC: 5034/371530; CD: 1665/375445](17); (4) the IBD GWAS dataset of the UK biobank, which contained only IBD GWAS summary data [cases/controls for IBD: 7045/449282]. The data were obtained from the GWAS Catalog and IEU OpenGWAS project databases (18).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eeQTL GWAS dataset\u003c/h3\u003e\n\u003cp\u003eA total of 5 eQTL datasets from different tissues were used in this study. The blood eQTL dataset was obtained from eQTLGen(19), where the fully significant cis-eQTLs (false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of 16144 genes were obtained from 31684 blood samples of healthy Europeans. The brain eQTL dataset was obtained from the PsychENCODE consortia, where the fully significant cis-eQTLs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with expression of \u0026gt;\u0026thinsp;0.1 fragments per kilobase per million mapped fragments in at least 10 samples and all SNP (single nucleotide polymorphism) information) of 15188 genes were obtained from 1387 brain samples of primarily European populations(20). Finally, we obtained the cis-eQTLs of the intestine, including all three available parts (small intestine, transverse colon and sigmoid colon), from the Genotype-Tissue Expression project (GTEx) V.8, which includes 4445, 8266 and 7362 genes from 174, 368 and 318 accordant tissue samples of the European population, respectively(21).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBidirectional two-sample MR analysis and meta-analysis\u003c/h2\u003e \u003cp\u003eIn this study, a bidirectional two-sample MR approach was used to evaluate the possible causal relationship between PD and IBD, which means that two diseases take turn as exposure or outcome, as showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All the GWAS datasets we used are publicly available, and the details are described above.\u003c/p\u003e \u003cp\u003eTo obtain a plausible result from MR analysis, which aims to mimic randomized controlled trials (RCTs) in real life, three principal rules must be followed when selecting an eligible instrument variant (IV), which is the first and most important step to perform MR: (1) IV is robustly associated with the exposure; (2) no confounder is associated with IV and outcome; (3) IV is only associated with the outcome through the exposure(22). Therefore, first, to obtain IVs closely associated with exposure, we selected SNPs that reached genome-wide significance (p value\u0026thinsp;\u0026lt;\u0026thinsp;5*10^-8); then, the intercept of MR‒Egger was calculated as pleiotropy in the post-MR analysis; finally, any IV associated with phenotype related to outcome was removed through the FastTraitR method with a p value\u0026thinsp;\u0026lt;\u0026thinsp;5*10^-5. F-statistics were calculated with each SNP chosen from exposures as [beta/SE]\u003csup\u003e2\u003c/sup\u003e, which were computed to quantify the strength of instruments (over 10 was considered sufficient).\u003c/p\u003e \u003cp\u003eAfter IVs were chosen, MR was performed between exposure and outcome. First, we harmonized the frequency of affected alleles according to exposure and outcome to ensure that they matched each other. Second, for the specific details of MR, we clumped SNPs based on linkage disequilibrium (LD, r2\u0026thinsp;=\u0026thinsp;0.0001) and genomic region (clump window 1,00000 kilobases) to obtain more reliable results. Palindromic genetic variants were discarded, and proxies of missing genetic variants with an LD score\u0026thinsp;\u0026gt;\u0026thinsp;0.8 were used in further MR analysis. The final results from the inverse-variance weighted (IVW) method were used as the main MR results because they integrate all the effects of IVs with the highest reliability. All the statistically significant IVW results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) used in the next step of the meta-analysis are expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). For the meta-analysis, the OR values of the corresponding exposure-outcome groups were combined using a fixed-effects model with a heterogeneity score of I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50%.\u003c/p\u003e \u003cp\u003eIn sensitivity analyses, 4 other MR methods, including MR‒Egger, weighted median mode, simple mode and weighted mode, were also used to assist in IVW interpretation, and post-MR analysis, including heterogeneity tests (Cochrane\u0026rsquo;s Q value was calculated to evaluate the heterogeneity of IVs, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate the presence of heterogeneity of IVs) and pleiotropy tests (MR‒Egger regression intercept tests with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate the presence of horizontal pleiotropy), were performed to determine whether heterogeneity and pleiotropy existed in the MR results(23, 24). Due to heterogeneity, a multiplicative random effect IVW model was adopted, and if pleiotropy existed, the whole MR result was discarded. Moreover, Steiger filtering analysis was applied to ensure that the effect of direction was from exposure to outcome but not reverse(25). Finally, the MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) method was used to detect outliers that differed from other IVs and calculate the corrected causal effect after removing the outliers(26). Meta-analysis was reperformed with corrected results to support and verify the final conclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePLACO and FUMA analysis\u003c/h2\u003e \u003cp\u003ePLACO was used to identify the potential pleiotropic SNPs between PD and IBD (including its subtypes)(27). This method merged the two GWAS datasets of PD and IBD patients into one. Then, the generated GWAS data were subjected to FUMA analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fuma.ctglab.nl/snp2gene\u003c/span\u003e\u003cspan address=\"https://fuma.ctglab.nl/snp2gene\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to characterize potential pleiotropic genes between two diseases by setting P\u003csub\u003ePLACO\u003c/sub\u003e \u0026lt; 10E-6 in a\u0026thinsp;\u0026plusmn;\u0026thinsp;250 kb radius and with LD r2\u0026thinsp;\u0026gt;\u0026thinsp;0.2 into a single genetic locus(28).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eeQTL MR analysis\u003c/h2\u003e \u003cp\u003eThe fundamentals of eQTL MR analysis are identical to those of two-sample MR analysis except that the eQTLs of each genome are selected as the exposure data. To obtain IVs that can represent the expression level of accordance genes, we selected SNPs with both a genome-wide significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5E-08 and an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 within \u0026plusmn;\u0026thinsp;100 kb from each gene\u0026rsquo;s coding region (except for eQTLs from GTEX, for which we selected SNPs with a genome-wide significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5E-06 to obtain more SNPs to avoid only one SNP remaining to perform MR)(29). The IVs were then clumped at r2\u0026thinsp;\u0026lt;\u0026thinsp;0.3 within 1,00000 kilobases(29). The rest of the analysis process was exactly the same as that used for the two-sample MR analysis mentioned above, and the results with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate statistical significance. In this study, we applied drug-target MR analysis to explore the novel causal genes shared by both PD and IBD with eQTL datasets from the human brain, blood and intestine, which could further reveal the links between them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eColocalization analysis\u003c/h2\u003e \u003cp\u003eSubsequent colocalization analysis was performed for drug targets over tier 2, deciding whether the eQTL SNP is significantly associated with the expression level of a gene, the disease outcome, or both via the following five hypotheses: PPH0, no association with either trait; PPH1, association with the exposure but not the outcome; PPH2, association with the outcome but not the exposure; PPH3, association with the exposure and outcome, with distinct causal variants; and PPH4, association with the exposure and outcome, with a shared causal variant. A low PPH3\u0026thinsp;+\u0026thinsp;PPH4 value cannot support the null hypothesis(30). We therefore restricted our analysis to genes with a PPH3\u0026thinsp;+\u0026thinsp;PPH4 value\u0026thinsp;\u0026ge;\u0026thinsp;0.8(30).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePathway enrichment analysis and protein‒protein network\u003c/h2\u003e \u003cp\u003eTo investigate the biological functions and signaling pathways of the genes identified via MR analysis, enrichment analysis was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods(31).\u003c/p\u003e \u003cp\u003eFurthermore, to explore the interactions between previously reported pleiotropic genes and genes identified in this study, a PPI network was constructed by using the Search Tool for the Retrieval of Interacting Genes (STRING) database version 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eR package\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed in R (version 4.3.0) with the following packages: \u0026ldquo;TwoSampleMR\u0026rdquo; (version 0.5.7)(25), \u0026ldquo;FastTraitR\u0026rdquo; (version 1.0), \u0026ldquo;MRPRESSO\u0026rdquo; (version 1.0), \u0026ldquo;PLACO\u0026rdquo; (version 0.1.1), and \u0026ldquo;coloc\u0026rdquo; (version 5.2.3, default setting). Data visualization was conducted using R packages, including \u0026ldquo;forestploter\u0026rdquo; and \u0026ldquo;heatmap\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePD as exposure and IBD as outcome\u003c/h2\u003e \u003cp\u003eA total of 19 SNPs associated with PD were identified as IVs, but after harmonization with IBD, CD and UC, only some of the IVs remained [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e], and all the F statistics were greater than 10 (ranging from 29.91-774.77) [Supplementary Table\u0026nbsp;1].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity analysis [Supplementary Table\u0026nbsp;2], no pleiotropic effect was detected by MR‒Egger intercept, and heterogeneity existed in each instrument estimation evaluated by Cochran\u0026rsquo;s Q test statistics, which was solved with a multiplicative random effects model. Potential outliers, which resulted in potential pleiotropy assessed by the global test, were identified by MR PRESSO for IBD, CD and UC outcomes, and the results remained similar after outlier correction [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]. Steiger filtering detected no \u0026lsquo;FALSE\u0026rsquo; direction in significant results [Supplementary Table\u0026nbsp;2].\u003c/p\u003e \u003cp\u003eIn the meta-analysis of estimates from IVW, the combined ORs for PD on IBD, CD, and UC were 1.050 [95% CI: 1.014, 1.086], 1.044 [95% CI: 0.995, 1.095] and 1.063 [95% CI: 1.016, 1.12], respectively, and no heterogeneity\u0026thinsp;\u0026gt;\u0026thinsp;50% was found. It seems that PD had a positive causal effect on IBD and UC, nearly on CD. These results were robust to MR-PRESSO correction (IBD 1.038 [95% CI: 1.007, 1.070], CD 1.044 [95% CI: 0.995, 1.095], UC 1.046 [95% CI: 1.005, 1.089]).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIBD as exposure and PD as outcome\u003c/h2\u003e \u003cp\u003eWhen the direction of causal analysis was opposite, the number of SNPs representing IBD, CD and UC varied [Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e], but all the F statistics were greater than 10, indicating sufficient strength [Supplementary Table\u0026nbsp;1] (ranging from 29.91\u0026ndash;774.77).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, similar to the PD-to-IBD analysis, no pleiotropic effect was detected, heterogeneity was resolved with multiplicative random effects, and the results remained similar after MR-PRESSO outlier correction [Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]. Steiger filtering detected no \u0026lsquo;FALSE\u0026rsquo; direction between exposure and outcome [Supplementary Table\u0026nbsp;2].\u003c/p\u003e \u003cp\u003eIn the meta-analysis of estimates from IVW, the combined ORs for IBD, CD and UC on PD were 1.003 [95% CI: 0.973, 1.034], 1.035 [95% CI: 1.004, 1.067] and 1.008 [95% CI: 0.977, 1.040], respectively, and no heterogeneity\u0026thinsp;\u0026gt;\u0026thinsp;50% was found. CD had a positive causal effect on PD but not IBD or UC on PD. These results were robust after MR-PRESSO correction (IBD 0.990 [95% CI: 0.964, 1.017], CD 1.029 [95% CI: 1.001, 1.058], UC 1.012 [95% CI: 0.982, 1.044]).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePLACO with FUMA identified pleiotropic genes\u003c/h2\u003e \u003cp\u003eTo identify more possible pleotropic genes between PD and IBD (including their subtypes), we selected the two GWAS datasets with the largest sample sizes and SNP numbers (Nalls MA and de Lang et al.). Then, the GWAS data were subjected to PLACO to obtain overlapping GWAS data. FUMA helped us to annotate our newly obtained GWAS data, and as a result, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC, respectively, which provided us with a possible range of shared drug targets [Supplementary Table\u0026nbsp;3].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eShared drug targets between PD and IBD\u003c/h2\u003e \u003cp\u003eWe used eQTL datasets from the brain, blood and intestine, including the small intestine, transverse colon and sigmoid colon, to extract eQTLs of the corresponding genes. A total of 15188 genes were extracted from the brain eQTL dataset, and 7427 genes remained after clumping; 16144 genes were extracted from the blood eQTL dataset, and 15055 genes remained after clumping; 4445 genes were extracted from the small intestine eQTL dataset, and 4169 genes remained after clumping; 8266 genes were extracted from the transverse colon eQTL dataset, and 7809 genes remained after clumping; 7362 genes were extracted from the sigmoid colon eQTL dataset, and 6975 genes remained after clumping. After performing MR on both PD and IBD with these gene symbols, 733 genes were found to have a significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) causal effect on both PD and IBD in only one type of tissue (defined as tier 3 drug target), 57 genes (51 protein coding genes) were found to have a significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) causal effect in two types of tissues (defined as tier 2 drug target), and 9 genes were found to have a significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) causal effect in all types of tissues, including blood, brain and intestine (defined as tier 1 drug target)[supplementary table 3]. We believe that only genes in tier 2 have the potential to link PD and IBD through the GBA, so a total of 60 protein-coding genes over tier 2 were included in the next step of the analysis, and the beta values of their effects are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we aimed to identify genes that had same direction of effects on PD and IBD in the same tissues. In summary, 10 genes had a positive effect on all types of tissues (\u003cem\u003eGPR161\u003c/em\u003e, \u003cem\u003eLRRK2, MAEL\u003c/em\u003e, \u003cem\u003eMAPT\u003c/em\u003e, \u003cem\u003eSLC41A3\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e, \u003cem\u003eNMT1\u003c/em\u003e, \u003cem\u003eATP23\u003c/em\u003e, \u003cem\u003eZYG11B\u003c/em\u003e, \u003cem\u003eand ERCC3\u003c/em\u003e); 12 genes had a negative effect on all types of tissues (\u003cem\u003eCPEB1\u003c/em\u003e, \u003cem\u003eCTSS\u003c/em\u003e, \u003cem\u003eHORMAD1\u003c/em\u003e, \u003cem\u003eMAN2B2\u003c/em\u003e, \u003cem\u003eSRPK1\u003c/em\u003e, \u003cem\u003eTBRG4\u003c/em\u003e, \u003cem\u003eTXNDC16\u003c/em\u003e, \u003cem\u003eENTR1\u003c/em\u003e, \u003cem\u003eZNF641\u003c/em\u003e, \u003cem\u003eDCAKD\u003c/em\u003e, \u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eand HLA-DOB\u003c/em\u003e); and 2 genes (\u003cem\u003eASB1 and GALK2\u003c/em\u003e) were found to have a consistent effect on the same tissues, but an inverse effect between different tissues and the remaining genes with antagonistic effects can be seen as separate drug targets for PD and IBD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe then compared our results with the list of pleiotropic genes and found that 70 genes overlapped with the results from eQTL MR analysis, including 18 genes classified beyond tier 2, which showed significant overlap with our drug targets [Supplementary Table\u0026nbsp;3].\u003c/p\u003e \u003cp\u003eWe also summarized 44 pleiotropic genes reported between PD and IBD in previous studies and found that 10 genes overlapped with our drug targets in tier 2, and 17 genes overlapped with our PLACO with FUMA-identified pleiotropic genes [Supplementary Table\u0026nbsp;3](1, 32, 33). Notably, \u003cem\u003eXPO1\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e, \u003cem\u003eKANSL1\u003c/em\u003e, \u003cem\u003ePNKD\u003c/em\u003e, \u003cem\u003eTMBIM1\u003c/em\u003e, \u003cem\u003eMAPT\u003c/em\u003e, and \u003cem\u003eLRRK2\u003c/em\u003e were found in all three groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eResults of colocalization analysis\u003c/h2\u003e \u003cp\u003eColocalization analysis was subsequently performed to further determine whether the eQTLs of 58 genes shared causal genetic variants using the SNPs within \u0026plusmn;\u0026thinsp;100 kb of the gene coding region [Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e]. The results suggested that most identified genes likely shared a causal variant with only one disease (PD or IBD). However, several genes (\u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eLRRK2\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e, \u003cem\u003eNRBP1\u003c/em\u003e, \u003cem\u003eand PPM1G\u003c/em\u003e) produced significant results in both diseases, which means that they were shared at the same time. We then focused on \u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eLRRK2\u003c/em\u003e, and \u003cem\u003eRAB29\u003c/em\u003e because they had concordant effects on the same tissues. \u003cem\u003eLRRK2\u003c/em\u003e shares causal variants in the blood, brain, transverse colon and sigmoid colon between the two diseases. \u003cem\u003eRAB29\u003c/em\u003e shares causal variants in the brain, small intestine, transverse colon and sigmoid colon between the two diseases. \u003cem\u003eHLA-DQA2\u003c/em\u003e shares causal variants in the blood, small intestine, transverse colon and sigmoid colon between the two diseases. Notably, \u003cem\u003eLRRK2\u003c/em\u003e and \u003cem\u003eRAB29\u003c/em\u003e were also found to overlap among drug targets, our pleiotropic genes and previously identified pleiotropic genes, indicating that they may play important roles in PD and IBD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment and PPI network analysis\u003c/h2\u003e \u003cp\u003eWe conducted enrichment analysis using GO and KEGG methods to explore the potential shared pathogenesis underlying PD and IBD. Interestingly, we found that all positive genes were enriched in pathways related to mitochondrial regulation, protein localization, and processing [Supplementary Fig.\u0026nbsp;1]. Conversely, genes with protective effects were mainly enriched in antigen processing and presentation via major histocompatibility complex (MHC) class II proteins and the phagosome pathway according to both GO and KEGG analyses [Supplementary Fig.\u0026nbsp;1].\u003c/p\u003e \u003cp\u003eWe then constructed separate PPI networks for positive genes and negative genes between the two diseases [Supplementary Fig.\u0026nbsp;2]. \u003cem\u003eLRRK2\u003c/em\u003e was found to interact with \u003cem\u003eMAPT\u003c/em\u003e and \u003cem\u003eRAB29\u003c/em\u003e, while \u003cem\u003eHLA-DQA2\u003c/em\u003e closely interacted with \u003cem\u003eCTSS\u003c/em\u003e and \u003cem\u003eHLA-DOB\u003c/em\u003e [Supplementary Fig.\u0026nbsp;2], consistent with the enriched biological pathways.\u003c/p\u003e \u003cp\u003eFinally, we constructed a PPI network based on the combination of genes beyond tier 2 with previous identified pleiotropic genes, which were significantly connected and enriched (P\u0026thinsp;=\u0026thinsp;1.05E-04) and converged on antigen processing, mitochondrial protein processing, and immune cell regulation, aligning with our enrichment analysis results and enhancing the credibility of our results [Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is now mounting evidence supporting the observational association and genetic correlation between PD and IBD. We evaluated their causality using a meta-analysis based on MR. Additionally, more than 200 genes were identified as pleiotropic genes, and a total of 799 genes were identified (733 tier 1 drug targets, 57 tier 2 drug targets, and 9 tier 3 drug targets). Among these, \u003cem\u003eLRRK2\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e, and \u003cem\u003eHLA-DQA2\u003c/em\u003e have emerged as the most important drug targets due to their concordant effects and colocalization on both diseases. Notably, \u003cem\u003eLRRK2\u003c/em\u003e and \u003cem\u003eRAB29\u003c/em\u003e were identified as pleiotropic genes, and \u003cem\u003eRAB29\u003c/em\u003e and \u003cem\u003eHLA-DQA2\u003c/em\u003e were first reported as drug targets for both diseases. All three genes highlight a new direction for drug development targeting these two diseases.\u003c/p\u003e \u003cp\u003eOur meta-analysis revealed that PD may have a positive causal effect on IBD [OR: 1.038, 95% CI: 1.007\u0026ndash;1.070] and UC [OR: 1.046, 95% CI: 1.005\u0026ndash;1.089], while CD may have a positive causal effect on PD [OR: 1.029, 95% CI: 1.001\u0026ndash;1.058]. We believe that previous studies did not reach the same conclusion because they relied on a single GWAS dataset. To address this issue, we conducted a meta-analysis using diverse European populations, including GWAS data from the UK Biobank, FinnGen, and other sources, to minimize potential bias. Our final results suggest a possible bidirectional association between PD and IBD.\u003c/p\u003e \u003cp\u003eDecades ago, Braak et al. considered the role of the GI system in PD and were the first to hypothesize that PD originates in the gut based on the finding of accumulated α-synuclein (α-syn) in enteric nerves(34). Braak proposed that α-syn can be transported from the intestine to the brain via the vagus nerve(34). This is supported by evidence that α-syn spreads in a caudo-rostral direction to the brain in the early stages of PD(35). This may explain why CD may have a causal effect on PD, as enteric α-syn expression is increased in CD patients but not in UC patients(36). However, the communication between the gut and brain is bidirectional, as supported by various pieces of evidence. One study from Sweden reported that the incidence of IBD significantly increased following PD diagnosis (adjusted RR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 1.20\u0026ndash;1.80)(7). Experimentally, one study revealed that damage to the nigrostriatal dopaminergic system in rats led to increased colonic levels of inflammatory and oxidative stress markers, along with activation of the proinflammatory arm of the local renin-angiotensin system(37). Other studies have suggested that α-syn might accumulate in the brain and be transported to enteric nerves via the vagus nerve or blood, causing intestinal inflammation(38\u0026ndash;41).\u003c/p\u003e \u003cp\u003eAlthough the link between PD and IBD is still debated, it is confidently assumed to be mediated through the GBA. In our study, LRRK2, RAB29, and HLA-DQA2 emerged as the three most important shared drug targets. Notably, this is the first time that RAB29 and HLA-DQA2 have been reported as druggable genes. This finding highlights new potential drug targets for both PD and IBD. The abnormal regulation of three gene-related pathways can be recognized as an indicator of disease development.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLRRK2\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e, and \u003cem\u003eHLA-DQA2\u003c/em\u003e are the three most valuable identified drug targets in this article. \u003cem\u003eLRRK2\u003c/em\u003e is a well-known gene and protein associated with both PD and CD. It is a large protein (2527 amino acids, 286 kDa) with multiple domains that functions as a kinase, a GTPase, or a scaffold for protein interactions(42). The G2019S mutation in \u003cem\u003eLRRK2\u003c/em\u003e is the most common mutation associated with PD and may account for up to 1% of all PD cases(43). Increasing evidence suggests that \u003cem\u003eLRRK2\u003c/em\u003e plays a critical role in both PD and IBD. For example, the G2019S mutation, along with the N2018D and M2397T mutations in \u003cem\u003eLRRK2\u003c/em\u003e, can increase the risk of both PD and CD(1). At present, LRRK2-in-1 and other small molecule antagonist drugs targeting LRRK2-G2019S and inhibit \u003cem\u003eLRRK2\u003c/em\u003e kinase activity have been developed for PD patients and are in the clinical trial stage, but there is no evidence that they can be applied to CD patients(44).\u003c/p\u003e \u003cp\u003e \u003cem\u003eRAB29\u003c/em\u003e, often reported to be closely associated with \u003cem\u003eLRRK2\u003c/em\u003e, has not been previously linked to IBD pathogenesis. It is believed to act as a master regulator of \u003cem\u003eLRRK2\u003c/em\u003e, controlling its activation, localization, and potentially biomarker phosphorylation. As a substrate of \u003cem\u003eLRRK2\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e undergoes \u003cem\u003eLRRK2\u003c/em\u003e-mediated phosphorylation, which might act as a negative feedback loop to prevent \u003cem\u003eRAB29\u003c/em\u003e from activating \u003cem\u003eLRRK2\u003c/em\u003e kinase activity(45). This finding suggested that the kinase activity of \u003cem\u003eLRRK2\u003c/em\u003e plays an important role in both diseases, with \u003cem\u003eLRRK2\u003c/em\u003e and \u003cem\u003eRAB29\u003c/em\u003e harmonically controlling it. Additionally, they regulate phagocytosis and lysosome-related organelle biogenesis(45, 46).\u003c/p\u003e \u003cp\u003e \u003cem\u003eHLA-DQA2\u003c/em\u003e, on the other hand, belongs to the human leukocyte antigen (HLA class II) alpha chain family. The protein it encodes is located in intracellular vesicles and plays a central role in the peptide loading of MHC class II molecules by helping release the CLIP molecule from the peptide binding site. \u003cem\u003eHLA-DQA2\u003c/em\u003e is involved in immune regulation in both PD and IBD, making it a critical target for understanding and potentially treating these diseases(47).\u003c/p\u003e \u003cp\u003eWe have summarized the genetic correlation between PD and IBD for comparison with our results. It is rational to find an obvious overlap. However, we went a step further by utilizing eQTLs from various GBA tissues to identify novel drug targets beyond tier 2. These newly discovered drug targets are closely associated with previously reported causal genes and are enriched in pathways related to mitochondria and antigen presentation. The risk genes were found to be enriched in pathways related to mitochondrial regulation, mitochondrial protein localization and processing. Abnormalities in these pathways are recognized as central mechanisms in multiple neurodegenerative diseases and contribute to IBD inflammation(48, 49). Antigen presentation and processing, which are related to protective genes, are essential for establishing immune tolerance and effective immune responses. Reduced antigen presentation can worsen immune conditions in both PD and IBD patients. Therefore, biological processes related to mitochondrial function and immune regulation may represent core pathogenic mechanisms shared between PD and IBD.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the study lacked protein quantitative trait locus (pQTL) datasets. Due to the small amount of pQTL data and poor overlap between the pQTL and eQTL datasets, we opted for eQTL datasets with a broader range of genes to identify more potential drug targets.\u003c/p\u003e \u003cp\u003eSecond, while PD originates in the basal ganglia, the eQTL data we used were from the human parietal lobes due to the limited availability of eQTL data from the basal ganglia.\u003c/p\u003e \u003cp\u003eThird, the number of genes aggregated from different eQTL datasets varies, which could result in some MR results being missed due to gene misalignment. Additionally, the sample sizes of the GTEx eQTL datasets are relatively small; however, we utilized three different GTEx eQTL datasets to cross-validate our findings. However, data from other non-European populations are lacking.\u003c/p\u003e \u003cp\u003eFinally, incorporating an eQTL dataset from peripheral nerves is recommended, as the vagus nerve is considered a possible connection pathway between PD and IBD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study not only established a reliable causal relationship and identified drug targets between PD and IBD but also provided a novel approach for studying the genetic correlation between these two diseases. The causal genes identified in multiple GBA tissues were enriched in pathways related to mitochondrial protein processing and localization, as well as antigen presentation and processing. These findings indicate possible common pathogenic pathways via the GBA. LRRK2, RAB29, and HLA-DQA2 are the three most compelling drug targets for further treatment of both IBD and PD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInflammatory bowel disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGut-brain-axis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLACO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePleiotropic analysis under the composite null hypothesis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFUMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional mapping combined with annotation of genetic associations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eexpression quantitative trait locus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein‒protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrohn's disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUlcerative colitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRisk ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor histocompatibility complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotype-Tissue Expression project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCTs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized controlled trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrument variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse-variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR-PRESSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMR Pleiotropy Residual Sum and Outlier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTRING\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSearch Tool for the Retrieval of Interacting Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor histocompatibility complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discover rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on previously collected and published data. In all original studies, ethical approval and consent to participate were obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode\u0026nbsp;availability: The R\u0026nbsp;code is available\u0026nbsp;upon\u0026nbsp;request from the corresponding author.\u003c/p\u003e\n\u003cp\u003eData availability: Most data generated or analysed during this study are included in this published article [and its supplementary information files]. The summary statistic data availability is described in supplementary table 4. The rest data is available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of the FinnGen study. Funding information of the genome wide association studies is specified in the cited studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeiming Wang\u0026nbsp;collected, processed the data and wrote the article. Xiaoyin Bai contributed to\u0026nbsp;the\u0026nbsp;literature research. Xiaoyin Bai, Yingmai Yang and Hong Yang contributed to manuscript modification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Capital Health Research and Development of Special Foundation (2022-2-4014), CAMS Innovation Fund for Medical Sciences (2022-I2M-C\u0026amp;T-B-011), National Natural Science Foundation of China (81970495), National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-0222022-PUMCH-C-018, 2022-PUMCH-A-074, 2022-PUMCH-A-179), and National Key Clinical Specialty Construction Project (ZK108000).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee HS, Lobbestael E, Vermeire S, Sabino J, Cleynen I. Inflammatory bowel disease and Parkinson\u0026apos;s disease: common pathophysiological links. Gut. 2021;70(2):408-17.\u003c/li\u003e\n\u003cli\u003eKeely S, Talley NJ, Hansbro PM. Pulmonary-intestinal cross-talk in mucosal inflammatory disease. 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Annu Rev Physiol. 2022;84:435-59.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, Inflammatory bowel disease, Gut-brain-axis, Mendelian randomization, drug target, Quantitative trait loci","lastPublishedDoi":"10.21203/rs.3.rs-4594793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4594793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Mounting evidence suggests that Parkinson’s disease (PD) and inflammatory bowel disease (IBD) are closely associated and becoming global health burdens. However, the causal relationshipsand common pathogeneses between them are uncertain. Furthermore, they are uncurable. Thus, we aimedto identify the causal relationships and novel therapeutic targets shared between them based on their common pathophysiological mechanisms in gut-brain-axis (GBA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A meta-analysis on bidirectional Mendelian randomization (MR) utilizing various datasets was performed to estimate their causal relationship. Then, pleiotropicanalysis under the composite null hypothesis(PLACO) with functional mapping combined withannotation of genetic associations (FUMA) analysis were conducted to identify pleiotropic genes. Next, blood, brain and intestine expression quantitative trait locus (eQTL) were taken to perform drug-target MR finding common causal genes in two diseases. Colocalization analysis ensured the eQTLs of corresponding gene colocalized with disease. Enrichment analysis and protein‒proteininteraction (PPI) network were done to explorecommon pathogenesis pathways. Genes passed all analysis were regarded as drug targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our MR meta-analysis revealed thebidirectional causal relationship between diseases, with combined ORs for PD on IBD, CD, UC (1.050 [95% CI: 1.014-1.086], 1.044 [95% CI: 0.995-1.095], 1.063 [95% CI: 1.016-1.120]); for IBD, CD, UC on PD (1.003 [95% CI: 0.973-1.034], 1.035 [95% CI: 1.004-1.067], 1.008 [95% CI: 0.977-1.040]). Overall, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC. Total of733 genes were classified as tier 3 (found in only one tissue) druggable targets, 57 as tier 2 (found in two tissues, 51 protein-coding genes) and 9 as tier 3 (found in three tissues). Among 60 protein-coding druggable targets over tier 2, 18 overlapped with pleiotropic genes and enriched in mitochondria, antigen presentation, processing and immune cell regulation pathways. Three druggable genes (\u003cem\u003eLRRK2\u003c/em\u003e, \u003cem\u003eRAB29\u003c/em\u003e and \u003cem\u003eHLA-DQA2\u003c/em\u003e) passed colocalization analysis. \u003cem\u003eLRRK2\u003c/em\u003e and \u003cem\u003eRAB29\u003c/em\u003e were reported to be pleiotropic genes, and \u003cem\u003eRAB29\u003c/em\u003e and \u003cem\u003eHLA-DQA2\u003c/em\u003ewere reported for the first time as potential drug targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis study established areliable causal relationship, possible shared drug targets and common pathogenesis pathways of two diseases, which had important implications for intervention and treatment of two diseases simultaneously.\u003c/p\u003e","manuscriptTitle":"Possible Linking and Treatment between Parkinson’s Disease and Inflammatory Bowel Disease: A Study of Mendelian Randomization Based on Gut–Brain Axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-20 17:17:35","doi":"10.21203/rs.3.rs-4594793/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-07-23T19:59:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-23T14:36:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T10:02:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2024-06-17T10:31:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a7cc208-c663-4708-b3c3-fdb1321c6fda","owner":[],"postedDate":"August 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:06:32+00:00","versionOfRecord":{"articleIdentity":"rs-4594793","link":"https://doi.org/10.1186/s12967-024-06045-2","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-01-11 15:57:33","publishedOnDateReadable":"January 11th, 2025"},"versionCreatedAt":"2024-08-20 17:17:35","video":"","vorDoi":"10.1186/s12967-024-06045-2","vorDoiUrl":"https://doi.org/10.1186/s12967-024-06045-2","workflowStages":[]},"version":"v1","identity":"rs-4594793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4594793","identity":"rs-4594793","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}