The Mediating Effect of 3-Methoxytyrosine on TWEAK Levels and Primary Sclerosing Cholangitis: A Genetic Perspective

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The Mediating Effect of 3-Methoxytyrosine on TWEAK Levels and Primary Sclerosing Cholangitis: A Genetic Perspective

https://doi.org/10.21203/rs.3.rs-3993883/v1

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Primary sclerosing cholangitis (PSC) is a chronic liver disease characterized by inflammation, fibrosis, and bile stasis. Inflammatory proteins and metabolites may be involved in PSC pathogenesis. However, a comprehensive analysis is currently lacking. In this study, we employed a two-step Mendelian randomization (MR) analysis to quantify the proportion of the effect of metabolites-mediated inflammation-related proteins on PSC. The dataset includes information on PSC, levels of 91 inflammation-related proteins, and levels of 1400 metabolites in plasma. PSC data (n = 14,890) were obtained from the International PSC Study Group, while inflammation-related proteins data came from a study involving 14,824 participants. Metabolites data were sourced from a Genome-Wide Association Study with 8,299 individuals. Furthermore, we employed a two-step Mendelian randomization (MR) analysis to quantify the proportion of the effect of metabolites-mediated inflammation-related proteins on PSC. After rigorous screening, we found through MR analysis that a higher genetically predicted tumor necrosis factor ligand superfamily member 12 (TWEAK) levels (inverse-variance weighted method, odds ratio (OR) 1.53, 95% confidence interval (CI) 1.23–1.91) were associated with an increased risk of PSC. There was no compelling evidence indicating that genetically predicted PSC had an effect on TWEAK levels (OR 1.004, 95% CI 0.99–1.02). The proportion of genetically predicted TWEAK levels mediated by 3-methoxytyrosine levels was estimated to be 6.38%. Finally, we've shown a causal relationship between TWEAK levels and PSC, with some mediation by 3-methoxytyrosine. TWEAK's pivotal role in PSC as an inflammation-related protein highlights its potential as a critical therapeutic target.

Health sciences/Diseases/Gastrointestinal diseases/Biliary tract disease/Bile duct disease/Primary sclerosing cholangitis

Biological sciences/Genetics/Genetic association study/Genome wide association studies

Mendelian randomization

Causal relationship

Single nucleotide polymorphism

Primary Sclerosing Cholangitis (PSC) is a chronic cholestatic liver disease marked by a complex interplay of inflammation, fibrosis and bile stasis (Dyson et al. 2018). The incidence of PSC is as high as 16.2 cases per 100,000 individuals, with the highest rates observed in Northern Europe and markedly lower rates in Asia (Boonstra et al. 2012). Unfortunately, the lack of specific therapeutic options shortens the timeframe from PSC diagnosis to either liver transplantation or mortality to a mere 13.2 years (Boonstra et al. 2013).

Growing evidence suggests interactions between inflammatory-related proteins and PSC. Aberrant inflammatory reactions can lead to tissue damage and are a central pathogenic mechanism in autoimmune diseases (Zhao et al. 2023b). Research indicates that the promotion of mucosal immunity mediated by interleukin (IL)-17 plays a role in the pathophysiology of PSC (Kunzmann et al. 2020). Additionally, PSC patients often exhibit elevated levels of inflammatory factors such as tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), and IL8 (Mueller et al. 2011). Furthermore, metabolites, serving as small molecules in metabolic reactions, can influence disease risk and serve as targets for therapeutic intervention (Wishart 2016). Additionally, serum circulating levels of metabolites involved in bile acid metabolism (Poupon et al. 2000), cholesterol metabolism (Kowdley 1998), lipoprotein metabolism (Kowdley 1998; Bell et al. 2015) and amino acid metabolism (ter Borg et al. 2005) have been found to differ in healthy populations and in patients with PSC. There is a close correlation between inflammatory-related proteins and metabolites. Numerous studies have shown that gut flora metabolites can promote the expression of inflammation-related proteins and the development of PSC (Yang et al. 2024). Conversely, research has also indicated that inflammation-related proteins, such as IFN-γ and TNF-α, pro-inflammatory cytokines, can stimulate indoleamine 2,3-dioxygenase to promote the metabolism of tryptophan into kynurenine. This leads to its elevated expression in patients with PSC (Bell et al. 2015).

At present, comprehensive research integrating analysis of inflammatory-related proteins, metabolites, and PSC is notably deficient. The intricate and complex causal relationships among these factors necessitate urgent, detailed analysis to identify potential targets for PSC onset and treatment direction.

Mendelian randomization (MR) is a potential causal inference method that utilizes genetic variations as instrumental variables to infer the impact of exposure factors on outcomes from observational data (Emdin et al. 2017). MR can mitigate the effects of unmeasured confounders or biases while leveraging Mendelian inheritance patterns to avoid reverse causation (Emdin et al. 2017). In this study, we collected recently published summary statistics from genome-wide association studies (GWAS) for 91 inflammation-related proteins, 1400 serum metabolites, and PSC. By employing the mediation MR method, we aimed to explore causal relationships among these three factors and investigate the pathogenesis of PSC, as well as potential therapeutic targets.

Study design

The data used in our analysis were publicly available and were approved by the institutional review committee in the respective studies. Therefore, no ethical committee review was required for this study. Additionally, all generated results are presented in the article and its supplements.

In this study, we explored the reciprocal causal relationship between various inflammation-related proteins and PSC by two-sample, bidirectional mendelian randomization (Fig. 1A). In our study, single nucleotide polymorphisms (SNPs) were defined as instrumental variables (IVs) (Davey Smith et al. 2014).

GWAS summary data sources

The data used in our study were all publicly available. The PSC dataset (n = 14,890) was obtained from the International PSC Study Group, as reported by Sun-Gou Ji et al., which investigated the relationship between PSC quantity and single nucleotide polymorphisms (SNPs) (Ji et al. 2017). The data of the 91 inflammation-related proteins (Table S1) we cited are derived from an extensive genomic study, utilizing the Olink Target platform to measure plasma proteins in 14,824 participants. This study represents a protein quantitative trait locus (pQTL) investigation, aiming to identify the genetic influences on the expression of inflammation-related proteins (Zhao et al. 2023a). The data for the 1400 metabolites (Table S2) cited originated from a large-scale Genome-Wide Association Study involving 8,299 individuals from the Canadian Longitudinal Study on Aging cohort, encompassing measurements of 1,091 distinct metabolites and 309 metabolite ratios (Chen et al. 2023). Furthermore, this study provides detailed information for the aforementioned data (Table 1). All GWAS data are from different consortia or organizations, and thus there is no sample overlap.

Table 1

Details of the genome-wide association studies and datasets used in our analyses
Items	Sample size	Data sources	PMID	Data Download Link
PSC (ieu-a-1112)	14,890	IPSCSG	27992413	https://gwas.mrcieu.ac.uk/; ID: ieu-a-1112
1400 metabolites	8,299	CLSA	36635386	https://www.ebi.ac.uk/gwas/; Accession numbers GCST90199621-90201020
91 inflammatory proteins	1,4744	Nature Immunology	37563310	https://www.ebi.ac.uk/gwas/; Accession numbers GCST90274758 to GCST90274848
PSC, Primary sclerosing cholangitis; IPSCSG, International PSC Study Group; CLSA: Canadian Longitudinal Study on Aging.

Instrumental variable selection and data harmonization

We included SNPs that were genome-wide significant (P < 1 × 10^− 5). Then, these SNPs were clustered based on linkage disequilibrium (window size = 10,000 kb and r² < 0.001). Additionally, palindromic and ambiguous SNPs were excluded from IVs for Mendelian randomization analysis (Hemani et al. 2018). The F statistic was calculated by the variance explained by SNPs for each exposure, i.e. [(N – K – 1)/K]/[R² /(1 – R² )], where K is the number of genetic variants, N is the sample size. We removed weak instrumental variables (F-statistics < 10) (Burgess et al. 2011).

Statistical analysis

We performed MR analysis using R software (version 4.2.0, http://www.r-project.org) and the “Two-Sample MR” package (version 0.5.6) (Broadbent et al. 2020).Moreover, the online tool PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/) was used to assess all known phenotypes related to the considered genetic instruments in our analyses.

Primary analysis

Figure 1 shows a schematic summary of the analysis. We conducted a two-sample bidirectional MR to evaluate the mutual causality between inflammation-related proteins and PSC (Fig. 1A), which was designated as the total effect.

Inverse variance weighting (IVW) uses meta-analysis to combine the Wald ratios of causal effects for each SNP (Hemani et al. 2018; Burgess et al. 2013). In addition, the MR-Egger (Burgess et al. 2017), weighted-median (Bowden et al. 2016) and weighted mode (Hartwig et al. 2017) methods were employed as supplements to IVW. Various methodologies tailored to distinct validity assumptions were utilized to derive MR estimates. The application of IVW hinges on the assumption that all SNPs serve as valid instrumental variables, thereby ensuring the method's capacity to yield precise estimations. MR-Egger scrutinizes directional pleiotropy within instrumental variables, with the intercept offering an estimate of the average pleiotropy associated with genetic variation. The weighted median method exhibits a superior ability to maintain heightened precision, evidenced by its smaller standard deviation when compared to MR-Egger analysis. Notably, in the presence of horizontal pleiotropy, the weighted median method provides a consistent estimate, even when 50% of genetic variants are deemed invalid instrumental variables (Zhang et al. 2021). The weighted mode-based estimators assume that the most common causal effect is consistent with the true causal effect. Hence, the remaining instruments could be invalid (that is, violate the assumptions of MR) without biasing the estimated causal effect.

Mediation analysis

We also performed mediation analyses using a two-step MR design to explore whether the presence of a metabolite mediated a causal relationship between inflammatory factor proteins and PSC outcomes (Fig. 1B). The overall effect can be decomposed into an indirect effect (a × b in Fig. 1B) and a direct effect (c’ in Fig. 1B) effect (Carter et al. 2021). We calculated the percentage mediated by the mediating effect by dividing the indirect effect by the total effect.

Sensitivity analysis

Heterogeneity was assessed using MR Egger and IVW methods. Cochrane’s Q value was used to evaluate the heterogeneity of the genetic tools, with P > 0.05 suggesting no significant heterogeneity. MR Egger intercept method was applied to assess the horizontal pleiotropy of the genetic tools, and P > 0.05 indicated the absence of horizontal pleiotropy. Finally, leave-one-out analysis was used to validate the effect of each SNP on the overall causal estimates.

The workflow diagram for this study was shown in Fig. 2.

Association of 91 inflammatory proteins and PSC

In the initial phase, our study systematically screened 91 inflammation proteins using the IVW method, ultimately pinpointing 9 factors that may be associated with PSC (Table S3). These factors include Tumor Necrosis Factor Ligand Superfamily Member 12 levels, Natural Killer Cell Receptor 2B4 levels, Monocyte Chemoattractant Protein-3 levels, TNF-beta levels, C-X-C Motif Chemokine 9 levels, Interleukin-8 levels, Delta and Notch-like Epidermal Growth Factor-Related Receptor levels, Interleukin-12 Subunit Beta levels and Interleukin-10 Receptor Subunit Beta levels.

In a subsequent reverse MR analysis, we observed an inverse causal relationship between three genetically predicted inflammatory proteins and PSC: C-X-C motif chemokine 9 levels, Interleukin-12 subunit beta levels and Interleukin-8 levels (Table S3). Therefore, these 3 inflammatory proteins will be excluded from the study.

In the final phase, IVW, MR-Egger, weighted mode and weighted median regression were employed to estimate the causal relationship between genetically predicted levels of 6 inflammatory proteins and PSC. Across all four MR methods, there was broad and consistent support for the positive association of Tumor necrosis factor ligand superfamily member 12 (TWEAK) levels with PSC (IVW odds ratio [OR] per SD increase in PSC = 1.53 [95% CI, 1.23–1.91], P = 1.71E-04; MR-Egger OR per SD increase in PSC = 1.95 [95% CI, 1.24–3.06], P = 8.05E-03; weighted median OR per SD increase in PSC = 1.72 [95% CI, 1.29–2.29], P = 1.98E-04; weighted mode OR per SD increase in PSC = 1.75 [95% CI, 1.29–2.39], P = 1.66E-03) (Fig. 3). The characteristics of SNPs associated with TWEAK and PSC in the bidirectional Mendelian randomization analysis are extensively documented in the supplementary files (Table S4, S5). However, the remaining five inflammatory proteins were unable to meet all rigorous screening criteria and were therefore excluded from this study (Table S6).

Association of TWEAK with 1400 Metabolites

Utilizing bidirectional Mendelian randomization analysis, we successfully identified a causal relationship between TWEAK and PSC. In order to delve deeper into the potential presence of a mediator in the pathway from TWEAK to PSC among 1400 metabolites, we meticulously screened the correlation between TWEAK and these metabolites using the IVW method. Metabolites exhibiting a P < 0.05 were then included in the subsequent analysis. Given this, we employed the IVW method and identified a potential causal relationship between TWEAK and 49 metabolites (P < 0.05) (Table S7).

Association of 49 metabolites with PSC

To further investigate the potential associations between the 49 identified metabolites and PSC, we systematically examined their correlations using the IVW method. The results revealed that among these 49 metabolites, three exhibited a potential association with PSC (P < 0.05). Specifically, these metabolites are 3-methoxytyrosine (3-MT) levels, N-succinyl-phenylalanine levels, and 2-hydroxy-4- (methylthio) butanoic acid levels (Table S8).

However, it is noteworthy that N-succinyl-phenylalanine levels and 2-hydroxy-4-(methylthio) butanoic acid levels demonstrated a negative correlation with PSC, opposing the overall directional effect. Consequently, these metabolites were excluded from further consideration in this study (Table S8).

In the final analysis, we found that 3-MT levels are causally related to PSC among these 49 metabolites (IVW OR per SD increase in PSC = 1.42 [95% CI, 1.12–1.80], P = 3.29E-03; weighted median OR per SD increase in PSC = 1.41 [95% CI, 1.09–1.83], P = 9.84E-03); weighted mode OR per SD increase in PSC = 1.52 [95% CI, 1.09–2.13], P = 2.87E-02); MR Egger OR per SD increase in PSC = 1.35 [95% CI,0.79–2.31], P = 2.92E-01), and the direction of estimation is consistent for all four methods (Fig. 2). The characteristics of SNPs associated with TWEAK to 3-MT and 3-MT to PSC in the MR analyses have been extensively documented in the supplementary files (Table S9, S10).

Proportion of the association between TWEAK and PSC mediated by 3-methoxytyrosine levels

In summary, we have ultimately discovered that 3-methoxytyrosine levels can act as a mediator in the pathway from TWEAK to PSC. We observed an association between elevated TWEAK levels and increased 3-methoxytyrosine levels, which, in turn, were correlated with an elevated risk of PSC. As illustrated in Fig. 4, our study demonstrated that the indirect effect was 0.03, constituting 6.38% of the heightened risk of PSC associated with TWEAK, while the direct effect was 0.40.

Sensitivity analysis

In the above analysis, a "leave-one-out" approach was employed for sensitivity analysis to investigate whether the causal relationships were influenced by any specific SNP. The results indicated that the stepwise exclusion of each SNP did not result in a significant alteration of the model's effect estimates or qualitative inferences (Fig. S1, S2, S3, S4). Cochrane's Q value was used to evaluate the heterogeneity of the genetic tools. No evidence of horizontal pleiotropy was observed in any of the aforementioned analyses (Table S11). In the heterogeneity analysis, upon verification, it was found that no heterogeneity existed in all analyses, except for the analysis involving the relationship between 3-methoxytyrosine levels and PSC (Table S11). This limitation is acknowledged as one of the constraints in our study.

Currently, the etiological factors and treatment strategies for PSC remain contentious (Dyson et al. 2018). In this study, we conducted a comprehensive exploration of the correlation between 91 inflammation-related proteins, 1400 plasma metabolites, and PSC using MR analysis based on existing GWAS. Our findings suggest a correlation between the genetically predicted TWEAK and an elevated risk of PSC (a 53.11% increase in PSC risk for each 1 standard deviation increase in TWEAK), with 6.34% of this effect mediated through 3-MT. To date, we are pioneers in employing MR analysis to thoroughly explore the correlation between 91 inflammation-related proteins and PSC. Concurrently, we've confirmed that 3-MT, observed among the spectrum of 1400 metabolites, mediates their causal relationship.

As a member of the tumor necrosis factor (TNF) ligand family, TWEAK is a multifaceted and versatile cytokine that regulates inflammatory pathways by inducing diverse cellular responses, contingent upon cell types and their microenvironment (Bertin et al. 2013). TWEAK has a dual role: promoting tissue regeneration after acute injury but hindering tissue repair in chronic diseases with persistent activation, particularly by inhibiting progenitor cell differentiation (Burkly et al. 2007). As an autoimmune disease, PSC is commonly associated with inflammatory bowel disease, with 70% of patients experiencing both conditions simultaneously (Sabino et al. 2016). In the case of inflammatory bowel disease, such as patients with ulcerative colitis, Kawashima et al. observed a significant upregulation of TWEAK expression in the intestinal mucosa compared to normal mucosal tissues (Kawashima et al. 2011). Similarly, elevated expression of TWEAK has been observed in autoimmune diseases such as systemic lupus erythematosus (Wang et al. 2012), lupus nephritis (Zhi-Chun et al. 2012), rheumatoid arthritis (Park et al. 2013), when compared to healthy control groups, and this expression has been correlated with the severity of the diseases. Moreover, Tumor Necrosis Factor Receptor Superfamily Member-12A (TNFRSF12A/Fibroblast-Inducible 14, FN14) acts as the specific binding receptor for TWEAK (Dohi et al. 2012). Observational studies have noted a significant increase in the expression of both TWEAK and FN14 in patients with primary biliary cholangitis and obstructive cholestatic disorders (Liao et al. 2023). Research confirms that bile acids can activate the Fn14-NFκB/Caspase-1/GSDMD signaling pathway, aggravating cholestatic liver injury, and TWEAK further amplifies this effect (Liao et al. 2023). Given the pivotal role of bile acids in the onset of PSC (Liwinski et al. 2020), coupled with our research findings, we hypothesize that TWEAK may play a significant role in the development of PSC.

Through MR, we have identified 3-MT as a potential mediator of PSC development induced by TWEAK. 3-MT is an O-methylated product of L-dopa catalyzed by catechol-O-methyltransferase (Goldstein et al. 2003), potentially involved in this process. In most cells, O-methylated compounds containing an amine moiety undergo further metabolic breakdown. Consequently, 3-MT may be converted into its corresponding glucuronide or sulfate forms, facilitating its excretion from the body, potentially via bile (Goldstein et al. 2003). This suggests a connection between 3-MT and the hepato-biliary system. Importantly, studies indicate a correlation between the expression of dopamine receptor D2 and oxidative damage and inflammation in the liver, possibly through the TGF-β1/Smads and NFκB pathways (Zhao et al. 2021). As a metabolite of dopamine, 3-MT may have a reasonable association with inflammation-related proteins and liver-related diseases. However, further research is needed to elucidate the exact nature of this relationship.

This study faces several limitations. Firstly, despite conducting MR analysis on published GWAS datasets, the conclusions lack experimental support, as they rely solely on genetic IVs and causal inference through multiple MR analytical approaches. Secondly, the scarcity of research on 3-MT raises concerns about its relationship with TWEAK and PSC, as the association is established only at the MR analysis level and lacks robust literature support. Thirdly, heterogeneity is observed in the MR analysis of 3-MT and PSC. Lastly, our study reveals a notably low genetic prediction (6.38%) of TWEAK mediated by 3-MT levels.

Through MR analysis of GWAS data encompassing 91 inflammation-related proteins, 1400 serum metabolites, and PSC, we unveiled a causal relationship between TWEAK and PSC. Notably, a portion of this relationship is mediated by 3-MT levels. Moreover, the pivotal role of TWEAK as an inflammation-related protein in PSC underscores its potential as a critical therapeutic target for managing this condition.

Acknowledge

Authors’ Contributions：J. Zhou and Y. Xu designed the study; J. Zhou and Z. Liu extracted data; J. Zhou analyzed and interpreted the data; J. Zhou wrote the manuscript.

Competing interests

The authors declare no competing interests.

Fundings

This study was funded by the Jiangsu University Medical Education Collaborative Innovation Fund Project (No. JDY2023018) and the Medical Research Project of Jiangsu Health Commission (No. Z2021010).

Availability of data and materials

Data are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

This study was based on publicly available summary data and required no ethics approval or participant consent.

Consent for publication

Not applicable.

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No competing interests reported.

FigS1.tif
Supplementary Figure S1: Leave-one-out plot to visualize causal effect of TWEAK on PSC risk when leaving one SNP out. Exposure: TWEAK, Tumor necrosis factor ligand superfamily member 12 levels; Outcome: PSC, Primary sclerosing cholangitis.
FigS2.tif
Supplementary Figure S2: Leave-one-out plot to visualize causal effect of PSC on TWEAK risk when leaving one SNP out. Exposure: TWEAK, Tumor necrosis factor ligand superfamily member 12 levels; Outcome: PSC, Primary sclerosing cholangitis.
FigS3.tif
Supplementary Figure S3: Leave-one-out plot to visualize causal effect of TWEAK on 3-MT risk when leaving one SNP out. Exposure: TWEAK, Tumor necrosis factor ligand superfamily member 12 levels; Outcome: 3-MT, 3-methoxytyrosine levels.
FigS4.tif
Supplementary Figure S4: Leave-one-out plot to visualize causal effect of 3-MT on PSC risk when leaving one SNP out. Exposure: 3-MT, 3-methoxytyrosine levels. Outcome: PSC, Primary sclerosing cholangitis.
Supplementarytables.xlsx

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The Mediating Effect of 3-Methoxytyrosine on TWEAK Levels and Primary Sclerosing Cholangitis: A Genetic Perspective

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Abstract

Figures

Introduction

Methods

Study design

GWAS summary data sources

Instrumental variable selection and data harmonization

Statistical analysis

Primary analysis

Mediation analysis

Sensitivity analysis

Results

Association of 91 inflammatory proteins and PSC

Association of TWEAK with 1400 Metabolites

Association of 49 metabolites with PSC

Proportion of the association between TWEAK and PSC mediated by 3-methoxytyrosine levels

Sensitivity analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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