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Sjogren’s Syndrome (SS) is a common systemic autoimmune disease that predominantly affects women [1]. Inflammation and tissue destruction of the salivary and lacrimal glands result in oral and ocular dryness, fatigue, and arthralgia [2]. In addition, extra-glandular manifestations are present in 30-40% of patients, such as interstitial lung disease, peripheral neuropathy, and interstitial nephritis [3,4].

Although the etiology of SS remains incompletely understood, and effective treatment is lacking [5], prior research has highlighted the interferon signaling pathway as a major therapeutic target [6,7].

However, genetic variations, epigenetic modifications, and inflammatory cytokine levels distinctly influence IFN-associated gene expression [7,8] suggesting a complex interplay in SS pathogenesis.

Thus, elucidating the intricacies of these interactions could shed light on the SS pathogenesis and the dysregulated molecular mechanisms involved, potentially unveiling new therapeutic targets and strategies for disease prevention.

The relationship between IFN signalling and SS, is underlined by both genetic predispositions and epigenetic modifications in SS pathogenesis [9,10]. Genetic studies have uncovered links between SS and genes active in both innate and adaptive immune pathways, emphasizing the significance of interferon signalling. Notable genes such as IRF5, STAT4, and TNFAIP3, which are integral to interferon signaling pathways, are associated with SS. This genetic linkage suggests that aberrations in interferon signaling are a hallmark of SS, leading to the chronic inflammation observed in patients. Epigenetic research further elucidates the impact of DNA methylation on the expression of interferon-regulated genes in SS. Findings indicate a trend of hypomethylation at these genes, which correlates with their increased activity. Therefore, the convergence of genetic and epigenetic evidence highlights the dysregulation of interferon signaling as a pivotal factor in SS pathogenesis. Targeted modulation of the interferon signalling pathway and modification of epigenetic signatures could provide new therapeutic avenues for patients with SS.

Numerous studies have demonstrated the involvement of IFNassociated genes in SS, and Genome-Wide Association Studies (GWAS) have identified genomic loci related to SS. However, there may not be a direct causal relationship between genetic variants and disease because of the Linkage Disequilibrium (LD) structure of the genome and the complex interactions among gene expression, DNA methylation, and inflammatory cytokines. Moreover, little is known about the interactions between causal IFN genes, DNA methylation, and inflammatory cytokines in SS-affected tissues. A multi-omics integrated approach can be used to reveal the complex relationships between SS causal IFN genes, DNA methylation, and inflammatory cytokines to explore therapeutic targets for SS.

We conducted a multi-omics Mendelian Randomization (MR) study to explore the potential causality and interaction among IFNassociated gene expression, DNA methylation, inflammatory cytokines, and SS. Linear regression models and the transcriptome meta-analysis identified IFN-associated DEGs in SS. Utilizing the SMR approach, we integrated expression Quantitative Trait Locus (eQTLs) and mQTLs with SS GWAS summary statistics, as well as integrally analyzing MSG tissues eQTLs with SS GWAS to reveal potential causal IFN-associated genes for SS in the blood and MSG. Furthermore, colocalization analysis was used to integrate eQTLs with inflammatory cytokines GWAS summary statistics to reveal potential interactions between causal IFN-associated genes and inflammatory cytokines.

Data resources and study design

We extracted IFN-associated genes from the GeneCards database using the keyword “interferon” and a relevance score ≥ 3. From the Gene Expression Omnibus (GEO) database, we obtained three public transcriptome datasets from SS patients and healthy controls. Linear regression models and the transcriptome meta-analysis identified IFNassociated Differentially Expressed Genes (DEGs) in SS. GWAS for SS were sourced from FinnGen and UK Biobank. The data for inflammatory cytokines were sourced from 91 cytokines and growth factors in a Nordic population. Blood eQTL were sourced from eQTL Gen, encompassing genetic data of 31,684 individuals. Additionally, blood mQTL summary data originated from two European cohorts. Minor Salivary Gland (MSG) tissue eQTL data were acquired from the Genotype-Tissue Expression (GTEx) project (Figure 1). This study specifically focused on cis-eQTLs and cis-mQTLs, denoting Single Nucleotide Polymorphisms (SNPs) located within a 1-Mb distance from both the beginning and end of a gene. Comprehensive details about the datasets utilized in this study are available in the additional file.

 

Figure 1: Research design. Identifying causal IFN-associated genes and interactions among gene expression, DNA methylation, and inflammatory cytokines in SS Pathogenesis.

Note: Genes associated with IFN signalling were sourced from the Gene Cards database. Additionally, three transcriptomic datasets for SS from the Gene Expression Omnibus (GEO) were utilized to detect differentially expressed IFN-associated genes in SS. These DEGs underwent Cell Type-Specific Expression Analysis (CSEA) to ascertain the cell types where they were predominantly expressed.

Using a three-step SMR method, an integrated examination of SS GWAS, along with eQTLs and mQTLs in blood, was carried out to delineate causal relationships among blood IFN-associated genes, DNA methylation, and SS pathogenesis (P SMR multi<0.05; HEIDI test P>0.01, and Cochran’s Q P>0.05). Subsequently, blood cis-eQTLs were integrated with GWAS data on inflammatory cytokines, identifying interactions through colocalization analysis (PPH4>0.5). Further, cis-eQTLs from Minor Salivary Gland (MSG) tissue sourced from the GTEx database were analyzed and integrated with SS GWAS data, to identify IFN-associated causal genes in the MSG (P SMR multi<0.05; HEIDI test P>0.01; Cochran’s QP>0.05). Then, ciseQTLs of IFN-associated causal genes in the MSG were integrated with GWAS data on inflammatory cytokines, assessing potential interactions between MSG IFN-associated genes and inflammatory cytokines via colocalization analysis (colocalization PPH4>0.5). Sensitivity analyses were employed to evaluate heterogeneity using Cochran's Q statistic in the MR-Egger and Inverse Variance Weighting (IVW) methods, with P>0.05 indicating no heterogeneity.

Statistical analysis

IFN-associated DEGs and cell-type-specific enrichment analysis: We analyzed IFN-associated DEGs using linear regression models adjusted for age, sex, and additional covariates. Fixed-effects meta-analyses of DEGs in the three datasets were then conducted using the R package metafor. Cell Type-Specific Expression Analysis (CSEA) was utilized to pinpoint the specific cell types that are enriched by IFN-associated (DEGs). Given the disease characteristics of SS, our research concentrated on cell types found in blood and MSG tissues. Additionally, we examined the regulatory aspects of DNA methylation sites. For these analyses, we utilized tools such as eFORGE.

Summary data-based Mendelian Randomization (SMR): We conducted analyses using established SMR multi-tools to determine whether the effects of SNPs on phenotypes are mediated through DNA methylation, gene expression, and inflammatory cytokines. Use SMR methods to integrate and analyze GWAS and multiple quantitative trait loci data to improve the detection of potentially causal SNPs.

Using a three-step SMR approach to determine the causal relationship between blood IFN-associated genes, DNA methylation, and SS.

Step one: Expression of blood IFN-associated genes served as the exposure, SNPs were instruments, and SS was considered the outcome.

Step two: Blood DNA methylation was exposure, SNPs were instruments, and SS was the outcome. Based on the results of the first two steps,

Step three: Blood DNA methylation was exposure, SNPs were instruments, and blood IFN-associated gene expressions were the outcome. Ultimately, candidate blood IFN-associated genes for SS need to fulfill the following conditions: P SMR multi<0.05, genomewide suggestive significance in eQTLs/mQTLs, and GWAS (P<1 × 10^-5), and Heterogeneity in the Dependent Instrument (HEIDI) test (P>0.01).

Using the same SMR approach as above, MSG tissue analysis was performed to determine the MSG IFN-associated causal genes in SS. One step SMR is needed: Expressions of MSG IFN-associated genes served as the exposure, SNPs were instruments, and SS was considered the outcome. Sensitivity analyses were employed to evaluate heterogeneity using Cochran's Q statistic in the MR-Egger and Inverse Variance Weighting (IVW) methods, with P>0.05 indicating no heterogeneity.

Colocalization analysis: Colocalization analysis serves as an effective method for identifying overlaps between causal variants in molecular and disease phenotypes, offering valuable insights into the molecular pathways of complex diseases. Due to limitations in assessing the causal relationship between SS and inflammatory cytokines, colocalization analysis was applied to reveal potential interactions between the expression of the SS-causal gene and inflammatory cytokines, utilizing the coloc R package, with a threshold of PPH4>0.5.

Differentially expressed IFN-associated genes

To investigate the role of IFN signalling genes in SS, three transcriptomic datasets were utilized. Gene expression from SS patients (n=71) and healthy controls (HCs, n=53) was compared using meta-analysis. Subsequently, 711 IFN-associated genes were extracted from Gene Cards, and linear regression models yielded 331 IFNassociated DEGs (Figure 2A). In addition, we performed CSEA on 331 IFN-associated DEGs, which were predominantly enriched in Tcells, macrophages, monocytes, and natural killer cells (Figure 2B).

 

Figure 2: Differentially expressed IFN-associated genes and celltype-specific enrichment analysis.

Note: A) Linear regression model and meta-analysis identified 331 differentially expressed interferon-associated genes between SS and Healthy Control (HC) patients. In the depicted volcano plot, metaanalytic effect sizes are on the x-axis whereas-log10-transformed P values are on the y-axis. Red markers signify the 331 significantly Differentially Expressed Genes (DEGs), while black markers depict genes that are not significantly differentially expressed. A dashed line delineates the significance boundary (P<0.05). b) The Cell TypeSpecific Expression Analysis (CSEA) was performed to determine which cell type these IFN-associated DEGs were specifically enriched in. The y-axis represents cell types from blood and Minor Salivary Gland (MSG) tissue.

Integration of SS GWAS and blood IFN‑associated eQTL/ mQTL data

Observing that nearly half (46.55%, 331 of 711) of IFN-associated DEGs are present in SS patients compared to HCs, we propose that these gene expression alterations might shed light on potential causal mechanisms in SS. Additionally, the association of DNA methylation in promoters or enhancers with gene expression and its significant role in regulating disease-related genes are well-documented. We aim to uncover causal genes for SS and delve into their epigenetic regulation mechanisms within the blood. Utilizing a three-step SMR strategy, we focused on results that remained significant across all SMR stages and passed rigorous sensitivity analyses, suggesting these as plausible causal genes. By integrating 331 IFN-associated DEG cis-eQTLs with their cis-mQTLs alongside the SS GWAS data, we aim to elucidate the genetic and epigenetic basis of SS.

Integrating eQTL Gene data with GWAS for SS identified 23 IFNassociated genes that met our significance criteria (P SMR multi<0.05; HEIDI test P>0.01, and Cochran’s Q P>0.05), detailed. Furthermore, by combining the same SS GWAS with mQTL data, we pinpointed 70 DNA methylation probes associated with 38 genes within a 1 Mb range. A subsequent analysis that merged potential SS-causal ciseQTLs and cis-mQTLs highlighted 11 DNA methylation probes potentially influencing five adjacent genes: SH2B3, LGALS9, CD40, GRB2, and DTX3L. These findings, which passed our stringent significance and heterogeneity tests (P SMR multi<0.05; HEIDI test P>0.01, and Cochran’s Q P>0.05), offer new insights into the epigenetic regulation of genes potentially causal for SS.

The results of blood-based enrichment analysis of mQTL sites showed that DNA methylation sites were mainly enriched in: primary monocytes (P value=0.00931), primary B cells (P value 215=0.00142), primary T cells (P value=0.00029), natural killer cells (P value=0.000126) from peripheral blood, and primary T cells from cord blood (P value =0.00681).

Potential blood IFN-associated SS‑causal genes regulated by DNA methylation

Blood SMR preferentially selected SH2B3, encoding SH2B3 (also LNK, lymphocyte adaptor protein) in the SH2B adaptor family of proteins, which are involved in IFN signalling activities through growth factors and cytokine receptors such as the JAK-STAT pathway. This study demonstrated significant SNP signals linked to SH2B3 across data from eQTL/mQTL, and SS GWAS. The DNA methylation probe cg26359133 is located in the 5UTR region of SH2B3 3668bp from the gene start site. Methylation at this site negatively affected SH2B3 expression (β SMR=-0.44) and SS onset (β SMR=-0.23), whereas SH2B3 expression was positively correlated with SS (β SMR=0.50). Our findings propose a mechanism whereby low DNA methylation levels increase SH2B3 expression, thereby increasing SS risk (Figure 3A, B).

 

Figure 3: The three-step SMR and colocalization analysis identified blood tissue potential IFN-associated causal genes (SH2B3) in SS and interactions with DNA methylation and inflammatory cytokines.

Note: A) SH2B3 locus plots reveal consistent genetic effects of SS GWAS, cis-mQTL, and cis-eQTL (P value<1 × 10⁻⁵). B) The threestep SMR analysis demonstrated causal relationships between DNA methylation-mediated SH2B3 gene expressions and the onset of SS (P SMR multi<0.05; HEIDI test P>0.01, and Cochran’s QP>0.05). Sequentially, the analysis includes SMR of SH2B3 gene expression to SS GWAS, gene methylation to SS GWAS, and finally, gene methylation to SH2B3 expression. C) Locus comparisons for SH2B3 gene expression and inflammatory cytokines (CCL19, IL-2Rβ, PD-L1, and TGF-α) via colocalization (PPH4>0.5).

Another key example is LGALS9 (Galectin 9), which modulates cell-cell and cell-matrix interactions and it reflects hyperactivity of B cell and IFN signalling activation in SS35. Our findings indicate a positive causal association between DNA methylation at probe cg06852032 and LGALS9 expression (β SMR=3.10). Probe cg06852032 is located in the gene body and 3UTR region of LGALS9, 272 bp from the gene termination site. Consistent observations showed that elevated gene expression of LGALS9 (β SMR=0.06) and methylation levels (β SMR=0.34) may increase SS risk. Therefore, it is hypothesized that genetic variants increase SS risk by upregulating LGALS9 expression through DNA methylation modifications (Figure 4A, B).

 

Figure 4: The three-step SMR and colocalization analysis identified blood tissue potential IFN-associated causal genes in SS and interactions with DNA methylation and inflammatory cytokines.

Note: A) LGALS9 locus plots reveal consistent genetic effects of SS GWAS, cis-mQTL, and cis-eQTL (P value<1 × 10⁻⁵). B) The three-step SMR analysis demonstrated causal relationships between DNA methylation-mediated LGALS9 gene expressions and the onset of SS (P SMR multi<0.05; HEIDI test P>0.01, and Cochran’s QP>0.05). Sequentially, the analysis includes SMR of LGALS9 gene expression to SS GWAS, gene methylation to SS GWAS, and finally, gene methylation to LGALS9 expression. C) Locus comparisons for LGALS9 gene expression and inflammatory cytokines (CD5, IL-10, and CST5) via colocalization (PPH4>0.5). D) The left illustrates the causal relationship between CD40 expression and SS GWAS (P SMR multi<0.05; HEIDI test P>0.01), whereas the right presents locus comparisons for CD40 cis-eQTL and inflammatory cytokines GWAS via colocalization (PPH4>0.5).

Integration of inflammatory cytokines GWAS and blood IFN‑associated eQTL data

Genetics and inflammatory cytokines studies are recognized for their critical roles in SS, which can provide insight into the dysregulated molecular mechanisms. IFN‑associated gene expression in the blood interacts with inflammatory cytokines in the pathogenesis of SS. Therefore, we hypothesized that by integrating IFN-associated cis-eQTL from blood and inflammatory cytokines GWAS, new candidate targets for the interaction of blood IFN‑associated genes with inflammatory cytokines would be provided.

Our findings identified five blood IFN‑associated genes with potential causal effects in SS. To further explore the interactions between candidate genes and inflammatory cytokines, we integrated inflammatory cytokines GWAS with cis-eQTLs through colocalization analysis. Aim to investigate whether there are common genetic effects of blood IFN‑associated gene expression and inflammatory cytokines. Ultimately, a total of three candidate genes, SH2B3, LGALS9, and CD40, were detected to interact with inflammatory cytokines at a threshold of PPH4>0.5.

Blood IFN‑associated SS‑causal genes interacting with inflammatory cytokines

We found that SNPs regulating SH2B3 expression may also regulate the levels of C-C motif chemokine 19 (CCL19; PPH4=0.58), interleukin-2 receptor subunit beta (IL-2Rβ; PPH4=0.55), programmed cell death ligand 1 (PD-L1; PPH4=0.75), transforming growth factor alpha (TGF-α; PPH4=0.52). Our research points to the possibility that genetic variants in SH2B3 may regulate both its expression and the levels of CCL19, IL-2Rβ, PD-L1, and TGF-α, potentially heightening the risk of SS (Figure 3C). Prior research has indicated that these inflammatory cytokines are also involved in SS pathogenesis. CCL19, a B cell chemokine, correlates with reduced blood memory B cells, and CCL19/CCR7 is significantly expressed in SS patients, which may serve as a potential biomarker. IL-2Rβ (CD122, Interleukin-2 receptor subunit beta), together with CD25 and CD132, forms the heterotrimeric receptor (IL-2R). By binding to IL-2R, IL-2 regulates T-cell activation and cellular immune responses. Increased plasma levels of IL-2R were observed to correlate positively with the severity of SS. PD-L1 expression mediated by IFN/JAK/ STAT, is elevated in salivary gland epithelial cells (SGECs) of SS patients, which contributes to the activation of SGECs and disease perpetuation. TGF-α has a trophic effect on vascular endothelium, Salivary Gland (SG) vesicles, ducts, and mucosal epithelium, and is reduced in SS patients. Therefore, we hypothesized that genetic variation regulates SH2B3 expression and concomitantly regulates the levels of CCL19, IL-2Rβ, PD-L1, and TGF-α, which in turn promote SS pathogenesis.

Further colocalization analyses revealed that LGALS9 expression correlates genetically with T-cell surface glycoprotein CD5 (CD5; PPH4=0.72), Interleukin-10 (IL-10; PPH4=0.74), and Cystatin D (CST5; PPH4=0.51), suggesting potential interactions with CD5, IL-10, and CST5 (Figure 3D). These inflammatory cytokines have also been reported to be correlated with SS. For instance, T-cell surface glycoprotein CD5, a negative co-stimulator in TCR signaling, shows decreased expression and functionality on peripheral blood lymphocytes of SS patients, potentially increasing SS risk. IL-10, a key anti-inflammatory mediator, is markedly elevated in the peripheral blood of SS patients, and IL-10-producing regulatory B (Breg) cells restrain the effector T follicular helper (Tfh) cell response in SS. Additionally, a positive correlation exists between IL-10 levels and ESSDAI, useful for monitoring SS disease activity. Our findings indicate that genetic variations in LGALS9 could concurrently influence its expression and the levels of CD5, IL-10, and CST5, thereby elevating the risk of SS.

Integration of IFN‑associated minor salivary gland tissue eQTL and SS/inflammatory cytokines GWAS data

Genetic influences on gene expression differ between blood and MSG tissues, potentially indicating variations in the genes that cause SS. In turn, genetic variation regulates the over-activation of interferon signalling, leading to elevated levels of inflammatory cytokines, which manifest as chronic inflammation of SG tissue. Therefore, the integration of MSG tissue eQTLs and inflammatory cytokine GWAS data will provide new candidate targets for SG tissue geneinflammatory cytokine interactions.

Combining MSG tissue eQTL data from GTEx (n=144) with SS GWAS, SMR analysis indicated a potential causal link between SS and thirteen MSG-expressed genes (P SMR multi <0.05, HEIDI P>0.01, and Cochran’s QP>0.05).

To further explore the interactions between MSG IFN-associated gene expressions and inflammatory cytokines, we integrated GWAS summary statistics for inflammatory cytokines with potential causal cis-eQTLs using colocalization analysis. The aim was to assess whether genetic influences on MSG gene expression overlap with those affecting inflammatory cytokines. SNPs within the human Major Histocompatibility Complex (MHC) region were excluded due to their high Linkage Disequilibrium (LD), potentially biasing subsequent analyses. The analysis revealed four gene-cytokine pathway pairs with a PPH4>0.5, including APOBEC3G, IFI27L2, SH2B3, and TMEM50B.

IFN‑associated potential SS‑causal genes involved in minor salivary gland gene expression and inflammatory cytokines interactions

Through SMR and colocalization analysis, we identified APOBEC3G as a candidate IFN causal gene interacting with inflammatory cytokines in SS MSG tissues (Figure 5A). The research demonstrated a negative correlation between APOBEC3G expression and SS development (β SMR=-0.04). Moreover, Fms-related tyrosine kinase 3 ligand (Flt3L; PPH4=0.58) and Delta and Notch-like epidermal growth factor related receptor (DNER; PPH4=0.64) shared genetic effects with APOBEC3G expression. Flt3L levels have also been reported to be associated with SS, particularly in SS lymphomas. Our results indicate that genetic variations in APOBEC3G could concurrently influence its gene expression and the levels of Flt3L and DNER, thus involving SS pathogenesis.

IFI27L2 is involved in interactions between MSG gene expression and inflammatory cytokines, with its expression positively linked to SS risk (β SMR=0.05) (Figure 5). Furthermore, IFI27L2 expression shares genetic effects with NGF-β (PPH4=0.55), which is upregulated in SS SGECs and associated with MSG inflammation severity. This suggests that genetic variations in IFI27L2 may interact with NGF-β through the interferon signaling pathway, contributing to SS pathogenesis.

TMEM50B is another IFN‑associated potential causal gene (Figure 5C). This study found TMEM50B expression to be negatively associated with SS risk (β SMR=-0.05). Furthermore, TMEM50B expression shares genetic effects with Interleukin-1α (IL-1α; PPH4=0.72), suggesting a possible interaction with IL-1α. IL-1α, a member of the Interleukin-1 (IL-1) family, is a widespread and crucial pro-inflammatory cytokine. Inflammation and damage in SGEC are marked by the release of IL-1α and IL-1β, leading to progressive SG damage and dysfunction. Therefore, genetic variations may influence SS pathogenesis by regulating TMEM50B expression and IL-1α levels.

 

Figure 5: SMR and colocalization analyses identified Minor Salivary Gland (MSG) IFN-associated genes and their interactions with inflammatory cytokines in SS. The left panel depicts SMR results showing the association between gene expressions and SS GWAS (P SMR multi<0.05; HEIDI test P>0.01), whereas the right panel displays locus comparisons for cis-eQTL and inflammatory cytokines GWAS via colocalization (PPH4>0.5).

This study established a potential causal relationship between the expression of interferon signalling gene expression and SS, and using multi-omics data, it revealed the interactions and molecular mechanisms between the expression of IFN-associated causal genes, DNA methylation, and inflammatory cytokines. We identified 331 IFN-associated DEGs and integrated SS GWAS with eQTLs/mQTLs of DEGs to identify five IFN-associated SS-causal genes associated: LGALS9, SH2B3, CD40, GRB2, and DTX3L. Meanwhile, colocalization analyses by further integrating inflammatory cytokine GWAS data identified a common genetic effect between SH2B3, LGALS9, CD40 expression, and inflammatory cytokines. In addition, using the same approach of integrating MSG eQTLs with SS and inflammatory cytokine GWAS data, four MSG tissue IFN-associated SS-causal genes, APOBEC3G, IFI27L2, TMEM50B, and SH2B3, were identified as being involved in the interaction between SG gene expression and inflammatory cytokines. Previous research has not thoroughly examined the causal relationship between interferon signalling gene expression and SS, nor the intricate interactions between epigenetic modifications and inflammatory cytokines.

Consequently, our study is crucial for identifying IFN-associated causal genes in blood and SG tissues, and for elucidating the complex interactions among interferon signalling, DNA methylation, and inflammatory cytokines in SS.

Increasingly, biomarkers derived from blood tissue are utilized to diagnose and predict SS activity, as well as to uncover the complex etiology and molecular mechanisms of the disease. SMR analysis of blood tissues revealed five potential causal relationships between IFN genes and SS, mediated by epigenomic and transcriptomic regulation. These findings underscore the significant involvement of epigenetic factors and gene expression in SS pathogenesis. Among these genes (LGALS9, SH2B3, CD40, GRB2, and DTX3L), colocalization analysis confirmed the association of LGALS9, SH2B3, and CD40 with inflammatory cytokines.

For instance, SH2B3 (also LNK, lymphocyte adaptor protein) was involved in IFN signalling activities through growth factors and cytokine receptors. Several studies have found that CCL19 associated with a variety of autoimmune diseases, is highly expressed in SS blood and salivary glands. CCL19 attracts dendritic cells, and antigenbinding B cells by binding to CCR7, causing inflammation in the blood and salivary glands. Furthermore, it has also been shown in large cohorts to correlate with anti-Sjögren’s Syndrome-Related Antigen A (SSA) antibody and IgG levels in SS patients. IL-2Rβ, a subunit of IL-2R, is involved in the IL-2 signalling pathway in SS.

However, elevated levels of IL-2R and impaired IL-2/IL-2R signalling lead to diminished immunosuppression of regulatory T cells (Tregs) in SS patients, correlated with the severity of SS disease. Enhancement of regulatory T cells by low-dose IL-2 restores the Th17/Treg balance, which is effective and well tolerated in SS patients. PD-L1, a transmembrane protein, is expressed in myeloid cells and regulated by interferon signalling pathways. In patients with SS, PD-L1 is up-regulated by IFN/JAK/STAT in SGECs. It serves a dual function, associated with epithelial cell survival and resistance to IFN-mediated apoptosis. PD-L1 reduces the activation of T cells and IFN-γ production, while also contributing to the activation of SGECs and the persistence of the disease. In the present study, we confirmed that hypomethylation levels upregulated SH2B3 gene expression, which potentially interacted with CCL19, IL-2Rβ, PD-L1, and TGF-α, thereby increasing the risk of SS development.

LGALS9, also known as Galectin-9, is part of the galectin family and binds beta-galactoside. It is induced by IFNα signalling, upregulated in SS, regulates B-cell activation as a feedback mechanism, and monitors SS disease activity. T-cell surface glycoprotein CD5, a negative co-stimulator in TCR signalling, shows decreased expression and functionality on peripheral blood lymphocytes of SS patients, potentially increasing SS risk. Galectin-9 regulates B-1a cell activation by increasing colocalization with the inhibitory co-receptor CD5, thereby modulating TLR4 signal transduction. It is well recognized that a significant number of cells within SS lesional tissue express the CD5 cell surface molecule. CD5+ Breg cells are crucial in restraining the effector Tfh cell response by producing IL-10 during SS development. IL-10, a critical antiinflammatory mediator, is significantly elevated in the peripheral blood of SS patients, with IL-10-producing Breg cells helping to restrain the effector Tfh cell response in SS. Additionally, a positive correlation exists between IL-10 levels and ESSDAI, useful for monitoring SS disease activity.

Previous research indicates that the Tim-3/Galectin-9 signaling pathway may regulate the function of CD4+ T cell subsets, impacting IL-10 levels. Importantly, our study confirms that DNA methylation positively regulates LGALS9 expression, and elevated transcript levels of LGALS9 may increase the risk of SS. Furthermore, we identified a shared genetic effect among LGALS9, CD5, IL-10, and CST5, indicating a link between DNA methylation, LGALS9 expression, these cytokines (CD5, IL-10, and CST5), and SS risk.

CD40, belonging to the Tumor Necrosis Factor (TNF) gene superfamily, encodes proteins serving as receptors on antigenpresenting cells of the immune system and are essential in SS for mediating a variety of immune and inflammatory responses. For example, CD40-CD154 mediated T-B cell interactions result in aberrant lymphocyte activation, SG inflammation, and subsequent tissue damage. CD40 gene expression correlates with CCL4, which has been demonstrated in previous studies. CCL4 levels were significantly elevated in the MSG, submandibular gland, and lacrimal gland, suggesting that it is closely involved in the pathogenesis of SS. In addition, IL-13 release from B cells requires CD40 cross-linking and cytokine signalling. IL-13 regulates the function of glandular tissue and the recruitment of mast cells to the gland, causing the progression of SS immunopathology. The present study confirms that the interaction of CD40 expression with inflammatory cytokines (CCL4, IL-10) is involved in the pathogenesis of SS.

SS is a chronic inflammatory autoimmune disease characterized by lymphocytic infiltration of exocrine glands, with a predominance of salivary glands. Using MSG eQTLs (the most relevant tissue type) to study their genetic influence on IFN gene expression may be more relevant than studies in blood. We identified APOBEC3G, IFI27L2, TMEM50B, and SH2B3 as potential IFN-associated SS causal genes in MSG tissues based on SMR analyses, where SH2B3 was also a relevant gene in blood. Nonetheless, the relationship between SH2B3 expression and SS in blood was different from that in MSG, indicating a tissue-specific influence on the pathogenesis of SS. In addition, colocalization analyses of gene expression with inflammatory cytokines confirmed the presence of common genetic effects of these gene expressions (APOBEC3G, IFI27L2, TMEM50B, and SH2B3) with inflammatory cytokines.

APOBEC3G (Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G), one of the APOBEC3 genes, is a member of the large apolipoprotein B mRNA editing enzyme catalytic polypeptide-like family which plays a critical role in innate immunity. The APOBEC3 family is highly expressed in macrophages, lymphoid cells, and dendritic cells, and dysregulated APOBEC3 activity has been implicated in genome mutagenesis in cancer. APOBEC3G was notably upregulated in MSG tissues obtained from SS lymphoma patients, which exhibited a more widespread distribution across myeloid, B-lymphocyte, and T-lymphocyte clusters. Furthermore, our research colocalized the genetic influences on APOBEC3G expression with those of inflammatory cytokines. Both Flt3L and DNER exhibited shared genetic effects with APOBEC3G expression, indicating possible interactions among this gene, Flt3L and DNER. Interestingly, serum levels of Flt3L are elevated in patients with SS and are associated with high disease activity scores, aberrant B cell distribution, and increased risk of developing lymphoma. Flt3L serves as a predictor of SS lymphoma, and may prove valuable as a predictive marker for lymphoproliferative disorders in SS. Elevated levels of Flt3L preferentially enhance the proliferation of type-I interferon-producing plasmacytoid dendritic cells. Increased levels of Flt3L may trigger autoimmune diseases. For example, patients with rheumatoid arthritis exhibit increased Flt3L levels in both serum and the synovial fluid of inflamed joints. Moreover, DNER is associated with inflammation of articular cartilage. This suggests that the genetic variant regulating APOBEC3G might simultaneously regulate its gene expression as well as the levels of Flt3L and DNER, thereby participating in the pathogenesis of SS.

Our study found that IFI27L2 expression was positively associated with the risk of developing SS and shared genetic effects with NGF-β. IFI27L2 (Interferon Alpha Inducible Protein 27 Like 2, also ISG12B), a member of the Interferon-Stimulated Gene (ISG)12 family, has an important role in the apoptotic properties induced by type 1 interferon. Notably, IFI27 was selected as a prospective biomarker for SS101. NGF-β has also been reported to have increased expression in SS Salivary Gland Epithelial Cells (SGECs), correlating with MSG inflammatory grade. Thus, we hypothesize that genetic variation regulates IFI27L2 expression and interacts with NGF-β via the interferon signalling pathway, thereby promoting SS pathogenesis. Additionally, this study highlighted a potential interaction between TMEM50B (Transmembrane Protein 50B) gene expression, negatively correlated with SS risk, and IL-1α in SS pathogenesis. IL-1α, one of the Interleukin (IL)-1 family members, is a widely distributed and pivotal pro-inflammatory cytokine.

Studies suggest that inhibiting IL-1 may be a therapeutic strategy worth considering in SS. This study has certain limitations. Firstly, although it utilizes GWAS data from FinnGen and UK Biobank, it may not fully capture the genetic and epigenetic diversity of the general population.

This limitation could restrict the generalizability of the findings to all SS patients, especially those from varied ethnic backgrounds. Secondly, the cross-sectional nature of the genetic and epigenetic data may not adequately reflect the dynamic changes in gene expression and methylation status, which are crucial for understanding the chronic progression of SS.

This study uncovered a causal relationship between interferon signalling and SS, revealing complex interactions among IFNassociated causal gene expression, DNA methylation, and inflammatory cytokines in SS pathogenesis. This offers new evidence for the involvement of interferon signalling in the pathogenic process of SS and provides fresh insights into the interactions among epigenetic, genetic variants, and inflammatory cytokines for in-depth studies of pathogenesis and molecular mechanisms.

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Not applicable.

IFN-associated genes from the GeneCards database were obtained from https://www.genecards.org. SS transcriptome datasets from the Gene Expression Omnibus (GEO) database were obtained from https:// www.ncbi.nlm.nih.gov/geo/. Minor salivary gland (MSG) tissue eQTL data were acquired from the Genotype-Tissue Expression (GTEx) project: https://gtexportal.org/home/downloads/adult-gtex/overview. Cell Type-Specific Expression Analysis (CSEA): https:// bioinfo.uth.edu/CSEADB/.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential competing interests.

Research design and guidance: JH, XZ, XT. Data collection and analysis: JH, XS. Manuscript writing and revision: JH, FP, XS. Suggestions for research content and revision: RL, ZG. Manuscript revision and editing: MY, WL, KY. All authors have endorsed the final version of the manuscript.

This research received funding from the Science and Technology Innovation Project of China Academy of Chinese Medical Sciences (no. CI2021A01502, no. CI2021A01510), the National Natural Science Foundation of China (82374285), Central High-level Chinese Medicine Hospital Clinical Research and Achievement Translation Capacity Enhancement Project: Clinical Research Integration Talent Special Project (Innovation Team Cultivation Project) (HLCMHPP2023002), and Special Funds for Basic Research Operating Costs of Central-level Public Welfare Research Institutes (ZZ15-XY-PT-11, ZZ15-YQ-023).

The authors would like to thank the UK Biobank and the FinnGen study for providing publicly accessible SS GWAS summary statistics for this analysis. Special thanks to China Academy of Chinese Medical Sciences for their support.

1. Qin B, Wang J, Yang Z (2015) Ann Rheum Dis 74: 1983-1989.

   [] [] []

2. Mariette X, Criswell LA (2018) . N Engl J Med 378: 931-939.

   [] [] []

3. Brito-Zerón P, Acar-Denizli N, Ng WF (2020) Rheumatology (Oxford) 59: 2350-2359.

   [] [] []

4. Jasiek M, Karras A, Le Guern V (2017) . Rheumatology (Oxford) 56: 362-370.

   [] [] []

5. Seror R, Nocturne G, Mariette X (2021) Nat Rev Rheumatol 17: 475-486.

   [] [] []

6. Verstappen GM, Kroese FGM (2023) Nat Rev Rheumatol 19: 468-469.

   [] [] []

7. Thorlacius GE, Björk A, Wahren-Herlenius M (2023) . Nat Rev Rheumatol 19: 288-306.

   [] [] []

8. Nocturne G, Mariette X (2018) Nat Rev Rheumatol 14: 133-145.

   [] [] []

9. Gottenberg JE, Cagnard N, Lucchesi C (2006) Proc Natl Acad Sci USA 103: 2770-2775.

   [] [] []