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Heterogeneity of human leukocyte antigen-G (HLA-G) expression in patients with Sjögren’s syndrome
*Corresponding author E-mail address: falkhulaifi@iau.edu.sa (F Alkhulaifi)
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Received: ,
Accepted: ,
Abstract
To investigate the association between HLA-G gene polymorphisms and susceptibility to Sjögren’s syndrome (SS), and to evaluate serum soluble HLA-G (sHLA-G) levels in a Saudi population. HLA-G 14-bp Ins/Del and six selected single-nucleotide polymorphisms (SNPs) were genotyped in SS patients and healthy controls using multiple genetic models. Serum sHLA-G levels were quantified by Enzyme-Linked Immunosorbent Assay (ELISA). No significant associations were observed between the analyzed HLA-G polymorphisms and SS susceptibility. However, serum sHLA-G levels were significantly elevated in SS patients compared to controls (P = 0.005), indicating potential relevance to disease immunopathogenesis. While HLA-G gene variants showed no association with SS, increased sHLA-G levels may serve as a promising biomarker for the disease. Further studies are warranted to validate these findings and clarify the role of sHLA-G in SS pathophysiology.
Keywords
Human leukocyte antigen-G
Single nucleotide polymorphisms
Sjögren’s syndrome
Soluble HLA-G
1. Introduction
Sjögren’s syndrome (SS) is a chronic, systemic autoimmune disorder predominantly targeting the exocrine glands, resulting in hallmark manifestations such as keratoconjunctivitis sicca and xerostomia. The pathophysiology of SS is defined by extensive lymphocytic infiltration of exocrine tissues, resulting in progressive glandular dysfunction and impaired secretory function. SS manifests in two clinical forms: primary SS (pSS), which arises as an isolated autoimmune disorder, and secondary SS, which occurs in association with other systemic autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and multiple sclerosis (MS).
Although extensive research has been conducted, the underlying etiology of SS remains incompletely understood. Current evidence suggests a multifactorial origin involving complex interactions among genetic predisposition, environmental triggers, and hormonal influences (Negrini et al., 2022). SS has a reported prevalence ranging from approximately 0.1% to 4.8% in the general population and exhibits a strong female predominance, with a female-to-male ratio nearing 9:1. The condition predominantly affects women, with peak incidence occurring during the fourth and fifth decades of life. Additionally, epidemiological data suggest an increased susceptibility among elderly individuals, indicating a disproportionate impact on older populations. This demographic pattern suggests that age and sex may be significant factors in the susceptibility and progression of SS, highlighting the need for further investigation into the underlying mechanisms contributing to this disparity (Parisis et al., 2020). It has been reported that SS is seven times more prevalent in older individuals, though the disease can also manifest in younger populations. Interestingly, SS exhibits distinct gender differences, which have led researchers to hypothesize that sex hormones may influence the disease’s progression through their regulatory effect on immune responses (Angum et al., 2020). Furthermore, research has identified single-nucleotide polymorphisms (SNPs) in multiple immune-regulating genes, specifically in genes encoding cytokines and Human Leukocyte Antigen (HLA) molecules, as contributing factors to the pathogenesis of SS (Lessard et al., 2013).
HLA-G is a non-classical molecule of the major histocompatibility complex (MHC) class I, serving a crucial role in the regulation of immune tolerance. Unlike classical HLA class I molecules, which are highly polymorphic and ubiquitously expressed to mediate antigen presentation to cytotoxic T lymphocytes, HLA-G exhibits limited polymorphism and tissue-restricted expression. Its primary function is to modulate immune responses, thereby maintaining immune homeostasis and preventing excessive immune activation (Naito and Okada, 2022). Soluble HLA-G (sHLA-G) refers to the circulating form of HLA-G molecules, which can modulate immune responses through interaction with inhibitory receptors on various immune cells. Genetic polymorphisms in the HLA-G gene may influence its expression and contribute to the susceptibility or progression of autoimmune diseases. Notably, HLA-G is highly expressed at immune-privileged sites such as the placenta, where it plays a critical role in preventing maternal immune rejection of the fetus. Moreover, HLA-G binds to and modulates several immune cell receptors, including ILT2, ILT4, and KIR2DL4, thereby inhibiting the activity of natural killer (NK) cells, T-cells, and antigen-presenting cells (APCs) (Zhuang et al., 2021). Owing to its significant immunomodulatory role, HLA-G has attracted considerable interest as a potential player in the pathogenesis of autoimmune diseases, including SS (Khatri et al., 2022).
Research on the genetic factors influencing SS in Saudi Arabia remains relatively limited; however, the unique genetic profile of the Saudi population presents a valuable opportunity to investigate the role of HLA-G polymorphisms in the pathogenesis of SS. Despite substantial advances in understanding SS, there have been no studies focusing on Middle Eastern populations or individuals of Arab ancestry that explore the genetic polymorphisms of the HLA-G gene. Consequently, the genetic basis of SS among Arab populations remains underexplored, and the role of HLA-G polymorphisms in influencing disease susceptibility is not thoroughly understood.
To bridge this knowledge gap, the present study investigates HLA-G expression profiles and genetic polymorphisms in patients with SS. Given the impact of population-specific genetic variations on disease susceptibility, pathogenesis, and clinical progression, analyzing HLA-G polymorphisms in this cohort may provide valuable insights into the immunogenetic mechanisms underlying SS.
By identifying potential genetic markers linked to SS, this research aims to advance our understanding of the dysregulation of HLA-G-mediated immune tolerance in affected individuals. Furthermore, elucidating the association between HLA-G polymorphisms and SS pathophysiology may guide the development of precision-based therapeutic strategies, contributing to more targeted and effective interventions for this autoimmune disorder.
2. Materials and Methods
2.1 Patient population
This study is part of a comprehensive clinical investigation into SS among patients in Saudi Arabia. Conducted between October 2018 and May 2019, this cross-sectional study enrolled a total of 80 Saudi male and female volunteers, aged 18 to 77 years. The cohort was stratified into two distinct groups: Group 1 comprised 40 healthy Saudi individuals who were seronegative for SS-specific autoantibodies, exhibited no clinical manifestations of the disease, and served as representative controls within the general Saudi population. Group 2 included 40 Saudi patients with a definitive SS diagnosis, recruited during routine follow-ups at the rheumatology and pulmonary clinics of King Saud University Medical City in Riyadh. All patients fulfilled the classification criteria established by the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) for SS (Table 1).
| Variable | SS patients | Control | P-value | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Mean ± SD | Min. | Max. | N | Mean ± SD | Min. | Max. | <0.001 | |
| Age | 41 | 58.76 ± 12.7 | 18 | 83 | 71 | 48.87 ± 14.49 | 24 | 83 | |
The exclusion criteria for this study included any confirmed diagnosis of malignancy, major psychiatric disorders, or end-organ failure. Clinical data, including age and sex, were collected during routine clinic visits. Immunosuppressive treatments were not modified for patients who had been receiving such medications for at least three months prior to blood sample collection.
2.2 Sample preparation
This study was approved by the Research Ethics Committee of the Medical City at King Saud University. Relevant family histories were documented, and written informed consent was obtained from all participants prior to sample collection. Peripheral blood samples (5 mL) were collected from both patients and controls into EDTA-containing tubes. Plasma was separated by centrifugation, aliquoted, and stored at −80°C until further analysis.
2.3 DNA extraction
Genomic DNA was extracted from peripheral blood cells using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA, USA) in accordance with the manufacturer’s protocol. The concentration and purity of the isolated DNA were evaluated using the NanoDrop™ 2000/2000C Spectrophotometer (Thermo Scientific, USA). Subsequently, the labeled DNA samples were stored at −20°C.
2.4 HLA-G genotyping
The HLA-G gene was genotyped to evaluate the 14-bp Insertion/Deletion (Ins/Del) polymorphism in the 3’ untranslated region (3’UTR) using Polymerase Chain Reaction (PCR). Specific primers targeting the 3′UTR were employed: forward (5′GTGATGGGCTGTTTAAAGTGTCACC’3) and reverse (3′GGAAGGAATGCAGTTCAG-CATGA’5). The PCR cycling conditions followed the protocol described by Al Omar et al. (2015). The PCR products were analyzed based on fragment sizes (210/224 bp) by detecting the presence or absence of specific bands in a 3% agarose gel, which was stained with ethidium bromide and visualized using a UV transilluminator with a gel documentation system (BioRad Gel Doc™ XR+, Hercules, CA, USA).
2.5 SNP selection
In this study, six SNPs within the immune checkpoint gene HLA-G (rs17875394, rs1233333, rs9380142, rs1063320, rs1710, and rs915668) were identified using the dbSNP database (https://www.ncbi.nlm.nih.gov/snp/), selected based on a minor allele frequency (MAF) ≥ 5% and a Hardy-Weinberg equilibrium (HWE) P-value > 0.005. Genotyping was performed through VIC- and FAM-labeled allelic discrimination using TaqMan Assays-on-Demand (Applied Biosystems) following the manufacturer’s instructions on an ABI Prism 7500 Real-Time PCR System (Applied Biosystems, Foster City, USA). The PCR reaction mixture (10 µL) consisted of 0.26 µL of 2× SNP Genotyping Assay, 5.5 µL of 2× Power Taq Master Mix, 2.24 µL of nuclease-free water, and 2 µL of genomic DNA (100 ng/μL). Thermal cycling conditions included an initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s, annealing at 55°C for 30 seconds, extension at 72°C for 30 s, and a final extension at 72°C for 5 min. To ensure genotyping accuracy, approximately 5% of the samples were randomly selected for re-genotyping, confirming data reproducibility.
2.6 Serum quantification of sHLA-G
sHLA-G levels in the serum were quantified using the Human HLA-G Enzyme-Linked Immunosorbent Assay (ELISA) Kit (Thermo Fisher Scientific, Cat# EEL051) following the manufacturer’s protocol. Briefly, 100 µL of serum was added to pre-coated wells, incubated with detection antibodies, and developed using a chromogenic substrate. Optical density was measured at 450 nm, and concentrations were calculated against a standard curve from recombinant HLA-G. All samples and standards were run in duplicate for accuracy.
2.7 Statistical analysis
The association between alleles and genotypes was evaluated by calculating odds ratios (ORs) with 95% confidence intervals (CI) across five genetic models: co-dominant, dominant, recessive, over-dominant, and log-additive. These analyses were performed using the SNPStats software. Model comparison was conducted using the Akaike Information Criterion (AIC), with the lowest AIC value indicating the best-fitting model. All SNPs were tested for deviations from Hardy-Weinberg equilibrium using a Chi-square test, and associations were deemed statistically significant at a threshold of P < 0.05.
3. Results
3.1 Association of HLA-G polymorphisms with SS
This study characterized the 14-bp Ins/Del polymorphism and six additional SNPs of the HLA-G gene in both patients with SS and healthy Saudi individuals. Additionally, plasma levels of sHLA-G were measured to investigate potential associations between sHLA-G secretion and HLA-G genotypes in this cohort. This comparison aimed to explore genetic and molecular differences between SS patients and healthy controls.
The allelic and genotypic distribution of HLA-G polymorphisms between patients with SS and healthy controls was analyzed using various genetic models (codominant, dominant, recessive, over dominant, and additive). No significant associations were identified between the rs17875394 alleles and genotypes in both cohorts. In the codominant model, the frequency of the G/G genotype in SS patients was 100%, compared to 97.5% in the control group (P = 0.4), while the A/G genotype was not observed in the SS group. For rs1233333, the A and G allele frequencies did not show significant differences between SS patients and controls (P = 0.3), with the A allele present in 59% of SS patients and 51% of controls. Genotypic analysis revealed that the A/A genotype was slightly more frequent in SS patients (37%) compared to controls (29.5%), though this difference was not statistically significant (P = 0.5). Similar non-significant results were observed across dominant, recessive, and over-dominant models. The analysis of rs9380142 also yielded non-significant findings, with the A allele frequency at 76% in SS patients and 73% in controls (P = 0.8). Likewise, the genotypic distribution (A/A, A/G, and G/G) showed no significant associations between the two groups across all genetic models. Similarly, for rs1063320, rs1710, and rs915668, no statistically significant differences in allele or genotype frequencies were observed between SS patients and controls. Across all models, the odds ratios and confidence intervals suggest minimal genetic influence of these polymorphisms on SS susceptibility in this cohort (Table 2).
| Gene | Locus | Model | Genotype | Sjogren % | Control% | OR (95% CI) | P-value | AIC | BIC |
|---|---|---|---|---|---|---|---|---|---|
| HLA-G | rs17875394 | Alleles | G | 34 (100%) | 79 (0.99%) | 1 | 1 | - | - |
| A | 0 (0%) | 1 (0.01%) | 0.00 (0.00-NA) | ||||||
| Codominant | G/G | 17 (100%) | 39 (97.5%) | 1 | 0.4 | 72.8 | 76.8 | ||
| A/G | 0 (0%) | 1 (2.5%) | 0.00 (0.00-NA) | ||||||
| rs1233333 | Alleles | A | 51 (0.59) | 62 (0.51%) | 1 | 0.3 | - | - | |
| G | 35 (0.41) | 60 (0.49) | 1.4 (0.81- 2.46) | ||||||
| Codominant | A/A | 16 (37%) | 18 (29.5%) | 1 | 0.5 | 145.6 | 153.6 | ||
| G/A | 19 (44.2%) | 26 (42.6%) | 0.82 (0.34-2.01) | ||||||
| G/G | 8 (18.6%) | 17 (27.9%) | 0.53 (0.18-1.55) | ||||||
| Dominant | A/A | 16 (37.2%) | 18 (29.5%) | 1 | 0.4 | 144 | 149.7 | ||
| G/A-G/G | 27 (62.8%) | 43 (70.5%) | 0.71 (0.31-1.62) | ||||||
| Recessive | A/A-G/A | 35 (81.4%) | 44 (72.1%) | 1 | 0.3 | 143.8 | 149 | ||
| G/G | 8 (18.6%) | 17 (27.9%) | 0.59 (0.23-1.53) | ||||||
| Over-dominant | A/A-G/G | 24 (55.8%) | 35 (57.4%) | 1 | 0.9 | 145 | 150 | ||
| G/A | 19 (44.2%) | 26 (42.6%) | 1.07 (0.48-2.34) | ||||||
| Log-additive | --- | --- | --- | 0.74 (0.43-1.25) | 0.3 | 143.7 | 149 | ||
| rs9380142 | Alleles | A | 65 (0.76%) | 89 (0.73%) | 1 | 0.8 | - | - | |
| G | 21 (0.24) | 33 (0.27%) | 1.96 (0.61- 2.16) | ||||||
| Codominant | A/A | 25 (58.1%) | 34 (55.7%) | 1 | 0.9 | 146.8 | 154.7 | ||
| A/G | 15 (34.9%) | 21 (34.4%) | 0.97 (0.42-2.25) | ||||||
| G/G | 3 (7%) | 6 (9.8%) | 0.68 (0.15-2.98) | ||||||
| Dominant | A/A | 25 (58.1%) | 34 (55.7%) | 1 | 0.8 | 145 | 150 | ||
| A/G-G/G | 18 (41.9%) | 27 (44.3%) | 0.91 (0.41-2.00) | ||||||
| Recessive | A/A-A/G | 40 (93%) | 55 (90.2%) | 1 | 0.6 | 144.8 | 150 | ||
| G/G | 3 (7%) | 6 (9.8%) | 0.69 (0.16-2.91) | ||||||
| Over-dominant | A/A-G/G | 28 (65.1%) | 40 (65.6%) | 1 | 0.96 | 145 | 150 | ||
| A/G | 15 (34.9%) | 21 (34.4%) | 1.02 (0.45-2.32) | ||||||
| Log-additive | --- | --- | --- | 0.88 (0.48-1.62) | 0.5 | 144.9 | 150.2 | ||
| rs1063320 | Alleles | G | 56 (0.65%) | 57 (0.61%) | 1 | 1 | - | - | |
| C | 30 (0.35%) | 37 (0.39%) | 1.21 (0.66- 2.22) | 0.6 | |||||
| Codominant | G/G | 18 (41.9%) | 16 (34%) | 1 | 0.7 | 130 | 137.5 | ||
| C/G | 20 (46.5%) | 25 (53.2%) | 0.71 (0.29-1.74) | ||||||
| C/C | 5 (11.6%) | 6 (12.8%) | 0.74 (0.19-2.90) | ||||||
| Dominant | G/G | 18 (41.9%) | 16 (34%) | 1 | 0.4 | 128 | 133 | ||
| C/G-C/C | 25 (58.1%) | 31 (66%) | 0.72 (0.30-1.69) | ||||||
| Recessive | G/G-C/G | 38 (88.4%) | 41 (87.2%) | 1 | 0.9 | 128.6 | 133.6 | ||
| C/C | 5 (11.6%) | 6 (12.8%) | 0.90 (0.25-3.19) | ||||||
| Over-dominant | G/G-C/C | 23 (53.5%) | 22 (46.8%) | 1 | 0.5 | 128.2 | 133.2 | ||
| C/G | 20 (46.5%) | 25 (53.2%) | 0.77 (0.33-1.75) | ||||||
| Log-additive | --- | --- | --- | 0.81 (0.43-1.53) | 0.5 | 128.2 | 133.2 | ||
| rs1710 | Alleles | C | 56 (0.65%) | 69 (0.58%) | 1 | 0.4 | - | - | |
| G | 30 (0.35%) | 49 (0.42%) | 1.33 (0.75- 2.36) | ||||||
| Codominant | C/C | 18 (41.9%) | 18 (30.5%) | 1 | 0.5 | 143.5 | 151.4 | ||
| G/C | 20 (46.5%) | 33 (55.9%) | 0.61 (0.26-1.43) | ||||||
| G/G | 5 (11.6%) | 8 (13.6%) | 0.63 (0.17-2.28) | ||||||
| Dominant | C/C | 18 (41.9%) | 18 (30.5%) | 1 | 0.2 | 141.5 | 146.7 | ||
| G/C-G/G | 25 (58.1%) | 41 (69.5%) | 0.61 (0.27-1.39) | ||||||
| Recessive | C/C-G/C | 38 (88.4%) | 51 (86.4%) | 1 | 0.8 | 142.8 | 148 | ||
| G/G | 5 (11.6%) | 8 (13.6%) | 0.84 (0.25-2.77) | ||||||
| Gene | Locus | Model | Genotype | Sjogren % | Control% | OR (95% CI) | P-value | AIC | BIC |
| Over-dominant | C/C-G/G | 23 (53.5%) | 26 (44.1%) | 1 | 0.4 | 142 | 147.2 | ||
| G/C | 20 (46.5%) | 33 (55.9%) | 0.69 (0.31-1.51) | ||||||
| Log-additive | --- | --- | --- | 0.73 (0.40-1.34) | 0.3 | 141.9 | 147.1 | ||
| rs915668 | Alleles | G | 57 (0.66%) | 74 (0.62%) | 1 | 0.6 | - | - | |
| C | 29 (0.34%) | 46 (0.38%) | 1.22 (0.68- 2.18) | ||||||
| Codominant | G/G | 19 (44.2%) | 22 (36.7%) | 1 | 0.7 | 145.4 | 153.3 | ||
| C/G | 19 (44.2%) | 30 (50%) | 0.73 (0.32-1.70) | ||||||
| C/C | 5 (11.6%) | 8 (13.3%) | 0.72 (0.20-2.59) | ||||||
| Dominant | G/G | 19 (44.2%) | 22 (36.7%) | 1 | 0.4 | 143.4 | 148.6 | ||
| C/G-C/C | 24 (55.8%) | 38 (63.3%) | 0.73 (0.33-1.63) | ||||||
| Recessive | G/G-C/G | 38 (88.4%) | 52 (86.7%) | 1 | 0.8 | 143.9 | 149.2 | ||
| C/C | 5 (11.6%) | 8 (13.3%) | 0.86 (0.26-2.82) | ||||||
| Over-dominant | G/G-C/C | 24 (55.8%) | 30 (50%) | 1 | 0.6 | 143.6 | 148.9 | ||
| C/G | 19 (44.2%) | 30 (50%) | 0.79 (0.36-1.74) | ||||||
| Log-additive | --- | --- | --- | 0.81 (0.45-1.47) | 0.5 | 143.5 | 148.8 |
Abbreviations; OR, odds ratio; CI, confidence interval, n, number of individuals; Boldfaced values indicate a significant difference at the P ≤ 0.05 level.
The allelic and genotypic distribution of HLA-G 5′ polymorphisms and the 14-bp Insertion/Deletion (Ins/Del) polymorphism were analyzed in patients with SS and healthy controls. Several genetic models, including codominant, dominant, recessive, over-dominant, and additive, were employed for analysis.
For the HLA-G 14-bp Ins/Del polymorphism, the codominant model did not reveal a statistically significant difference between SS patients and controls. The del/del genotype was observed in 19.4% of SS patients and 9.1% of controls, while the del/ins genotype was present in 67.7% of SS patients and 77.3% of controls. The ins/ins genotype appeared in 12.9% of SS patients and 13.6% of controls. The P-value for the codominant model was 0.4, indicating no significant genotypic association. Similarly, the dominant and recessive models yielded no statistically significant results (P = 0.2 and P = 0.9, respectively), suggesting no significant influence of this polymorphism on SS susceptibility in this cohort (Table 3).
| Genetic model type | Genotype/variant | Controls | SS patients | Control vs patients | |||||
|---|---|---|---|---|---|---|---|---|---|
| Count | % | Count | % | OR (95% CI) | P-value | AIC | BIC | ||
| Codominant | del/del | 4 | 9.1 | 6 | 19.4 | 1 | |||
| del/ins | 34 | 77.3 | 21 | 67.7 | 0.41 (0.10-1.63) | 0.4 | 106 | 113 | |
| ins/ins | 6 | 13.6 | 4 | 12.9 | 0.44 (0.07-2.66) | ||||
| Dominant | del/del | 4 | 4 | 6 | 19.4 | 1 | 0.2 | 104 | 108.7 |
| del/ins + ins/ins | 40 | 90.9 | 25 | 80.7 | 0.42 (0.11-1.62) | ||||
| Recessive | del/del + del/ins | 38 | 86.4 | 27 | 87.1 | 1 | 0.9 | 105.7 | 110 |
| ins/ins | 6 | 13.6 | 4 | 12.9 | 0.94 (0.24-3.65) | ||||
| Over-dominant | del/del + ins/ins | 10 | 22.7 | 10 | 32.3 | 1 | 0.4 | 104.9 | 109.5 |
| del/ins | 34 | 77.3 | 21 | 67.7 | 0.62 (0.22-1.73) | ||||
| Log-additive | --- | --- | --- | --- | --- | 0.66 (0.27-1.63) | 0.4 | 104.9 | 109.5 |
In contrast, the analysis of sHLA-G levels revealed a statistically significant difference between SS patients and controls. The mean serum concentration of sHLA-G was notably higher in SS patients (14.5 ± 0.62) compared to controls (11.8 ± 0.65), with a P-value of 0.005, suggesting that sHLA-G may serve as a potential biomarker of disease activity in SS (Table 4, Fig. 1).
| N | Mean | Std. error mean | P value | |
|---|---|---|---|---|
| control | 15 | 11.8 | .65 | .005 |
| SS | 13 | 14.5 | .62 |
- Elevated levels of sHLA-G in patients with SS compared to healthy controls.
4. Discussion
SS is a clinically and immunologically heterogeneous autoimmune disorder, 0underscoring the critical need for reliable biomarkers to enhance patient stratification for precise disease monitoring, prognostic assessment, and the optimization of therapeutic strategies. To address this need, we integrated genetic data with clinical parameters to explore potential genetic determinants associated with SS susceptibility. Despite comprehensive analyses, no statistically significant associations were identified between specific HLA-G polymorphisms and the risk of SS development in the studied patient cohort.
To the best of our knowledge, this work represents the first report establishing a direct correlation between sHLA-G risk variants and SS susceptibility in patients from Saudi Arabia and the broader Middle Eastern population. We further assessed the association between several HLA-G polymorphisms and SS susceptibility within a Saudi patient cohort. While no significant genetic associations were observed, elevated levels of sHLA-G were detected in the serum of SS patients compared to healthy controls. These findings contribute to the growing body of evidence suggesting a role for HLA-G in modulating immune responses in autoimmune diseases, though the exact mechanisms remain to be elucidated.
Previous research has explored the role of HLA-G polymorphisms in various autoimmune diseases, including SLE, RA, and MS (Morandi et al., 2013; Gautam et al., 2020). In line with our findings, Cavalcanti et al. (2017) reported elevated sHLA-G levels in patients, suggesting that sHLA-G may have a functional role in immune regulation that operates independently of genetic variation. The lack of significant genetic association between HLA-G polymorphisms and SS in our study contrasts with findings in some other autoimmune diseases. For example, Silva et al. (2016) demonstrated a significant association between HLA-G 14-bp insertion/deletion polymorphisms and Type 1 diabetes.
This discrepancy may be attributed to disease-specific mechanisms or differences in the genetic backgrounds of the populations studied. In our cohort, the 14-bp Ins/Del polymorphism did not show any significant association with SS, suggesting that this genetic variant may not play a major role in SS within the Saudi population. Differences in the genetic architecture across populations underscore the importance of population-specific studies, as genetic factors may have varied contributions to disease susceptibility depending on the ethnic and geographic context.
The elevated levels of sHLA-G observed in our study are consistent with findings in other autoimmune disorders. Morandi et al. (2013) reported similar increases in sHLA-G in MS. These studies suggest that sHLA-G plays an active role in modulating immune responses, potentially by inducing immune tolerance and suppressing inflammatory processes. In the context of SS, elevated sHLA-G may reflect a compensatory mechanism aimed at dampening the ongoing autoimmune response. However, the precise role of sHLA-G in SS pathogenesis remains to be fully elucidated, and further functional studies are necessary to determine whether this molecule could serve as a reliable biomarker for disease activity.
Understanding the functional effect of genetic variants, particularly in genes coding for immune checkpoint molecules such as HLA-G, is crucial for advancing personalized medicine approaches. Our results suggest that while HLA-G polymorphisms may not be directly implicated in SS susceptibility in the Saudi population, sHLA-G could potentially serve as a biomarker for disease activity. These findings open avenues for future research aimed at exploring therapeutic interventions that target immune checkpoint molecules. Given that immune checkpoint inhibitors have shown promise in the treatment of cancers and some autoimmune diseases, targeting HLA-G could offer new strategies for managing SS, particularly in patients who exhibit elevated levels of sHLA-G. Contrary to our findings, several studies have demonstrated significant associations between specific HLA-G polymorphisms and various autoimmune disorders across different populations. For example, Gautam et al., 2020 reported a strong correlation between HLA-G genetic variants and RA. These discrepancies underscore the necessity for further investigations to elucidate the role of HLA-G in the pathogenesis of diverse autoimmune diseases across distinct ethnic and genetic backgrounds. In the context of SS, future research should aim to increase cohort sizes and explore additional immune-related genetic loci to achieve a more comprehensive understanding of the genetic determinants contributing to disease susceptibility and heterogeneity.
In conclusion, our study highlights the potential importance of sHLA-G in the pathogenesis of SS, while also suggesting that HLA-G polymorphisms may not significantly influence susceptibility in the Saudi population. Future research should focus on elucidating the functional role of sHLA-G and its potential as a therapeutic target, as well as exploring other genetic and environmental factors that may contribute to SS risk. Personalized medicine approaches that incorporate biomarkers such as sHLA-G could pave the way for more effective treatment strategies in autoimmune diseases like SS.
5. Conclusions
sHLA-G levels were significantly elevated in patients with SS, suggesting its potential as a biomarker of disease activity. No significant associations were found between HLA-G gene variants and SS susceptibility. Further research is needed to clarify the immunomodulatory role of HLA-G in the pathogenesis and clinical progression of SS.
CRediT authorship contribution statement
Safa A Alqarzae: Formal analysis, Practical and experimental work, data curation, Writing - Original Draft. Maha Daghestani: writing final manuscript and editing. Bashaer Alqahtani: data curation. Mohammed A. Omair: writing final manuscript and editing. Fadwa M Alkhulaifi: formal analysis, Writing - Original Draft. Sheka Yagub Aloyouni: Writing - Original Draft. Rasha Alonaizan: Writing - Original Draft. Suliman Alomar: Conceptualization, visualization and methodology, Funding acquisition.
Declaration of competing interest
The authors declare that they have no competing financial interests or personal relationships that could have influenced the work presented in this paper.
Data availability
The datasets generated and/or analyzed during the study are not publicly available due to ethical constraints; however, they can be provided by the corresponding author upon reasonable request.
Declaration of Generative AI and AI-assisted technologies in the writing process
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Funding
This study was supported by the Ongoing Research Funding Program (ORF-2025-35) at King Saud University, Riyadh, Saudi Arabia, and by the Princess Nourah bint Abdulrahman University Researchers Supporting Project (PNURSP2025R713), Riyadh, Saudi Arabia.
Institutional review board statement
Ethical approval for this study was obtained from the Institutional Review Board (IRB) of the College of Medicine at King Saud University (Approval No. E-23-8343). All procedures were conducted in compliance with the ethical principles outlined in the Declaration of Helsinki.
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