Genetic Mutations in TNFSF11 Were Associated With the Chronicity of Hepatitis C Among Chinese Han Population

Introduction

With the introduction of all-oral direct-actingantiviral therapy, a substantial breakthrough has been made in chronic HCV infection treatment during the past decades (1). However, due to HCV's significant heterogeneity and high variability, virus reinfection following successful treatment remains an important public health problem (2). The HCV pathogenesis and progression are complex and interact with its biological characteristics, environmental behavior factors, host immunity, and genetic background.

The activation of NF-κB and NF-κB-dependent inflammatory pathways are important to chronic HCV infection and its related cirrhosis and HCC. The NF-κB ligand (RANKL) receptor activator, a 316-amino acid transmembrane protein, is highly expressed in different immune cells including T or dendritic cells. RANKL can be induced by inflammatory factors such as interleukin 1, tumor necrosis factor α, and transforming growth factor β (3). Besides, RANK and osteoprotegerin (OPG) are RANKL receptors, and the RANKL/RANK/OPG pathway is important for cellular immune responses such as cell death and proliferation, inflammation, and immunity (4). The communication pathways mediated by TNFSF/TNFRSF are essential for numerous developmental, homeostatic, and stimulus-responsive processes. Both innate and adaptive immune cells are controlled by TNFSF/TNFRSF members in a manner that is crucial for the coordination of various mechanisms driving either co-stimulation or co-inhibition of the immune response (5). Different cellular immune responses can be triggered by genetic differences between different individuals. During the past decades, many studies have identified that immune cytokines SNPs were significantly associated with HCV spontaneous clearance and virological response, except for viral and environmental factors (6–9). Studies have shown that multiple TNFSF and TNFRSF gene SNPs are related to autoimmune diseases, suggesting that these SNPs play an important role in immunity. rs8086340 and rs2073618 are closely related to the occurrence of rheumatoid arthritis (10), and rs9525641 may affect the susceptibility and severity of AS disease (11). However, no research has addressed TNFSF and TNFRSF genetic polymorphisms' impacts on HCV-related chronic liver diseases. Considering that China has the largest number of HCV infections (about 10 million patients), we examined the relationships between OPG-RANKL-RANL pathway genes SNPs rs9525641, rs2073618, and rs8686340, and HCV infection outcomes in a high-risk Chinese population.

Methods

Study Participants

This study included three HCV infection high-risk groups. In the present study, 2,800 subjects were recruited from 2008 to 2016, including 722 hemodialysis patients from 9 hemodialysis centers in southern China, 459 drug users from a Nanjing compulsory detoxification center, and 1,619 paid blood donors from 6 Zhenjiang villages. All research objects voluntarily signed the informed consent. The exclusion criteria were: (1) patients under 18 years and over 80 years; (2) patients with interferon treatment history; (3) patients co-infected with HBV and HIV; (4) patients who suffer from autoimmune diseases or malignant tumors; (5) patients with other liver diseases. All patients were diagnosed with patient's clinical symptoms and biochemical examination indicators by experienced doctors, strictly following international standards. Patients were grouped according to their HCV antibodies and viral load. The participants were categorized into three groups: (1) A, uninfected control (anti-HCV and HCV RNA negative); (2) B, spontaneous HCV clearance (anti-HCV positive and HCV RNA negative); (3) C, persistent HCV infection cases (anti-HCV and HCV RNA positive).

This study was conducted strictly under the “Declaration of Helsinki” and was approved by the Ethics Committee of Nanjing Medical University (2017445).

We interviewed each participant with trained personnel and collected demographic data and environmental exposure history information through a structured survey. All participants were informed and agreed to participate in this study before recruitment.

Data and Blood Sample Collection

After the interview, we collected venous blood samples (~10 mL) from each participant, separated the plasma and white blood cells, and stored them at −80°C until assays. The detection of subjects' anti-HCV antibodies, HCV RNA, and HCV genotypes was performed with Jurong City People's Hospital and Yixing City People's Hospital, using third-generation enzyme-linked immunosorbent assay (ELISA) (Architect Anti-HCV assay, Abbott Laboratories, Abbott Park, IL, USA), Trizol LS reagent (Takara Biotech, Tokyo, Japan) and the murex HCV serotype ELISA kit (Abbott, Wiesbaden, Germany), respectively. The HCV RNA load detection limit was 1 × 10³ IU/ml and all serological tests were performed with the same analytical systems.

SNP Selection and Genotyping

Candidate TNFSF11/TNFRSF11A/TNFRSF11B gene Tag SNPs were selected by searching the 1,000 Genomes Project (http://www.1000genomes.org/) or the HapMap (http://www.hapmap.org/) databases. The selected SNPs were filtered with the following criteria: (1) minor allele frequency >5% in Han Chinese population, acquired from the Haploview software (version 4.2; Broad Institute, Cambridge, MA, USA); (2) Hardy-Weinberg equilibrium test p ≥ 0.05; (3) reported SNPs from previous studies associated with immune-related disorders; (4) combined bioinformatics data from Regulome DB (http://regulome.stanford.edu/). Finally, three SNP candidates (rs9525641, rs8086340, and rs2073618) were chosen for genotyping. Primers and probes are presented in Supplementary Table 1.

Genomic DNA was isolated from subjects' peripheral blood leukocytes using protease K digestion, phenol-chloroform extraction, and ethanol precipitation. SNPs genotyping was performed with a Taman allelic discrimination assay on the LightCycler^® 480 IIReal-Time PCR System (Roche, Switzerland). All genotyping was performed without knowing the subjects' case or control status. Each SNP accordance rate was 100% for the repeated experiments of 10% random samples. Additionally, the genotyping success rates for these polymorphisms were above 95%. All tests were carried out following the manufacturer's instructions and were performed with the same analytical systems.

In silico Analysis

The function of SNPs was predicted using the Regulome DB online database, HaploReg database (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) and Vienna RNA Web Servers (http://rna.tbi.univie.ac.at/cgi-bin/RNAWeb/Suite/RNAfold.cgi). The Regulome DB online database annotated SNPs with known and predicted regulatory elements in Homo sapiens genome intergenic regions was used to obtain SNPs' Regulome DB scores. Different Regulome DB score represents different combinations of the above and detailed information on all Regulome DB scores (Supplementary Table 2). The HaploReg database can be used for exploring chromatin states, conservation, and regulatory motif alterations within a set of genetically linked variants. Moreover, RNA secondary structures were predicted using the Vienna RNA Web Servers based on its latest package (Version 2.3.1).

Statistic Analysis

The demographic and clinical data distribution among the three groups was compared using the χ² test. HWE was assessed among control subjects by the goodness-of-fit χ² test. Logistic regression, with age, gender, and high-risk population adjustments, was used to analyze the relationship between the three SNPs and HCV infection outcome according to four genetic models, showing odds ratio and 95% confidence intervals. Hierarchical analysis was used to control confounding factors' effects on the results and Q tests were used to determine heterogeneity between subgroups. Statistical analyzes were performed using stata14.0, and a two-sided p < 0.05 was considered statistically significant.

Results

Demographic and Clinical Characteristics of Participants

According to the HCV antibody and RNA, subjects were divided into three groups. The demographic and clinical characteristics distribution among the HCV-uninfected control, the spontaneous HCV clearance, and the persistent infection groups are presented in Table 1. No significant differences in age or gender distribution among groups were detected (p = 0.185 and 0.095, respectively). On the other hand, alanine aminotransferase (ALT), aspartate aminotransferase (AST), infection routes, and HCV genotype differed (p < 0.001).

TABLE 1

Table 1. Demographic and clinical characteristics among HCV control, spontaneous clearance, and persistent infection groups.

SNPs rs9525641, rs8086340, and rs2073618 genotype frequencies in the HCV-uninfected control followed the Hardy-Weinberg equilibrium (p = 0.637, 0.250, and 0.113, respectively). This indicated that the control was a representative group.

Associations Between SNP Candidates and HCV Infection Outcomes

The genotype distribution rs9525641, rs8086340, and rs2073618 among groups are shown in Table 2. To analyze the association between these SNPs and HCV infection susceptibility, we first combined patients from groups B and C in an HCV-infected group and compared it with the control (A). However, no significant association was observed in the logistic regression analyses between these three SNPs and HCV infection susceptibility (p > 0.05). To determine the association between these SNPs and HCV chronicity, the B group was selected as a control and compared to C. The regression analysis of a co-dominant model—corrected for age, gender, and high-risk population—showed that patients carrying the rs9525641-C gene were significantly associated with HCV chronic diseases (adjusted OR = 1.518, 95% CI: 1.079–2.136, p = 0.017), as for a recessive (adjusted OR = 1.203, 95% CI: 1.018–1.420, p = 0.030), and additive models (adjusted OR = 1.545, 95% CI: 1.150–2.075, p = 0.004). However, no correlation was observed between the two other genotypes and HCV infection chronicity (p > 0.05).

TABLE 2

Table 2. Genotypes distributions of three SNPs among persistent infection, spontaneous clearance and control group.

Stratified Analysis

To control age, gender, high-risk population, and HCV genotypes bias in each population, we performed a stratified analysis to explore the association between the rs9525641 genotype and HCV chronicity using a recessive model (Table 3). Results showed that the rs9525641 variant genotypes were significantly associated with an increased chronic HCV infection risk among people ≥50 years (adjusted OR = 1.562, 95% CI: 1.079–2.262, p = 0.018), females (adjusted OR = 1.667, 95% CI: 1.145–2.429, p = 0.008), ALT <40 U/L (adjusted OR = 1.532, 95% CI: 1.074–2.286, p = 0.018), and AST <40 U/L (adjusted OR = 1.552, 95% CI: 1.095–2.201, p = 0.014). Additionally, considering data heterogeneity in different subgroups, we performed a heterogeneity test. Results showed no significant heterogeneity between groups (p > 0.05).

TABLE 3

Table 3. Stratified analysis the association of rs9525641 with HCV chronicity.

Bioinformatics Analysis

The rs9525641 genotype had a Regulome DB score of 5, suggesting its potential functions such as transcription factor binding or DNase peak. Based on the HaploReg database, rs9525641 overlaps promoter histone marks, enhancer histone marks, and DNase and FXR motifs. Furthermore, 22 SNPs were in linkage disequilibrium with rs9525641 in the Asian population (r² > 0.8) (available at HaploReg database). Results of the 22 SNPs are presented in Supplementary Table 3. The in?uence of those SNPs on the RANKL mRNA secondary structure was predicted using the RNAfold Web Server. Six SNPs presented local structure changes (rs17458177, rs1325799, rs17536328, rs7984870, rs9533155, and rs3742257) (Supplementary Figures 1–6). Moreover, rs17458177-C and -T alleles showed a difference in the lowest free energy (−18.40 vs. −18.90 kcal/mol), suggesting that mutations might affect RANKL transcription. Specific information for the other SNPs can be found in the Supplementary Figures 1–6.

Discussion

The RANKL message is detected in the peripheral lymph nodes and bone marrow, thymus, spleen, Peyer's patches, brain, heart, skin, skeletal muscle, kidney, liver, lung, and mammary tissues (12). The RANKL/RANK system has been shown to play a critical role in the immune system, including lymph-node development, lymphocyte differentiation, dendritic cell survival, and T-cell activation and tolerance induction (13). RANKL can regulate lymph-node organogenesis, T- and B-lymphocyte development, and osteoclast differentiation. Some studies also indicated that RANKL regulates the thymus microenvironment by autoimmune regulators expression activation (14). Additionally, at the molecular level, RANK interacts with RANKL to activate the transcription factor NF-κB along with TNF receptor-related factor family signaling molecules (15). Considering that the NF-κB function is related to RANKL/RANK, and NF-κB has been linked to chronic hepatitis C (16–18), we hyphothesized that the RANKL/RANK pathway polymorphisms would affect the HCV infection outcome.

RANKL, RANK, and OPG are encoded by TNFSF11 (gene map locus 13q14), TNFRSF11A (gene map locus 18q22.1), and TNFRSF11B (gene map locus 8q24), respectively (19). The TNFSF11 gene structure is highly conserved among mammals, consisting of five exons that span 33.9 kb in humans (10). SNPs located near TNFSF11, TNFRSF11A, and TNFRSF11B have been reported to be closely associated with Paget's disease (20), osteoporotic fractures (21), cardiovascular diseases (22), ankylosing spondylitis (11), and breast (23), and esophageal cancers (24). In this study, we showed that subjects who carried the rs9525641-C allele were more likely to develop HCV chronicity than those with the rs9525641-T allele. Furthermore, in the stratification analyses based on age, gender, and high-risk population, we found that the rs9525641-C allele was associated with HCV chronicity among elders, females, and persons with ALT and AST <40 U/L. Interestingly, RANKL can cause various degenerative bone diseases, such as rheumatoid arthritis and osteoporosis. These diseases are mostly female. Our research shows that RANKL is related to the chronicity of HCV in women. This suggests to some extent the combined effect of gender factors and the RANKL system in disease progression, which is worthy of further discussion. However, the heterogeneity test showed no significant heterogeneity in any pair-wise comparison (p > 0.05), indicating that these variables did not materially affect the results.

Although rs9525641 is located in the TNFSF11 intronic region, this variant might play an important role in gene transcription regulation or might be in linkage disequilibrium with other functional SNPs, such as rs17458177, rs1325799, rs17536328, rs7984870, rs9533155, and rs3742257. We also calculated the degree of linkage between the candidate SNPs, r² < 0.1 indicates that there is no linkage disequilibrium among these three SNPs. The bioinformatics analysis indicated that these variants could regulate gene transcription, mRNA export, and protein translational efficiency. In the present study, we found two possible TNFSF11 biological processes using the STRING database website prediction (https://string-db.org/cgi/network.pl?taskId=kGEafB5GcPa7:) extracellular signal-regulated kinase 1 (ERK1) and ERK2 cascade positive regulation via TNFSF11-mediated signaling; and the tumor necrosis factor-mediated signaling pathway. It has been reported that the HCV non-enveloped particles' intriguing cellular internalization properties can activate the ERK1/2 pathway that could be important in the HCV life cycle and infection pathogenesis (25). Additionally, Fletcher et al. reported that several TNF superfamily members – including TNF-α, TNF-β, TWEAK, and LIGHT – can promote HCV entry via NF-κB-mediated activation of myosin light chain kinase and tight junctions disruption (26). Therefore, TNFSF11 may be involved in these pathways and contribute to the hepatitis C chronic process. However, these hypotheses are based on computer simulations and functional evaluations using biological assays. Thus, it should be warranted in future studies.

RANK is an intrinsic hematopoietic cell surface receptor that stimulates NF-κB receptor activation, plays a central role in T and dendritic cells, and promotes lymph node development (27)^. Previously, several studies have found that the TNFRSF11 rs8680340 was closely related to the anti-citrullinated peptide antibody and age at natural menopause (28). Additionally, the TNFRSF11B rs2073618 was significantly associated with Type 2 Diabetes (29), rheumatoid arthritis (30), and volumetric bone mineral density (31). However, in this study, no significant association was observed between TNFRSF11A rs8086340, TNFRSF11B rs2073618, and HCV infection outcome.

Our study also has some limitations. First, this study was performed with the Chinese Han population, requiring further reproduction in different ethnic populations. Second, we did not collect immune markers data in the RANKL/RANK pathway and could not check the association between immune markers data and the target SNPs. Third, we did not collect complete virus subtypes and viral load data and could not check the association between virus data and the target SNPs. Hence, we should improve the collection of this part of the data in the later stagethe and possible immune mechanism requires to be further studied and verified. Furthermore, we did not explore how the functional mechanisms of these SNPs affect HCV chronicity using molecular biology approaches. Then, functional studies are required in the future to explore how these polymorphisms impact chronic HCV infection.

Overall, our findings suggested that the rs9525641 TNFSF11 polymorphism might affect HCV chronicity in a high-risk Chinese population. Larger well-designed epidemiological studies with ethnically diverse populations and functional evaluations are warranted to confirm these findings before the effect of these variants can be fully and accurately evaluated.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SY and R-BY: conceptualization. Y-QH: formal analysis, investigation, and writing–original draft. PH: funding acquisition and methodology. Y-DW and J-JW: resources. J-JW and FZ: software. SY: supervision. Y-QH and X-YY: writing–review and editing. All authors contributed to the article and approved the submitted version.

Funding

This study was supported in part by National Natural Science Foundation of China (no. 81703273), Natural Science Foundation of Jiangsu Province (no. BK20171054), Natural Science Foundation of Yunnan Province (no. 2019FA005), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Conflict of Interest

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

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We are grateful to all patients and workers for donating this research.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2021.743406/full#supplementary-material

Supplementary Figure 1. The influence of rs17458177 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 bases downstream of the mutation). The minimum free energies for the C and T allele of rs17458177 were estimated at −18.40 and −18.90 kcal/mol, respectively, by RNAfold Wed Server.

Supplementary Figure 2. The influence of rs1325799 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the G and A allele of rs1325799 were estimated at −11.80 and −14.00 kcal/mol, respectively, by RNAfold Wed Server.

Supplementary Figure 3. The influence of rs17536328 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the C and T allele of rs17536328 were estimated at −17.4 and −16.60 kcal/mol, respectively, by RNAfold Wed Server.

Supplementary Figure 4. The influence of rs7984870 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the G and C allele of rs7984870 were estimated at −14.40 and −15.50 kcal/mol, respectively, by RNAfold Wed Server.

Supplementary Figure 5. The influence of rs9533155 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the C and G allele of rs9533155 were estimated at −14.80 and −15.50 kcal/mol, respectively, by RNAfold Wed Server.

Supplementary Figure 6. The influence ofrs3742257 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the T and C allele of rs3742257 were estimated at −31.70 and −27.80 kcal/mol, respectively, by RNAfold Wed Server.

Supplementary Table 1. Probes and primers of investigated TNFSF and TNFRSF SNPs for Taman assay.

Supplementary Table 2. The detailed information on different RegulomeDB scores.

Supplementary Table 3. Annotation of variannts with stong linkage disequilibrim with SNP rs 9525641 in HaploRegV4.1.

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