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CXCL9-11 polymorphisms are associated with liver fibrosis in patients with chronic hepatitis C: a cross-sectional study

Abstract

Background and aims

CXCL9-11 polymorphisms are related to various infectious diseases, including hepatitis C virus (HCV) infection. In this study, we analyzed the association between CXCL9-11 polymorphisms and liver fibrosis in HCV-infected patients.

Methods

We performed a cross-sectional study in 389 patients who were genotyped for CXCL9-11 polymorphisms (CXCL9 rs10336, CXCL10 rs3921, and CXCL11 rs4619915) using the Sequenom’s MassARRAY platform. The primary outcome variable was the liver stiffness measurement (LSM). We established three cut-offs of LSM: LSM ≥ 7.1 kPa (F ≥ 2—significant fibrosis), LSM ≥ 9.5 kPa (F ≥ 3—advanced fibrosis), and LSM ≥ 12.5 kPa (F4—cirrhosis).

Results

Recessive, overdominant and codominant models of inheritance showed significant values, but the overdominant model was the best fitting our data. In this case, CXCL9 rs10336 AG, CXCL10 rs3921 CG and CXCL11 rs4619915 AG were mainly associated with lower values of LSM [(adjusted GMR (aGMR) = 0.85 (p = 0.005), aGMR = 0.84 (p = 0.003), and aGMR = 0.84 (p = 0.003), respectively]. Patients with CXCL9 rs10336 AG genotype had lower odds of significant fibrosis (LSM ≥ 7.1 kPa) [adjusted OR (aOR) = 0.59 (p = 0.016)], advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 0.54 (p = 0.010)], and cirrhosis (LSM ≥ 12.5 kPa) [aOR = 0.56 (p = 0.043)]. Patients with CXCL10 rs3921 CG or CXCL11 rs4619915 AG genotypes had lower odds of significant fibrosis (LSM ≥ 7.1 kPa) [adjusted OR (aOR) = 0.56 (p = 0.008)], advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 0.55 (p = 0.013)], and cirrhosis (LSM ≥ 12.5 kPa) [aOR = 0.57 (p = 0.051)]. Additionally, CXCL9-11 polymorphisms were related to lower liver stiffness under a codominant model of inheritance, being the heterozygous genotypes also protective against hepatic fibrosis. In the recessive inheritance model, the CXCL9 rs10336 AA, CXCL10 rs3921 CC and CXCL11 rs4619915 AA were associated with higher LSM values [(adjusted GMR (aGMR) = 1.19 (p = 0.030), aGMR = 1.21 (p = 0.023), and aGMR = 1.21 (p = 0.023), respectively]. Moreover, patients with CXCL9 rs10336 AA genotype had higher odds of significant fibrosis (LSM ≥ 7.1 kPa) [adjusted OR (aOR) = 1.83 (p = 0.044)] and advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 1.85 (p = 0.045)]. Furthermore, patients with CXCL10 rs3921 CC or CXCL11 rs4619915 AA genotypes had higher odds of advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 1.89 (p = 0.038)].

Conclusions

CXCL9-11 polymorphisms were related to likelihood of having liver fibrosis in HCV-infected patients. Our data suggest that CXCL9-11 polymorphisms may play a significant role against the progression of CHC and could help prioritize antiviral therapy.

Background

Hepatitis C virus (HCV) leads to the development of chronic liver disease or liver-related death worldwide [1, 2]. Around 80% of the subjects exposed to the HCV usually develop chronic hepatitis C (CHC), which may result in significant liver fibrosis, cirrhosis and end stage liver disease [3]. Moreover, CHC constitutes a major indication for liver transplantation in the United States [4] and Europe [5]. However, the impact of HCV infection is highly variable, ranging from minimal rates of fibrosis progression to extensive fibrosis and cirrhosis in a few years [6]. Additionally, HCV antiviral therapy may reduce the clinical consequences of CHC, but patients with cirrhosis, despite HCV eradication, remain at risk of disease progression [7,8,9].

The management of patients with CHC depends on their clinical stage [10], since patients without cirrhosis have a much longer median survival time than those with cirrhosis [6]. Thus, early recognition of patients with CHC at high risk for developing liver fibrosis and cirrhosis is important, because it ensures optimal preventive management strategies that can modify the course of CHC disease [11]. The hepatic biopsy is the gold standard test to diagnose and quantify liver fibrosis, but this procedure is associated with several drawbacks [12, 13]. Liver stiffness measurement (LSM) using transient elastography is a noninvasive method based on liver elasticity observations, which may accurately predict the presence of fibrosis/cirrhosis in patients with CHC [12, 13].

The pathogenic mechanisms of the accelerated progression of liver injury are incompletely understood; but single nucleotide polymorphisms (SNPs), liver inflammatory profile and the impaired immune response are thought to be important determinants of the evolution of liver disease [14]. In this context, the lymphocyte migration from the periphery to liver parenchyma mediated by chemokines constitutes a major event for development of liver fibrosis in response to HCV infection [15]. CXCL chemokine ligand (CXCL) 9 [monokine induced by interferon γ (Mig)], CXCL10 [interferon-γ—inducible protein-10 (IP-10)], and CXCL11 (interferon-inducible T cell α chemoattractant; I-TAC) are produced in the liver by infected hepatocytes during CHC. This response induces migration of activated T cells (T-helper/T-cytotoxic type-1 cell (Th1/Tc1) response) from the periphery to the infected liver parenchyma via chemokine (C-X-C motif) receptor 3 [16]. It has been reported an increase of CXCL9 expression in hepatocytes of HCV-infected patients, and a correlation between CXCL9 levels and liver fibrosis [17, 18]. However, an anti-fibrotic role of CXCL9 has also been described [19,20,21]. Besides, increased CXCL10 levels have been associated with liver injury in HCV-infected patients [17, 21,22,23,24,25,26], and CXCL11 expression levels have been related to both portal and lobular inflammation; a clear relationship with liver disease in CHC has not been found [17, 22, 27]. Additionally, CXCL9-11 chemokines have been significantly correlated with the level of hepatoprotective cytokines such as IL6 and IL10, suggesting their involvement in a counter-regulatory response during the progression of liver disease [17].

CXCL9-11 polymorphisms have been related to severity in various infectious diseases such as those caused by enterovirus-71 [28], hepatitis B infection [29, 30], malaria [31], Chagas disease [32], and tuberculosis [33]. Besides, we have found an association between CXCL9-11 polymorphisms and liver disease in human immunodeficiency virus (HIV)/HCV-coinfected patients [34]. These patients have a damaged immune system due to HIV infection, causing hepatitis C virus to evolve more aggressively [35, 36]. However, to our knowledge, there is no information regarding the relationship between CXCL9-11 polymorphisms and liver disease in HCV-monoinfected patients.

In the present study, we analysed the association between CXCL9-11 polymorphisms and liver fibrosis, considering LSM values, in HCV-infected patients.

Patients and methods

Patients

We carried out a cross-sectional study on 389 HCV-infected patients who had liver stiffness assessed by transient elastometry in Virgen de la Salud Hospital (Toledo, Spain) between 2005 and 2015. The study was conducted in accordance with the Declaration of Helsinki, all patients gave their written consent for the study, and the Research Ethic Committee (“Comité de Ética de la Investigación”) approved the study.

The selection criteria were: (1) detectable HCV RNA by polymerase chain reaction; (2) availability of a valid LSM; (3) availability of DNA sample. The exclusion criteria were: (1) co-infection with hepatitis B virus or human immunodeficiency virus; (2) clinical evidence of hepatic decompensation at enrollment or a prior history of hepatic decompensation; and (3) hepatocellular carcinoma at enrollment or a previous history of hepatocellular carcinoma.

Clinical and laboratory data

Clinical and epidemiological data were obtained from medical records. The time since HCV diagnosis was calculated starting from the diagnosis date to the date of last visit in our study. High alcohol intake was considered to be ≥20 grams/day in women and ≥60 grams/day in men [37]. Patients could have been treated with HCV therapy [peg-Interferon (IFN)-α/ribavirin] according to clinical guidelines [38, 39]. However, we only included non-responder patients with detectable serum HCV-RNA.

Liver stiffness measurement

Liver stiffness measurement was assessed by transient elastography (FibroScan®, Echosens, Paris, France). Results were expressed in kilopascals (kPa) with a range of 2.5–75 kPa. Transient elastography was performed by a trained operator (medical doctor), and measurements were considered reliable when the interquartile-range-to-median ratio was <0.30 [40]. For at least ten successful measurements.

Outcome variable

The primary outcome variable was the LSM examined as continuous and categorical variable. Three different cut-offs of LSM [41], were established to stratify all patients: LSM ≥ 7.1 kPa (F ≥ 2—significant fibrosis), LSM ≥ 9.5 kPa (F ≥ 3—advanced fibrosis), and LSM ≥ 12.5 kPa (F4—cirrhosis).

Genotyping of CXCL9-11 polymorphisms

We analyzed three SNPs within the CXCL9-11 family (CXCL9 rs10336, CXCL10 rs3921 and CXCL11 rs4619915) [34, 42]. DNA samples were genotyped at the Spanish National Genotyping Center (CeGen-PRB2; http://www.usc.es/cegen/) by using the iPLEX® Gold technology and Agena Bioscience’s MassARRAY platform (San Diego, CA, USA).The quality control was performed according to the CeGen criteria, which includes a genotyping call-rate over 95% for all the SNPs; duplicated samples on each plate to check for technical replicates; a negative control; and three positive Coriell controls from Human Genetic Cell Repository (NA10860, NA10861 and NA11984). These controls were included on each batch to exclude DNA contamination and ensure a technically correct laboratory process, respectively.

A control healthy population was obtained the at the 1000 Genomes Project’s website (http://www.1000genomes.org/home): This database provides a broad representation of common human genetic variation detected by next generation sequencing in multiple populations [43]. Our control population was the Iberian population in Spain (IBS), which includes 107 individuals.

Statistical analysis

To analyze the differences among groups according to CXCL9-11 genotypes, Mann–Whitney U test and Kruskal–Wallis were used for continuous variables and Chi square test or Fisher’s exact test for categorical variables.

For the genetic association study, generalized linear model (GLM) was used to analyze the outcome variables with CXCL9-11 polymorphisms. This analysis was carried out according to dominant, codominant, overdominant, recessive and additive models. These analyses were tested according to the goodness of fit evaluated by Akaike information criterion (AIC) value and Bayesian information criterion (BIC). On the one hand, GLM with a gamma distribution was used to investigate the association between CXCL9-11 polymorphisms and the LSM (continuous variable). Besides, natural logarithmic transformation (Ln) of LSM was used due to a skewed distribution. The GLM gives the differences between groups and the geometric mean ratio (GMR), or the ratio by which the geometric mean of the original outcome is multiplied. On the other hand, GLM with binomial distribution (logit-link) was used to investigate the association of CXCL9-11 polymorphisms with categorical outcome variables (F ≥ 2, F ≥ 3, and F4). This test gives the differences between groups and the odds ratio (OR) for categorical outcome variables. For univariate model, we included each SNP separately. For multivariate model, each SNP together with all available relevant covariates (multivariate model) were included using the Enter algorithm (Forced Entry). The covariates that we have had available for inclusion in our study were age, gender, time since HCV diagnosis, HCV genotype, injection drug use, high alcohol intake, and non-responder patients to HCV antiviral therapy prior to baseline (patients who failed therapy). All statistical tests were performed with the Statistical Package for the Social Sciences (SPSS) 21.0 software (IBM Corp., Chicago, USA). All p values were two-tailed and statistical significance was defined as p value <0.05.

In addition, Hardy–Weinberg equilibrium (HWE) and pairwise linkage disequilibrium (LD) were analyzed by Haploview 4.2 software. Haplotype-based association test was performed with Plink software (http://pngu.mgh.harvard.edu/~purcell/plink/).

Results

Patient characteristics

Table 1 shows the characteristics of 389 chronic HCV-infected patients, 71 were cirrhotic (LSM ≥ 12.5 kPa). In brief, 58.6% were male, the median age was 49.6 years, 19.3% reported a high intake of alcohol, 17% were prior injection drug users, 81.2% were infected by HCV genotype 1, and 20.8% were patients treated with antiviral therapy (peg-IFN-α/ribavirin) who did not achieved sustained virological response (non-responders). The median LSM was 6.9 kPa and the median time since HCV diagnosis was 8.6 years.

Table 1 Clinical and epidemiological characteristics of HCV-infected patients

CXCL9-11 polymorphisms

Table 2 shows allelic and genotypic frequencies of CXCL9-11 polymorphisms. All SNPs had a minimum allele frequency >5%, displayed less than 5% of missing values, and were in HWE (p > 0.05). No significant differences were found between studied patients and IBS population. In addition, the allelic and genotypic frequencies in our dataset were in accordance with data listed on the NCBI SNP database for European population (http://www.ncbi.nlm.nih.gov/projects/SNP/).

Table 2 Summary of Hardy–Weinberg Equilibrium test and allelic and genotypic frequencies genotypes for CXCL9-11 polymorphisms in HCV-infected patients compared to Iberian population (data from HapMap) (http://browser.1000genomes.org/index.html)

A strong LD among CXCL9-11 SNPs was found (Fig. 1; D’ ≥ 0.97), meaning that there is no evidence of recombination between these SNPs. Furthermore, the R-squared among CXCL9-11 SNPs was also high (Fig. 1; R-squared ≥ 0.92), indicating that these polymorphisms could substitute each other.

Fig. 1
figure 1

Pairwise linkage disequilibrium (LD) pattern for CXCL9-11 polymorphisms. Each diagonal represents a different SNP, with each square representing a pairwise comparison between two SNPs

CXCL9-11 polymorphisms and liver stiffness

The values of LSM and fibrosis stages according to CXCL9-11 polymorphisms are shown in Table 3. The CXCL9 rs10336 AG, CXCL10 rs3921 CG and CXCL11 rs4619915 AG were linked to lower values of LSM (p ≤ 0.002) and frequencies of F ≥ 2 (LSM ≥ 7.1 kPa; p ≤ 0.004), F ≥ 3 (LSM ≥ 9.5 kPa; p ≤ 0.003) and F4 (LSM ≥ 12.5 kPa; p ≤ 0.038) than patients with homozygote genotypes.

Table 3 Summary of LSM and fibrosis stages according to CXCL9-11 polymorphisms (CXCL9 rs10336, CXCL10 rs3921, and CXCL11 rs4619915) in HCV-infected patients

The association analysis was tested under five different genetic models of inheritance. Recessive, overdominant and codominant models showed significant values, but the overdominant model was the best fitting our data according to AIC and BIC.

The overdominant inheritance model was the model that best fitted to our data, and the association values are shown in Table 4. In this case, we also found that CXCL9 rs10336 AG, CXCL10 rs3921 CG and CXCL11 rs4619915 AG were mainly associated with lower values of LSM [(adjusted GMR (aGMR) = 0.85 (p = 0.005), aGMR = 0.84 (p = 0.003), and aGMR = 0.84 (p = 0.003), respectively]. Moreover, patients with CXCL9 rs10336 AG genotype had lower odds of significant fibrosis (LSM ≥ 7.1 kPa) [adjusted OR (aOR) = 0.59 (p = 0.016)], advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 0.54 (p = 0.010)], and cirrhosis (LSM ≥ 12.5 kPa) [aOR = 0.56 (p = 0.043)]. Furthermore, patients with CXCL10 rs3921 CG or CXCL11 rs4619915 AG genotypes had lower odds of significant fibrosis (LSM ≥ 7.1 kPa) [adjusted OR (aOR) = 0.56 (p = 0.008)], advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 0.55 (p = 0.013)], and cirrhosis (LSM ≥ 12.5 kPa) [aOR = 0.57 (p = 0.051)]. Additionally, we also found that CXCL9-11 polymorphisms were related to lower liver stiffness under a codominant model of inheritance (data not shown), being the heterozygous genotypes also protective against hepatic fibrosis.

Table 4 Relationship between CXCL9-11 polymorphisms (CXCL9 rs10336, CXCL10 rs3921, and CXCL11 rs4619915) and liver fibrosis in HCV-infected patients

Table 5 shows the relationship between CXCL9-11 polymorphisms and liver stiffness under a recessive inheritance model. The CXCL9 rs10336 AA, CXCL10 rs3921 CC and CXCL11 rs4619915 AA were associated with higher LSM values [aGMR = 1.19 (p = 0.030), aGMR = 1.21 (p = 0.023), and aGMR = 1.21 (p = 0.023), respectively]. Moreover, patients with CXCL9 rs10336 AA genotype had higher odds of significant fibrosis (LSM ≥ 7.1 kPa) [adjusted OR (aOR) = 1.83 (p = 0.044)] and advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 1.85 (p = 0.045)]. Furthermore, patients with CXCL10 rs3921 CC or CXCL11 rs4619915 AA genotypes had higher odds of advanced fibrosis (LSM ≥ 9.5 kPa) [aOR = 1.89 (p = 0.038)].

Table 5 Relationship between CXCL9-11 polymorphisms (CXCL9 rs10336, CXCL10 rs3921, and CXCL11 rs4619915) and liver fibrosis in HCV-infected patients (recessive inheritance model)

CXCL9-11 haplotypes and liver fibrosis

Only three major haplotypes were found (Table 6), which included over 99.3% of patients: GGG, ACA, and AGG. However, we did not found any significant association.

Table 6 Association between CXCL9-11 haplotypes and liver fibrosis stage in HCV-infected patients

Discussion

In our study, we found that CXCL9-11 polymorphisms, under an overdominant inheritance model, were associated with lower LSM values and fibrosis stage. This association was particularly important with significant fibrosis (F ≥ 2, LSM ≥ 7.5 kPa) and advanced liver fibrosis (F ≥ 3, LSM ≥ 9.5 kPa). To our knowledge, this is the first study that reports a relationship between CXCL9-11 polymorphisms and liver fibrosis in HCV-infected patients. Additionally, this work confirms the previous findings of our group in HIV/HCV-coinfected patients [34]; suggesting that CXCL9-11 polymorphisms may play a key role in the development of liver fibrosis during CHC.

Hepatitis C virus infection is characterized by an increased production of cytokines and chemokines, including the C-X-C motif chemokine receptor 3-associated chemokines [16, 23, 26, 44], which seems to be an important determinant of histological progression in HCV-infected patients [27, 45]. In this context, CXCL9-11 chemokines are expressed on many cell types, and play a crucial role in CHC through the recruitment to the liver parenchyma of inflammatory cells (monocytes/macrophages, activated T lymphocytes, natural killers and dendritic cells). These cells potentially contribute to the host immune response against the HCV, as well as to the hepatic inflammation and the cascade of events that may lead to fibrogenesis and liver disease progression [15, 22, 27, 45].

There is not much available information of the relationship between CXCL9-11 polymorphisms and liver fibrosis in CHC. CXCL9 variants have been related to liver fibrosis in mice and humans [21]. CXCL10 rs1439490 (G-201A) polymorphism has been associated with liver disease in patients infected with hepatitis B virus [29]. In a recent article published by our group, we found that CXCL9 rs10336 AA, CXCL10 rs3921 CC and CXCL11 rs4619915 AA genotypes were related to higher likelihood of severity of liver fibrosis in HIV/HCV-coinfected patients under a recessive model [34]. Here, the homozygous genotypes for the minor alleles showed susceptibility to the liver disease. In the current study, we also showed that CXCL9-11 polymorphisms were also related to higher LSM values and higher odds of severity of liver fibrosis under a recessive model of inheritance. However, the overdominant model was the one that fitted best to our data, since it had lower values of AIC and BIC. In the overdominant model, the presence of heterozygous genotypes were linked to protection of severity of the liver disease. Thus, heterozygous individuals have a higher fitness than homozygous subjects (heterozygote advantage), such as in the sickle cell anemia, in which homozygote subjects do not have malaria protection or have dramatic propensity to sickle cell anemia; while heterozygote subjects have fewer physiological effects and partial resistance to malaria [46]. However, we do not have data to explain the mechanism by which this occurs.

The three CXCL9-11 SNPs analyzed in this study are located at the 3′ untranslated region (UTR) of their respective genes. 3′UTR is a putative regulatory region, which could be targeted by microRNAs (miRNAs). These molecules are small non-coding RNAs that regulates expression by binding to its mRNA target site. Thus, polymorphisms in 3′UTR region could create, destroy or modify the efficiency of miRNA binding to the 3′UTR of a gene, resulting in gene dysregulation at posttranscriptional level. In order to investigate whether CXCL9-11 polymorphisms could affect mRNA target sites for miRNAs, we performed an in silico analysis by using PolymiRTS Database 3.0 [47]. We found that rs10336 A and rs4619915 A allele generates two miRNA binding sites for hsa-miR-4519 and hsa-miR-5089-3p respectively, whereas rs10336 G and rs4619915 G allele disrupt these miRNA target sites. The lack of binding of these miRNAs to the region surrounding these variants could lead to higher CXCL9-11 levels and, consequently, could modulate the fibrotic effect of these proteins since serum levels of CXCL9 were positively associated with the severity of liver fibrosis [17]. Additionally, the three CXCL9-11 polymorphisms seem to be implicated in changes in the chromatin state in several immune-related cell lines. Genetic variation in regulatory elements might coordinate changes in chromatic states and gene expression at both local and distal sites [48]. By using rVarBase [49], we detected that rs10336 has been associated with a ‘enhancer’ chromatin state in monocytes, rs3921 to a weak transcription in primary B cells and monocytes, and rs4619915 to weak transcription in monocytes among others. All these findings support the idea that CXCL9-11 polymorphisms could affect the production of the CXCL9-11 chemokine levels. In fact, both CXCL9 rs10336 TT and CXCL10 rs3921 GG genotypes have also been related to higher cellular expression of CXCL9 and CXCL10 in other infectious disease, such as Chagas infection, where they were also related to a higher severity of chronic Chagas cardiomyopathy [32]. In this regard, our data are in line with data of Nogueira et al. [32], because patients with CXCL9 rs10336 TT and CXCL10 rs3921 GG genotypes (recessive inheritance model) had higher LSM values and fibrosis stages, possibly due to increased production of these chemokines that could allow the development and maintenance of the infiltration of Th1 cells in liver parenchyma. However, it is necessary to take into account that CXCL9, CXCL10, and CXCL11 genes are clustered at chromosome 4q21.2, within a range of 32 kb [50, 51], where there is a high number of additional SNPs, which may be tightly linked to CXCL9 rs10336, CXCL10 rs3921, and CXCL11 rs4619915 polymorphisms [50, 51]. Thereby, rs10336, rs3921, and rs4619915 polymorphisms could be functionally responsible for the observed effect or could just be polymorphisms in high LD with the causal mutation. Therefore, this association that we observed might be due to the proximity to other SNPs that may modulate CXCL9-11 expression, or a direct effect of CXCL9-11 polymorphisms on CXCL9-11 expression. In this regard, CXCL9-11 levels or CXCL9-11 expression could be very helpful and convincing to reinforce the hypothesis that the observed effect of CXCL9-11 SNPs have a biological effect, but such information were not available in this study.

In this study, we analyzed the association between haplotypes and liver fibrosis stages, but we did not find any significant association. The explanation for this lack of association is possibly due to the association of each individual SNP with liver fibrosis under an overdominant model of inheritance, which assumes that both alleles in heterozygous have the strongest impact over the trait. Furthermore, we also compared allelic and genotypic frequencies of CXCL9 rs10336, CXCL10 rs3921 and CXCL11 rs4619915 polymorphism between HCV-infected patients and healthy people (IBS), and no significant differences were found, indicating that our cohort did not have any significant bias regarding the distribution of CXCL9-11 polymorphisms.

Moreover, in terms of translational research, the discriminating power was low for finding patients with liver fibrosis (data not shown). Complex human diseases are under the control of many genes that contribute each one of them with modest individual effects, and only big effects would be detected in small populations. However, the combination of CXCL9-11 polymorphisms together with other biomarkers (plasmatic, genetic, etc.) could add value to the prediction of the development of advanced fibrosis.

This study has several limitations that must be considered to ensure a correct interpretation of the data. Firstly, this is a cross-sectional study, which entails lack of uniformity. Even with this drawback, the association between CXCL9-11 polymorphisms and liver stiffness detected is strong. Secondly, CXCL9-11 chemokines levels in plasma could not be analyzed because we did not have access to plasma samples. Thirdly, since allele frequencies vary among ethnicities, and our study has been carried out entirely in HCV-infected white Europeans, it would be necessary to perform an independent replication of this study for different ethnic groups. Fourthly, in our study, about 20% of the patients were non-responders to IFN-alpha therapy during the follow-up. However, we did not find any significant effect on liver fibrosis progression in the multivariate analysis. Besides, when we excluded non-responder patients, significant associations between CXCL9-11 were also found (data not shown). Fifthly, time since HCV infection is one of the most important factors determining fibrosis in HCV infected patients, but it is difficult to determine because it involves lead-time bias. In this regard, we did not have access to the date of HCV infection in most patients. Instead, we used the time since HCV diagnosis, which may also accumulate bias due to the variability in time from the HCV infection to the HCV diagnosis. Thus, some patients may have HCV infection a long time before HCV diagnosis, when they show symptoms of chronic liver disease, while some may be diagnosed with HCV infection not long after the onset of infection, especially those who had a regular check-up.

Conclusion

In conclusion, CXCL9-11 polymorphisms were related to likelihood of having liver fibrosis in HCV-infected patients. Our data suggest that CXCL9-11 polymorphisms may play a significant role against the progression of CHC and could help prioritize antiviral therapy.

Abbreviations

HCV:

hepatitis c virus

CHC:

chronic hepatitis c

LSM:

liver stiffness measurement

SNPs:

single nucleotide polymorphisms

CXCL:

CXCL chemokine ligand

HIV:

human immunodeficiency virus

IFN:

interferon

kPa:

kilopascals

F ≥ 2—significant fibrosis:

LSM ≥ 7.1 kPa

F ≥ 3—advanced fibrosis:

LSM ≥ 9.5 kPa

F4—cirrhosis:

LSM ≥ 12.5 kPa

UTR:

3′ untranslated region

IBS:

Iberian population in Spain

GLM:

generalized linear model

GMR:

geometric mean ratio

OR:

odds ratio

HWE:

Hardy-Weinberg equilibrium

LD:

linkage disequilibrium

miRNAs:

microRNAs

References

  1. Asrani SK, Larson JJ, Yawn B, Therneau TM, Kim WR (2013) Underestimation of liver-related mortality in the United States. Gastroenterology 145(375–382):e371–e372

    Google Scholar 

  2. Shepard CW, Finelli L, Alter MJ (2005) Global epidemiology of hepatitis C virus infection. Lancet Infect Dis 5:558–567

    Article  PubMed  Google Scholar 

  3. Ghany MG, Kleiner DE, Alter H, Doo E, Khokar F, Promrat K, Herion D, Park Y, Liang TJ, Hoofnagle JH (2003) Progression of fibrosis in chronic hepatitis C. Gastroenterology 124:97–104

    Article  PubMed  Google Scholar 

  4. Razavi H, Elkhoury AC, Elbasha E, Estes C, Pasini K, Poynard T, Kumar R (2013) Chronic hepatitis C virus (HCV) disease burden and cost in the United States. Hepatology 57:2164–2170

    Article  PubMed  PubMed Central  Google Scholar 

  5. Adam R, Karam V, Delvart V, O’Grady J, Mirza D, Klempnauer J, Castaing D, Neuhaus P, Jamieson N, Salizzoni M et al (2012) Evolution of indications and results of liver transplantation in Europe. A report from the European Liver Transplant Registry (ELTR). J Hepatol 57:675–688

    Article  PubMed  Google Scholar 

  6. Westbrook RH, Dusheiko G (2014) Natural history of hepatitis C. J Hepatol 61:S58–S68

    Article  PubMed  Google Scholar 

  7. Terrault NA, Hassanein TI (2016) Management of the patient with SVR. J Hepatol 65:S120–S129

    Article  PubMed  Google Scholar 

  8. Serfaty L (2016) Follow-up of patients with chronic hepatitis C and a sustained viral response. Liver Int 36(Suppl 1):67–71

    Article  PubMed  Google Scholar 

  9. van der Meer AJ, Feld JJ, Hofer H, Almasio PL, Calvaruso V, Fernandez-Rodriguez CM, Aleman S, Ganne-Carrie N, D’Ambrosio R, Pol S et al (2017) Risk of cirrhosis-related complications in patients with advanced fibrosis following hepatitis C virus eradication. J Hepatol 66:485–493

    Article  PubMed  Google Scholar 

  10. EASL (2015) EASL recommendations on treatment of hepatitis C 2015. J Hepatol 63:199–236

    Article  Google Scholar 

  11. Tsochatzis EA, Bosch J, Burroughs AK (2014) Liver cirrhosis. Lancet 383:1749–1761

    Article  PubMed  Google Scholar 

  12. Resino S, Sanchez-Conde M, Berenguer J (2012) Coinfection by human immunodeficiency virus and hepatitis C virus: noninvasive assessment and staging of fibrosis. Curr Opin Infect Dis 25:564–569

    Article  PubMed  Google Scholar 

  13. Castera L (2015) Noninvasive assessment of liver fibrosis. Dig Dis 33:498–503

    Article  PubMed  Google Scholar 

  14. Heim MH, Bochud PY, George J (2016) Host—hepatitis C viral interactions: the role of genetics. J Hepatol 65:S22–S32

    Article  PubMed  Google Scholar 

  15. Heydtmann M, Adams DH (2009) Chemokines in the immunopathogenesis of hepatitis C infection. Hepatology 49:676–688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zeremski M, Petrovic LM, Talal AH (2007) The role of chemokines as inflammatory mediators in chronic hepatitis C virus infection. J Viral Hepat 14:675–687

    CAS  PubMed  Google Scholar 

  17. Tacke F, Zimmermann HW, Berres ML, Trautwein C, Wasmuth HE (2011) Serum chemokine receptor CXCR3 ligands are associated with progression, organ dysfunction and complications of chronic liver diseases. Liver Int 31:840–849

    Article  CAS  PubMed  Google Scholar 

  18. Zeremski M, Dimova R, Brown Q, Jacobson IM, Markatou M, Talal AH (2009) Peripheral CXCR3-associated chemokines as biomarkers of fibrosis in chronic hepatitis C virus infection. J Infect Dis 200:1774–1780

    Article  CAS  PubMed  Google Scholar 

  19. Liang YJ, Luo J, Lu Q, Zhou Y, Wu HW, Zheng D, Ren YY, Sun KY, Wang Y, Zhang ZS (2012) Gene profile of chemokines on hepatic stellate cells of schistosome-infected mice and antifibrotic roles of CXCL9/10 on liver non-parenchymal cells. PLoS ONE 7:e42490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sahin H, Borkham-Kamphorst E, Kuppe C, Zaldivar MM, Grouls C, Al-samman M, Nellen A, Schmitz P, Heinrichs D, Berres ML et al (2012) Chemokine Cxcl9 attenuates liver fibrosis-associated angiogenesis in mice. Hepatology 55:1610–1619

    Article  CAS  PubMed  Google Scholar 

  21. Wasmuth HE, Lammert F, Zaldivar MM, Weiskirchen R, Hellerbrand C, Scholten D, Berres ML, Zimmermann H, Streetz KL, Tacke F et al (2009) Antifibrotic effects of CXCL9 and its receptor CXCR3 in livers of mice and humans. Gastroenterology 137:309–319

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Patel K, Remlinger KS, Walker TG, Leitner P, Lucas JE, Gardner SD, McHutchison JG, Irving W, Guha IN (2014) Multiplex protein analysis to determine fibrosis stage and progression in patients with chronic hepatitis C. Clin Gastroenterol Hepatol 12(2113–2120):e2111–e2113

    Google Scholar 

  23. Harvey CE, Post JJ, Palladinetti P, Freeman AJ, Ffrench RA, Kumar RK, Marinos G, Lloyd AR (2003) Expression of the chemokine IP-10 (CXCL10) by hepatocytes in chronic hepatitis C virus infection correlates with histological severity and lobular inflammation. J Leukoc Biol 74:360–369

    Article  CAS  PubMed  Google Scholar 

  24. Romero AI, Lagging M, Westin J, Dhillon AP, Dustin LB, Pawlotsky JM, Neumann AU, Ferrari C, Missale G, Haagmans BL et al (2006) Interferon (IFN)-gamma-inducible protein-10: association with histological results, viral kinetics, and outcome during treatment with pegylated IFN-alpha 2a and ribavirin for chronic hepatitis C virus infection. J Infect Dis 194:895–903

    Article  CAS  PubMed  Google Scholar 

  25. Roe B, Coughlan S, Hassan J, Grogan A, Farrell G, Norris S, Bergin C, Hall WW (2007) Elevated serum levels of interferon- gamma -inducible protein-10 in patients coinfected with hepatitis C virus and HIV. J Infect Dis 196:1053–1057

    Article  CAS  PubMed  Google Scholar 

  26. Berenguer J, Fernandez-Rodriguez A, Jimenez-Sousa MA, Cosin J, Zarate P, Micheloud D, Lopez JC, Miralles P, Catalan P, Resino S (2012) High plasma CXCL10 levels are associated with HCV-genotype 1, and higher insulin resistance, fibrosis, and HIV viral load in HIV/HCV coinfected patients. Cytokine 57:25–29

    Article  CAS  PubMed  Google Scholar 

  27. Marra F, Tacke F (2014) Roles for chemokines in liver disease. Gastroenterology 147(577–594):e571

    Google Scholar 

  28. Li JA, Chen ZB, Lv TG, Han ZL (2014) Genetic polymorphism of CCL2-2518, CXCL10-201, IL8+ 781 and susceptibility to severity of Enterovirus-71 infection in a Chinese population. Inflamm Res 63:549–556

    Article  CAS  PubMed  Google Scholar 

  29. Xu Z, Liu Y, Liu L, Li X, Bai S, Rong Y, Wang H, Mao Y, Xin S, Xu D (2013) Association of interferon-gamma induced protein 10 promoter polymorphisms with the disease progression of hepatitis B virus infection in Chinese Han population. PLoS ONE 8:e72799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Deng G, Zhou G, Zhang R, Zhai Y, Zhao W, Yan Z, Deng C, Yuan X, Xu B, Dong X et al (2008) Regulatory polymorphisms in the promoter of CXCL10 gene and disease progression in male hepatitis B virus carriers. Gastroenterology 134:716–726

    Article  CAS  PubMed  Google Scholar 

  31. Wilson N, Driss A, Solomon W, Dickinson-Copeland C, Salifu H, Jain V, Singh N, Stiles J (2013) CXCL10 gene promoter polymorphism-1447A> G correlates with plasma CXCL10 levels and is associated with male susceptibility to cerebral malaria. PLoS ONE 8:e81329

    Article  PubMed  PubMed Central  Google Scholar 

  32. Nogueira LG, Santos RH, Ianni BM, Fiorelli AI, Mairena EC, Benvenuti LA, Frade A, Donadi E, Dias F, Saba B et al (2012) Myocardial chemokine expression and intensity of myocarditis in Chagas cardiomyopathy are controlled by polymorphisms in CXCL9 and CXCL10. PLoS Negl Trop Dis 6:e1867

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tang NL, Fan HP, Chang KC, Ching JK, Kong KP, Yew WW, Kam KM, Leung CC, Tam CM, Blackwell J, Chan CY (2009) Genetic association between a chemokine gene CXCL-10 (IP-10, interferon gamma inducible protein 10) and susceptibility to tuberculosis. Clin Chim Acta 406:98–102

    Article  CAS  PubMed  Google Scholar 

  34. Pineda-Tenor D, Berenguer J, Garcia-Alvarez M, Guzman-Fulgencio M, Carrero A, Aldamiz-Echevarria T, Tejerina F, Diez C, Jimenez-Sousa MA, Fernandez-Rodriguez A et al (2015) Single nucleotide polymorphisms of CXCL9-11 chemokines are associated with liver fibrosis in HIV/HCV-coinfected patients. J Acquir Immune Defic Syndr 68:386–395

    Article  CAS  PubMed  Google Scholar 

  35. Re VL III, Kallan MJ, Tate JP, Localio AR, Lim JK, Goetz MB, Klein MB, Rimland D, Rodriguez-Barradas MC, Butt AA et al (2014) Hepatic decompensation in antiretroviral-treated patients co-infected with HIV and hepatitis C virus compared with hepatitis C virus-monoinfected patients: a cohort study. Ann Intern Med 160:369–379

    Article  PubMed  PubMed Central  Google Scholar 

  36. Macias J, Berenguer J, Japon MA, Giron JA, Rivero A, Lopez-Cortes LF, Moreno A, Gonzalez-Serrano M, Iribarren JA, Ortega E et al (2009) Fast fibrosis progression between repeated liver biopsies in patients coinfected with human immunodeficiency virus/hepatitis C virus. Hepatology 50:1056–1063

    Article  CAS  PubMed  Google Scholar 

  37. O’Shea RS, Dasarathy S, McCullough AJ (2010) Alcoholic liver disease. Hepatology 51:307–328

    Article  PubMed  Google Scholar 

  38. Pham B, Stern A, Chen W, Sander B, John-Baptiste A, Thein HH, Gomes T, Wodchis WP, Bayoumi A, Machado M et al (2011) Preventing pressure ulcers in long-term care: a cost-effectiveness analysis. Arch Intern Med 171:1839–1847

    Article  PubMed  Google Scholar 

  39. Soto-Rifo R, Limousin T, Rubilar PS, Ricci EP, Decimo D, Moncorge O, Trabaud MA, Andre P, Cimarelli A, Ohlmann T (2012) Different effects of the TAR structure on HIV-1 and HIV-2 genomic RNA translation. Nucleic Acids Res 40:2653–2667

    Article  CAS  PubMed  Google Scholar 

  40. Castera L, Forns X, Alberti A (2008) Non-invasive evaluation of liver fibrosis using transient elastography. J Hepatol 48:835–847

    Article  PubMed  Google Scholar 

  41. Castera L, Vergniol J, Foucher J, Le Bail B, Chanteloup E, Haaser M, Darriet M, Couzigou P, De Ledinghen V (2005) Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 128:343–350

    Article  PubMed  Google Scholar 

  42. Pineda-Tenor D, Berenguer J, Jimenez-Sousa MA, Guzman-Fulgencio M, Aldamiz-Echevarria T, Carrero A, Garcia-Alvarez M, Diez C, Tejerina F, Briz V, Resino S (2014) CXCL9, CXCL10 and CXCL11 polymorphisms are associated with sustained virologic response in HIV/HCV-coinfected patients. J Clin Virol 61:423–429

    Article  CAS  PubMed  Google Scholar 

  43. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR, Consortium GP (2015) A global reference for human genetic variation. Nature 526:68–74

    Article  PubMed  Google Scholar 

  44. Hu S, Ghabril M, Amet T, Hu N, Byrd D, Yang K, Vuppalanchi R, Saxena R, Desai M, Lan J et al (2014) HIV-1 coinfection profoundly alters intrahepatic chemokine but not inflammatory cytokine profiles in HCV-infected subjects. PLoS ONE 9:e86964

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zeremski M, Petrovic LM, Chiriboga L, Brown QB, Yee HT, Kinkhabwala M, Jacobson IM, Dimova R, Markatou M, Talal AH (2008) Intrahepatic levels of CXCR3-associated chemokines correlate with liver inflammation and fibrosis in chronic hepatitis C. Hepatology 48:1440–1450

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Aidoo M, Terlouw DJ, Kolczak MS, McElroy PD, ter Kuile FO, Kariuki S, Nahlen BL, Lal AA, Udhayakumar V (2002) Protective effects of the sickle cell gene against malaria morbidity and mortality. Lancet 359:1311–1312

    Article  CAS  PubMed  Google Scholar 

  47. Bhattacharya A, Ziebarth JD, Cui Y (2014) PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res 42:D86–D91

    Article  CAS  PubMed  Google Scholar 

  48. Grubert F, Zaugg JB, Kasowski M, Ursu O, Spacek DV, Martin AR, Greenside P, Srivas R, Phanstiel DH, Pekowska A et al (2015) Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162:1051–1065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Guo L, Du Y, Qu S, Wang J (2016) rVarBase: an updated database for regulatory features of human variants. Nucleic Acids Res 44:D888–D893

    Article  CAS  PubMed  Google Scholar 

  50. Zlotnik A, Yoshie O (2000) Chemokines: a new classification system and their role in immunity. Immunity 12:121–127

    Article  CAS  PubMed  Google Scholar 

  51. Erdel M, Theurl M, Meyer M, Duba HC, Utermann G, Werner-Felmayer G (2001) High-resolution mapping of the human 4q21 and the mouse 5E3 SCYB chemokine cluster by fiber-fluorescence in situ hybridization. Immunogenetics 53:611–615

    Article  CAS  PubMed  Google Scholar 

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Authors’ contributions

Conceptualization: MAJS, AZG, DPT, and SR. Resources and data curation: AZG, DPT, MAJS, LMM, AFR, SV, JJSR, TAV, and JSM. Formal analysis: MAJS and SR. Investigation: MAJS, AZG, and DPT. Writing—original draft preparation: MAJS, AZG, and SR. Writing—Review & Editing: LMM, AFR, DPT, and PR. Visualization, supervision and funding acquisition: SR. All authors read and approved the final manuscript.

Acknowledgements

The authors thank the Spanish National Genotyping Center (CEGEN-PRB2-ISCIII) for providing SNP genotyping services (http://www.cegen.org). It is supported by grant PT13/0001, ISCIII-SGEFI/FEDER.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets analyzed during the current study available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the Research Ethic Committee of the Instituto de Salud Carlos III and was conducted in accordance with the Declaration of Helsinki. All patients gave their written informed consent.

Funding

This work has been supported by grants given by Fondo de Investigación de Sanidad en España (FIS) [Spanish Health Founds for Research] [Grant Numbers PI14CIII/00011]. MAJS, LMM and AFR are supported by “Instituto de Salud Carlos III” [Grant Numbers CD13/00013, CD14/00002, and CP14CIII/00010, respectively].

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Correspondence to Salvador Resino.

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Jiménez-Sousa, M.Á., Gómez-Moreno, A.Z., Pineda-Tenor, D. et al. CXCL9-11 polymorphisms are associated with liver fibrosis in patients with chronic hepatitis C: a cross-sectional study. Clin Trans Med 6, 26 (2017). https://doi.org/10.1186/s40169-017-0156-3

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