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. 2021 Jun 23;16(6):e0253470. doi: 10.1371/journal.pone.0253470

Inflammasome genes polymorphisms may influence the development of hepatitis C in the Amazonas, Brazil

Diana Mota Toro 1, Rajendranath Ramasawmy 1,2,3,4,5, Pedro Vieira Silva Neto 1, Grenda Leite Pereira 1, Priscila Santos Sarmento 1, Hanna Lara Silva Negreiros Dray 6, Keyla Santos Sousa 6,7, Juliana Santos Affonso 6,7, Jéssica Albuquerque Silva 6, Nadja Pinto Garcia 6, Marilú Victória Barbieri 2,3, Flamir Silva Victória 2,3, Eduardo Antônio Donadi 8, Allyson Guimarães Costa 1,2,3,5,7,6,9,*, Mauricio Morishi Ogusku 1,5,10, Aya Sadahiro 1,5, Andréa Monteiro Tarragô 1,5,6,7, Adriana Malheiro 1,2,5,6,7,*
Editor: Cinzia Ciccacci11
PMCID: PMC8221483  PMID: 34161370

Abstract

Hepatitis C is considered a major public health problem caused by the hepatitis C virus (HCV). Viral infections are known to induce production of IL1β through the signaling pathway of inflammasomes. Emerging evidences suggest that Inflammasome genes may influence the immune response against HCV as the host genetic background may contribute to the balance between acute and chronic inflammation. We investigated in 151 patients with chronic hepatitis C and 206 healthy blood donors’ individuals (HD). Polymorphisms in the IL1B and IL18 genes were genotyped by PCR-RFLP, while NLRP3, CARD8, CTSB and AIM2 by RT- PCR. Serum assay of IL-1β cytokine was performed by ELISA. 84 patients presented mild fibrosis (<F2) and 67 advanced fibrosis (≥ F2). Among the HD individuals the NLRP3-rs10754558 C/C genotype correlated with higher IL-1β levels compared to the G/G genotype. Similar pattern was observed in patients with hepatitis C, mean circulating IL-1β levels were 21,96 ± 4.5 and 10,62 ± 3.3pg/mL among the C/C and G/G genotypes, respectively. This pattern holds even after stratification of the patients into mild fibrosis and advanced fibrosis, demonstrating that the NLRP3-rs10754558 or another polymorphism in linkage disequilibrium with it possibly has an influence on the processing of pro-IL-1β. Notably, higher levels of IL-1β (Mann–Whitney test, p<0.0001) were observed among patients (mean ± SEM: 19,24 ±3.pg/mL) when compared with controls (mean ± SEM: 11,80 ±1.0pg/mL). Gene-gene interaction showed that individuals heterogyzotes for both CARD8-rs2009373 and IL1B-rs16944 are less prone to hepatitis C development (padj = 0.039). Similarly, herozygote carriers for CTSB-rs1692816 and AIM2-rs1103577 (padj = 0.008) or for IL18-rs187238 and NLRP3-rs10754558 (padj = 0.005), have less chances to the development of hepatitis C. However, between subgroups of <F2 and ≥F2, individuals homozygous for the T allele of CARD8-rs2009373 and heterozygous for IL18-rs187238 (padj = 0.028), have mild form of fibrosis.

Introduction

Hepatitis C is an inflammatory liver disease caused by the hepatitis C virus (HCV). Nearly 71 million individuals are estimated to be infected worldwide and around 70% will progress to chronic form of the disease and may develop liver fibrosis, cirrhosis and hepatocellular carcinoma [1, 2].

Persistent inflammatory response activates immunoregulatory mechanisms and stimulates the production of TGF-β and IL-10, regulatory cytokines. TGF-β1 is a potent activator of hepatic stellate cell (HSCs) trans differentiation in myofibroblasts that stimulate the mechanisms of hepatic fibrosis [3–8]. IL-1β is also capable of stimulating fibrosis by activating HSCs cells and contributing to the development and maintenance of fibrosis in the liver [9].

IL-1β induces COX2, nitric oxide and TNF-α, and also modulates various cellular processes during chronic HCV infection [10]. Persistent hepatic macrophage IL-1β production in patients with chronic HCV attracts immune cells to the liver, enhancing inflammation [11, 12]. Exacerbated proinflammatory cytokine production contributes to tissue damage and development of autoinflammatory diseases [13].

Processing of pro-IL1β and pro-IL-18 proinflammatory cytokines into their bioactive forms requires the formation of molecular complexes called inflammasomes [14]. NLRP3 inflammasome is extensively studied and its gene expression is positively regulated by the transcription factor NF-kB [15–19]. CARD8 negatively regulates inflammasome activity, inhibiting activation of transcription factor NF-kB and regulating activation of inflammatory caspases [20, 21]. CARD8 regulates bioactive IL-1β secretion through inhibitory interaction with NLRP3 caspase-1 [20] and / or acting through inhibitory mechanisms in pro-IL-1β formation via the NF-kB pathway [20–23].

Growing evidence suggests that inflammasome genes may influence the immune response against HCV. Since nearly two-third of HCV-infected individuals progress to chronic form of the disease while the rest can eliminate the virus or remain asymptomatic, this suggests that the host genetic background may play an important role in the clinical outcome of HCV infection. In the present study, we analyzed whether polymorphisms in inflammasomes genes IL1B rs16944, IL18 rs187238, NLRP3 rs10754558, CARD8 rs2009373, CTSB rs1692816 and AIM2 rs1103577 are associated with susceptibility to HCV infection and liver fibrosis in hepatitis C patients in the Amazon population.

Material and methods

Ethics approval

This study was reviewed by the Ethics Committee of the Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM) and granted under the file (CAAE 49652815.8.0000.0009 and CAAE 0024.112.000–10). All of the study participants provided a signed written informed consent form prior to the enrollment in the study, according to Declaration of Helsinki and Resolution 466/12 of the Brazilian National Health Council for research involving human subjects. All patients were treated following the specifications of the Brazilian Ministry of Health [24].

Samples and clinical data

The study was performed at the Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (FMT-HVD) in Manaus, the capital city of the Amazonas State, during 2016–2017. The study population was described elsewhere [25]. Briefly, 206 Healthy blood donors (HD) were randomly selected from HEMOAM and 151 HCV infected patients were recruited at the FMT-HVD. Among the HCV infected patients, 84 and 67 had < F2 and ≥F2, respectively.

Sample collection and genomic DNA extraction

Sample collection and genomic DNA extraction are also described elsewhere [25]. Briefly, genomic DNA was extracted from peripheral blood samples using the QIAamp DNA Blood Mini Kit (QIAGEN, Chatsworth, CA, USA) according to the manufacturer’s instructions.

Molecular analysis of polymorphisms

6 SNPs in inflammasome genes (CARD8, CTSB, NLRP3, AIM2, IL1B, IL18) were selected according to previously reported association studies and public databases (NCBI dbSNP and Human genome GRCh37.p13).

The NLRP3 rs10754558, CTSB rs1692816, CARD8 rs2009373 and AIM2 rs1103577 were identified by Real Time PCR. Whereas IL1B rs16944 and IL18 rs187238 of were identified by PCR-RFLP.

Real-time PCR reactions were performed with 3.5μL of ultrapure water, 5μL of Master Mix genotyping (1x), 0.5μL of TaqMan® assay (20x), containing 36μM of each primer and 8μM of each TaqMan® probe, 1μL genomic DNA, with final volume of 10μL (S1 Table). Applied Biosystems QuantStudio™ 3 Thermocycler by Thermofisher Scientific was used for amplification of fragments of interest, with the following cycling parameters: 95oC for 10 minutes for activation, 50 cycles at 92oC for 15 seconds for denaturation and 50 cycles at 60oC for 90 seconds for annealing and extension.

PCR-RFLP were performed in the Eppendorf Mastercycler ep in a final volume of 25uL consisting of ~20ng genomic DNA, 2U Platinum™Taq DNA polymerase (Thermo Fisher Scientific), 2.5μL10x buffer (100 mmol/L Tris-HCl (pH 8.3) and 500 mmol/L KCl), 1μL MgCl2 (1.5 mmol/L), 1μL dNTPs (40 mmol/L), and 0.25 pmol/L each of forward and reverse primer. A total of 10μL of PCR product was digested with 5U of restriction enzyme (AvaI for IL1B rs16944 and MboII for IL18 rs187238) with their specified buffer. The restrictions enzymes are from New England Biolabs, Ipswich, MA, USA. The primers, PCR cycling conditions, and restriction endonucleases are shown in S2 Table. After digestion with the restriction enzymes, the fragments generated were size-separated by electrophoresis in a 2% - 4% agarose gel stained with GelRed™ Nucleic Acid Gel Stain (Biotium, Hayward, CA, USA), and visualized with the UV light Gel Doc™ XR +System (Bio-Rad Corporation, Hercules, CA, USA) with a photo documentation system.

Serum cytokine assay

Serum concentrations of IL-1β was measured by the Enzyme-Linked Immunosorbent Assay (ELISA) sandwich immunoassay. Dosages were made from plasma of subjects in the Hepatitis C patient group and controls. We use a commercial BD® Biosciences (San Jose, CA, USA) human BDTM OptEIA® Set II kit for cytokine IL-1β.

Genotype association test and statistical analyzes

Demographic, clinical and laboratory data were presented in tables and graphs, prepared using the Excel program (Microsoft Corporation). Categorical variables are expressed as absolute value (n) and relative frequency (%). Statistical analysis between independent groups was performed by Chi-square or Fisher’s exact test for categorical variables and Student’s t-test, Mann-Whitney test or ANOVA for continuous variables. Logistic regression was performed to identify risk factors associated with the specific outcome (advanced fibrosis), as well as a description of their "odds ratio" and 95% confidence interval. A significant threshold of p <0.05 was adopted.

R software version 3.2.2 (www.r-project.org) was used to perform genotypes association and inheritance modelling (package SNP assoc version 1.9–2). Data were adjusted for sex and age. Polymorphisms were evaluated for Hardy-Weinberg equilibrium (HWE) and the association analysis with risk or protection for infection and fibrosis was performed for Codominant, dominant, recessive and Overdominate genetic inheritance models. The best genetic inheritance model was chosen according to the lowest value of Akaike Information Criterion (AIC).

Results

Basic characteristics of the study population

151 patients with chronic HCV infection and 206 healthy controls were included in the study. Among the HCV patients, 83 (55%) were males and 68 (45%) females, with a mean age of 57.8 ± SD 11.2 years. Of the HD, 144 (70%) were males and 62 (30%) females, with a mean age of 32 ± SD 10.8 years. The distribution of sexes was significantly different between the two studied groups (p < 0.05). Males were predominant in both groups (p = 0,003).

49 patients were treatment naïve. 84 patients (55.6%) had mild fibrosis (<F2) and 67 (44.4%) (≥F2) advanced fibrosis according to APRI (AST to Platelet Ratio Index) and FIB4 (Fibrosis-4) indices. Patients with advanced fibrosis (≥F2) had a higher mean age (61.03 ± 9.3), with p = 0.001. About viral genotypes, we detected 103 (68%) genotype 1, 32 (21%) genotype 3, 10 (7%) genotype 2 and one patient had the hepatitis C genotype 4.

Frequency of genotypes and alleles

The genotypes and allele frequencies of all the studied SNPs are shown in Table 1. No significant deviation from the Hardy-Weinberg equilibrium was observed in any of the studied SNPs among the HD and the patients with HCV.

Table 1. Relation of allele and genotype frequencies of SNPs with the hepatites C susceptibility.

Genotype and Allele HD Patients p value OR (95% CI) p value OR (95% CI) padj1 OR (95% CI) padj2 Comparisons
IL1β rs16944 (n = 203) (n = 151)
T/T 66 (33%) 44 (29%) 0.242 (R) 1.56 (0.93–2.62) 0.094 (R) 1.68 (0.99–2.85) 0.054 (D) 1.75 (0.83–3.68) 0.138 T/Tvs C/T-C/C
T/T-C/T vs C/C
T/T-C/C vs C/T
C/T 102 (50%) 70 (46%)
C/C 35 (17%) 37 (25%)
T 234 (58%) 158 (52%) 0.159
C 172 (42%) 144 (48%)
IL18 rs187238 (n = 202) (n = 149)
C/C 22 (11%) 18 (12%) 0.727 (O) 84 (0.55–1.29) 0.428 (O) 0.86 (0.56–1.32) 0.491 (D) 0.72 (0.36–1.46) 0.363 G/G vs G/C-C/C
G/G-G/C vs C/C
G/G–C/C vs G/C
C/G 94 (46%) 63 (42%)
G/G 86 (43%) 68 (46%)
C 138 (34%) 99 (33%) 0.795
G 266 (66%) 199 (67%)
CARD8 rs2009373 (n = 202) (n = 151)
T/T 43 (21%) 28 (19%) 0.418 (D) 0.74 (0.47–1.17) 0.194 (D) 0.74 (0.46–1.18) 0.201 (R) 0.46 (0.18–1.16) 0.098 C/C vs C/T-T/T
C/CC/T vs T/T
C/C-T/T vs C/T
C/T 105 (52%) 73 (48%)
C/C 54 (27%) 50 (33%)
T 191 (47%) 129 (43%) 0.228
C 213 (53%) 173 (57%)
CTSB rs1692816 (n = 202) (n = 151)
C/C 56 (28%) 45 (30%) 0.472 (O) 0.85 (0.55–1.29) 0.439 (O) 0.84 (0.55–1.29) 0.420 (O) 0.52 (0.25–1.08) 0.772 C/C vs A/C-A/A
C/C-A/C vs A/A
C/C-A/A vs A/C
A/C 98 (49%) 67 (44%)
A/A 48 (24%) 39 (26%)
C 210 (52%) 157 (52%) 0.998
A 194 (48%) 145 (48%)
NLRP3 rs10754558 (n = 206) (n = 151)
G/G 13 (6%) 15 (10%) 0.450 (R) 1.64 (0.75–3.55) 0.211 (R) 1.71 (0.78–3.74) 0.179 (R) 0.55 (0.16–1.91) 0.352 C/C vs C/G-G/G
C/C–C/G vs G/G
C/C-G/G vs C/G
G/C 74 (36%) 53 (35%)
C/C 119 (58%) 83 (55%)
G 100 (24%) 83 (27%) 0.331
C 312 (76%) 219 (73%)
AIM2 rs1103577 (n = 200) (n = 151)
T/T 39 (20%) 29 (19%) 0.306 (D) 71 (0.45–1.12) 0.140 (D) 0.69 (0.43–1.10) 0.117 (D) 0.75 (0.35–1.61) 0.461 C/C vs C/T–T/T
C/C-C/T vs T/T
C/C-T/T vs C/T
T/C 108 (54%) 71 (47%)
C/C 53 (27%) 51 (34%)
T 186 (47%) 129 (43%) 0.318
C 214 (54%) 173 (57%)
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HD: Healthy blood donors. OR: odds ratio. 95% CI: 95% confidence interval. Padj1: p value adjusted by sex. Padj2: p value adjusted by sex and age. The best genetic inheritance model was adopted for each SNP, according to the Akaike Information Criterion (AIC), where C = codominant, D = dominant, R = recessive, O = overdominant. The statistical analyses were conducted using the chi-square test. p < 0.05 is considered significant. Genetic models: Codominant: comparison of homozygote frequency for polymorphic allele, with heterozygote and homozygote for wild allele, simultaneously expressing both alleles. Dominant: Homozygote frequency comparison for wild allele with heterozygote + homozygote for polymorphic allele. Recessive: Comparison of the frequency of Homozygote for wild + heterozygous allele with homozygous for polymorphic allele. Overdominat: Comparison of frequencies Homozygote for wild allele + homozygote for polymorphic allele with heterozygote, which evaluates the heterozygous genotype.

The frequency distribution of the IL1B rs16944 genotypes T/T, T/C, and C/C were 29%, 46%, and 25% among the patients and 33%, 50%, and 17%, among the HD, respectively (p = 0.2). Comparison of IL1B C/T rs16944 genotypes between patients with HCV infection and HD showed that carriers homozygote for the C allele seem to be susceptible to HCV infection after adjusting for age padj = 0.054, [OR = 1.68 (95% CI = 0.99–2.85)] as shown in Table 1. The other SNPs did show any significant association with susceptibility or resistance to HCV infection (Table 1).

We stratified the patients with HCV into mild fibrosis (<F2) and advanced fibrosis (≥F2) patients to look for if any of the SNP may indicate severity of the disease, as shown in Table 2. We observed that individual’s homozygote for the A allele of CTSB rs1692816 SNP have mild fibrosis compared to individuals with the C allele p = 0.044 [OR = 0.46 (95% CI = 0.21–1.00)]. However, when correcting for gender and age, individual’s homozygote for the A allele still have 50% less chances of developing advanced fibrosis p = 0.091; [OR = 0.50 (95% CI = 0.22–1.13)].

Table 2. Relation of allele and genotype frequencies of SNPs with the fibrosis susceptibility.

Genotype and Allele <F2 ≥F2 p value OR (95% CI) p value OR (95% CI) padj1 OR (95% CI) padj2 Comparisons
IL1β rs16944 (n = 84) (n = 67)
T/T 20 (24%) 24 (36%) 0.498 (O) 0.58 (0.30–1.11) 0.095 (D) 0.54 (0.27–1.11) 0.091 (D) 0.51 (0.24–1.08) 0.076 T/Tvs C/T-C/C
T/T-C/T vs C/C
T/T-C/CvsC/T
C/T 44 (52%) 26 (39%)
C/C 20 (24%) 17 (25%)
T 84 (50%) 74 (55%) 0.665
C 84 (50%) 60 (45%)
IL18 rs187238 (n = 82) (n = 67)
C/C 9 (11%) 9 (13%) 0.720 (O) 0.62 (0.32–1.19) 0.147 (O) 0.61 (0.32–1.19) 0.143 (O) 0.53 (0.26–1.08) 0.075 G/G vs G/C-C/C
G/G-G/C vs C/C
G/G–C/C vs G/C
C/G 39 (48%) 24 (36%)
G/G 34 (41%) 34 (51%)
C 57 (35%) 42 (31%) 0.824
G 107 (65%) 92 (69%)
CARD8 rs2009373 (n = 84) (n = 67)
T/T 15 (18%) 13 (19%) 0.492 (D) 1.90 (0.94–3.84) 0.069 (D) 1.88 (0.93–3.80) 0.076 (O) 1.72 (0.87–3.39) 0.115 C/C vs C/T-T/T
C/C-C/T vs T/T
C/C-T/T vs C/T
C/T 36 (43%) 37 (55%)
C/C 33 (39%) 17 (25%)
T 66 (39%) 63 (47%) 0.402
C 102 (61%) 71 (53%)
CTSB rs1692816 (n = 84) (n = 67)
C/C 24 (29%) 21 (31%) 0.389 (R) 0.46 (0.21–1.00) 0.044 (R) 0.46 (0.21–1.00) 0.045 (R) 0.50 (0.22–1.13) 0.091 C/C vs A/C-A/A
C/C-A/C vs A/A
C/C-A/A vs A/C
A/C 33 (39%) 34 (51%)
A/A 27 (32%) 12 (18%)
C 81 (48%) 76 (57%) 0.339
A 87 (52%) 58 (43%)
NLRP3 rs10754558 (n = 51) (n = 67)
G/G 4 (5%) 11 (16%) 0.072 (C) 0.53 (0.26–1.09) 0.012 0.52 (0.25–1.08) 0.012 (C) 0.47 (0.22–1.01) 0.016 C/C vs C/G-G/G
C/C–C/G vs G/G
C/C-G/G vs C/G
G/C 36 (43%) 17 (25%)
C/C 44 (52%) 39 (58%)
G 44 (26%) 39 (29%) 0.853
C 124 (74%) 95 (71%)
AIM2 rs1103577 (n = 84) (n = 67)
T/T 14 (17%) 15 (22%) 0.909 (R) 1.44 (0.64–3.25) 0.376 (R) 1.46 (0.65–3.29) 0.363 (R) 1.31 (0.56–3.08) 0.534 C/C vs C/T–T/T
C/C-C/T vs T/T
C/C-T/T vs C/T
C/T 42 (50%) 29 (43%)
C/C 28 (33%) 23 (34%)
T 70 (42%) 59 (44%) 0.918
C 98 (58%) 75 (56%)
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Fibrosis degree was assessed using METAVIR score. The statistical analyses were conducted using the chi-square test. p < 0.05 is considered significant. OR: odds ratio. 95% CI: 95% confidence interval. pAdj1: p value adjusted by sex. pAdj2: p value adjusted by sex and age. The best genetic inheritance model was adopted for each SNP, according to the Akaike Information Criterion (AIC), where C = codominant, D = dominant, R = recessive, O = overdominant. Genetic models: Codominant: comparison of homozygote frequency for polymorphic allele, with heterozygote and homozygote for wild allele, simultaneously expressing both alleles. Dominant: Homozygote frequency comparison for wild allele with heterozygote + homozygote for polymorphic allele. Recessive: Comparison of the frequency of Homozygote for wild + heterozygous allele with homozygous for polymorphic allele. Overdominat: Comparison of frequencies Homozygote for wild allele + homozygote for polymorphic allele with heterozygote, which evaluates the heterozygous genotype.

Gene-gene interaction of the SNPs between healthy blood donors and patients with hepatitis C

To evaluate whether polymorphisms in different genes could have a combined effect and influence susceptibility to HCV infection and/or development of liver fibrosis, we performed a gene-gene interaction analysis. Statistically significant results are reported in Tables 3 and 4.

Table 3. Gene-gene interaction between healthy blood donors (n = 202) and patients with hepatitis C (n = 151).

Gene-gene interaction p value Median p value adjusted Median Association with
CARD8 rs2009373 vs IL1B rs16944
CARD8 C/T: C/T IL1B 0.032 -0.907 0.039 -0.104 Protection
CARD8 rs2009373 vs IL18 rs187238
CARD8 C/T: G/C IL18 0.056 -0.981 0.059 -0.104 -
CTSB rs1692816 vs AIM2 rs1103577
CTSB A/C: C/T AIM2 0.002 -0.865 0.008 -0.089 Protection
IL18 rs187238 vs NLRP3 rs10754558
IL18 G/C: C/G NLRP3 0.092 -0.913 0.005 -0.098 Protection
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Median: Median value (Average of minimum and maximum residues) generated by the statistical program R is the parameter that determines whether it is a risk or protection factor. Median values greater than 1 are associated with risk and less than 1 with protection; Adjusted p value: p value adjusted by gender and age. p < 0.05 is considered significant.

Table 4. Gene-gene interaction between subgroups of patients with hepatitis C (<F2 n = 84 and ≥F2 n = 67) and relation of SNPs with the fibrosis.

Gene-gene interaction p value Median p value adjusted Median Association
CARD8 rs2009373 vs IL18 rs187238
CARD8 T/T: G/C IL18 0.051 -0.633 0.028 -0.522 Protection
CTSB rs1692816 vs AIM2 rs1103577
CTSB A/A: C/T AIM2 0.060 -0.417 0.052 -0.413 Protection
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Median: Median value (Average of minimum and maximum residues) generated by the statistical program R is the parameter that determines whether it is a risk or protection factor. Median values greater than 1 are associated with risk and less than 1 with protection; Adjusted p value: p value adjusted by gender and age. Fibrosis degree was assessed using METAVIR score. p < 0.05 is considered significant.

Gene-gene interaction showed that individuals heterozygotes for both CARD8 rs2009373 and IL1B rs16944 are less prone to hepatitis C development (padj = 0.039). Similarly, heterozygote carriers for CTSB rs1692816 and AIM2 rs1103577 (padj = 0.008) or for IL18 rs187238 and NLRP3 rs10754558 (padj = 0.005), have less chances to the development of hepatitis C (Table 3).

Gene-gene interaction of the SNPs between mild fibrosis (<F2) and advanced fibrosis (≥F2)

In the analysis of gene interaction between subgroups of mild fibrosis (<F2) and advanced fibrosis (≥F2), individuals homozygous for the T allele of CARD8 rs2009373 and heterozygous for IL18 rs187238 (padj = 0.028), have mild form of fibrosis (Table 4).

Influence of the SNPs on IL1β serum concentration

Of all the polymorphisms studied (S1 and S2 Figs), only NLRP3 rs10754558 showed an influence on the serum cytokine concentration of IL-1β (Fig 1).

Fig 1. Serum concentration of IL-1β cytokine in patients with hepatitis C.

Fig 1

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Treated (A) and untreated (B), stratified according to the degree of hepatic fibrosis, analyzed as a function of the NLRP3 rs10754558 polymorphisms. Data are expressed as mean ± standard deviation of circulating concentration (pg/mL) of IL-1β cytokine. Statistical analyzes were performed by ANOVA (nonparametric analysis of variance), with Kruskal-Wallis test, followed Dunn’s post-test to compare pairs.

Among the HD individuals the NLRP3 rs10754558 C/C genotype correlated with higher IL-1β levels (mean ± SD: 13.39 ± 1.4 pg/mL) compared to the G/G genotype (mean ± SD: 7,69 ± 2.0 pg/mL). Similar pattern was observed in patients with hepatitis C, mean circulating IL-1β levels were 21.96 ± 4.5 and 10.62 ± 3.3 pg/mL among the C/C and G/G genotypes, respectively. This pattern holds even after stratification of the patients into mild fibrosis and advanced fibrosis, demonstrating that the NLRP3 rs10754558 or another polymorphism in linkage disequilibrium with it possibly has an influence on the processing of pro-IL-1β.

Notably, higher levels of IL-1β (Mann–Whitney test, p < 0.0001) were observed among patients (mean ± SD: 19.24 ± 3 pg/mL) when compared with controls (mean ± SD: 11.80 ± 1.0 pg/mL) (Fig 1). Our data show that individuals with the C/C genotype have a higher concentration of IL-1β than those with the G allele.

Discussion

NLRP3 inflammasome is a cytoplasmic sensor that, upon activation, recruits various proteins to form a multiprotein complex, activating caspase-1. Caspase-1 process pro-IL1β into IL-1β cytokine. NLRP3 inflammasome is regulated by caspase recruitment domain 8 (CARD8) [20]. Recently, many studies have evidenced that the NLRP3 inflammasome is involved in the recognition of HCV and processing pro-IL-1β in liver macrophages and also hepatocytes infected with HCV [26–28].

The genetic background of an individual may affect the control, susceptibility and chronicity of HCV infection [29]. A recent study evaluated 201 Egyptian hepatitis C patients and found that the IL1B rs1143629 located in intron A/A genotype was prevalent in HCV patients [OR = 1.7 (95% CI = 1–2.8)] [30]. In this study, we performed the rs16944 that is located in the promoter region at position -511 and showed that individuals with the C/C genotype were prevalent among the patients with HCV and have 58% chances higher of developing hepatitis (OR = 1.58 [95% CI 0.88–2.9]) compared to carriers homozygous for the T allele, suggesting polymorphisms present in the IL1B gene may be associated to the development of hepatitis caused by HCV. Comparing the genotypes with circulating plasma levels of IL-1β, did show any influence of the SNP on the plasma levels.

Interestingly, the IL1B rs16944 allele C has been identified as a genetic marker for the development of hepatocarcinoma in patients with chronic HBV infection [31]. Furthermore, genome-wide association study in southwest China revealed 6 new loci, including the rs16944 SNP in the IL1B gene, associated with an increased prevalence rate of chronic hepatitis B [32]. Altogether, the rs16944 may be a genetic modifier of hepatitis caused by either HBV or HCV.

We observed higher plasma levels of IL1β among the patients with HCV compared to the HD. Similarly in other studies, higher concentrations of plasma circulating cytokines IL-18 and IL-1β were observed in hepatitis B patients [33]. In hepatitis C virus infection, studies show that serum IL-18 and IL-1β concentrations are higher in patients with chronic HCV infection and HCV-related cirrhosis compared with healthy controls [34–36].

NLRP3 rs10754558 polymorphism (3’UTR region) has been studied in several infectious diseases (HIV-1 and HTLV1 infection) [37, 38], neoplastic processes [39], hematological diseases [40], inflammatory and autoimmune disorders [41, 42]. The NLRP3 rs10754558 variant has been correlated with higher mRNA expression by either altering expression of an enhancer activity or mRNA stability [43]. In this study, individuals with the C/C genotype have a higher concentration of IL-1β than those with the G allele.

In one study among patients with Hepatitis C (sustained virological responders against non-responders groups), the NLRP3 rs10754558 had no influence on treatment [30]. We did not observe any differences in alleles or genotypes frequencies between the HD and the patients. However, heterozygosity for NLRP3 rs10754558 seems to associate with severe disease. The loss of heterozygosity among ≥F2, a decrease from 43% among the <F2 compared to 25% among the ≥F2, may suggest that individual with this genotype dies earlier compared to individuals homozygous for GG or CC. Notably, patients ≥F2 are older than the patients <F2. Interestingly, we observed an increase from 5% of the GG genotype among the <F2 to 16% among the ≥F2.

Considering that the various components of inflammasomes interact, we evaluated whether polymorphisms located in different genes could interact and have a combined effect on HCV infection and influence the development of liver fibrosis. Gene-gene interaction analysis was performed between cases and controls, as well as comparisons between groups of patients with mild (<F2) and advanced fibrosis (≥F2).

Gene-gene interaction showed that individuals heterozygotes for both CARD8 rs2009373 and IL1B rs16944 are less prone to hepatitis C development (padj = 0.039). Similarly, heterozygote carriers for CTSB rs1692816 and AIM2 rs1103577 (padj = 0.008) or for IL18 rs187238 and NLRP3 rs10754558 (padj = 0.005), have less chances to the development of hepatitis C.

In the analysis of gene interaction between subgroups of mild fibrosis (<F2) and advanced fibrosis (≥F2), individuals homozygous for the T allele of CARD8 rs2009373 and heterozygous for IL18 rs187238 (padj = 0.028), have mild form of fibrosis. Altogether, the approach of gene-gene interactions demonstrates that not only one gene but several genes are involved in the development of hepatitis.

The production of cytokines IL-1β and IL-18, resulting from the activation of inflammasomes, can modulate adaptive immune responses in complex and diverse ways. IL-18 stimulates the production of IFN-γ and the proliferation of Th1 cells. IL-1β can induce the survival and proliferation of naïve T cells, via positive regulation of the IL-2 receptor. Humoral responses can also be increased by IL-1β, directly via increased proliferation of B cells or indirectly by the positive regulation of costimulatory molecules in T cells [42].

Therefore, the activation of the inflammasome can significantly affect an adaptive immune response, which is necessary for the successful elimination of HCV [26, 44]. However, it is necessary to maintain the balance in these immune responses, to avoid cell damage and tissue impairment, as in the case of patients with chronic hepatitis C, who have a disrupted immune response against HCV [45, 46].

This study has some limitations. Our sample size is small and does not allow intra-comparison of the genotype’s combination studied with IL-1β cytokine. However, it showed that the combinations of these polymorphisms seem to influence in the chronic hepatic disease. Further studies are needed to confirm this preliminary finding.

Conclusion

The present study demonstrated an association between inflammasomes genes and development of hepatitis C. We note that individuals homozygotes for the C allele of the IL1B C/T rs16944 seem to be susceptible to HCV infection and individuals homozygote for the A allele still have 50% less chances of developing advanced fibrosis. NLRP3 rs10754558 C/C genotype showed an influence on the serum cytokine concentration of IL-1β. Studies of these genes in different world populations should help understand the importance of these variants in the role of host genetic variability in the clinical presentation and development of hepatitis C and fibrosis hepatic. However, further studies are recommended to confirm our findings.

Contribution to the field statement

Hepatitis C is an inflammatory liver disease caused by the hepatitis C virus (HCV). Nearly 71 million individuals are estimated to be infected worldwide and around 70% will progress to chronic form of the disease. Since nearly two-third of HCV-infected individuals progress to chronic form of the disease while the rest can eliminate the virus or remain asymptomatic, this suggests that the host genetic background may play an important role in the clinical outcome of HCV infection.

NLRP3 inflammasome is a cytoplasmic sensor that, upon activation, recruits various proteins to form a multiprotein complex, activating caspase-1. Caspase-1 process pro-IL1β into IL-1β cytokine. Recently, many studies have shown that the NLRP3 inflammasome is involved in the recognition of HCV and processing pro-IL-1β in hepatic macrophages and hepatocytes infected with HCV. The production of cytokines IL-1β and IL-18, resulting from the activation of inflammasomes, can modulate immune responses in complex and diverse ways.

We believe that genetic background of an individual, related to inflammasome complex proteins, may affect the control, susceptibility and chronicity of HCV infection, possibly because it influences the magnitude of the antiviral immune response and inflammation during infection, with consequences for the patient’s clinical presentation.

Supporting information

S1 Fig. Serum concentration of IL-1β cytokine in 151 patients with hepatitis C, stratified according to the degree of hepatic fibrosis, analyzed as a function of the IL1β (rs16944), IL-18 (rs187238), CARD8 (rs2009373), CTSB (rs1692816), AIM2 (rs1103577) polymorphisms.

Data are expressed as mean ± standard deviation of circulating concentration (pg/mL) of IL-1β cytokine. Statistical analyzes were performed by ANOVA (nonparametric analysis of variance), with Kruskal-Wallis test, followed by Dunns post-test to compare pairs.

(TIF)

Click here for additional data file. (176.5KB, tif)
S2 Fig. Serum concentration of IL-1β cytokine of 49 patients with hepatitis C, without treatment, stratified according to the degree of hepatic fibrosis, analyzed as a function of the IL1β (rs16944), IL-18 (rs187238), CARD8 (rs2009373), CTSB (rs1692816), AIM2 (rs1103577) polymorphisms.

Data are expressed as mean ± standard deviation of circulating concentration (pg/mL) of IL-1β cytokine. Statistical analyzes were performed by ANOVA (nonparametric analysis of variance), with Kruskal-Wallis test, followed by Dunns post-test to compare pairs.

(TIF)

Click here for additional data file. (179.1KB, tif)
S1 Table. Sequences of the probes used for Real Time PCR genotyping.

(DOCX)

Click here for additional data file. (15.2KB, docx)
S2 Table. Primers and PCR conditions for the studied polymorphisms.

(DOCX)

Click here for additional data file. (16.1KB, docx)

Acknowledgments

We acknowledge the collaboration of Ambulatório de Hepatopatia da Fundação de Medicina Tropical -Doutor Heitor Vieira Dourado (FMT-HVD) and laboratory support of the Laboratório de Genômica da Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM), Laboratório de Micologia (INPA) and Laboratório de Imunologia Celular (UFAM).

Data Availability

Due to ethical restrictions regarding patient privacy, data are available upon request. Data are available upon request from the Ethics Committee for researchers from Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (CEP/FMT-HVD - cep@fmt.am.gov.br), for researchers who meet the criteria for access to confidential data. Additional requests for the data may be sent to the corresponding author or coauthors Allyson G. Costa (allyson.gui.costa@gmail.com); Adriana Malheiro (malheiroadriana@yahoo.com.br); Andréa Monteiro Tarragô (andrea_s_monteiro@hotmail.com); Rajendranath Ramasawmy (ramasawm@gmail.com).

Funding Statement

This work was funded by Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) (Pró-Estado Program - #002/2008, #007/2018 and #005/2019, PAMEQ Program - #004/2019, PAPAC Program - #005/2019 and PECTI-AM/SAÚDE Program #004/2020), Rede Genômica de Vigilância em Saúde do Estado do Amazonas (REGESAM), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Universal Program - #407818/2016), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Brazilian Ministry of Health.

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Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil

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Reviewer #1: Article Report

Title: Inflammasome genes polymorphisms may influence the development of Hepatitis C in

the Amazonas, Brazil

Summary of article: Diana Mota Toro et al, from Programa de Pós-Graduação em Imunologia Básica e Aplicada, Universidade Federal do Amazonas, Manaus, Amazonas, Brazil. The main focus of this research work is to Hepatitis C and associated polymorphisms of inflammasomes. Author investigated 151 patients with chronic hepatitis C and 206 healthy blood donors’ individuals (HD).

Polymorphisms in the IL1B and IL18 genes were genotyped by PCR-RFLP, while

NLRP3, CARD8, CTSB and AIM2 by RT- PCR.

Serum assay of IL-1β cytokine was performed by ELISA. 84 patients presented mild fibrosis (<f2) 67="" advanced="" and="" fibrosis="">Comment: Minor revision

The work is very important in terms of better understanding of association between inflammasomes genes and development of hepatitis C. However, below comments should be address in main content with references to make it more informative and attractive for wide users.

1. Why author choose PCR-RFLP and RT-PCR for polymorphism study. Reason to use PCR-RFLP should be mentioned in main text.

2. Progression of disease depends on multigenic expression of genes in pathway. Why author choose only 6 genes for this study.

3. Genetic background of populations also responsible for false association in the study. Cast and categories along with regional influence might be responsible for various results. Author justify the concern of population stratification if data available about case and control subjects.

4. Sample size is one of challenges in case-control study, however mild vs advanced fibrosis and combined data should be evaluated with statistical corrections for correct p value. Author explain the severity of disease and other gene role in discussion part.

5. How heterogeneity and homogeneity influence the expression of gene and thus the varied clinical out come in HCV.

6. Is there any other study of similar gene and region population in HCV, Kindly mention latest reference to make it for more better understanding.</f2)>

Reviewer #2: The manuscript entitled “Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil” by Toro et al. sheds some light on the role of the inflammasome genes variations in the in the progression of the chronic hepatitis C in Brazilian patients.

The study is very well designed and well written. I have very few comments.

1. Line 183-185: please adjust the sentence.

2. Table 3 and table 4: Please show the number of patients in each sub-group because I think the numbers are so small especially in table 4. So, the authors should be very cautious when they draw conclusions from these tables, and they should state this clearly in the study.

3. Line 330-340: these two paragraphs are redundant with the results so please try to support your finding with possible explanation or supporting studies if any.

4. Also, in the discussion, it will be great if the authors explain how the findings of this study could help managing the HCV patients in the future.

**********

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Reviewer #1: Yes: Dr. Anshuman Mishra

Reviewer #2: No

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Attachment

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PLoS One. 2021 Jun 23;16(6):e0253470. doi: 10.1371/journal.pone.0253470.r002

Author response to Decision Letter 0

15 Apr 2021

Manaus, April 08th, 2021

To: Narasimha Reddy Parine, Ph.D.

Academic Editor

Plos One

PONE-D-20-35265

Dear Editor,

We very much appreciate the kind consideration given to our manuscript (MS) PONE-D-20-35265, entitled "Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil". We hope that the replies to the reviewer’s comments will have satisfactorily improved the MS.

Below, we present all the queries made by the reviewers. The changes requested are clearly outlined in the revised manuscript and marked in yellow. We have prepared a list of answers to the reviewers’ comments, which are highlighted in “bold italic”. In the response to each query, we are also including the modified part, as it is in the revised manuscript.

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We acknowledge the comment of Plos One Team and inform that Journal Requirements were revised.

Reviewers' comments:

Reviewer reports:

Reviewer #1:

Title: Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil

Summary of article: Diana Mota Toro et al, from Programa de Pós-Graduação em Imunologia Básica e Aplicada, Universidade Federal do Amazonas, Manaus, Amazonas, Brazil. The main focus of this research work is to Hepatitis C and associated polymorphisms of inflammasomes. Author investigated 151 patients with chronic hepatitis C and 206 healthy blood donors’ individuals (HD).

Polymorphisms in the IL1B and IL18 genes were genotyped by PCR-RFLP, while

NLRP3, CARD8, CTSB and AIM2 by RT- PCR.

Serum assay of IL-1β cytokine was performed by ELISA. 84 patients presented mild fibrosis (Comment: Minor revision

The work is very important in terms of better understanding of association between inflammasomes genes and development of hepatitis C. However, below comments should be address in main content with references to make it more informative and attractive for wide users.

We acknowledge the encouraging comment of Reviewer #1, which clearly indicates that our message was well understood.

1. Why author choose PCR-RFLP and RT-PCR for polymorphism study. Reason to use PCR-RFLP should be mentioned in main text.

We do understand your worry about using two different techniques for alleles discrimination. We used PCR-RFLP for alleles discrimination for IL1B rs16944 and IL18 rs187238 as the technique is very well established in the literature and is less expensive than RT-PCR using TaqMan probes on demand. However, we have included in the text Line 147-152 for readers to understand the technique PCR-RFLP.

2. Progression of disease depends on multigenic expression of genes in pathway. Why author choose only 6 genes for this study.

You are right to point that disease progression depends on multigenic expression of genes in pathway. In this study we chose these genes because they are the most studied in various inflammatory diseases. However, ongoing research in our laboratory to decipher the immunogenetics mechanism involved in the development of the disease is pursuing.

3. Genetic background of populations also responsible for false association in the study. Cast and categories along with regional influence might be responsible for various results. Authors justify the concern of population stratification if data available about case and control subjects.

Thank you for reminding us about spurious association due to genetics background. Cases and controls were carefully selected to avoid genetic heterogeneity. Furthermore, all the participants are unrelated individuals to avoid bias association.

4. Sample size is one of challenges in case-control study, however mild vs advanced fibrosis and combined data should be evaluated with statistical corrections for correct p value. Authors explain the severity of disease and other gene role in discussion part.

We agree with you that sample size is very important in genetics association studies especially when stratifications into different groups are performed. Conscious of this, we have adjusted all the p values for sex and age. We did not performed corrections of p value for multiple comparisons as we think that Bonferroni corrections is too conservative. Furthermore, we think that our study can be a lead for other groups to replicate or not these associations in different populations. We pointed these limitations in the discussion section (line357-361).

5. How heterogeneity and homogeneity influence the expression of gene and thus the varied clinical outcome in HCV.

We believe that genetic heterogeneity or homogeneity may the same influence on gene expression in a pathway because we all have the same genes. However, we agree that environmental factors may play a role. Furthermore, evolution may have shaped genetics substructure as we often observed different population allele frequency for a given variant. That is why we always select our population of study from the same genetic background. It might be philosophical today in post-genomic era as always race was attributed to it.

6. Is there any other study of similar gene and region population in HCV, Kindly mention latest reference to make it for more better understanding.

To the best of our knowledge, we believed to have referred most of articles pertinent to or study. Finally, we appreciate very much your positive comments to help us improve our manuscript.

Reviewer #2:

The manuscript entitled “Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil” by Toro et al. sheds some light on the role of the inflammasome genes variations in the in the progression of the chronic hepatitis C in Brazilian patients.

The study is very well designed and well written. I have very few comments.

We acknowledge the encouraging comment of Reviewer #2, which clearly indicates that our message was well understood.

1. Line 183-185: please adjust the sentence.

We have improved the sentence to make it understandable.

2. Table 3 and table 4: Please show the number of patients in each sub-group because I think the numbers are so small especially in table 4. So, the authors should be very cautious when they draw conclusions from these tables, and they should state this clearly in the study.

We did state this in the limitations of the study.

3. Line 330-340: these two paragraphs are redundant with the results so please try to support your finding with possible explanation or supporting studies if any.

We corrected in the text.

4. Also, in the discussion, it will be great if the authors explain how the findings of this study could help managing the HCV patients in the future.

We included a paragraph about this. Line 361-364.

We believed that the new changes have significantly improved the quality of our manuscript. We would like to thank Plos One members and reviewers for their dedication in providing their valuable and interesting comments on this article.

We sincerely hope that the revised version of our manuscript meets the high standards of Plos One publications and is, therefore, acceptable for publication in this journal.

With best wishes,

Allyson Guimarães da Costa (on behalf of all authors)

Decision Letter 1

Cinzia Ciccacci

7 Jun 2021

Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil

PONE-D-20-35265R1

Dear Dr. Costa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Cinzia Ciccacci

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Accepted with selected respons recitation in the main text. Perticularly querry no. 2 to 4 is important.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dr. Anshuman Mishra

Acceptance letter

Cinzia Ciccacci

15 Jun 2021

PONE-D-20-35265R1

Inflammasome genes polymorphisms may influence the development of Hepatitis C in the Amazonas, Brazil

Dear Dr. Costa:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Cinzia Ciccacci

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Serum concentration of IL-1β cytokine in 151 patients with hepatitis C, stratified according to the degree of hepatic fibrosis, analyzed as a function of the IL1β (rs16944), IL-18 (rs187238), CARD8 (rs2009373), CTSB (rs1692816), AIM2 (rs1103577) polymorphisms.

    Data are expressed as mean ± standard deviation of circulating concentration (pg/mL) of IL-1β cytokine. Statistical analyzes were performed by ANOVA (nonparametric analysis of variance), with Kruskal-Wallis test, followed by Dunns post-test to compare pairs.

    (TIF)

    Click here for additional data file. (176.5KB, tif)
    S2 Fig. Serum concentration of IL-1β cytokine of 49 patients with hepatitis C, without treatment, stratified according to the degree of hepatic fibrosis, analyzed as a function of the IL1β (rs16944), IL-18 (rs187238), CARD8 (rs2009373), CTSB (rs1692816), AIM2 (rs1103577) polymorphisms.

    Data are expressed as mean ± standard deviation of circulating concentration (pg/mL) of IL-1β cytokine. Statistical analyzes were performed by ANOVA (nonparametric analysis of variance), with Kruskal-Wallis test, followed by Dunns post-test to compare pairs.

    (TIF)

    Click here for additional data file. (179.1KB, tif)
    S1 Table. Sequences of the probes used for Real Time PCR genotyping.

    (DOCX)

    Click here for additional data file. (15.2KB, docx)
    S2 Table. Primers and PCR conditions for the studied polymorphisms.

    (DOCX)

    Click here for additional data file. (16.1KB, docx)
    Attachment

    Submitted filename: Plos One Report.docx

    Click here for additional data file. (14.7KB, docx)

    Data Availability Statement

    Due to ethical restrictions regarding patient privacy, data are available upon request. Data are available upon request from the Ethics Committee for researchers from Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (CEP/FMT-HVD - cep@fmt.am.gov.br), for researchers who meet the criteria for access to confidential data. Additional requests for the data may be sent to the corresponding author or coauthors Allyson G. Costa (allyson.gui.costa@gmail.com); Adriana Malheiro (malheiroadriana@yahoo.com.br); Andréa Monteiro Tarragô (andrea_s_monteiro@hotmail.com); Rajendranath Ramasawmy (ramasawm@gmail.com).

    Articles from PLoS ONE are provided here courtesy of PLOS

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