HIV infection is characterized by generalized immune activation with high levels of circulating cytokines, such as tumour necrosis factor (TNF)-α, interleukin (IL)-6, and IL-10 [1–3]. TNF-α, IL-6 and the chemokine IL-8 accelerate viral replication in monocytes through activation of the nuclear transcription factor NF-κB [4–7]. The loss of CD4 cells characterizing the course of chronic HIV infection might be ascribed to persistent immune activation .
Anti-inflammatory IL-10 downregulates pro-inflammatory TNF-α and inhibits HIV replication in vitro in macrophages  and we have previously found that decreased production of IL-10 as well as other cytokines is a predictor of mortality in HIV-infected patients . In apparent contradiction, plasma levels correlate positively with HIV RNA and inversely with CD4 cell count , but this may simply reflect a concomitant upregulation of IL-10 in a context of immune activation. Recent studies have proposed that IL-10 opposes viral clearance and hence facilitates chronic infection in viral mice models [11,12]. Hence, the role of IL-10 in HIV infection is ambiguous.
The balance between pro- and anti-inflammatory cytokines may be influenced by genetic constitution. Single nucleotide polymorphisms in cytokine promoter regions can change the affinity of transcription factors and hence the rate of transcription and ultimately systemic cytokine concentrations [13,14]. The IL-10 −1082A>G polymorphism has been linked to increased IL-10 production [15–17]. Only a few reports exist on the effect of single nucleotide polymorphisms of TNF-α and IL-10 promoters during HIV infection, but IL-10 −592C>A has been linked to faster progression of HIV .
The present study was carried out in a cohort of HIV-infected individuals from rural Zimbabwe. Polymorphisms were identified, survival was registered, and CD4 cell counts, plasma HIV RNA, soluble TNF receptor II (sTNF-rII), which has been shown to reflect TNF-α levels but fluctuate less [19–21], IL-8 and IL-10 were measured.
The Mupfure Schistosomiasis and HIV Cohort has been described in detail elsewhere . The cohort was initiated in November 2002 in Mupfure and adjacent areas, Shamva District, Mashonaland Central Province, Zimbabwe. According to local sources (E Gomo, personal communication), the population is homogeneous Shona. Individuals coinfected with HIV and schistosomiasis were recruited (n = 156). Concurrently, three control groups were established: HIV only (n = 42), schistosomiasis only (n = 133), and uninfected (n = 47). Clinical examination and blood sampling were performed at baseline and then at 3 months and 3 years after baseline.
The Medical Research Council of Zimbabwe (MRCZ/A/918) and the Central Medical Scientific Ethics Committee of Denmark (624-01-0031) approved the study. Oral and written informed consent was obtained from all participants. There was no public scheme for antiretroviral therapy in Zimbabwe at the time of the study, and it can be assumed that all participants were antiretroviral therapy naive.
HIV status was determined in the field on a dry blood spot (Determine, Abott, Tokyo, Japan) and verified by two enzyme-linked immunosorbent assay (ELISA) tests on serum (Recombigen, Cambridge Biotech, Galway, Ireland; Ortho, Raritan, New Jersey, USA) with no disagreements between tests.
Shistosoma haematobium was diagnosed by microscopy of urine samples filtered on Nytrel filters on three consecutive days  (Vestergaard Frandsen, Kolding, Denmark). Diagnosis of Shistosoma mansoni and other helminth eggs or parasites was assessed by the modified formol–ether concentration technique .
Blood was drawn into tubes coated with ethylenediaminetetraacetic acid and kept cool until separation a maximum of 4 h after sampling. Plasma was transferred to cryotubes and stored in liquid nitrogen until shipment on dry ice; in Copenhagen, samples were stored at −80°C awaiting analysis.
Plasma HIV RNA was determined by the Roche Amplicor HIV-1 Monitor test version 1.5 (Hoffmann-La Roche, Basel, Switzerland), CD4 cell counts with FacsCalibur (Becton-Dickinson, San Jose, California USA), and leukocyte counts with Hematology Analyzer SF 3000 (Sysmex, Ramsey, Minnesota, USA).
Plasma IL-10 and IL-8 were measured by cytometric bead array (CBA Human Inflammation Kit; BD Biosciences, San Diego, California, USA) as previously described . Plasma sTNF-rII was assessed by ELISA (Quantikine, R&D Systems, Minneapolis, Minnesota, USA).
DNA was extracted from purified peripheral mononuclear cells stored in liquid nitrogen (QIAamp DNA Blood Midi, Qiagen, Hilden, Germany). Genetic material was unavailable for seven participants (four of them were HIV positive; none were registered as deceased). Genotypes for the four single nucleotide polymorphisms were determined by fluorescence-based real-time PCR (ABI PRISM 7900 SDS, Applied Biosystems, Foster City, California, USA). Predeveloped assays were used according to the manufacturer's descriptions [TNF −238 G>A, rs361525 (C_2215707_10); TNF −308 G>A, rs1800629 (C_7514879_10); IL-10 −592 C>A, rs1800872 (C_1747363_10); IL-10 −1082 A>G, rs1800896 (C_1747360_10); Applied Biosystems]. PCR amplification was performed in a total reaction volume of 5 μl. The reaction mixture consisted of 1 μl of 0.4 μg/μl gDNA, primer and probe mix, nuclease-free water and 2× TaqMan Universal MasterMix (Applied Biosystems). Allelic discrimination was performed after PCR (cycle profile: 50°C for 2 min, 95°C for 10 min, plus 40 cycles of 95°C for 15 s and 60°C for 1 min).
Statistical analyses were performed using SAS 9.1 (SAS Institute, Cary, North Carolina, USA). Log-transformed values were used when appropriate to approximate normal distribution.
Kaplan–Meier plots were drawn with subjects stratified into groups according to genotype, or baseline IL-10, IL-8, and sTNF-rII tertiles. Groups were compared by the log rank test.
Univariate Cox proportional hazards models were fitted for the parameters and supplemented with multivariate models. Results are shown as P values and hazard ratios (HR) with confidence intervals (CI). Distribution of genotypes between groups was tested by the χ2 test or Fisher's exact test.
Analysis of covariance (ANCOVA) was assessed to compare the decrease in CD4 cell count between groups.
Differences in baseline plasma IL-10, IL-8, and sTNF-rII according to HIV and schistosomiasis status were evaluated by two-way analysis of variance (ANOVA). Results were presented graphically as geometric means with 95% CI. Only IL-10 levels above the lower limit of detection were included in the two-way ANOVA. This was supplemented with a logistic regression with IL-10 categorized as above or below the lower limit of detection with covariates HIV and schistosomiasis status.
Univariate and multivariate linear regressions were performed within each HIV stratum with inflammation markers as predictors for CD4 cell count and plasma HIV RNA. Multivariate analyses included predictors IL-10, IL-8, sTNF-rII, S. mansoni and S. haematobium status, age and sex. Levels of IL-10, IL-8, and sTNF-rII were Log10-transformed, hence, a one-unit change of the predictor represented a 10-fold increase.
For all ANOVA and ANCOVA, interactions between strata were assessed and cross-products excluded when insignificant.
Baseline characteristics of the cohort have been described previously . Briefly, 198 HIV-infected and 180 HIV-uninfected participants were included. HIV-infected individuals were 83% female and had a mean age of 33 years (range, 19–59), a median CD4 cell count of 320 cells/μl (interquartile range, 185–503) and a median plasma HIV RNA of 63 250 copies/ml (interquartile range, 12 800–180 000). The HIV-uninfected participants were 77% female and had a mean age of 33 years (range 18–63).
HIV-infected subjects were followed for 4.3 years or until death. The total follow-up time was 631 years (median 3.6 person-years, 25–75 percentiles, 3.0–4.1) and 58 deaths were recorded . As previously described, the 289 schistosomiasis-infected individuals were treated with praziquantel at baseline or at the 3-month follow-up, and schistosomiasis infection at baseline or schistosomiasis intensity did not predict mortality .
Cytokine single nucleotide promoter polymorphisms
Mortality did not differ according to genotype for IL-10 −592, TNF-α −238 or TNF-α −308. However, mortality was predicted by IL-10 −1082 (Fig. 1a). A univariate proportional hazards model showed a protective effect of the minor G allele when mortality was compared between carriers and noncarriers (HR for G-carriers, 0.47; 95% CI, 0.27–0.82; P < 0.01). The age and sex distribution did not differ according to carriage of the G allele, and age and sex adjustments did not change the results. Multivariate analysis, including all four polymorphisms, baseline CD4 cell count, HIV RNA, age and sex adjustments, did not change results and showed lower mortality among G-carriers (HR, 0.47; 95% CI, 0.24–0.90; P < 0.05). Possible effects of schistosomiasis were excluded by adding schistosomiasis status at baseline to the model. Results did not change and there was no tendency to an effect of schistosomiasis whether it was added as one covariate (schistosomiasis or not) or two covariates (S. haematobium or not and S. mansoni or not). Furthermore, the distribution of IL-10 −1082 genotypes did not differ according to schistosomiasis infection (χ2 test, P = 0.50), and the proportion of G-carriers was similar in the schistosomiasis infected (48%) and uninfected (45%). Similarly, no differences were found in the distribution of genotypes between the S. haematobium infected and uninfected (χ2 test, P = 0.52) or between the S. mansoni infected or uninfected (χ2 test, P = 0.48).
When IL-10 −592 and −1082 haplotypes were constructed, it was still only carriage or not of the −1082 G allele that predicted mortality.
To evaluate the prognostic strength of IL-10 −1082 further, its effect on the rate of CD4 cell count decrease was evaluated. At the 3-year follow-up, 149 HIV-infected participants were alive and 94 reported for the follow-up (63%). The CD4 cell counts at this point were compared in an ANCOVA between carriers and noncarriers of IL-10 −1082G, adjusted for baseline CD4 cell count, age and sex. The decrease in CD4 cell count was attenuated in G-carriers compared with individuals homozygous for A (mean ratio G-carriers/AA, 1.41; 95% CI, 1.10–1.79; P < 0.01; Fig. 1b). Results were not altered by addition of baseline HIV RNA to the model; in this multivariate model, IL-10 −1082 was a stronger predictor of CD4 cell decrease than HIV RNA (P < 0.01 and P < 0.05, respectively). When schistosomiasis status at baseline was added to the model, either as one covariate or split in two according to S. haematobium or S. mansoni, no effects were found and the results for the IL-10 −1082 effect were not altered (P < 0.01).
Age and sex distribution was similar in carriers and noncarriers of IL-10 −1082G. Baseline CD4 cell counts did not differ according to IL-10 −1082 genotype but plasma HIV RNA was lower among G-carriers (mean difference, 0.25 log10 copies/ml; 95% CI, 0.00–0.50; P < 0.05). IL-10 levels did not differ according to IL-10 −1082 among HIV-infected (Kruskal–Wallis, P = 0.24) or uninfected (P = 0.78) participants. Neither did levels differ according to IL-10 −592. However, when IL-10 was categorized into high or low according to the IL-10 median and modelled in multivariate logistic regression as the dependent variable with both IL-10 polymorphisms, age and sex as predictors among HIV-infected participants, or in all participants and adjusted for HIV, an effect of the IL-10 −1082 was found (P < 0.05). This reflected a higher proportion of individuals with IL-10 concentrations above the median in individuals homozygous for the G allele (odds ratio for high IL-10, GG versus AA/AG, 3.2; 95% CI, 1.1–9.1). Results were not altered when further adjustments for CD4 cell count and HIV RNA were included.
Plasma sTNF-rII did not differ according to TNF-α or IL-10 genotypes, whether the polymorphisms were tested separately or combined into haplotypes.
Linkage disequilibrium was found between IL-10 −592, IL-10 −1082, TNF-α −238 and TNF −308 polymorphisms. For IL-10 −1082 and IL-10 −592 only the A–A, A–C, and G–C haplotype combinations existed, as also found in Caucasian populations  (i.e., no G–A combination exists).
There was no difference in the distribution of genotypes between HIV-infected and uninfected individuals for IL-10 −1082, TNF-α −238, and TNF-α −308, and Hardy–Weinberg equilibrium was found among HIV-infected and uninfected individuals for all three polymorphisms. A trend towards fewer HIV-infected individuals heterozygous for IL-10 −592 was observed (Fisher's exact test, P = 0.07; Table 1). Among HIV-infected individuals, Hardy–Weinberg disequilibrium was found for this polymorphism (P < 0.01) whereas the distribution was in Hardy–Weinberg equilibrium among HIV-uninfected individuals. The allele frequencies were similar between HIV-infected and uninfected participants.
Baseline cytokine levels and HIV status
After assessment of the effect of single nucleotide polymorphisms on mortality, CD4 cell count decrease, and baseline cytokine levels, the association between baseline cytokine levels and HIV parameters was investigated.
Two-way ANOVA was performed to determine the effects of HIV and schistosomiasis status on levels of plasma IL-10, IL-8 and sTNF-rII. For IL-10, including only samples with measurable levels, there were no differences between HIV or schistosomiasis strata (Fig. 2a). Similarly, IL-10 above the lower limit of detection was not predicted by HIV or schistosomiasis in a logistic regression. Higher IL-8 concentrations were found among HIV-infected individuals (P < 0.05, Fig. 2b). Concentrations of sTNF-rII were higher in HIV-infected individuals (P < 0.0001, Fig. 2c) with no difference according to schistosomiasis strata. The ratio of TNF-rII to IL-10 was higher in HIV-infected individuals (P < 0.01) but did not differ according to schistosomiasis status. Similar results for all analyses were obtained after age and sex adjustments (data not shown).
Univariate regression analyses were performed with plasma IL-10, IL-8 and sTNF-rII as predictors for CD4 cell count or plasma HIV RNA (Table 2). Univariately, IL-10 correlated negatively with CD4 cell count and positively with HIV RNA among HIV-infected individuals; IL-8 did not correlate with CD4 cell count but correlated univariately with HIV RNA. Only sTNF-rII was an independent predictor of CD4 cell count and plasma HIV RNA; correlating negatively with CD4 cell count among HIV-infected and uninfected participants, univariately as well as multivariately. It also correlated positively with HIV RNA in univariate and multivariate analyses.
Survival and baseline cytokine levels
The effect of immune activation as measured by the three parameters on HIV-related mortality was evaluated. The subjects were stratified into three equally sized groups according to baseline values of IL-10, IL-8 and sTNF-rII, and Kaplan–Meier plots were produced (Fig. 3). IL-10, IL-8 and sTNF-rII all predicted mortality in univariate proportional hazards models: HR 2.1 (95% CI, 1.2–3.5) with IL-10 10-fold higher (P <0.01), HR 2.8 (95% CI, 1.1–6.6) with IL-8 10-fold higher (P < 0.05), and HR 54 (95% CI, 18–163) with sTNF-rII 10-fold higher (P < 0.0001). A multivariate model was fitted including baseline IL-10, IL-8, sTNF-rII, CD4 cell count, HIV RNA, age and sex. In this analysis, sTNF-rII was an independent predictor of mortality [HR 6.3 (95% CI, 1.6–25) for sTNF-rII 10-fold higher; P < 0.01]. It also predicted survival at a level of significance comparable to HIV RNA (P < 0.01) and CD4 cell count (P < 0.05). IL-10 and IL-8 did not predict mortality in multivariate analysis. Further adjustments for baseline S. haematobium and S. mansoni status did not alter results.
The TNF-rII to IL-10 ratio did not predict mortality in log-rank or Cox analyses.
This study evaluated the effect of IL-10 and TNF-α promoter polymorphisms and their association with progression, mortality and plasma levels of IL-10, IL-8 and sTNF-rII in the chronic phase of HIV infection.
The IL-10 −1082A>G polymorphism had a marked effect on survival. Carriers of the minor allele G had lower plasma HIV RNA at baseline but even after adjusting for CD4 cell count and plasma HIV RNA, the HR among G-carriers was 0.47 when compared with noncarriers. Additionally, the decrease in CD4 cell count among participants reporting for the 3-year follow-up was attenuated in G-carriers. These two main findings mutually support each other, as the latter analysis excluded the majority of the deceased individuals included in the former analysis. However, verification in other studies is important before drawing a definite conclusion about the protective effect of IL-10 −1082G. Previous reports have linked the G allele to increased IL-10 expression and production [15–17,28], and we also found an indication of higher baseline plasma IL-10 levels associated with the G allele. Intriguingly, it has been reported that the number of males homozygous for the G allele is increased among centenarians compared with younger controls . Studies have indicated that the −1082G protects against various other diseases in which inflammation plays a significant role [30,31]. Faster progression among HIV-infected IL-10 −592A-carriers has been reported . This is in agreement with our results as the IL-10 −592A is always linked to the −1082A (but not vice versa).
We did not find differences in the distribution of IL-10 −1082 in HIV-infected and uninfected individuals, indicating that this polymorphism does not affect the risk of acquiring HIV. However, as noncarriers harboured higher HIV RNA levels at baseline, this may enhance the risk of HIV transmission.
Circulating IL-10 is decreased by HAART [3,32] and levels are stable in HIV-infected individuals who are long-term nonprogressors, whereas they increase in progressing patients . Hence, IL-10 could be interpreted to accelerate HIV. However, as evident in the multivariate analysis, only sTNF-rII was an independent predictor of CD4 cell count and plasma HIV RNA. We propose that the univariate finding merely reflected the upregulation of plasma IL-10 as a positive feedback to immune activation and that IL-10 is indeed opposing immune activation and viral replication. Our current finding that high producers of IL-10 experience markedly lower mortality and an attenuated CD4 cell count decrease compared with low producers support this hypothesis. Recent findings in models of viral infection in mice show that blockade of the IL-10/IL-10 receptor pathway may change the course of an otherwise chronic infection into a rapidly resolving acute infection [11,12]. However, in the light of our findings, indicating that IL-10 inhibits HIV replication, this may not be a feasible approach to control human HIV infection. Indeed, it could be hypothesized that lack of IL-10 would turn the chronic HIV infection into an acute infection with lytic CD4 cell loss. HIV clearance through a strong proinflammatory response mediated by inhibiting IL-10 signalling may not be possible, as immune activation in itself acts to accelerate HIV replication [4,7,8,33]. Conversely, producers of high IL-10 concentrations may control the infection, possibly by inhibiting the proinflammatory drive on HIV replication and minimizing the immunopathogenic effects of chronic immune activation.
When the genotype distributions of our HIV-uninfected participants were compared with control groups from other studies, we found that the distributions of TNF −238 and −308 genotypes were similar to previous reports from the Gambia [34,35]. The allele frequency of IL-10 −592A in our cohort was similar to the previously described among African Americans , which was higher than among Caucasians [15,18,36]. The allelic frequency of IL-10 −1082G was similar to the frequency described in a report from the Gambia  but higher frequency of the G allele was reported in a study from the UK . Finally, we acknowledge that we cannot exclude that the G allele is merely a marker for effects of other genes in the vicinity of the allele. We have assumed that IL-10 −1082G has the same effect on IL-10 production as previously reported. Even though there was no indication of genetic admixture, it must be mentioned as a possible source of bias. Moreover, only schistosomiasis coinfection was routinely assessed. The presence of other nonsymptomatic coinfections could possibly also have influenced our conclusions. Verification of our results and mechanistic studies are needed to confirm the protective effect of IL-10 −1082G and IL-10 per se.
The univariate correlations between HIV RNA/CD4 cell count and plasma IL-10 have been shown previously , although not in Africa. However, we also found plasma HIV RNA to correlate univariately with IL-8 levels. Increased circulating IL-8 in HIV-infected patients has previously been described , but to our knowledge a correlation between plasma IL-8 and HIV RNA has thus far not been established.
Higher levels of sTNF-rII among HIV-infected individuals and correlations with HIV RNA and CD4 cell count have been reported by ourselves and others [39,40] but not in an African context. The finding that sTNF-rII predicted mortality independently of CD4 cell count and HIV RNA suggests that other parameters (e.g., the presence of coinfections and genetic constitution) modulate the level of immune activation induced in response to a given level of HIV RNA. This level of immune activation may again determine the rate of CD4 cell loss. The predictive strength calls for further investigation of sTNF-rII as an alternative marker of progression in HIV.
This study supports a role for the anti-inflammatory IL-10 as an inhibitor of HIV replication since survival was doubled in carriers of IL-10 −1082G, an allele linked to increased IL-10 production. Moreover, the decrease in CD4 cell count among survivors reporting for the 3-year follow-up was attenuated in carriers of the IL-10 −1082G allele, although this analysis excluded the majority of the deceased individuals included in the survival analysis. While these results mutually support each other, further studies are needed to confirm our results before drawing definite conclusions. Even though plasma IL-10 correlated positively with HIV progression, and higher levels predicted mortality, these effects could be fully explained by upregulation in a context of immune activation since only sTNF-rII was an independent predictor of CD4 cell count, plasma HIV RNA and mortality. Indeed, sTNF-rII was a strong predictor of mortality and could be evaluated as an alternative prognostic marker.
We thank the following for their contribution to the study: the Mupfure Community; the Village Health Workers; the Environmental Health Technician, the technical team of E. N. Kurewa, N. Taremeredzwa, W. Mashange, C. Mukahiwa, S. Nyandoro, W. Soko, B. Mugwagwa, and E. Mashiri; the Department of Haematology at Parirenyatwa Hospital (R. Mafirakureba, D. Mawire and B. Mudenge); and the Department of Virology at Rigshospitalet, Copenhagen (M. Luneborg-Nielsen).
Sponsorship: The study was supported by grants from the Danish AIDS Foundation (F01-18, F01-19); Fonden Til Lægevidenskabens Fremme; the DANIDA Health Programme in Zimbabwe (2001); the Centre of Inflammation and Metabolism (Danish National Research Foundation DG 02-512-555); the Cluster of International Health, Copenhagen University; and the Danish National Research Foundation (2117-05-0147).
Note: The authors have no commercial or other association that might pose a conflict of interest.
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