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. Author manuscript; available in PMC: 2017 Sep 10.
Published in final edited form as: AIDS. 2016 Sep 10;30(14):2159–2168. doi: 10.1097/QAD.0000000000001179

Polymorphisms in the CD14 and TLR4 genes independently predict CD4+ T-cell recovery in HIV-infected individuals on antiretroviral therapy

Yean K YONG 1, Esaki M SHANKAR 1,2, Ajantha SOLOMON 3, Tim SPELMAN 3,6,9, Christopher K FAIRLEY 7,8, Julian ELLIOTT 5,6, Jennifer HOY 5, Paul CAMERON 3,5, Adeeba KAMARULZAMAN 1,4, Sharon R LEWIN 3,5,10,†
PMCID: PMC5007213  NIHMSID: NIHMS793355  PMID: 27281059

Abstract

Background

Chronic HIV infection leads to marked depletion of CD4+ T cells in the gastrointestinal (GI) tract and increased microbial translocation measured by an increase in circulating lipopolysaccharide (LPS) levels. Here, we hypothesised that single-nucleotide polymorphisms (SNPs) in genes encoding the Toll-like receptor 4 (TLR4) and CD14, the principal receptors for LPS, were associated with CD4+ T-cell recovery post-antiretroviral therapy (ART).

Methods

Prospective study of predominantly Caucasian HIV-infected subjects receiving suppressive ART for at least 12 months. We analysed the CD14 SNPs C-260T and the TLR4 SNPs A+896G, C+1196T. We also determined the levels of LPS and soluble CD14 (sCD14) in plasma samples collected pre- and post-ART initiation. CD4+ T-cell recovery was assessed by linear mixed models.

Results

Following ART, individuals with a TT genotype compared to a CT or CC genotype for CD14 C-260T SNP showed higher levels of sCD14 (p=0.008 and 0.003 respectively). The CC genotype for the CD14 C-260T SNP, compared to CT or TT and the TLR4 SNP (AC/GT) compared to the homozygous genotype (AA/CC) were both independently associated with enhanced long-term CD4+ T-cell recovery (>3 months; p<0.001).

Conclusions

Polymorphisms in CD14 and TLR4 are independently associated with long-term CD4+ T-cell recovery in HIV-infected individuals post-ART.

Keywords: C-260T CD14 SNP, HIV, CD4 recovery, microbial translocation, LPS, sCD14

INTRODUCTION

Combination antiretroviral therapy (ART) has led to a dramatic reduction in morbidity and mortality of individuals infected with human immunodeficiency virus (HIV). Successful clinical outcomes are dependent on adequate recovery of CD4+ T cells following ART [1]. Several studies have shown that ~30% of patients fail to restore CD4+ T-cell counts >500cells/μL despite years of suppressive therapy [2]. There is now increasing evidence to show that patients failing to achieve CD4+ T-cell counts >500 cells/μL are at increased risk of developing serious non-AIDS events [3], including cardiovascular disease (CVD), hypertension, liver disease, non-AIDS malignancies and neurocognitive impairment [4]. Recent studies suggest that normalization of CD4+ T-cell counts should be defined as >900cell/μL [5].

HIV disease progression is associated with increased adaptive and innate immune activation (IA) [6], which is multi-factorial including increased expansion of CMV-specific T cells [7] and persistent HIV RNA at low levels [8]. However, a major contributing factor to immune activation is the depletion of CD4+ T cells in gut-associated lymphoid tissue (GALT) during the acute HIV infection [9–11] leading to increased microbial translocation (MT). Upon initiation of ART, MT is reduced as measured by reduction in the circulating levels of lipopolysaccharide (LPS), but this rarely reaches the levels of HIV uninfected controls [9, 10, 12, 13]. A case-control study that compared CD4+ T-cell non-responders (CD4+ T-cell counts <200cells/μL after 24 months of suppressive ART) versus full responders (>400cells/μL after 24 months of suppressive ART), showed that LPS was significantly increased in non-responders [10].

Toll-like receptor 4 (TLR4) and CD14 expressed on macrophages, monocytes, dendritic cells (DCs) and neutrophils [14], are the main cellular sensors of LPS [15]. Binding of LPS to these receptors induces a signaling cascade leading to activation of NF-κB, and eventual secretion of pro-inflammatory cytokines [16]. CD14 can also be found in plasma in a soluble form [17] and soluble CD14 (sCD14) is elevated in HIV-infected individuals despite long-term suppressive ART [18]. Elevated sCD14 has also been associated with CVD, neurological abnormalities, lymphoma and mortality in ART-treated HIV-infected individuals [4]. Collectively, these data demonstrate that host responses to elevated LPS levels could influence the rate of CD4+ T-cell recovery post-ART, and consequently to development of important clinical end-points in HIV disease.

Polymorphisms in the genes encoding for CD14 and TLR4 have been shown to determine responsiveness to LPS [19]. A common single-nucleotide polymorphism (SNP) found at position -260 of the CD14 promoter (where a C→T change occurs) enhances the transcriptional activity of CD14, which is associated with an increase in high-density membrane-bound CD14 on monocytes as well as elevated levels of sCD14 in tissue culture supernatants along with other pro-inflammatory cytokines such as TNF-α and IL-6 [20, 21]. Others have shown evidence for an association between the CD14 159TT genotype and inflammatory conditions including chronic periodontitis [22], myocardial infarction [23], Crohn’s disease [24], and susceptibility to brucellosis [25].

The main TLR4 SNPs that have been studied include A+896G (rs4986790) and C+1196T (rs4986791) [26]. Compared to non-carriers, carriers of A+896G/C+1196T showed blunted inflammatory responses to inhaled LPS [26] as well as reduced plasma IL-6, IL-1β and C-reactive protein (CRP) levels in response to LPS administration ex vivo [27]. Others have shown that both these TLR4 SNPs were associated with reduced prevalence of diabetic neuropathy in type 2 diabetes [28], and that carriers of these SNPs were more susceptible to severe bacterial infections [29, 30].

In this study, we hypothesized that SNPs in the TLR4 and CD14 genes will have an impact on CD4+ T-cell recovery in HIV-infected individuals following initiation of ART secondary to a change in response to LPS. Using multivariate analysis, we examined the clinical and biological factors associated with restoration of CD4+ T cells and found that both TLR4 and CD14 SNPs were independently associated with enhanced long-term CD4+ T-cell recovery.

METHODS

Patients

Patients were recruited from the Alfred Hospital and the Melbourne Sexual Health Centre (Melbourne, Australia) based on previously published inclusion criteria [13]. All plasma and peripheral blood mononuclear cells (PBMCs) were collected at pre-ART, and one time-point at least 12 months after the initiation of ART. The clinical specimens were processed by standard protocols within 24hrs of collection. Demographic and clinical parameters such as date of birth, gender, HIV exposure category, diagnosis of AIDS-defining illnesses (ADIs) before initiation of ART, co-infection with hepatitis B and C viruses, and initial ART treatment regimen were obtained from the Victorian HIV Service Database of the Alfred Hospital. All CD4+ T-cell counts and plasma HIV RNA results were also collected longitudinally from ART initiation to the most recent data available. All participants provided written informed consent and the protocols were approved by the Research and Ethics Committee of Alfred Health.

CD14 and TLR4 genotyping

DNA was obtained from cryopreserved PBMCs. Genotyping of a common polymorphism in CD14, C-260T (rs2569190) and two common polymorphisms in the TLR4 gene, A+896G (rs4986790) and C+1196T (rs4986791) was performed using the Sequenom MassARRAY iPLEX platform at the Australian Genome Research Facility.

Plasma LPS and sCD14

MT was estimated by measuring LPS levels using a commercial chromogenic limulus amebocyte lysate (LAL) assay (Lonza Walkersville Inc., Walkersville, MD), and sCD14 levels using a Quantikine sCD14 enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, MN) as previously described [13]. All samples were assayed in duplicate, and samples from the same patient (i.e. pre- and post-ART samples) were always assayed on the same plate to minimise interassay variation. Additionally, two samples were used as internal quality control (QC) for each assay. The results of each plate were only included if the coefficient of variance (CV) of the two QC samples was <15%.

Statistical analysis

Genotype frequencies were tested for Hardy-Weinberg equilibrium by chi-square test. The association between TLR4 and CD14 SNPs, LPS and sCD14 was analysed using the Kruskal-Wallis test with a post-hoc two-tailed Mann-Whitney U test. Linear mixed model (LMM) regression analysis was used to adjust for differences in the LPS levels across the genotypes tested. Plasma levels of LPS and sCD14 of a paired-sample (pre- and post-ART) were compared using the Wilcoxon signed-rank test. Piecewise regression by LMM was used to determine the association between TLR4 and CD14 SNPs with the rate of CD4+ T-cell recovery at two different time-periods separately (i.e. baseline to 3 months, and 3 months onwards) after ART initiation. The association between TLR4 and CD14 SNPs and other clinical parameters to CD4+ T-cell counts post-ART were first assessed using univariate LMM. Variables with p<0.2 were considered as candidates and included in the multivariate analysis. Variables with p<0.05 were considered as independent predictors. As CD4+ T-cell count was an outcome variable that was significantly skewed and was resistant to sensible transformation (log, square-root transformation) the use of maximum likelihood estimation was precluded. In this case generalised estimating equation (GEE) was used in the LMM analysis.

RESULTS

Patient demography and clinical characteristics

We recruited 156 Caucasian patients, and DNA was available from 125 patients. Cohort demographics and clinical details of the patients are described in Table 1. After initiation of ART, all patients had viral suppression (plasma HIV RNA <400copies/mL) throughout the follow-up period except three patients who had a single episode of virological failure (plasma HIV RNA >400copies/mL). The success rate of amplification to determine the genotype was 100%. Of the 125 patients examined, 54 were heterozygous and 34 homozygous for the CD14 -260T allele; and 12 heterozygous for the TLR-4 SNPs. There were no patients homozygous for the TLR4 SNPs. This resulted in an allele frequency of 0.49 for CD14 SNP (χ2 test p=0.13) and 0.048 for TLR4 SNPs (χ2 test p=0.573) that was in HWE. Both TLR4 SNPs A+896G and C+1196T were co-segregated.

Table 1.

Clinical and demographic details of the cohort

Characteristics No. (%) of patients
(N=125)
Gender
 Male 113 (90.4)
 Female 12 (9.6)
Median of age at ART initiation (IQR), year 40 (34 – 48)
Positive for hepatitis C virus antibodies (Anti-HCV) 11 (8.8)
Positive for hepatitis B virus surface antigen (HBsAg) 9 (7.2)
Median nadir CD4+ T cell count (IQR), cell/μL 202 (114 – 284)
Median baseline viral load (IQR), copies/mL 100,000 (44,050 – 135,638)
Median time on ART (IQR), years 2.4 (1.6 – 4.5)
History of AIDS-defining illness 31 (24.8)
Trimethoprim and sulfamethoxazole at ART initiation 60 (48)
ART regimen†
 NNRTI-based 91 (72.8)
 PI-based 29 (23.2)
 Triple NRTI-based 4 (3.2)
CD14 (C-260T) genotype frequency
 CC 37 (29.6)
 CT 54 (43.2)
 TT 34 (27.2)
TLR4 (A+869G and C+1196T) genotype frequency
 AA/CC 113 (90.4)
 AG/CT 12 (9.6)
 GG/TT 0 (0)
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Data are no. (%) of patients, unless otherwise indicated. ART, antiretroviral therapy; CD14, cluster of differentiation-14; TLR4, Toll-like receptor-4; IQR, interquartile range; NNRTI, nonnucleoside reverse-transcriptase inhibitor; NRTI, nucleoside reverse-transcriptase inhibitor; PI, protease inhibitor.

†

one patient had no data of treatment regime.

Suppressive ART for 12 months resulted in significant decrease of plasma LPS and sCD14 levels

The effects of long-term suppressive ART on HIV viral load, CD4+ T-cell count MT and innate immune activation were assessed in plasma samples of patients before and at one time-point after at least 12 months of suppressive ART. We showed that both LPS and sCD14 levels were significantly reduced following ART (both p<0.0001) (Wilcoxon signed rank test, data not shown), consistent with multiple prior studies [9, 13, 31]. We found a weak correlation between sCD14 and LPS levels (r=0.2044; p=0.0222) pre-ART (Figure 1A), and also between pre-ART CD4+ T-cell count with LPS (r=−0.195; p=0.029) and sCD14 (r=−0.404; p<0.0001) (Figure 1B–C). Pre-ART HIV viral load was not correlated with plasma levels of LPS; whilst it correlated with the levels of sCD14 (r=0.329; p=0.0002), and inversely with CD4+ T-cell count (r=−0.268; p=0.003) (Figure 1D–F).

Figure 1. Correlation between CD4+ T-cell count, LPS, sCD14 levels and HIV viral load in plasma samples collected from patients before ART (light grey circles) and after ART (dark grey circles).

Figure 1

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Spearman test was used to compare the correlation between these variables. sCD14, soluble CD14; LPS, lipopolysaccharide.

SNPs in CD14 but not TLR4 were associated with increased sCD14 levels post-ART

We next compared the levels of CD4+ T-cell count, LPS, sCD14 and HIV viral load in plasma among different CD14 and TLR4 genotypes. We found that CD4+ T-cell count and sCD14 in the post-ART samples was significantly different amongst the different CD14 genotypes (Kruskal-Wallis test, p=0.024 and p=0.006 respectively) but not in the pre-ART samples. A post-hoc analysisshowed that genotype CC was associated with significant higher level of CD4 T-cell count compared to CT (p=0.013; Mann Whiteny U test) and a trend of higher CD4 T-cell count compared to TT genotype (p=0.058) (Figure 2A, bottom left).

Figure 2. Clinical and plasma parameters pre-ART and post-ART in HIV-infected individuals with different SNPs in CD14 and TLR4.

Figure 2

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The CD4+ T-cells, LPS, sCD14 and plasma HIV viral load in individuals with A. CD14-260 genotypes and B. TLR4 +896 genotypes. C. Spearman correlations in pre-ART samples between LPS and sCD14 (left panel), sCD14 and CD4+ T-cell count (middle panel) and LPS and CD4+ T-cell count (right panel), stratified by CD4+ T-cell count, viral load and SNPs in CD14 and TLR4. The bar represents the strength of association (r values) where bars point to the right for a positive association and to the left for a negative association. The light grey represents a p value >0.05 (non-significant association) and the dark grey represents a p value <0.05 (significant association), where **** <0.0001, *** <0.001 and ** <0.01 and * <0.05. KW, Kruskal-Wallis test was used to compare across 3 genotypes. MN, Mann-Whitney U test, post-hoc test to compare the across 2 genotypes.

Genotype CC and CT were associated with lower levels of plasma sCD14 as compared to TT (p=0.0026 and p=0.083 respectively); whilst the difference between CC and CT were not significant (p=0.6) (Figure 2A). Using a regression model adjusted for LPS levels post-ART and years on ART, the CC genotype compared to the CC and CT genotypes was independently associated with lower sCD14 levels (hazard ratio [HR] −0.11, 95% confidence interval [CI] −0.13 – −0.077; p<0.001).

The CD14 genotypes were not associated with CD4+ T-cell count, plasma sCD14 and HIV viral load pre-ART, and LPS pre- and post-ART (Figure 2A, B). The TLR4 SNPs, A+896G and C+1196T were not associated with these four variables pre- or post-ART (Figure 2A, B). Together, we concluded that only the presence of CD14 CC genotype was associated with lower sCD14 levels post-ART.

The correlation of sCD14, LPS levels and CD4+ T-cell counts depends on the CD4 and plasma HIV viral load

As the plasma levels of LPS, sCD14 and CD4+ T-cell count were poorly correlated, we speculated if this could be influenced by the degree of immune recovery. We therefore performed a post-hoc analysis stratified by baseline CD4+ T-cell levels and plasma HIV viral load and showed that the levels of LPS and sCD14 were more strongly correlated in individuals with baseline CD4+ T-cell count <100 cells/μL (r=0.622; p=0.0009) and baseline HIV viral load >5log/mL (r=0.385; p=0.002). In this cohort, a similar correlation was not observed among individuals with baseline CD4 >100 cells/μL and HIV viral load <5log/mL. We also found that the levels of LPS and sCD14 were more strongly correlated in individuals with CD14 −260TT (r=0.484; p=0.0037) and TLR4 +896AA (r=0.246; p=0.008) the two genotypes that were associated with an increased responsiveness towards LPS stimulation (Figure 2C, left panel)

SNPs in CD14 and TLR4 were independently associated with enhanced CD4+ T-cell recovery

In order to assess the independent influence of CD14 and TLR4 SNPs on CD4+ T-cell recovery following suppressive ART, we performed multivariate piece-wise regression by LMM, controlling for clinical and biological factors that have previously been shown to be associated with CD4+ T-cell reconstitution. In general, recovery of CD4+ T cells during viral suppression occurs in two phases [32]. During the first 3 months of ART, the number of CD4+ T cells rapidly increases due to T-cell homeostasis [33, 34], improved thymic output [35] and redistribution of T cells into peripheral circulation [36], followed by a slower phase of T cell repopulation that takes up to several years post-ART and is largely driven by T-cell proliferation and enhanced thymic production [37]. Considering the biphasic pattern of CD4+ T-cell recovery, LMM was performed at two different time periods following initiation of ART i.e. from baseline to 3 months; and 4 months onwards as we have previously reported [38].

In unadjusted analyses from baseline to 3 months, multiple factors were significantly associated with early CD4+ T-cell recovery (Table 2). In adjusted analyses, baseline CD4+ T-cell counts (β-coefficient=0.87; 95% CI=0.779–0.966; p<0.001) was associated with enhanced early CD4+ T-cell recovery; whilst receiving a PI regimen (β-coefficient=−38.81; 95% CI=−67.39 – −10.23; p=0.008) was associated with decreased early CD4+ T-cell recovery (Table 2).

Table 2.

Predictors for CD4+ T-cell recovery from baseline to 3 months on ART

Variable Univariate Coef. (95% CI) p value Multivariate Coef. (95% CI) p
Baseline CD4+ T-cell count, per 100 cells/μL 0.90 (0.84,0.96) <0.001 0.87 (0.779, 0.966) <0.001
Log (Baseline viral load, per 100,000 copies/mL) 0.0 (−0.00001, 0.00003) 0.312 … …
Time-dependent covariate viral load, per 100,000 copies/mL −6.81 (−17.17, 3.56) 0.198 −2.23 (−8.035, 3.568) 0.45
Age at ART initiation, year −0.076 (−1.87, 1.72) 0.933 … …
ART regimen
 NNRTI regimena … … … …
 PI regimen −57.84 (−103.74, −11.94) 0.014 −38.81 (−67.39, −10.23) 0.008
 Triple NRTI regimen −79.49 (−184.03, 25.06) 0.136 −20.53 (−114.54, 73.47) 0.669
Trimethoprim/sulfamethoxazole at ART initiation −170.81 (−204.91, −136.71) <0.001 −17.63 (−47.59, 12.32) 0.249
Pre-ART LPS level, per 100 pg/mL −27.75 (−56.49, 1.00) 0.059 −3.32 (−22.19, 15.54) 0.73
Post-ART LPS level, per 100 pg/mL −26.26 (−103.92, 51.40) 0.507 … …
Log (Pre-ART sCD14 level, per 100,000 pg/mL) −144.99 (−204.19, −85.78) <0.001 13.69 (−30.32, 57.72) 0.542
Log (Post-ART sCD14 level, per 100,000 pg/mL) 93.62 (20.97, 166.27) 0.012 −9.61 (−61.7, 42.48) 0.718
Previous AIDS-defining illness
 Yesa … … … …
 No 73.51 (31.70, 115.32) 0.001 15.65 (−13.20, 44.50) 0.288
Gender
 Malea … … … …
 Female −33.84 (−99.47, 31.79) 0.312 … …
Positive for hepatitis B surface antigen (HBsAg) 53.81 (−21.96, 129.58) 0.164 −26.38 (−69.09, 16.34) 0.226
Positive for hepatitis C virus antibody (Anti-HCV) −64.00 (−126.85, −1.15) 0.046 15.22 (−32.48, 62.93) 0.532
Mode of transmission
 MSMa … … … …
 IDU −118.40 (−266.80, 29.30) 0.118 −63.33 (−160.14, 33.48) 0.200
 MSM + IDU −119.30 (−284.86, 46.27) 0.158 79.55 (−27.84, 186.95) 0.147
 Transfusion −166.00 (−314.40, −17.60) 0.028 −58.89 (−221.99, 104.20) 0.479
 Unknown −108.31 (−156.14, −60.48) <0.001 −13.72 (−47.15, 19.72) 0.421
CD14 SNP (C-260T)
 CC −25.03 (−67.54, 17.49) 0.249 16.64 (−11.48, 44.76) 0.246
 T-carriagea … … … …
TLR4 SNPs (A+896G & C+1196T)b
 AA/CC … … … …
 AG/CT 1.85 (−63.89, 67.58) 0.956 −6.01 (−51.55, 39.53) 0.796
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Univariate analyses by generalised estimating equation (GEE) using mixed-effect modelling were performed to identify potential predictors of CD4 recovery starting from baseline to 3 months post-ART. Variable for which p<0.2 were considered as candidates. The candidate predictors were then included in a multivariate GEE mixed-effect model and variables for which p<0.05 were considered as independent predictors.

ART, antiretroviral therapy; coef., coefficient; CI, confidence interval; NNRTI, non-nucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; LPS, lipopolysaccharide; sCD14, soluble CD14; IDU, injecting drug use; MSM, men having sex with men; TLR4, Toll-like receptor 4.

a

reference group

b

both TLR4 SNPs (A+896G & C+1196T) are co-segregated

Next, we investigated the determinants associated with late CD4+ T-cell recovery i.e. four months and beyond. Unadjusted analyses are shown in Table 3. In an adjusted model, multiple clinical parameters remained significant including baseline CD4+ T-cell count, age at ART initiation, receiving trimethoprim and sulfamethoxazole (TMP-SMX) therapy at the time of initiation of ART, post-ART LPS level, pre- and post-ART sCD14 levels, previous ADIs, gender, positive for HBV antigen, mode of HIV transmission as well as both CD14 and TLR4 genotype. Our preliminary analysis showed that individuals with CD14 -260CC compared to CT and TT had a higher CD4+ T-cell count (β-coefficient 117 (p=0.002) and 87 (p=0.0026) respectively). Therefore, TT and CT were combined and compared with CC in subsequent analyses. The pooled β-coefficient of CD14 -206CC genotype compared to CT/TT was independently associated with enhanced late CD4+ T-cell recovery (β-coefficient=65.71; 95% CI=42.51–88.92; p<0.001). In the same model, the TLR4 SNPs (AG/CT) as compared to the genotype AA/CC were also independently associated with enhanced late CD4+ T-cell recovery (β-coefficient =111.87; 95% CI=78.33–159.41; p<0.001) (Table 3).

Table 3.

Predictors for CD4+ T-cell recovery after 4 months on ART.

Variable Univariate Coef. (95% CI) p value Multivariate Coef. (95% CI) p value
Baseline CD4+ T-cell count, per 100 cells/μL 0.80 (0.73, 0.87) <0.001 0.69 (0.61, 0.78) <0.001
Log (Baseline viral load, per 100,000 copies/mL) −3.49e−6 (−0.001, 0.0) 0.751 … …
Time-dependent covariate viral load, per 100,000 copies/mL 6.43 (−9356, 22.42) 0.431 … …
Age of ART started, year −3.37 (−4.50, 2.25) <0.001 −3.64 (−4.73, −2.55) <0.001
ART regimen
 NNRTI regimena … … … …
 PI regimen 9.52 (−17.07, 36.12) 0.483 … …
 Triple NRTI regimen −35.86 (−101.81, 30.09) 0.287 … …
Trimethoprim/sulfamethoxazole at ART initiation −217.41 (−239.53, −195.29) <0.001 −85.05 (−112.15, −57.96) <0.001
Pre-ART LPS level, per 100 pg/mL −39.86 (−56.08, −23.63) <0.001 −9.3 (−25.2, 6.6) 0.252
Post-ART LPS level, per 100 pg/mL −109.89 (−164.37, −55.41) <0.001 −95.78 (−145.83, −45.73) <0.001
Log (Pre-ART sCD14 level, per 100,000 pg/mL) −219.63 (−257.72, −181.54) <0.001 −94.04 (−139.0, −49.04) <0.001
Log (Post-ART sCD14 level, per 100,000 pg/mL) −60.44 (−104.36, −16.53) 0.007 19.18 (−25.16, 63.52) 0.397
Previous AIDS-defining illness
 Yesa … … … …
 No 49.36 (22.51, 76.21) <0.001 75.73 (48.07, 103.38) <0.001
History of virologic failure
 Yes 121.45 (52.39, 190.52) <0.001 52.3 (−16.61, 121.2) 0.137
 Noa … … … …
Gender
 Malea … … … …
 Female −52.84 (−93.73, −11.95) 0.011 −180.86 (−230.16, −131.55) <0.001
Positive for hepatitis B surface antigen 153.55 (107.92, 199.18) <0.001 54.85 (13.37, 96.32) 0.010
Positive for hepatitis C virus antibody 13.87 (−26.45, 54.20) 0.5 … …
Mode of transmission
 MSMa … … … …
 IDU 68.84 (3.54, 134.15) 0.039 32.20 (−39.16, 103.57) 0.376
 MSM + IDU 5.00 (−142.61, 152.61) 0.947 181.77 (37.02, 326.51) 0.014
 Transfusion −227.55 (−344.01, −111.09) <0.001 −170.30 (−311.14, −29.46) 0.018
 Unknown −3.63 (−33.13, 25.86) 0.809 30.79 (0.23, 61.35) 0.048
CD14 SNP (C-260T)
 CC 79.94 (55.47, 104.41) <0.001 65.71 (42.51, 88.92) <0.001
 T-carriagea … … … …
TLR4 SNPs (A+896G & C+1196T)b
 AA/CCa … … … …
 AG/CT 16.38 (−23.99, 56.75) 0.427 111.87 (78.33, 159.41) <0.001
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Univariate analyses by generalised estimating equation (GEE) using mixed-effect modelling were performed to identify potential predictors of CD4 recovery starting from 4th month onwards post-ART. Variable for which p<0.2 were considered as candidates. The candidate predictors were then included in a multivariate GEE mixed-effect model and variables for which p<0.05 were considered as independent predictors. ART, antiretroviral therapy; coef., coefficient; CI, confidence interval; NNRTI, non-nucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; LPS, lipopolysaccharide; sCD14, soluble CD14; IDU, injecting drug use; MSM, men having sex with men; TLR4, Toll-like receptor 4.

a

reference group

b

both TLR4 SNPs (A+896G & C+1196T) are co-segregated

DISCUSSION

In the present study, we sought to investigate the relationship of TLR4 and CD14 SNPs with LPS and sCD14 and CD4+ T-cell recovery in HIV-infected individuals who were on suppressive ART. We found significant associations between TLR4 and CD14 SNPs with the trajectory of late recovery of CD4+ T-cell counts following ART using LMM. In addition, using a multivariate analysis, we found that the TLR4 genotype +896AG/+1196CT and CD14 genotype -260CC were independently associated with enhanced late CD4+ T-cell recovery post-ART. Finally, we showed that plasma LPS and sCD14 were significantly correlated in HIV-infected participants with a CD4 T-cell count <100cells/μL, HIV viral load > 5 log/mL and in individuals carrying the CD14 -260TT and TLR4 +896AA SNPs, the genotypes associated with an increased responsiveness or sensitivity towards LPS stimulation.

In this study, we assessed the changes in CD4+ T-cell counts over time after initiation of ART as a continuous variable using GEE analysis, controlling for time varying covariates. Although several others have used Cox regression to study CD4+ T-cell recovery [39, 40], we believe that the LMM approach is more robust because it takes into account the heterogeneity in both the rate and magnitude of CD4+ T-cell count increases following ART [2, 41].

The TLR4 SNPS in exon 3 – A+896G and C+1196T – are common non-synonymous SNPs with replacement of an aspartic acid (Asp) residue with glycine (Gly) at amino acid position 299 (Asp299Gly); and threonine with an isoleucine at position 399 (Thr399Ile), respectively [42]. Both SNPs are known to affect the extracellular domain of TLR4. Nonetheless, the precise mechanism whereby these SNPs affect LPS signaling is not clearly understood. A recent study suggested that the SNPs could interfere with TLR4 dimerization, and therefore prevent the assembly of intracellular docking platforms for adapter recruitment leading to the hyporesponsiveness to LPS stimulation [43]. In line with this, we demonstrated in a multivariate model that carriage of TLR4 +896AG/+1196CT was associated with greater late CD4+ T-cell reconstitution, independent of LPS levels. However, since TLR4 +896AG was less prevalent (9.6%), we also felt that the SNP effect might have been masked by the effect of the more prevalent SNP i.e. CD14 -260CC (29.6%) in the univariate analysis. Therefore, the TLR4 +896AG SNP only showed its effect after the adjustment in multivariate model.

The SNP in the CD14 gene, C-260T (also known as C-159T) is a common genetic polymorphism, which occurs in the promoter region of the CD14 [44] and is reported to occur in ~48% of Caucasians [45]. This SNP (C→T) reportedly reduces the binding affinity of suppressor proteins (Sp) to its binding domain and hence enhances the promoter activity of CD14 [46]. Consistent with this finding, we found that the CC genotype was associated with lower plasma levels of sCD14 (and TT genotype with higher sCD14), independent of LPS. Interestingly, we only observed this in plasma collected post-ART. This potentially could be due the high level of immune activation pre-ART which may have masked the effects of CD14 C-260T SNPs.

We have recently examined the relationship of these same polymorphisms and cardiovascular disease in a cohort of individuals on ART recruited in Malaysia [47]. We are currently examining the relationship of these polymorphisms to CD4 recovery but given that this cohort is entirely Asian and they initiated ART late (median CD4 = 63 cells/μL compared to median CD4= 202 cells/μL in this manuscript), we anticipate that the relationship between the polymorphisms and CD4 recovery that we describe here may be quite different. Ideally, the relationship between the polymorphisms described here and CD4 recovery should be examined in a replicate cohort of similar ethnicity which we are currently planning.

There were a number of limitations in our study. First, although this was a longitudinal study, patients were enrolled post-ART and therefore samples were obtained at varying time-points following ART initiation. We adjusted for time between initiating ART and sample collection in the multivariate model, but our results could have been confounded by loss to follow up or death, although death is now significantly reduced in HIV-infected individuals on ART [48]. Second, only a small fraction of our patients were carriers of the rare allele for TLR4 SNPs, and therefore this analysis had less statistical power. Although there is a plausible biological basis to support our findings of the genetic association between TLR4 and CD14 SNPs and recovery of CD4+ T cells following ART, given the small sample size, these findings require confirmation in an independent cohort of similar ethnicity. Lastly, the CD4/CD8 T-cell ratio has previously been shown to be associated with T-cell activation, senescence, exhaustion [49] as well as a strong predictor of HIV disease progression, CD4 T cell recovery [40] and aging-related diseases [50, 51]. In this cohort, baseline CD8 data was available only from a limited number of patients, and therefore was not included in the analyses.

In summary, we demonstrated enhanced late CD4+ T-cell recovery in HIV-infected individuals receiving ART with SNPS in TLR4 +896AG/+1196CT and CD14 -260CC. Taken together, these data suggest that apart from MT, genetic polymorphisms that alter the host response to LPS stimulation could also be an important determinant of CD4+ T-cells reconstitution following ART. The current study provides the first evidence to show that CD4+ T-cell recovery in HIV-infected individuals on ART is associated with genotypes related to LPS metabolism. These findings, once repeated in an independent cohort, could potentially inform novel interventions to enhance CD4 recovery in those at greatest risk.

Acknowledgments

The authors are grateful to all the participants, clinical and laboratory staff of the Alfred Hospital and Melbourne Sexual Health Centre for assistance with patient recruitment, specimen collection and cooperation. We also thank Victorian HIV Blood and Tissue Bank and the Victorian Infectious Diseases Reference Laboratory for access to stored samples. We thank Pushparaj Velayudham for his assistance and Reena Rajasuriar for helpful discussions and review of the manuscript.

Grant support.

This work was supported by a grant from the High Impact Research-Ministry of Higher Education, Malaysia (UM.C/625/1/HIR/MOHE/MED/01), University of Malaya Research Grant (UMRG) (RG448-12HTM) by the Health and Translational Medicine Research Cluster to EMS. SRL is supported by the National Health and Medical Research Council of Australia and the Delaney AIDS Research Enterprise (DARE), National Institutes of Health (U19 AI096109).

Footnotes

Disclosure of potential conflict of interest.

No potential conflicts of interest were disclosed.

Author Contributions

Conceived and designed the experiments: YKY EMS SRL. Performed the experiments: YKY. Analyzed the data: YKY TS. Contributed reagents/materials/analysis tools: AS, CKF, JE, JH, PC, AK. Wrote the manuscript: YKY EMS TS SRL.

Presented in part: 7th IAS Conference on HIV Pathogenesis, Treatment and Prevention. 30 June – 3 July 2013, Kuala Lumpur, Malaysia. Abstract number: WEPE453

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