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MHC-driven HIV-1 control on the long run is not systematically determined at early times post-HIV-1 infection

Antoni, Guillemettea,b,*; Guergnon, Julienc,d,*; Meaudre, Célinec,d; Samri, Assiac,d; Boufassa, Faroudyb; Goujard, Cécileb,e; Lambotte, Oliviere,f; Autran, Brigittec,d,g; Rouzioux, Christineh; Costagliola, Dominiquei,j; Meyer, Laurencea,b,*; Theodorou, Ioannisc,d,g,*

doi: 10.1097/QAD.0b013e328360a4bd
Basic Science

Introduction: Human leukocyte antigen (HLA) class I-driven long-term protection against HIV-1 is mainly associated with HLA-B*27 and HLA-B*57. This effect is observed early after infection. Clarification needs to be established concerning the moment of action for the other HLA-B or HLA-C alleles.

Methods: HLA-B and HLA-C alleles from 111 individuals that control HIV-1 disease for over 8 years and from 747 seroconverters frequencies were compared. Also, HLA-B and HLA-C influence on early levels of plasma HIV-RNA, cellular HIV-DNA, CD4, CD8 and CD4/CD8 ratio was evaluated among the seroconverters. We performed univariate, multivariate and haplotypic analyses in order to disentangle the respective contribution of the HLA-B and HLA-C genes.

Results: The haplotypes analysis shows three patterns of protective effects of HLA-B and HLA-C alleles or haplotypes. First, the HLA B*57, HLA-B*27, HLA-B*13 and HLA-C*14 alleles, which have a strong effect on long-term disease control, also influence at least one of the early infection phenotypes. Second, HLA-B*52 has a strong effect during early time points on HIV-RNA without significant effect on the long-term control of HIV-1. Finally, the HLA-B*14-C*08 haplotype has a strong effect on the long-term protection, without influencing early viral control.

Conclusion: Our study highlighted independent effects of HLA-B and HLA-C alleles on HIV-disease progression. Furthermore, some alleles appeared to be specifically associated with either long-term control or early virological parameters, suggesting different immunological mechanisms according to the disease stages.

Supplemental Digital Content is available in the text

aEpidemiology and Public Health Service, APHP, Hôpital du Kremlin Bicêtre, Université Paris-Sud

bINSERM U1018, Centre for Research in Epidemiology and Population Health, Le Kremlin Bicêtre

cUPMC Univ Paris 06, Laboratory of Immunity and Infection

dINSERM, Laboratory of Immunity and Infection, Paris

eService de Médecine Interne, AP-HP, Hôpital Bicêtre, Univ Paris-Sud 11

fINSERM U1012, Le Kremlin-Bicêtre

gHôpital Pitié Salpêtrière, APHP

hHôpital Necker, APHP, Université Paris Descartes

iUPMC Univ Paris 06

jINSERM, Paris, France.

*Guillemete Antoni, Julien Guergnon, Laurence Meyer and Ioannis Theodorou contributed equally to the writing of this article.

Correspondence to Julien Guergnon, PhD, INSERM, Paris, France. E-mail:

Received 26 September, 2012

Revised 4 February, 2013

Accepted 26 February, 2013

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website (

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Host genetics has been deeply studied in the context of HIV infection for almost two decades now, demonstrating that host genes variants could determine part of the variation in susceptibility to infection [1–3] or disease progression [4–6]. Concerning disease progression, the human major histocompatibility complex (MHC) locus has been clearly identified as the main actor of the outcome for infected individuals [5,7–13]. Given that human leukocyte antigen (HLA) molecules are highly polymorphic, HIV-infected patients are far from being ‘equal’ against the virus and MHC genetic variation explains up to 15–20% of interindividual differences in HIV-viral levels and CD4 cell count decline [14].

Several studies have demonstrated that a few HLA-B alleles, driving appropriate CD8+ responses, are the major genetic determinants for HIV-replication control and/or disease progression [9,12,15–17]. Variability of natural killer cell response mediated through the killer cells immunoglobulin-like receptors (KIR) also influences the outcome of the infection. Copy number variations of KIR3DL1 and KIR3DS1 gene associate to HIV-1 control at the viral load set point.

In most studies analyzing the influence of MHC variation in HIV disease, investigators focused on AIDS-free survival, viral replication at set point or HLA alleles preferentially found in long-term nonprogressors (LTNPs). However, several questions about the protective effect of HLA alleles at different stages of HIV pathogenesis are still open.

A study by Altfeld et al. [12] showed that HLA-B*27, HLA-B*57 and HLA-B*51 alleles associate with strong T-cell responses during primary infection and identified relevant immunodominant T-cell epitopes. However no direct link with the patient's early viral load (HIV-RNA) has been established. Indeed few or no data are available on the effect of HLA alleles on viral load during primary infection. A study from Gao et al. [18] suggested that the B*57 protective effect can be observed as soon as early stages of HIV disease, whereas the B*27 allele only delays progression to AIDS-defining illness without slowing down the drop to 200 cells/μl. Altogether, no study has yet been designed to analyze at once which HLA-B and HLA-C alleles influence the long-term outcome of HIV disease and/or the virological and immunological parameters early after infection.

Another partially unsolved question concerns the effect of HLA-C alleles on early viral loads and whether this is a clear HLA-C or an HLA-B-C haplotypic effect. Data from primary infection can provide evidence about the subtle balance of HLA-C-related effects involving both peptide selection on HIV and or HLA-C expression. In this study, we performed HLA-B-C haplotypic association studies and tried to disentangle the respective role of these two genes in two distinct disease stages of HIV disease. We first analyzed HLA-B-C haplotypes in a cohort of ‘Extreme’ individuals enrolling both LTNPs and HIV controllers and then we studied the effect of these haplotypes on quantitative phenotypes soon after seroconversion: plasma HIV-RNA levels; cellular HIV-DNA levels; CD4 cell count; and CD4/CD8 ratio in a large cohort of seroconverters infected for less than 3 months [5,19–22].

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Patients and methods


The main characteristics are presented in Table S1,

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Extreme patients

This group included all white patients with available samples from two preexisting cohorts, which enrolled LTNP (66 patients from the ANRS-CO15 ALT cohort) and HIV controllers (45 patients from ANRS-EP36 National Observatory) [5,19]. At enrollment in the ALT cohort, time as first HIV-positive test had to be more than 8 years, and the patients had to be antiretroviral-naive, without any AIDS symptom, and with CD4 cell count more than 600 per μl without any decrease during the last 5 years. Patients from HIC cohort were eligible if they presented with an untreated asymptomatic HIV-1 infection for more than 10 years with plasma HIV-1 RNA levels below 400 copies/m in more than 90% of samples tested as HIV diagnosis [22]. Before combining the controllers and LTNP cohorts, we compared their HLA-B and HLA-C groups frequencies (table S2, No major difference was observed: only HLA-B07 and HLA-B57 tended to be less frequent in LTNP than in controllers, with P-values however far above the Bonferroni threshold of P = 0.0017 (P = 0.016 and P = 0.047, respectively). These differences could be explained by multiple testing.

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This group of patients is made of 747 white patients enrolled in the ANRS-CO06 PRIMO cohort. This ongoing cohort has enrolled since November 1996 patients presenting during HIV-1 primary infection, mainly diagnosed on the basis of a negative or incomplete western blot (no anti p-68 or anti p-37) (95% of the patients), or on the basis of an interval of less than 3 months between a negative and a positive ELISA test [20,21].

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Biological markers

HIV-RNA measurements were performed on sites using commercial assays according to manufacturer's recommendations as previously reported [23].

Frozen pellets of peripheral blood mononuclear cells (PBMC) were centralized and HIV-DNA levels were quantified using a real time PCR HIV-DNA assay as previously described [24], with amplification in the LTR gene and a cutoff value at five HIV-DNA copies/PCR (Biocentric, Bandol, France). Results were expressed as the copy number per million PBMC.

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HLA-B and HLA-C genotyping

HLA-B and HLA-C typing were performed using sequence-specific oligonucleotides PCR amplifications (PCR-SSO) as previously described [25] according to the manufacturer's protocol (LABType One-Lambda).

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Statistical analyses

Extreme phenotype versus seroconverters

HLA-B and HLA-C genotypes frequencies were first compared between the two groups by the mean of a chi-square or a Fisher test, according to the allele frequencies. For each allele one after one, we considered a dominant model and computed the OR (odds-ratio) of being “Extreme” (ORextreme) by pooling together genotypes presenting one or two copies of the considered allele with the other genotypes as reference. Multivariate logistic regressions were then performed guided by linkage disequilibrium between HLA-B and HLA-C loci in order to identify independent markers.

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Early levels of HIV-RNA and HIV-DNA, CD4 cells count and CD4/CD8 ratio phenotypes

In the PRIMO sample, the associations between the quantitative phenotypes measured at the time of HIV-1 primary infection and the HLA-B and HLA-C alleles were estimated by the mean of multiple linear regression models. HIV-RNA/-DNA, CD4 cell count and CD4/CD8 ratio were normalized to obtain a convergence in the models destined to estimate HLA B-C haplotype effects. The regressions of HIV-RNA and HIV-DNA levels were systematically adjusted for sex and time since infection, and the regressions of CD4 cell count levels and CD4/CD8 ratio were further adjusted for tobacco smoking [26]. HLA alleles were first analyzed one after each other considering a dominant model, and secondary included in a multivariate linear regression, based on results from linkage disequilibrium.

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Multiple testing

At the first stage of the analysis (univariate screening), a stringent Bonferroni correction was applied. As we excluded from the analyses alleles showing a low frequency below 3% in the three samples (ALT, HIC and PRIMO), we performed 17 tests for HLA-B and 12 tests for HLA-C. We then considered the associations showing a P-value lower than 0.0017 significant. As haplotype and multivariate analysis were carried on selected alleles, we used the usual P less than 0.05 threshold.

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Haplotypic analysis

HLA-B-C Haplotypes were analyzed using the ‘haplo.glm’ function from the ‘Haplo Stats’ R package. The ‘haplo.glm’ function is based on an iterative two-step EM, with the prosterior probabilities of pair of haplotypes per patient used as weights to update the regression coefficients, and the regression coefficients used to update the posterior probabilities [27]. Here, we present haplotypes comprising alleles that were found to be significantly associated with the Extreme status or with at least one of the quantitative phenotypes, and considered all others alleles referred as “BXX” or “CwXX”.

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Linkage disequilibrium analysis

The linkage disequilibrium between HLA-B and –C alleles was assessed by the mean of pairwise r 2 and D’, calculated according to the haplotype frequencies obtained from the EM algorithm previously described.

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Long-term nonprogressors and HIV controllers versus seroconverters

We first compared HLA-B and HLA-C allele frequencies in the Extreme group of patients to the seroconverters from the PRIMO cohort. In the univariate analysis, HLA-B alleles significantly (P Bonferroni < 0.0017) associated with the Extreme status were B*57, B*27, and B*14 (Table 1). The B*35 allele was conversely under-represented among Extreme (7.4%) compared with PRIMO patients (21%). Among HLA-C alleles, those that passed the Bonferroni threshold were C*06, C*08 and C*14, with increased frequencies in the Extreme compared with PRIMO patients.

Table 1

Table 1

We studied all linkage disequilibriums between each allele significantly associated with the Extreme phenotype in the univariate analysis and any other alleles (Table S3, Altogether, seven noticeable linkage disequilibrium (D′ > 0.3 and r 2 > 0.1) measures were identified: B*14-C*08; B*57-C*06; B*27 and C*01; B*27 and C*02; B*35 and C*04; B*13 and C*06; and B*51 and C*14.

A multivariate analysis was set up to identify the independent protective markers from the haplotypes identified by linkage disequilibrium. These independent protective alleles were B*57 (ORextreme = 18.55), C*14 (ORextreme = 6.03) B*14 – or C*08 – (ORextreme = 5.27), B*27 (ORextreme = 4.54), B*13 (ORextreme = 7.66) (Table 1). We also identified B*35 allele as deleterious, although adjusted association did not reach statistical significance. Inclusion of additional alleles one after each other in the previous model did not influence these associations.

Altogether, the multivariate model was able to eliminate alleles with a mild effect (B*35) or in linkage disequilibrium with protective alleles (C*06).

Similar results were observed when the analysis was restricted to patients infected through sexual route only (706 seroconvertors versus 67 from the “Extreme” group) (not shown).

We carried on haplotypic analyses (Fig. 1) including alleles associated with the Extreme phenotype and alleles in linkage disequilibrium with them. We found that five haplotypes displayed odds ratios more than 4, demonstrating a strong protective effect of these haplotypic associations. Given the quasi perfect linkage disequilibrium between B*14 and C*08, it was impossible to distinguish an allelic effect. Indeed, only one haplotype comprised these alleles (B*14-C08, ORextreme = 4.26, P = 6.9e-06). In contrast, we were able to detect a leading effect of B*57 on C*06, both B*57-C*06 and B*57-CXX haplotypes were significantly over-represented among Extreme patients (ORextreme = 8.18 and ORextreme = 18.23, respectively) while BXX-C*06 was not. Interestingly, accounting for C*06 allele allowed us to observe a trend towards a protective effect of the B*13 allele (ORextreme = 4.17, P = 0.07). Therefore, C*06 univariate association with the Extreme phenotype was essentially due to its association to HLA-B*57 and HLA-B*13.

Fig. 1

Fig. 1

Concerning the other studied haplotypes, we concluded that the effects observed for B*27 in the univariate analyses were still significant in any haplotype background (B*27-C01: ORextreme = 3.60 or B*27-C*02: ORextreme = 5.59). Furthermore, the only haplotype including C*14 was B*51-C*14 (ORextreme = 6.22). It was noteworthy that whereas a protective effect of B*51 has been previously described [28], our haplotype analysis could only observe a protective effect for the haplotype B*51-C14 and none for B*51 phased with another C allele. Finally, a weak and not significant deleterious effect could be observed for B*35-C*04, while C*04 itself did not appear to have any deleterious effect.

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Virological and immunological parameters during seroconversion

We then challenged the hypothesis that patients able to control HIV disease over the long run are also able to exhibit favorable HIV virological and immunological parameters during primo-infection (Table 2). Alleles associated with lower HIV-RNA and/or HIV-DNA levels were B*27, B*57, B*13 and C*06, this latter being in linkage disequilibrium with B*57 and B*13, with at least −0.6 log for HIV-RNA and −0.3 log for HIV-DNA. Again, the only deleterious alleles were B*35 and C*04, significantly associated with higher HIV-DNA (+0.17 log HIV-DNA for both alleles).

Table 2

Table 2

Concerning the immunological parameters, we observed no significant association for CD8+ T-cells levels (data not shown), and only C*06 was significantly associated with higher CD4 levels in the univariate analysis (+65 for untransformed CD4 levels, Table 3), probably reflecting linkage disequilibrium with both B*57 and B*13. Interestingly, the study of the CD4/CD8 ratio pointed out three associations reaching the Bonferroni threshold: the respective effects on CD4/CD8 ratio were +0.17 for B*27 and B*57, and +0.09 for C*06 (Table 3).

Table 3

Table 3

Surprisingly, some alleles associated with the Extreme status were not significantly associated with any studied phenotype in PRIMO patients. Indeed, no significant association was found between the virological or immunological phenotypes at seroconversion and B*14-C*08 haplotype. In addition, the C*14 allele which was significantly associated with the Extreme phenotype, showed only a trend toward lower HIV-DNA (−0.32 log HIV-DNA, P = 0.02) and higher CD4 count (+110 for untransformed CD4 levels, P = 0.01) but without reaching statistical significance after correction for multiple testing.

On the contrary, the analysis of the virological and immunological parameters during seroconversion shows that the HLA-B*52 allele was strongly influencing HIV-RNA levels during the first weeks postinfection, although not significantly associated with the Extreme group.

Based on linkage disequilibrium data (Table S3,, we retained in the multivariate analyses and identified as independent factors B*13, B*27, B*52, and B*57 alleles for HIV-RNA (effects on log HIV-RNA are respectively −0.7, −0.6, −1.0, and −0.9); B*13, B*27, B*35 and B*57 alleles for HIV-DNA (effects on log HIV-DNA are −0.4, −0.4, +0.1, −0.6, respectively); and B*27 and B*57 alleles for the ratio CD4/CD8 (effects on untransformed ratio are +0.16 for these two alleles). Inclusions of the remaining alleles in the multivariate models presented in Tables 2 and 3 did not change the associations.

Finally, we still observed in the multivariate model a strong effect of B*52 (−1.0 log) on HIV-RNA during seroconversion.

We then carried on haplotypic analyses considering the same haplotypes as we did for the Extreme phenotype (Fig. 2 and Fig. 3).

Fig. 2

Fig. 2

Fig. 3

Fig. 3

Consistent with what was observed for the Extreme phenotype, we confirmed highly significant modulations of the virological parameters (HIV-RNA and HIV-DNA, Fig. 2) associated with the B*57-C*06, B*13-C*06 and B*27-C*02 haplotypes. However, no significant association was observed between C*06 or C*02 and any virological quantitative phenotype when C*06 was not associated to B*57 or B*13 and when C*02 was not associated with B*27. Concerning the effects of these haplotypes on immunological parameters (Fig. 3), the B*57-C*06 haplotype was significantly associated to CD4/CD8 ratio. The most significantly associated haplotypes to immunological parameters, after B*57-C*06, were B*13-C*06 and B*27-C*02.

The B*52 allele found associated with HIV-RNA was always associated with C*12. B*52-C*12 haplotype was significantly associated with lower levels of HIV-RNA (−1 log HIV-RNA, Fig. 2), while haplotypes constituted by C*12 and any other HLA-B allele were not.

B*14-C*08 (strongly associated with the Extreme phenotype) was not significantly associated with any of the virological or immunological phenotype at seroconversion. The effect of the B51-C*14 haplotype on early HIV-RNA level was weak (−0.27 log HIV-RNA) and not significant, although we observed for this haplotype lower HIV-DNA levels (−0.38 log HIV-DNA, P = 0.004) and higher CD4 cell count (P = 0.01).

Finally, only one haplotype, B*35-C*04 appeared as potentially deleterious (higher HIV-DNA, lower CD4 cell count and CD4/CD8), but none of the corresponding P-value was below 0.05, and no effect on early HIV-RNA level was observed.

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Plasma HIV-RNA (HIV replication), cellular HIV-DNA (HIV intracellular reservoir) and peripheral blood CD4 levels during primary infection are robust predictive and independent markers of disease progression but also excellent phenotypic markers for genotype-phenotype correlations studies [20,21,29]. In this study, we focused on HLA-B and HLA-C alleles and haplotypes in order to gain knowledge about the impact of MHC-related genetic variation on HIV disease. Data presented herein refine the level and the timing of the HLA-B or HLA-C-associated effects on HIV-1 infection and point out that different patterns of MHC-related protection exist.

First, we found that HLA-B*57, HLA-B*27, HLA-B*13 and HLA-B*52 alleles have an HLA-C independent effect leading to either lower HIV-RNA and HIV-DNA levels, or CD4/CD8 ratios during seroconversion. It is noteworthy that HLA-B alleles better explained the variability of the ratio CD4/CD8, a marker of HIV replication, than the variability of CD4 or CD8 alone. Among HLA-C alleles, only HLA-C*14 had an HLA-B*-independent effect at early time points, reducing HIV-DNA without affecting HIV-RNA.

We also searched for HLA-B and HLA-C alleles or haplotypes associated with the Extreme phenotype (controllers or LTNP) by comparing Extreme patients to seroconverters. The cohort of seroconvertors is a good reference cohort of HIV-infected patients, although we are aware that some unrecognized controllers and LTNP might belong to the seroconvertors cohort, decreasing the power of the analyses. However, they likely represent less than 1% of the cohort [30].

Protective allelic or haplotypic repertoire is slightly different from what has been observed as protective during seroconversion. Indeed, besides B*27, B*57, B*13 and C*14, we found that the HLA-B*14-C*08 haplotype is a strong marker of the ‘Extreme’ phenotype, although not significantly associated with studied phenotypes at seroconversion. Interestingly, the B*14 and C*08 contribution to the total HIV-1-specific CD8+ T-cell response during primary infection was previously evaluated to only 20%, compared with B*27 and B*57 alleles that contribute to 65 and 66%, respectively [12], supporting our findings showing no strong B*14-C08 association with protection during early infection. HLA-B*13 associated with the ‘Extreme’ phenotype probably for similar reasons than B*27 and B*57 alleles. First, B*13 has been shown to promote strong and rapid anti-HIV cytotoxicity through lymphocytes (CTL) responses [31,32]. In the meantime, epitopes presented by B*13 allele are mainly from Gag (and Nef) and mutations occurring in their sequence are likely associated with fitness cost for the virus [31,32]. Concerning B*52, we failed to find a significant effect on the long-term control, but we may have lacked statistical power.

As HLA-B and HLA-C protective alleles are mostly in linkage disequilibrium, it is essential to distinguish the own effect of HLA-B or HLA-C allele by means of haplotype analysis. We were able to exclude an intrinsic role for C*01, C*02 and C*06 alleles. Concerning the HLA-B*51-C*14 haplotype, even though HLA-B*51 is considered as a protective allele [28,33,34], our data support a very weak effect of B*51 for the long-term protection towards HIV infection. Therefore, the protective effect observed in the B*51-C*14 patients is likely due to C*14 itself or to the B*51-C*14 haplotype.

Taken together, these data allow the distinction of three patterns of protective effects of HLA-B* and HLA-C* alleles or haplotypes. First, The HLA B*57, HLA-B*27, HLA-B*13 and HLA-C*14 alleles which have a strong effect on long-term disease control, also influence at least one of the early infection phenotypes. Second, HLA-B*52 has a strong effect during early time points on HIV-RNA without significant effect on the long-term control of HIV-1. Finally, the HLA-B*14-C*08 haplotype has a strong effect on the long-term protection, without influencing early viral control.

HLA-class I driven protection suggests that the CD8 cell-mediated responses restricted by these alleles should be key in such control. However, our previous results as those of other groups suggest distinct mechanisms for protective HLA alleles or haplotypes, measured as CD8 T-cell responses to the virus antigens [12,15,17,35]. We have also observed (data not shown) that the association between B*14-C*08 and disease control is not mediated by a stronger CD8+-mediated response. The protection linked to this haplotype could then be due to KIR-HLA-C*08 interaction or to other MHC genetic variants in linkage disequilibrium with this haplotype. A recent study presented interesting results about sequence similarity between HLA-C alleles and their susceptibility to be dowregulated by miR-148a [36]. The level of HLA-C receptor cell surface expression being correlated to the HIV viral load and the disease progression, protective HLA-C alleles should belong to the miR-148a escape alleles [37–39]. Unfortunately, the two HLA-C protective alleles found in our study are HLA-C*08 and HLA-C*14, and belong to the escape and the inhibited group, respectively.

Finally, we could not observe a significant reduction of the B*35 allele frequency in the ‘Extreme’ group of patients in multivariate and haplotypic analyses. It is noteworthy that we could observe a significant effect of HLA-B*35 in the univariate analysis, consistently with previous results [35]. However, in our analyses, B*35 allele is deleterious if compared with all other alleles, and not if compared with neutral alleles. Moreover, the four-digits genotyping in our study was not available. Therefore, the deleterious B*3502/03 subtypes could not be distinguished from the B*3501, which displays no effect on the HIV-infection outcome [35,40]. It must be noted that the B*35-C*04 haplotype is not able to give information about the previously described deleterious alleles B*3502 and B*3503 [35,41].

In conclusion, the strength of our study relied on considering several phenotypes measured early after HIV infection and several years after, and also on the consideration of HLA B-C haplotypes that allowed a refined classification of the protective strength of HLA-B and HLA-C alleles towards HIV-1 disease progression. Results observed for B*14-C*08 and B*51-C*14 haplotypes illustrate the complexity of finding HIV modifying genes in the MHC locus but also raise again the possibility that MHC genes in linkage with HLA-protecting alleles that do not influence the peptide repertoire presented to CD8 T cells might be important for the control of HIV disease.

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We thank all the patients for their participation in the PRIMO, ALT and HIC cohorts studies. G.A. performed statistical analyses, wrote the article; J.G. designed and performed genotyping experiments, wrote the article; C.M. performed genotyping experiments; C.G., O.L., A.S. and F.B. contributed to the acquisition of patients data; B.A. wrote article; C.R. performed HIV-RNA and cellular HIV-DNA loads, D.C. designed the statistical analyses, L.M. designed the study and supervised the statistical analyses, wrote the article, I.T. designed the study, supervised genotyping experiments, wrote the article.

The ANRS (Agence Nationale de Recherche sur le SIDA et les Hépatites Virales) PRIMO, ALT and HIC studies are funded by the ANRS. J.G. received funding from ANRS.

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Conflicts of interest

There are no conflicts of interest.

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HIV-1; long-term nonprogression; MHC haplotypes; seroconversion; viral load

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