Introduction
The global impact of HIV-1/AIDS is enormous, with 25 million deaths due to HIV-1/AIDS since it was first recognized in 1981 (www.unaids.org/en/knowledge centre/HIV Data/EpiUpdate/EpUpdArchive/2006). Sub-Saharan Africa has only 10% of the world's total population, but 67% of all people living with HIV-1/AIDS (www.unaids.org/en/knowledge centre/HIV Data/EpiUpdate/EpUpdArchive/2006). Unfortunately, this is also one of the world's poorest regions, and many people have no access to quality healthcare (www.unaids.org/en/knowledge centre/HIV Data/EpiUpdate/EpUpdArchive/2006). Although effective prevention programs have been developed, they have not been implemented at a large scale and the best hope of ending this pandemic is an effective vaccine. To achieve this, we need a better understanding of protective immunity against HIV-1.
Not every individual exposed to HIV-1 becomes infected with the virus. This variability in susceptibility to HIV-1 infection and disease has been documented in several cohort studies [1,2] and can be attributed to both host [3] and viral factors. Host factors that are correlated with susceptibility to infection and disease progression include HIV-specific CD8+ and CD4+ T-cell responses [3–7], mucosal immunity [8,9], chemokine receptor polymorphisms [10,11], and human leukocyte antigens (HLA) [1,12–14]. Polymorphisms in genes influencing the immune response to HIV-1 may account for the variation in susceptibility to HIV-1 infection between individuals with similar risk of exposure.
The HLA complex, located on the short arm of chromosome 6, is the most polymorphic system in the human genome. It is fundamental in the immune system's response to infectious agents, as HLA class I and II molecules restrict presentation of antigenic peptides to CD8+ and CD4+ T cells, respectively [15]. HLA class II molecules are expressed on professional antigen presenting cells (APCs) and present exogenously derived epitopes to CD4+ helper T cells, which are important in antibody and cytokine production, help CD8+ mediated response, and are involved in other immune responses [16]. Previous studies have shown that individuals may differ in their immune responses to HIV-1 due to differences in their HLA alleles, ability to present antigen to CD4+ T cells [12,14].
HLA-DQ consists of variable α and β chains that form membrane-anchored heterodimers. The DQ heterodimers form a nine pocket peptide binding cleft that opens at the ends to allow binding of peptides of various lengths [17]. The combinations of DQ α1 (DQA1) and DQ β1 (DQB1) determine the DQ molecule's specificity and diversity for antigen presentation. This specificity is essential in distinguishing self and nonself antigens, making HLA-DQ an important determinant in transplant compatibility. In addition, DQ antigens have been associated with many autoimmune disorders such as celiac disease [18–21], multiple sclerosis (MS) [22–24], type 1 diabetes mellitus [25,26], asthma [27], and systemic lupus erythematosus (SLE) [28–30]. However, the roles of DQ alleles in infectious diseases have been less well studied. Given the significance of HLA class II in CD4+ immunity and its down regulation by HIV-1 Tat protein [31,32], the potential importance of DQ genes in the susceptibility to HIV-1 infection should not be ignored. HLA-DQ has been reported to correlate with susceptibility to HIV-1 infection and disease in several small cohort studies [33–35].
In this study, we investigated the role of DQ genes in the differential susceptibility to HIV-1 infection observed in Pumwani Sex Worker cohort in Nairobi, Kenya. A subgroup of women in this cohort remains seronegative and PCR negative to HIV-1, despite heavy exposure to HIV-1 infection through active sex work [3]. As HIV-1 specific CD4+ T cell response is associated with relative resistance to HIV-1 infection [4,36,37], identifying DQ antigens and elucidating their roles in resistance/susceptibility to HIV-1 infection in the Pumwani cohort may contribute to the development of effective vaccines.
Materials and methods
Study subjects
The Pumwani Sex Worker cohort was established in Nairobi, Kenya in 1985. The present study was conducted among 978 women enrolled between 1985–2005. The overall HIV infection rate in the Pumwani cohort is greater than 70%. Women were classified as HIV-1 resistant if they remained HIV seronegative, PCR negative for at least 3 years while continuing active sex work, and were negative at the time of this study. The women classified as resistant in this study were all enrolled in the cohort before 1999 with an average follow-up time of 9.6 +/− 4.3 years. There were 978 women typed in this study. Some of the individuals were either new enrollees or their follow-up time was not long enough to meet the criteria for resistance. These individuals were excluded for comparisons between HIV-positive (susceptible) and HIV-resistant women (serology and PCR negative for at least 3 years, and negative at the time of study) in the analysis. The number of women typed differed slightly between the DQA1 (956) and DQB1 (978) groups due to a lack of high-quality DNA. This study has been approved by the Ethics Committee of the University of Manitoba and the Ethics and Research Committee of Kenyatta National Hospital, and informed consent was obtained from all women.
DNA preparation
DNA was extracted from whole blood, buffy coat, B-cells and peripheral blood mononuclear cells (PBMC) using QIAmp DNA Mini Kit (QIAgen Inc., Mississauga, Ontario, Canada). The DNA was quantified by standard ultraviolet spectrophotometric analysis.
PCR amplification and sequencing DQ genes
PCR amplification of exon 2 of DQA1 and DQB1 followed the methods of Luo et al.[38]. Five μl of PCR reaction was visualized on a 1.5% agarose gel stained with ethidium bromide to check for amplification of the correctly sized PCR products. The remaining PCR products were then purified using a MultiScreen HTS PCR filter plate (Millipore, Bedford, Massachussetts, USA) and vacuum apparatus. The purified PCR products were sequenced with BigDye cycle sequencing kits (Applied Biosystems, Foster City, California, USA) and analyzed using an ABI3100 Prism Genetic Analyzer.
HLA typing
Genotyping software CodonExpress based on a taxonomy-based sequence analysis [38] was used to genotype HLA-DQA1 and DQB1 genes. Since only exon 2 of DQA1 and DQB1 is involved in forming the peptide binding cleft and is the most polymorphic region of both genes, DQA1 and DQB1 were typed using exon 2 sequences only. When allele differences were in exons other than exon 2, we simplified the allele name expression, for example, DQA1*010101 includes DQA1*010101 or DQA1*010102. Quality control of the typing results was performed using Sequencher (Gene Codes Corporation).
Statistical analysis
Analyses were performed with SPSS 13.0 for Windows statistical analysis software package. Python for Populations (PyPop) 32-0.6.0 software for population genetics [39] was used to estimate HLA class II allele and haplotype frequencies, as well as Hardy–Weinberg equilibrium. The probability of being HLA typed may depend on HIV status and duration of follow up. Since we have not retained blood samples from all women enrolled during the first few years of the project, we carried out a sensitivity analysis that adjusted for this effects, using sampling weights based on the Hurvitz-Thompson theory [40]. In this analysis the probability of being HLA typed was estimated using logistic regression with follow-up and HIV status as covariables and the inverse of this probability for each woman, after standardization, was used as a sample weight. The standard univariate Fisher's exact test (P value, odds ratio, confidence interval 95% [(Hutchon JR. http://www.hutchon.net/ConfidOR.htm. (Accessed 2007)] was utilized to determine the relationship between binary outcomes and explanatory variables. Kaplan–Meier plots with log rank tests were used to examine seroconversion rates. Only women enrolled before 2001 were included in the survival analysis. Multivariate binary logistic regression analyses were performed to determine potential linkage with the HLA alleles that were previously reported to associate with HIV-1 resistance or susceptibility [14].
Results
Major DQA1 and DQB1 allele and haplotype frequencies in the Pumwani cohort
Sixteen DQA1 alleles were identified in the Pumwani cohort. The most common DQA1 alleles in the cohort were DQA1*010201 (31%), DQA1*050101 (22.6%), and DQA1*010101 (16.5%). These three DQA1 alleles account for 70% of all DQA1 alleles in the cohort. The DQA1 allele/genotype frequencies closely matched those expected for Hardy–Weinberg equilibrium with the exception of lower than expected heterozygotes for DQA1*010201 (P = 0.003) and DQA1*0402 (P = 0.048), as well as lower than expected frequencies for genotypes DQA1*010101–010201 (P = 0.045) and DQA1*010201–050101 (P = 0.027). Higher than expected homozygosity for DQA1*010201 was also observed (P = 0.002).
Although 37 DQB1 alleles were identified in the Pumwani cohort, the major DQB1 alleles in the cohort were DQB1*030101 (20.68%), DQB1*0602 (17.20%), DQB1*0201 (16.07%) and DQB1*050101 (16.17%). These four alleles account for 70% of all DQB1 alleles in the Pumwani cohort. The DQB1 allele and genotype frequencies matched those expected for Hardy–Weinberg equilibrium with the exception of lower than expected frequencies for DQB1*030101 heterozygotes (P = 0.032), genotypes DQB1*0201/030101 (P = 0.036) and DQB1*0602/0603 (P = 0.030). Higher than expected frequencies were observed for genotypes DQB1*0201/0402 (P = 0.012) and DQB1*030101 homozygotes (P = 0.003).
For alleles that did not match Hardy–Weinberg expected values, several checks were performed to exclude sources of error. All sequences were retyped, and confirmed with the original typing, thereby ruling out typing errors. We also examined the possibility of allele dropout, or uneven amplification of the two alleles due to quality/quantity of DNA, especially in cases where homozygote frequency was higher than expected. We also ruled this out as a source of error, due to the presence of polymorphisms in the intron regions of the samples. As the typing was found to be accurate, other factors might contribute to the deviations from Hardy–Weinberg equilibrium including nonrandom mating, selection by other diseases, migration, or the Wahlund effect.
The DQA1 and DQB1 heterodimers form a binding pocket that determines the specificity of the DQ molecule in antigen presentation. Therefore, the DQA1-DQB1 haplotype describes the full repertoire of antigen presentations by the DQA1 and DQB1 molecules in combination. Using PyPop 32-0.6.0, we estimated 216 distinct DQA1-DQB1 haplotypes in the Pumwani cohort. The most common DQ haplotypes were DQA1*010201-DQB1*0602 (13.56%), DQA1*010101-DQB1*050101 (13.28%), and DQA1*050101-DQB1*030101 (13.03%). These three major haplotypes accounted for almost 40% of all DQ haplotypes in the Pumwani cohort.
We compared DQA1 and DQB1 allele frequencies of the Pumwani cohort with those of other sub-Saharan African populations and observed very similar frequency distributions (data not shown) [http://www.ncbi.nlm.nih.gov/projects/mhc/ihwg.cgi. (Accessed 2006)].
Distributions of DQA1 and DQB1 alleles and haplotypes in HIV-1 resistant and infected women in the Pumwani cohort
We compared frequencies of DQA1 and DQB1 alleles and DQ haplotypes of HIV-1 resistant women with those of HIV-1 infected women in the cohort. We did not observe significant differences in DQA1 allele frequency distributions between HIV-1 resistant and HIV-1 positive women except that DQA1*0402 was only observed in the HIV-1 positive women (Table 1). Several significant differences in DQB1 allele frequency distributions were observed between HIV-1 resistant women and HIV-1 positive women (Table 1). The frequencies of DQB1*0609, DQB1*0603 and DQB1*050301 were significantly higher in HIV-1 resistant women than in HIV-1 positive women (P = 0.047, P = 0.039 and P = 0.055, respectively), whereas significantly higher frequencies of DQB1*0602 were observed in HIV-1 positive women (P = 0.036) (Table 1). We also observed that the frequencies of several DQA1-DQB1 haplotypes containing these differentially distributed DQB1 alleles were also significantly different between the HIV-1 resistant women and the HIV-1 positive group (Table 2). The frequency of DQA1*010201-DQB1*0603 haplotype was significantly higher in the HIV-1 resistant group (P = 0.044), whereas that of DQA1*010201-DQB1*0602 was significantly higher in the HIV-1 positive group (P = 0.024) (Table 2). Several haplotypes were only observed in the HIV-1 positive group including DQA1*0504-DQB1*0201, DQA1*010201-DQB1*0201, DQA1*0402-DQB1*0402 and DQA1*0402-DQB1*030101 (Table 2). These results are exploratory, and further studies should be carried out to verify these findings.
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Table 1: Distribution of frequencies of major DQA1 and DQB1 alleles in HIV-1 resistant and positive women in the Pumwani cohort.
Table 2: Frequencies of major haplotypes in HIV-1 resistant and positive women in the Pumwani cohort.
DQ haplotypes that were identified in two or fewer than two individuals were designated as rare DQ haplotypes. We compared the distribution of rare DQ haplotypes between HIV-1 resistant group and the HIV-1 positive group and did not observe significant differences in rare haplotype frequencies between the two groups (P = 0.398).
Associations of DQ genotypes with resistance/susceptibility to HIV-1 infection
Women who have DQB1*0603 and DQB1*0609 were more likely to be resistant to HIV-1 infection [P = 0.037, odds ratio (OR) = 3.25, 95% confidence interval (CI) = 1.12–9.47] (Table 3). The presence of DQB1*050301 also appeared to offer protection from HIV-1 infection (P = 0.055, OR = 12.77, 95% CI = 1.44–112) (Table 3). DQB1*0602 (P = 0.048, OR = 0.68, 95% CI = 0.44–1.05) and DQA1*010201–DQB1*0602 haplotype (P = 0.039, OR = 0.64, 95% CI = 0.41–1.03) increased the susceptibility to HIV-1 infection, while DQA1*010201–DQB1*0603 conferred protection from HIV-1 (P = 0.044, OR = 17.33, 95% CI = 1.79–168).
Table 3: DQ genotypes and haplotypes associated with resistance and susceptibility to HIV-1 infection.
Several DQ haplotypes were only found in the HIV-1 positive group (OR = 0.30–0.31, 95% CI = 0.03–3.70), including DQA1*010201-DQB1*0201, DQA1*0504-DQB1*0201, DQA1*0402-DQB1*0402 and DQA1*0402-DQB1*030101. Kaplan–Meier survival analysis showed that women with these haplotypes seroconverted faster when compared with women without them [Fig. 1. (a)–(d)]. We also noted that women with the DQA1*010201-DQB1*0602 haplotype, associated with increased susceptibility to HIV-1, seroconverted faster than women without this haplotype, and women who are homozygous for DQA1*010201-DQB1*0602 fared even worse [Fig. 1. (e)].
Fig. 1: Kaplan–Meier survival plots showing time to seroconversion. (a) Women with DQA1*010201-DQB1*0201 (broken line) seroconverted more rapidly than women without this haplotype (solid line).(b) Women with DQA1*0504-DQB1*0201 (broken line) seroconverted more rapidly than women without this haplotype. (c) Women with DQA1*0402-DQB1*0402 (broken line) seroconverted more rapidly than women without this haplotype.(d) Women with DQA1*0402-DQB1*030101 (broken line) seroconverted more rapidly than women without this haplotype. (e) Women with DQA1*010201-DQB1*0602 seroconverted more rapidly than women without this haplotype (solid line). The effect is additive, with homozygotes (dotted line) for this haplotype seroconverting more rapidly than individuals with just one copy (broken line).
Sensitivity analysis, using weighted analyses analogous to those above yielded associations that were consistent with the results obtained using nonweighted analyses, showed that differences in time of enrollment did not impact on our findings.
DQ haplotypes with differential susceptibility to HIV-1 infection are independent of previously reported associations
To examine whether the DQ haplotype associations identified in this study were due to linkage with previously reported HLA alleles or supertypes, we conducted binary logistic regression analysis. As seen in Table 4, the associations of DQB1*0603, DQB1*0609 and DQA1*010201-DQB1*0603 haplotype with resistance were independent of HLA-DRB1*01 genotype and HLA A2/6802 supertype, which had previously been shown to associate with HIV-1 resistance in this cohort [14]. Also, the association of DQA1*010201-DQB1*0602 haplotype with susceptibility to HIV-1 infection was independent of HLA-A*2301, which was previously shown to associate with increased susceptibility to HIV-1 infection in the Pumwani cohort [14] (Table 4).
Table 4: Binary logistic regression analysis.
Discussion
Many host factors are involved in natural resistance to HIV-1 [1–14,41,42]. Our study shows that HLA-DQ, a key factor in CD4+ T cell activation, is important in the protective immunity to HIV-1 infection in the highly exposed but uninfected female sex workers in the Pumwani cohort, providing further evidence that CD4+ T-helper cells play an integral role in HIV-1 infection and immunity [37,43].
The associations of DQB1*0603 and DQA1*010201-DQB1*0603 haplotype with HIV-1 resistance in the Pumwani cohort are consistent with the previous report that DQB1*0603 allele was associated with protection from HIV-1 infection in Caucasians [35]. The observed associations of DQB1*0602 genotype and the DQB1*010201-0602 haplotype with susceptibility to HIV-1 infection in the Pumwani cohort also agree with observations that DQB1*0602 was associated with increased HIV-1 susceptibility in Caucasians [35,44] and was associated with accelerated seroconversion in a Zambian discordant couple cohort when present on a haplotype with DRB1*1503 [44,45]. This consistency among different ethnic populations, which experience infections from different subtypes of HIV-1, suggests that the influence of ethnicity and viral subtype diversity on the role of DQ in resistance/susceptibility to HIV-1 infection is not as great as previously thought. Thus, understanding the role of DQ in HIV-1 resistance/susceptibility could have broader applications. Furthermore, our observations in the Pumwani cohort would likely be applicable to other populations in sub-Saharan Africa because of the similarity in DQ allele/haplotype frequencies in the region [http://www.ncbi.nlm.nih.gov/projects/mhc/ihwg.cgi. (Accessed 2006)].
HLA alleles have been reported to be associated with over 40 autoimmune, allergic, neoplastic and infectious diseases, but linkage disequilibrium in the HLA region makes it difficult to attribute disease risk to individual genes/alleles. HLA class II alleles have been identified as the major genetic factors influencing many chronic inflammatory diseases [46], indicating that class II antigens play an important role in the immune response. The DQ alleles and haplotypes associated with HIV-1 resistance/susceptibility in the Pumwani cohort have also been associated with other diseases. DQA1*0102-DQB1*0602 haplotype was associated with increased susceptibility to narcolepsy [47] and multiple sclerosis [48], and decreased susceptibility to Type 1 diabetes [49,50]. DQB1*050301 was associated with increased risk of vitiligo [51] and pemphigus vulgaris in a Chinese population [52], but had a protective effect against Type 1 diabetes in Czechs [53]. DQB1*0603 was found to be protective against Type 1 diabetes in Czechs [53] and British Caucasians [54]. Both DQB1*0609 and DQB1*0602 were protective against Type 1 diabetes in Jewish and Israeli Arab populations [55]. As autoimmune diseases are often associated with greater immune activation, these observations suggest a mechanism for DQ immune response similar to foreign pathogens and the lack of self-tolerance observed in autoimmunity. The incidence of autoimmune disorders in the Pumwani cohort is unknown. Future studies should determine whether alleles associated with autoimmunity in other populations have similar associations in the Pumwani cohort.
The DQ molecule's peptide binding groove presents peptides to CD4+ T cells, imparting its role in self-tolerance and adaptive response to pathogens [46]. The αβ pairing at the DQ locus depends on polymorphisms in the allelic regions α44-53 and β85-90 [56–58]. Important amino acid residues have been identified in the HLA-DQ domains [58]. Analysis of the protein products encoded by HLA will help elucidate why certain alleles associate with differential HIV-1 susceptibility. The associations of two DQ haplotypes, DQA1*010201-DQB1*0602 and DQA1*010201-DQB1*0603 with different outcomes of HIV-1 infection could be due to the amino acid differences between DQB1*0602 and DQB1*0603 as the two different DQB1 alleles pair with the same DQA1 alleles. DQB1*0602 and DQB1*0603 have differences at several important residues. At residue 9 on β strand 1, presumed to contact peptide pocket 9 [58], DQB1*0602 encoded Phe, whereas DQB1*0603 encoded Tyr. Although these amino acids are conserved substitutions [59], they may still convey some functional or structural differences in the DQ molecule. At residue 30 on β strand 2, presumed to contact peptide pocket 6 and hydrogen bonds to the peptide [58], DQB1*0602 encoded Val whereas DQB1*0603 encoded His. These amino acids are nonconserved substitutions [59], which may have functional consequences on the antigen presentation of the DQ molecule. DQB1*0602 and DQB1*0609, which are also associated with different outcomes of HIV-1 infection in the Pumwani cohort, also differed at residue 9, with DQB1*0609 encoding Tyr. Residue 57 on the helix is presumed to contact peptide pocket 9 and hydrogen bonds to the peptide [58]. At this residue, DQB1*0602 encoded Asp whereas DQB*0609 encoded Val. Residue 70, which is on the helix and may contact peptide pocket 4 and the T-cell receptor [58], encodes Gly for DQB1*0602 and Arg for DQB1*0609. The differences at residues 57 and 70 are nonconserved substitutions [59] and may have significant effects on peptide binding. It will be important to determine the epitopes of these DQ haplotypes and subsequent immune responses after the differential antigen presentation by DQ haplotypes that are associated with resistance/susceptibility to HIV-1 infection.
Variability in tolerating epitope mutations could influence the ability of DQ molecules in antigen presentation. The tolerance of epitope mutations of alleles associated with differential susceptibility to HIV-1 infection could be different, therefore further molecular modeling of HLA-DQ will allow for a better understanding of the effect of residue changes and the interactions between α and β chains, and a better understanding of the DQ molecule's role in HIV-1 resistance and susceptibility.
Although the exact mechanism for HLA-DQ disease associations is unclear, it is likely that its role involves the initiation of CD4+ T helper cell immune responses, creating a cytokine-rich environment and helping the CD8+ T cell response, which controls viral spread during acute and chronic infection [60,61]. Further studies should determine if the identified alleles vary in their ability to prime the anti-HIV-1-specific immune response, resulting in differential virus transmissibility.
The differential susceptibility to HIV-1 infection involves many factors. We showed that HLA-DQ genes play an important role in the natural resistance to HIV-1 infection in the Pumwani cohort. Although it is likely that this involves direct immune responses to HIV-1, alternative but equally important pathways cannot be ruled out. Identification of DQ alleles/haplotypes associated with resistance/susceptibility to HIV-1 infection will help in the design of HIV vaccines targeting specific HLA-DQ alleles/haplotypes. Ultimately, the full repertoire of HLA antigens possessed by an individual is the key in determining their immune response to infectious pathogens, so we expect that full class I and class II haplotype analysis will provide further insight into other potential associations and linkages between protective/susceptible alleles, as well as the effect of whole haplotypes on anti-HIV-1 immunity.
Acknowledgements
We thank Thomas Bielawny, Sue Ramdahin, John Rutherford, and Leslie Slaney for technical assistance; and Dr Gary Van Domselaar for the data converting program. We thank the dedicated nurses and staff who work with the Pumwani Sex Worker Cohort, Jane Njoki, Jane Kamene, Elizabeth Bwibo, Edith Amatiwa; and the women of the Pumwani cohort for their participation in this study. Dr Francis A. Plummer is a Tier I CIHR Canada Research Chair. Presented at Keystone Symposium: HIV Pathogenesis, Whistler, British Columbia, Canada, 25–30 March 2007, and XVI International AIDS Conference, Toronto, Ontario, Canada, 13–18 August 2006.
Sponsorship: This study was supported by a grant from the Bill and Melinda Gates Foundation and the Canadian Institutes of Health Research (HOP-43135) through the Grand Challenges in Global Health Initiative. This study was also supported through a grant from the NIH (R01 AI56980).
Conflict of interest: None.
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