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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Science. Author manuscript; available in PMC Dec 11, 2011.
Published in final edited form as:
PMCID: PMC3235490
NIHMSID: NIHMS331514

The Major Genetic Determinants of HIV-1 Control Affect HLA Class I Peptide Presentation

The International HIV Controllers Study,* Writing team, Florencia Pereyra,1,2, Xiaoming Jia,3, Paul J. McLaren,4,5, Amalio Telenti,6 Paul I.W. de Bakker, co-chair,4,5,7,8, Bruce D. Walker, co-chair,1,9, Analysis team, Xiaoming Jia,3 Paul J. McLaren, project leaders,4,5 Stephan Ripke,4,10 Chanson J. Brumme,1 Sara L. Pulit,4,5 Amalio Telenti,6 Mary Carrington,1,11 Carl M. Kadie,12 Jonathan M. Carlson,13 David Heckerman,13 Paul I.W. de Bakker, chair,4,5,7,8, Study design, Florencia Pereyra,1,2 Paul I.W. de Bakker,4,5,7,8, Robert R. Graham,14 Robert M. Plenge,4,15 Steven G. Deeks,16 Bruce D. Walker, chair,1,9, SNP genotyping, HLA typing, and sample management, Lauren Gianniny,4 Gabriel Crawford,4 Jordan Sullivan,4 Elena Gonzalez,4 Leela Davies,4 Amy Camargo,4 Jamie M. Moore,4 Nicole Beattie,4 Supriya Gupta,4 Andrew Crenshaw,4 Noël P. Burtt,4 Candace Guiducci,4 Namrata Gupta,4 Mary Carrington,1,11 Xiaojiang Gao,11 Ying Qi,11 Yuko Yuki,11 HIV controllers recruitment and sample management, Florencia Pereyra, project leader,1,2 Alicja Piechocka-Trocha,1 Emily Cutrell,1 Rachel Rosenberg,1 Kristin L. Moss,1 Paul Lemay,1 Jessica O’Leary,1 Todd Schaefer,1 Pranshu Verma,1 Ildiko Toth,1 Brian Block,1 Brett Baker,1 Alissa Rothchild,1 Jeffrey Lian,1 Jacqueline Proudfoot,1 Donna Marie L. Alvino,1 Seanna Vine,1 Marylyn M. Addo,1 Todd M. Allen,1 Marcus Altfeld,1 Matthew R. Henn,4 Sylvie Le Gall,1 Hendrik Streeck,1 Bruce D. Walker, chair,1,9, AIDS Clinical Trials Group, David W. Haas,17 Daniel R. Kuritzkes,2 Gregory K. Robbins,18 Robert W. Shafer,19 Roy M. Gulick,20 Cecilia M. Shikuma,21 Richard Haubrich,22 Sharon Riddler,23 Paul E. Sax,2 Eric S. Daar,24 Heather J. Ribaudo,25 HIV controllers referral team, Brian Agan,26 Shanu Agarwal,27 Richard L. Ahern,18 Brady L. Allen,28 Sherly Altidor,29 Eric L. Altschuler,30 Sujata Ambardar,31 Kathryn Anastos,32 Ben Anderson,33 Val Anderson,34 Ushan Andrady,34 Diana Antoniskis,35 David Bangsberg,1,18 Daniel Barbaro,36 William Barrie,37 J. Bartczak,38 Simon Barton,39 Patricia Basden,40 Nesli Basgoz,18 Suzane Bazner,1 Nicholaos C. Bellos,41 Anne M. Benson,40 Judith Berger,42 Nicole F. Bernard,43 Annette M. Bernard,44 Christopher Birch,1 Stanley J. Bodner,45 Robert K. Bolan,46 Emilie T. Boudreaux,47 Meg Bradley,1 James F. Braun,48 Jon E. Brndjar,49 Stephen J. Brown,50 Katherine Brown,51 Sheldon T. Brown,52 Jedidiah Burack,53 Larry M. Bush,54 Virginia Cafaro,55 Omobolaji Campbell,18 John Campbell,56 Robert H. Carlson,57 J. Kevin Carmichael,58 Kathleen K. Casey,59 Chris Cavacuiti,60 Gregory Celestin,61 Steven T. Chambers,62 Nancy Chez,63 Lisa M. Chirch,64 Paul J. Cimoch,65 Daniel Cohen,66 Lillian E. Cohn,67 Brian Conway,68 David A. Cooper,69 Brian Cornelson,60 David T. Cox,70 Michael V. Cristofano,71 George Cuchural, Jr.,72 Julie L. Czartoski,73 Joseph M. Dahman,74 Jennifer S. Daly,75 Benjamin T. Davis,18 Kristine Davis,76 Sheila M. Davod,18 Steven G. Deeks,16 Edwin DeJesus,77 Craig A. Dietz,78 Eleanor Dunham,64 Michael E. Dunn,79 Todd B. Ellerin,80 Joseph J. Eron,81 John J.W. Fangman,82 Claire E. Farel,2 Helen Ferlazzo,83 Sarah Fidler,84 Anita Fleenor-Ford,85 Renee Frankel,86 Kenneth A. Freedberg,18 Neel K. French,87 Jonathan D. Fuchs,88 Jon D. Fuller,89 Jonna Gaberman,90 Joel E. Gallant,91 Rajesh T. Gandhi,18 Efrain Garcia,92 Donald Garmon,93 Joseph C. Gathe, Jr.,94 Cyril R. Gaultier,95 Wondwoosen Gebre,96 Frank D. Gilman,97 Ian Gilson,98 Paul A. Goepfert,99 Michael S. Gottlieb,100 Claudia Goulston,101 Richard K. Groger,102 T. Douglas Gurley,103 Stuart Haber,104 Robin Hardwicke,105 W. David Hardy,24 P. Richard Harrigan,106 Trevor N. Hawkins,107 Sonya Heath,99 Frederick M. Hecht,16 W. Keith Henry,108 Melissa Hladek,109 Robert P. Hoffman,110 James M. Horton,111 Ricky K. Hsu,112 Gregory D. Huhn,113 Peter Hunt,16 Mark J. Hupert,36 Mark L. Illeman,114 Hans Jaeger,115 Robert M. Jellinger,116 Mina John,117 Jennifer A. Johnson,2 Kristin L. Johnson,18 Heather Johnson,36 Kay Johnson,118 Jennifer Joly,64 Wilbert C. Jordan,119 Carol A. Kauffman,120 Homayoon Khanlou,121 Robert K. Killian,122 Arthur Y. Kim,18 David D. Kim,123 Clifford A. Kinder,124 Jeffrey T. Kirchner,125 Laura Kogelman,126 Erna Milunka Kojic,127 P. Todd Korthuis,128 Wayne Kurisu,97 Douglas S. Kwon,1 Melissa LaMar,93 Harry Lampiris,16 Massimiliano Lanzafame,129 Michael M. Lederman,130 David M. Lee,28 Jean M.L. Lee,73 Marah J. Lee,131 Edward T.Y. Lee,132 Janice Lemoine,133 Jay A. Levy,16 Josep M. Llibre,134 Michael A. Liguori,112 Susan J. Little,22 Anne Y. Liu,2 Alvaro J. Lopez,135 Mono R. Loutfy,136 Dawn Loy,137 Debbie Y. Mohammed,30 Alan Man,35 Michael K. Mansour,18 Vincent C. Marconi,138 Martin Markowitz,139 Rui Marques,140 Jeffrey N. Martin,16 Harold L. Martin, Jr.,141 Kenneth Hugh Mayer,66 M. Juliana McElrath,73 Theresa A. McGhee,142 Barbara H. McGovern,126 Katherine McGowan,2 Dawn McIntyre,59 Gavin X. Mcleod,143 Prema Menezes,81 Greg Mesa,144 Craig E. Metroka,29 Dirk Meyer-Olson,145 Andy O. Miller,146 Kate Montgomery,147 Karam C. Mounzer,148 Ellen H. Nagami,1 Iris Nagin,149 Ronald G. Nahass,150 Margret O. Nelson,18 Craig Nielsen,151 David L. Norene,152 David H. O’Connor,153 Bisola O. Ojikutu,18 Jason Okulicz,154 Olakunle O. Oladehin,18 Edward C. Oldfield, III,155 Susan A. Olender,156 Mario Ostrowski,136 William F. Owen, Jr.,157 Eunice Pae,1 Jeffrey Parsonnet,158 Andrew M. Pavlatos,159 Aaron M. Perlmutter,160 Michael N. Pierce,218 Jonathan M. Pincus,161 Leandro Pisani,162 Lawrence Jay Price,163 Laurie Proia,164 Richard C. Prokesch,137 Heather Calderon Pujet,165 Moti Ramgopal,166 Almas Rathod,1 Michael Rausch,167 J. Ravishankar,168 Frank S. Rhame,169 Constance Shamuyarira Richards,170 Douglas D. Richman,22 Gregory K. Robbins,18 Berta Rodes,171 Milagros Rodriguez,162 Richard C. Rose, III,172 Eric S. Rosenberg,18 Daniel Rosenthal,173 Polly E. Ross,174 David S. Rubin,175 Elease Rumbaugh,35 Luis Saenz,162 Michelle R. Salvaggio,176 William C. Sanchez,177 Veeraf M. Sanjana,178 Steven Santiago,162 Wolfgang Schmidt,179 Hanneke Schuitemaker,180 Philip M. Sestak,181 Peter Shalit,182 William Shay,104 Vivian N. Shirvani,183 Vanessa I. Silebi,184 James M. Sizemore, Jr.,185 Paul R. Skolnik,89 Marcia Sokol-Anderson,186 James M. Sosman,153 Paul Stabile,187 Jack T. Stapleton,188 Sheree Starrett,189 Francine Stein,83 Hans-Jurgen Stellbrink,190 F. Lisa Sterman,191 Valerie E. Stone,18 David R. Stone,192 Giuseppe Tambussi,193 Randy A. Taplitz,22 Ellen M. Tedaldi,194 Amalio Telenti,6 William Theisen,2 Richard Torres,195 Lorraine Tosiello,196 Cecile Tremblay,197 Marc A. Tribble,198 Phuong D. Trinh,199 Alice Tsao,1 Peggy Ueda,1 Anthony Vaccaro,200 Emilia Valadas,201 Thanes J. Vanig,202 Isabel Vecino,203 Vilma M. Vega,137 Wenoah Veikley,107 Barbara H. Wade,204 Charles Walworth,65 Chingchai Wanidworanun,205 Douglas J. Ward,206 Daniel A. Warner,207 Robert D. Weber,208 Duncan Webster,209 Steve Weis,203 David A. Wheeler,210 David J. White,211 Ed Wilkins,212 Alan Winston,84 Clifford G. Wlodaver,213 Angelique van’t Wout,180 David P. Wright,214 Otto O. Yang,24 David L. Yurdin,215 Brandon W. Zabukovic,216 Kimon C. Zachary,18 Beth Zeeman,1 and Meng Zhao217

Abstract

Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA–viral peptide interaction as the major factor modulating durable control of HIV infection.

Hiv infection is characterized by acute viremia, often in excess of 5 million viral particles per milliliter of plasma, followed by an average 100-fold or greater decline to a relatively stable plasma virus load set point (1). In the absence of antiretroviral therapy, the level of viremia is associated with the rate of CD4+ T cell decline and progression to AIDS. There is substantial interperson variability in the virus load set point, with most individuals having stable levels exceeding 10,000 RNA copies/ml. Yet a small number of people demonstrate sustained ability to control HIV replication without therapy. Such individuals, referred to as HIV controllers, typically maintain stable CD4+ cell counts, do not develop clinical disease, and are less likely to transmit HIV to others (2).

To determine the genetic basis for this rare phenomenon, we established a multinational consortium (www.hivcontrollers.org) to recruit HIV-1 controllers, who are defined by at least three measurements of plasma virus load (VL) < 2000 RNA copies/ml over at least a 12-month period in the absence of antiviral therapy. We performed a genome-wide association study (GWAS) in the HIV controllers (median VL, CD4 count, and disease duration of 241 copies/ml, 699 cells/mm3, and 10 years, respectively) and treatment-naïve chronically infected individuals with advanced disease (median VL and CD4 count of 61,698 copies/ml and 224 cells/mm3, respectively) enrolled in antiviral treatment studies led by the AIDS Clinical Trials Group. After quality control and imputation on the basis of HapMap Phase 3 (3), we obtained data on 1,384,048 single-nucleotide polymorphisms (SNPs) in 974 controllers (cases) and 2648 progressors (controls) from multiple populations (table S1).

After stratification into European, African American, and Hispanic ethnic groups (fig. S1), we tested each SNP for association using logistic regression, including the major principal components as covariates to correct for population substructure (4). In the largest group, comprising 1712 individuals of European ancestry, we identified 313 SNPs with genome-wide significance, defined by P < 5 × 10−8 due to correction for multiple comparisons (table S2). All SNPs that reached genome-wide significance were located in the major histocompatibility complex (MHC) region on chromosome 6 (Fig. 1A). We obtained similar results for the other two ethnic groups and in a meta-analysis of all participants (fig. S2). We also performed a genome-wide analysis to test the influence of local chromosomal ancestry in the African American sample (4), but we detected no signal outside the MHC (figs. S3 and S4). The impact of the MHC was further underscored when we specifically tested published associations related to HIV disease progression outside the MHC. Only variants in the CCR5-CCR2 locus—namely, CCR5Δ32 deletion polymorphism (5), C927T in CCR5 (6), and Val64→Ile64 in CCR2 (7)—replicate with nominal statistical significance in our study (Fig. 1B and table S3).

Fig. 1
Genome-wide association results in the European sample. (A) Manhattan plot of 1.3 million autosomal SNPs. Only SNPs in the MHC on chromosome 6 reach genome-wide significance, indicated by the horizontal dotted line (P < 5 × 10−8 ...

Closer examination of the significant SNPs within the MHC showed that they are located within a 3-Mb region concentrated around class I human leukocyte antigen (HLA) genes (fig. S5), but extensive linkage disequilibrium (LD) makes precise assignment of causal variants challenging (8). Therefore, we used stepwise regression to define independent markers associated with host control. From the initial set of 313 SNPs that reached genome-wide significance in the European sample, for which the greatest numbers of participants were available, we found only four independent markers of association (Table 1). rs9264942, located 35 kb upstream of HLA-C and a putative variant associated with HLA-C expression levels [odds ratio (OR) = 2.9, P = 2.8 × 10−35, where an OR > 1 indicates a protective effect], and rs2395029, a proxy for HLA-B*57:01 (OR = 5.3, P = 9.7 × 10−26), had been previously reported to be associated with virus load set point after acute infection (9). We also defined rs4418214, a noncoding SNP near MICA (OR = 4.4, P = 1.4 × 10−34), and rs3131018 in PSORS1C3, a gene implicated in psoriasis (OR = 2.1, P = 4.2 × 10−16). These four SNPs explain 19% of the observed variance of host control in the European sample; together with those in CCR5, these SNPs explain 23%, using Nagelkerke’s approximation (Fig. 1C) (10).

Table 1
Association results for the independent SNPs in the MHC identified with stepwise regression in the European and African American samples. The odds ratio and frequency is given for the A1 allele, where OR > 1 indicates a protective effect. Odds ...

In the smaller African American sample, we observed 33 SNPs with genome-wide significance, four of which were identified as independent markers, but all differed from those in the European sample (Table 1). This suggests that shared causal variants are tagged by different SNPs in these two populations or that the mechanism of control differs with ethnicity. Only rs2523608 was previously identified, in a recent study of virus load set point in African Americans (11). Despite no evidence for historical recombination (D’ = 1), this SNP is only weakly correlated (r2 < 0.1) with HLA-B*57:03, the class I allele most strongly associated with durable control of HIV in populations of African ancestry (11-13). In the Hispanic sample, which was much smaller, the most significant SNP was rs2523590, 2 kb upstream of HLA-B, also identified in the African American sample described here.

Given the localization of significant SNPs entirely to the HLA class I region, as well as previous studies showing HLA alleles to affect disease progression (13-20), we next sought to evaluate whether these SNP and HLA associations might be due to specific amino acids within HLA. Because HLA types were available for only a portion of the entire cohort, we developed a method to impute classical HLA alleles and their corresponding amino acid sequences (4) on the basis of haplotype patterns in an independent data set collected by the Type 1 Diabetes Genetics Consortium (T1DGC) (21). This data set contains genotype data for 639 SNPs in the MHC that overlap with genotyped SNPs in our GWAS and classical HLA types for class I and II loci at four-digit resolution in 2767 unrelated individuals of European descent.

We imputed HLA types in the European sample of our study and validated the imputations by comparing to empirical four-digit HLA typing data collected for class I loci in a subset (n = 371) of the HIV controllers. The quality of the imputations was such that the imputed and true frequencies for all HLA alleles in this subset were in near-perfect agreement (Fig. 2A) (r2 = 0.99). Furthermore, the positive predictive value was 95.2% and the sensitivity was 95.2% at two-digit resolution (92.7 and 95.6%, respectively, at four-digit resolution) for HLA alleles with frequency >2% (Fig. 2B). This indicates that the performance of the imputation was generally excellent for common alleles, consistent with previous work (22). We used HLA allele imputations in all participants (even those with HLA types defined by sequencing) for association analyses to avoid systematic bias between cases and controls. Lower imputation quality would only decrease power, not increase the false-positive rate, because cases and controls would be equally affected.

Fig. 2
Imputation quality of classical HLA alleles in the European sample. (A) Concordance between imputed (y-axis) and observed (x-axis) frequencies of classical HLA types in 371 HIV-1 controllers with four-digit HLA types obtained through Sanger sequencing. ...

We tested all HLA alleles for association via logistic regression, adjusting for the same covariates used in SNP analysis (tables S4 and S5). The most significant HLA association is B*57:01 (OR = 5.5, P = 1.4 × 10−26), which explains the proxy association of rs2395029 in HCP5. With the use of stepwise regression modeling in the European sample of controllers and progressors, we were able to implicate B*57:01, B*27:05, B*14/Cw*08:02, B*52, and A*25 as protective alleles and B*35 and Cw*07 as risk alleles. These associations are consistent with earlier studies that highlighted a role for HLA class I loci (13-20), and particularly HLA-B alleles in control of HIV, which indicated that the imputations are robust. Collectively explaining 19% of the variance of host control, these HLA allele associations are consistent with the effects of the four independent SNPs.

Virus-infected cells are recognized by CD8+ T cells after presentation of short viral peptides within the binding groove of HLA class I, and HIV-specific CD8+ T cells are strongly associated with control (23). We thus evaluated whether the SNP associations identified in the GWAS, and the HLA associations derived from imputation, might be due to specific amino acid positions within the HLA molecules, particularly those involved in the interaction between the viral peptide and the HLA class I molecule. Using the official DNA sequences defined for known HLA alleles (24), we encoded all variable amino acid positions within the coding regions of the HLA genes in each of the previously HLA-typed 2767 individuals in the T1DGC reference panel, and we used this data set to impute the amino acids in the cases and controls (4). Among a total of 372 polymorphic amino acid positions in class I and II HLA proteins, 286 are biallelic like a typical nonsynonymous coding SNP. The remaining 86 positions accommodate more than two amino acids; position 97 is the most diverse in HLA-B with six possible amino acids observed in European populations.

After imputing these amino acids in the European sample, we used logistic regression to test all positions for association with host control (fig. S6 and table S6). Notably, position 97 in HLA-B was more significant (omnibus P = 4 × 10−45) than any single SNP in the GWAS, and three amino acid positions (67, 70, and 97), all in HLA-B, showed much stronger associations than any single classical HLA allele, including B*57:01 (Fig. 3A). Moreover, allelic variants at these positions were associated with substantial frequency differences between cases and controls (Fig. 3B). These results indicate that the effect of HLA-B on disease outcome could be mediated, at least in part, by these positions. These three amino acid positions are located in the peptide binding groove, which suggests that conformational differences in peptide presentation at these sites contribute to the protective or susceptible nature of the various HLA-B allotypes. Although both innate and adaptive mechanisms could be at play, the hypothesis that HLA affects peptide presentation and subsequent T cell functionality is supported by experimental data showing substantial functional differences between CTL targeting identical epitopes but restricted by different HLA alleles (25).

Fig. 3
Associations at amino acids in HLA-B in the European sample. (A) Association results for all variable amino acid positions, as calculated by the omnibus test. Colors denote conventional pocket positions. P values for significant classical HLA-B alleles ...

We next performed stepwise regression modeling and identified six residues as independent markers associated with durable control of HIV. These include Arg97, Cys67, Gly62, and Glu63, all in HLA-B; Ser77 in HLA-A; and Met304 in HLA-C, which collectively explain 20% of the observed variance (similar to the variance explained by the seven classical HLA alleles described above). With the exception of Met304 in the transmembrane domain of HLA-C, these residues are all located in the MHC class I peptide binding groove, again suggesting that the binding pocket—and, by inference, the conformational presentation of class I-restricted epitopes—plays a key role in host control.

Having identified these amino acid positions as strong candidates to account for the SNP and HLA association signals in this study, we next investigated their effects on protection or risk, revealing allelic variants at these positions linked to both extremes (Table 2). HLA-B position 97 (omnibus P = 4 × 10−45), located at the base of the C pocket, has important conformational properties for peptide binding (26). Position 97 has six allelic variants: Protective haplotypes B*57:01, B*27:05, and B*14 are uniquely defined by Val97 (3% frequency in controls), Asn97 (4%) and Trp97 (3%), respectively; the other amino acids at this position (Ser, Thr, Arg) segregate on a diverse set of haplotypes. Ser97 (27% frequency) lies on risk haplotypes Cw*07, B*07, and others, where-as Thr97 (11%) lies on protective B*52 (and others). Arg97 is the most common amino acid (51%) and is carried by risk allele B*35, among others. The importance of this amino acid position to host control is underscored by conditional analyses revealing significance when we adjust incrementally for Val97 (omnibus test for position 97, P = 3 × 10−20), Asn97 (P = 2 × 10−9), and Trp97 (P = 7 × 10−5). Thus, at a single position within the peptide binding groove (position 97, C-pocket), discrete amino acids are associated with opposite disease outcomes, even after controlling for B*57 and B*27, alleles associated with host control.

Table 2
Haplotypes defined by the four independent SNPs, classical HLA alleles, and amino acids associated with host control in the European sample. Haplotypes are ordered by the estimated odds ratio, where the most common haplotype was taken as reference (OR ...

We also found similar discordant associations for alleles at positions 67, 63, and 62 (Table 2), all of which line the α1 helix along the peptide binding groove and help shape the B-pocket (Fig. 4). At position 67 (omnibus P = 2 × 10−42), risk haplotypes B*35 and B*07 carry aromatic residues Phe67 and Tyr67, respectively, whereas protective B*57:01, B*27:05, and B*14 alleles carry sulfur-containing residues Met67 or Cys67. Position 62 (P = 5 × 10−27) is biallelic (Arg/Gly) with the Gly62 allele segregating with protective alleles B*57:01 and B*58 (<1% frequency, OR = 1.7, P = 0.2). Adjacent position 63 (P = 9 × 10−16) is also biallelic (Glu/Asn) with Glu63 appearing in complete LD (D’ = 1) with B*57:01, B*27:05, and B*52. In contrast, at this position the risk alleles B*07 (14% frequency, OR = 0.5, P = 1 × 10−7) and B*35 both carry Asn63. Position 70 (omnibus P = 3 × 10−39) accommodates four alleles that are tightly coupled with positions 67 and 97: Ser70 appears exclusively with Met67 (which defines B*57 and B*58), Gln70 with Tyr67, and Lys70 with Asn97 (B*27). Hence, these data create a consistent and parsimonious model that can explain the associations of classical HLA-B alleles by specific amino acids lining the binding groove (and residues tightly coupled to them), which are expected to have an impact on the three-dimensional structure of the peptide-MHC complex.

Fig. 4
Three-dimensional ribbon representation of the HLA-B protein based on Protein Data Bank entry 2bvp (30), highlighting amino acid positions 62, 63, 67, 70, and 97 lining the peptide binding pocket. The peptide backbone of the epitope is also displayed. ...

To further investigate the role of individual amino acid positions in HLA-B, we implemented a permutation procedure to assess how consistent the above observations are with a null model in which there is no relation between amino acids at a particular position and host control (4). The results of this procedure provided evidence that multiple amino acid positions in the peptide binding groove are indeed associated with host control (table S7), including positions 62, 63, 67, 70, and 97, thus providing a structural basis for the effect of HLA-B on host control (Fig. 4).

Within HLA-A position 77, which lies on the α helix contributing to the F-pocket, we identified a weaker but still significant association (omnibus P = 3 × 10−6). Ser77 (6% frequency, OR = 2.0, P = 2 × 10−6) is carried by only two HLA-A alleles (joint r2 = 1): A*25 (2.4% frequency, OR = 2.6, P = 1 × 10−5) and A*32 (3.2%, OR = 1.6, P = 0.02). Given its location and earlier association evidence for the A10 supertype (27), HLA-A could play a role in host control, although the evidence is not as strong as for HLA-B.

The signals within HLA-C are less straight-forward to interpret. Position 304 is a biallelic variant (Val/Met) located in the transmembrane domain (Met304, 28% frequency, OR = 2.3, P = 7 × 10−23). Met304 is in moderate LD (r2 = 0.5) with rs9264942, which is known to be associated with HLA-C expression levels (28). Addition of this SNP to a multivariate model of all six amino acids is marginally significant (P = 0.013) but eliminates the effect of Met304 (P = 0.06). Similarly, addition of rs9264942 to a multivariate model of all seven independent classical HLA alleles is also significant (P = 2 × 10−4) but eliminates the effect of Cw*07 (P = 0.08). These observations make it difficult to determine the extent to which epitope presentation in the HLA-C peptide binding pocket is important for host control. Thus, rs9264942 could be a proxy for not only many protective and risk HLA alleles (predominantly at HLA-B), but also for an independent effect on HLA-C gene expression, differentially affecting the response to HIV (29).

We next evaluated associations for the SNPs in the MHC, classical HLA alleles, and amino acids in a second independent cohort of untreated HIV-infected persons from Switzerland (fig. S7 and tables S8 and S9) (4), in whom virus load set point was measured as a quantitative trait. Allelic variants at positions 67, 70, and 97 were also associated with highly significant differences in virus load set point in this second cohort (Fig. 3C). The effect estimates of all variable amino acids in HLA-B (r2 > 0.9) and, to a lesser degree, those in HLA-C (r2 > 0.8) in that cohort are in excellent agreement (figs. S8 and S9). As before, position 97 in HLA-B is the most significant association (omnibus P = 1 × 10−13). The HLA-A associations (A*25 or Ser77) did not replicate, which reduces the likelihood that HLA-A plays a major role in host control.

In the African American sample (fig. S10), the most significant HLA allele association was observed for two-digit B*57 (OR = 5.1, P = 1.7 × 10−21) and four-digit B*57:03 (OR = 5.1, P = 2.8 × 10−17; tables S10 and S11), consistent with previous studies (11-13). Position 97 in HLA-B (omnibus P = 2 × 10−25) is again the most significant amino acid (table S12). The consistency of these results demonstrates that imputation and association testing at amino acid resolution in multiple ethnicities can resolve disparate SNP associations in the MHC and help with fine-mapping of classical HLA associations.

Altogether, these results link the major genetic impact of host control of HIV-1 to specific amino acids involved in the presentation of viral peptides on infected cells. Moreover, they reconcile previously reported SNP and HLA associations with host control and lack of control to specific amino acid positions within the MHC class I peptide binding groove. Although variation in the entire HLA protein is involved in the differential response to HIV across HLA allotypes, the major genetic effects are condensed to the positions highlighted in this study, indicating a structural basis for the HLA association with disease progression that is probably mediated by the conformation of the peptide within the class I binding groove. The most significant residue, position 97 in the floor of the peptide binding groove of HLA-B, is associated with the extremes of viral load, depending on the expressed amino acid. This residue has been shown to have important conformational properties that affect epitope-contacting residues within the binding groove (26, 30) and has also been implicated in HLA protein folding and cell-surface expression (31).

Although the main focus of this study was on common sequence variation, it remains an open question as to the role of variants outside the MHC and the contribution of epistatic effects and epigenetic regulation. Additional factors also contribute to immune control of HIV, including fitness-altering mutations, immuno-regulatory networks, T cell help, thymic selection, and innate effector mechanisms such as killer cell immunoglobulin-like receptor recognition (23), some of which are influenced by the peptide-HLA class I complex. However, the combination and location of the significant amino acids defined here are most consistent with the genetic associations observed being modulated by HLA class I restricted CD8+ T cells. These results implicate the nature of the HLA-viral peptide interaction as the major genetic factor modulating durable control of HIV infection and provide the basis for future studies of the impact of HLA-peptide conformation on immune cell induction and function.

Supplementary Material

Supplementary data

Acknowledgments

This work was made possible through a generous donation from the Mark and Lisa Schwartz Foundation and a subsequent award from the Collaboration for AIDS Vaccine Discovery of the Bill and Melinda Gates Foundation. This work was also supported in part by the Harvard University Center for AIDS Research (grant P-30-AI060354); University of California San Francisco (UCSF) Center for AIDS Research (grant P-30 AI27763); UCSF Clinical and Translational Science Institute (grant UL1 RR024131); Center for AIDS Research Network of Integrated Clinical Systems (grant R24 AI067039); and NIH grants AI28568 and AI030914 (B.D.W.); AI087145 and K24AI069994 (S.G.D.); AI069513, AI34835, AI069432, AI069423, AI069477, AI069501, AI069474, AI069428, AI69467, AI069415, Al32782, AI27661, AI25859, AI28568, AI30914, AI069495, AI069471, AI069532, AI069452, AI069450, AI069556, AI069484, AI069472, AI34853, AI069465, AI069511, AI38844, AI069424, AI069434, AI46370, AI68634, AI069502, AI069419, AI068636, and RR024975 (AIDS Clinical Trials Group); and AI077505 and MH071205 (D.W.H.). The Swiss HIV Cohort Study is supported by the Swiss National Science Foundation (SNF grants 33CSC0-108787 and 310000-110012). S. Ripke acknowledges support from NIH/National Institute of Mental Health (grant MH085520). This project has been funded in whole or in part with funds from National Cancer Institute/NIH (grant HHSN261200800001E to M. Carrington). The content of this publication does not necessarily reflect the views or policies of the U.S. Department of Health and Human Services, nor does the mention of trade names, commercial products, or organizations imply endorsement by the U.S. government. This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

References and Notes

1. McMichael AJ, Borrow P, Tomaras GD, Goonetilleke N, Haynes BF. Nat. Rev. Immunol. 2010;10:11. [PMC free article] [PubMed]
2. Deeks SG, Walker BD. Immunity. 2007;27:406. [PubMed]
3. The International HapMap 3 Consortium Nature. 2010;467:52. [PMC free article] [PubMed]
4. See supporting online material on Science Online for detailed background on the analyses that we performed.
5. Dean M, et al. Science. 1996;273:1856. [PubMed]
6. Martin MP, et al. Science. 1998;282:1907. [PubMed]
7. Smith MW, et al. Science. 1997;277:959. [PubMed]
8. de Bakker PIW, et al. Nat. Genet. 2006;38:1166. [PMC free article] [PubMed]
9. Fellay J, et al. Science. 2007;317:944. 10.1126/science.1143767. [PMC free article] [PubMed]
10. Nagelkerke NJD. Biometrika. 1991;78:691.
11. Pelak K, et al. J. Infect. Dis. 2010;201:1141. [PMC free article] [PubMed]
12. Pereyra F, et al. J. Infect. Dis. 2008;197:563. [PubMed]
13. Costello C, et al. AIDS. 1999;13:1990. [PubMed]
14. Klein MR, et al. J. Infect. Dis. 1994;169:1244. [PubMed]
15. Kaslow RA, et al. Nat. Med. 1996;2:405. [PubMed]
16. Carrington M, et al. Science. 1999;283:1748. [PubMed]
17. Migueles SA, et al. Proc. Natl. Acad. Sci. U.S.A. 2000;97:2709. [PMC free article] [PubMed]
18. Flores-Villanueva PO, et al. Proc. Natl. Acad. Sci. U.S.A. 2001;98:5140. [PMC free article] [PubMed]
19. Carrington M, O’Brien SJ. Annu. Rev. Med. 2003;54:535. [PubMed]
20. Kiepiela P, et al. Nature. 2004;432:769. [PubMed]
21. Brown WM, et al. Diabetes Obes. Metab. 2009;11(suppl. 1):2. [PMC free article] [PubMed]
22. Leslie S, Donnelly P, McVean G. Am. J. Hum. Genet. 2008;82:48. [PMC free article] [PubMed]
23. Virgin HW, Walker BD. Nature. 2010;464:224. [PubMed]
24. Robinson J, et al. Nucleic Acids Res. 2009;37:D1013. [PMC free article] [PubMed]
25. Leslie A, et al. J. Immunol. 2006;177:4699. [PubMed]
26. Fagerberg T, Cerottini JC, Michielin O. J. Mol. Biol. 2006;356:521. [PubMed]
27. Catano G, et al. PLoS ONE. 2008;3:e3636. [PMC free article] [PubMed]
28. Stranger BE, et al. Nat. Genet. 2007;39:1217. [PMC free article] [PubMed]
29. Thomas R, et al. Nat. Genet. 2009;41:1290. [PMC free article] [PubMed]
30. Stewart-Jones GB, et al. J. Immunol. 2005;175:2459. [PubMed]
31. Blanco-Gelaz MA, et al. Int. Immunol. 2006;18:211. [PubMed]
32. Pettersen EF, et al. J. Comput. Chem. 2004;25:1605. [PubMed]
33. Single-letter abbreviations for the amino acid residues referred to in Table 2 are as follows: Glu E, Phe F, Gly G, Lys K, Met M, Asn N, Gln Q, Arg R, Ser S, Val V, Trp W, Tyr Y.
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