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of June 16, 2017.
Adaptive Interactions between HLA and
HIV-1: Highly Divergent Selection Imposed
by HLA Class I Molecules with Common
Supertype Motifs
Mina John, David Heckerman, Ian James, Lawrence P. Park,
Jonathan M. Carlson, Abha Chopra, Silvana Gaudieri, David
Nolan, David W. Haas, Sharon A. Riddler, Richard
Haubrich and Simon Mallal
Supplementary
Material
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The Journal of Immunology is published twice each month by
The American Association of Immunologists, Inc.,
1451 Rockville Pike, Suite 650, Rockville, MD 20852
Copyright © 2010 by The American Association of
Immunologists, Inc. All rights reserved.
Print ISSN: 0022-1767 Online ISSN: 1550-6606.
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J Immunol 2010; 184:4368-4377; Prepublished online 15
March 2010;
doi: 10.4049/jimmunol.0903745
http://www.jimmunol.org/content/184/8/4368
The Journal of Immunology
Adaptive Interactions between HLA and HIV-1: Highly
Divergent Selection Imposed by HLA Class I Molecules with
Common Supertype Motifs
Mina John,*,† David Heckerman,‡ Ian James,* Lawrence P. Park,* Jonathan M. Carlson,‡
Abha Chopra,* Silvana Gaudieri,*,x David Nolan,*,† David W. Haas,{,|| Sharon A. Riddler,#
Richard Haubrich,** and Simon Mallal*,†
hole-genome association studies have shown that the
most significant genetic determinants of HIV disease
outcome are within the MHC and, more specifically, the
highly variable HLA class I loci (1, 2). This builds upon many
studies showing associations between HLA class I genotypes and
HIV disease progression (3). The HLA class I allele repertoire of an
individual determines which peptide epitopes derived from a pathogen may be presented to Ag-specific CD8 T cells. The bulk of
genetic variation among .2000 known HLA class I subtypes
worldwide is within exons 2 and 3, coding for the peptide-binding
region of the mature HLA protein (4). Polymorphism in this region
provides for the broadest defense against a variety of different
pathogens and variability within a pathogen species. There was
allelic divergence at MHC class I loci during primate evolution, and
the combination of early human colonization history and diverse
W
selection pressures of prevalent microbes in different regions of the
world led to the global diversity of HLA alleles today (5, 6). Although the complex interactions between founder effects, pathogen-driven selection, and population admixtures are continuous,
the timescale of human evolution is such that the HLA allele distribution of modern human populations still reflects, in large part,
the early ancestry of the individuals within them (7).
In the context of this evolutionary underpinning of HLA diversity,
the capacity of a relatively new human pathogen, such as HIV, to
evade diverse HLA-restricted T cell responses is remarkable (3).
HIV mutations that abrogate HLA–peptide binding (8), TCR recognition of infected cells (9), or disrupt intracellular epitope processing (10–12) become positively selected in individuals and then
become apparent as HLA allele-specific viral polymorphisms in
a population (13–18). Therefore, linked covariations in the HIV
*Centre for Clinical Immunology and Biomedical Statistics, Institute for Immunology and Infectious Diseases, Murdoch University; †Department of Clinical Immunology and Immunogenetics, Royal Perth Hospital; xSchool of Anatomy and Human
Biology and Centre for Forensic Science, University of Western Australia, Perth,
Western Australia, Australia; ‡Microsoft Research, Microsoft, Redmond, WA
98052; {Department of Microbiology and Immunology and ||Department of Medicine
Center for Human Genetics Research, Vanderbilt University School of Medicine,
Nashville, TN 37203; #Graduate School of Public Health, University of Pittsburgh,
Pittsburgh, PA 15213; and **Antiviral Research Center, University of California, San
Diego, San Diego, CA 92103
The GenBank (www.ncbi.nlm.nih.gov/Genbank/) accession numbers for AACTG
5142/5128 cohort HIV-1 sequences reported in this paper are GQ371216–GQ371763
(gag), GQ371764–GQ372317 (pol), GQ372318–GQ372824 and GQ398382–
GQ398387 (nef), and GU727870–GU731062 (remaining genes). Patient-specific genetic (HLA) data cannot be posted publicly; however, data may be shared with collaborators for specific research projects subject to approval by the appropriate study teams
and Institutional Review Boards governing AIDS Clinical Trials Group A5128 and the
Western Australian HIV Cohort Study materials and data. Please contact the corresponding author.
Received for publication November 20, 2009. Accepted for publication January 29,
2010.
This work was supported by Grant RO1 AI060460 from the National Institute of
Allergy and Infectious Diseases. The AIDS Clinical Trials Group is supported by Grant
AI-68636, and the Vanderbilt DNA Resources Core is supported by Grant RR024975.
The AIDS Clinical Trials Group Clinical Trials Sites that collected DNA were supported by National Institutes of Health Grants AI64086, AI68636, AI68634, AI069471,
AI27661, AI069439, AI25859, AI069477, AI069513, AI069452, AI27673, AI069419,
AI069474, AI69411, AI69423, AI69494, AI069484, AI069472, AI069501, AI69467,
AI069450, AI32782, AI69465, AI069424, AI38858, AI069447, AI069495, AI069502,
AI069556, AI069432, AI46370, AI069532, AI046376, AI34853, and AI069434. The
project was also supported by the Australian National Health and Medical Research
Council and the Bill and Melinda Gates Foundation.
www.jimmunol.org/cgi/doi/10.4049/jimmunol.0903745
The content of this study is the responsibility of the authors and does not necessarily
represent the official views of National Institute of Allergy and Infectious Diseases or
the National Institutes of Health.
Address correspondence and reprint requests to Dr. Mina John, Department of Clinical Immunology and Immunogenetics, Level 2, North Block, Royal Perth Hospital,
197 Wellington Street, Perth, Western Australia 6000, Australia. E-mail address:
[email protected]
The online version of this article contains supplemental material.
Abbreviations used in this paper: ACTG, AIDS Clinical Trials Group; LD, linkage
disequilibrium; UC, unclassified supertype; WA, Western Australia.
Copyright Ó 2010 by The American Association of Immunologists, Inc. 0022-1767/10/$16.00
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Currently, 1.1 million individuals in the United States are living with HIV-1 infection. Although this is a relatively small proportion of
the global pandemic, the remarkable mix of ancestries in the United States, drawn together over the past two centuries of continuous
population migrations, provides an important and unique perspective on adaptive interactions between HIV-1 and human genetic
diversity. HIV-1 is a rapidly adaptable organism and mutates within or near immune epitopes that are determined by the HLA class I
genotype of the infected host. We characterized HLA-associated polymorphisms across the full HIV-1 proteome in a large, ethnically
diverse national United States cohort of HIV-1–infected individuals. We found a striking divergence in the immunoselection patterns
associated with HLA variants that have very similar or identical peptide-binding specificities but are differentially distributed
among racial/ethnic groups. Although their similarity in peptide binding functionally clusters these HLA variants into supertypes,
their differences at sites within the peptide-binding groove contribute to race-specific selection effects on circulating HIV-1 viruses.
This suggests that the interactions between the HLA/HIV peptide complex and the TCR vary significantly within HLA supertype
groups, which, in turn, influences HIV-1 evolution. The Journal of Immunology, 2010, 184: 4368–4377.
The Journal of Immunology
Materials and Methods
Study populations
The 555 study subjects drawn from 55 participating centers across the United
States were enrolled in AIDS Clinical Trials Group (ACTG) protocol A5142
(26) and provided DNA under protocol A5128 (27). Protocol A5128 facilitated storage and retrieval of DNA samples from A5142/A5128 participants for ACTG genetics studies. Race/ethnic groups were predefined
categories specified by standard ACTG protocol, and individuals were selfclassified into these categories at study enrollment. Selected analyses also
used a comparator population of 245 individuals recruited into the Western
Australian (WA) HIV Cohort Study, a population-based, observational cohort study. In both cohorts, patients provided written informed consent to
these investigations, and studies received approval from their respective
Institutional Review Boards prior to commencement.
HLA genotyping
HLA class I genotypes (at HLA-A, -B, and -C loci) were determined based on
locus-specific PCR amplification of exons 2–3 and were all resolved to
unambiguous four-digit-level resolution using standard DNA sequencebased typing. Ambiguities were resolved following sequencing with allelespecific subtyping primers. Sequence electropherograms were analyzed
using Assign (Conexio Genomics, Applecross, Australia). HLA allele frequencies in the ACTG study population were compared with published data
derived from the U.S. National Marrow Donor Program random sampling of
.1000 anonymous unrelated subjects self-classified into five major ethnic
groups in the United States (7).
HIV-1 sequencing
Standard bulk sequencing, using a nested PCR approach, was used for the
United States cohort HIV-1 sequencing. HIV-1 RNA was isolated from
pretreatment-stored plasma using lysis buffer. After reverse transcription
(Superscript III Reverse Transcriptase; Invitrogen, Carlsbad, CA), two
overlapping 59 and 39 fragments, ∼6 kb in length and spanning the full
HIV-1 genome, were amplified by nested PCR. Bidirectional sequencing
on all fragments was performed using an ABI 3130XL Analyzer (Hitachi,
Singapore), with iterative gap filling using alternative primers. Electropherograms were analyzed and edited using Assign (Conexio Genomics).
Standard pre- and post-PCR guidelines were followed, and a customized
laboratory information-management system tracked single PCRs with individual sample numbers, including well positions, controls, and other
PCR details. Because of the nested PCR approach, contamination was
monitored using PCR negative controls for first- and second-round PCRs.
The large overlapping region between 59 and 39 fragments allowed an
inbuilt test of sequencing precision and contamination, and individual
sequence fragments were analyzed for phylogenetic relatedness routinely.
HIV-1 sequences were aligned against the full genome of reference sequence HXB2 as near full-length genomes or partial-length sequences with
intervening gaps. All insertion/deletion mixtures (predominantly in env,
but detected at low frequencies in all proteins) resulting in stretches of
unresolvable International Union of Pure and Applied Chemistry mixture
codes were removed, causing gaps in sequence. Areas not sequenced because of repeated sequencing or amplification failures also created gaps. A
table showing the number of sequenced bases, number and proportion of
mixtures, and number and size of gaps for gag, pol, and nef per individual
is provided in the supplemental data. For the computation of single-site
HIV–HLA associations as described below, the United States cohort sequences were combined with previously generated WA HIV Cohort data to
generate a total sequence dataset of 800 patient-derived sequences, and
HLA genotypes and associations were adjusted for potential phylogenetic
clustering across the two cohorts. For the construction of maximum-likelihood phylogenetic trees in this analysis (28), genome alignments considered HIV-1 genes (for gag: n = 746, pol: n = 781, nef: n = 616, and env: n =
742) or 1-kb sequence blocks (covering rev: n = 708, tat: n = 720, vif: n =
708, vpr: n = 709, and vpu: n = 715). All gene and sequence blocks included
in the analysis were required to have .300 nucleotides; however, the median level of coverage within all analyzed non-env genes was 98.2–100%
per gene and 79.14% in env.
Computation of single-site HLA–HIV-1 polymorphism
associations
Associations between HLA alleles and HIV polymorphisms were tested
using methods developed to incorporate viral phylogenetic tree structure
and HLA linkage disequilibrium (LD) into correlations and have been used
in published studies (15, 29, 30). First, standard maximum-likelihood
phylogenetic trees were constructed from sequences covering Gag, Pol,
Nef, and Env as separate proteins and 1-kb sequence blocks covering Rev,
Tat, Vif, Vpr, and Vpu. For every viral amino acid and HLA allele combination, a likelihood ratio test was used to evaluate whether a model incorporating viral phylogenetic structure and HLA-mediated selection
pressure explained the observed data better than did a model assuming
neutral evolution according to the tree only. Two generative models for
each residue in viral sequence were created representing the two alternative models; the likelihood of the observations was maximized over the
parameters of both models using an Expectation-Maximization algorithm.
A p value was generated using a likelihood ratio test based on those
likelihoods. For every contingency table for a given amino acid–HLA
allele pair, associations were defined as denoting “attraction”: enrichment
of the specified amino acid in the specified HLA group, “repulsion”: depletion of the amino acid in the non-HLA allele group, “escape”: depletion
of non-amino acid in the HLA group, and “reversion”: enrichment of
amino acid in the non-HLA group. Thus, escape/reversion associations
identified the revertant/nonadapted/wild-type amino acid and repulsion/
attraction associations identified the adapted amino acid for a given HLAassociated amino acid substitution. The numbers of individuals with/
without each HLA allele and with/without each viral polymorphism had to
exceed an actual value of 3 to limit instability due to the use of largesample approximations and the possibility of misclassification. All comparisons were tested using HLA genotypes at two- and four-digit resolution to maximize specificity for effects by four-digit subtypes with
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proteome, which may compensate or precondition primary escape
mutations (19–21), are also HLA allele specific (22). Evidence that
adaptive changes in HIV accumulate in populations as a function of
the frequency of the selecting HLA alleles has been published (23).
The extent to which the HLA signatures from these studies and their
population dynamics can be generalized more broadly to other
populations depends, to a significant extent, on how HLA subtypes
differentially distributed among different racial groups, even within
allelic families, diverge or converge in their interactions with HIV
peptides. There has been limited information about this from
population-based studies because low-resolution HLA genotyping
has been used, because particular molecular splits of broad HLA
types predominate in less admixed cohorts, or because comparisons
across cohorts may be confounded by different viral subtypes. The
worldwide clade B epidemic includes immunogenetically distinct
populations in Asia, the Pacific region, the Caribbean, and particularly Central and South America, where pathogen diversity has
been associated with significant HLA allelic diversification within
lineages, particularly at the HLA-B locus (24). Human migrations
have also led to increasing racial/ethnic diversity in western
countries affected by subtype B HIV, exemplified by the United
States epidemic (25). The extent to which the selection effects of
HLA in distinct ethnic groups leads to population-level divergence
and subsequent clustering of viral genome or epitope diversity has
not been fully elucidated. Therefore, the United States epidemic
presents the unique conditions required to characterize differential
HIV-1 adaptation patterns in diverse ethnic groupings in the context
of one predominant viral subtype.
Conversely, the imprints of selection in HIV should inform an
understanding of diversifying selective forces on HLA peptidebinding specificities and more subtle HLA gene variations affecting
TCR recognition of HLA/peptide complexes. The latter may be
underestimated in more homogenous populations, in which a spread
of related HLA variants is not present. In this study, we used highresolution HLA genotyping, self-identified race/ethnicity information, sequences of all HIV genes, and published and novel
analytical methods to examine convergence and divergence of HLAassociated adaptation patterns in HIV-1. We used a large cohort
drawn from 55 centers throughout the country as a snapshot of
contemporary United States genetic and HIV-1 diversity and the
adaptive interactions between them.
4369
4370
HIV-1 ADAPTATION TO DIVERSE HLA
and viral sequences were permuted, and the standard R2 was compared
with the randomization distribution. Five hundred permutations were used
to estimate p values, thus truncating p values at 1/500.
different peptide binding but retain sensitivity for effects seen only at the
two-digit level because of low-frequency subtypes. A multivariate analysis
was used to identify and eliminate HLA associations driven by positive LD
between HLA alleles (31). For every viral amino acid at each codon, the
HLA allele with the strongest association was added to the list of identified
associations. Then, individuals expressing this allele were removed from
the dataset, and the analysis was repeated. This standard forward-selection
procedure was iterated until no HLA allele yielded an association with
uncorrected p , 0.05. Because there were multiple comparisons made in
the analyses, we used q values, which estimate the false-discovery rate
among identified associations compared with a randomly permuted dataset
(32). Only associations with q # 0.2, indicating a 20% false-discovery
rate, are presented in the paper.
Web resources Source code for the program used to compute HLA–HIV-1
polymorphism associations is available at www.microsoft.com/science.
Results
The study population consisted of 222 white non-Hispanics (40%),
211 black non-Hispanics (38%), 108 Hispanics (regardless of race;
19.5%), 11 Asians/Pacific Islanders (2%), and 3 others (1 Alaskan
Native American, 1 identified as of more than one race; and 1 “other/
unknown”). These proportions were comparable with those reported
as United States HIV prevalence estimates—white (34.6%), black
(46.1%), Hispanic (17.5%), and Asian/Pacific Islander (1.4%)—by
the Centers for Disease Control and Prevention based on the national
HIV/AIDS Reporting System up to 2006 (25). Other epidemiological and clinical data are provided in Supplemental Table I.
Correlations between HLA matching and viral sequence
similarity
Correlations were tested between pairwise viral sequence dissimilarity and
the matching of HLA genotype in individuals carrying those sequences as
a more global analysis of the influence of HLA on viral sequence divergence. Analyses were restricted to cases that had $90% sequence
coverage within the protein window used and to windows for which $80
cases satisfied this restriction. Dissimilarity of HLA genotypes was measured by the number of nonmatching alleles, counting homozygote alleles
as two alleles. Viral sequence dissimilarity was measured by pairwise
differences in amino acid using the binary Manhattan metric, which adjusts
automatically for missing values. Correlations were tested over full-length
Gag, and then localized correlations were tested over sliding intervals of
10 aa across Gag representing an epitope-length window. All analyses
were carried out using SPlus 8.0 for HLA-A, -B, and -C loci combined and
separately. Because the pairwise dissimilarity scores were not independent,
significance was assessed by randomization tests in which HLA genotypes
HLA class I genetic diversity
A
C
50.0%
40.0%
45.0%
35.0%
40.0%
30.0%
35.0%
30.0%
25.0%
25.0%
20.0%
20.0%
15.0%
15.0%
C*1801
C*1601
C*1701
C*1502
C*1505
C*1402
C*1203
C*0802
C*1202
C*0801
C*0704
C*0701
C*0702
C*0501
C*0602
C*0304
C*0401
C*0102
C*0302
0.0%
C*0303
5.0%
0.0%
A*0101
A*0201
A*0202
A*0205
A*0206
A*0301
A*1101
A*2301
A*2402
A*2501
A*2601
A*2902
A*3001
A*3002
A*3101
A*3201
A*3301
A*3303
A*3402
A*3601
A*6601
A*6602
A*6801
A*6802
A*6803
A*7401
A*8001
5.0%
C*0202
10.0%
10.0%
B
30.0%
Black
25.0%
White
Hispanic
20.0%
15.0%
10.0%
5.0%
B*5 8 0 2
B* 8 1 0 1
B* 5 8 0 1
B* 5 7 0 3
B* 5 5 0 1
B* 5 7 0 1
B* 5 3 0 1
B* 5 1 0 1
B* 5 2 0 1
B* 5 0 0 1
B* 4 8 0 1
B* 4 9 0 1
B* 4 4 0 3
B* 4 5 0 1
B* 4 4 0 2
B* 4 2 0 1
B* 4 2 0 2
B* 4 0 0 1
B* 4 0 0 2
B* 3 9 0 5
B* 3 8 0 1
B* 3 9 0 1
B* 3 5 1 2
B* 3 7 0 1
B* 3 5 0 1
B* 3 5 0 3
B* 1 8 0 1
B* 2 7 05
B* 1 5 0 3
B* 1 5 1 0
B* 1 4 0 2
B* 1 5 0 1
B* 0 8 0 1
B* 1 3 0 2
B* 0 7 0 2
0.0%
FIGURE 1. HLA allele carriage rate in groups defined by self-identified race/ethnicity for HLA-A (A), HLA-B (B), and HLA-C (C) loci. Only HLA
alleles with .3% carriage frequency are included (n = 555).
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HLA diversity within the study population varied substantially
according to race/ethnicity (Figs. 1, 2). Of HLA alleles carried by
$3% of individuals within at least one ethnic group, 20 HLA-A and
31 HLA-B alleles were identified. Of these, nine (45%) HLA-A and
six (18.7%) HLA-B alleles were common to all racial/ethnic groups.
The proportion of relatively race-specific genotypes was greater
within the HLA-B locus than for HLA-A. Hence, 1 of 13 (7.7%)
HLA-A and 5 of 16 (31.3%) HLA-B alleles could be considered
relatively specific to the white population. Similarly, 2 of 18 (11.1%)
HLA-A and 6 of 16 (37.5%) HLA-B alleles were specific to Hispanics, whereas the black population provided the largest proportion
The Journal of Immunology
4371
Black
Black
HLA-B
A*7401A03(10.4%)
A*0202A02 (8.1%)
A*3402A03 (6.6%)
A*3601A01 (4.3%)
A*6601A03 (3.8%)
HLA-A
A*3002A01
A*3301A03
A*0201A02
A*3303A03
A*0301A03
A*0101A01
A*3201A01
A*2902A01A24
A*6801A03;A*2301A24
A*3001A01A03
A*6802A02
A*2501A01 (4.5%)
A*1101A03
A*2402A24
A*2601A01
A*3101A03
B*5301B07-C*0401 (23.7%)
B*1503B27-C*0202 (10.9%)
B*5802B58-C*0602 (8.5%)
B*4501B44 (9.5%)
B*5703B58 (9.0%); B*5801B58 (8.5%)
B*1510B27 (7.6%); B*8101B07 (4.3%)
B*4901UC-C*0701
B*0801B08-C*0701
B*3501B07-C*0401
B*5101B07
B*4403B44
B*1801B44
A*0206A02 (9.3%)
A*6803A03 (9.3%)
B*4001B44-C*0304 (10.4%)
B*5701B58-C*0602 (5.4%)
B*3503B07-C*0401 (4.1%) B*1402B27-C*0802
B*3701B44-C*0602 (3.2%)
B*4402B44
B*1501B62 (14.9%)
B*2705B27
Hispanic
White
B*4201B07-C*1701
B*0702B07-C*0702
Hispanic
White
B*1302UC-C*0602 (5.0%)
B*3512B07-C*0401 (4.7%)
B*5201B62-C*1202 (4.6%)
B*4202UC-C*1701 (3.7%)
B*3905B27-C*0702 (11.1%)
B*4002B44 (10.2%)
Hispanic
C*1801 (9.5%)
C*0602
C*0202
C*0704 (7.2%)
White
C*0401
C*0701
C*0702
C*0501
C*0304
C*0802
C*1601
C*0303
C*1203
C*0102
C*1502
C*1701
C*0302
C*0102 (7.4%)
C*1202 (4.6%)
Hispanic
FIGURE 2. Prevalence of HLA alleles in relation to specific race/ethnic groups in the United States cohort. HLA-B/C genotype combinations in which
the relevant HLA-B allele is accompanied by its haplotypic HLA-C allele at a frequency .80% in the cohort are also shown. Supertype groups are in
superscript. UC, unclassified supertype.
of race-specific HLA-A alleles (5 of 17, 29.1%) and HLA-B alleles (8
of 16, 50.0%). Phylogenetic trees based on the peptide-binding regions of all HLA class I alleles were constructed (Supplemental Fig.
1). Several groups of HLA alleles that appeared more closely associated within a lineage included highly race-specific alleles, such as
HLA-Bp5701 and -Bp5703, which differ at only two residues in the
peptide-binding domain (Supplemental Fig. 2), but are distributed
almost exclusively in whites and blacks, respectively (Fig. 2). These
trees reproduced many previously noted relationships, although
strong support for phylogenetic relationships between HLA-A and -B
alleles is limited by high levels of intra-allelic recombination at these
loci through human and primate evolution (33).
The gene frequencies of HLA alleles within the study subpopulations were formally compared with frequencies in the only
contemporary large-scale population-based sampling of United States
ethnic groups available (7) (Fig. 1, Supplemental Table II). After
adjustment for the number of comparisons made over each HLA/
ethnic group combination, there were no significant differences in the
frequency of any HLA-A allele between whites and blacks; however,
there was enrichment of HLA-Bp5703 in the black study cohort
subpopulation (5.07%) compared with blacks in the healthy reference
population (0.4%, p = 0.000067) (7). Among Hispanics, HLA-
Ap6803, -Bp4201, -Cp0305, and -Cp1701 were increased in the HIVinfected cohort compared with background rates (p = 0.004, p =
0.016, p = 0.042, and p = 0.00027, respectively), whereas HLACp0304 was depleted (p = 0.022).
HIV-1 genetic diversity
HIV-1 sequences were identified as clade B in 97% of cases, based on
phylogenetic analysis of all HIV genes. There were six minor clusters
with bootstrap values .70% detected in these analyses, all of which
had less than five individuals within them, with no consistent sharing
of study site, city, or ethnicity to suggest recent transmission clusters.
When United States gag, pol, and nef sequences were compared with
those from geographically distant individuals from WA (n = 245, 87%
subtype B), sequences from Australian individuals self-classified as
white interdigitated with United States whites and all other subpopulations from the United States (Fig. 3, Supplemental Fig. 3).
HIV-1 adaptation to HLA
We carried out a comprehensive analysis of HLA-associated viral
polymorphisms across the full HIV-1 proteome, with adjustment for
potential phylogenetic relatedness between viral sequences and LD
between coinherited HLA alleles. This analysis identified 874
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Black
HLA-C
4372
HIV-1 ADAPTATION TO DIVERSE HLA
FIGURE 3. Example of United States and WA HIV Cohort HIV-1 sequence phylogenetic trees. HIV-1 pol is shown (gag and nef are shown in
Supplemental Fig. 3). Only sequences with .75% coverage are included.
Trees constructed using neighbor-joining method using Kimura-two-parameter with pairwise deletion. Cohort and self-identified race/ethnicity
(black, white, and Hispanic) associated with individual sequences and
main subtype assignments are indicated. Other race/ethnicity categories in
the WA HIV Cohort and ACTG cohort are not labeled. Analysis was
performed using MEGA v3.1.
significant associations of unique amino acid polymorphisms and
HLA-A (238, 27%), HLA-B (438, 50%), or HLA-C (198, 23%)
alleles (q values , 0.2) (Fig. 4A–C, Supplemental Fig. 4 for all full
proteome adaptation maps). In all proteins, HLA-B associations
were the most common, except for Vpu, in which HLA-A dominated. The majority of associations involved substitutions between
the population consensus amino acid and a codominant dimorphism; however, more complex patterns involving minor amino
acids at highly polymorphic positions (Env A319T, entropy 0.88) or
HLA alleles driving to more than one alternative adaptation (e.g.,
HLA-Ap0301 Gag K28R/Q and HLA-Bp0801 Nef K94M/Q/E/N)
or away from more than one susceptible amino acid at the same
position (e.g., HLA-Cp0602 Pol A/N709x) were also apparent. As
noted in previous studies, associations frequently overlapped with
the same or opposing effects (13–18). In the latter case, adaptation
for one HLA allele corresponded to reversion for other HLA alleles
Differential constraints to HLA-driven adaptation in the HIV
proteome
In 95.7% of identified nonadapted and adapted pairs in HLA-driven
polymorphisms, the amino acids were at a minimal single-nucleotide distance from each other, with the remainder having twonucleotide differences. HLA-associated polymorphisms were
dominated by a small group of amino acid pairs with similar
physicochemical properties: positively charged (K/R: 17.8%),
negatively charged (E/D: 14.5%), nonpolar aliphatic (V/I: 5.3%, L/I:
4.6%, L/V: 3.1%), and bulky aromatic residues (Y/F: 3.8%), suggesting that protein function and structure contingent upon these
properties would tend to be preserved. At a protein level, the density
of HLA associations differed between proteins in the following
(descending) order: Nef . Vpr . Rev . Vif . Tat . Gag . Pol .
Vpu . Env and at subprotein level: Nef . Vpr . p17 . Vif .
p2p7p1p6 . Rev . p24 . Gag/Pol TF . Vpu . Integrase .
Protease . Env . Tat . RT (Supplemental Fig. 5). When average
Shannon entropy scores (48) were plotted against HLA association
density per protein, Nef and Env had higher variability (lesser
constraint), but levels of HLA selection were dissimilar (Supplemental Fig. 5). We detected sparse HLA class I–associated selection in Env, despite the high degree of general variability. Gag and
Pol had less HLA selection than Nef in the face of higher constraint.
This may imply that the CD8 T cell responses selecting changes in
Gag and Pol epitopes (against constraint), and to a lesser degree,
Nef epitope (without constraint), are qualitatively stronger. In
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and vice versa. Over the whole proteome, there were 202 statistically significant associations at sites #4 aa flanking experimentally
characterized and published CD8 T cell epitopes with matching
HLA restriction (34) and 165 additional associations within or near
putative epitopes based on the Epipred epitope-prediction algorithm (35). (The CD8 T cell epitope prediction program used is
available at http://atom.research.microsoft.com/bio/epipred.aspx.)
For those associations with corresponding known or high-probability putative epitopes, mutation was extraepitopic in 132 cases
(36%). In all cases matching known T cell escape mutations as
classically described, Epipred indicated a low prediction score for
the adapted epitope sequence relative to the nonadapted one. There
were other patterns of predicted T cell reactivity in relation to HLAdriven changes, including where immunoreactivity was known or
predicted for the adapted epitope. The remaining HLA associations
not within or flanking known epitopes may represent viral covariation associated with a primary HLA-driven adaptation; however, other factors that contribute to this proportion may include the
relative underrepresentation of known epitopes in polymorphic
regions and restricted by less-studied HLA alleles and the false
discovery rate of 20%.
The first described naturally occurring variant p24 epitopes,
GGKKKYKF and DCKTILKAL, shown to abolish cytotoxicity of
variant-expressing cells corresponded to HLA-Bp0801–associated
polymorphisms Gag L31F and K331R, respectively, in this study
(36). Similarly, many well-characterized mutations observed to
evolve and escape CD8 T cell responses experimentally in direct
immunological studies of individuals were reproduced in this
study as HLA–HIV-1 polymorphism associations (8–12, 37–47)
(Supplemental Fig. 4). There was a partial overlap between the
associations detected in this study and the specific associations
reported by other studies with other populations (13–18). However, complete concordance is not expected, given significant
differences in ethnic/HLA distributions, population size, and viral
subtype composition. Formal concordance and stratification
analyses accounting for these differences and involving more
populations are underway.
The Journal of Immunology
contrast, Env is subject to humoral and other selective forces, and
insertion/deletion polymorphisms and other complex variability in
Env may reduce the power to detect HLA class I associations. The
remaining accessory proteins had higher HLA selection than did
Gag and Pol, as expected, but the distribution within this group did
not indicate a simple direct correlation between entropy and HLAassociated selection. Notably, Vpu was the most polymorphic
protein but had the least HLA-associated selection after Env.
Cumulative HLA-driven adaptation per individual
Selection profiles of HLA alleles within supertypes and allelic
lineages
HLA-associated adaptation in HIV was mapped in a manner analogous to resistance maps for antiretroviral drugs such that the unique
immunoselection profile across the whole HIV-1 proteome is shown
for every given HLA allele in the cohort (Fig. 4A–C, Supplemental
Fig. 4). Then the convergence or divergence in immunoselection
patterns associated with different HLA alleles could be visualized.
Because the selection imprints on HIV-1 should partly reflect the
binding specificity of HLA peptide-binding regions, we examined
HLA alleles within defined supertypes (49): more specifically those
related in phylogenetic trees based on exon 2 and 3 sequences
(Supplemental Fig. 1). The degree of admixture in the United States
cohort meant that such alleles were comparable in population frequency, so that divergence in their immunoselection would not be
explained by unequal statistical power. Across all of the main relevant groupings, there was some minor overlap in the epitopes
targeted by grouped alleles; however, overall, there was a striking
lack of overlap in the selection patterns within those epitopes. For
example, HLA-Bp5701, -B5703, -Bp5801, and -Bp5802 are of the
B58 supertype and have marked race/ethnic group specificity (Figs.
1, 2). HLA-Bp5701/3 and -B5801 had clustered adaptations around
well-known epitopes in p24 (TW10 and IW9), Integrase (SW10),
Rev (RY10), and Nef (HW9); however, there were only two singlecodon changes, Gag T242N in TW10 and Pol T840A (or integrase
T125A), which was common to all three alleles (Fig. 4A). HLABp5802, associated with rapid HIV subtype C disease progression,
had only one shared adaptation with other alleles in the HLA-B58
group that are associated with slow HIV-1 disease progression
(HLA-Bp5701–associated Rev T15x) and no shared adaptations
with the most closely related HLA-Bp5801 (50). Within the B7
supertype, HLA-Bp0702 is present in white and black populations;
however, HLA-Bp8101 and -Bp4201 are rare in whites. As with the
B58 group, the selection profiles of these alleles were largely
nonoverlapping (Fig. 4B). Across selected allele pairs within the
B44 supertype (e.g., HLA-Bp4001 and -Bp4002, HLA-Bp4402
and -B4403) that have extremely conserved exon 2 and 3 sequences
(Supplemental Fig. 2) and strong phylogenetic support for close
lineage (33), there was little evident overlap in the selection these
alleles imposed on HIV (Fig. 4C). Differential immunoselection in
the Gag KF11 epitope by HLA-Bp5701–restricted T cells in subtype B HIV-1 and HLA-Bp5703–restricted T cells in subtype
C HIV-1 (51) as well as B7 supertype divergence in relation to
shared epitopes in HIV-1 subtype C (52), was shown to be associated with strongly divergent patterns of TCR recruitment and
qualitatively different T cell responses. The patterns of HIV-1
subtype B adaptations associated with all HLA-A and -B alleles
within all defined HLA supertypes and broad allelic families for all
HLA-C alleles relevant to this population (Supplemental Fig. 4)
suggest that this is a widespread, general phenomenon. As such,
subtle HLA gene variations can make a significant impact on viral
diversity at the population level, through diversification of viral
peptide sampling and perhaps more extensively through differential
TCR usage by the same peptides.
The influence of HLA on viral sequence divergence
We then sought to reconcile the observations that, on the one hand,
race-specific HLA alleles have very divergent viral-adaptation
patterns in terms of single-site associations, whereas on the other
hand, there was no clustering of viral sequences by race/ethnic
group in the phylogenetic trees (Fig. 3). Given the multiple diversifying forces on viral sequences and underlying constraint, we
analyzed the correlations between pairwise viral sequence divergence and HLA divergence (or nonmatching of HLA alleles) in
individuals carrying those sequences. When whole protein lengths
were considered, HLA-A/B/C matching did not correlate significantly with greater viral sequence similarity (e.g., p = 0.06 in
Gag), consistent with the phylogenetic trees. However, when
shorter sliding intervals of 10 aa within viral proteins were considered, the localized correlations within a number of intervals
became significant, with p values based on 500 randomizations of
the data (Fig. 5A). Thus, the influence of HLA diversity on HIV-1
diversity is demonstrably localized within epitope-length windows, and the correlation plots effectively visualized a landscape
of HLA-driven divergent evolution of HIV-1. In contrast to phylogenetic relationships calculated using full-protein sequences and
encompassing all influences on diversity, these profiles represent
the CD8 T cell view of HIV-1, not as a whole replicating virus or
functional proteins but as an assortment of 8-11mer peptides.
Given that our analyses captured primary polymorphisms and
covarying adaptation patterns, this also suggests that most covariation is highly localized and proximal to the primary adaptation site in HIV-1. This is supported by explicit population-based
analyses of amino acid covariation in HIV-1 (22) and is the case in
most experimentally characterized examples of HIV-1 immune
escape and compensatory mutation (8, 19–21).
Having established the areas of strongest HLA-driven epitope
diversity, we split the correlations by race/ethnic group and by HLA
loci (Fig. 5B–D, Supplemental Fig. 6). We chose to focus on the
example of Gag for these detailed analyses, given the importance
of this protein to immunological control and as a common vaccine
Ag (53). In these plots, the only strongly significant HLA-B peak
(2log p . 2) shared between white and black subpopulations
corresponded to clustered HLA-Bp57/5801–associated Gag
T242N/I247 and G248, as described in previous analyses. Otherwise, the HLA–HIV-1 correlation landscape was strikingly different among all racial/ethnic groups. The absence of the T242Nassociated peak in Hispanics may be related, in part, to the
complete absence of the B57/58 group in Amerindians. There
were unique peaks in the Hispanics corresponding to HLA alleles
more common in Hispanics and adaptations detected in phylogeny-corrected analyses (e.g., HLA-Bp4002 Gag 219P in Hispanics
alone) as in other groups (e.g., HLA-Bp0702 Gag in whites alone).
Peaks not corresponding to specific HLA associations could also
be caused by HLA-lineage correlations in this analysis or by
HLA-selection effects not sufficiently strong to be detected with
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In each study subject, we examined all HIV-1 residues in which an
HLA association matched their own HLA class I alleles. The residues at these positions were identified as being adapted, nonadapted,
or missing if within a sequence gap and counted as a proportion of all
HLA-associated sites relevant to the individual. Although the population-level analysis pointed to differences in HLA-association
density across proteins, it is notable that within individuals, the
average proportion of all relevant (individual-specific) HLA-associated sites that were in the adapted state was uniformly high across
Vif (66%), Env (64%), Nef (58%), Vpr (57%), Gag (55%), Pol (54%),
and Tat (40%) and was lower in Rev (28%) and Vpu (27%).
4373
4374
HIV-1 ADAPTATION TO DIVERSE HLA
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FIGURE 4. Maps of unique HLA-associated adaptations (q value , 0.2) in the HIV-1 genome for selected grouped HLA alleles present in the cohort
within the HLA-B58 supertype (A), B7 supertype (B), and B44 supertype (C). The nonadapted (susceptible/revertant) amino acids are displayed above the
The Journal of Immunology
1.5
0.0
0.5
1.0
-log(p)
2.0
2.5
3.0
A
4375
0
100
200
300
400
500
Residue
C
0
100
200
300
400
500
2.0
1.0
-log(p)
2.0
0.0
0.0
0.0
1.0
-log(p)
2.0
1.0
D
0
100
Residue
200
300
Residue
400
500
0
100
200
300
400
500
Residue
FIGURE 5. Plots of significance (2logP) of correlations between nonmatching in HLA genotype and viral sequence dissimilarity over sliding windows
of 10 aa in HIV-1 Gag. A, The p values determined by randomizations are plotted for correlation analyses of HLA-A, -B, and -C loci combined in all United
States cohort individuals. Correlation significance values are plotted for HLA-B locus nonmatching in particular in white (B, n = 222), black (C, n = 209),
and Hispanic (D, n = 107) subpopulations.
the current level of statistical power. In either case, these plots
show the extent to which race-specific HLA alleles could shape
viral epitope diversity, and, in turn, the CD8 T cell responses that
they elicit in distinct ways in different human populations.
Discussion
The global diversity of HLA derives from the unique demographic
histories of human ancestral groups spanning 100,000–200,000 y,
as well as the selection imposed by the diverse microbial environments into which these groups migrated and survived in isolation (5, 6). In this context, the convergence of African,
European, Asian, and Amerindian ancestries into a single United
States population, as sampled in this study, brings together a diversity of HLA alleles across multiple allelic lineages and with
widely differing evolutionary histories. This type of population
sampling, with ascertainment not specifically based on race/ethnicity, provides a more relevant sampling of HLA diversity for
HIV-1 vaccine and therapeutics research, and the major subpopulations studied herein are comparable in size to those in the
largest, national healthy population study of contemporary HLA
genetic diversity in the United States (7). Although the different
HLA distributions in this study cohort of HIV-infected individuals
predominantly reflect ancestry groups, as seen in the background
population, the statistically significant enrichment of HLA-
Bp5703, which is associated with slower HIV-1 disease progression, is notable and could be evidence of HIV-1 infection as
a selective influence on the frequency of particular HLA alleles in
a HIV-prevalent population within a contemporary timeframe.
Given that those individuals with the extremes of slow disease
progression are not likely to be enrolled in this cohort, such effects
may only be underestimated in this study. However, it is not clear
what underpins the differences in other alleles among Hispanics. It
is also possible that all of these differences are caused by some
differential enrollment of certain groups into the study cohort or
the background population study, although the basis for these
effects for these particular alleles is unknown.
By examination of the selection effects of these diverse HLA
alleles on HIV-1 sequences, aspects of ancestral HLA evolution are
apparent. There is a dominance of the HLA-B in selection, as seen in
immunological studies of HIV-1 (50) and adding to accumulating
evidence of the strongest pathogen-driven balancing selection operating at the HLA-B locus (5, 6). Because most serological/broad
allelic families of HLAs and supertypes contain subtypes that traverse human ancestral groups and there is adequate statistical
power to compare these subtypes, in this study, we show that there
is striking divergence across most alleles. It is HIV-1 itself that
reveals the functional differences between HLA alleles in a way
that is independent of, and without specific reference to, their
line in blue text, and adapted amino acids are shown below the line in red text. Blanks indicate where only the nonadapted or the adapted amino acid was
evident from the leaf associations in the analysis. Locations of published CD8 T cell epitopes are shown as boxes at association sites.
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-log(p)
B
4376
ulations within the broader HIV-1 subtype B epidemic in which
many of these HLA alleles are represented.
Acknowledgments
We thank the study teams, study sites, and participants in the U.S. Adult
ACTG A5142 and A5128 protocols and the WA HIV Cohort Study and
colleagues in the Centre for Clinical Immunology and Biomedical Statistics. We also thank Susan Herrmann, Shay Leary, and Anthony Fordham
for assistance with manuscript preparation.
Disclosures
The authors have no financial conflicts of interest.
References
1. Fellay, J, K.V. Shianna, D Ge, S. Colombo, B. Ledergerber, M. Weale, K. Zhang,
C. Gumbs, A. Castagna, A. Cossarizza, et al. 2007. A whole-genome association
study of major determinants for host control of HIV-1. Science 317: 944–947.
2. Limou, S., S. Le Clerc, C. Coulonges, W. Carpentier, C. Dina, O. Delaneau,
T. Labib, L. Taing, R. Sladek, C. Deveau, et al. 2009. Genomewide association
study of an AIDS-nonprogression cohort emphasizes the role played by HLA
genes (ANRS Genomewide Association Study 02). J. Infect. Dis. 199: 419–
426.
3. Goulder, P. J., and D. I. Watkins. 2008. Impact of MHC class I diversity on immune
control of immunodeficiency virus replication. Nat. Rev. Immunol. 8: 619–630.
4. Hughes, A.L., and M. Nei. 1988. Pattern of nucleotide substitution at major
histocompatibility complex class I loci reveals overdominant selection. Nature
335: 167–170.
5. Meyer, D., R. M. Single, S. J. Mack, H. A. Erlich, and G. Thomson. 2006.
Signatures of demographic history and natural selection in the human major
histocompatibility complex Loci. Genetics 173: 2121–2142.
6. Prugnolle, F., A. Manica, M. Charpentier, J. F. Guégan, V. Guernier, and
F. Balloux. 2005. Pathogen-driven selection and worldwide HLA class I diversity. Curr. Biol. 15: 1022–1027.
7. Cao, K., J. Hollenbach, X. Shi, W. Shi, M. Chopek, and M. A. Fernández-Viña.
2001. Analysis of the frequencies of HLA-A, B, and C alleles and haplotypes in
the five major ethnic groups of the United States reveals high levels of diversity
in these loci and contrasting distribution patterns in these populations. Hum.
Immunol. 62: 1009–1030.
8. Kelleher, A. D., C. Long, E. C. Holmes, R. L. Allen, J. Wilson, C. Conlon,
C. Workman, S. Shaunak, K. Olson, P. Goulder, et al. 2001. Clustered mutations
in HIV-1 gag are consistently required for escape from HLA-B27-restricted
cytotoxic T lymphocyte responses. J. Exp. Med. 193: 375–386.
9. Leslie, A. J., K. J. Pfafferott, P. Chetty, R. Draenert, M. M. Addo, M. Feeney,
Y. Tang, E. C. Holmes, T. Allen, J. G. Prado, et al. 2004. HIV evolution: CTL
escape mutation and reversion after transmission. Nat. Med. 10: 282–289.
10. Draenert, R., S. Le Gall, K. J. Pfafferott, A. J. Leslie, P. Chetty, C. Brander,
E. C. Holmes, S. C. Chang, M. E. Feeney, M. M. Addo, et al. 2004. Immune
selection for altered antigen processing leads to cytotoxic T lymphocyte escape
in chronic HIV-1 infection. J. Exp. Med. 199: 905–915.
11. Allen, T. M., M. Altfeld, X. G. Yu, K. M. O’Sullivan, M. Lichterfeld, S. Le Gall,
M. John, B. R. Mothe, P. K. Lee, E. T. Kalife, et al. 2004. Selection, transmission,
and reversion of an antigen-processing cytotoxic T-lymphocyte escape mutation
in human immunodeficiency virus type 1 infection. J. Virol. 78: 7069–7078.
12. Yokomaku, Y., H. Miura, H. Tomiyama, A. Kawana-Tachikawa, M. Takiguchi,
A. Kojima, Y. Nagai, A. Iwamoto, Z. Matsuda, and K. Ariyoshi. 2004. Impaired
processing and presentation of cytotoxic-T-lymphocyte (CTL) epitopes are
major escape mechanisms from CTL immune pressure in human immunodeficiency virus type 1 infection. J. Virol. 78: 1324–1332.
13. Moore, C. B., M. John, I. R. James, F. T. Christiansen, C. S. Witt, and
S. A. Mallal. 2002. Evidence of HIV-1 adaptation to HLA-restricted immune
responses at a population level. Science 296: 1439–1443.
14. Brumme, Z. L., C. J. Brumme, D. Heckerman, B. T. Korber, M. Daniels,
J. M. Carlson, C. Kadie, T. Bhattacharya, C. Chui, J. Szinger, et al. 2007. Evidence of differential HLA class I-mediated viral evolution in functional and
accessory/regulatory genes of HIV-1. PLoS Pathog. 3: e94.
15. Brumme, Z.L, I. Tao, S. Szeto, C.J. Brumme, J.M. Carlson, D. Chan, C. Kadie,
N. Frahm, C. Brander, B.D. Walker, D. Heckerman, and P.R. Harrigan. 2008.
Human leukocyte antigen-specific polymorphisms in HIV-1 Gag and their association with viral load in chronic untreated infection. AIDS 22: 1277–1286.
16. Rousseau, C. M., M. G. Daniels, J. M. Carlson, C. Kadie, H. Crawford,
A. Prendergast, P. Matthews, R. Payne, M. Rolland, D. N. Raugi, et al. 2008.
HLA class I-driven evolution of human immunodeficiency virus type 1 subtype c
proteome: immune escape and viral load. J. Virol. 82: 6434–6446.
17. Wang, Y. E., B. Li, J. M. Carlson, H. Streeck, A. D. Gladden, R. Goodman,
A. Schneidewind, K. A. Power, I. Toth, N. Frahm, et al. 2009. Protective HLA
class I alleles that restrict acute-phase CD8+ T-cell responses are associated with
viral escape mutations located in highly conserved regions of human immunodeficiency virus type 1. J. Virol. 83: 1845–1855.
18. Brumme, Z. L., M. John, J. M. Carlson, C. J. Brumme, D. Chan, M. A. Brockman,
L. C. Swenson, I. Tao, S. Szeto, P. Rosato, et al. 2009. HLA-associated immune
escape pathways in HIV-1 subtype B Gag, Pol and Nef proteins. PLoS One 4: e6687.
Downloaded from http://www.jimmunol.org/ by guest on June 16, 2017
phylogenetic relatedness or supertype. These results suggest that
alleles within a supertype are functionally very different in terms of
the HLA-peptide-TCR interaction, even those apparently very
closely related phylogenetically and within the same two-digit
genotype. The parallel selection effects of HLA-Bp5701 and
-Bp5703, which have been a strong focus in HIV research, seem to
be the exception rather than the rule. This is consistent with the
markedly different effects on HIV-1 disease progression associated
with HLA-B58 (50) and -B35 subtypes (54) and suggests that the
HLA allelic variation across the many other HLA groupings, particularly at HLA-B, have a functional evolutionary basis. Therefore, it is important for clinical applications that HLA alleles of
a supertype or family are not assumed to elicit qualitatively equal
T cell responses, despite promiscuously bound epitopes.
Just as these analyses provide a view of “immunology taught by
viruses,” the counterevolution of HIV-1 provides an approach to
“virology taught by the immune system” (55). The large number of
HLA associations across all HIV-1 proteins reveals the enormous
scope of subtype B HIV-1 adaptability against a wide span of global
HLA diversity. Cumulative per-individual adaptation, as a proportion of sites relevant to the autologous HLA repertoire, is high
across most HIV-1 proteins, although Vpu stands out as an exception
to the rule in this and other respects having high entropy but low
density of HLA associations and dominant HLA-A locus selection
(Supplemental Fig. 5). Finally, the analyses of HLA–viral sequence
dissimilarity correlations show the extent to which overall HLA
repertoire drives viral sequence divergence in a global sense, given
the degree of constraint and the other immune and nonimmune influences on viral epitope diversity at any one site. Ultimately, it is
diversity within the viral peptidome that directly affects applications
in CD8 T cell immunotherapy or vaccines. Within these immunologically relevant sequence windows, diversity is influenced by
HLA to the extent that the landscape of HLA-driven viral diversity
differs among race/ethnic groups (Fig. 5). Such divergence could
impact HIV vaccine efficacy across these groups or the extrapolation of efficacy in one group based on viral sequence data from
another group, if not informed by knowledge of variances in HLA
alleles at high resolution and their divergent imprints on viral sequence. It is important to recognize that this divergence is clear
within epitope windows and driven by definable changes at single
residues (Fig. 4, Supplemental Fig. 4) but is not visible in standard
phylogenetic trees (Fig. 3). Given the functional relevance of these
changes, this could be important in understanding the optimal CD8
T cell responses in certain populations in which strong population
substructures exist (e.g., Asia or Central and South America).
Therefore, the host genetic admixture within the United States
provides the unique opportunity to characterize and measure these
effects and would be enhanced by studies of specifically targeted
populations that are likely to shape circulating viral epitopes in
distinct ways (56).
Although studies of larger and combined HLA–viral sequence
datasets from different countries and regions will benefit from the
general increase in statistical power, this study illustrates the value
of broad population-based sampling, inclusion of immunogenetically distinct groupings, individual-level ancestry information, and
high-resolution HLA genotyping in characterizing the specific
interactions underpinning HLA–HIV coevolution. By mapping
these interactions as proteome-wide putative immunoselection
profiles of individual HLA alleles (Fig. 4, Supplemental Fig. 4), as
well as “hotspots” of HLA-driven epitope divergence (Fig. 5), it is
hoped that this information can be more readily used for the
significant amount of HIV immunology, virology, and vaccine
research involving study subjects within the United States, from
where these genetic data were directly drawn, as well as in pop-
HIV-1 ADAPTATION TO DIVERSE HLA
The Journal of Immunology
39. Goulder, P. J., R. E. Phillips, R. A. Colbert, S. McAdam, G. Ogg, M. A. Nowak,
P. Giangrande, G. Luzzi, B. Morgan, A. Edwards, et al. 1997. Late escape from
an immunodominant cytotoxic T-lymphocyte response associated with progression to AIDS. Nat. Med. 3: 212–217.
40. Feeney, M. E., Y. Tang, K. Pfafferott, K. A. Roosevelt, R. Draenert, A. Trocha,
X. G. Yu, C. Verrill, T. Allen, C. Moore, et al. 2005. HIV-1 viral escape in infancy
followed by emergence of a variant-specific CTL response. J. Immunol. 174: 7524–
7530.
41. Sipsas, N. V., S. A. Kalams, A. Trocha, S. He, W. A. Blattner, B. D. Walker, and
R. P. Johnson. 1997. Identification of type-specific cytotoxic T lymphocyte responses to homologous viral proteins in laboratory workers accidentally infected
with HIV-1. J. Clin. Invest. 99: 752–762.
42. Geels, M. J., M. Cornelissen, H. Schuitemaker, K. Anderson, D. Kwa, J. Maas,
J. T. Dekker, E. Baan, F. Zorgdrager, R. van den Burg, et al. 2003. Identification of
sequential viral escape mutants associated with altered T-cell responses in a human
immunodeficiency virus type 1-infected individual. J. Virol. 77: 12430–12440.
43. Jones, N. A., X. Wei, D. R. Flower, M. Wong, F. Michor, M. S. Saag, B. H. Hahn,
M. A. Nowak, G. M. Shaw, and P. Borrow. 2004. Determinants of human immunodeficiency virus type 1 escape from the primary CD8+ cytotoxic T lymphocyte response. J. Exp. Med. 200: 1243–1256.
44. Furutsuki, T., N. Hosoya, A. Kawana-Tachikawa, M. Tomizawa, T. Odawara,
M. Goto, Y. Kitamura, T. Nakamura, A. D. Kelleher, D. A. Cooper, and
A. Iwamoto. 2004. Frequent transmission of cytotoxic-T-lymphocyte escape
mutants of human immunodeficiency virus type 1 in the highly HLA-A24positive Japanese population. J. Virol. 78: 8437–8445.
45. Allen, T. M., X. G. Yu, E. T. Kalife, L. L. Reyor, M. Lichterfeld, M. John,
M. Cheng, R. L. Allgaier, S. Mui, N. Frahm, et al. 2005. De novo generation of
escape variant-specific CD8+ T-cell responses following cytotoxic T-lymphocyte
escape in chronic human immunodeficiency virus type 1 infection. J. Virol. 79:
12952–12960.
46. Leslie, A., D. Kavanagh, I. Honeyborne, K. Pfafferott, C. Edwards, T. Pillay,
L. Hilton, C. Thobakgale, D. Ramduth, R. Draenert, et al. 2005. Transmission
and accumulation of CTL escape variants drive negative associations between
HIV polymorphisms and HLA. J. Exp. Med. 201: 891–902.
47. Pillay, T., H. T. Zhang, J. W. Drijfhout, N. Robinson, H. Brown, M. Khan,
J. Moodley, M. Adhikari, K. Pfafferott, M. E. Feeney, et al. 2005. Unique acquisition of cytotoxic T-lymphocyte escape mutants in infant human immunodeficiency virus type 1 infection. J. Virol. 79: 12100–12105.
48. Shannon, C. E. 1948. A mathematical theory of communication. Bell Syst. Tech.
J. 27: 623–656.
49. Sidney, J., B. Peters, N. Frahm, C. Brander, and A. Sette. 2008. HLA class I
supertypes: a revised and updated classification. BMC Immunol. 9: 1 10.1186/
1471-2172-9-1. PubMed doi:10.1186/1471-2172-9-1.
50. Kiepiela, P., A. J. Leslie, I. Honeyborne, D. Ramduth, C. Thobakgale, S. Chetty,
P. Rathnavalu, C. Moore, K. J. Pfafferott, L. Hilton, et al. 2004. Dominant influence of HLA-B in mediating the potential co-evolution of HIV and HLA.
Nature 432: 769–775.
51. Yu, X. G., M. Lichterfeld, S. Chetty, K. L. Williams, S. K. Mui, T. Miura,
N. Frahm, M. E. Feeney, Y. Tang, F. Pereyra, et al. 2007. Mutually exclusive
T-cell receptor induction and differential susceptibility to human immunodeficiency virus type 1 mutational escape associated with a two-amino-acid difference between HLA class I subtypes. J. Virol. 81: 1619–1631.
52. Leslie, A., D. A. Price, P. Mkhize, K. Bishop, A. Rathod, C. Day, H. Crawford,
I. Honeyborne, T. E. Asher, G. Luzzi, et al. 2006. Differential selection pressure
exerted on HIV by CTL targeting identical epitopes but restricted by distinct
HLA alleles from the same HLA supertype. J. Immunol. 177: 4699–4708.
53. Kiepiela, P., K. Ngumbela, C. Thobakgale, D. Ramduth, I. Honeyborne,
E. Moodley, S. Reddy, C. de Pierres, Z. Mncube, N. Mkhwanazi, et al. 2007.
CD8+ T-cell responses to different HIV proteins have discordant associations
with viral load. Nat. Med. 13: 46–53.
54. Gao, X., G. W. Nelson, P. Karacki, M. P. Martin, J. Phair, R. Kaslow,
J. J. Goedert, S. Buchbinder, K. Hoots, D. Vlahov, et al. 2001. Effect of a single
amino acid change in MHC class I molecules on the rate of progression to AIDS.
N. Engl. J. Med. 344: 1668–1675.
55. Nolan, D., S. Gaudieri, and S. Mallal. 2006. Host genetics and viral infections:
immunology taught by viruses, virology taught by the immune system. Curr.
Opin. Immunol. 18: 413–421.
56. Avila-Rios, S., C. E. Ormsby, J. M. Carlson, H. Valenzuela-Ponce, J. BlancoHeredia, D. Garrido-Rodriguez, C. Garcia-Morales, D. Heckerman, Z. L. Brumme,
S. Mallal, et al. 2009. Unique features of HLA-mediated HIV evolution in
a Mexican cohort: a comparative study. Retrovirology 6: 72.
Downloaded from http://www.jimmunol.org/ by guest on June 16, 2017
19. Schneidewind, A., M. A. Brockman, R. Yang, R. I. Adam, B. Li, S. Le Gall,
C. R. Rinaldo, S. L. Craggs, R. L. Allgaier, K. A. Power, et al. 2007. Escape
from the dominant HLA-B27-restricted cytotoxic T-lymphocyte response in Gag
is associated with a dramatic reduction in human immunodeficiency virus type 1
replication. J. Virol. 81: 12382–12393.
20. Brockman, M. A., A. Schneidewind, M. Lahaie, A. Schmidt, T. Miura,
I. Desouza, F. Ryvkin, C. A. Derdeyn, S. Allen, E. Hunter, et al. 2007. Escape
and compensation from early HLA-B57-mediated cytotoxic T-lymphocyte
pressure on human immunodeficiency virus type 1 Gag alter capsid interactions
with cyclophilin A. J. Virol. 81: 12608–12618.
21. Crawford, H., J. G. Prado, A. Leslie, S. Hué, I. Honeyborne, S. Reddy, M. van
der Stok, Z. Mncube, C. Brander, C. Rousseau, et al. 2007. Compensatory
mutation partially restores fitness and delays reversion of escape mutation within
the immunodominant HLA-Bp5703-restricted Gag epitope in chronic human
immunodeficiency virus type 1 infection. J. Virol. 81: 8346–8351.
22. Carlson, J. M., Z. L. Brumme, C. M. Rousseau, C. J. Brumme, P. Matthews,
C. Kadie, J. I. Mullins, B. D. Walker, P. R. Harrigan, P. J. Goulder, and
D. Heckerman. 2008. Phylogenetic dependency networks: inferring patterns of
CTL escape and codon covariation in HIV-1 Gag. PLoS Comput. Biol. 4: e1000225.
23. Kawashima, Y., K. Pfafferott, J. Frater, P. Matthews, R. Payne, M. Addo,
H. Gatanaga, M. Fujiwara, A. Hachiya, H. Koizumi, et al. 2009. Adaptation of
HIV-1 to human leukocyte antigen class I. Nature 458: 641–645.
24. Fernández-Viña, M. A., A. M. Lázaro, C. Y. Marcos, C. Nulf, E. Raimondi,
E. J. Haas, P. Stastny, and M. A. Fernández-Viña. 1997. Dissimilar evolution of
B-locus versus A-locus and class II loci of the HLA region in South American
Indian tribes. Tissue Antigens 50: 233–250.
25. Centers for Disease Control and Prevention (CDC). 2008. HIV prevalence estimates–United States, 2006. MMWR Morb. Mortal. Wkly. Rep. 57: 1073–1076.
26. Riddler SA, R. Haubrich, A.G. DiRienzo, L. Peeples, W.G. Powderly, K.L.
Klingman, K.W. Garren, T. George, J.F. Rooney, B. Brizz, et al.; AIDS Clinical
Trials Group Study A5142 Team. 2008. Class-sparing regimens for initial
treatment of HIV-1 infection. N. Engl. J. Med. 358: 2095–2106.
27. Haas, D. W., G. R. Wilkinson, D. R. Kuritzkes, D. D. Richman, J. Nicotera,
L. F. Mahon, C. Sutcliffe, S. Siminski, J. Andersen, K. Coughlin, et al, and Adult
AIDS Clinical Trials Group. 2003. A multi-investigator/institutional DNA bank
for AIDS-related human genetic studies: AACTG Protocol A5128. HIV Clin.
Trials 4: 287–300.
28. Guindon, S., and O. Gascuel. 2003. A simple, fast, and accurate algorithm to
estimate large phylogenies by maximum likelihood. Syst. Biol. 52: 696–704.
29. Bhattacharya, T., M. Daniels, D. Heckerman, B. Foley, N. Frahm, C. Kadie,
J. Carlson, K. Yusim, B. McMahon, B. Gaschen, et al. 2007. Founder effects in
the assessment of HIV polymorphisms and HLA allele associations. Science
315: 1583–1586.
30. Carlson, J. M., C. Kadie, S. Mallal, and D. Heckerman. 2007. Leveraging hierarchical population structure in discrete association studies. PLoS One 2: e591.
31. Cattley, S. K., J. F. Williamson, G. K. Tay, O. P. Martinez, S. Gaudieri, and
R. L. Dawkins. 2000. Further characterization of MHC haplotypes demonstrates
conservation telomeric of HLA-A: update of the 4AOH and 10IHW cell panels.
Eur. J. Immunogenet. 27: 397–426.
32. Storey, J. D., and R. Tibshirani. Statistical significance for genomewide studies.
2003. Proc Natl Acad Sci. USA 100: 9440–9445.
33. McKenzie, L. M., J. Pecon-Slattery, M. Carrington, and S. J. O’Brien. 1999.
Taxonomic hierarchy of HLA class I allele sequences. Genes Immun. 1: 120–129.
34. Korber, B. T., C. Brander, B.F. Haynes, R. Koup, J.P. Moore, B.D. Walker, and
D.I. Watkins. eds. HIV Molecular Immunology 2006/2007. Los Alamos National
Laboratory, Theoretical Biology and Biophysics, Los Alamos, NM. LA-UR 074752.
35. Heckerman, D., C. Kadie, and J. Listgarten. 2006. Leveraging information across
HLA alleles/supertypes improves epitope prediction. J Comput. Biol. 14: 736–746.
36. Phillips, R. E., S. Rowland-Jones, D. F. Nixon, F. M. Gotch, J. P. Edwards,
A. O. Ogunlesi, J. G. Elvin, J. A. Rothbard, C. R. M. Bangham, C. R. Rizza, and
A. J. McMichael. 1991. Human immunodeficiency virus genetic variation that
can escape cytotoxic T cell recognition. Nature 354: 453–459.
37. Price, D. A., P. J. Goulder, P. Klenerman, A. K. Sewell, P. J. Easterbrook,
M. Troop, C. R. Bangham, and R. E. Phillips. 1997. Positive selection of HIV-1
cytotoxic T lymphocyte escape variants during primary infection. Proc. Natl.
Acad. Sci. USA 94: 1890–1895.
38. Milicic, A., C. T. Edwards, S. Hué, J. Fox, H. Brown, T. Pillay, J. W. Drijfhout,
J. N. Weber, E. C. Holmes, S. J. Fidler, et al. 2005. Sexual transmission of single
human immunodeficiency virus type 1 virions encoding highly polymorphic
multisite cytotoxic T-lymphocyte escape variants. J. Virol. 79: 13953–13962.
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