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Supplementary Information
Identification of HLA Class-I restriction of HIV peptides targeted
Associations between recognition of individual peptides and expression of particular
HLA class I molecules was sought using Fisher’s exact test. For each peptide targeted,
the strongest association between HLA allele expression and peptide recognition was
sought. In order to avoid failure to detect HLA-peptide associations because of >1
response being directed towards a single peptide, once the strongest association had been
identified, further associations with recognition of the same peptide were sought
following exclusion of the allele already identified. Overall there were 278 peptides
targeted by >1 subject. No HLA associations were identified for 150 of these peptides
(86% of which were targeted by <3 subjects), one HLA association for 89 peptides, two
HLA associations for 29 peptides, 3 associations for 8 peptides, 4 associations for one
peptide and 5 associations for one peptide. To correct for multiple comparisons, the
Bonferroni correction was used, so that associations of 0.05<p<0.001 were lost following
this correction for 45 alleles analysed. Comparison of the contribution of HLA-A, -B and
-C alleles to the CTL response was made using unpaired t tests, that is, comparing the
mean number of peptide associations per HLA allele at each locus.
Influence of HLA on Viral Load and CD4 count
To test for the differential contribution that HLA-A, -B and -C alleles made to towards
variation in viral load of the cohort as a whole, we first tested for differences in viral
loads between individuals grouped according to their HLA-A type, using a one way
analysis of variance (ANOVA). This was repeated, grouping individuals in turn by their
HLA-B and HLA-C type. Analysis of individual HLA associations with viral load was
undertaken by comparison of viral loads in subjects expressing the relevant allele with
those in subjects not expressing that allele (unpaired t test). This analysis was undertaken
for the alleles expressed in >5% of the 706 study subjects. Bonferroni correction for 38
multiple tests resulted in loss of significance of associations for which 0.05>p>0.00135.
The same steps were repeated to examine associations between HLA Class I expression
and absolute CD4 count.
Influence of HLA on viral evolution
To identify associations between expression of HLA alleles and the occurrence of Nef
and Gag amino acid sequence polymorphism in the Durban cohort, mutual information
tests of every HLA by every position in the alignment were run; listed in Fig 4 are those
with p<0.001 by a Monte Carlo test; these were confirmed with Fisher’s exact test (p
values shown in Fig 4A). For Nef (Fig 4A) there were 123 sequences. Contingency tests
were performed excluding unknown amino acids from direct sequencing. All 65 different
HLA alleles, irrespective of their frequency, were run against 123 informative positions
for Nef (out of 220 total positions, 74 were invariant and 23 had only one amino acid
difference and were excluded from the test). Shown are uncorrected p values.
To test for differences in the associations of HIV polymorphism in the Perth cohort16, we
considered the proportion of residues over the entire expressed genome at which
polymorphism away from population consensus showed differential rates analysing each
of the 3 different allele groups, HLA-A, -B and -C separately. The use of overall
association within HLA-A or –B or –C helps overcome issues of different powers of
detection for individual alleles as a result of different frequencies of occurrence. Our
method assumes that HIV polymorphisms from the consensus are independent across
residues and proteins.
Logistic regression models were used to relate binary polymorphism away from
population consensus across different HLA alleles, equivalent to testing for unequal
polymorphism rates across all allelic groups. This was done for each amino acid position
in the expressed HIV-1 genome and separately for each set of HLA-A, -B and –C alleles
analysed. These included all alleles that had a carriage frequency of >7%: HLA-A*0101
(29.0%), A*0201 (50.2%), A*0301 (20.8%), A*1101 (18.4%), A*2402 (16.7%), A*2902
(7.8%), HLA-B*0702 (20.8%), B*0801 (20.8%), B*1501 (10.2%), B*1801 (7.8%),
B*2705 (7.8%), B*3501 (9.0%), B*4001 (12.2%), B*4402 (16.7%), B*5101 (9.8%),
B*5701 (8.6%), HLA-Cw*0102 (13.9%), Cw*0303 (9.4%), Cw*0304 (15.9%),
Cw*0401 (20.0%), Cw*0501 (19.2%), Cw*0602 (17.6%), Cw*0701 (29.8%), Cw*0702
(26.1%), Cw*0802 (9.0%), Cw*1502 (8.6%). Likelihood ratio tests were used to
determine the p-values for the hypotheses of no differences between alleles within the
HLA sets. For each of HLA-A, -B and –C, the numbers of p-values <0.001 were assessed
and compared pairwise using McNemar exact tests to accommodate correlations between
the binary outcomes based on polymorphisms at the same residue. HLA-B-associated
polymorphism was significantly more common than HLA-A but not than HLA-C
(p=0.0003 and 0.12, respectively), and HLA-C-associated polymorphism was
significantly more common than HLA-A (p=0.041).