Simulation of A3G-induced mutations was carried out as previously described (117). Briefly, the whole genome of the HIV-1 isolate HXB2 BRU was obtained from NCBI. This sequence was chosen since it was used in previous works and model systems that studied the role of A3G on HIV CTL escape (117, 119). A3G-induced mutations (G-to-A) on the 5′-most dG in A3G's trinucleotide hotspot motifs considering the sense of the +ve sense strand (GGG, GGA, and GGT) were manually simulated and translated to amino acid sequence. For this analysis, we considered first-round mutations. For multiple back-to-back A3G hotspots, all possible amino acid alteration consequences of A3G-induced mutations were considered.

To generate a comprehensive list of all potential CTL epitopes of HIV, we considered all HIV peptides that are predicted to be efficiently processed by proteasomes and also bind to HLA class I molecules. We identified the portions of the HIV genome encoding known CTL epitopes using the HIV Molecular Immunology Database (http://www.hiv.lanl.gov/content/immunology/tables/ctl_summary.html). We evaluated the predicted MHC binding affinity of wild-type and A3G-mutated variant CTL epitope sequences of Gag, Pol, Env, and Nef restricted to HLA-A02:01, -A03:01, -B57:01, and -B35:01. We utilized epitope prediction algorithms enabling us to investigate the impact of mutations at A3G hotspots within or in flanking regions of the predicted and known epitopes on HLA affinity binding, and epitope processing. Here we used NetMHCpan 4 (http://www.cbs.dtu.dk/services/NetMHCpan/) using artificial networks (ANN) to construct a fine CTL epitope map based on HLA-I binding. The NetMHCpan 4 server predicts binding of peptides to any HLA molecule of a known sequence using ANNs (127, 136, 141). Then the Immune Epitope Databases (IEDB) server (http://tools.iedb.org/processing/) was applied for further prediction based on proteasomal cleavage, TAP transporter efficiency and HLA binding affinity to improve the selection of potential epitopes. To evaluate the impact of A3G alterations on HLA-binding, we only considered predicted epitopes with a high-rank score between 0 and 0.5 percentile as strong HLA binders and 0.5–2.00 percentile as weak HLA binders (136). We then calculated a Delta from the wild-type sequence HLA binding score to evaluate the change in predicted HLA affinity caused by A3G-induced mutation. We set a Delta of 0.1 as a threshold of significant difference for enhanced or diminished HLA binding affinity, based on distribution analysis of the difference values. We used NetChop 3.1 (http://www.cbs.dtu.dk/services/NetChop/) to display the impact of the mutation on proteasomal cleavage. The program C-term 3.0 network is trained with a database consisting of 1260 publicly available HLA class I ligands (using only the C-terminal cleavage site of the ligands). The highest probabilities of cleavage (threshold set at 0.5) were applied based on default program recommendation (142). To predict proteasomal cleavage sites, wild-type and A3G-induced mutated polypeptide of Gag, Pol, Env, and Nef were submitted to NetChop 4.

To investigate the enrichment of A3G hotspots of CTL epitopes Gag, Pol, Env, and Nef restricted to HLA-A2:01, HLA-A3:01, HLA-B57:01, and HLA-B35:01, we counted the number of A3G hotspot motifs (GGA, GGG, and GGT) in CTL epitope-encoding regions vs. non-CTL epitope-encoding sequences. We normalized for gene size by dividing the frequency of hotspots by the total number of analyzed nucleotides in each gene. The normalized hotspot frequencies at the nucleotide levels were calculated for sequences encoding CTL epitopes restricted to each individual HLA, and non-CTL epitope encoding sequences. Then, the ratio of hotspot frequency was determined for inside to outside epitope-encoding sequences, for each A3G hotspot motif and each restricting HLA. As controls to evaluate potential A3G sequence enrichment in sequences encoding CTL epitopes, we conducted a parallel analysis with randomly shuffled HIV genomic sequence using the “Shuffle DNA” function of the Sequence Manipulation Suite (http://www.bioinformatics.org/sms2/shuffle_dna.html) resource. The HIV sequence was randomly shuffled six independent times, and A3G hotspot enrichment analysis was performed for each hotspot motif and restricting HLA, using the same border locations of sequences encoding CTL epitope sequences in the actual HIV genomic sequence. MATLAB was used to describe the distribution of amino acid alterations in epitope-surrounding regions, considering a 32-amino acid boundary around each 8–11 mer epitope, with a limit of either 2 or 4 A3G-induced amino acid changes on either the N- or C-terminal sides of each CTL epitope within this boundary considered to be a clustered pattern. Graphpad Prism 5 was used to generate the schematic graph to display the distribution of A3G-induced mutation within and in the flanking regions of CTL epitopes Gag, Pol, Env, and Nef.

For analysis of amino acid alteration consequences of A3G enrichment in CTL-encoding sequences, affected amino acids (as a result of non-synonymous A3G-mediated mutations), or non-affected amino acids (as a result of silent A3G-mediated mutations) were determined, and frequencies of amino acid consequences inside or outside epitope-encoding sequences were normalized to the total amino acid number of regions of each polypeptide (Gag, Pol, Env, and Nef) present in epitope or non-epitope regions, for each restricting HLA. Then, the ratio of normalized non-synonymous to silent substitutions in CTL epitope encoding region to non-CTL epitope encoding region was calculated for A3G-induced mutations of Gag, Pol, Env, and Nef, restricted to each HLA. The ratio of non-synonymous and silent A3G-mediated amino acid substitutions in the CTL epitope encoding region to non-CTL epitope encoding region was calculated in two ways for each individual A3G hotspot motif. First, the ratio of A3G-induced mutations resulting in non-synonymous residue changes to silent mutations inside CTL epitopes was divided by the same ratio determined for regions of each polypeptide located outside CTL epitopes. Second, the frequency of A3G-mediated non-synonymous mutations inside CTL epitopes was divided by the frequency of A3G-mediated non-synonymous mutations outside CTL epitopes, ignoring silent consequences.

To analyze the distribution pattern of A3G hotspot positioning in the HIV genome, we divided the entire length of the genome into 60 bp stretches and counted the number of A3G hotpots whose mutation would result in non-synonymous amino acid changes or stop codon generation. This pattern was plotted and compared against the known mutational gradient of the HIV genome as previously described (35, 143). In the same manner, to analyze the distribution pattern of A3G hotspots whose mutation is predicted to result in CTL escape, we considered, for each HLA, A3G hotpots that fall in CTL epitope-encoding sequences and whose mutation caused a predicted decrease in HLA binding affinity, as described above. For each HLA, we also generated a map of the positions of CTL epitope-encoding sequences across the entire HIV genome. We derived a map of normalized escape potential which we defined as the number of CTL-escape inducing A3G hotspots in each 60 bp segment, normalized (divided) by the total number of A3G hotspots within the segment. Based on the normalized escape potential and the number of CTL epitopes encoded in each 60 bp segment, for each HLA, we derived a map of escape factor which we defined as the product of the number of CTL epitopes and normalized escape potential. Thus, the escape factor value represents the potential for A3G-induced mutations to generate CTL-escape in any given 60 bp increment of the HIV genome.

We utilized NetMHCpan 4 and IEDB using ANN to construct a CTL epitope map based on proteasomal cleavage, TAP transporter efficiency and HLA-binding affinity. Using entire peptide sequences Gag, Pol, Env, and Nef, we generated a list of all potential HLA-binding peptide epitopes. Although the binding affinity data covers 172 HLA molecules (136), we restricted our analysis to HLA-A02:01, -A03:01, -B57:01, and -B35:01 because HLA-A02:01 and A03:01 are frequent in the population, and HLA-B57:01 and -35:01 correlate with robust and weak HIV-specific CTL responses, respectively. Based on the potential definition of all possible epitopes through the presence of HLA-binding anchor residues in the HIV proteome, the number of all putative potential epitopes is significantly higher than those that actually elicit CTL responses, due to limitation in either epitope processing, presentation to TCR, and the many complex physiological and immune response dynamics that underlie the CTL response that cannot be accounted for by epitope prediction algorithms (144). Nevertheless, we noted that the set of epitopes that we generated on the bases of HLA binding and proteasomal processing predictions included the majority (~70%) of experimentally-verified CTL epitopes listed in the HIV Molecular Immunology Database (Table S1). In addition to the predicted set of epitopes, we also included in our analyses experimentally-known CTL epitopes.

Thus, we generated an epitope list which includes all potential CTL epitopes restricted by HLA-A2:01, -A3:01, -B57:01, and -B35:01. In total, for Gag, Pol, Env, and Nef, we identified 14-12-14-10, 19-33-26-21, 22-14-20-8, and 8-6-9-8 epitopes restricted to HLA-A2:01, -A3:01, -B57:01, and -B35:01, respectively (Table 1 and Table S1). To dissect the role of A3G at each step of CTL epitope generation, we simulated A3G mutations in sequences encoding Gag, Pol, Env, and Nef. A3G mutations (G-to-A) on the 5′-most dG in A3G hotspot trinucleotide motifs (GGG, GGA, and GGT) were simulated and translated to the peptide sequence (Figure 1). We found 16-16-21-13 (Pol), 8-10-11-7 (Gag), 5-2-6-5 (Nef), and 13-8-15-3 (Env) restricted toHLA-A2:01, -A3:01-, -B57:01-, and -B35:01-, respectively, whose encoding sequences contain A3G hotpots. After simulation of A3G-induced mutations at these motifs, we identified 33-33-44-20 possible mutated epitopes restricted to HLA-A2:01, -A3:01, -B57:01, and -B35:01 for Pol, of which 25 alterations include stop codons (Table 1 and Table S1). These numbers were 8-20-22-8 and 12 stop codons for Gag, 11-4-14-15 and 9 stop codons for Nef, 28-20-31-6, and 29 stop codons for Env. These results indicate that A3G-induced mutations can potentially alter CTL epitopes restricted to all four examined HLAs in Gag, Pol, Env, and Nef (Table 1 and Table S1). Considering all predicted wild-type epitopes, Gag, Pol, Env, and Nef contained 21, 40, 26, and 12% of the predicted CTL epitopes respectively. 25, 30, 26, and 19 of all CTL epitopes were restricted to HLA-A2:01, -A3:01, -B57:01, and -B35:01, respectively. After simulating A3G-induced mutations, 22, 42, 25, and 11% of all mutated epitopes came from Gag, Pol, Env, and Nef and 26, 23, 33, 18% of all epitopes were restricted to HLA-A2:01, -A3:01, -B57:01, and -B35:01, respectively. This initial comparison between the distribution of wild-type vs. A3G-mutated CTL epitopes suggested a bias for A3G-mediated mutations in HLA-B57-restricted epitopes, consistent with previous suggestions for a role of A3G in mediating decreased CTL recognition for peptides restricted to protective HLAs such as B57 (117, 118).

To examine the specific impacts of A3G-induced mutations on HLA-binding, we considered predicted epitopes with a high-rank NetMHCpan 4 score between 0 and 0.5 percentile as strong HLA binders and 0.5–2.0 percentile as weak HLA binders, according to default parameters of the prediction algorithm (136) (Figure 1, Table S1). However, we noted that 30% of experimentally-verified epitopes exhibit out of range and low HLA binding affinity scores; hence, their aforementioned absence in the total predicted pool of CTL epitopes (Table S1). To evaluate the change in predicted HLA affinity that occurred as a result of each A3G-induced mutation, we calculated a Delta value from the wild-type sequence HLA binding score. We set 0.1 as a threshold of difference for increased or reduced HLA binding affinity because below this value poor correlation was observed between the predicted HLA affinity rank and absolute nM affinities.

Although A3G-induced stop codons would not lead to infectious virus production, viral genomes containing stop codons can produce immunogenic truncated peptides which contain CTL epitopes (120). Thus, we considered all A3G-mutations, including stop codon generators (Figure 2A, top panel), or excluding stop codons (Figure 2A, bottom panel). Considering all A3G-induced mutations, 25, 46, 32, and 14% of HLA-A2:01-, -A3:01-, -B57:01-, and -B35:01-restricted epitopes exhibited increased HLA-binding affinity after A3G simulation mutation. Conversely, 75, 54, 68, and 86% of HLA-A2:01-, -A3:01-, -B57:01-, and -B35:01-restricted epitopes exhibited decreased HLA-binding affinities as a result of A3G-induced mutations (Figure 2A, top panel). Excluding A3G-mediated stop codons, 32, 51, 62, and 24% of HLA-A2:01-, -A3:01-, -B57:01-, and -B35:01-restricted epitopes increased HLA-binding affinity after A3G simulation mutation, whilst 68, 49, 38, and 76% of A3G-induced mutations HLA-A2:01-, -A3:01-, -B57:01-, and -B35:01-restricted epitopes led to decreased HLA-binding affinities (Figure 2A, bottom panel).

Next, we sought to break down the effect of A3G-induced mutations at epitope-HLA anchor vs. non-anchor residues. A3G-induced mutations in non-anchor residues were predicted to lead to enhanced, diminished, or abolished HLA binding affinity for epitopes restricted to all 4 HLAs, with diminished/abolished HLA binding being the most common predicted outcome for HLA-A2:01-, -A3:01-, - and -B35:01-restricted epitopes (74, 53, 80% of all A3G mutations, respectively); for HLA-B57:01 however, that outcome was nearly equal to potential for enhanced HLA affinity (35%) (Figure 2B). Escape mutations that reduce class I HLA binding commonly occur at the N-terminal (amino acid position 2 in the peptide epitope) and/or the C terminal (e.g., amino acid position 9 in a 9mer peptide epitope) anchor residues in most epitopes (84, 96, 145). Thus, we then considered A3G-induced mutations that target the anchor residues. A3G-induced mutations in anchor residues were predicted to lead to enhanced, diminished or abolished HLA binding affinity for epitopes restricted to all 4 HLAs, with diminished/abolished HLA binding being the most common predicted outcome for -B57:01-, - and -B35:01-restricted epitopes (33, 7% of all A3G mutations, respectively). In contrast, for HLA-A2:01- and -A3:01, A3G-induced mutations at anchor residues mediated diminished/abolished HLA binding affinity with the same or much lower levels than enhanced HLA binding affinities (2 and 1%, respectively) (Figure 2C).

Considering all epitopes that were within the aforementioned HLA-binding threshold range, 2 and 23% of A3G-induced amino acid changes targeted the N and C-terminal anchor positions, respectively. Twenty-three percent is approximately two-fold higher than expected by random chance (84, 146): if A3G-induced mutations were equally distributed amongst all residues in a pool of 8–11 mer peptides, then each residue ought to have a ~10% probability of being targeted. In total, A3G can potentially generate a stop codon in 30% of all epitopes that contain A3G hotspots. Of these, 43% led to a stop codon at the most C-terminal position, of which the overwhelming majority (92%) were restricted to HLA-B57:01, since tryptophan (the TGG codon) is the C-terminal anchor for HLA-B57:01 (Figure 2A, compare top and bottom panels). This codon, which has a high likelihood of containing an A3G hotspot motif dependent on the next downstream nucleotide, is the most susceptible codon for generating a stop due to G-to-A mutation (37, 38). Also, we observed 30% of all potential A3G-induced mutations in CTL epitopes were located at residue positions 3, 5, and 7 which are key for TCR recognition (147), while 40% of A3G-mediated substitutions targeted residue positions 1, 4, 6, 8, 9, and 10. These results indicate that the N- and C-terminal anchor residues are under- and over-targeted by A3G for mutation, whilst the middle positions are apparently equally targeted. Furthermore, the increased A3G targeting of the most C-terminal anchor residue reflects its overwhelming propensity for stop codon generation in HLA-B57:01-restricted epitopes (Figure 2A). Based on these analyses, we conclude that A3G-induced mutations can increase or decrease HLA-binding affinities of potential CTL epitopes; however, the major outcome considering all mutations (non-synonymous amino acid changes and stop codons) at all residues (anchor and non-anchor) was decreased HLA-binding affinity. These results are consistent with previous observations that the CTL-epitope-encoding sequences of HIV have evolved to channel A3G-induced mutations to mediate CTL escape. On the other hand, we also observed the generation of 18 neo-epitopes based on enhanced HLA-binding affinity.

We utilized NetChop 3.1 to examine the impact of A3G-induced mutations on the proteasomal processing of HIV proteins, which is the step before peptide epitope generation for HLA binding. We submitted the entire sequences of Gag, Pol, Env, and Nef to NetChop, either in wild-type format, or including all possible A3G-induced mutations. On the entire peptide sequence, we overlaid the map of HLA-A2:01, -A3:01-, -B57:01, and -B35:01-restricted CTL epitopes. Each residue within wild-type or A3G-mutated Gag, Pol, Env, and Nef proteins was then assigned a cleavage prediction score (default threshold of 0.5 is considered a cleavage site) (142), and scores at each residue position were compared between wild-type and A3G-mutated proteins (Supplementary File 1: excel table). For this analysis, we considered two categories of A3G-induced mutations: those that fell within individual CTL epitopes, or those that fell outside but within six amino acids upstream or downstream of the N-or C- terminal residues of the epitope (148). A3G-induced mutations that generated new/enhanced cleavage sites within a CTL epitope, or abolished/decreased cleavage within the six amino acids adjacent to the epitope would likely lead to diminished proteasomal processing of the epitope. Conversely, A3G mutations that enhanced cleavage in the adjacent region of an epitope or abolished/decreased cleavage within the epitope itself would likely lead to enhanced proteasomal processing of the epitope.

In this manner, we quantified the impact of A3G-induced mutations on CTL epitope proteasomal cleavage (Supplementary File 1, Table 2). Considering all A3G-mediated mutations, for epitopes restricted to HLA-A2:01, -A3:01-, -B57:01, and -B35:01, respectively, 42, 53, 43, 45% of all A3G-induced mutation events resulted in decreased predicted proteasomal processing, whilst 58, 47, 57, and 55% of mutations resulted in generation of sites predicted to enhance proteasomal processing. If epitopes were categorized by protein of origin rather than restricting HLA, for Gag, Pol, Env, and Nef, respectively, 54, 43, 41, and 53% of all A3G-induced mutations resulted in decreased predicted proteasomal processing, whilst 46, 57, 59, and 47% resulted in enhanced predicted processing. Excluding A3G-mediated stop codon generation, for epitopes restricted to HLA-A2:01, -A3:01-, -B57:01, and -B35:01, respectively, 43, 56, 53, and 43% of all A3G-induced mutations resulted in decreased predicted proteasomal processing, whilst, 57, 44, 43, and 57% resulted in enhanced proteasomal processing.

In total, including A3G-mediated stop codon generation 54% of all A3G-induced mutations that could potentially impact proteasomal cleavage were predicted to lead to enhanced CTL epitope production, whilst 46% could potentially decrease CTL epitope production. These numbers are 51 and 49%, respectively, whist A3G-mediated stop codon generation is excluded (Table 2). These results indicate that there has not been a strong evolutionary pressure maintained on the viral genome for utilizing A3G toward CTL escape at the level of modulating proteasomal processing for CTL epitope generation.

We then investigated whether mutations mediated by A3G are clustered around the CTL epitopes, with more A3G hotspots being present either near the N- or C-terminal boundaries of epitopes, than expected at random. Considering a limit of 4 mutational A3G hotspots (A3G hotspots whose mutation would lead to non-synonymous substitutions) in 32 residues around each epitope, there appeared to be a marked paucity of such clustering (Table 3, Figure S1); however, when this limit was lowered to 2 mutational A3G hotspots, instances of clustering expectedly rose to 50–70% of epitopes. When all A3G hotspots were considered, this clustering proportion rose to 70–80%; thus, if non-mutational A3G hotspots can alter aspects of epitope production pre-translation (e.g., splicing, expression, etc.) this could be considered a significant trend.

We then investigated the enrichment of A3G hotspots (GGA, GGG, and GGT) inside vs. outside genomic sequences encoding CTL epitopes restricted to HLA-A2:01, HLA-A3:01, HLA-B57:01, and HLA-B35:01 in Gag, Pol, Env, and Nef genes (Table S2, Figure 3). First, we normalized for total nucleotide length of each gene and calculated the ratio of normalized A3G hotspot frequencies inside to outside epitope-encoding sequences for each protein's CTL epitopes. Thus, an inside: outside ratio >1 would be indicative of A3G hotspot enrichment in CTL-encoding sequences. As a control, we subjected the entire HIV genomic sequence to a random shuffling process, six independent times, but retained the positions/borders of the CTL epitope-encoding sequences. We then conducted the same analysis and expectedly arrived at ratios of ~1 (Figure 3A). We did not observe a generalizable trend of hotspot enrichment (ratio >1) in CTL-encoding sequences; however, when compared to the hypothetical ratio of 1 and the randomly shuffled control analyses, we noted that sequences encoding epitopes restricted to HLA-A2:01 and HLA-B57:01 often exhibited the highest enrichment ratios of 2-2.5 for at least 1-2 out of the 3 A3G hotspot motifs (Table S2, Figure 3A). These data are consistent with our previous observations that viral genomic sequences encoding more immunogenic CTL epitopes (restricted to more common HLA alleles, or those that elicit a more effective CTL response) have evolved to maintain A3G hotspots. Since A3G-mediated mutations which lead to stop codon generation would most often lead to non-infectious genomes, it is difficult to envision how maintenance of such A3G motifs to alter CTL epitopes at the cost of producing a non-infectious virus could be advantageous for the virus. Thus, in our enrichment analyses which was conducted to measure the extent to which the viral genome has evolved to utilize A3G toward CTL escape, we excluded A3G hotspots that would lead to stop codons. Rather, we considered these separately by examining the frequency and positional distribution of stop codon-generating A3G motifs in Gag, Pol, Env, and Nef. We found that between 18 and 43% of stop codons are positioned in the first quarter of each peptide (Figure S2), and there was a general trend of more frequent A3G-mediated stop codon generation in Pol and Env, as compared to Gag and Nef.

Having examined gene sequence A3G hotspot enrichment, we sought to measure the potential consequences at CTL epitope protein level. To this end, all simulated A3G-induced mutations were translated to protein sequences as described above. Since it is known that A3G can mutate the entire viral genome at low levels, epitopes with multiple hotspots and multiple mutated versions were considered independently. We then quantified A3G-induced non-synonymous and silent substitutions that fell within or outside of CTL epitopes of Gag, Pol, Env, and Nef restricted to HLA-A2:01, HLA-A3:01, HLA-B57:01 and HLA-B35:01. Next, we determined the ratio of A3G-induced mutations that caused non-synonymous residue changes to A3G-induced mutations which resulted in silent mutations within CTL epitopes and divided this by the same ratio determined for regions of each polypeptide that fell outside CTL epitopes. This analysis was carried out for each individual A3G hotspot motif, and as a total for all amino acids affected by A3G mutations within each polypeptide (Table S2 and Figure 3B). Thus, a ratio of >1 would indicate that the genomic sequence of HIV has evolved to channel A3G-induced mutations into amino acid changes, more often within CTL epitope-encoding regions as compared to sequences outside these portions. Indeed, we observed numerous instances of significant preferential channeling (ratios up to 4.5) toward non-synonymous residue changes in CTL epitopes of Gag, Pol, Env, and Nef as a result of A3G-induced mutations (Figure 3B). If we ignored A3G-driven silent consequences and evaluated the ratio of only A3G-mediated non-synonymous mutations inside CTL epitope-encoding regions to A3G-mediated non-synonymous mutations outside CTL epitope-encoding regions, we observed that in 28/48 graphed bars (58% of all measurements) the ratio was ≥ 1, with some ratios in the 2–3 range (Figure 3C). In general, the bias for A3G mutations to translate to non-synonymous rather than silent amino acid mutations was more pronounced for Gag, Pol and Nef as compared to Env, consistent with the former three polypeptides housing the vast majority of HIV's CTL epitopes (Figures 3B,C compared to Figure 3A).

In principle, non-synonymous amino acid changes arising from A3G mutations can enhance or diminish antigen presentation as the proteasomal processing and HLA binding levels (Figure 2, Table 2). To examine the distribution patterns of A3G hotspots that could potentially lead to CTL escape, we generated a map of all A3G hotspots across the entire HIV genome and overlaid this map on the experimentally-determined and well-known gradient of G to A mutations across the HIV genome (Figure 4A). In the context of A3G action, this twin gradient has been suggested to be a consequence of HIV genome's replication dynamics. Certain portions of the HIV minus strand genome remain single-stranded for a longer period compared to other segments, due to dynamics of RNA digestion by RT, the role of the Polypurine tracts (PPT), and subsequent positive sense strand polymerization. These segments are thus more available for A3G targeting resulting in a mutation gradient (34, 35, 143). We observed that regions near the central PPT and the C-terminal end (Nef) that are more highly mutated are rich in A3G hotspots, consistent with the notion that the viral genome has positioned hotspots in genomic locations that are more prone to being targeted by the A3G enzyme (Figure 4A). For each HLA, we plotted the number of CTL-epitope encoding sequences (in Gag, Pol, Env, and Nef) at incremental positions along the entire viral genome length (Figure 4B, top graph of each panel). We also plotted a normalized escape potential graph which represents the likelihood that an A3G hotspot located inside a CTL epitope-encoding sequence can generate a CTL-escape mutation. This was calculated by counting the A3G hotspots predicted to lower HLA binding affinities and normalizing these by the total abundance of A3G hotspots in the given CTL-epitope encoding region (Figure 4B, middle graph of each panel). Considering the number of CTL-epitopes encoded by a given genomic location (top panel), as well as the normalized escape potential of the sequences encoding this epitope (middle panel), we then generated an escape factor map which represents the compound potential for A3G to cause CTL escape across the HIV genome, for each HLA (Figure 4B, bottom graph of each panel). First, we noted that the potential for A3G-generated CTL escape was present throughout the length of the genome, for epitopes restricted to all 4 HLAs; however, it was generally more frequent in regions of the genome with a higher mutational potential and less frequent in regions known to be mutated at lower rates (comparing escape factor maps in Figure 4B to the mutational gradient in Figure 4A). Secondly, epitopes restricted to HLA-A2:01 and HLA-B57:01 exhibited overall higher abundance and frequent positioning of escape-inducing A3G hotspots, with HLA-B57:01-restricted epitopes also containing the highest escape factor values (Figure 4B). Thirdly, regions encoding for Gag, Pol and Nef contained generally a higher density of potentially CTL escape-inducing A3G hotspots, as compared to Env. The polypeptide and HLA-specific patterns observed are consistent with the A3G hotspot enrichment analysis (Figure 3) and taken together suggest the evolution of HIV genome to position A3G hotspot motifs in CTL-encoding regions, and highly mutable regions of the HIV genome, such that they preferentially yield CTL escape-inducing non-synonymous amino acid changes.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.