Deep mutational scanning of Env

We used the deep mutational scanning approach in Fig 1A to estimate the effects of all single amino-acid mutations to Env. We applied this approach to Env from the LAI strain of HIV [46]. LAI is a CXCR4-tropic subtype B virus isolated from a chronically infected individual and then passaged in human T-lymphocytes. We chose this strain because LAI and the closely related HXB2 strain have been widely used to study Env’s structure and function [8–11, 47–49], providing extensive biochemical data with which to benchmark our results. LAI’s Env is 861 amino acids in length. We mutagenized amino acids 31–702 (throughout this paper, we use the HXB2 numbering scheme [50]). We excluded the N-terminal signal peptide and the C-terminal cytoplasmic tail, since mutations in these regions can alter Env expression in ways that affect viral infectivity in cell culture [51–53]. The region of Env that we mutagenized spanned 677 residues, meaning that there are 677 × 63 = 42,651 possible codon mutations, corresponding to 677 × 19 = 12,863 possible amino-acid mutations.

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Fig 1. Deep mutational scanning workflow.

(A) We created libraries of HIV proviral plasmids with random codon mutations in env, and generated mutant viruses by transfecting these plasmid libraries into 293T cells. Since cells receive multiple plasmids, there may not be a link between viral genotype and phenotype at this stage. To establish this link and select for functional variants, we passaged the viruses twice at low multiplicity of infection (MOI) in SupT1 cells. We deep sequenced env before and after selection to quantify the enrichment or depletion of each mutation, and used these data to estimate the preference of each site for each amino acid. Each mutant library was paired with a control in which cells were transfected with a wildtype HIV proviral plasmid to generate initially wildtype viruses that were passaged in parallel with the mutant viruses. Deep sequencing of these wildtype controls enabled estimation of the rates of apparent mutations arising from deep sequencing and viral replication. (B) We performed the entire experiment in triplicate. Additionally, we passaged the replicate-3 transfection supernatant in duplicate (replicate 3b). We also performed the second passage of replicate 3b in duplicate (replicates 3b-1 and 3b-2).

https://doi.org/10.1371/journal.ppat.1006114.g001

To create plasmid libraries containing all these mutations, we used a previously described PCR mutagenesis technique [31] that creates multi-nucleotide (e.g, gca→CAT) as well as single-nucleotide (e.g, gca→gAa) codon mutations. We created three independent plasmid libraries, and carried each library through all subsequent steps independently, meaning that all our measurements were made in true biological triplicate (Fig 1B). We Sanger sequenced 26 clones to estimate the frequency of mutations in the plasmid mutant libraries (S1 Fig). There were an average of 1.4 codon mutations per clone, with the number of mutations per clone roughly following a Poisson distribution. The deep sequencing described in the next section found that at least 79% of the ≈10⁴ possible amino-acid mutations were observed at least three times in each of the triplicate libraries, and that 98% of mutations were observed at least three times across all three libraries combined. The plasmid libraries therefore sampled most amino-acid mutations to Env.

We produced virus libraries by transfecting each plasmid library into 293T cells. The viruses in the resulting transfection supernatant lack a genotype-phenotype link, since each cell is transfected by many plasmids. We therefore passaged the transfection supernatants twice in SupT1 cells at an MOI of 0.005 to create a genotype-phenotype link and select for functional variants. Importantly, neither 293T nor SupT1 cells express detectable levels of APOBEC3G [54, 55], which can hypermutate HIV genomes [56, 57]. This is a crucial point: although HIV encodes a protein that counteracts APOBEC3G, a fraction of viruses will lack a functional version of this protein and so have their genomes hypermutated in APOBEC3G-expressing cells. For each library, we passaged 5 × 10⁵ infectious particles in order to maintain library diversity. We used Illumina deep sequencing to quantify the frequency of each mutation before and after passaging. In order to increase the sequencing accuracy, we attached unique molecular barcodes or “Primer IDs” to each PCR amplicon [58–61]. We sequenced the plasmids to assess the initial mutation frequencies, and sequenced non-integrated viral DNA [62] from infected SupT1 cells to assess the mutation frequencies in the viruses. A concern is that errors from sequencing and viral replication (e.g., from viral reverse transcriptase) would introduce bias. To address this concern, we paired each mutant library with a control in which we generated wildtype virus from unmutated plasmid. Sequencing the control plasmids and viruses enabled us to estimate and statistically correct for the rates of these errors (S2 Fig). Overall, these procedures allowed us to implement the deep mutational scanning workflow in Fig 1.

Most mutations are under purifying selection, but a few sites experience selection for cell-culture adaptation mutations

Our deep mutational scanning experiments require that selection purge the virus libraries of non-functional variants. As an initial gene-wide measure of selection, we analyzed how different types of codon mutations (nonsynonymous, synonymous, and stop-codon mutations) changed in frequency after selection. In these analyses, we corrected for background errors from PCR, sequencing, and viral replication by subtracting the mutation frequencies measured in our wildtype controls from those measured in the mutant libraries (S2 Fig).

Stop-codon mutations are expected to be uniformly deleterious. Indeed, after correcting for background errors, stop codons were purged to <1% of their initial frequency in the twice-passaged viruses for each replicate, indicating strong purifying selection (see the data for “all sites” in Fig 2A). The second viral passage is important for complete selection, as stop codons remain at about ≈16% of their initial frequency in viruses that were only been passaged once (S3 Fig).

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Fig 2. Selection purged mutations in most of env, but favored mutations at a few sites.

(A) For each replicate, we deep sequenced the initial plasmids (DNA) and the viruses after two rounds of passaging (P2). Bars show the per-codon mutation frequency averaged across sites after subtracting error rates determined from the wildtype controls (S2 Fig). When mutation frequencies are averaged across all sites, selection purged stop codons to <1% of their frequency in the initial DNA. Selection only slightly reduced the average frequency of nonsynonymous mutations; however, this average results from two distinct trends. For ≈4% of sites, the frequency of nonsynonymous mutations in the twice-passaged viruses (f^P2) increased >3-fold relative to the frequency in the initial plasmid DNA (f^DNA). For all other sites, the frequency of nonsynonymous mutations decreased substantially after selection. (B) The sites at which the error-corrected mutation frequency increased >3-fold are similar between replicates, indicating consistent selection for tissue-culture adaptation at a few positions. The left Venn diagram shows the overlap among replicates in the sites with a >3-fold increase. The right Venn diagram shows the expected overlap if the same number of sites per replicate are randomly drawn from Env’s primary sequence. This difference is statistically significant, with P < 10⁻⁴ when comparing the actual overlap among all three replicates to the random expectation. Another summary view of selection on env is provided by S4 and S5 Figs.

https://doi.org/10.1371/journal.ppat.1006114.g002

Interpreting the frequencies of nonsynonymous mutations is more nuanced, as different amino-acid mutations have different functional effects. However, a large fraction of amino-acid mutations are deleterious to any protein [63–65]. Therefore, one might expect that the frequency of nonsynonymous mutations would decrease substantially in the twice-passaged mutant viruses. But surprisingly, even after correcting for background errors, the average frequency of nonsynonymous mutations in the passaged viruses is ≈90% of its value in the mutant plasmids (see the data for “all sites” in Fig 2A). However, the average masks two disparate trends. In each library, a few sites exhibit large increases in the frequency of nonsynonymous mutations, whereas this frequency decreases by nearly two-fold for all other sites (see the data for the subgroups of sites in Fig 2A).

An obvious hypothesis is that at a few sites, amino-acid mutations are favored because they are adaptive for viral replication in cell culture. Consistent with this hypothesis, the sites that experienced large increases in mutation frequencies are similar among the three replicates (Fig 2B), suggestive of reproducible selection for mutations at these sites. Moreover, these sites are spatially clustered in Env’s crystal structure in regions where mutations are likely to enhance viral replication in cell culture (Fig 3 and S1 Table). One cluster of mutations disrupts potential glycosylation sites at the trimer apex (Fig 3A). This result suggests that some of the glycans that help shield Env from antibodies in nature [6, 66] actually decrease viral fitness in the absence of immune selection. This idea is consistent with previous studies showing that that loss of glycosylation sites can enhance viral infectivity in cell culture [67–69]. A second cluster overlaps sites where mutations influence Env’s conformational dynamics, which are commonly altered by cell-culture passage [70, 71]. It has been hypothesized that neutralization-resistant Envs primarily assume conformations that mask conserved antibody epitopes, while lab-adapted variants more efficiently sample different conformations associated with CD4 binding [72]. Thus, the adaptive mutations we observe may enable Env to more efficiently use CD4 in cell culture, but would not be selected in nature because they expose conserved epitopes. A third cluster is at the co-receptor binding interface (Fig 3B), where mutations may enhance viral entry in cell culture. Therefore, while most of Env is under purifying selection against changes to the protein sequence, a few sites are under selection for cell-culture adapting amino-acid mutations.

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Fig 3. Sites of recurrent cell-culture mutations mapped on Env’s structure.

The 25 sites from Fig 2B where the mutation frequency increased >3-fold in at least two replicates after cell-culture passage. (A) Trimeric Env (PDB 5FYK [73]) with one monomer in grey and the others in white, oriented so the membrane-proximal region is at the bottom. Sites of cell-culture mutations are shown as spheres, colored red-to-blue according to primary sequence. Most of these sites fall in one of three clusters. Mutations in the first cluster disrupt potential glycosylation sites at Env’s apex. The second cluster includes or is adjacent to sites where mutations are known to affect Env’s conformational dynamics [74, 75]. (B) The third cluster is near the co-receptor binding surface. This panel shows an apex-down view of monomeric gp120 (grey) in complex with CD4 (green) (PDB 3JWD [48]). Sites of recurrent cell-culture mutations are shown as spheres colored according to primary sequence as in panel A. The black bar indicates cropping of CD4. (C) The same view as panel B, but the spheres now show sites known to affect binding to CXCR4 [10] or CCR5 [76]. Note the extensive overlap between the spheres in this panel and panel B.

https://doi.org/10.1371/journal.ppat.1006114.g003

If our experiments are indeed identifying mutations to LAI that are beneficial in cell culture, then one expectation is that some of these mutations might fix after prolonged passage of LAI in cell culture. Interestingly, almost exactly such an experiment was performed in the early study of HIV. The LAI strain used in our study was initially isolated from a chronically infected individual and then passaged in cell culture for a short period of time before cloning [46, 77]. HXB2, another common lab strain, is derived from a variant of LAI that was repeatedly passaged in a variety of cell lines, initially as a contaminant of other viral stocks [78, 79]. There are 23 amino-acid differences between the Env proteins of LAI and HXB2. Although the predecessor for HXB2 was not passaged in the same SupT1 cell line that we used, if its passage in other cell lines led to mutations that were generally adaptive to cell culture, then we would expect them to introduce amino acids in HXB2 that are also selected in our deep mutational scan of LAI. Indeed, we found that most differences between LAI and HXB2 introduced mutations to amino acids that our experiments suggest are more preferred in cell culture than the wildtype LAI amino acid (S2 Table). Thus, our results are consistent with the expectation that HXB2 is more adapted to cell culture than LAI.

The average error-corrected frequency of synonymous mutations changes little after selection (an average decrease to 96% of the original frequency; see the data for “all sites” in Fig 2A). This overall trend is consistent with the fact that synonymous mutations usually have smaller functional effects than nonsynonymous mutations. However, synonymous mutations can sometimes have substantial effects [21, 80–82], particularly in viruses like HIV that are under strong selection for RNA secondary structure and codon usage [83, 84]. To assess selection on synonymous mutations on a more site-specific level, we examined the change in frequency of multi-nucleotide codon mutations across env’s primary sequence (Fig 4). The rationale behind examining only multi-nucleotide codon mutations is that they are not appreciably confounded by errors from PCR, deep sequencing, or de novo mutations from viral replication (S2 and S4 Figs). In a region roughly spanning codons 500 to 600, selection strongly purged both synonymous and nonsynonymous multi-nucleotide codon mutations (Fig 4). This region contains env’s Rev-response element (RRE) [85], a highly structured region of RNA that is bound by the Rev protein to control the temporal export of unspliced HIV transcripts from the nucleus [86, 87]. The finding of strong selection on the nucleotide as well as the amino-acid sequence of the RRE region of Env therefore agrees with our biological expectations.

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Fig 4. Selection depleted multi-nucleotide codon mutations in the Rev-response element (RRE).

This plot shows a 51-codon sliding-window average of the fold change in per-codon multi-nucleotide mutation frequency after two rounds of viral passage, with data plotted for the center point in each window. The strongest depletion of both synonymous and nonsynonymous mutations occurred in the RRE, which is an RNA secondary structure important for viral replication.

https://doi.org/10.1371/journal.ppat.1006114.g004

The preference for each amino acid at each site in Env

The previous section examined broad trends in selection averaged across many sites. But our data also enable much more fine-grained estimates of the preference for every amino-acid at every position in Env. We define a site’s preference for an amino acid to be proportional to the enrichment or depletion of that amino acid after selection (correcting for the error rates determined using the wildtype controls), normalizing the preferences for each site so that they sum to one. We denote the preference of site r for amino acid a as π_r,a, and compute the preferences from the deep-sequencing data as described in [88]. Since we mutagenized 677 residues in Env, there are 677 × 20 = 13,540 preferences. If selection in our experiments exactly parallels selection in nature and there are no shifts in mutational effects as Env evolves, then these preferences are the expected frequencies of each amino acid at each site in an alignment of Env sequences that have reached evolutionary equilibrium under a mutation process that introduces each amino acid with equal probability [31, 89].

Fig 5 shows Env’s site-specific amino-acid preferences after averaging across replicates and re-scaling to account for the stringency of selection in our experiments (details of this re-scaling are in the next section). As is immediately obvious from Fig 5, sites vary dramatically in their tolerance for mutations. Some sites strongly prefer a single amino acid, while other sites can tolerate many amino acids. For instance, site 457, an important receptor-binding residue [8], has a strong preference for aspartic acid. However, this site is adjacent to a variable loop (sites 460–469) where most sites tolerate many amino acids. Another general observation is that when sites tolerate multiple amino acids, they often prefer ones with similar chemical properties. For instance, sites 225 and 226 prefer hydrophobic amino acids, while sites 162 to 164 prefer positively charged amino acids.

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Fig 5. Env’s site-specific amino-acid preferences.

The amino-acid preferences averaged across replicates and re-scaled to account for differences in the stringency of selection between our experiments and natural selection. At each site, the preferences for each of the 20 amino acids sum to one, with the height of each letter proportional to the preference for that amino acid at that site. Letters are colored according to hydrophobicity. The overlay bar indicates the gp120 variable loops, other regions of gp120, and gp41. The LAI wildtype (WT) sequence is shown below the overlay bar. Black dots indicate sites where mutations are known to disrupt CD4 binding (Table 1). Sites are numbered using the HXB2 scheme [50]. Numerical values of the preferences before and after re-scaling are in S1 and S2 Files, respectively.

https://doi.org/10.1371/journal.ppat.1006114.g005

To confirm that our experiments captured known constraints on Env’s function, we examined mutations that have been characterized to affect key functions of Env. Table 1 lists mutations known to disrupt an essential disulfide bond, binding to receptor or co-receptor, or protease cleavage. In almost all cases, the deleterious mutation introduces an amino-acid that our experiments report as having a markedly lower preference than the wildtype amino acid. Therefore, our measurements largely concord with existing knowledge about mutations that affect key aspects of Env’s function.

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Table 1. Our experimental estimates are mostly concordant with existing knowledge about the effects of mutations to functionally or structurally important parts of Env.

https://doi.org/10.1371/journal.ppat.1006114.t001

A crucial aspect of any high-throughput experiment is assessing the reproducibility of independent replicates. Fig 5 shows the average of the preferences measured in each replicate. Fig 6A shows the correlations among the 13,540 site-specific amino-acid preferences estimated from each of the three replicates. The correlations are modest, indicating substantial replicate-to-replicate noise. In principle, this noise could arise from differences in the initial plasmid mutant libraries, bottlenecks during the generation of viruses by transfection, bottlenecks during viral passaging, or bottlenecks during the sequencing of proviral DNA from infected cells. Analysis of technical replicates of the first or second round of viral passaging indicates that most of the noise arises from bottlenecks during the viral passaging or sequencing steps. Specifically, measurements from replicate 3 are no more correlated to those from replicates 3b-1 or 3b-2 (which are repeated passages of the same transfection supernatant, Fig 1B) than they are to those from totally independent replicates (compare Fig 6 and S6 Fig). However, replicates 3b-1 and 3b-2 (which shared the first of the two viral passages, Fig 1) do yield more correlated measurements than independent replicates (S6 Fig). The existence of bottlenecks during viral passage is also suggested by the data in S4 and S5 Figs. Therefore, the experimental reproducibility could probably be increased by passaging more infectious viruses at each step.

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Fig 6. The amino-acid preferences are modestly correlated among experimental replicates, but the sites tolerate similar numbers of amino acids and prefer similar amino acids across replicates.

(A) Correlations between the site-specific amino-acid preferences from each replicate. (B) Correlations between the effective number of amino acids tolerated per site. For each site r, the effective number of tolerated amino acids is , where H_r is the Shannon entropy of that site’s amino-acid preferences. This number ranges between 1 and 20, with 20 indicating all amino acids are preferred equally and 1 indicating only a single amino acid is preferred. (C) Correlations between the preference-weighted hydrophobicities. For each site r, the preference-weighted hydrophobicity is ∑_a π_r,a × X_a where π_r,a is the preference of r for amino acid a, and X_a is the Kyte-Doolittle hydropathy [90] of a. The fact that both the effective number of tolerated amino acids and the hydrophobicities are more correlated than the amino-acid preferences means that when different amino acids are preferred at a site in different experimental replicates, the number and chemical properties of the preferred amino acids are similar. Each plot shows the Pearson correlation coefficient and associated P-value. Similar data for replicates 3b-1 and 3b-2 are in S6 Fig. The plots in this and subsequent figures show all 20 amino-acid preferences for each site; although only 19 of these preferences are independent parameters, all 20 values are shown because otherwise the correlation will depend on which value is excluded.

https://doi.org/10.1371/journal.ppat.1006114.g006

If bottlenecks cause each replicate to sample slightly different mutations, then perhaps the total number of tolerated mutations per site will be similar between replicates, even if the exact mutations differ. To test this hypothesis, we computed the effective number of amino acids tolerated at each site as the exponential of the Shannon entropy of the site’s amino-acid preferences. Fig 6B shows that the effective number of amino acids tolerated at each site is more correlated between replicates than the preferences themselves. We further reasoned that even if bottlenecking causes slight variations in the preferred amino acids between replicates, each site would still tend to prefer amino acids with similar chemical characteristics. To test this hypothesis, we quantified the extent that each site preferred hydrophobic or hydrophilic amino acids by computing a site-specific hydrophobicity score from the amino-acid preferences. Fig 6C shows that these preference-weighted hydrophobicities are more correlated between replicates than the preferences. Therefore, even though there is replicate-to-replicate noise in the exact amino acids preferred at a site, the effective number of tolerated amino acids and the chemical properties of these amino acids are similar among replicates.

The amino-acid preferences correlate with amino-acid frequencies in HIV sequence alignments at most sites, but deviate at positions subject to selection pressures absent from our experiments

In the previous section, we showed that our experimentally measured amino-acid preferences captured the constraints on Env’s biological function for sites with known mutational effects (Table 1). If this is true across the entire protein, then our measurements should correlate with the frequencies of amino acids in natural HIV sequences. Table 2 shows that there is a modest correlation (Pearson’s R ranging from 0.29 to 0.36) between the preferences from each experimental replicate and the frequencies in an alignment of HIV-1 group-M sequences (a phylogenetic tree of these sequences is in Fig 7A; sites in Env variable loops that can not be reliably aligned are excluded as described in the Methods). Since each replicate suffers from noise due to partial bottlenecking of the viral diversity, we hypothesized that averaging the preferences across replicates should make them more accurate. Indeed, averaging the replicates increased the correlation to R = 0.4 (Table 2).

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Fig 7. Correlations between amino-acid preferences and frequencies in natural HIV sequences.

(A) Phylogenetic tree of the HIV-1 group-M sequences in the alignment. (B) Correlation between alignment frequencies and preferences. The preferences are the replicate averages re-scaled by the stringency parameter in Table 2. (C) The correlation if evolution is simulated along the phylogenetic tree assuming that the preferences correctly describe the actual selection. (D) There is no correlation in a control simulation in which preferences are randomized among sites. (E), (F) Correlation between preferences and alignment frequencies as a function of relative solvent accessibility (RSA). Red lines show the actual correlation. Dark and light gray show the range of correlations in the middle 80% and 100% of 100 simulations. For both plots, data are shown until the subset of sites that meets the RSA cutoff becomes less than 10% of all sites in Env; this is why neither x-axis extends all the way from 0 to 1. Correlation coefficients are Pearson’s R.

https://doi.org/10.1371/journal.ppat.1006114.g007

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Table 2. Correlation of amino-acid preferences with amino-acid frequencies in nature.

https://doi.org/10.1371/journal.ppat.1006114.t002

The concordance between deep mutational scanning measurements and natural sequence variation is improved by accounting for differences in the stringency of selection in the experiments compared to natural selection [89, 91]. Specifically, if the measured preference is π_r,a and the stringency parameter is β, then the re-scaled preference is . A stringency parameter of β > 1 means that natural evolution favors the same amino acids as the experiments, but with greater stringency. Table 2 shows that for all replicates, the stringency parameter that maximizes the correlation is >1. Therefore, natural selection prefers the same amino acids as our experiments, but with greater stringency.

After averaging across replicates and re-scaling by the optimal stringency parameter, the Pearson correlation is 0.44 between our experimentally measured preferences and amino-acid frequencies in the alignment of naturally occurring HIV sequences (Fig 7B). Is this a good correlation? At first glance, a correlation of 0.44 seems unimpressive. But we do not expect a perfect correlation even if the experiments perfectly concord with selection on Env in nature. There are several factors that are expected to reduce the correlation between the experimentally measured preferences and amino-acid frequencies in natural sequences. First, our experiments examine the effects of mutations to Env from the LAI strain. However, it is well known that epistasis can cause the effects of mutations to differ among homologs of the same protein [92, 93], and many examples of this phenomenon have been documented in HIV Env [94–97]. Therefore, our measurements for the LAI Env are probably not completely generalizable to all other strains. In addition, natural HIV sequences are drawn from a phylogeny (Fig 7A), not an ideal ensemble of all possible Env sequences. The frequencies of amino acids in this phylogeny reflect evolutionary history as well as natural selection. For instance, if several amino acids are equally preferred at a site, one is likely to be more frequent in the alignment due to historical contingency. Additionally, natural evolution is influenced by the genetic code and mutation biases: a mutation from the tryptophan codon TGG to the valine codon GTT is extremely unlikely even if valine is more preferred than tryptophan. Mutation biases inherent in reverse transcription [98] or APOBEC3G-induced hypermutation [54] could also bias some evolutionary outcomes over others. Therefore, the correlation will be imperfect even if the preferences completely concord with natural selection—the question is how the actual correlation compares to what is expected given the phylogenetic history and mutation biases.

To determine the expected correlation if the experimentally measured amino-acid preferences reflect conserved constraints in Env, we simulated evolution along the phylogenetic tree in Fig 7A under the assumption that the experimentally measured preferences exactly match natural selection. Specifically, we used pyvolve [99] to simulate evolution using the experimentally informed site-specific codon substitution models described in [91], which define mutation-fixation probabilities in terms of the amino-acid preferences. In addition to the preferences and the stringency parameter β = 2.1 from Table 2, the substitution models in [91] require specification of parameters reflecting biases in the mutation process. We estimated nucleotide mutation bias parameters of ϕ_A = 0.55, ϕ_C = 0.15, ϕ_G = 0.11, and ϕ_T = 0.18 from the frequencies at the third-nucleotide codon position in sequences in the group-M alignment for sites where the most common amino acid had 4-fold codon degeneracy. We used the transition-transversion ratio of κ = 4.4 estimated in [100]. For these simulations, we scaled the branch lengths so that the average pairwise protein divergence was the same in the actual and simulated alignments.

The correlation between the preferences and amino-acid frequencies in a representative simulated alignment is shown in Fig 7C. As this plot illustrates, the expected correlation is only about 0.46 if the experimentally measured preferences exactly describe natural selection on Env under our model. The simulated frequencies in Fig 7C show the same pattern of bi-modality (most values near zero or one) as the actual frequencies in Fig 7B despite the fact that the preferences used in the simulations allow multiple amino acids at most sites (see Fig 5). This fact illustrates that bi-modality in the amino-acid frequencies can arise from the historical contingency inherent in a phylogenetic tree even if multiple amino acids are tolerated at most sites. As a control, we also simulated evolution using substitution models in which the preferences have been randomized among sites (Fig 7D); as should be the case, there is no correlation in these control simulations. So the actual correlation is nearly as high as expected if natural selection concords with the preferences measured in our experiment.

We next investigated if there are parts of Env for which there is an especially low correlation between our experimentally measured preferences and natural amino-acid frequencies. For instance, antibodies exert selection on the surface of Env in nature [6, 7, 101, 102]. We therefore examined the actual and simulated correlations between the preferences and frequencies as a function of solvent accessibility (Fig 7E and 7F). For all sites (right side of Fig 7E, left side of Fig 7F), the actual correlation is only slightly lower than the range of correlations in 100 simulations. For more buried sites, both the simulated and actual correlations increase (Fig 7E), presumably because sites in the core of Env tend to have stronger preferences for specific amino acids. But as sites become more surface-exposed, the actual correlation drops below the value expected from the simulations (Fig 7F). Therefore, our experiments provide a relatively worse description of natural selection on Env’s surface than its core—probably because the evolution of the protein’s core is shaped mostly by inherent functional constraints that are effectively captured by our experiments, whereas the surface is subject to selection pressures (e.g., antibodies) that are not modeled in our experiments.

Comparing disulfide-bonded cysteines and glycosylation sites vividly illustrates this dichotomy between inherent functional constraints and external selection pressures. Env has 10 highly conserved disulfide bonds, most of which are essential for the protein’s inherent function [49]. Env also has numerous N-linked glycosylation sites, many of which are also highly conserved in nature, where they help shield the protein from antibodies [6, 66]. In contrast to the disulfides, only some glycosylation sites are important for Env’s function in the absence of immune selection [67, 69]. Fig 8 shows that our experimentally measured preferences are highly correlated with natural amino-acid frequencies at the sites of the disulfides, but not at the glycosylation sites. This result can easily be rationalized: the disulfides are inherently necessary for Env’s function, whereas many glycosylation sites are important largely because of the external selection imposed by antibodies. Our experiments therefore accurately reflect the natural constraints on the former but not the latter.

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Fig 8. The correlation between the experimentally measured preferences and amino-acid frequencies in natural sequences is low at glycosylation sites, but high at disulfide-bonded cysteines.

(A) The logo plots show the frequencies of amino acids in the group-M alignment or the amino-acid preferences from our experiments at a subset of potential N-linked glycosylation sites (see S7 Fig for all 30 sites). The glycosylation sites are conserved in nature, but tolerant of mutations in our experiment. The scatter plot shows that there is a poor correlation between the preferences and natural amino-acid frequencies at all 22 alignable glycosylation sites: red triangles represent the first position in each glycosylation site, whereas gray circles represent all other sites. (B) There is much better concordance between the preferences and natural amino-acid frequencies for Env’s disulfide-bonded cysteines. The logo plots show each pair of cysteines for a subset of disulfides (see S7 Fig for all 10 disulfides). The scatter plot shows that there is a strong correlation between the preferences and natural amino-acid frequencies at all disulfide-bonded cysteines.

https://doi.org/10.1371/journal.ppat.1006114.g008

The fact that we found well-tolerated mutations at all of Env’s glycosylation sites (S7A Fig) might seem surprising given that other studies have shown that some glycosylation sites are important for Env’s function in certain HIV strains [67, 69]. However, these studies were all performed in HIV strains substantially diverged from LAI. A study in HXB2 (which is closely related to LAI) found that individual mutations are at least partially tolerated at all glycosylation sites in Env’s gp120 subunit when assaying for viral infectivity in cell culture [103]. Therefore, glycosylation sites may be especially expendable in the LAI strain used in our study.

Env has a low mutational tolerance in broadly neutralizing antibody epitopes

Different sites in Env evolve at different rates in natural HIV sequences. For instance, sites on the apical surface of Env evolve especially rapidly [104]. These differences in evolutionary rate arise from two factors. First, some sites are inherently better at tolerating mutations without disrupting Env’s essential functions. Second, some sites are under stronger immune selection for rapid sequence change. However, since Env in nature is under selection both to maintain its function and escape immunity, it is difficult to deconvolve these factors.

Our experiments estimate each site’s inherent tolerance for mutations under selection purely for Env’s function in cell culture, without the confounding effects of immune selection (for the remainder of this section, we define a site’s mutational tolerance as the Shannon entropy of its amino-acid preferences shown in Fig 5). We can therefore assess whether regions of Env that evolve rapidly or slowly in nature also have unusually high or low inherent tolerance to mutations.

We focused on two regions of Env. First, we analyzed portions of the protein classified as “variable loops” due to extensive variation in nature [105, 106]. These loops are frequently targeted by antibodies that drive rapid sequence evolution [102, 107]. Because these loops evolve rapidly, we hypothesized they would have a high inherent mutational tolerance. But an alternative hypothesis is that their rapid evolution more attributable to strong selection from antibodies than an unusually high mutational tolerance. Second, we focused on epitopes of antibodies that broadly neutralize many HIV strains. Because these epitopes are highly conserved in nature and often overlap with regions of known functional constraint [108–113], we hypothesized they would have a low mutational tolerance. However, an alternative hypothesis is that these epitopes evolve slowly not because they are mutationally intolerant but simply because they are under weaker immune selection. Indeed, broad immune responses targeting these epitopes only develop in 20% of infected individuals and generally only after multiple years of infection [114].

In testing these hypotheses, it is important to control for other properties known to affect mutational tolerance. This can be done by using multiple linear regression to simultaneously analyze how several independent variables affect the dependent variable of mutational tolerance. Relative solvent accessibility (RSA) is the strongest determinant of mutational tolerance in proteins [115], so we included RSA as a variable in the regression. The region of env that contains the RRE is under strong nucleotide-level constraint [85–87, Fig 4], so we also included being in the RRE as a binary variable in the regression. We defined the variable loops as indicated in Fig 5, and included being in one of these loops as a binary variable in the regression. Finally, we used crystal structures to delineate broadly neutralizing antibody epitopes. We focused on broadly neutralizing antibodies targeting the CD4 binding site, since most other broadly neutralizing antibodies target either glycans (which are subject to pressures that are not well-modeled in our experiments; Fig 8A) or a membrane-proximal region of gp41 that is not fully resolved in crystal structures of trimeric Env making it impossible to correct for RSA. Specifically, we analyzed the three antibodies with the greatest breadth from [116]: VRC01 (PDB 3NGB [117]), 12A21 (PDB 4JPW [118]), and 3BNC117 (PDB 4JPV [118]). We defined a site as part of an epitope if it was within a 4Å inter-atomic distance of the antibody, and included the number of epitopes in which a site is found as a discrete variable in the regression.

The results of the multiple linear regression are in Table 3. As expected, increased solvent accessibility is strongly associated with increased mutational tolerance, whereas presence in the RRE is strongly associated with decreased mutational tolerance. After correcting for these effects, sites in broadly neutralizing epitopes have significantly reduced mutational tolerance. In contrast, sites in the variable loops have higher mutational tolerance, but this effect is not statistically significant. Some of the loops are more variable in nature than others [119]. However, even when the loops are considered independently, none of these regions has a statistically significant association with mutational tolerance (S3 Table). Overall, this analysis provides statistical confirmation of something that is widely assumed in the study of HIV: broadly neutralizing antibodies are unique because they target regions of Env that are inherently intolerant of mutations. However, we fail to find strong statistical support for the hypothesis that variable loops are especially tolerant of mutations. Thus, the rapid evolution of these loops in nature is probably more attributable to strong immune selection than exceptionally high inherent mutational tolerance.

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Table 3. Broadly neutralizing antibody epitopes have significantly lower mutational tolerance than other sites in Env.

https://doi.org/10.1371/journal.ppat.1006114.t003

Data and computer code

The computer code to analyze the sequencing data and generate the figures is provided in a series of IPython notebooks in S3 File. Illumina sequencing data are available from the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under the accession numbers in S10 File.

Sequence numbering

We use the HXB2 numbering system [50] unless otherwise noted. The “variable loop” definitions were taken from http://www.hiv.lanl.gov/, not including the flanking disulfide-bonded cysteines as part of the loops.

Codon mutant libraries

We created the codon mutant libraries in the context of the pro-viral genomic plasmid pLAI, which encodes the LAI strain of HIV [46]. This plasmid was obtained from the lab of Michael Emerman. The plasmid sequence is in S4 File.

We created codon mutant libraries of env using the PCR mutagenesis technique described in [31] (see also [33, 38]) except that we performed two total rounds of mutagenesis rather than the three rounds in [31]. The codon tiling mutagenic primers are in S5 File. The end primers were: 5’-ttggaatttctggcccagaccgtctcatgagagtgaaggagaaatatcagcacttg-3’ and 5’-catctgctgctggctcagc-3’. We created three replicate libraries by performing all the steps independently for each replicate starting with independent plasmid preps.

We cloned the PCR mutagenized env amplicons into the LAI plasmid with high efficiency to create plasmid mutant libraries. To seamlessly clone the PCR products into the proviral plasmid, we created a recipient version of the plasmid that had env replaced by GFP flanked by restriction sites for BsmBI, which cleaves outside its recognition sequence. We named this recipient plasmid pLAI-δ env-BsmBI; its sequence is in S6 File. We digested both this recipient plasmid and the gel-purified PCR amplicons with BsmBI (there are BsmBI sites at either end of the PCR amplicon), gel purified the digested PCR products, and ligated them into the plasmid using a T4 DNA ligase. We column purified the ligation products, electroporated them into competent cells (Invitrogen,12033-015), and plated the transformed cells on LB plates supplemented with 100 μg/mL ampicillin. For each of the three replicate libraries, we performed enough transformations to yield >1.4 million unique colonies as estimated by plating dilutions of each transformation on separate plates. Control ligations lacking an insert yielded at least 10-fold fewer colonies. The transformed cells were scraped from the plates, grown in liquid LB-ampicillin at 37°C for ∼4 hours, and mini-prepped to obtain the plasmid mutant libraries. For the wildtype controls, we prepped three independent cultures of the wildtype LAI proviral plasmid.

Generation and passaging of viruses

We generated the mutant virus libraries by transfecting the mutant plasmid libraries into 293T cells obtained from the American Type Culture Collection (ATCC). For each replicate, we transfected two 12-well tissue-culture plates to increase the diversity of the generated viruses. Specifically, we plated 293T cells at 2.4×10⁵ cells/well in D10 media (DMEM supplemented with 10% FBS, 1% 200 mM L-glutamine, and 1% of a solution of 10,000 units/mL penicillin and 10,000 μg/mL streptomycin). The next day, we transfected each well with 1 μg plasmid using BioT (Bioland Scientific LLC, B01-01). For the three wildtype controls we used the same process but with only a single 12-well plate per replicate. At one day post-transfection, we aspirated the old media, replacing it with fresh D10. At ∼60 hours post-transfection, we filtered the transfection supernatants through 0.4 μm filters. To remove residual plasmid DNA from the transfection, we then treated the filtrate with DNase-I (Roche, 4716728001) at a final concentration of 100 U/mL in the presence of 10 mM magnesium chloride (Sigma, M8266) at 37°C for 20–30 minutes. We froze aliquots of the DNase-treated supernatant at -80°C. Aliquots were thawed and titered by TZM-bl and TCID-50 assays as described below.

We passaged the transfection supernatants in SupT1 cells obtained from the NIH AIDS Reagent Program [126]. SupT1 cells were maintained in a media identical to the D10 described above except that the DMEM was replaced with RPMI-1640 (GE Healthcare Life Sciences, SH30255.01). Before infecting cells, for replicates 1, 2, and 3 (but not replicate 3b), we first filtered thawed transfection supernatants through a 0.2 μm filter in an effort to remove any large viral aggregates. We then infected 10⁸ SupT1 cells with 5 × 10⁵ TZM-bl units of the mutant library transfection supernatant in a final volume of 100 mL SupT1 culture medium in a vented tissue-culture flask (Fisher Scientific, 14-826-80). In parallel, we passaged 10⁵ TZM-bl units of transfection supernatant for each wildtype control in 20 million SupT1 cells in a final volume of 20 mL. At one day post-infection, we pelleted cells at 300×g for 4 minutes and resuspended in fresh media to the same volume as before. At two days post-infection, we added fresh media equal to the volume already in the flask to dilute the cells and provide fresh media. We harvested virus at three days post-infection (for replicates 1, 2, and 3) or four days post-infection (for replicate 3b) by pelleting cell debri at 300×g for 4 minutes and then collecting the viral supernatant for storage at -80°C. To remove residual culture media and plasmid DNA from the cell pellets, we washed pellets two times in PBS. The washed cells were resuspended in PBS to a final concentration of 10⁷ cells/mL, and aliquots were frozen at -80°C for DNA purification.

We conducted a second passage by infecting new cells with the passage-1 viral supernatants. The second passage differed from the first passage in the following ways: Before infecting cells, we filtered passage-1 supernatant of replicate 3b-2 through a 0.2 μm filter but did not filter any of the other replicates. We also had to modify the passaging conditions for some replicates due to low titers of the passage-1 supernatants. For viruses in which the passage-1 supernatant was at too low a concentration to infect at an MOI of 0.005 in the volumes indicated above, we added additional passage-1 supernatant, and then reduced the volume to that indicated above during the day-one media change. As stated in the Results section, passaging more than 5 × 10⁵ TZM-bl units of the mutant library at each step would probably help increase reproducibility between experimental replicates.

Virus titering by TCID₅₀ and TZM-bl assays

We measured viral titers using TZM-bl reporter cells obtained from the NIH AIDS Reagent Program [127]. Specifically, we added 2×10⁴ cells in 0.5 mL D10 to each well of a 48-well plate. We made dilutions of viral inoculum and infected cells with 100 uL of each dilution. At 2 days post-infection, we fixed cells in a solution of 1% formaldehyde and 0.2% glutaraldehyde in PBS for 5 minutes at room temperature, washed with PBS to remove the fixing solution, and stained for beta-galactosidase activity with a solution of 4 mM potassium ferrocyanide, 4 mM potassium ferricyanide, and 0.4 mg/mL X-gal in PBS at 37°C for 50 minutes. After washing cells with PBS to remove the staining solution, we used a microscope to count the number of blue cells per well, computing the viral titer as the number of blue cells per mL of viral inoculum.

We were concerned that the infectious titer in SupT1 cells might differ from the TZM-bl titers. We therefore also performed TCID₅₀ assay to directly measure infectious titers in SupT1 cells. To do this, we made dilutions of viral transfection supernatant in a 96-well tissue-culture plate and added SupT1 cells at a final concentration of 2.5×10⁵ cells/mL in a final volume of 180 μL/well. At 4 and 8 days post-infection, we passaged supernatant 1:10 into fresh media to prevent cells from becoming over confluent. At 12 days post-infection, we measured the titer of culture supernatants using the TZM-bl assay to determine which SupT1 infections had led to the production of virus. Based on binary scoring from these TZM-bl assays, we calculated titers using the Reed-Muench formula [128] as implemented at https://github.com/jbloomlab/reedmuenchcalculator. At least for the LAI strain used in our experiments, the SupT1 TCID₅₀ titers were approximately equal to the TZM-bl titers. Therefore, we used only the less time-consuming TZM-bl assay for all subsequent titering.

Generation of samples for Illumina sequencing

We purified non-integrated viral DNA from aliquots of frozen SupT1 cells using a mini-prep kit (Qiagen, 27104) with ∼10⁷ cells per prep. In some cases, we then concentrated the purified DNA using Agencourt AMPure XP beads (Beckman Coulter, A63880) using a bead-to-sample ratio of 1.0 and eluting with half of the starting sample volume.

We next generated PCR amplicons of env to use as templates for Illumina sequencing. We created these amplicons from plasmid or mini-prepped non-integrated viral DNA by PCR using the primers 5’-agcgacgaagacctcctcaag-3’ and 5’-acagcactattctttagttcctgactcc-3’. PCRs were performed in 20 μl or 50 μl volumes using KOD Hot Start Master Mix (71842, EMD Millipore) with 0.3 μM of each primer and 3 ng/μl of mini-prepped DNA or 0.3 ng/μl of plasmid as template. The PCR program was:

1. 95°C, 2 minutes
2. 95°C, 20 seconds
3. 70°C, 1 second
4. 64.3°C, 10 seconds (cooling to this temperature at 0.5°C/second)
5. 70°C, 1 minute 48 seconds
6. Go to 2, 27 times
7. hold at 4°C

For replicate 3b, there were a few modifications: the annealing temperature was 64.9°C, the extension time was 54 seconds, and we performed only 25 cycles. To quantify the number of unique template molecules amplified in each PCR, we performed standard curves using known amounts of template env in pro-viral plasmid, and ran the the bands on an agarose gel alongside our amplicons for visual quantification. We performed a sufficient number of PCR reactions to ensure that amplicons from plasmid were coming from > 10⁶ unique template molecules, and amplicons from viral DNA were coming from ∼2 × 10⁵ template molecules. All PCR products were purified with Agencourt beads (using a sample-to-bead ratio of 1.0) and quantified by Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies, P7589).

We deep sequenced these amplicons using the strategy for barcoded-subamplicon sequencing in [38], dividing env into six subamplicons (this is a variation of the strategy originally described in [58–60]). The sequences of the primers used in the two rounds of PCR are in S9 File. Our first-round PCR conditions slightly differed from [38]: our 25 μL PCRs contained 12.5 μL KOD Hot Start Master Mix, 0.3 μM of each primer, and 5 ng of purified amplicon. For replicates 1, 2, and 3, the first-round PCR program was:

1. 95°C, 2 minutes
2. 95°C, 20 seconds
3. 70°C, 1 seconds
4. 60°C, 10 seconds (cooling to this temperature at 0.5°C/second)
5. 70°C, 10 seconds
6. Go to 2, 10 times
7. 95°C, 1 min
8. hold 4°C

For replicate 3b, we used the same program, but with 9 PCR cycles instead of 11. Prior to the second round PCR, we bottlenecked each subamplicon by diluting it to a concentration that should have yielded between 3 and 5×10⁵ unique single-stranded molecules per subamplicon per sample. We purified the second-round PCR products using Agencourt beads, quantified with PicoGreen, pooled in equimolar amounts, and purified by agarose gel electrophoresis, excising DNA corresponding to the expected ∼500 base pairs in length. We sequenced the purified DNA using multiple runs of an Illumina MiSeq with 2×275 bp paired-end reads.

Analysis of deep-sequencing data

We used dms_tools (http://jbloomlab.github.io/dms_tools/), version 1.1.dev13, to filter and align the deep-sequencing reads, count the number of times each codon mutation was observed both before and after selection, and infer Env’s site-specific amino-acid preferences using the algorithm described in [88]. The code that performs this analysis is in S3 File. Figures summarizing the results of the deep sequencing are also in this supplementary file.

Alignment of group-M env sequences

We downloaded the 2014 filtered web alignment of env from http://www.hiv.lanl.gov/, including all subtypes for HIV-1/SIVcpz. We then curated this alignment in the following ways. First, we removed sequences differed in length from HXB2 (including gap characters) or contained a premature stop codon, ambiguous residue, or frame-shift mutation. Next, we removed columns in the alignment for which we lacked deep mutational scanning data, columns that had >5% gap characters, or columns in variable loops that appeared poorly aligned by eye. Finally, we randomly selected 30 sequences per subtype for group-M subtypes A, B, C, D, F, and G, for a total of 180 sequences. The resulting alignment is in S7 File. The phylogenetic tree in Fig 7 was inferred using RAxML [129] with the GTRCAT substitution model.

Computing relative solvent accessibilities

We computed absolute solvent accessibilities based on the PDB structure 4TVP (including all three Env monomers after removing antibody chains) using DSSP [130, 131]. We normalized absolute solvent accessibilities to relative ones using the maximum accessibilities provided in the first table of [132]. The relative solvent accessibilities are listed in S8 File.