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Selection analyses of paired HIV-1 gag and gp41 sequences obtained before and after antiretroviral therapy

Most HIV-1-infected individuals with virological failure on a pharmacologically-boosted protease inhibitor (PI) regimen do not develop PI-resistance protease mutations. One proposed explanation is that HIV-1 gag or gp41 cytoplasmic domain mutations might also reduce PI susceptibility. In a recent study of paired gag and gp41 sequences from individuals with virological failure on a PI regimen, we did not identify PI-selected mutations and concluded that if such mutations existed, larger numbers of paired sequences from multiple studies would be needed for their identification. In this study, we generated site-specific amino acid profiles using gag and gp41 published sequences from 5,338 and 4,242 ART-naïve individuals, respectively, to assist researchers identify unusual mutations arising during therapy and to provide scripts for performing established and novel maximal likelihood estimates of dN/dS substitution rates in paired sequences. The pipelines used to generate the curated sequences, amino acid profiles, and dN/dS analyses will facilitate the application of consistent methods to paired gag and gp41 sequence datasets and expedite the identification of potential sites under PI-selection pressure.


Background & Summary
HIV-1 protease mutations responsible for protease inhibitor (PI) resistance are now uncommon in patients with virological failure on an initial PI-containing regimen, particularly regimens including pharmacologically-boosted lopinavir, atazanavir, or darunavir [1][2][3] One explanation for the infrequent occurrence of PI-resistance mutations in protease is that mutations outside of protease might reduce PI susceptibility even in the absence of primary PI resistance protease mutations. Indeed, many studies have reported that gag cleavage and non-cleavage site mutations may compensate for the reduced fitness associated with primary PI-resistance protease mutations [4][5][6][7][8][9][10][11] and several studies of pseudotyped viruses reported that genetic loci in matrix (MA) gag 12,13 and in the gp41 cytoplasmic domain (CD) 14 can reduce PI susceptibility in the absence of PI-resistance protease mutations.
To identify gag and gp41 mutations under selective PI pressure, we recently sequenced gag and/or gp41 in 61 individuals with virological failure on a PI or a control nonnucleoside RT inhibitor (NNRTI) containing regimen 15 . We quantified nonsynonymous and synonymous mutations in both genes and identified sites exhibiting signal for directional or diversifying selection. We also used published gag and gp41 polymorphism data to highlight mutations displaying a high selection index, defined as changing from a conserved or common amino acid variant to an uncommon amino acid variant. The rationale for this latter analysis is that most drug-resistance mutations in established targets of antiviral therapy including protease, RT, integrase, and the extracellular domain of gp41 are amino acid variants at sites that are non-polymorphic in the absence of selective drug pressure.
In our previous study, many amino acid mutations were found to emerge in gag and in gp41-CD in both the PI-and NNRTI-treated groups. However, in neither gene, were there discernible differences between the two groups in overall numbers of mutations, mutations displaying evidence of diversifying or directional selection, or mutations with a high selection index. Based on this previous study, we concluded that if gag and/or gp41 encoded PI-resistance mutations, they might not be confined to repeated mutations at a few sites, and that multiple studies with large numbers of paired sequences from individuals with virological failure on a PI-containing regimen would need to be pooled to identify such mutations. To facilitate such studies, we provide here a detailed description of the methods used to generate the datasets and analytic results used in our previous study.
The selection index analyses, in particular, require additional exposition because they used data derived from the curation and annotation of gag and gp41 sequences from more than 500 GenBank submission sets and/or peer-reviewed publications to determine the polymorphism rates at each gag and gp41 position. The annotation of these references according to the treatment status of the individuals in the references is included as part of this manuscript's Data Citation. The selection index analyses, also required performing quality control analyses of each gag and gp41 sequence and determining the prevalence of each mutation at each position. Finally, the HyPhy scripts described in this manuscript make it possible to exactly replicate each of the maximum likelihood estimates of the ratio of nonsynonymous and synonymous substitution rates presented in our original manuscript.

Methods
Gag and gp41 sequences of paired samples obtained before and after PI or NNRTI therapy The sequences described in our previous manuscript were obtained from HIV-1-infected individuals in Northern California who had genotypic resistance tests performed between April 2001 and June 2013 and from participants in the ACTG A5202 clinical trial 3,15 . The sequences were derived from plasma virus samples obtained before and after therapy from 41 previously PI-naïve subjects who had received a ritonavir-boosted PI-containing regimen or from 20 control subjects who received an NNRTI-containing regimen. Among the 41 PI-treated subjects, paired sequences before and after PI treatment were available for both gag and gp41 in 11 individuals, for gag alone in 13 individuals, and for gp41 alone in 17 individuals. Among 20 NNRTI control subjects, paired sequences before and after NNRTI treatment were available for both gag and gp41 in 13 individuals, for gag alone in three individuals, and gp41 alone in four individuals. Table 1 (available online only) contains the GenBank accessions for each of the paired protease, gag and gp41 sequences from the 41 PI-and 20 NNRTI-treated individuals. This study was approved by the Institutional Review Boards (IRBs) of Stanford University, KPNC, and the NIH ACTG and all study methods were performed in accordance with the guidelines of these IRBs. Informed consent was required for participation in the ACTG 5202 trial. The Stanford University and KPNC IRBs provided a waiver of informed consent for the study of remnant KPNC samples that were unlinked to individual protected health information.
The plasma samples were processed and underwent direct PCR Sanger sequencing as described in our previous manuscript 15 . Each gag, and gp41 sequence was aligned using the Translation Align option with the ClustalW algorithm for multiple sequence alignment using the Geneious R11 software 16 . The parameters used were the default values (cost matrix: BLOSUM; gap open cost: 10; gap extend cost: 0.1). The multiple sequence alignment was then manually edited using the subtype B consensus sequence 17 . Manual edits were required for the gag but not the gp41 sequence because gag contained many more indels than gp41. The manual edits were primarily the shifting of indels to be consistent with the remaining sequences and the subtype B consensus. Nucleotide insertions were then stripped from the sequence prior to subsequent analyses. The original and aligned sets of FASTA sequences for gag and gp41 are in the files gagOriginal.fas, gagAligned.fas, gp41Original.fas, and gp41Aligned.fas (Data Citation 1). The insertions in gag and gp41 are in file insertions.csv. Neighbor-joining trees for each gene, created by HyPhy version 2.3.2 using the TN93 distance, confirmed that each pair of sequences clustered by individual. All trees (in Newick format) are included in Data Citation 1. Data Citation 1 also contains the initial and edited gag gene alignments in data/gag.geneious (Data Citation 1).
Pairwise sequence comparisons and dN/dS analyses of gag and gp41 Tables 2 and 3 summarize the median proportions of pairwise nucleotide and amino acid changes, pairwise dN/dS ratios, and median proportions of IUPAC ambiguities in the pre-and post-treatment gag and gp41 sequences, respectively. Pairwise dN/dS ratio estimation was implemented using a custom HyPhy v2.3.2 script scripts/pairwise-estimator-dnds.bf (Data Citation 1); this script reads in a collection of aligned coding sequences, splits them into host pairs (based on patient ID encoded in the FASTA sequence name), and estimates dN/dS by maximum likelihood using the MG94xREV codon substitution model 18 . The script also optionally restricts the analysis to a contiguous region of the alignment, for example to focus on a specific protein domain.
As noted in the previous manuscript, there was no difference in median proportions of pairwise nucleotide and amino acid changes and pairwise dN/dS ratios in gag and gp41 between the PI-and NNRTI-treated patients. Also, as previously noted, there was a significant reduction in the median proportion of ambiguous nucleotides in gag between baseline and follow-up among the PI-treated patients: 0.4% (IQR:0.1% to 0.8%) vs 0.0% (IQR:0% to 0.6%; p = 0.02 Mann-Whitney U test). Table 4 lists each of the amino acid changes that occurred at gag cleavage sites.

Positional dN/dS selection analyses of gag and gp41
We ran the fixed effects likelihood (FEL) method, as implemented in HyPhy v2.3.2, to detect codon sites exhibiting diversifying selection in gag and gp41 on the post-treatment branches using a p-value of 0.05 (refs 18,19). This analysis requires annotated phylogenetic trees (e.g., internalFiles/phylo/ gagNNRTIs.tre (Data Citation 1)), i.e. trees where post-treatment branches are marked for testing. A convenient tool for annotating trees can be found at http://phylotree.hyphy.org. We also used Hyphy v2.3.2 to fit a model of episodic directional selection (MEDS) to the post-treatment branches pressure, also using a p-value of 0.05 (ref. 20). Table 5 shows the gag positions with evidence of diversifying selection and the gag mutations with evidence of directional selection within the PI and NNRTItreatment groups. Table 6 shows the gp41 positions with evidence of diversifying selection and evidence of directional selection within the PI and NNRTI-treatment groups. Both of these analyses identify candidate positions and mutations that are most likely to be under selective drug pressure. However, as is the case with any statistical testing procedures, it is possible that some of the positions and/or mutations are misclassified as either false positives or false negatives. Files containing shell scripts are provided to enable users to repeat these selection analyses with the same set of parameters that we used.  Collection of previously published gag sequences from ARV-naïve individuals We downloaded the complete set of 7,550 one-per-person aligned complete gag sequences from the Los Alamos National Laboratories (LANL) HIV Sequence Database 21 . We filtered 565 sequences that contained either large deletions or missing nucleotides (n = 238), more than 3 frameshift mutations (n = 281), or 3 or more signature APOBEC mutations (n = 46) defined as mutations at highly conserved positions that were likely to occur in sequences containing stop codons and that occurred in an appropriate dinucleotide context: GG → AG for APOBEC3G GA → AA for APOBEC3F (ref. 22). Applying the Local FDR Poisson distribution using the R LocFDRPois package to our data, we found that presence of ≥3 signature APOBEC mutations was associated with a 0.99 likelihood of a sequence having undergone APOBEC-mediated G to A hypermutation 23 . Table 7 lists the 45 signature APOBEC mutations that we identified and Fig. 1 shows the distribution of the number of signature APOBEC mutations per gag sequence. We then used Batch Entrez to submit each of the Accession IDs to GenBank 24 and parsed the XML results to aggregate sequences into GenBank submission sets, henceforth referred to as studies, sharing either the same PubMed ID or the same Title and Author List fields. We reviewed the 264 studies reporting three or more individuals. Of these studies, 164 (62.1%) comprised solely ART-naïve individuals, 75 (28.4%) comprised individuals whose treatment status was unknown, and 25 (9.5%) comprised individuals who were ART-experienced. A summary of these 264 studies is provided in gagStudies.csv (Data Citation 1).
We then determined the proportion of each amino acid at each position in gag for the complete set of 5,365 one-per-person group M ART-naïve sequences as well as for the four LANL-designated subtypes (A, B, C, and CRF01_AE) for which at least 100 sequences were present. Site-specific amino acids present in 0.1% or fewer sequences were considered unusual. Fig. 2 shows that the numbers of gag sequences according to the number of unusual mutations per sequence monotonically decreases until n = 10 unusual mutations. Therefore, we excluded sequences containing ≥11 unusual mutations since these 27 sequences were considered to be at high risk of poor sequence quality. We then recalculated the proportion of each amino acid at each position for the remaining 5,338 sequences. The original and aligned sets of FASTA sequences for the 5,338 one-per-person filtered sequences from these studies are in gagNaiveOriginal.fas and gagNaiveAligned.fas (Data Citation 1). The header for each sequence contains the GenBank accession number and the LANL-designated subtype. The file gagAAPrevalence.csv (Data Citation 1) lists the proportion of each amino acid at each gag position. In this file insertions, deletions, and mixtures are indicated by "ins", "del", and "X", respectively. Figure 3 displays the distribution of amino acids at each of the 500 gag positions in the one-per-person group M HIV-1 gag sequences from ARV-naïve individuals.

Collection of previously published gp41 sequences from ARV-naïve individuals
We downloaded the complete set of 7,489 one-per-person aligned complete gp41 sequences from the LANL HIV Sequence Database 21 . We filtered 453 sequences that contained either large deletions or missing nucleotides (n = 234), more than 3 frameshift mutations (n = 89), or 3 or more signature APOBEC mutations (n = 130) defined as mutations at highly conserved positions that were likely to occur in sequences containing stop codons and that occurred in an appropriate dinucleotide context 22 . Applying the Local FDR Poisson distribution using the R LocFDRPois package to our data, we found that presence of ≥3 signature APOBEC mutations was associated with 0.97 likelihood of a sequence having Median % IUPAC Ambiguities (interquartile range) undergone APOBEC-mediated G to A hypermutation 23 . Table 8 lists the 47 signature APOBEC mutations and Fig. 4 shows the distribution of the number of signature APOBEC mutations per sequence. We then used Batch Entrez to submit each of the Accession IDs to GenBank 24 and parsed the XML results to aggregate sequences into GenBank submission sets (or studies) sharing either the same PubMed ID or the same Title and Author List fields. We reviewed the 329 studies reporting five or more individuals. Of these studies, 191 (58.1%) comprised solely ART-naïve individuals, 95 (28.9%) comprised individuals whose treatment status was unknown, and 43 (13.0%) comprised individuals who were ARTexperienced. A summary of these 329 studies is provided in gp41Studies.csv (Data Citation 1).
We then determined the proportion of each amino acid at each position in gp41 for the complete set of 4,263 one-per-person group M ART-naïve sequences as well as for each of the four LANL-designated subtypes (A, B, C, and CRF01_AE) for which at least 100 sequences were present. Amino acids occurring at a proportion ≤0.1% were considered unusual. Figure 5 shows that the numbers of gp41 sequences according to the number of unusual mutations per sequence monotonically decreases until n = 7 unusual mutations. Therefore, we excluded sequences containing ≥8 unusual mutations, since these 21 sequences were considered to be at high risk of poor sequence quality. We then recalculated the proportion of each amino acid at each position for the remaining 4,242 sequences. The original and aligned sets of sequences for the 4,242 one-per-person filtered sequences from these studies in FASTA format are in gp41NaiveOriginal.fas and gp41NaiveAligned.fas (Data Citation 1). The header for each sequence contains the GenBank accession number and the LANL-designated subtype. The file gp41AAPrevalence. csv (Data Citation 1) lists the proportion of each amino acid at each gp41 position. In this file insertions, deletions, and mixtures are indicated by "ins", "del", and "X", respectively. Figure 6 displays the distribution of amino acids at each of the 345 gp41 positions in the one-per-person group M HIV-1 gp41 sequences from ART-naïve individuals.  Gag and gp41 selection indexes We used empirical gag and gp41 amino acid site frequencies to calculate a selection index for each amino acid change that developed during therapy defined as follows: log 10 of the ratio of the proportion of the pre-therapy amino acid in PI-naïve individuals divided by the proportion of the post-therapy amino acid in PI-naïve individuals (fold change). Amino acid changes with a high selection index were defined as changing from a highly conserved or relatively common amino acid variant at a position to an amino acid with a prevalence at least 10 times less common (i.e., a selection index ≥1.0). The distribution of all selection indexes for gag and gp41 according to treatment was plotted using an R script that accepts as input the list of amino acid changes between pairs of sequences and data on the proportion of each amino acid in an external database. The script and the resulting figures are located at scripts/make-graphical-summary.r, reports/gag-mutations.pdf, and reports/gp41-mutations.pdf (Data Citation 1). As no statistical model for the expected distribution of selection indexes for proteins under selective drug pressure has been developed, the plots are useful primarily for identifying loci at which changes from a conserved to an unusual amino acid were clustered. As noted in our previous manuscript, we found no discernible difference in either gag or gp41 in the distribution of selection indexes between PI-and NNRTI-treated individuals.

Code availability
The code used in this manuscript includes the set of Python (version 3.5.2), R (version 3.2.3), and Linux shell scripts that are available on Github (https://github.com/hivdb/gag-gp41) and in the gag-gp41.zip file submitted to Dryad Digital Repository (Data Citation 1). The Github site and the Dryad zip file also includes the 24 files cited in this Data Descriptor. There are no restrictions on accessing or using the code or files as they are released under the open source MIT License.

Data Records
The original and aligned pre-and post-treatment gag and gp41 sequences from our previous published study are available in four FASTA files in which each sequence header contains four fields: GenBank accession ID, PID, treatment time point, and treatment category (for example KY579846| 118827_PIs_Pre). The sequence files, which are named gagOriginal.fas, gagAligned.fas, gp41Original. fas, and gp41Aligned.fas, are located in the directory data/fasta/ (Data Citation 1). Table 1    . The file data/insertions.csv (Data Citation 1) lists each of the insertions in gag and gp41 as these were removed during sequence alignment. The Newick representation of the neighbour-joining trees for the aligned gag and gp41 sequences are in the directory data/phylo/ (Data Citation 1). Tables 2, 3, and 4 summarize the differences between pre-and posttreatment sequences for gag, the gag cleavage sites, and gp41, respectively. The Linux shell scripts that pass parameters to Hyphy batch language scripts used to perform the pairwise dN/dS analysis, FEL diversifying selection analysis, and MEDS directional selection analysis are named run-pairwise.sh, run-fel.sh, and run-meds.sh, respectively (Data Citation 1). They are available in the directory scripts/. The summarized results of the pairwise dN/dS analysis are in Tables 2 and 4. The results of FEL and MEDS are in Tables 5 and 6.
The file data/naiveStudies/gagStudies.csv (Data Citation 1) contains the list of all studies with three or more individuals from whom gag sequences were obtained for analyzing mutation prevalence. The files data/naiveStudies/gagNaiveOriginal.fas and data/naiveStudies/gagNaiveAligned.fas (Data Citation 1) contain the 5,338 one-per-person quality-control filtered original and aligned sequences from these studies. Table 7 lists the gag signature APOBEC mutations. Figure 1 shows the distribution of the number of gag signature APOBEC mutations prior to quality control filtering. Figure 2 shows the distribution of the number of unusual amino acids per gag sequence. The file gagAAPrevalence.csv (Data Citation 1) lists the proportion of each amino acid at each gag position according to HIV-1 subtype in one-per-person sequences filtered for an excess of signature APOBEC mutations and unusual amino acids. report/gagnaive-indels.pdf (Data Citation 1) displays the distribution of insertions and deletions at each position in gag. Figure 3 displays the proportions of all gag amino acid variants present at 1.0% or greater frequency of one-per-person sequences.          The file data/naiveStudies/gp41Studies.csv (Data Citation 1) contains the list of all studies with five or more individuals from whom gp41 sequences were obtained for analyzing mutation prevalence. The files data/naiveStudies/gp41NaiveOriginal.fas and data/naiveStudies/gp41NaiveAligned.fas (Data Citation 1) contain the 4,242 one-per-person quality-control filtered original and aligned sequences from these studies. Table 8 lists the gp41 signature APOBEC mutations. Figure 4 shows how the distribution of the number of gp41 signature APOBEC mutations prior to quality control filtering. Figure 5 shows the distribution of the number of unusual amino acids per gp41 sequence. The file gp41AAPrevalence.csv (Data Citation 1) lists the proportion of each amino acid at each gp41 position according to HIV-1 subtype in one-per-person sequences filtered for an excess of signature APOBEC mutations and unusual amino acids. report/gp41-naive-indels.pdf (Data Citation 1) displays the distribution of insertions and deletions at each position in gp41. Figure 6 displays the proportions of all gp41 amino acid variants present in ≥1.0% of one-per-person sequences.

H K A R V I L A E A M S
The file scripts/run-basic.py and scripts/make-graphical-summary.r (Data Citation 1) contain the script that accept a list of amino acid changes between pairs of sequences and data on the proportion of each mutation to generate a plot showing the selection indexes for each mutation. The files reports/gagmutations.pdf, and reports/gp41-mutations.pdf contain the output of these scripts.

Technical Validation
Several concerns arise when calculating positional amino acid prevalence from large numbers of sequences in public databases: (i) Do any of the sequences contain nucleotide sequence errors introduced by those who submitted the sequence?; (ii) Do any of the sequences contain annotation errors introduced by those who submitted the sequence or by database curators?; (iii) Have errors been introduced during sequence alignment resulting in the spurious alignment of nonhomologous positions and secondarily inaccurate mutation proportion data?; and (iv) In the case of HIV-1, do the sequences have evidence of  Figure 4. Distribution of gp41 APOBEC signature mutations. Distribution of the number of gp41 APOBEC signature mutations in the complete set of 7,166 one-per-person quality-control filtered (i.e. sequences with large numbers of missing nucleotides and frame shifts were excluded) aligned complete gp41 sequences from the LANL HIV sequence database. The 130 sequences containing ≥3 APOBEC signature mutations were considered to be at high risk of having been subject to APOBEC-mediated G-to-A hypermutation and were excluded from our amino acid prevalence calculations. APOBEC-mediated G-to-A hypermutation or other evidence for biological artefact consistent with a nonviable virus protein?
In our analyses, we used the LANL HIV Sequence Database 21 to retrieve complete group M HIV-1 gag and gp41 sequences from previously published studies submitted to GenBank 24 . Despite the fact that GenBank is the standard database for sequences determined by dideoxynucleoside sequencing and that the LANL HIV Sequence Database is a curated HIV sequence database, we performed additional analyses to address the concerns cited in the previous paragraph. This process involved first removing sequences containing large gaps, multiple frame shift mutations, and an excess of signature APOBEC mutations. This was followed by removing a small number of sequences containing high numbers of unusual mutations.
The approach for identifying likely APOBEC-mediated G-to-A hypermutation was similar to an approach that we previously described for HIV-1 protease, RT, and integrase 25 . We first identified 45 gag   and 47 gp41 signature APOBEC mutations. Overall, 23 of the signature mutations were stop codons at positions for which the highly conserved consensus amino acid was tryptophan (W) and 69 were highly unusual amino acids at conserved positions that usually occurred in a sequence containing one or more stop codons. The distribution of the number of signature APOBEC mutations per sequence was then used to exclude a small proportion of sequences with ≥3 signature APOBEC mutations: 0.6% for gag and 1.7% for gp41. Following these steps, we plotted the distribution of pairwise uncorrected nucleotide distances for group M HIV-1 gag and gp41 and for those subtypes for which more than 100 sequences were available ( Figs. 7 and 8). The intra-and inter-subtype pairwise distances for both genes clustered around previously reported genetic distances for these genes 26 . The absence of highly divergent gag or gp41 sequences is consistent with our attention to sequence alignment and sequence quality in creating our curated sequence datasets and amino acid profiles.
We also identified a previous study of gag amino acid variation and found that 92.9% (914) of the 984 amino acids which we detected at a prevalence of at least 1.0% of PI-naïve individuals were detected among the 993 amino acids detected at a prevalence of at least 1.0% in this earlier study 27 . This previous study was similar in design to ours with the following exceptions: (i) Sequences were obtained from all published studies through 2012 regardless of whether the individuals from whom the sequences were obtained were PI-experienced, PI-naïve, or of uncertain PI treatment history; (ii) Hypermutated sequences were excluded using the Los Alamos Hypermut tool 28 ; and (iii) No isolates were excluded solely on the basis of having a large number of unusual mutations. We did not identify a similarly large study of gp41 amino acid variation. The exclusion of outlier sequences is a logical approach to creating useful sequence sets and alignments from which mutation proportion data can be calculated. Nonetheless, highly unusual sequences may not always reflect erroneous or artefactual data. As part of our technical validation pipeline, we have identified those sequences that were excluded should other researchers be interested in their analysis.

Usage Notes
The complete set of files including tables, figures, sequence files, tab-delimited files, and code files are available as a Dryad data citation and on the GitHub repository. The Dryad data citation provides a stable permanent snapshot of the analyses described in this manuscript. The GitHub repository will evolve as new studies are published and as more published studies are reviewed to expand the sets of filtered annotated gag and gp41 sequences from PI-naïve individuals.
Other researchers sequencing gag and/or gp41 sequences before and after PI therapy will be able to pool our data with theirs and to perform the same dN/dS and selection index analyses using the software described in this manuscript and provided on Dryad and GitHub.
Several other aspects of our data and code will be useful to other researchers even if they are not planning to perform the same analyses described in this manuscript: (i) the signature APOBEC mutations for gag and gp41 will be useful for the study of sequences of these two genes; (ii) the gag and gp41 sequence sets, publication summaries, and mutation prevalence files will also be useful to other researchers studying these genes; and (iii) the Hyphy and shell scripts will be useful to other researchers performing dN/dS analyses on sequence pairs. In particular, the HyPhy script for pairwise analysis has not been previously published.