Restriction of V3 region sequence divergence in the HIV-1 envelope gene during antiretroviral treatment in a cohort of recent seroconverters
© Gall et al; licensee BioMed Central Ltd. 2013
Received: 2 November 2011
Accepted: 27 April 2012
Published: 18 January 2013
Dynamic changes in Human Immunodeficiency Virus 1 (HIV-1) sequence diversity and divergence are associated with immune control during primary infection and progression to AIDS. Consensus sequencing or single genome amplification sequencing of the HIV-1 envelope (env) gene, in particular the variable (V) regions, is used as a marker for HIV-1 genome diversity, but population diversity is only minimally, or semi-quantitatively sampled using these methods.
Here we use second generation deep sequencing to determine inter-and intra-patient sequence heterogeneity and to quantify minor variants in a cohort of individuals either receiving or not receiving antiretroviral treatment following seroconversion; the SPARTAC trial. We show, through a cross-sectional study of sequence diversity of the env V3 in 30 antiretroviral-naive patients during primary infection that considerable population structure diversity exists, with some individuals exhibiting highly constrained plasma virus diversity. Diversity was independent of clinical markers (viral load, time from seroconversion, CD4 cell count) of infection. Serial sampling over 60 weeks of non-treated individuals that define three initially different diversity profiles showed that complex patterns of continuing HIV-1 sequence diversification and divergence could be readily detected. Evidence for minor sequence turnover, emergence of new variants and re-emergence of archived variants could be inferred from this analysis. Analysis of viral divergence over the same time period in patients who received short (12 weeks, ART12) or long course antiretroviral therapy (48 weeks, ART48) and a non-treated control group revealed that ART48 successfully suppressed viral divergence while ART12 did not have a significant effect.
Deep sequencing is a sensitive and reliable method for investigating the diversity of the env V3 as an important component of HIV-1 genome diversity. Detailed insights into the complex early intra-patient dynamics of env V3 diversity and divergence were explored in antiretroviral-naïve recent seroconverters. Long course antiretroviral therapy, initiated soon after seroconversion and administered for 48 weeks, restricts HIV-1 divergence significantly. The effect of ART12 and ART48 on clinical markers of HIV infection and progression is currently investigated in the SPARTAC trial.
Primary infection by Human Immunodeficiency Virus type 1 (HIV-1) is usually initiated by a single or small number of infecting virions . Quantitative assessment of HIV-1 evolution can be determined by measuring HIV sequence diversity, the genetic variation at a given time, and divergence, the genetic distance to a reference point, usually the founder virus. In the absence of immune pressure HIV-1 diversifies and diverges from the founder virus by random mutation. This occurs during virus dissemination from the site of primary infection and the establishment of a peak viremia . Extensive viral diversity arises from the high error rate of the HIV-1 reverse transcriptase, the rapid virus turnover, and the large population size. Diversity is constrained upon initiation of the CD8 cytotoxic T lymphocyte (CTL) response . Under intense CTL selection, escape variants occur and expand within the infectious virus pool. Single genome amplification (SGA) sequencing and deep amplicon sequencing by Roche/454 technology show many viral CTL escape variants can exist in parallel at the time of declining viral load and establishment of viral set point in both HIV and Simian Immunodeficiency virus (SIV) infections [3, 4].
Longitudinal analysis of HIV-1 sequence variation following seroconversion shows virus divergence and diversity accumulate co-linearly at approximately 1% per year . Sequence diversity increases, eventually plateaus, and then in some cases declines over time. During the diversity plateau, however, divergence continues to increase until, at a later time, divergence stabilisation is also reached . The emergence of CXCR4-tropic variants may occur immediately prior to the peak of diversity, with stabilisation of divergence occurring during periods of low CD4+ T cell numbers . Although the dynamics of HIV-1 diversity during primary infection are well studied, less is known about the dynamics of diversity and divergence following seroconversion and clinical latency, or the effect of antiretroviral therapy (ART) on altering the course of diversification and divergence. High HIV-1 envelope (env) diversity is linked to slower disease progression and effective immune control. Specifically, Heteroduplex Tracking Assay (HTA) analysis of populations of HIV-1 env V1/V2 and V4/V5 regions showed that high levels of sequence diversity were linked with slower disease progression and that rapid CD4+ T cell decline was associated with lower env diversification . Using the same assay over shorter time intervals revealed that selective pressures on V1/V2 and V4/V5 are intense and continually evolving . Together this suggests that continued virus replication, in the absence of drug therapy, but in the presence of adaptive immune responses drives divergence and diversity.
That intense selective pressure leads to HIV-1 mutational escape has also been shown in the development of antiviral drug resistance. Interestingly, recent data as revealed by deep sequencing suggest that rather than providing a strong selective pressure on the env gene and constraining diversity, treatment with the CCR5 antagonist Vicriviroc resulted in V3 env diversity increasing during treatment and was coupled with an expansion of rare sequence variants during drug resistance selection . This suggested that continued evolutionary pressure resulted in the observable exploration of the sequence space. Whether immune selective pressures result in the same drive of diversification has not been determined to the same detail by HTA which is a semi-quantitative and non sequence based method. In contrast, for patients on successful highly active antiretroviral therapy (HAART), an absence of evolution has been shown for both the env C2-V3-C3  and the protease encoding region . However, these studies were based on consensus and clonal sequencing, and did not analyse the impact of the length of therapy on the diversification and divergence of the virus.
Sequencing using second generation sequencing technology (Roche/454 Life Sciences)  has proven to be a promising alternative to traditional clonal sequencing. It surpasses the potential of clonal sequencing with regard to sensitivity, throughput, and ability to quantify minor variants. However, the drawbacks of a higher sequencing error rate [8, 12–14] and the potential generation of recombinants during sample preparation by PCR [15, 16] have to be taken into account when minor variants are analyzed. Deep sequencing of the env V3 loop has been utilized in a limited number of chronically infected patients to detect small minority X4-tropic viruses before  and during ART with a CCR5 antagonist , as well as their emergence after interruption of this therapy  or HAART .
Here, we show the application of deep sequencing of the HIV-1 env V3 to determine the diversity of HIV-1 in patients at primary infection with HIV-1 subtype B prior to initiating ART. We show the detailed and complex intra-patient dynamics of diversity and divergence following primary infection. Importantly we provide evidence that ART initiated soon after seroconversion can affect the dynamics of HIV diversification and divergence.
Sequence data and filtering
Deep sequencing data for the env V3 region of 80 patient and 3 control samples were generated using 12 Genome Sequencer FLX Instrument runs. Either 1/4 or 1/8 region of a PicoTiterPlate was used for each pool of 5 to 12 samples. For one sample pool, sequencing yielded an average of 34.13 MB (megabases) of total data from 115284 reads with a read length of 297.87 ± 52.69 (mean ± SD). The average number of initial reads generated was 13929 (508–88144) for patient samples and 16954 (15739–19215) for control samples. Various filtering steps were applied to the deep sequencing data (see Methods and Additional file 1: Table S1). The resulting average sequencing depth for patient samples was 6908-fold (354–29387). For the control sequences from cloned HIV envelopes E6 and H8, the sequencing depth following data filtering was 9783-fold (E6) and 15524-fold (H8); and for the “mixed” HIV env control E6:H8, the sequencing depth was 11491-fold. Phred quality scores for non-consensus bases in the env V3 region were analysed for all samples (Additional file 2: Figure S1). The median Phred score was 32.50 with a range of 21.17 to 39.86. Therefore, the median of the probability of a nucleotide being called incorrectly was 0.05% (range 0.76%–0.01%). However, as Phred scores only account for base calling errors, we also assessed the overall error rate of our entire amplification and sequencing process.
Error rates in 454 deep sequencing
Determination of sequence accuracy of control samples
% correct nt
% erroneous nt
% correct nt
% erroneous nt
Homopolymeric regions a
% correct nt
% erroneous nt
Ratios of major and minor variants and the ability of our methods to generate recombinant sequences were also assessed by using the “mixed” control E6:H8 combined at a ratio of 9 and 1. The two major sequence variants were identical to the plasmid sequences of E6 (77.90% of the sequences) and H8 (11.86%). All remaining sequences were present at low frequencies of ≤ 0.40% (not shown). At least one recombinant sequence was identified within these minor variants at a very low frequency of 0.02% (2/11491 reads). A second recombinant sequence with a frequency of 0.01% (1/11491 reads) differed from the first only by an inversion of two nucleotides at the end of a homopolymer tract (not shown). Our error profile was almost identical to that of Wang et al. , therefore using the same Poisson model of 454 sequencing errors, we estimated that we require a depth of greater than 750-fold to detect a sequence present at 1.5%, and at that level we were conservatively well in excess of both homopolymeric, non-homopolymeric, base calling errors and recombinant error rates.
Sequence diversity of the HIV envV3 at primary infection
Deep sequencing of the env V3 region of 30 patients at primary infection with HIV-1 subtype B showed a complex pattern of major and minor sequence variants (Figure 1). The composition of major and minor variants differed between individuals with the major sequence variant accounting for up to 93.3% of the sequences (subject 15) or only 16.2% (subject 25). Between one (subjects 10, 15 and 30) and 12 (subject 25) error corrected genuine sequence variants (present at greater than 1.5% of the total sequences) were found in each individual. Of note, for the individuals with one major variant the sequences are effectively clonal. Although frequencies of major and minor sequence variants appear informative we wished to examine HIV-1 env sequence diversity in the more formal framework of Shannon Entropy.
Coreceptor tropism of envV3 variants at primary infection
Utilization of the CCR5 or CXCR4 receptor as an HIV coreceptor was predicted based on env V3 variants of the 30 patients as well as two control samples. Results of predictions by (a) the PSSM method using the X4R5 matrix, (b) the PSSM method using the SINSI matrix and (c) the Geno2pheno [coreceptor] tool are shown in Additional file 3: Figure S2. For the majority of the variants, CCR5 usage was predicted using all methods. Sequence variants associated with virus using the CXCR4 coreceptor were clearly identified in subject 18. The 2nd (frequency 21.82%), 5th (3.29%) and 7th (1.82%) most frequent variants were associated with CXCR4 coreceptor usage, totaling 26.93% of the sequences found in this patient. CXCR4 coreceptor predictions were consistent for the three methods for the variants with frequencies of 21.82% and 3.29%. For these two variants, PSSM scores applying the X4R5 matrix were −2.35 and PSSM scores using the SINSI matrix were 1.93; the false positive rate (FPR) for CXCR4 usage obtained by the Geno2pheno [coreceptor] tool was 1.7%. However, for the variant with a frequency of 1.82%, the PSSM SINSI matrix (score −0.45) and the Geno2pheno [coreceptor] tool (FPR 1.5%) predicted CXCR4 usage, while the PSSM X4R5 matrix predicted a dual-tropic phenotype (score −6.88).
Envsequence diversity at primary infection is independent of clinical markers of HIV infection
Dynamics of sequence diversity of the envV3 following primary infection
Dynamics of sequence divergence of the envV3 following primary infection
Influence of ART on the divergence of the viral population
Deep sequencing using second generation sequencing technology  offers unprecedented possibilities to rapidly generate large and quantitative data and to detect minor variants of pathogens. Elucidation of the error rates of our procedures shows they are consistent with previous studies  with per-nucleotide error rates of 0.11% and 0.10%, respectively, for the new GS FLX Titanium chemistry. We determined that under our conditions recombinant sequences were observed less frequently, 3/11491 (0.03%) than the 0.11% and 0.15% found by Tsibris et al. . Understanding the error associated with the entire sequencing process allowed the reliable analysis of sequence variants of the env V3 in a cross-sectional study of 30 patients at primary HIV infection. Our per-nucleotide error rate allowed the derivation of a frequency of detection for the least abundant sequence above experimental errors. This was defined as a frequency of 1.5%, and therefore all minor variants with frequencies above this cut-off were considered to be true variants.
The degree of sequence diversity of the env V3 was considerable among patients, with the proportion represented by the major variant ranging from 93.3% and 16.2% of the sequenced population. Although amplification was performed using primers in conserved regions of env, we cannot exclude the possibility of cis-linkage between the primer binding sites and the V3 region biasing our measures of diversity by preferentially amplifying one or more variants. Nevertheless our data are consistent with HTA assays which also revealed strong ‘monoclonal’ env bands in a number of individuals during early HIV infection [6, 7]. Coreceptor usage prediction revealed that more than ¼ of the env V3 variants in subject 18 at primary infection were predicted as capable of using the CXCR4 coreceptor. Deep env V3 sequencing is therefore a valuable tool for the sensitive and quantitative assessment of HIV diversity and divergence as well as coreceptor tropism at primary infection .
Intra-individual differences in HIV population diversity are apparent at primary infection and here we arbitrarily assigned individuals into 3 diversity groups (low, medium and high) based on Shannon Entropy. The degree of HIV sequence diversity has been associated with CD4 counts later in infection . Here, however, we were unable to correlate viral load, time from seroconversion, or CD4 counts with the frequency of sequence variants or the Shannon entropy of each individual’s HIV-1 env V3 population. As the patients included in this study were selected based on their detectable viral loads (average 223,737 RNA copies per ml at baseline), 1 ml plasma was used for RNA extraction and 10 μl RNA were used for the initial one-step RT-PCR, it is unlikely that a bias in sequence diversity was due to undersampling of the virus population during sample processing. However, it is clear that a 1 ml plasma sample from an adult represents only a fraction of the total blood volume of ~ 5 liters. In addition, focusing on post seroconversion samples may be too short a time frame to see longer term correlations with these other measures of HIV replication. Limiting the analysis to the env V3 may affect the power to find correlations with clinical markers of HIV infection.
Serial sampling of three individuals with high, medium and low diversity following primary infection suggests that increases in diversity and divergence from the virus present immediately following primary infection occurred as previously described , and these properties are detectable even over 60 weeks of sampling. The depth of sequencing obtained in our study allowed a much greater insight into the HIV population dynamics of these three individuals than it could be achieved by clonal sequencing. Subject 26, classed as low initial sequence diversity showed minimal increase in HIV divergence over time. Interestingly, at 419 days the re-emergence of variants occurred that were identical or closely related to variants present immediately post seroconversion. If this represents re-emergence from an archived provirus pool or the emergence of a variant continuously replicating in a different reservoir but excluded from the peripheral blood until day 419 is not clear. Re-emergence of archived variants has been documented following interruption from prolonged HAART [19, 20], and deep sequencing of provirus populations from macrophages indicates higher and distinct HIV diversity than T lymphocytes . It is likely therefore that combined deep sequence analysis of viral and proviral populations at primary infection will reveal more details of the intra-individual HIV evolution over time. Subject 17, classified with initial medium sequence diversity had an uncomplicated pattern of increasing HIV sequence divergence, and the emergence of different diverse populations over time, in agreement with previous analysis . Subject 25, classed as high initial sequence diversity showed evidence of two distinct HIV populations at primary infection. Both virus lineages persisted following primary infection suggesting they are representative of viruses with equivalent fitness. Whether they represent the result of transmission of more than a single virus as has been observed in approximately 25% of primary infection , or are the result of a super-infection during seroconversion cannot be determined by deep sequencing of the env V3. Furthermore, no conclusions can be made about the other 27 subjects, as only two samples were analysed for each of these subjects.
As HIV sequence divergence is affected by immune response and antiviral therapy, we determined if differences in divergence were observable at ~ 60 weeks after primary infection in individuals on the short (12 weeks, ART12) or long (48 weeks, ART48) course antiviral therapy following seroconversion. The divergence of the population from the founder virus was significantly lower after ART48 than in the non-treated control group, consistent with the successful suppression of HIV-1 evolution during HAART as previously reported [9, 10]. In contrast, ART12 did not have a significant effect on the divergence at ~ 60 weeks after primary infection. As viral loads are not significantly different on both treatment arms at ~ 60 weeks after primary infection, the observed difference in divergence between ART12 and ART48 most likely reflects prolonged virus replication inhibition in the ART48 arm. Therefore, virus divergence at ~ 60 weeks after primary infection provides a measure of the previous extent of antiretroviral suppression of HIV replication. These observed differences are unlikely to remain with continued virus replication in the absence of antiretroviral therapy. Whether more short lived effects on the ART12 arm are visible at earlier times is not clear, however the HIV evolutionary properties analysed here suggest a lack of efficacy of ART12 in the prolonged alteration of HIV evolutionary dynamics. Whether ART12 affects clinical markers of HIV infection and progression in the framework of the SPARTAC trial is currently under investigation.
We conclude, after analysis of 80 patient samples and appropriate controls, that deep sequencing of the HIV-1 env V3 can successfully be applied to ascertain HIV-1 diversity in clinical settings. It shows detailed and complex intra-patient dynamics of env V3 diversity and divergence following primary infection. Importantly, we found that long course antiretroviral therapy for 48 weeks (ART48), initiated soon after seroconversion, clearly produces a measurable difference in HIV sequence evolution. Such therapy may therefore have a more pronounced impact on markers of HIV infection and progression than short course antiretroviral therapy (ART12). Our analysis of the env V3 as a marker for HIV-1 genome diversity is, therefore, informative in detecting HIV-1 treatment effects.
Thirty study subjects diagnosed with primary HIV-1 infection (PHI) were selected from the Short Pulse Antiretroviral Therapy at Seroconversion (SPARTAC) study. PHI was confirmed by one of the following criteria: i) positive HIV-1 serology following a negative test within 6 months, ii) low level “incident” result using a detuned HIV-1 ELISA, iii) rising antibody levels detected by progression of protein bands on Western blot, or iv) positive HIV-1 proviral PCR or p24-antigen ELISA in the absence of HIV-1 specific antibodies . The participants studied longitudinally were randomised three ways, to no therapy (Control) or 12 weeks (ART12) or 48 weeks (ART48) antiretroviral therapy shortly after seroconversion, with 10 subjects in each group. Only subjects with plasma viral loads greater than 30,000 RNA copies per ml at enrolment were selected for study. Samples collected at enrolment from subjects E6 and H8, who were not included in deep sequencing studies, were used to construct cloned HIV-1 envelopes for use as sequencing controls.
Viral RNA extraction
Viral RNA was extracted from EDTA plasma collected at enrolment and after ~ 60 weeks from all subjects, and longitudinally over ~ 60 weeks from three subjects, using a QIAamp Viral RNA Mini Kit (Qiagen, Crawley, UK). Briefly, 1 ml of EDTA treated plasma was centrifuged for 1 hour at 25,000 g. The resultant pellet was resuspended in 140 μl nuclease-free phosphate buffered saline before continuing with the RNA purification according to the manufacturer’s instructions. RNA was eluted in 50 μl of elution buffer.
Full length HIV-1 env was reverse transcribed and PCR amplified from plasma viral RNA from subjects E6 and H8 using primers EnvF (5′-GGG CTC GAG GCG GCC GCG AGC AGA AGA CAG TGG CAA TG-3′) and EnvR (5′-AAA TCT AGA GGG CCC CCA CTT GCC ACC CAT RTT A-3′). Amplified env genes were cloned into pcDNA3.1 (+) using restriction sites ApaI and XbaI. Single clones were purified from transformed MACH I strain E.coli using a GeneJET™ Plasmid Miniprep Kit (Fermentas, York, UK). Eight micrograms of each plasmid were linearised with 50 units of ApaI restriction enzyme and digested DNA was purified using a GeneJET™ Gel Extraction Kit (Fermentas). One-hundred nanograms of purified DNA were transcribed with T7 polymerase (Invitrogen, Paisley, UK) in the supplied buffer supplemented with 0.4 mM of each nucleotide, 100 ng/μl BSA (New England Biolabs, Hitchen, UK), 2 units/μl of RNaseOUT™ Ribonuclease Inhibitor (Invitrogen) and in the presence of 5 mM DTT. Reactions were incubated for 2 hours at 37°C. Template DNA was digested with one unit of DNAase I (Invitrogen) at 37°C for 30 min and reactions were terminated with a final concentration of 2.5 mM EDTA (pH 8.0) for 10 min at 65°C. Transcripts were purified using an RNeasy kit (Qiagen) with on-column DNase digestion according to the manufacturer’s instructions. The integrity and quantity of RNA was determined using a Bioanalyzer 2100 (Agilent Technologies, Wokingham, UK). Elimination of DNA template was confirmed by quantitative real-time PCR using SYBR Green PCR master mix (Sigma Aldrich, Gillingham, UK), which did not produce a fluorescent signal after 35 amplification cycles in a LightCycler 2.0 (Roche Applied Science, Lewes, UK).
Reverse transcription and nested polymerase chain reaction
Viral RNA and in vitro transcribed RNA were reverse transcribed and PCR amplified in a single-tube reaction using SuperScript® III One-Step RT-PCR System with Platinum® Taq High Fidelity (Invitrogen). Each 50 μl reaction contained 200 nM of primer CV1 (5′-TAA CAT GTG GAA AAA TAA CAT GGT-3′), 200 nM of primer CV4 (5′-AGA AAA ATT CTC CTC CAC AAT TAA A-3′) and 10 μl plasma RNA extract or 104 copies of RNA transcript. Cycling conditions were 55°C for 30 min (reverse transcription), 94°C for 2 min (hot start), 35 cycles of 94°C for 30 s, 55°C for 30 s, 68°C for 30 seconds, and a final incubation at 68°C for 5 min. Second round PCR was performed using Phusion® Hot Start High-Fidelity DNA Polymerase (Finnzymes). Each 25 ul reaction contained 12.25 ul Nuclease-Free Water, 5 ul Phusion® HF Buffer (5x), 0.5 ul dNTP mix (10 mM each), 1 ul each of primers JAE1 (5′-CAC AGT ACA ATG TAC ACA TG-3′) and JAE4 (5′-ACA ATT TCT GGG TCC CCT CC-3′) (20 pmol/ul), 0.25 ul Phusion® Hot Start DNA Polymerase (2 U/μl) and 5 ul of template first round PCR product. Cycling conditions were 98°C for 30 s (initial denaturation), 40 cycles of 98°C for 10 s, 60°C for 20 s, 72°C for 15 s, and a final incubation at 72°C for 10 min. Amplicons with a size of 387 bp were visualized by 2% agarose gel electrophoresis for 1 h at 120 V in Tris-acetate-EDTA buffer.
Deep sequencing of amplicons
Nested PCR amplicons were sequenced without further purification using the Genome Sequencer FLX Instrument and GS FLX Titanium series reagents (Roche/454 Life Sciences) according to the manufacturer’s instructions. In short, single-stranded DNA libraries were prepared with the GS FLX Titanium Rapid Library Preparation Kit, using one of the 12 Multiplex Identifier (MID) adaptors for each sample. Between 5 and 12 libraries were pooled and immobilized onto DNA capture beads. Clonal amplification was carried out by emulsion PCR. Emulsions were broken, beads were enriched, loaded onto 1/4 or 1/8 of a PicoTiterPlate and sequencing-by-synthesis was performed .
Read assembly and filtering
One SFF file per region of the PicoTiterPlate was generated by the Genome Sequencer FLX System Software (Roche/454 Life Sciences). Using the sfffile and sffinfo commands of the SFF tools, the SFF file was split into separate SFF files based on the MIDs, and FASTA files containing sequence reads for each sample were created. Sequence reads were mapped against a HIV-1 subtype B env sequence which matched the samples with regard to the country of origin (UK) and the collection date (2005) [GenBank: FJ653428] using the software Segminator [18, 22]. Thereby reads with a length < 70 nt and/or low identity to the reference sequence were removed. The pipeline for data processing, filtering and assembly is summarised in Additional file 6: Figure S5. Alignments were trimmed to the env V3 region and visually inspected in BioEdit version 184.108.40.206 . All reads not spanning the entire env V3 region as well as misaligned reads were removed. To obtain a final alignment, reads containing ‘Ns‘were removed, as these correspond to positions where no nucleotide was incorporated during sequencing in greater than three consecutive flows. Additional file 1: Table S1 summarizes how many sequences were discarded at each step of the filtering process. Subsequently, duplicate sequences were eliminated with the ElimDupes tool  which generated an alignment comprising only the unique sequences found in each sample, and a table showing the numbers and names of the duplicates represented by each unique sequence. Frequencies of sequence variants as well as process error rates for the control samples were calculated and the most frequent unique sequences within each sample were extracted from the alignments with a combination of Excel and customized python™ scripts. All figures were computed in R version 2.10.1 .
The Highlighter tool  was used to visualize alignments and to screen for recombinant sequences. These were verified with the manual bootscan method  implemented in the Recombination Detection program version 3.42 [28, 29].
Shannon information entropy
where N(t) is the number of unique sequence variants that are present at time t, and f i (t) is the proportion of the ith sequence variant . The more diverse the variants present in amounts comparable to one another, the greater the resulting entropy value.
Genetic distance and divergence analysis and Neighbor-joining trees
To analyse the dynamics of sequence divergence of the env V3 following primary infection in three patients in detail, sequence divergence was calculated as the pairwise genetic distance (number of nucleotide substitutions per site) of a sequence variant at a given time from the consensus sequence at time 0. Prior to computation, the appropriate model of nucleotide substitution was determined using jModelTest version 0.1.1 . Genetic distances were estimated under the General Time Reversible model of nucleotide substitution, with proportion of invariable sites and gamma-distributed rate heterogeneity (GTR + I + G), using PAUP* version 4.0b10  and Neighbor-joining trees were reconstructed under the same model. Trees were edited with the program FigTree version 1.3.1 .
To explore the impact of ART on divergence of the viral population over time in all 30 patients, mean intra-patient root to tip distances, defined as genetic distance (in number of nucleotide substitutions per site) from each sequence to the root, were used. Distances were calculated under the uncorrected (p) model of nucleotide substitution using PAUP* version 4.0b10. The null hypothesis that the root to tip distances are equal in the three groups was tested using Wilcoxon rank sum tests using R version 2.10.1.
Coreceptor usage prediction
Accession numbers for sequencing data
The Roche/454 Life Sciences sequencing data obtained in this study is available from the EMBL/GenBank®/DDBJ Sequence Read Archive under study accession number ERP001147.
Trial Steering Committee (TSC)
Independent TSC Members: A Breckenridge (Chair), P Clayden, C Conlon, F Conradie, J Kaldor*, F Maggiolo; F Ssali
Country Principal Investigators: D A Cooper, P Kaleebu, G Ramjee, M Schechter, G Tambussi, J Weber
Data and Safety Monitoring Committee (DSMC)
T Peto (Chair) A McLaren (in memoriam), V Beral, G Chene, J Hakim
Co-ordinating Trial Centre
MRC Clinical Trials Unit, London (A Babiker, K Porter, M Thomason, F Ewings, M Gabriel, D Johnson, K Thompson, A Cursley*, K Donegan*, E Fossey*, P Kelleher*, K Lee*, B Murphy*, D Nock*)
Central Immunology Laboratories and Repositories
The Peter Medawar Building for Pathogen Research, University of Oxford, UK (R Phillips, J Frater, L Ohm Laursen*, N Robinson, P Goulder, H Brown)
Central Virology Laboratories and Repositories
Jefferiss Trust Laboratories, Imperial College, London, UK (M McClure, D Bonsall*, O Erlwein*, A Helander*, S Kaye, M Robinson, Lisa Cook*, Gemma Adcock*, Parvin Ahmed*)
Clinical Endpoint Review Committee
N Paton, S Fidler
Investigators and Staff at Participating Sites
Australia: St Vincent’s Hospital, Sydney (A Kelleher), Northside Clinic, Melbourne (R Moore), East Sydney Doctors, Sydney, (R McFarlane), Prahran Market Clinic, Melbourne (N Roth), Taylor Square Private Clinic, Sydney (R Finlayson), The Centre Clinic, Melbourne (B Kiem Tee), Sexual Health Centre, Melbourne (T Read), AIDS Medical Unit, Brisbane (M Kelly), Burwood Rd Practice, Sydney (N.Doong) Holdsworth House Medical Practice, Sydney (M Bloch) Aids Research Initiative, Sydney (C Workman).
Coordinating centre in Australia: Kirby Institute University of New South Wales, Sydney (P Grey, D A Cooper, A Kelleher, M Law).
Brazil: Projeto Praça Onze, Hospital Escola São Francisco de Assis, Universidade federal do Rio de Janeiro, Rio de Janeiro (M Schechter, P Gama, M Mercon*, M Barbosa de Souza, C Beppu Yoshida, J R Grangeiro da Silva, A Sampaio Amaral, D Fernandes de Aguiar, M de Fátima Melo, R Quaresma Garrido
Italy: Ospedale San Raffaele, Milan (G Tambussi, S Nozza, M Pogliaghi, S ChiappettaL Della Torre, Elisa Gasparotto,), Ospedale Lazzaro Spallanzani, Roma (G D’Offizi, C Vlassi, A Corpolongo)
Cape Town: Desmond Tutu HIV Centre, Institute of Infectious Diseases, Cape Town
(R Wood, J Pitt, C Orrell, F Cilliers, R Croxford, K Middelkoop, L G Bekker, C Heiberg, J Aploon, N Killa, E Fielder, T Buhler)
Johannesburg: The Wits Reproductive Health and HIV Institute, University of Witswatersrand, Hillbrow Health Precinct, Johannesburg. (H Rees, F Venter, T Palanee) Contract Laboratory Services, Johannesburg Hospital, Johannesburg (W Stevens, C Ingram, M Majam, M Papathanasopoulos)
Kwazulu-Natal: HIV Prevention Unit, Medical Research Council, Durban (G Ramjee, S Gappoo, J Moodley, A Premrajh, L Zako)
Uganda: MRC/Uganda Virus Research Institute, Entebbe (H Grosskurth, A Kamali, P Kaleebu, U Bahemuka, J Mugisha*, H F Njaj*)
Spain: Hospital Clinic-IDIBAPS. University of Barcelona, Barcelona (J M Miro, M López-Dieguez*, C. Manzardo, JA Arnaiz, T. Pumarola, M. Plana, M. Tuset, MC Ligero, MT García, T. Gallart, JM Gatell)
UK and Ireland: Royal Sussex County Hospital, Brighton (M Fisher, K Hobbs, N Perry, D Pao, D Maitland, L Heald), St James’s Hospital, Dublin (F Mulcahy, G Courtney, S O’Dea, D Reidy), Regional Infectious Diseases Unit, Western General Hospital and Genitourinary Dept, Royal Infirmary of Edinburgh, Edinburgh (C Leen, G Scott, L Ellis, S Morris, P Simmonds), Chelsea and Westminster Hospital, London (B Gazzard, D Hawkins, C Higgs), Homerton Hospital, London (J Anderson, S Mguni), Mortimer Market Centre, London (I Williams, N De Esteban, P Pellegrino, A Arenas-Pinto, D Cornforth*, J Turner*) North Middlesex Hospital (J Ainsworth, A Waters), Royal Free Hospital (M Johnson, S Kinloch, A Carroll, P Byrne, Z Cuthbertson), Barts & the London NHS Trust, London (C Orkin, J Hand, C De Souza), St Mary’s Hospital, London (J Weber, S Fidler, E Hamlyn, E Thomson*, J Fox*, K Legg, S Mullaney*, A Winston, S Wilson, P Ambrose), Birmingham Heartlands Hospital (S Taylor, G Gilleran).
Imperial College Trial Secretariat
S Keeling, A Becker
Imperial College DSMC Secretariat
Left the study team before the trial ended
This work was supported by the Wellcome Trust. We are grateful for support from the NIHR Biomedical Research Centre funding scheme at Imperial College Healthcare NHS Trust and University College London Hospitals NHS Foundation Trust. The funding sources had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. The authors would like to thank Vicky Murray and Tarryn Porter for excellent technical assistance and Simon Watson for computational biology assistance. Abbott Laboratories generously provided Kaletra/Aluvia (lopinavir and low-dose ritonavir) for the African sites.
- Keele BF, Giorgi EE, Salazar-Gonzalez JF, Decker JM, Pham KT, Salazar MG, Sun C, Grayson T, Wang S, Li H, et al: Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci U S A. 2008, 105: 7552-7557. 10.1073/pnas.0802203105.PubMed CentralView ArticlePubMedGoogle Scholar
- Ganusov VV, Goonetilleke N, Liu MK, Ferrari G, Shaw GM, McMichael AJ, Borrow P, Korber BT, Perelson AS: Fitness costs and diversity of CTL response determine the rate of CTL escape during the acute and chronic phases of HIV infection. J Virol. 2011, 85: 10518-10528. 10.1128/JVI.00655-11.PubMed CentralView ArticlePubMedGoogle Scholar
- Fischer W, Ganusov VV, Giorgi EE, Hraber PT, Keele BF, Leitner T, Han CS, Gleasner CD, Green L, Lo CC, et al: Transmission of single HIV-1 genomes and dynamics of early immune escape revealed by ultra-deep sequencing. PLoS One. 2010, 5: e12303-10.1371/journal.pone.0012303.PubMed CentralView ArticlePubMedGoogle Scholar
- Love TM, Thurston SW, Keefer MC, Dewhurst S, Lee HY: Mathematical modeling of ultradeep sequencing data reveals that acute CD8+ T-lymphocyte responses exert strong selective pressure in simian immunodeficiency virus-infected macaques but still fail to clear founder epitope sequences. J Virol. 2010, 84: 5802-5814. 10.1128/JVI.00117-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Shankarappa R, Margolick JB, Gange SJ, Rodrigo AG, Upchurch D, Farzadegan H, Gupta P, Rinaldo CR, Learn GH, He X, et al: Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. J Virol. 1999, 73: 10489-10502.PubMed CentralPubMedGoogle Scholar
- Riddle TM, Shire NJ, Sherman MS, Franco KF, Sheppard HW, Nelson JA: Sequential turnover of human immunodeficiency virus type 1 env throughout the course of infection. J Virol. 2006, 80: 10591-10599. 10.1128/JVI.00644-06.PubMed CentralView ArticlePubMedGoogle Scholar
- Harrington PR, Nelson JA, Kitrinos KM, Swanstrom R: Independent evolution of human immunodeficiency virus type 1 env V1/V2 and V4/V5 hypervariable regions during chronic infection. J Virol. 2007, 81: 5413-5417. 10.1128/JVI.02554-06.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsibris AM, Korber B, Arnaout R, Russ C, Lo CC, Leitner T, Gaschen B, Theiler J, Paredes R, Su Z, et al: Quantitative deep sequencing reveals dynamic HIV-1 escape and large population shifts during CCR5 antagonist therapy in vivo. PLoS One. 2009, 4: e5683-10.1371/journal.pone.0005683.PubMed CentralView ArticlePubMedGoogle Scholar
- Mens H, Pedersen AG, Jorgensen LB, Hue S, Yang Y, Gerstoft J, Katzenstein TL: Investigating signs of recent evolution in the pool of proviral HIV type 1 DNA during years of successful HAART. AIDS Res Hum Retroviruses. 2007, 23: 107-115. 10.1089/aid.2006.0089.View ArticlePubMedGoogle Scholar
- Parera M, Ibanez A, Clotet B, Martinez MA: Lack of evidence for protease evolution in HIV-1-infected patients after 2 years of successful highly active antiretroviral therapy. J Infect Dis. 2004, 189: 1444-1451. 10.1086/382485.View ArticlePubMedGoogle Scholar
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437: 376-380.PubMed CentralPubMedGoogle Scholar
- Rozera G, Abbate I, Bruselles A, Vlassi C, D’Offizi G, Narciso P, Chillemi G, Prosperi M, Ippolito G, Capobianchi MR: Massively parallel pyrosequencing highlights minority variants in the HIV-1 env quasispecies deriving from lymphomonocyte sub-populations. Retrovirology. 2009, 6: 15-10.1186/1742-4690-6-15.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang C, Mitsuya Y, Gharizadeh B, Ronaghi M, Shafer RW: Characterization of mutation spectra with ultra-deep pyrosequencing: application to HIV-1 drug resistance. Genome Res. 2007, 17: 1195-1201. 10.1101/gr.6468307.PubMed CentralView ArticlePubMedGoogle Scholar
- Huse SM, Huber JA, Morrison HG, Sogin ML, Welch DM: Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 2007, 8: R143-10.1186/gb-2007-8-7-r143.PubMed CentralView ArticlePubMedGoogle Scholar
- Meyerhans A, Vartanian JP, Wain-Hobson S: DNA recombination during PCR. Nucleic Acids Res. 1990, 18: 1687-1691. 10.1093/nar/18.7.1687.PubMed CentralView ArticlePubMedGoogle Scholar
- Salazar-Gonzalez JF, Bailes E, Pham KT, Salazar MG, Guffey MB, Keele BF, Derdeyn CA, Farmer P, Hunter E, Allen S, et al: Deciphering human immunodeficiency virus type 1 transmission and early envelope diversification by single-genome amplification and sequencing. J Virol. 2008, 82: 3952-3970. 10.1128/JVI.02660-07.PubMed CentralView ArticlePubMedGoogle Scholar
- Swenson LC, Moores A, Low AJ, Thielen A, Dong W, Woods C, Jensen MA, Wynhoven B, Chan D, Glascock C, Harrigan PR: Improved Detection of CXCR4-Using HIV by V3 Genotyping: Application of Population-Based and “Deep” Sequencing to Plasma RNA and Proviral DNA. J Acquir Immune Defic Syndr. 2010, 54: 506-510. 10.1097/QAI.0b013e3181d0558f.View ArticlePubMedGoogle Scholar
- Archer J, Braverman MS, Taillon BE, Desany B, James I, Harrigan PR, Lewis M, Robertson DL: Detection of low-frequency pretherapy chemokine (CXC motif) receptor 4 (CXCR4)-using HIV-1 with ultra-deep pyrosequencing. Aids. 2009, 23: 1209-1218. 10.1097/QAD.0b013e32832b4399.PubMed CentralView ArticlePubMedGoogle Scholar
- Chun TW, Davey RT, Ostrowski M, Shawn Justement J, Engel D, Mullins JI, Fauci AS: Relationship between pre-existing viral reservoirs and the re-emergence of plasma viremia after discontinuation of highly active anti-retroviral therapy. Nat Med. 2000, 6: 757-761. 10.1038/77481.View ArticlePubMedGoogle Scholar
- Joos B, Fischer M, Kuster H, Pillai SK, Wong JK, Boni J, Hirschel B, Weber R, Trkola A, Gunthard HF: HIV rebounds from latently infected cells, rather than from continuing low-level replication. Proc Natl Acad Sci U S A. 2008, 105: 16725-16730. 10.1073/pnas.0804192105.PubMed CentralView ArticlePubMedGoogle Scholar
- Fidler S, Oxenius A, Brady M, Clarke J, Cropley I, Babiker A, Zhang HT, Price D, Phillips R, Weber J: Virological and immunological effects of short-course antiretroviral therapy in primary HIV infection. Aids. 2002, 16: 2049-2054. 10.1097/00002030-200210180-00010.View ArticlePubMedGoogle Scholar
- Archer J, Rambaut A, Taillon BE, Harrigan PR, Lewis M, Robertson DL: The evolutionary analysis of emerging low frequency HIV-1 CXCR4 using variants through time–an ultra-deep approach. PLoS Comput Biol. 2010, 6: e1001022-10.1371/journal.pcbi.1001022.PubMed CentralView ArticlePubMedGoogle Scholar
- Hall TA: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser. 1999, 41: 95-98.Google Scholar
- R Development Core Team: R: A language and environment for statistical computing. 2009, Vienna, Austria: R Foundation for Statistical ComputingGoogle Scholar
- Salminen MO, Carr JK, Burke DS, McCutchan FE: Identification of breakpoints in intergenotypic recombinants of HIV type 1 by bootscanning. AIDS Res Hum Retroviruses. 1995, 11: 1423-1425. 10.1089/aid.1995.11.1423.View ArticlePubMedGoogle Scholar
- Martin DP, Posada D, Crandall KA, Williamson C: A modified bootscan algorithm for automated identification of recombinant sequences and recombination breakpoints. AIDS Res Hum Retroviruses. 2005, 21: 98-102. 10.1089/aid.2005.21.98.View ArticlePubMedGoogle Scholar
- Martin DP, Williamson C, Posada D: RDP2: recombination detection and analysis from sequence alignments. Bioinformatics. 2005, 21: 260-262. 10.1093/bioinformatics/bth490.View ArticlePubMedGoogle Scholar
- Shannon CE: Prediction and entropy of printed English. Bell Syst Technical J. 1951, 30: 50-64.View ArticleGoogle Scholar
- Posada D: jModelTest: phylogenetic model averaging. Mol Biol Evol. 2008, 25: 1253-1256. 10.1093/molbev/msn083.View ArticlePubMedGoogle Scholar
- Swofford DL: PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. 2003, Sunderland, Massachusetts: Sinauer AssociatesGoogle Scholar
- Jensen MA, Li FS, AB van’t Wout, Nickle DC, Shriner D, He HX, McLaughlin S, Shankarappa R, Margolick JB, Mullins JI: Improved coreceptor usage prediction and genotypic monitoring of R5-to-X4 transition by motif analysis of human immunodeficiency virus type 1 env V3 loop sequences. J Virol. 2003, 77: 13376-13388. 10.1128/JVI.77.24.13376-13388.2003.PubMed CentralView ArticlePubMedGoogle Scholar
- Sing T, Low AJ, Beerenwinkel N, Sander O, Cheung PK, Domingues FS, Buch J, Daumer M, Kaiser R, Lengauer T, Harrigan PR: Predicting HIV coreceptor usage on the basis of genetic and clinical covariates. Antivir Ther. 2007, 12: 1097-1106.PubMedGoogle Scholar
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