Phylogenetic analysis consistent with a clinical history of sexual transmission of HIV-1 from a single donor reveals transmission of highly distinct variants
- Suzanne English1,
- Aris Katzourakis2,
- David Bonsall3,
- Peter Flanagan1,
- Anna Duda1,
- Sarah Fidler3,
- Jonathan Weber3,
- Myra McClure3,
- SPARTAC Trial Investigators1,
- Rodney Phillips†1, 4, 5 and
- John Frater†1, 4, 5Email author
© English et al; licensee BioMed Central Ltd. 2011
Received: 15 January 2011
Accepted: 7 July 2011
Published: 7 July 2011
To combat the pandemic of human immunodeficiency virus 1 (HIV-1), a successful vaccine will need to cope with the variability of transmissible viruses. Human hosts infected with HIV-1 potentially harbour many viral variants but very little is known about viruses that are likely to be transmitted, or even if there are viral characteristics that predict enhanced transmission in vivo. We show for the first time that genetic divergence consistent with a single transmission event in vivo can represent several years of pre-transmission evolution.
We describe a highly unusual case consistent with a single donor transmitting highly related but distinct HIV-1 variants to two individuals on the same evening. We confirm that the clustering of viral genetic sequences, present within each recipient, is consistent with the history of a single donor across the viral env, gag and pol genes by maximum likelihood and Bayesian Markov Chain Monte Carlo based phylogenetic analyses. Based on an uncorrelated, lognormal relaxed clock of env gene evolution calibrated with other datasets, the time since the most recent common ancestor is estimated as 2.86 years prior to transmission (95% confidence interval 1.28 to 4.54 years).
Our results show that an effective design for a preventative vaccine will need to anticipate extensive HIV-1 diversity within an individual donor as well as diversity at the population level.
A successful HIV-1 vaccine would be designed based upon the antigenicity of transmissible viruses. At the global level, multiple subtypes with evidence of on-going evolution  result in a level of diversity that has already frustrated all efforts to synthesize a universal HIV-1 vaccine . Additionally, substantial virus diversity develops within a single host during chronic infection , and it is unclear which viral variants are transmissible to a new host. Recent efforts have concentrated on inferring variant transmissibility by characterizing the precise genetic and antigenic features of viruses found during very early stages of infection [4–9].
Single viral variants are detected in a significant proportion of new HIV-1 infections in vivo, indicating a profound genetic bottleneck [6, 10]. The degree of genetic bottleneck has been associated with the route of transmission [11–13]. Another factor associated with the number of infecting variants is the presence of genitourinary infections . Together, these data suggest that differences in the degree of genetic bottleneck are related to variations in mucosal defence and its integrity.
However, the actual mechanism of this genetic bottleneck remains unclear, and studies may be confounded by variations in both the risk of transmission among donors and the diversity of transmissible virions within donors . The highest risk of transmission occurs during primary infection when the population size of infectious virus peaks . However, viral diversity within the acutely-infected donor is limited, potentially making transmitted viruses indistinguishable in the recipient [4–6, 11, 15].
Furthermore, genetic analysis has also indicated that mucosal defence and integrity are not the only explanations for the apparent genetic bottleneck. Demographic models have been developed that avoid unsupported prior assumptions about the degree of genetic bottleneck . Viral variability was compared  in gag and env genes after transmission in mother-to-child transmission cases and in men who have sex with men (MSM). Viral variability after transmission was not consistently associated with the route of transmission . In addition, a severe genetic bottleneck may be a sufficient, but not a necessary, condition for random transmission of genetic variability .
If transmission of viral variability is not random, then transmission may occur by natural selection [17, 18]. However, transmissibility has not yet been associated with specific viral characteristics. Most new, sexually-transmitted HIV-1 infections are CCR5-tropic [4, 19], but this may reflect biased representation of these variants in genital fluids [20, 21]. In eight cases of heterosexual transmission of subtype C , transmitted variants tended to have fewer potential N-linked glycosylation sites (PNLGSs) and shorter hypervariable loops than the average variant in the donor host. In addition, recipient env-pseudotyped virus was more susceptible to neutralization by donor serum than donor env-pseudotyped virus . A study of 35 subtype A cases from Kenya, and 13 subtype B cases from the USA  found that recently-infected persons had viruses with shorter, less-glycosylated V1V2 loops compared with a database of viruses . However, studies of subtype B have not shown a consistent decrease in hypervariable loop length or the number of PNLGSs [24, 25]. Therefore, there is no firm evidence that natural selection determines transmission of viral variants.
Animal models of HIV infection that use the closely-related simian immunodeficiency virus (SIV) have also demonstrated that many different variants circulating within the host are transmissible. A low-dose, intrarectal inoculum of SIV was given to 18 rhesus macaques  to mimic physiological concentrations. Although between one and five variants initiated new infections, the viruses transmitted to all macaques collectively reflected the diversity within the inoculum . Another study  demonstrated a stochastic pattern of V1V2 variant transmission from an inoculum. Therefore, a broad range of viruses circulating in a single donor may be potentially transmissible at any one time, consistent with the hypothesis that transmission of viral variants is a random process.
To demonstrate that this lack of predictability is also true for HIV-1 transmission in humans, we present an unusual case consistent with a clinical history of one male having transmitted significantly divergent HIV-1 variants to two recipients on the same evening. We show that, as with macaques, diversity in early infection is limited and compatible with transmission of a single variant to each recipient, but also that a single donor can transmit two very different HIV-1 strains contemporaneously. Furthermore, we do not find any evidence that this between-host genetic divergence is evidence of selection pressure from either humoral or cellular immunity during or since transmission. Finally, if transmission is a random process, we hypothesize that a protective vaccine would need to cover the breadth of transmissible variation within individual donors as well as population-wide diversity.
Results and Discussion
Case history of a single, third party exposure and recent seroconversion
P1 and P2 were sampled on the same day when they enrolled in the SPARTAC trial, 63 days post-exposure. Both participants were randomized to receive no therapy. Plasma for sequencing was re-sampled on the same date from both participants, on day 235 post-exposure. P1 reported exposure to a fourth party after day 63 and before day 235. Evidence of HIV-1 super-infection in P1 was seen on plasma collected at day 235 (data not shown). On day 245, P1 was diagnosed with acute hepatitis C virus (HCV) infection (Figure 1) having been negative for HCV by PCR and antibody on day 29. He commenced treatment with ribavirin and interferon after day 245. Therefore, all time-points after day 63 were excluded from further phylogenetic analysis.
The CD4+ count and plasma viral load values for P1 and P2 are shown in Figure 1. Despite the same exposure, P1 and P2 followed different clinical courses. P1 maintained a CD4+ T cell count greater than 350 cells/mm3 during the first 310 days of untreated infection compared with P2, who had only two CD4+ readings greater than 350 cells/mm3 over the first 249 days of infection. The plasma viral load for P1 was consistently lower than P2 after day 96, with the exception of a second peak reading in P1 taken on day 249, after the detection of HIV-1 super-infection and acute HCV infection. Therefore, P2 appeared to progress more rapidly than P1.
Further clinical laboratory evidence was consistent with the history of a single donor because the time window for one participant to have infected the other was short. Participants P1 and P2 were both positive for p31 antigen on Western Blot on day 63. Therefore, the minimum estimated time since the onset of detectable viraemia (> 50 copies/ml) of approximately 47.4 days [30, 31]. Thus, the estimated maximum pre-viraemic phase for either participant was 15 to 16 days. Since, the estimated pre-viraemic phase for HIV-1 lasts between 7 and 25 days [30–33], one participant could have infected the other only between day 7 and day 9 post-exposure to the third party. However, peak viral load in acutely infected subjects is reached 7 or more days after the onset of detectable viraemia [6, 12, 34] and the infectiousness of a donor MSM is low if his viral load is 400 copies/ml or less . Therefore, while the laboratory evidence did not exclude this alternative scenario, it was unlikely that one participant infected the other.
Sequences for phylogenetic analysis obtained from multiple viral genes
Between-host phylogenetic analysis supports the clinical history of a single donor
By both maximum likelihood (ML) and Bayesian MCMC based analyses, sequences from P1 and P2 were highly related and clustered to the exclusion of all other sequences, consistent with a common donor (Figure 2, Additional Files 1 and 2). We demonstrated the statistical support for the robustness of the cluster by both methods (Figure 2 - ML bootstrap values for three genes were: env 100%, gag 99.9% and pol 99.3%, and Bayesian MCMC based posterior probabilities were: 100% for env, gag and pol). We could not use phylogenetic inference to exclude the possibility that one participant infected the other, since such techniques cannot prove the direction of transmission in a forensic sense . For example, we could not exclude the possibility that two strains were transmitted to one participant and that an initially infectious strain was out-competed to extinction prior to day 63. However, results from other studies suggested this was unlikely [5, 6, 13, 40, 41]. Therefore, phylogenetic analyses were consistent with the clinical history that a single, third party contemporaneously transmitted the divergent strains that infected P1 and P2.
Significant between-host divergence observed in transmitted HIV-1 env and polgenes
We measured the inter-host distance for stem branches, which are the internal branches separating the within-patient sequences. For the gag gene fragment, which we expected to be the most conserved fragment, the inter-host distance was 0.54% by ML analysis (Figure 2c). The inter-host distance for the env fragment, which we expected to be the least conserved of the three, was 3.81%(Figure 2a). For the pol fragment, the inter-host distance was 1.93% (Figure 2e). The inter-host distance for env contrasts with the smaller mean distance within each participant. For env, the mean within-patient distance was 0.54% by ML analysis in both participants across the gap-stripped 1305 nucleotide alignment, consistent with the history of recent infection (Figure 2a). In addition, sequence analysis of day 235 plasma also failed to detect env or pol sequences from P1 in P2 and vice versa (data not shown). Therefore, despite sharing highly similar gag genes, consistent with the clinical history of a common origin, P1 and P2 appeared to be infected with remarkably different env variants and, to a lesser extent, pol variants.
Envdivergence quantified by estimating the tMRCA
Potential antigenic variation in the gp120 proteins of transmitted viruses
However, demonstrating a high level of divergence did not answer whether each patient received divergent variants at random or whether there was selection at transmission. Transmission of divergent env gp120 variants could be due to hard selection for differences in antigenicity in each recipient. Hard selection involves selective mortality of variants . In rhesus macaques, SIV envelope proteins appear be under hard selection at transmission due to neutralizing antibodies . Attempts have been made to infer the antigenicity of HIV-1 envelope proteins to neutralizing antibodies from the number of potential N-linked glycosylation sites (PNLGSs) in gp120 [22, 48]. Therefore, we hypothesized that differences in the number of PNLGSs in gp120 would indicate potential between-host differences in viral antigenicity.
We then looked at the positions that were not 100% identical, to see if there was any evidence of potential hard selection in each recipient during transmission. In particular, we focussed on the V1V4 region that is implicated in susceptibility to neutralizing antibodies. Previous studies of this region have suggested that fewer PNLGSs in this region increases the susceptibility of highly related strains to neutralizing antibody [22, 24, 25, 49]. We found a higher mean number of PNLGSs across V1V4 in P2 (24 sites, range 23-25) than P1 (19 sites, range 18-20; p < 0.0001, unpaired T-test). These data indicated that there could be a difference in susceptibility to neutralizing antibodies between these two strains, consistent with a non-random model of transmission.
No autologous or cross-neutralization observed despite potential antigenic variation
We hypothesized that differences at PNLGSs might equate to differences in neutralization that would explain the transmission of divergent env variants [22, 24, 25, 49]. Therefore, we investigated whether the viral isolates from P1 and P2 had different neutralization profiles. Viruses pseudotyped with full-length day 63 env sequences from P1 and P2 were tested against autologous or heterologous serum from each participant sampled at day 186 post-exposure. However, the env pseudotypes for both P1 and P2 were only poorly neutralized or cross-neutralized (half maximal inhibitory concentration, IC50, of serum ≤ 1:20, Additional File 4). Therefore, it seemed unlikely that a humoral response was responsible for the detection of different env variants in P1 and P2, consistent with transmission being a random process.
However, envelope proteins are not only potentially under immune selection at transmission but also might be selected for an increased ability to enter cells. We used the data from our neutralization assay to estimate the infectivity of the env pseudotyped viruses in vitro. Pseudoviruses derived from P1 sequences were approximately 2.5 times (P < 0.05) more infectious in vitro than pseudoviruses from P2, after normalization to reverse transcriptase levels (Additional File 5). We noted between-host diversity in C2C4, including differences in glycosylation. C2C4 encodes discontinuous regions involved in CD4 and co-receptor binding [50–52]. Inferred gp120 protein sequences were analysed with several algorithms that were evaluated by Low and colleagues , to detect differences in predicted co-receptor usage and minimize the possibility of missing CXCR4/CCR5 dual-use variants. However, these algorithms predicted that all sampled viruses from P1 and P2 would use CCR5. Our experiment was not specifically set up to test infectivity so all these results must be interpreted with caution. In addition, potential differences in infectivity do not explain why both viruses were able to cause productive infection in different individuals. Therefore, we found no evidence to reject a random model of transmission.
HLA Class 1 restricted responses and potential selection pressure around transmission
We also investigated HIV-1 specific cellular immune responses, to exclude another potential source of hard selection in each participant that might influence our results. Clinical progression and viral load have been associated with host HLA Class I type in chronic infection [54–56]. HLA Class I restricts the ability of host cytotoxic T lymphocytes (CTLs) to recognize and destroy infected cells. Furthermore, sequencing studies have detected evidence consistent with escape from CTL responses within weeks of HIV-1 infection . The role that CTLs play in preventing established viral infection in humans remains unclear. However, vaccination of rhesus macaques to produce detectable CTL responses is associated with partial protection from infection , and HIV-1 specific CTL responses have been detected in persons who remain PCR/ELISA negative despite high-risk exposure [59–61]. Therefore, we hypothesized that CTL responses during and after transmission were a potential source of hard selection in P1 and P2.
Firstly, we compared the Class I HLA type of P1 and P2 with the clinical data to see if there was evidence of selection. P1 possessed HLA-A*0201, A*2402, HLA-B*1402, B*3543, Cw*0102, Cw*0802; P2 possessed HLA-A*0101, A*2901, B*0801, B*5001, Cw*0602, Cw*0701. Neither participant possessed HLA types that are strongly associated with protection from progression in chronic infection [62, 63]. However, P2, who progressed quickest, possessed the HLA-A*0101 B*0801 haplotype that is associated with more rapid progression . Therefore, we hypothesize that host factors contribute to the different clinical outcome in these participants and that the viruses had been under different selection pressures since transmission.
Detectable CTL responses do not explain between-host divergence in env
We investigated whether different CTL responses could have influenced detection of divergent variants in our study. Phylogenetic analysis assumes neutral evolution rather than natural selection . Therefore, we compared viral sequence data and γ-interferon ELISpot data from each participant to see if cytotoxic T lymphocyte responses since transmission may have accounted for observed between-host divergence in env [65, 66]. Sequence data were available for the two env gp120 optimal peptides against which P2 had a significant response: TVYYGVPVWK (HXB2 gp160 30-46) and SFEPIPIHY (HXB2 gp160 202-221). The inferred amino acid sequences for P1 were identical to the wild-type peptides at these epitopes: TVYYGVPVWR and SFEPIPIHY. P2 was also infected with wild-type TVYYGVPVWR, as well as both wild-type and mutant SFEPIPIHK sequences. Therefore, between-host genetic differences in env could not be attributed to detectable, env-directed CTL responses, and our data were still consistent with transmission of env variants being a random process.
We have quantified for the first time significant, between-host genetic divergence in HIV-1 variants that are likely to have been transmitted by a single donor to two recipients on the same night. Furthermore, these data indicate that currently it is not possible to predict which of the many HIV-1 variants circulating at the time of transmission will successfully seed a new infection. If transmission is a random process, then this represents a major hurdle that any HIV-1 vaccine design will need to overcome.
360 participants, 151 of whom were from the UK or Ireland, were recruited to the Short Pulse AntiRetroviral Therapy at HIV seroConversion (SPARTAC) trial (ISRCTN number 76742797; EudraCT number 2004-000446-20). Two male individuals from the UK cohort, P1 and P2, were identified on clinical history as having epidemiologically-linked infections: they were partners and had shared a sexual encounter with a single, third male on the same night. P1 and P2 were enrolled in the trial on the same day and followed up at the Jefferiss Trust Clinic, St. Mary's Hospital, Paddington, London, UK. They were both randomized to receive no therapy.
This study has been approved by the Multicentre Research Ethics Committee (MREC). All participants provided written informed consent before participating in this study.
Participant HLA type was determined to the oligo-allelic level using Dynal RELITM Reverse Sequence-Specific Oligonucleotide kits for the HLA-A, -B and -C loci (Dynal Biotech). To obtain four-digit typing, Dynal Biotech Sequence-Specific priming kits were used, in conjunction with the Sequence-Specific Oligonucleotide type.
Separation of PBMCs and plasma
Peripheral blood mononucleocyte (PBMC) and plasma samples were separated from fresh EDTA blood by Ficoll/Hypaque density gradient centrifugation. For PBMC collection, blood was diluted with R10 solution: RPMI 1640 (Sigma UK) with 10% fetal calf serum (FCS; Sigma, UK), 50 units/ml penicillin/streptomycin mix and 2 μM L-glutamine. The mixture was then layered over Lymphoprep separation medium (Gibco, UK). Samples were centrifuged at 100 × g at room temperature. The resultant layer of PBMC was removed and washed. 1 ml aliquots containing 5 × 106 cells were stored in cryotubes in liquid nitrogen at -180±C. For plasma collection, blood samples were prepared as above with dilution with R10, and the resulting plasma was collected in 1 ml aliquots and stored at -80±C.
Viral RNA extraction
1 ml aliquots of frozen plasma were used for each extraction. The plasma was centrifuged at 1600 × g and 4±C for 1 hour to pellet the virus. Excess plasma was removed and the pellet was resuspended in 140 μl of remaining plasma. RNA was then extracted with the QIAamp Viral RNA Minikit (Qiagen, UK) according to the manufacturer's instructions.
Reverse transcription and polymerase chain reaction (PCR)
For env, viral RNA was reverse transcribed using the SuperScript III Kit (Invitrogen, UK) to produce cDNA. 15 μl of viral RNA was added to 1.5 μl dH2O, 1.5 μl primer OFM19  (concentration 20 μM) and 1.5 μl dNTPs (concentration 10 mM). The mix was heated to 65°C for 5 min followed by 4°C for 1 mins to anneal the primers to the RNA. The reverse transcription (RT) reaction mix (5xBuffer: 6 μl, DTT: 1.5 μl; RNaseOUT 1.5 μl; SuperScript III 1.5 μl) was then added to make a final volume of 29 μl. The reaction mix was heated to 50°C for 60 min, followed by 55°C for 60 min and finally 75°C for 10 minutes. For gag and pol, viral RNA was reverse transcribed using the Reverse-iT 1st Strand Synthesis Kit (Abgene, UK). 18 μl of viral RNA was added to 1.5 μl primer (random decamers and oligodT supplied with the kit, concentration 20 μM). The mix was heated to 75°C for 5 min followed by 4°C for 2 min to anneal the primers to the RNA. The RT reaction mix (5×Buffer: 6 μl; dNTPs: 3 μl concentration 10 mM; RTase Blend 1.5 μl) was then added to make a final volume of 30 μl. The reaction mixture was heated to 42°C for 60 min followed by 75°C for 10 min. The HIV gag and pol genes were amplified by separate PCR reactions as described in detail elsewhere . The HIV env genes were amplified by PCR using a protocol for single genome amplification as described in detail elsewhere [5, 6].
Single genome amplification
Bacterial cloning was carried out for gag and pol using the TOPO TA "One Shot" Cloning Kit for Sequencing (Invitrogen, UK). Purified PCR products were ligated into the pCR4-TOPO vector. Escherichia coli were mixed on ice with the ligation mix and then transfected by heat shock at 42°C for 30 s. Cells were immediately removed to ice and then added to SOC medium (Invitrogen, UK) and placed on a shaking incubator at 37°C and < 1 × g for 1 hour. Cells were then spread on plates of 1× lysogeny broth (LB) agar (Sigma, UK) containing 0.1 μ g/ml ampicillin (Sigma, UK) and incubated overnight at 37°C. Negative controls were included. Colonies were then selected and added to individual wells containing 2× LB medium (Sigma, UK) with 0.05 μ g/ml kanamycin (Sigma, UK). The wells were incubated on a shaking incubator overnight at 37°C and < 1 × g. Bacteria were lysed and minipreps of clonal plasmid DNA (pDNA) were prepared using the Montage Miniprep96 Kit (Millipore, US).
Sequencing of population PCR, SGA and bacterial cloning DNA products was performed using BigDye technology in a 96-well plate. For population PCR and SGA products, 3 μl DNA was added to a mix containing 0.8 μl BigDye Terminator (Applied Biosystems, UK), 1.5 μl 5× sequencing buffer (Applied Biosystems, UK), 2 μl of primer (3.3 μM) and 2.7 μl dH2O. For bacteria-cloned pDNA, 4 μl of miniprep was added to a mix containing 1 μl BigDye Terminator, 1.5 μl 5× sequencing buffer, 1 μl of primer (3.3 μM) and 3.5 μl dH2O. The following cycling conditions were used: 96°C for 30 s, then 30 cycles of 96°C for 30 s, 50°C for 15 s and 60°C for 4 min. DNA for sequencing was precipitated on ice with 2 μl 3M sodium acetate, 10 μl dH2O, 50 μl ice-cold 100% ethanol for 5 min at -20°C, centrifuged at 600 × g for 80 min at 4°C, washed twice with ice-cold 70% ethanol and run on an ABI 3700 sequencer.
All sequences were manually edited using Sequencher v4.8 (Gene Codes Corporation, US) and manually aligned using Se-Al v2.0a11 [68, 69]. For env alignment, sequences were first aligned with MUSCLE v3.7  followed by manual alignment. Sequences containing stop codons or frameshifts were deleted prior to subsequent analysis. Where appropriate, reference sequences were obtained from the Los Alamos National Laboratory (LANL) HIV sequence database . For env, which contains many gaps and poorly aligned regions, gap stripping was undertaken first with GapStreeze set to 5% . In GapStreeze, the user sets a gap tolerance between 0% and 100%. A value of 5% will cause all columns in the alignment to be deleted if more than 5% of sequences contain a gap at that position. Sequences were manually edited in Se-Al v2.0a11 before and after gap-stripping.
Between-host phylogenetic analysis
Phylogenetic analysis of viral sequences sampled from P1 and P2 was carried out by several methods across the env, gag and pol gene-fragments. Prior to gap-stripping with GapStreeze, a likelihood mapping  analysis was run to ensure phylogenetic signal within env was significant. Likelihood mapping was implemented in Tree-Puzzle v5.3.rc7  and the env fragment was screened from the beginning of the coding start region to the end of gp120 (HXB2 nucleotide position 6225 to 7757). Additionally, full nucleotide sequences for the fragments from all three genes were visually screened in Highlighter , and the inferred protein sequence were screened visually using Jalview v2.6 [75, 76]. Phylogenetic trees were initially constructed using the maximum likelihood (ML) method with PhyML v3.0 software , and visualized in FigTree v1.3.1 . We chose the substitution model that gave the highest likelihood with PAUP*v4.0 : the generalized time reversible (GTR) model incorporating estimates of the proportion of invariant sites (I), and the shape parameter of a gamma distribution . ML branch support values were obtained by non-parametric bootstrapping using PhyML v3.0 (1000 replicates). Finally, phylogenetic analysis using a Bayesian MCMC based method was implemented in Mr Bayes v3.1.2 [81, 82]. An unconstrained branch length (exponential) prior was used to avoid enforcing a molecular clock . MrBayes v3.1.2 was run in duplicate for at least 50,000,000 steps for env and pol, sampling trees every 1,000 steps. MrBayes v3.1.2 was run in duplicate for at least 100,000,000 steps for gag, sampling every 10,000 steps. Convergence was assessed with Tracer v1.5  with all parameter estimates having effective sample sizes (ESSs) of > 300, because a high ESS reflects a low degree of correlation among samples . The consensus tree for each gene, with posterior probabilities for branch support, was generated and visualized in FigTree v1.3.1.
Inferring the tMRCA using a relaxed molecular clock
To determine the time to the most recent common ancestor (tMRCA) of the sequences isolated from the two participants, we used a Bayesian MCMC based approach. We tested our assumption that all of the observed evolution in env within the viral sequence sets from each participant had occurred within each host by demonstrating a star-like intra-host phylogeny, and confirming that intra-host divergence by ML was consistent with that predicted for early, monophyletic infection against other datasets [3, 5, 6, 12, 15]. We used a normal tMRCA prior for the sequences within each participant, calibrated to a mean of 63 days since exposure (standard deviation 1 day). We ran BEAST v1.5.4  for at least 100,000,000 steps, sampling every 10,000 steps, and employing an uncorrelated lognormal relaxed clock to allow for rate variation among branches [15, 44, 85–87]. Rate variation may occur if the two variants evolved at different rates, before or after transmission [15, 44]. The substitution model was the GTR model. The underlying demographic model was the Bayesian skyline plot with 10 steps, and was used as a flexible prior on the distribution of the inter-node intervals on the sampled phylogenetic topologies [15, 44, 85–87]. Convergence was assessed with Tracer v1.5, and all parameter estimates had ESSs of > 300 [15, 44, 85–87]. Convergence was not achieved when using estimated transmission time as the only prior; the ESSs for the prior and posterior probabilities remained < 100 after 300,000,000 steps [15, 44, 85–87]. To deal with this issue, a posterior mean rate of substitution prior was estimated from the posterior mean rate of another dataset, for a fragment of the env C2V5 region . This mean rate prior was normally distributed, with a mean of 8.18 × 10-3 substitutions per site per year (standard deviation of 1.15×10-3 substitutions per site per year) . The hypervariable regions were cut to be consistent with the original dataset after consultation with the authors . To achieve convergence, our relaxed-clock analysis also required the full-length C2V5 fragment, rather than the part-fragment used in the reference dataset that was missing the 5' end of the C2 region .
To determine the sensitivity of our results to the choice of prior, we also analysed the data under a strict molecular clock, calibrating the time of transmission to the same prior as under the relaxed molecular clock, but not enforcing a strong prior on the rate [15, 44]. We performed this analysis for C2V5 and our entire 1305 stripped env fragment. The mean rate prior from the reference dataset was necessary for the, relaxed clock analysis to converge, but our tMRCA estimate was robust to this choice of prior, as the most important prior for the tMRCA estimate was the time of transmission. Although calibration to the time since transmission may lead to an overestimate of the posterior substitution rate estimate , other studies have found that this effect is small for monophyletic infections [6, 12]. Both strict-clock and relaxed-clock analyses using the gag and pol fragments failed to achieve convergence after 300,000,000 steps, and no reference datasets were available for calibration of evolution in these fragments.
Potential N-linked glycosylation site analysis
We compared potential N-linked glycosylation sites (PNLGSs) between inferred amino acid sequences for the SGA samples env in from P1 and P2, using N-Glycosite .
Neutralization and infectivity assays
HIV env genes were amplified from reverse transcribed viral RNA, restriction-cloned in pcDNA3.1 (Invitrogen, UK) and co-transfected into 293T cells with an env deficient backbone, nl4.3Δenv (Dr M. Pizzato, University of Geneva). Virus-containing supernatants were harvested, assayed for reverse transcriptase activity , and titrated onto the HIV permissible cell-line, TZM-BL (also known as JC53-BL) using previously described techniques  with the following modifications: cell monolayers were fixed with 0.2% gluteraldehyde, stained with an X-gal substrate and air dried. Infected cells were counted with an AID v2.9 EliSpot plate-counter (AID GmbH, Germany). To test serum-mediated neutralizing responses, 400 focus forming units (FFUs) of titrated-pseudovirus were incubated with serial dilutions of heat inactivated autologous sera from participants. Neutralization was calculated as the percentage-reduction of FFUs compared to virus-only controls.
IFN-γ ELISpot assay
Where Mexp is the number of spots in the experimental well, is the mean number of spots in the negative control wells, and SD neg is the standard deviation of the negative control wells. NMOR is always a positive integer and all negative values are set to 0.
The authors would like to thank the participants and staff of the St. Mary's Hospital Jefferiss Wing clinic and the participants and staff involved in the SPARTAC trial. The authors would like to thank Mr David English for computing assistance in the rapid processing of MrBayes analyses. The authors would also like to thank the anonymous reviewers of this manuscript for their comments and suggestions. JF is supported by the Medical Research Council. JW and RP are supported by the Wellcome Trust (UK). REP is a NIHR Senior Investigator. SE is supported by the Commonwealth Scholarship Commission. AK is supported by the Royal Society.
SPARTAC Investigators. Trial Steering Committee A Breckenridge (Chair), C Conlon, D Cooper, F Conradie, J Kaldor, M Schechter, P Claydon, P Kaleebu, G Ramjee, F Ssali, G Tambussi, J Weber. Trial Physician Sarah Fidler. Trial Statistician Abdel Babiker. Data and Safety Monitoring Committee A McLaren (in memoriam), V Beral, G Chene, J Hakim. Central Virology Laboratories and Repositories Jefferiss Trust Laboratories, Imperial College, London, UK (M McClure, D Muir, I Blain, A Helander, O Erlwien, S Kaye). Clinical Endpoint Review Committee N Paton, S Fidler. Co-ordinating Trial Centres Australia: National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, Sydney (P Grey, D Cooper, T Kelleher, M Law). UK and Ireland: MRC Clinical Trials Unit, London (A Babiker, K Porter, P Kelleher, K Boyd, D Johnson, D Nock) Investigators and Staff at Participating Sites Australia: St Vincent's Hospital, Sydney (D Cooper), Carlton Clinic, Melbourne (J Anderson), 407 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), Centre for Immunology, Sydney (P Cunningham). Brazil: Projeto Praça Onze, Hospital Escola São Francisco de Assis, Universidade federal do Rio de Janeiro, Rio de Janeiro. (M Schechter, R Zajdenverg, M Merçon). Italy: Ospedale San Raffaele, Milan (G Tambussi, C Tassan Din, C Ronchetti, G Travi, V Rusconi, G de Bartolo), Ospedale Lazzaro Spallanzani, Roma (G D'Offizi, C Vlassi, A Corpolongo). South Africa:Capetown: Desmond Tutu HIV Centre, Institute of Infectious Diseases, Capetown (R Wood, J Pitt, L-G Bekker, J Aploon, L Fielder, N Killa, T Buhler) Johannesburg: Reproductive Health and HIV Research Unit, Bara Clinic, Chris Hani Baragwanath Hospital, Johannesburg (H Rees, J Moyes, S Walaza, K Moitse), Contract Laboratory Services, Johannesburg Hospital, Johannesburg (W Stevens, C Wallis, C Ingram, M Majam) Kwazulu-Natal: HIV Prevention Unit, Medical Research Council, Durban (G Ramjee, D Singh, T Mtambo, S Gappoo, H Somaroo, J Moodley, M Mills, A Premrajh, N Nozulu, K Naidoo). Uganda: MRC/Uganda Virus Research Institute, Entebbe (H Grosskurth, A Kamali, P Kaleebu, J Mugisha, U Bahemuka, F Lyagoba, P Tabuga). Spain: Hospital Clinic-IDIBAPS. Univ. of Barcelona. Barcelona (J M Miro, M López-Dieguez, F. Agüero, JA Arnaiz, T. Pumarola, M. Plana, M. Tuset, MC Ligero, C. Gil, T. Gallart, JM Gatell) UK and Ireland: Royal Sussex County Hospital, Brighton (M Fisher, L Heald, N Perry, D Pao, D Maitland), St James's Hospital, Dublin (F Mulcahy, G Courtney, 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, T Shaw), Chelsea and Westminster Hospital, London (B Gazzard, D Hawkins, C Higgs, C Mahuma), Homerton Hospital, London (J Anderson, L Muromba), Mortimer Market Centre, London (I Williams, J Turner, D Mullan, D Aldam), North Middlesex Hospital (J Ainsworth, A Waters), Royal Free Hospital (M Johnson, S Kinloch, A Carroll, P Byrne, Z Cuthbertson), St Bartholomew's Hospital, London (C Orkin, J Hand, C De Souza), St Mary's Hospital, London (J Weber, S Fidler, E Thomson, J Fox, K Legg, S Mullaney, A Winston, N Poulter, S Wilson) Trial Secretariat D Winogron, S Keeling.
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