Within the first weeks of infection, HIV rapidly disseminates through the body and establishes cellular HIV reservoirs.1 Understanding how HIV migrates, colonizes, and adapts to various anatomic compartments [eg, blood plasma, peripheral blood mononuclear cells (PBMCs), and the male genital tract] during the course of infection will likely be important for the development of effective eradication strategies.1,2
Viral compartmentalization within anatomic regions can occur as a consequence of tissue-specific genetic differentiation and restricted viral migration between anatomic sites or tissues.3 Such compartmentalization impacts HIV-associated pathogenesis and is associated with neurocognitive disease,4,5 development of drug resistance, and sexual transmission.6–12 For example, the male genital tract represents a unique compartment, which can be distinct from blood, including differences in drug resistance mutations, viral replication,13–16 and differential virus evolution17,18 in response to local environment factors19–22 and antiretroviral pharmacokinetics.23–25 Although these unique genotypic and phenotypic characteristics might have crucial implications for efficient HIV sexual transmission,6–12 semen-specific variants residing in these “sanctuaries” may also play an important role in reseeding the blood.
In this study, we analyzed deep sequencing data obtained from longitudinally collected samples from individuals with recent HIV infection to characterize the dynamics of viral spread between the blood and genital tract during early HIV infection.
MATERIAL AND METHODS
This study was approved by the UCSD Human Research Protections Program. All study participants provided voluntary, written informed consent before any study procedures were undertaken.
Participants, Samples, and Clinical Laboratory Tests
Six individuals with recent HIV infection participating in the San Diego Primary Infection Resource Consortium26 were enrolled in this study. Before the initiation of antiretroviral therapy, longitudinal samples of blood plasma (n = 25), seminal plasma (n = 15), and PBMC (n = 9) were collected with a median of 5 time points per individual (range: 3–7). HIV DNA levels were quantified in PBMCs and HIV RNA levels were quantified in both blood and seminal plasma27 (Table 1 and Figure S1, http://links.lww.com/QAI/A901). The estimated dates of infection (EDI) were determined using a series of well-defined stepwise rules to characterize stages of infection based on serologic and virologic criteria, as described by Le et al28 and summarized in Table S1, Supplemental Digital Content, http://links.lww.com/QAI/A903.
Next Generation Sequencing and Sequence Analyses
HIV RNA was extracted from blood and seminal plasma and HIV DNA was extracted from 5 million frozen PBMCs using standard methods.27 Deep sequencing of PCR-amplified env C2-V3 (HXB2 coordinates 6928–7344) was performed using the Roche 454 FLX Titanium platform (Basel, Switzerland) and sequence haplotypes were generated as described in Supplemental Digital Content, Table S2, http://links.lww.com/QAI/A904. For each sample, we computed the mean of all pairwise Tamura-Nei 93 distances between reads with at least 100 overlapping base pairs to measure the mean pairwise diversity.29
To evaluate if sampled viral populations within each individual were under selective pressure, we used the Fast Unconstrained Bayesian AppRoximation (FUBAR) program.30 This method exploits several computational shortcuts to speed up the detection of positive or purifying selection, leading to improved robustness against model misspecification and permitting the analysis of large data sets for which selection analysis was previously intractable.30 A posterior probability threshold P of 0.9 was defined to detect codon sites under positive and purifying selection.
Viral compartmentalization and population subdivision for each individual data set was assessed by a stringent multilevel approach including FST31 and Slatkin–Maddison assessments of compartmentalization32 implemented in the HyPhy software package.33 Briefly for the FST, we compute the fixation index,31 defined as
, where πI is the estimate of mean pairwise intracompartmental Tamura-Nei (TN93) genetic distance,34 and πD is its intercompartmental counterpart. Both quantities were computed by comparing all reads from 2 different compartments, subject to the requirement that they share at least 100 aligned nucleotide positions. Statistical significance was derived via a 1000-fold population-structure randomization/permutation test. For a given sample to be considered compartmentalized, we required that: (1) FST test was statistically significant with a P-value of less than 0.05, (2) the FSTP-value remained significant when the haplotype frequency was ignored (ie, all haplotypes were assigned a relative weight of 1), and (3) the Slatkin–Maddison test for compartmentalization, based on the inferred maximum likelihood phylogeny, was also statistically significant.
The resulting collection of haplotypes obtained from each sample was used to reconstruct the spatial dynamics of HIV-1 across compartments. Briefly, we used a Bayesian discrete phylogeographic approach35 and a Markov chain Monte Carlo (MCMC) framework as implemented in BEAST v1.8.1 with BEAGLE.36,37 We applied a discretized gamma distribution (GTR + 4Γ) to account for among-site rate variation. Time scales of the trees were calibrated with the sampling dates available. We specified an uncorrelated lognormal molecular clock that allows rates to vary among the branches of the inferred phylogenies to infer the timescale of HIV evolution for each individual,38 with a gamma distribution prior. A Bayesian skyline tree prior was used as a coalescent demographic model. MCMC simulations were run for 250 million steps, subsampling parameters every 50,000 steps. Convergence of the chains was inspected using Tracer.v.1.6. Maximum clade credibility trees were obtained with TreeAnnotator v1.8.1 and visualized using FigTree 184.108.40.206 To obtain the expectations for the location state transitions, we estimated Markov jump counts39 along the branches of the posterior tree distribution.40
History of viral movements and estimated percentage of HIV-1 migration events from blood to semen and conversely from semen to blood were obtained using a “robust counting” procedure as implemented in BEAST package v220.127.116.11 Briefly, this method allows estimation of the expected number of location changes along the branches of a posterior tree distribution, and can be used to investigate intrahost HIV spatiotemporal dynamics.2 Here, we estimated the location state transition rates between compartments using a randomized subsample of an equal number of blood plasma and seminal sequences to avoid potential bias in spatial inference estimates that may arise from oversampling one location. Blood and seminal plasma sequences from each available time point were retained to maximize temporal evolutionary signal. To improve robustness, the analysis was repeated 3 times with a new random subsample each time. Posterior mean percentage and 95% Bayesian Credible Intervals for the proportion of migration events from and to blood plasma were obtained. A full description of these analyses is provided in the Supplemental Digital Content and in our previous manuscript.2
Selected markers of genital inflammation and cellular trafficking [monocyte chemotactic protein (MCP)-1, interleukin (IL)-6, tumor necrosis factor (TNF) α, interferon γ (IFN-γ), regulated on activation normal T-cell expressed and secreted (RANTES), and IFN-γ induced protein (IP)-10] were measured at baseline in seminal plasma using a bead-based multiplex array.21,41
Herpesvirus DNA and HIV RNA Extraction and Quantification From Seminal Plasma
We used quantitative Reverse Transcriptase polymerase chain reaction to measure levels of HIV RNA at each time point. Additionally, DNA levels from various human herpesviruses (HHV) in seminal plasma were measured at baseline (ie, cytomegalovirus, Epstein–Barr virus, herpes simplex virus types 1 and 2, and HHV types 6, 7, and 8).41,42
Statistical analyses were performed using SAS (version 9.4) and GraphPad Prism 6.0c software (GraphPad Software, Inc., San Diego, CA). We performed comparisons of inferred numbers of migration events across compartments between groups using the Fisher exact test. Evaluation of correlations between continuous variables was performed using the Spearman rank correlation.
HIV-infected participants (n = 6) were all men who have sex with men recently infected with HIV-1 subtype B. Participants had a mean age of 30 years, and no reported needle-sharing exposures. Baseline blood and semen samples were collected at a median of 81 days after the EDI [interquartile range (IQR), 35–108 days]. Additional longitudinal samples were collected serially for a median of 180 days (IQR, 82–761 days) after baseline, with a median of 4.5 time points per participant (IQR, 3–5.5). The median blood and seminal plasma viral load at baseline were 5.07 (IQR, 4.30–5.58) and 3.54 (IQR, 3.14–4.22), respectively, and the median CD4+ count was 629 cells per microliter (IQR, 506–691). Characteristics and demographics of the subjects are summarized in Table 1 and Figure S1, http://links.lww.com/QAI/A901.
Viral Characteristics in Blood and the Male Genital Tract
To compare the diversity of HIV-1 env between the male genital tract, peripheral blood plasma, and PBMCs, we calculated the mean pairwise diversity in samples from all 3 compartments. There was no significant difference in overall nucleotide diversity between sequences sampled from blood plasma, seminal plasma, and PBMC across the 6 individuals at any time point. Using a mixed-effects model to examine viral diversity in relation to time from the estimated date of infection within each individual, we found a significant increase in viral diversity over the course of infection in the HIV RNA population in seminal plasma (P = 0.03) but not in blood plasma (Figure 2, Supplemental Digital Content, http://links.lww.com/QAI/A902).
At baseline, we detected evidence of viral compartmentalization between the blood and genital compartments in 2 of 6 participants (subjects S3 and S6). Interestingly, both individuals lost compartmentalization at later time points, 182 days (subject S3) and 457 days (subject S6) postinfection, respectively. The remaining 4 participants were not compartmentalized at baseline but exhibited significant signal for compartmentalization between blood and semen at one or more postbaseline time points (Table 2). There was no association between the time from EDI and the presence of viral compartmentalization between blood and seminal plasma. None of the 6 individuals maintained viral compartmentalization between blood and seminal plasma throughout the analyzed time points.
As viral evolution in distinct anatomic compartments may be differentially affected by selection, we evaluated codon sites under positive and purifying selection within blood and seminal plasma for each participant. Overall, the number of sites under positive and diversifying selection were higher in blood and seminal plasma compared to PBMC (Table 3, Supplemental Digital Content, http://links.lww.com/QAI/A905) and the sites identified to be under positive and purifying selection varied between individuals and across compartments (data not shown).
HIV Phylodynamics Between Blood and Seminal Plasma
To further characterize the viral gene flow between blood and semen, we applied a Bayesian discrete phylogeographic approach to our sequence data sets.35 The inferred phylogenies and the distribution of node states across the MCMC analyses all suggested that ancestral sequences within the tree originated from blood plasma and were followed by spread to the PBMCs and genital tract (Fig. 1, colored nodes). We then used Markov jump counts to provide a quantitative estimate of gene flow between each compartment. Using random but evenly distributed subsamples to reduce sampling bias, we found a significantly higher number of migration events originating from blood plasma toward both seminal plasma and PBMC for all individuals (P < 0.01) (Fig. 2, Table 4, Supplemental Digital Content, http://links.lww.com/QAI/A906). Gene flow (ie, the expected number of location state transitions along branches of the tree39) from blood plasma to the genital tract and from blood plasma to PBMCs represented a mean of 47.0% (range: 29.6%–53.6%) and 38.0% (range: 3.4%–65.3%) of the total viral movement across the 3 compartments, respectively.
Correlates of Viral Diversity and Gene Flow
To explore possible inflammatory mechanisms connecting viral evolution and gene flow between blood and genital compartment, we measured levels of selected cytokines and chemokines (ie, MCP-1, IL-6, TNF-α, IFN-γ, RANTES, and IP-10) in seminal plasma at baseline (Table 5, Supplemental Digital Content, http://links.lww.com/QAI/A907). After adjustment for multiple comparisons, in our limited data set, we found no association between viral diversity or viral migration between semen and blood and the presence of HHV or increased levels of cytokines in the genital tract at baseline (Table 6, Supplemental Digital Content, http://links.lww.com/QAI/A908).
A better characterization of the viral dynamics of HIV in the genital tract is important not only to inform eradication strategies, but also to direct strategies to reduce infectiousness and reduce the risk of transmission. Previous works have reported compartmentalization of HIV between blood and semen and described the potential implications for HIV transmission and drug resistance.6–11 In our study, we found evidence of viral compartmentalization between the blood (PBMC or plasma) and seminal plasma in 6 HIV-infected men during the earliest phase of HIV infection. Interestingly none of the 6 individuals maintained viral compartmentalization between blood and seminal plasma throughout the entire study period. This is consistent with previous studies showing that compartmentalization of HIV variants can be a transient phenomenon,43–45 revealing the complex viral dynamics between blood and the genital tract as reviewed in Houzet et al.46 Similar to these human studies, variation was also seen in nonhuman primate models: studies in recently infected macaques showed both evidence of mixing of the SIV population between blood and the genital tract during acute SIV infection47 and genital tract sequestration following resolution of primary viremia and during chronic infection.48,49 The absence of compartmentalization between viral populations in the blood and the genital tract can be the consequence of ongoing viral trafficking or limited divergence rates over the course of infection. Here, we found increasing compartmentalization between viral populations of the blood and seminal plasma over time for 4 of 6 individuals (66.7%). The delayed appearance of viral segregation together with the significant increase in viral diversity in semen during the course of HIV infection might be a consequence of ongoing viral replication under selective pressures unique to the genital tract. Consequently, we evaluated the potential selective pressure by comparing the site under positive and purifying selection in blood and seminal plasma. Although the small number of participants evaluated and the short segment of HIV-1 env used represent an important limitation, we found that the number and the nature of sites identified to be under positive and purifying selection varied between individuals and across compartments. This is consistent with previous findings showing distinct local selective pressures that may be contributing to viral evolution and segregation of HIV variants in the genital tract.9,13–18
To further understand mechanisms underlying viral compartmentalization, we applied a discrete diffusion approach to phylogenies of all compartment-specific HIV sequence data to characterize the gene flow between blood and semen. This method permits to quantify the migration between compartments by estimating the Markov jump count of location state transitions along tree branches. Similar to our previous report,2 we found evidence of reciprocal migrations between the blood and genital tract but in this study, gene flow predominantly originated from blood plasma (Fig. 2), illustrating the complex intermixing of HIV subpopulations within the host. These findings are also consistent with a cross-sectional study of 5 HIV-infected men, which showed evidence of viral compartmentalization between the blood (PBMC or plasma) and the genital tract, and a predominant viral gene flow from blood plasma to genital tract using a cladistic measure of gene flow (Slatkin and Maddison test).50 Together these results illustrate that viral migration between anatomic compartments is a complex and dynamic bidirectional process.2
Previous reports have demonstrated that semen and blood are 2 distinct immunologic compartments with distinct cytokine profiles.21,22,51 Local immune activation and T-cell activation may provide an environment that can support virus replication with unique selective pressures,51 giving rise to a compartmentalized viral population. Consistent with this idea of local immune activation, a previous study of 16 men with chronic HIV infection demonstrated high levels of MCP-1 in the semen, but found no correlation with the viral compartmentalization.44 Here, we measured seminal levels of MCP-1, IL-6, TNF-α, IFN-γ, RANTES, and IP-10 and HHV DNA at baseline. Although we found no significant association between viral trafficking and local inflammation or the presence of HHV at baseline after adjustment for multiple comparisons, the limited sample size and the lack of longitudinal measures of these markers of inflammation and infection prevented any robust conclusion.
There are several important limitations of this study. Primarily, the small number of subjects evaluated is an important limitation. However, it is generally difficult to obtain paired genital and blood samples from recently infected individuals in a longitudinal manner, and so while our sample population is small, the study represents one of the largest such data sets in HIV-infected individuals. Our data were also generated with first-generation deep sequencing technology targeting a small C2-V3 env fragment, limiting our ability to delve into the selection pressures driving compartmentalization. Nevertheless, the phylogenetic signal present in this segment was enough to identify viral compartmentalization, and assess viral gene flow across compartments. Similar to most studies of viral dynamics in humans, sampling bias may have affected our observations. Viral populations are dynamic while sampling is static, and samples were not taken at the same point during infection for all individuals. Further limitations to our study include that there are inherent differences in physiology and evolutionary rates of archived viral DNA and viral RNA, and that there may be sampling biases between HIV DNA and HIV RNA populations, given that HIV RNA populations are usually more uniform than HIV DNA populations, at any one sampling time point; however, such sampling biases are limited in this study by the fact that the samples analyzed are from early infection, when overall body viral diversity is limited.52 Finally, our analysis might be affected by measurement bias, because haplotype reconstruction may affect the proportionality of viral variants, although the relative abundance of haplotypes is usually preserved.53
In summary, we used newly state-of-the-art Bayesian phylogeographic methods to evaluate the dynamics and determinants of HIV compartmentalization between blood and the male genital tract. Despite these listed limitations, our results are compatible with early spread of virus predominantly from blood plasma to genital tract, with consequent establishment of a local viral population, followed by intracompartmental evolution driven by specific local selective pressures and immune environment. It will be important to monitor these dynamics during HIV cure studies using latency reversing agents to determine if this viral compartment can act as a source for viral rebound.
1. Stekler J, Sycks BJ, Holte S, et al. HIV dynamics in seminal plasma
during primary HIV infection. AIDS Res Hum Retroviruses. 2008;24:1269–1274.
2. Chaillon A, Gianella S, Wertheim JO, et al. HIV migration between blood and cerebrospinal fluid or semen over time. J Infect Dis. 2014;209:1642–1652.
3. Nickle DC, Shriner D, Mittler JE, et al. Importance and detection of virus reservoirs and compartments of HIV infection. Curr Opin Microbiol. 2003;6:410–416.
4. Cowley D, Gray LR, Wesselingh SL, et al. Genetic and functional heterogeneity of CNS-derived tat alleles from patients with HIV-associated dementia. J Neurovirol. 2011;17:70–81.
5. Smith DM, Zárate S, Shao H, et al. Pleocytosis is associated with disruption of HIV compartmentalization
between blood and cerebral spinal fluid viral populations. Virology. 2009;385:204–208.
6. Eron JJ, Vernazza PL, Johnston DM, et al. Resistance of HIV-1 to antiretroviral agents in blood and seminal plasma
: implications for transmission. AIDS. 1998;12:F181–F189.
7. Smith DM, Wong JK, Shao H, et al. Long-term persistence of transmitted HIV drug resistance in male genital tract secretions: implications for secondary transmission. J Infect Dis. 2007;196:356–360.
8. Zhu T, Wang N, Carr A, et al. Genetic characterization of human immunodeficiency virus type 1 in blood and genital secretions: evidence for viral compartmentalization
and selection during sexual transmission. J Virol. 1996;70:3098–3107.
9. Pillai SK, Good B, Pond SK, et al. Semen-specific genetic characteristics of human immunodeficiency virus type 1 env. J Virol. 2005;79:1734–1742.
10. Delwart EL, Mullins JI, Gupta P, et al. Human immunodeficiency virus type 1 populations in blood and semen. J Virol. 1998;72:617–623.
11. Paranjpe S, Craigo J, Patterson B, et al. Subcompartmentalization of HIV-1 quasispecies between seminal cells and seminal plasma
indicates their origin in distinct genital tissues. AIDS Res Hum Retroviruses. 2002;18:1271–1280.
12. Zhu T, Mo H, Wang N, et al. Genotypic and phenotypic characterization of HIV-1 patients with primary infection. Science. 1993;261:1179–1181.
13. Sheth PM, Kovacs C, Kemal KS, et al. Persistent HIV RNA shedding in semen despite effective antiretroviral therapy. AIDS. 2009;23:2050–2054.
14. Lorello G, la Porte C, Pilon R, et al. Discordance in HIV-1 viral loads and antiretroviral drug concentrations comparing semen and blood plasma. HIV Med. 2009;10:548–554.
15. Politch JA, Mayer KH, Welles SL, et al. Highly active antiretroviral therapy does not completely suppress HIV in semen of sexually active HIV-infected men who have sex with men. AIDS. 2012;26:1535–1543.
16. Gianella S, Smith DM, Vargas MV, et al. Shedding of HIV and human herpesviruses in the semen of effectively treated HIV-1-infected men who have sex with men. Clin Infect Dis. 2013;57:441–447.
17. Tirado G, Jove G, Kumar R, et al. Differential virus evolution in blood and genital tract of HIV-infected females: evidence for the involvement of drug and non-drug resistance-associated mutations. Virology. 2004;324:577–586.
18. Byrn RA, Kiessling AA. Analysis of human immunodeficiency virus in semen: indications of a genetically distinct virus reservoir. J Reprod Immunol. 1998;41:161–176.
19. Vernazza PL, Eron JJ, Cohen MS, et al. Detection and biologic characterization of infectious HIV-1 in semen of seropositive men. AIDS. 1994;8:1325–1329.
20. Ping LH, Cohen MS, Hoffman I, et al. Effects of genital tract inflammation on human immunodeficiency virus type 1 V3 populations in blood and semen. J Virol. 2000;74:8946–8952.
21. Lisco A, Munawwar A, Introini A, et al. Semen of HIV-1-infected individuals: local shedding of herpesviruses and reprogrammed cytokine network. J Infect Dis. 2012;205:97–105.
22. Vanpouille C, Introini A, Morris SR, et al. Distinct cytokine/chemokine network in semen and blood characterize different stages of HIV infection. AIDS. 2016;30:193–201.
23. Trezza CR, Kashuba AD. Pharmacokinetics of antiretrovirals in genital secretions and anatomic sites of HIV transmission: implications for HIV prevention. Clin Pharmacokinet. 2014;53:611–624.
24. Fletcher P, Herrera C, Armanasco N, et al. Short communication: limited anti-HIV-1 activity of maraviroc in mucosal tissues. AIDS Res Hum Retroviruses. 2016;32:334–338.
25. Else LJ, Taylor S, Back DJ, et al. Pharmacokinetics of antiretroviral drugs in anatomical sanctuary sites: the male and female genital tract. Antivir Ther. 2011;16:1149–1167.
26. Butler DM, Delport W, Kosakovsky Pond SL, et al. The origins of sexually transmitted HIV among men who have sex with men. Sci Transl Med. 2010;2:18re1.
27. Gianella S, Mehta SR, Strain MC, et al. Impact of seminal cytomegalovirus replication on HIV-1 dynamics between blood and semen. J Med Virol. 2012;84:1703–1709.
28. Le T, Wright EJ, Smith DM, et al. Enhanced CD4+ T-cell recovery with earlier HIV-1 antiretroviral therapy. N Engl J Med. 2013;368:218–230.
29. Tamura K. Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G+C-content biases. Mol Biol Evol. 1992;9:678–687.
30. Murrell B, Moola S, Mabona A, et al. FUBAR: a fast, unconstrained bayesian AppRoximation for inferring selection. Mol Biol Evol. 2013;30:1196–1205.
31. Hudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow from DNA sequence data. Genetics. 1992;132:583–589.
32. Slatkin M, Maddison WP. A cladistic measure of gene flow inferred from the phylogenies of alleles. Genetics. 1989;123:603–613.
33. Pond SLK, Frost SDW, Muse SV. HyPhy: hypothesis testing using phylogenies. Bioinformatics. 2005;21:676–679.
34. Chen W, Wang H. Variance estimation for nucleotide substitution models. Mol Phylogenet Evol. 2015;90:97–103.
35. Lemey P, Rambaut A, Drummond AJ, et al. Bayesian phylogeography
finds its roots. PLoS Comput Biol. 2009;5:e1000520.
36. Drummond AJ, Suchard MA, Xie D, et al. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol Biol Evol. 2012;29:1969–1973.
37. Suchard MA, Rambaut A. Many-core algorithms for statistical phylogenetics. Bioinformatics. 2009;25:1370–1376.
38. Drummond AJ, Ho SYW, Phillips MJ, et al. Relaxed phylogenetics and dating with confidence. PLoS Biol. 2006;4.
39. Minin VN, Suchard MA. Counting labeled transitions in continuous-time Markov
models of evolution. J Math Biol. 2008;56:391–412.
40. O'Brien JD, Minin VN, Suchard MA. Learning to count: robust estimates for labeled distances between molecular sequences. Mol Biol Evol. 2009;26:801–814.
41. Gianella S, Smith DM, Daar ES, et al. Genital cytomegalovirus replication predicts syphilis acquisition among HIV-1 infected men who have sex with men. PLoS One. 2015;10:e0130410.
42. Gianella S, Massanella M, Richman DD, et al. Cytomegalovirus replication in semen is associated with higher levels of proviral HIV DNA and CD4+ T cell activation during antiretroviral treatment. J Virol. 2014;88:7818–7827.
43. Bull ME, Heath LM, McKernan-Mullin JL, et al. Human immunodeficiency viruses appear compartmentalized to the female genital tract in cross-sectional analyses but genital lineages do not persist over time. J Infect Dis. 2013;207:1206–1215.
44. Anderson JA, Ping L-H, Dibben O, et al. HIV-1 populations in semen arise through multiple mechanisms. PLoS Pathog. 2010;6:.
45. Gupta P, Leroux C, Patterson BK, et al. Human immunodeficiency virus type 1 shedding pattern in semen correlates with the compartmentalization
of viral Quasi species between blood and semen. J Infect Dis. 2000;182:79–87.
46. Houzet L, Matusali G, Dejucq-Rainsford N. Origins of HIV-infected leukocytes and virions in semen. J Infect Dis. 2014;210(suppl 3):S622–S630.
47. Fieni F, Stone M, Ma ZM, et al. Viral RNA levels and env variants in semen and tissues of mature male rhesus macaques infected with SIV by penile inoculation. PLoS One. 2013;8:e76367.
48. Whitney JB, Hraber PT, Luedemann C, et al. Genital tract sequestration of SIV following acute infection. PLoS Pathog. 2011;7:e1001293.
49. Le Tortorec A, Le Grand R, Denis H, et al. Infection of semen-producing organs by SIV during the acute and chronic stages of the disease. PLoS One. 2008;3:e1792.
50. Diem K, Nickle DC, Motoshige A, et al. Male genital tract compartmentalization
of human immunodeficiency virus type 1 (HIV). AIDS Res Hum Retroviruses. 2008;24:561–571.
51. Olivier AJ, Masson L, Ronacher K, et al. Distinct cytokine patterns in semen influence local HIV shedding and HIV target cell activation. J Infect Dis. 2014;209:1174–1184.
52. Shankarappa R, Margolick JB, Gange SJ, et al. Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. J Virol. 1999;73:10489–10502.
53. Jayasundara D, Saeed I, Maheswararajah S, et al. ViQuaS: an improved reconstruction pipeline for viral quasispecies spectra generated by next-generation sequencing. Bioinformatics. 2015;31:886–896.
Bayesian inference; phylogeography; compartmentalization; Markov; seminal plasma; PBMC
Supplemental Digital Content
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.