Over the past 30 years, HIV-1/AIDS has evolved into an increasingly heterogenous disease composed of multiple epidemics each influenced by a complex array of biological, behavioral, and cultural factors.1–3 Highly active antiretroviral therapy (HAART), introduced in Western World settings in 1995, has reduced morbility and mortality, stabilizing subtype B men who have sex with men (MSM) and intravenous drug user (IDU) epidemics.1–4 Heterosexual (HET) epidemics in Africa have diversified to several major subtypes (A, C, D, F, and G) and circulating recombinant (eg, CRF01_AE and CRF02_AG) forms.
Global initiatives to scale-up antiretroviral therapy (ART) over the last decade have led to 25%–50% reductions in infections in Africa and Asia despite weak health care systems.5–9 The landmark HIV Prevention Trials Network (HPTN) 052 trial showed that earlier ART initiation (550–350 vs. <250 cells/μL) could result in a 96% reduction in the risk of transmission in HIV serodiscordant couples.7 The success of HPTN 052, preexposure prophylaxis, and microbicide trials, and observational cohorts, has advanced the concept of “Treatment as Prevention” (TasP) to avert new infections at a population level.7–18 Guidelines have been revised to reflect these goals, recommending universal annual testing and immediate ART initiation for all persons.10,19,20
There remains a debate on the generalizability, feasibility, and sustainability of TasP initiatives.19–21 The resurgence of MSM epidemics and the rise in complex HET/IDU/MSM epidemics in Brazil, East Europe, China, and Southeast Asia emphasize the need for tailoring ART with other prevention interventions.
One of the central disputes surrounds the issue as to whether transmissions in early-stage infection, frequently undiagnosed, will compromise TasP strategies.22–26 Acute/early-stage infection has been postulated to account for 5%–70% transmissions depending on epidemiologic and mathematical modeling assumptions.27–30 Epidemiological analysis of MSM transmission dynamics is complicated by patterns of risk behavior, frequent anonymity of sexual partnerships, low risk of infection per coital act, and long infectivity periods.27–33
Phylogeny provides a unique framework to capture underlying structures of transmission networks that could not be otherwise identified.23,24,34–40 Phylogenetics can identify the genetic interrelatedness of viruses in HIV-infected persons.23,24,34–40 The “clustering” of sequences can infer transmission networks whereby dynamic HIV spread can be assessed on chronological and stage of infection time scales. Phylogenetic cluster analysis can be combined with epidemiological, demographic, and behavioral data to describe the underlying factors contributing to the growth of individual epidemics.23,24,35,36,41,42
This article will use phylogenetic findings based on the Montreal MSM cohort to illustrate the role of phylogeny in the design of prevention strategies. Transmission clustering is the driving force of 75% of the MSM epidemic wherein 1 infection can lead to 10 onward transmissions. These findings substantiate the necessity for targeted testing and immediate ART initiation to curb resurgent MSM epidemics.23,24,34,36,37,43–45
PHYLOGENETIC ANALYSIS OF MSM TRANSMISSION DYNAMICS
The Montreal MSM epidemic began in the early 1980s. By 2008, prevalence rates in sexually active MSM had risen to 15% despite low HIV incidence (0.62 per 100 person-years) with 75% of diagnosed persons receiving HAART.46 The provincial genotyping began in 2001 and has sequence data sets on half of the diagnosed HIV population. Transmission dynamics have been assessed based on phylogenetic analysis of coclustering patterns of newly diagnosed primary infections (subtype B, male only) over the last decade. Genotyping requisitions completed by prescribing physicians distinguish primary HIV infection (PHI) (PHI < 6 months post seroconversion) populations from chronic drug-naive (PHI > 6 months) and treatment-experienced populations.23,36 Viral transmission clustering has been based on robust criteria of high bootstrap values (>98%), short genetic distance (<1.5%), and similarity in signature mutational motifs.
In 2007, half of primary/early-stage infections (PHI < 6 months) were observed to cocluster with other primary infections although PHIs rarely coclustered with drug-naive and treated chronic populations (1% and 2.7%, respectively).36 High rates of coclustering of primary stage cohorts are consistent with frequent retransmissions among individuals who are recently infected and often unaware of their status.23,34,36–38,47–51
Three phylogenetic patterns of PHI clustering have been observed: unique “dead-end” primary infections, small cluster (2–4 PHI), and large cluster (5–60 PHI) networks (Fig. 1). The growth of the MSM epidemic can be attributed to the stepwise increase in large clustered transmissions, rising from 16 clusters in 2005 (n = 140, 9 PHI/cluster) to 60 clusters in 2012 (n = 750, 13 PHI/cluster). The cumulative contribution of large clusters to the epidemic has risen from 30% of the epidemic in 2005 to 54% of the epidemic in 2012 (Fig. 1). Unique transmissions have declined from 42% of infections in 2005 to 26% of infections in 2012. Small clusters (2–4 PHI) accounted for the remaining 28% and 20% infections in 2005 and 2012, respectively.
The temporal growth of individual small and large clusters highlights the role of primary (<6 months) and early-stage infection in onward transmission dynamics. Individual small clusters expanded over median 4.75-month periods with a 1- to 11-month interquartile range (Fig. 2). The temporal expansion of large clusters occurred over a median 11-month period with an 8- to 21-month interquartile range (Fig. 2). These results are similar to findings in the United Kingdom, the Netherlands, and France.23–25,34,36,37,52 Taken together, 25%–30% of transmissions in large clusters occur over a 6-month period and half of transmissions occur over a 14- to 17-month period (Fig. 2).
RELATIONSHIP TO OTHER STUDIES
Comparisons of MSM transmission dynamics have been confounded by the use of different inclusion criteria and methodologies. Molecular phylogeny studies have been assessed using acute/PHI (<6 months) and recent infection (<12–18 months) MSM cohorts and national genotyping programs that include chronic populations and different risk groups (MSM), hetersexual (HET), and intravenous drug users (IDU).23,34,36–38 The criteria for designation of transmission “clustering” have varied in bootstrap values (>95%–98%) and genetic distance (<0.015–0.045).34–36,51 The rates of coclustering of MSM early-stage infections have varied from 17% to 70% in different regional settings, based on differences in prevalence rates, demographics, and depth of sampling.23,34,36–38,47–51
Clustering in most MSM cohorts was related to early-stage infection and high CD4 cell count.36,44,51,53 The nationwide United Kingdom survey showed that 15%, 21%, and 15% of infections were interlinked to 1, 2–10, and >10 infections with high bootstrap values (>95%) and genetic distance below 4.5%.24 The transmission interval occurred over median 17-month intervals with 20% of infections occurring over 6-month intervals, confirming the role of early infection in onward transmission.24,34 The Brighton study showed 24% clustering of MSM infections with onward transmission related to recent infection, concomitant sexually transmitted disease, higher viral load, and unawareness of status; clustering was reduced by effective HAART.54 The Swiss HIV cohort, a mixture of HET, MSM, and IDU epidemics, showed 42% overall clustering (bootstrap values > 98%).55 Inclusion in clusters was associated with MSM transmission (52% clustering) and recent infection (<1 year post seroconversion, <0.5% ambiguity).55
PHYLOGENETIC INFERENCES AND PREVENTION STRATEGIES
Transmission clustering is clearly the driving force of MSM epidemics. The patterns of phylogenetic coclustering implicate a complex interplay of biological, behavioral, and interventional factors in the rise of large cluster transmission cascades. Although 75% of persons may ultimately receive HAART, there remains the precarious ART-free period of early-stage infection. The expansion of 60 clusters over 8- to 21-month intervals is inconsistent with a role of primary stage (<6 months), recent (1 year), and early stage (<24 months) in 25%, 50%, and 75% of onward transmissions. The duration of clusters indicate that onward transmission is not instantaneous but occurs over an extended period, involving the overlap of persons engaging in low- and high-risk behavior. Unawareness of status and poor testing habits are fueling onward transmission among treatment-naive individuals.
Although it has been postulated that early-stage infection will compromise TasP strategies, our findings argue that it is the delay in ART initiation that has contributed to the episodic development of new phylogenetic variants capable of overriding severe transmission bottlenecks. The failure to test, link to care, and initiation of early treatment is fueling the epidemic. This has had dangerous implications in the spread of drug resistance and the introduction of non–B subtypes.56–60 Six large clusters in our cohorts (n = 60, n = 29, n = 21, n = 9, n = 6, n = 6) harbor G190A or K103N, conferring resistance to first-generation nonnucleoside reverse transcriptase inhibitors.56 The crossover of non–B subtype HET and MSM epidemics has been rare, although 3 non–B subtype MSM clusters have arisen in Montreal, including CRF01_AE (n = 6) and CRF02_AG and l CRF_AB (n = 25) variants.44,59
Clearly, TasP interventions are needed to curb the development of drug-resistant subepidemics. High rates of transmitted drug resistance among drug-naive MSM and IDU populations have been related to clustering.56,58,59,61,62 This is of concern in resource-poor settings, where stavudine, didanosine, and nevirapine-based regimens may facilitate development of K65R or nonnucleoside reverse transcriptase inhibitor resistance.63,64 Pooled drug resistance testing may be needed to identify emergent resistance in resource-poor settings.65
The extended infectiousness of phylogenetic variants in large clusters may be related to multiple factors, including viral homogeneity, extended viremia, immature immune response, and risk behavior among those unaware of status.66 Fundamental research is needed to characterize the genotypic and phenotypic signatures of unique vs. cluster viral variants.67–72
These findings argue that the success of TasP will be predicated on timely diagnosis.73–75 SPOT, a Montreal community-based initiative, was begun in 2008, both as an intervention and a research study, to understand structural and attitudinal barriers to frequent testing and linkage to care. The site provides anonymous rapid testing and individualized motivational counseling. The SPOT findings point to the need to diversify services to reach priority populations who are less likely to use existing services. Half of the individuals seeking testing had not had an HIV test in the previous year. The overall rate of seropositivity was 2.1% (n = 36 of 1718) compared with the 0.14% seropositivity among MSM in the Montreal area (260,000 annual tests).
No persons at SPOT were identified with acute infections (n = 1682) using nucleic acid antigen testing, suggestive of a limited role of acute infection in transmission dynamics. Eight persons (25%) had primary infection (1–6 months since last test). Sequence-based assays, including nucleotide diversity, X4 env coreceptor usage, and next-generation sequencing and cluster association, were used to estimate recency of infection because half of newly diagnosed persons had not had a test in the previous year.76–80 Overall, 80% of seropositive persons had early-stage infection (<1 year) and were potentially infectious. Linkage to care and immediate ART is a viable option to curb the MSM epidemic.
PHYLODYNAMICS OF HET EPIDEMICS
The global expansion of relatively few viral subtypes is indicative of clustering at a global level. Subtype C accounts for half of worldwide infections, distributed mostly in Ethiopia, central and southern Africa, Brazil, India, and China.1,2,4 Subtypes A and CRF01_AE epidemics (17% of global infections) have spread from East Africa into Southeast Asia, China, and former Soviet Union nations through intravenous drug use (IDU), commercial sex work, and HET networks. Subtypes CRF02_AG and G (13% of global infections) have spread from West and North Africa into Europe.1,2,4 Subtype D remains mainly localized to Uganda. Subtype F, endemic in Angola, has spread to South America and Romania through MSM, IDU, and/or blood product infections. Newly emerging mosaic recombinant forms are emerging through the crossover of the HET, MSM, and IDU epidemics in different regional settings.81
The fastest growing epidemics worldwide are the IDU epidemics in Eastern Europe where subtypes A1 and CRF03_AB are most prevalent.82 In heavily populated regions, including India, China, and Southeast Asia, epidemics have rapidly shifted from predominant IDU epidemics to HET and MSM epidemics with selective expansion of subtype C, CRF07_BC, CRF08_BC, and CRF01_AE subtypes.81,83,84
There remains a paucity of phylogenetic studies on transmission dynamics of HET epidemics at the population level, although temporal cluster dynamics of the domestic subtype C epidemics in the United Kingdom seem to parallel those observed for the Montreal large cluster subset (Fig. 2).38 Phylogenetic clusters are relatively small (2–4 infections) and represent approximately 20% of transmission events. It will become increasingly important to monitor increased clustering with the extended use of ART in resource-poor settings.
Phylogenetics remain an endpoint metric in prevention trials of serodiscordant couples. The HPTN 052, Partners in Prevention, Zambia, and Uganda prevention trials showed that 21%, 26.5%, 13%, and 8%, respectively, of identified transmissions among enrolled couples were phylogenetically unlinked.16,17,28 Relationships outside partnerships may account for 10%–65% of HIV transmissions in sub-Saharan Africa.85
The HPTN 052 trial showed that the majority (83%) of linked transmission events involved the subtype C population in Africa, although this group represented only half of the recruited participants.7,86 The differential transmissibility of variants may affect the success of different clinical trials. A Botswana study showed that 34% of participants had extended viremia (>100,000 copies/mL) for median periods of 318 days (282–459 days), although no subtype differences were observed in The Partners to Prevention trial.87,88
FUTURE DIRECTIONS FOR PHYLOGENY IN PREVENTION
Testing, treatment, and other prevention interventions require major public health commitments. Phylogenetics can delineate underlying trends in regional settings to establish evidence-informed decisions.40 The integration of phylogenetic, epidemiological, clinical, and demographic data will be important in delineating the role of linkage to care, behavior, socioeconomic factors, and migration on transmission dynamics.40 Although early-stage infection may dominate in regional settings with universal access to health care and ART coverage, significant contributions of chronic stage infections may be related to socioeconomic factors, including lower awareness of status and poor linkage to care and treatment.89–94 Phylogenetic inferences of local epidemics may assist in the design of targeted prevention policies for distinct demographic groups, such as young adults and racial/ethnic minorities.92,95,96
The ultimate success of TasP will require improved strategies to target “Seek, Test, Link, Treat, and Retain” most-at-risk populations.97,98 Control interventions to limit HIV transmission are predicated on early diagnosis.27,29,30,33,54,99–101 Rapid testing programs are needed to target most-at-risk populations in a timely fashion. In Montreal, the SPOT site represents an MSM community-based initiative offering anonymous testing with peer group motivational counseling. The newly instituted clinic-based initiative, Actuel-sur-la-Rue, now provides rapid testing for HIV-1 and sexually transmitted diseases testing with linkage to care. The success of both testing initiatives in recruitment will be assessed in real time by phylogenetic analysis of cluster association and growth over time. Phylogeny will be used to assess the success of early treatment initiatives in reducing rates of clustering at a population level.
Sequence-based assays may be used to better monitor transmission dynamics and evaluate the impact of HIV prevention/intervention trials. The frequency of ambiguous calls in bulk sequencing can serve as a surrogate marker to distinguish recent infection (<0.44% ambiguity in the first year) from chronic infection (predictive value 98.7%).77,79 Single-genome amplification–direct sequencing, next-generation sequencing, and high-resolution melting assays may be applied in dating the recency of infection and viral evolution in a highly accurate manner.76,102–104
The upcoming HPTN 071 (PopART) and Mochudi HIV-1 prevention project in Botswana will examine the benefit of early ART on population level HIV-1 incidence in Africa. Phylogenetic analyses may be of assistance in monitoring the success of intervention trials, vis-à-vis, (1) assessment of viral linkage in partnerships, (2) clustering of transmission events, and (3) determination of the proportion of new infections attributable to acute and chronic stage infection.
Future research will broaden our knowledge of underlying mechanisms, leading to the preferential selection and expansion of transmitted ancestral strains. Phylodynamic inferences will be important in the design, implementation, and assessment of candidate public health and therapeutic and behavioral interventions for the ultimate prevention of new HIV infections.
The authors thank the patients, clinicians, and research staff participating in the the Quebec genotyping program, the SPOT study group, and the Montreal PHI cohort study and our coprincipal investigators Michel Roger, Joanne Otis, Robert Rousseau, and Jean-Pierre Routy.
1. Tebit DM, Arts EJ. Tracking a century of global expansion and evolution of HIV to drive understanding and to combat disease. Lancet Infect Dis. 2011;11:45–56.
2. Lihana RW, Ssemwanga D, Abimiku A, et al.. Update on HIV-1 diversity in Africa: a decade in review. AIDS Rev. 2012;14:83–100.
3. Beyrer C, Baral SD, van Griensven F, et al.. Global epidemiology of HIV infection in men who have sex with men. Lancet. 2012;380:367–377.
4. Hemelaar J, Gouws E, Ghys PD, et al.. Global trends in molecular epidemiology of HIV-1 during 2000-2007. AIDS. 2011;25:679–689.
5. Montaner JS, Lima VD, Barrios R, et al.. Association of highly active antiretroviral therapy coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study. Lancet. 2010;376:532–539.
6. Bendavid E, Grant P, Talbot A, et al.. Cost-effectiveness of antiretroviral regimens in the World Health Organization's treatment guidelines: a South African analysis. AIDS. 2011;25:211–220.
7. Cohen MS, Chen YQ, McCauley M, et al.. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365:493–505.
8. Donnell D, Baeten JM, Kiarie J, et al.. Heterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. Lancet. 2010;375:2092–2098.
9. Jia Z, Ruan Y, Li Q, et al.. Antiretroviral therapy to prevent HIV transmission in serodiscordant couples in China (2003-11): a national observational cohort study. Lancet. 2012;pii: S0140-6736(12)61898-4. doi: 10.1016/S0140-6736(12)61898-4.
10. Labarga P. New DHHS guidelines recommend antiretroviral therapy to all HIV-infected persons. AIDS Rev. 2012;14:154.
11. Thompson MA, Aberg JA, Hoy JF, et al.. Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel. JAMA. 2012;308:387–402.
12. Okwundu CI, Uthman OA, Okoromah CA. Antiretroviral pre-exposure prophylaxis (PrEP) for preventing HIV in high-risk individuals. Cochrane Database Syst Rev. 2012;7:CD007189.
13. Thigpen MC, Kebaabetswe PM, Paxton LA, et al.. Antiretroviral preexposure prophylaxis for heterosexual HIV transmission in Botswana. N Engl J Med. 2012;367:423–434.
14. Abdool Karim Q, Abdool Karim SS, Frohlich JA, et al.. Effectiveness and safety of tenofovir gel, an antiretroviral microbicide, for the prevention of HIV infection in women. Science. 2010;329:1168–1174.
15. Grant RM, Lama JR, Anderson PL, et al.. Preexposure chemoprophylaxis for HIV prevention in men who have sex with men. N Engl J Med. 2010;363:2587–2599.
16. Trask SA, Derdeyn CA, Fideli U, et al.. Molecular epidemiology of human immunodeficiency virus type 1 transmission in a heterosexual cohort of discordant couples in Zambia. J Virol. 2002;76:397–405.
17. Campbell MS, Mullins JI, Hughes JP, et al.. Viral linkage in HIV-1 seroconverters and their partners in an HIV-1 prevention clinical trial. PLoS One. 2011;6:e16986.
18. Celum C, Wald A, Lingappa JR, et al.. Acyclovir and transmission of HIV-1 from persons infected with HIV-1 and HSV-2. N Engl J Med. 2010;362:427–439.
19. Granich R, Williams B, Montaner J. Fifteen million people on antiretroviral treatment by 2015: treatment as prevention. Curr Opin HIV AIDS. 2013;8:41–49.
20. Granich RM, Gilks CF, Dye C, et al.. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet. 2009;373:48–57.
21. Baggaley RF, White RG, Hollingsworth TD, et al.. Heterosexual HIV-1 infectiousness and antiretroviral use: systematic review of prospective studies of discordant couples. Epidemiology. 2013;24:110–121.
22. Beyrer C, Sullivan PS, Sanchez J, et al.. A call to action for comprehensive HIV services for men who have sex with men. Lancet. 2012;280:424–438.
23. Brenner BG, Roger M, Stephens D, et al.. Transmission clustering drives the onward spread of the HIV epidemic among men who have sex with men in Quebec. J Infect Dis. 2011;204:1115–1119.
24. Leigh Brown AJ, Lycett SJ, Weinert L, et al.. Transmission network parameters estimated from HIV sequences for a nationwide epidemic. J Infect Dis. 2011;204:1463–1469.
25. Bezemer D, de Wolf F, Boerlijst MC, et al.. 27 years of the HIV epidemic amongst men having sex with men in the Netherlands: an in depth mathematical model-based analysis. Epidemics. 2010;2:66–79.
26. van Sighem A, Vidondo B, Glass TR, et al.. Resurgence of HIV infection among men who have sex with men in Switzerland: mathematical modelling study. PLoS One. 2012;7:e44819.
27. Powers KA, Ghani AC, Miller WC, et al.. The role of acute and early HIV infection in the spread of HIV and implications for transmission prevention strategies in Lilongwe, Malawi: a modelling study. Lancet. 2011;378:256–268.
28. Wawer MJ, Gray RH, Sewankambo NK, et al.. Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis. 2005;191:1403–1409.
29. Cohen MS, Shaw GM, McMichael AJ, et al.. Acute HIV-1 infection. N Engl J Med. 2011;364:1943–1954.
30. Pilcher CD, Joaki G, Hoffman IF, et al.. Amplified transmission of HIV-1: comparison of HIV-1 concentrations in semen and blood during acute and chronic infection. AIDS. 2007;21:1723–1730.
31. Alam SJ, Romero-Severson E, Kim JH, et al.. Dynamic sex roles among men who have sex with men and transmissions from primary HIV infection. Epidemiology. 2010;21:669–675.
32. Kim JH, Koopman JS. HIV transmissions by stage in dynamic sexual partnerships. J Theor Biol. 2012;298:147–153.
33. Kim JH, Riolo RL, Koopman JS. HIV transmission by stage of infection and pattern of sexual partnerships. Epidemiology. 2010;21:676–684.
34. Lewis F, Hughes GJ, Rambaut A, et al.. Episodic sexual transmission of HIV revealed by molecular phylodynamics. PLoS Med. 2008;5:e50.
35. Hue S, Clewley JP, Cane PA, et al.. HIV-1 pol gene variation is sufficient for reconstruction of transmissions in the era of antiretroviral therapy. AIDS. 2004;18:719–728.
36. Brenner BG, Roger M, Routy JP, et al.. High rates of forward transmission events after acute/early HIV-1 infection. J Infect Dis. 2007;195:951–959.
37. Bezemer D, van Sighem A, Lukashov VV, et al.. Transmission networks of HIV-1 among men having sex with men in the Netherlands. AIDS. 2010;24:271–282.
38. Hughes GJ, Fearnhill E, Dunn D, et al.. Molecular phylodynamics of the heterosexual HIV epidemic in the United Kingdom. PLoS Pathog. 2009;5:e1000590.
39. Yerly S, Junier T, Gayet-Ageron A, et al.. The impact of transmission clusters on primary drug resistance in newly diagnosed HIV-1 infection. AIDS. 2009;23:1415–1423.
40. Levy I, Mor Z, Anis E, et al.. Men who have sex with men, risk behavior, and HIV infection: integrative analysis of clinical, epidemiological, and laboratory databases. Clin Infect Dis. 2011;52:1363–1370.
41. Pillay D, Fisher M. Primary HIV infection, phylogenetics, and antiretroviral prevention. J Infect Dis. 2007;195:924–926.
42. Lam TT, Hon CC, Tang JW. Use of phylogenetics in the molecular epidemiology and evolutionary studies of viral infections. Crit Rev Clin Lab Sci. 2010;47:5–49.
43. Bezemer D, de Wolf F, Boerlijst MC, et al.. A resurgent HIV-1 epidemic among men who have sex with men in the era of potent antiretroviral therapy. AIDS. 2008;22:1071–1077.
44. Kouyos RD, von Wyl V, Yerly S, et al.. Molecular epidemiology reveals long-term changes in HIV type 1 subtype B transmission in Switzerland. J Infect Dis. 2010;201:1488–1497.
45. Yerly S, von Wyl V, Ledergerber B, et al.. Transmission of HIV-1 drug resistance in Switzerland: a 10-year molecular epidemiology survey. AIDS. 2007;21:2223–2229.
46. Lavoie E, Alary M, Remis RS, et al.. Determinants of HIV seroconversion among men who have sex with men living in a low HIV incidence population in the era of highly active antiretroviral therapies. Sex Transm Dis. 2008;35:25–29.
47. Pao D, Fisher M, Hue S, et al.. Transmission of HIV-1 during primary infection: relationship to sexual risk and sexually transmitted infections. AIDS. 2005;19:85–90.
48. Hue S, Clewley JP, Cane PA, et al.. Investigation of HIV-1 transmission events by phylogenetic methods: requirement for scientific rigour. AIDS. 2005;19:449–450.
49. German D, Sifakis F, Maulsby C, et al.. Persistently high prevalence and unrecognized HIV infection among men who have sex with men in Baltimore: the BESURE study. J Acquir Immune Defic Syndr. 2011;57:77–87.
50. Paraskevis D, Pybus O, Magiorkinis G, et al.. Tracing the HIV-1 subtype B mobility in Europe: a phylogeographic approach. Retrovirology. 2009;6:49.
51. Audelin AM, Cowan SA, Obel N, et al.. Phylogenetics of the Danish HIV epidemic: the role of very late presenters in sustaining the epidemic. J Acquir Immune Defic Syndr. 2012;62:102–108.
52. Frange P, Meyer L, Deveau C, et al.. Recent HIV-1 infection contributes to the viral diffusion over the French territory with a recent increasing frequency. PLoS One. 2012;7:e31695.
53. Chalmet K, Staelens D, Blot S, et al.. Epidemiological study of phylogenetic transmission clusters in a local HIV-1 epidemic reveals distinct differences between subtype B and non-B infections. BMC Infect Dis. 2010;10:262.
54. Fisher M, Pao D, Brown AE, et al.. Determinants of HIV-1 transmission in men who have sex with men: a combined clinical, epidemiological and phylogenetic approach. AIDS. 2010;24:1739–1747.
55. Ambrosioni J, Junier T, Delhumeau C, et al.. Impact of HAART on the molecular epidemiology of newly diagnosed HIV infections in Geneva, Switzerland. AIDS. 2012;26:2079–2086.
56. Brenner BG, Roger M, Moisi DD, et al.. Transmission networks of drug resistance acquired in primary/early stage HIV infection. AIDS. 2008;22:2509–2515.
57. Wittkop L, Gunthard HF, de Wolf F, et al.. Effect of transmitted drug resistance on virological and immunological response to initial combination antiretroviral therapy for HIV (EuroCoord-CHAIN joint project): a European multicohort study. Lancet Infect Dis. 2011;11:363–371.
58. Hue S, Gifford RJ, Dunn D, et al.. Demonstration of sustained drug-resistant human immunodeficiency virus type 1 lineages circulating among treatment-naive individuals. J Virol. 2009;83:2645–2654.
59. Wainberg MA, Zaharatos GJ, Brenner BG. Development of antiretroviral drug resistance. N Engl J Med. 2011;365:637–646.
60. Truong HM, Kellogg TA, McFarland W, et al.. Sentinel surveillance of HIV-1 transmitted drug resistance, acute infection and recent infection. PLoS One. 2011;6:e25281.
61. Castor D, Low A, Evering T, et al.. Transmitted drug resistance and phylogenetic relationships among acute and early HIV-1 infected individuals in New York city. J Acquir Immune Defic Syndr. 2012.
62. Castro E, Khonkarly M, Ciuffreda D, et al.. HIV-1 drug resistance transmission networks in southwest Switzerland. AIDS Res Hum Retroviruses. 2010;26:1233–1238.
63. Brenner BG, Coutsinos D. The K65R mutation in HIV-1 reverse transcriptase: genetic barriers, resistance profile and clinical implications. HIV Ther. 2009;3:583–594.
64. Brenner BG, Oliveira M, Doualla-Bell F, et al.. HIV-1 subtype C viruses rapidly develop K65R resistance to tenofovir in cell culture. AIDS. 2006;20:F9–F13.
65. Finucane MM, Rowley CF, Paciorek CJ, et al.. Estimating the prevalence of transmitted HIV drug resistance using pooled samples. Stat Methods Med Res. 2013. In press.
66. Marks G, Crepaz N, Janssen RS. Estimating sexual transmission of HIV from persons aware and unaware that they are infected with the virus in the USA. AIDS. 2006;20:1447–1450.
67. Gnanakaran S, Bhattacharya T, Daniels M, et al.. Recurrent signature patterns in HIV-1 B clade envelope glycoproteins associated with either early or chronic infections. PLoS Pathog. 2011;7:e1002209.
68. Ochsenbauer C, Edmonds TG, Ding H, et al.. Generation of transmitted/founder HIV-1 infectious molecular clones and characterization of their replication capacity in CD4 T lymphocytes and monocyte-derived macrophages. J Virol. 2012;86:2715–2728.
69. Salazar-Gonzalez JF, Salazar MG, Keele BF, et al.. Genetic identity, biological phenotype, and evolutionary pathways of transmitted/founder viruses in acute and early HIV-1 infection. J Exp Med. 2009;206:1273–1289.
70. Wood N, Bhattacharya T, Keele BF, et al.. HIV evolution in early infection: selection pressures, patterns of insertion and deletion, and the impact of APOBEC. PLoS Pathog. 2009;5:e1000414.
71. Freel SA, Picking RA, Ferrari G, et al.. Initial HIV-1 antigen-specific CD8+ T cells in acute HIV-1 infection inhibit transmitted/founder virus replication. J Virol. 2012;86:6835–6846.
72. Parrish NF, Wilen CB, Banks LB, et al.. Transmitted/founder and chronic subtype C HIV-1 use CD4 and CCR5 receptors with equal efficiency and are not inhibited by blocking the integrin alpha4beta7. PLoS Pathog. 2012;8:e1002686.
73. Smith K, Powers KA, Kashuba AD, et al.. HIV-1 treatment as prevention: the good, the bad, and the challenges. Curr Opin HIV AIDS. 2011;6:315–325.
74. Delva W, Wilson DP, Abu-Raddad L, et al.. HIV treatment as prevention: principles of good HIV epidemiology modelling for public health decision-making in all modes of prevention and evaluation. PLoS Med. 2012;9:e1001239.
75. Delva W, Eaton JW, Meng F, et al.. HIV treatment as prevention: optimising the impact of expanded HIV treatment programmes. PLoS Med. 2012;9:e1001258.
76. Poon AF, McGovern RA, Mo T, et al.. Dates of HIV infection can be estimated for seroprevalent patients by coalescent analysis of serial next-generation sequencing data. AIDS. 2011;25:2019–2026.
77. Kouyos RD, von Wyl V, Yerly S, et al.. Ambiguous nucleotide calls from population-based sequencing of HIV-1 are a marker for viral diversity and the age of infection. Clin Infect Dis. 2011;52:532–539.
78. Giorgi EE, Funkhouser B, Athreya G, et al.. Estimating time since infection in early homogeneous HIV-1 samples using a poisson model. BMC Bioinformatics. 2010;11:532.
79. Maldarelli F, Shao W, Dewar R, et al.. New bioinformatic algorithm to identify recent HIV-1 infection. Antivir Ther. 2010;15:A97.
80. Cousins MM, Laeyendecker O, Beauchamp G, et al.. Use of a high resolution melting (HRM) assay to compare gag, pol, and env diversity in adults with different stages of HIV infection. PLoS One. 2011;6:e27211.
81. Wu J, Meng Z, Xu J, et al.. New emerging recombinant HIV-1 strains and close transmission linkage of HIV-1 strains in the Chinese MSM population indicate a new epidemic risk. PLoS One. 2013;8:e54322.
82. Stanojevic M, Alexiev I, Beshkov D, et al.. HIV1 molecular epidemiology in the Balkans: a melting pot for high genetic diversity. AIDS Rev. 2012;14:28–36.
83. He X, Xing H, Ruan Y, et al.. A comprehensive mapping of HIV-1 genotypes in various risk groups and regions across China based on a nationwide molecular epidemiologic survey. PLoS One. 2012;7:e47289.
84. Neogi U, Bontell I, Shet A, et al.. Molecular epidemiology of HIV-1 subtypes in India: origin and evolutionary history of the predominant subtype C. PLoS One. 2012;7:e39819.
85. Bellan SE, Fiorella KJ, Melesse DY, et al.. Extra-couple HIV transmission in sub-Saharan Africa: a mathematical modelling study of survey data. Lancet. 2013;381:1561–1569.
86. Eshleman SH, Hudelson SE, Redd AD, et al.. Analysis of genetic linkage of HIV from couples enrolled in the HIV Prevention Trials Network 052 trial. J Infect Dis. 2011;204:1918–1926.
87. Novitsky V, Ndung'u T, Wang R, et al.. Extended high viremics: a substantial fraction of individuals maintain high plasma viral RNA levels after acute HIV-1 subtype C infection. AIDS. 2011;25:1515–1522.
88. Campbell MS, Kahle EM, Celum C, et al.. Plasma viral loads during early HIV-1 infection are similar in subtype C- and non-subtype C-infected African seroconverters. J Infect Dis. 2013;207:1166–1170.
89. Hernandez AL, Prejean J, Doshani M, et al.. Previous HIV testing among adults and adolescents newly diagnosed with HIV infection—National HIV Surveillance System, 18 Jurisdictions, United States, 2006-2009. MMWR Morb Mortal Wkly Rep. 2012;61:441–445.
90. Chen M, Rhodes PH, Hall IH, et al.. Prevalence of undiagnosed HIV infection among persons aged ≥13 years—National HIV Surveillance System, United States, 2005-2008. MMWR Surveill Summ. 2012;61:57–64.
91. Sifakis F, Flynn CP, Metsch L, et al.. HIV prevalence, unrecognized infection, and HIV testing among men who have sex with men—five U.S. cities, June 2004-April 2005. MMWR Morb Mortal Wkly Rep. 2005;54:597–601.
92. Oster AM, Wiegand RE, Sionean C, et al.. Understanding disparities in HIV infection between black and white MSM in the United States. AIDS. 2011;25:1103–1112.
93. Ayala G, Bingham T, Kim J, et al.. Modeling the impact of social discrimination and financial hardship on the sexual risk of HIV among Latino and Black men who have sex with men. Am J Public Health. 2012;102:S242–S249.
94. Aldous JL, Pond SK, Poon A, et al.. Characterizing HIV transmission networks across the United States. Clin Infect Dis. 2012;55:1135–1143.
95. Dennis AM, Hue S, Hurt CB, et al.. Phylogenetic insights into HIV transmission in North Carolina. AIDS. 2012;26:1813–1822.
96. Oster AM, Pieniazek D, Zhang X, et al.. Demographic but not geographic insularity in HIV transmission among young black MSM. AIDS. 2011;25:2157–2165.
97. Hull MW, Wu Z, Montaner JS. Optimizing the engagement of care cascade: a critical step to maximize the impact of HIV treatment as prevention. Curr Opin HIV AIDS. 2012;7:579–586.
98. McNairy ML, Cohen M, El-Sadr WM. Antiretroviral therapy for prevention is a combination strategy. Curr HIV/AIDS Rep. 2013;10:152–158.
99. Cohen MS, Fidler S. HIV prevention 2010: where are we now and where are we going? Curr Opin HIV AIDS. 2010;5:265–268.
100. Jacquez JA, Koopman JS, Simon CP, et al.. Role of the primary infection in epidemics of HIV infection in gay cohorts. J Acquir Immune Defic Syndr. 1994;7:1169–1184.
101. Miller WC, Rosenberg NE, Rutstein SE, et al.. Role of acute and early HIV infection in the sexual transmission of HIV. Curr Opin HIV AIDS. 2010;5:277–282.
102. Park SY, Love TM, Nelson J, et al.. Designing a genome-based HIV incidence assay with high sensitivity and specificity. AIDS. 2011;25:F13–F19.
103. Yang J, Xia X, He X, et al.. A new pattern-based method for identifying recent HIV-1 infections from the viral env sequence. Sci China Life Sci. 2012;55:328–335.
104. Cousins MM, Ou SS, Wawer MJ, et al.. Comparison of a high-resolution melting assay to next-generation sequencing for analysis of HIV diversity. J Clin Microbiol. 2012;50:3054–3059.