The widespread use of combination antiretroviral therapy (ART) in the United States has resulted in a substantial reduction of HIV-related morbidity and mortality.1 Mathematical models and clinical studies have also demonstrated that ART use reduces the risk of HIV transmission.2–4 However, the emergence of HIV drug-resistant variants and their transmission remain a major concern to the widespread use of ART, which can lead to higher probability of early virological failure in first-line ART.5–8
Estimates of the rates of transmitted drug resistance (TDR) in HIV epidemic vary throughout the world. There are several reasons for this variation, including differences in sampling, gender, race/ethnicity, location, time from seroconversion, duration of use of ART in the study population, and risk exposure category.9–13 Overall, the prevalence of TDR has been reported to range from 3.4% to 25.2% among ART-naive HIV-infected individuals in the United States9,14–29 and has been associated with the level of drug resistance in the community as a whole, “community drug resistance”.30
Continued monitoring in the same population can provide important insights into important trends of TDR that may impact clinical practice, such as which first-line ART regimens should be used and if baseline drug resistance testing should be performed. Our group has monitored TDR in San Diego County since 199616 and has documented TDR rates in both ART-naive patients with an unknown duration of HIV infection24 and those with recent infection.16,31 Our most recent report was in 2009, which found the overall prevalence of TDR among newly diagnosed HIV-infected patients in San Diego County to be 19%.31 The present study builds on this previous work by determining the prevalence, rate of change, and phylogenetic relationships of TDR in newly diagnosed and ART-naive HIV-infected individuals in San Diego County from 1996 through 2013.
Individuals enrolled between June 1996 and June 2013 in the University of California, San Diego Primary Infection Resource Consortium (SD PIRC), were included in this analysis. Inclusion criteria were (1) age over 18 years; (2) HIV-infected within the previous 12 months, as determined by laboratory diagnostics, documented evolution of HIV seroconversion within the preceding 12 months, or evidence of acute or early HIV infection as determined using a set of clinical, virologic, and serologic criteria; and (3) no ART exposure at the time of enrollment (treatment-naive), as previously described.32 After informed consent, clinical demographic characteristics and laboratory data were obtained at baseline from all participants. This study was approved by the UCSD human research protection program.
Genotypic Resistance Analysis
Blood specimens were collected before the initiation of therapy and within 1 month of enrollment into SD PIRC for drug resistance evaluation. Population sequencing of the partial HIV-1 pol coding region was performed (GeneSeq HIV-1; Monogram Biosciences, Inc., South San Francisco, CA or Viroseq v.2.0; Celera Diagnostics, Alameda, CA).33 Genotypic analysis was performed to detect mutations in the HIV-1 pol gene fragment-encoding protease (PR) and reverse transcriptase (RT), as previously described.32 Major drug resistance mutations (DRMs) were identified using the Stanford HIV database Calibrated Population Resistance Tool version 6.0 available on http://cpr.stanford.edu/cpr/index.html34 based on the 2009 World Health Organization surveillance of transmitted DRMs (SDRMs) list for nucleoside reverse transcriptase inhibitors (NRTIs), non-NRTIs (NNRTIs), and protease inhibitors (PIs).35 The presence of one or more major resistance mutations in any drug class was considered as TDR according to the SDRM list.
Identification of Transmission Clusters by Network Analysis
Cluster analyses were performed as previously described.36 Briefly, the Tamura-Nei 93 nucleotide substitution model (TN93)37 was used to compute genetic distance between all sequences, and a putative link was inferred if the TN93 genetic distance between 2 sequences was less than 1.5%. Elucidation of transmission clusters was performed by combining these inferred linkages.31
The HIV-1 subtypes and circulating recombinant forms were determined using 2 HIV-1 subtyping tools, namely the Rega HIV-1 subtyping tool version 3.038,39 and SCUEAL.40 The discordant subtyping results between the 2 tools were then analyzed using phylogenetic analysis in the Treemaker tool provided by HIV LANL Sequence Database that included all reference sequences from HIV-1 subtypes and circulating recombinant forms to make an informed assignment of subtype.41
An alignment of the 496 available sequences was created using MUSCLE42 and further curated manually using Bioedit software version 7.2.5 (Carlsbad, CA).43 To avoid the effect of homoplasy (convergent evolution) of drug resistance mutations on the phylogenetic analysis, all 29 codons associated with major DRM in PR and RT were removed from all of the sequences within the alignment. Phylogenetic approaches were then used to establish transmission clusters and interrelationships among viral sequences. Global phylogenetic relationships were estimated using a maximum likelihood approach with a bootstrap analyses with 1000 replicates using the general time reversible + Gamma (GTR + Γ) model of nucleotide substitution in FastTree version 2.1 (Berkeley, CA).44 Robust clusters were assessed by bootstrap support values (≥70%) with 1000 replicates. The trees were edited and visualized using FigTree version 1.4.1 (Edinburgh, UK).45
Prevalence values were calculated with a 95% Wilson score confidence interval (95% CI) for binomially distributed data. Categorical variables were compared using the χ2 test, Fisher exact test, or simple logistic regression analysis as appropriate. Continuous variables were compared using the Student t-test or the Mann–Whitney U test. Multiple binomial logistic regression analysis was used to determine the factors associated with drug resistance mutations and control the potential confounders. The yearly time periods were assessed with χ2 test for trend or the Cochran–Armitage test. All P-values were two-tailed tests, and the statistical significance level was set at P < 0.05. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC).
Characteristics of Subjects
A total of 496 SD PIRC participants with clinical or laboratory evidence of primary HIV infection were enrolled from 1996 through 2013. The majority of the study population was male (97%); 78% were white, 9.7% Native American, 6.8% black, 3.7% Asian, and 1.9% Pacific Islander (Table 1). The mean age of SD PIRC participants was 32 years at the time of resistance testing. The most commonly reported transmission risk factors were men who have sex with men (MSM, 90.3%) or MSM and intravenous drug use (MSM + IVDU; 3.2%), followed by heterosexual contact (2.4%). These data are consistent with the HIV epidemiology in San Diego County.46 At enrollment, mean baseline CD4 count was 530 cells per microliter and 3.4% having CD4 < 200 cells per microliter. Median viral load at enrollment was 97,808 HIV RNA copies per milliliter. Overall, most participants were infected with HIV-1 subtype B (97.4%). No single HIV-1 non-B subtype represented more than 0.4% of the sample.
Overall Prevalence of TDR
The percentage of ART-naive individuals with primary HIV infection enrolled in SD PIRC between 1996 and 2013 who harbored one or more DRM was 13.5% (67/496; 95% CI: 10.8% to 16.8%). The most common major DRM identified were associated with NNRTIs resistance at 8.5% (42/496; 95% CI: 6.3% to 11.3%), followed by PIs at 4.4% (22/496; 95% CI: 2.9% to 6.7%) and NRTIs at 3.8% (19/496; 95% CI: 2.4% to 5.9%) (Table 3). Dual- and triple-class TDR were found in 3.8% (19/496; 95% CI: 2.4 to 5.9) and 1.0% (5/49 6; 95% CI: 0.4% to 2.4%) of subjects. The K103N/S, NNRTIs-associated mutation, was the most frequent mutation observed in 7.3% of individuals, whereas most NRTIs DRMs were thymidine analogue mutations (TAMs) of which the most prevalent were the T215Y/F/I/S/D/E/C/V mutations (2%), followed by M41L (1.8%), whereas M46I/L was the most common PI DRM, which was found in 1.8% of individuals (see Supplemental Digital Content, https://links.lww.com/QAI/A743).
TDR Trends Throughout the Study Period
When the rates of TDR were compared among 4 time periods (1996–1999, 2000–2004, 2005–2009, and 2010–2013), we found a statistically significant increase over time in the proportion of participants with TDR (P = 0.005; Table 2), and this significance remains when controlling for potential confounders (P = 0.02). When comparing resistance by ART class (Table 3 and Fig. 1), TDR prevalence for NNRTIs significantly increased over the entire study period (P for trend = 0.005) that coincided with the observed increase in K103N/S mutation (P for trend = 0.005; Fig. 2 and Supplemental Digital Content, https://links.lww.com/QAI/A743). By contrast, the prevalence of NRTIs and PIs TDR was apparently stable over time (P = NS). The temporal trends for specific mutations are presented in the Supplemental Digital Content, (https://links.lww.com/QAI/A743).
Correlates of TDR
Characteristics of individuals with and without TDR were comparable for sex, age at enrollment, ethnicity, route of transmission, CD4 cell count, plasma HIV-RNA, baseline history of alcohol use and IVDU within 90 days of SD PIRC enrollment, and year of diagnosis (Table 2). In a univariate analysis, mean baseline CD4 cell count was significantly lower among individuals with TDR (P = 0.02; Table 2), but no difference was found in baseline median plasma viral load (P = 0.23; Table 2). Similarly, no significant association between TDR and other demographic factors, sexual practices, or use of recreational drugs were found. Given that only one factor was associated with TDR (baseline CD4 count), no significant associations became evident in multivariate analyses.
Phylogenetic and Network Analysis
A phylogenetic tree was inferred with the 496 HIV-1 partial pol sequences from the SD PIRC data set (Fig. 3). Given the limitations associated with phylogenetically analyzing such large numbers of sequences, we also used network analysis to obtain a deeper understanding of the underlying transmission network. We identified 52 transmission clusters (169 individuals, 34% of the cohort), of which 12 included at least one individual with a DRM. Of these clusters, 8 (66.6%) included at least 2 individuals carrying the same resistance mutation, and the K103N was the concordant mutation found in 7 out of these 8 clusters, whereas in the remaining one cluster, L90M was the concordant mutation. Phylogenetic analysis using FastTree software was used to confirm the existence of these 8 clusters, and all these clusters had bootstrap values ≥70% (Fig. 3), supporting the findings made using the network analysis.
Focusing on the individuals with primary infection, the prevalence of TDR was not significantly different among individuals who were part of clusters and those who were not [11.8% (20/169) vs. 16.8% (47/280), respectively; P = 0.49]. To further determine the probability of having the same DRM in the same identified cluster by chance, prevalence of TDR was also compared among all individuals of each transmission cluster, which contained at least 2 individuals sharing the same DRM. Significant overrepresentation of individuals sharing the same DRM was found in 6 of 8 clusters at 2 different nominal P-values of 0.005 and 0.02 (data not shown). We also found strong evidence that individuals with DRMs that were in a phylogenetic cluster were more likely to have a closest neighbor in the phylogenetic tree of all SD PIRC sequences with the same DRM than individuals with sequences harboring DRMs that were not found within a cluster (P = 0.002, data not shown).
This study estimated the prevalence of TDR among individuals with acute and early HIV infection in San Diego between 1996 and 2013. The combined prevalence of TDR to one or more drugs in the first 3 ART classes was approximately 13.5% over the 18 years of the study. Given the episodic nature of HIV transmission, we expected to observe year-to-year variation in TDR rates, similar to what has been shown in other recent studies47–49; however, we did observe a significant increase in the overall rate of TDR over time. The TDR rates found from 2005 to 2013 mirror the most recent CDC study evaluating TDR nationally between 2007 and 2010, which reported a 16.2% rate of TDR among ART-naive HIV-infected individuals across the United States.29 By contrast, studies in Europe have reported stabilizing and possibly decreasing trends in TDR prevalence in recent years.50 Similar to the CDC report, we found that the overall prevalence of TDR to NNRTIs was 8.5% in our cohort, whereas the CDC report by Kim et al found the prevalence of TDR to NNRTIs to be 8.1%. Our study found that dual-class resistance and triple-class resistance were slightly higher at 3.8% and 1.0%, respectively, than those of the CDC report of 2.1% and 0.5%, respectively. These differences may be secondary to differences in the study design where we only evaluated for TDR among individuals with recent HIV infection, whereas the CDC study examined individuals who were ART-naive, but the duration of infection was not always known. As such, the rates of TDR from the CDC study may be underestimations, since TDR mutations can become undetectable over time from reversion to wild-type sequences.51
In our study, TDR to NNRTIs was the most frequently observed DRM, consistent with other published studies.11,17 In the precombination ART era (before 1996), TDR in the US was primarily directed to NRTIs; however, when NNRTIs became widely available in 1996,52–56 the prevalence of recently HIV-infected individuals who had TDR to NNRTI increased over time, with the K103N the most frequently observed DRM. Specifically, 53.7% of individuals with DRM had the K103N mutation. This may be because the K103N DRM is often associated with early virologic failure with the most frequently used NNRTI efavirenz,57 has minimal effects on viral replication capacity,58 and may persist for long periods, even after discontinuation of NNRTI-based therapy.59,60 Generally, early virologic failure to efavirenz is conferred by a single mutation, but continuation of ART during virologic failure often leads to accumulation of multiple DRMs, which can lead to cross-class resistance.61
In this study, most DRMs to NRTIs were TAMs T215Y/F/I/S/D/E/C/V (2%) and M41L (1.8%), which mostly confer resistance to older generation NRTIs, zidovudine, and stavudine. However, the M41L alone can be a polymorphism that is not associated with reduced susceptibility to any NRTIs by itself.62,63 The rate of this TDR to NRTI with TAMs remained relatively stable throughout our observed study period. Since zidovudine and stavudine are rarely prescribed anymore, the persistence of these TAMs in our ART-naive cohort suggests that these TAMs and their revertants are evidence of ongoing TDR that carries DRMs that were selected for in-patients receiving zidovudine or stavudine earlier in the epidemic. This hypothesis is supported by several studies that have demonstrated that TAMs and their revertants persist for several years with little reversion to wild-type amino acids in the absence of antiretroviral selection pressure.51,64
The prevalence of TDR to PIs was lower than TDR to NNRTIs and NRTIs, which is consistent with other studies,64–66 and is likely the result of the high genetic barrier to develop DRMs to PIs.67,68 According to recent version guidelines for treating HIV infection,69,70 integrase strand transfer inhibitors (INSTIs) were listed as a “preferred” regimen for ART-naive HIV-infected individuals. Although, to date, the prevalence of TDR to INSTIs has been very rare,71–73 increased use will certainly lead to increasing TDR to INSTIs. Thus, monitoring of TDR should include evaluation for integrase mutations.
In most studies, individuals with TDR seemed to have higher baseline CD4 cell count than individuals without TDR11,74,75; however, our study found the opposite. This discrepancy was found in at least one other study76 and may be due to differences between cohorts. However, this association turned to nonsignificant in multivariate analysis. The most recent CDC report of TDR among ART-naive MSM in a large US study showed higher prevalence of TDR among MSM (17.4%) than among heterosexuals.77 In comparison, we found the prevalence of TDR among MSM to be lower at 13.4% than at 16.7% in the heterosexual population (P = 0.67, data not shown). Unfortunately, the small number of participants reporting heterosexual risk prevents us from generalizing our results to that population. These variations may reflect differences in the MSM population size, linkage or access to diagnosis, and care. Since new infection among MSM still remains an important factor driving HIV epidemic, especially in San Diego, TDR surveillance in this group should be regularly performed to identify and intervene on developing TDR trends.
This study had a rate of clustering with 34.1% of sequences segregating into 52 clusters. Several of these transmission clusters included individuals sharing the same DRM, in particular K103N mutation. This presence of TDR within transmission clusters accounted for almost 30% of DRMs in the cohort, which may be explained by reduced time for viral reversion of DRMs during clustered transmission.78 Several remaining clusters included individuals with different resistance mutations, which may reflect reversions, or sampling bias.
As with any other observational study, our study has limitations. First, we may have had a biased sample of the local population since potential participants were not selected using random sampling methods, and thus our study population might not be representative of our overall local population. Second, although the predominant risk factor in the San Diego epidemic is MSM, the SD PIRC is even more highly focused on this population with targeted HIV-testing campaigns,79 and thus MSM were most likely overrepresented. Third, this study did not evaluate TDR to other classes of antiretroviral medications, such as integrase inhibitors and fusion inhibitors. As the use of these agents increases, surveillance for TDR to the agents must be included. Finally, our data were not complete, and so demographic and clustering associations with TDR may have been missed.
Transmission of primary HIV-1 drug resistance continues to be an important public health threat. This study indicates that the prevalence of TDR has significantly increased over the past 18 years, specifically for TDR to NNRTIs. This study has also identified that TDR can occur within transmission clusters, which may be why the rate of TDR does not seem to be slowing despite the use of more effective ART over time. Taken together, this study reinforces the current recommendations for both baseline resistance testing to guide treatment decisions68 and the early treatment recently diagnosed individuals,80 since early HIV detection and treatment can prevent transmission of HIV drug resistance.
The authors thank the UCSD Center For AIDS Research (CFAR) and all patients who participated in the San Diego Primary Infection Cohort, to Josué Pérez Santiago and Mayuree Homsanit for invaluable statistical advice, and to Demetrius Dela Cruz for his administrative assistance.
1. Palella FJ Jr, Delaney KM, Moorman AC, et al.. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med. 1998;338:853–860.
2. Quinn TC, Wawer MJ, Sewankambo N, et al.. Viral load and heterosexual transmission of human immunodeficiency virus type 1. Rakai Project Study Group. N Engl J Med. 2000;342:921–929.
3. Attia S, Egger M, Muller M, et al.. Sexual transmission of HIV according to viral load and antiretroviral therapy: systematic review and meta-analysis. AIDS. 2009;23:1397–1404.
4. Das M, Chu PL, Santos GM, et al.. Decreases in community viral load are accompanied by reductions in new HIV infections in San Francisco. PLoS One. 2010;5:e11068.
5. Little SJ, Daar ES, D'Aquila RT, et al.. Reduced antiretroviral drug susceptibility among patients with primary HIV infection. JAMA. 1999;282:1142–1149.
6. 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.
7. Hingankar NK, Thorat SR, Deshpande A, et al.. Initial virologic response and HIV drug resistance among HIV-infected individuals initiating first-line antiretroviral therapy at 2 clinics in Chennai and Mumbai, India. Clin Infect Dis. 2012;54(suppl 4):S348–S354.
8. Phanuphak P, Sirivichayakul S, Jiamsakul A, et al.. Transmitted drug resistance and antiretroviral treatment outcomes in non-subtype B HIV-1-infected patients in South East Asia. J Acquir Immune Defic Syndr. 2014;66:74–79.
9. Booth CL, Geretti AM. Prevalence and determinants of transmitted antiretroviral drug resistance in HIV-1 infection. J Antimicrob Chemother. 2007;59:1047–1056.
10. Novak RM, Chen L, MacArthur RD, et al.. Prevalence of antiretroviral drug resistance mutations in chronically HIV-infected, treatment-naive patients: implications for routine resistance screening before initiation of antiretroviral therapy. Clin Infect Dis. 2005;40:468–474.
11. Grant RM, Hecht FM, Warmerdam M, et al.. Time trends in primary HIV-1 drug resistance among recently infected persons. JAMA. 2002;288:181–188.
12. Stadeli KM, Richman DD. Rates of emergence of HIV drug resistance in resource-limited settings: a systematic review. Antivir Ther. 2013;18:115–123.
13. Pham QD, Wilson DP, Law MG, et al.. Global burden of transmitted HIV drug resistance and HIV-exposure categories: a systematic review and meta-analysis. AIDS. 2014;28:2751–2762.
14. Boden D, Hurley A, Zhang L, et al.. HIV-1 drug resistance in newly infected individuals. JAMA. 1999;282:1135–1141.
15. Weinstock H, Respess R, Heneine W, et al.. Prevalence of mutations associated with reduced antiretroviral drug susceptibility among human immunodeficiency virus type 1 seroconverters in the United States, 1993-1998. J Infect Dis. 2000;182:330–333.
16. Little SJ, Holte S, Routy JP, et al.. Antiretroviral-drug resistance among patients recently infected with HIV. N Engl J Med. 2002;347:385–394.
17. Simon V, Vanderhoeven J, Hurley A, et al.. Evolving patterns of HIV-1 resistance to antiretroviral agents in newly infected individuals. AIDS. 2002;16:1511–1519.
18. Weinstock HS, Zaidi I, Heneine W, et al.. The epidemiology of antiretroviral drug resistance among drug-naive HIV-1-infected persons in 10 US cities. J Infect Dis. 2004;189:2174–2180.
19. Bennett D, McCormick L, Kline R. US surveillance of HIV drug resistance at diagnosis using HIV diagnostic sera. Paper presented at: 12th Conference on Retroviruses and Opportunistic Infections (CROI); February 22-25, 2005; Boston, MA. Abstract 674.
20. Shet A, Berry L, Mohri H, et al.. Tracking the prevalence of transmitted antiretroviral drug-resistant HIV-1: a decade of experience. J Acquir Immune Defic Syndr. 2006;41:439–446.
21. Parker MM, Gordon D, Reilly A, et al.. Prevalence of drug-resistant and nonsubtype B HIV strains in antiretroviral-naive, HIV-infected individuals in New York State. AIDS Patient Care STDS. 2007;21:644–652.
22. Wheeler W, Mahle K, Bodnar U. Antiretroviral drug-resistance mutations and subtypes in drug-naïve persons newly diagnosed with HIV-1 infection, United States, March 2003 to October 2006. Paper presented at: 14th Conference on Retroviruses and Opportunistic Infections (CROI); February 25-28, 2007; Los Angeles, CA. Abstract 648.
23. Ross L, Lim ML, Liao Q, et al.. Prevalence of antiretroviral drug resistance and resistance-associated mutations in antiretroviral therapy-naive HIV-infected individuals from 40 United States cities. HIV Clin Trials. 2007;8:1–8.
24. Smith D, Moini N, Pesano R, et al.. Clinical utility of HIV standard genotyping among antiretroviral-naive individuals with unknown duration of infection. Clin Infect Dis. 2007;44:456–458.
25. Hurt CB, McCoy SI, Kuruc J, et al.. Transmitted antiretroviral drug resistance among acute and recent HIV infections in North Carolina from 1998 to 2007. Antivir Ther. 2009;14:673–678.
26. Wheeler WH, Ziebell RA, Zabina H, et al.. Prevalence of transmitted drug resistance associated mutations and HIV-1 subtypes in new HIV-1 diagnoses, U.S.-2006. AIDS. 2010;24:1203–1212.
27. Youmans E, Tripathi A, Albrecht H, et al.. Transmitted antiretroviral drug resistance in individuals with newly diagnosed HIV infection: South Carolina 2005-2009. South Med J. 2011;104:95–101.
28. Sey K, Ma Y, Lan YC, et al.. Prevalence and circulation patterns of variant, atypical and resistant HIV in Los Angeles county (2007-2009). J Med Virol. 2014;86:1639–1647.
29. Kim D, Ziebell R, Saduvala N. Trend in transmitted HIV-1 ARV drug resistance-associated mutations: 10 HIV surveillance areas, US, 2007-2010. Paper presented at: 20th Conference on Retroviruses and Opportunistic Infections (CROI); March 3-6, 2013; Atlanta, GA. Abstract 149.
30. Tilghman MW, Perez-Santiago J, Osorio G, et al.. Community HIV-1 drug resistance is associated with transmitted drug resistance. HIV Med. 2014;15:339–346.
31. Smith DM, May SJ, Tweeten S, et al.. A public health model for the molecular surveillance of HIV transmission in San Diego
, California. AIDS. 2009;23:225–232.
32. 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.
33. Little SJ, Kosakovsky Pond SL, Anderson CM, et al.. Using HIV networks to inform real time prevention interventions. PLoS One. 2014;9:e98443.
34. Gifford RJ, Liu TF, Rhee SY, et al.. The calibrated population resistance tool: standardized genotypic estimation of transmitted HIV-1 drug resistance. Bioinformatics. 2009;25:1197–1198.
35. Bennett DE, Camacho RJ, Otelea D, et al.. Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PLoS One. 2009;4:e4724.
36. Wertheim JO, Leigh Brown AJ, Hepler NL, et al.. The global transmission network of HIV-1. J Infect Dis. 2014;209:304–313.
37. Tamura K, Nei M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol. 1993;10:512–526.
38. de Oliveira T, Deforche K, Cassol S, et al.. An automated genotyping system for analysis of HIV-1 and other microbial sequences. Bioinformatics. 2005;21:3797–3800.
39. Alcantara LC, Cassol S, Libin P, et al.. A standardized framework for accurate, high-throughput genotyping of recombinant and non-recombinant viral sequences. Nucleic Acids Res. 2009;37:W634–W642.
40. Kosakovsky Pond SL, Posada D, Stawiski E, et al.. An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1. PLoS Comput Biol. 2009;5:e1000581.
42. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–1797.
43. Hall TA. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser. 1999;41:95–98.
44. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.
45. Rambaut A. FigTree v1.4.1: Tree Figure Drawing Tool. 2009. Available at: http://tree.bio.ed.ac.uk/software/figtree/
. Accessed July 10, 2014.
46. Macchione N, Wooten W, Waters-Montijo K, et al.. HIV/AIDS Surveillance Program Epidemiology Report 2012. County of San Diego
Health and Human Services Agency, 2012. Available at: http://www.sandiegocounty.gov/hhsa/programs/phs/documents/HAEU_BiAnnual_Report_2012_final.pdf
. Accessed on August 25, 2014.
47. Vega Y, Delgado E, Fernandez-Garcia A, et al.. Epidemiological surveillance of HIV-1 transmitted drug resistance in Spain in 2004-2012: relevance of transmission clusters in the propagation of resistance mutations. PLoS One. 2015;10:e0125699.
48. Ambrosioni J, Sued O, Nicolas D, et al.. Trends in transmission of drug resistance and prevalence of non-b subtypes in patients with acute or recent HIV-1 infection in Barcelona in the last 16 Years (1997-2012). PLoS One. 2015;10:e0125837.
49. Rhee SY, Blanco JL, Jordan MR, et al.. Geographic and temporal trends in the molecular epidemiology and genetic mechanisms of transmitted HIV-1 drug resistance: an individual-patient- and sequence-level meta-analysis. PLoS Med. 2015;12:e1001810.
50. Vercauteren J, Wensing AM, van de Vijver DA, et al.. Transmission of drug-resistant HIV-1 is stabilizing in Europe. J Infect Dis. 2009;200:1503–1508.
51. Jain V, Sucupira MC, Bacchetti P, et al.. Differential persistence of transmitted HIV-1 drug resistance mutation classes. J Infect Dis. 2011;203:1174–1181.
52. Staszewski S, Morales-Ramirez J, Tashima KT, et al.. Efavirenz plus zidovudine and lamivudine, efavirenz plus indinavir, and indinavir plus zidovudine and lamivudine in the treatment of HIV-1 infection in adults. Study 006 Team. N Engl J Med. 1999;341:1865–1873.
53. Parienti JJ, Bangsberg DR, Verdon R, et al.. Better adherence with once-daily antiretroviral regimens: a meta-analysis. Clin Infect Dis. 2009;48:484–488.
54. Carpenter CC, Cooper DA, Fischl MA, et al.. Antiretroviral therapy in adults: updated recommendations of the International AIDS Society-USA Panel. JAMA. 2000;283:381–390.
55. Yeni PG, Hammer SM, Carpenter CC, et al.. Antiretroviral treatment for adult HIV infection in 2002: updated recommendations of the International AIDS Society-USA Panel. JAMA. 2002;288:222–235.
56. Yeni PG, Hammer SM, Hirsch MS, et al.. Treatment for adult HIV infection: 2004 recommendations of the International AIDS Society-USA Panel. JAMA. 2004;292:251–265.
57. Deeks SG. International perspectives on antiretroviral resistance. Nonnucleoside reverse transcriptase inhibitor resistance. J Acquir Immune Defic Syndr. 2001;26(suppl 1):S25–S33.
58. Gianotti N, Galli L, Boeri E, et al.. In vivo dynamics of the K103N mutation following the withdrawal of non-nucleoside reverse transcriptase inhibitors in human immunodeficiency virus-infected patients. New Microbiol. 2005;28:319–326.
59. Hare CB, Mellors J, Krambrink A, et al.. Detection of nonnucleoside reverse-transcriptase inhibitor-resistant HIV-1 after discontinuation of virologically suppressive antiretroviral therapy. Clin Infect Dis. 2008;47:421–424.
60. Palmer S, Boltz V, Maldarelli F, et al.. Selection and persistence of non-nucleoside reverse transcriptase inhibitor-resistant HIV-1 in patients starting and stopping non-nucleoside therapy. AIDS. 2006;20:701–710.
61. Bacheler L, Jeffrey S, Hanna G, et al.. Genotypic correlates of phenotypic resistance to efavirenz in virus isolates from patients failing nonnucleoside reverse transcriptase inhibitor therapy. J Virol. 2001;75:4999–5008.
62. Lindstrom A, Ohlis A, Huigen M, et al.. HIV-1 transmission cluster with M41L “singleton” mutation and decreased transmission of resistance in newly diagnosed Swedish homosexual men. Antivir Ther. 2006;11:1031–1039.
63. Shafer RW, Rhee SY, Pillay D, et al.. HIV-1 protease and reverse transcriptase mutations for drug resistance surveillance. AIDS. 2007;21:215–223.
64. Little SJ, Frost SD, Wong JK, et al.. Persistence of transmitted drug resistance among subjects with primary human immunodeficiency virus infection. J Virol. 2008;82:5510–5518.
65. Brenner BG, Routy JP, Petrella M, et al.. Persistence and fitness of multidrug-resistant human immunodeficiency virus type 1 acquired in primary infection. J Virol. 2002;76:1753–1761.
66. Pao D, Andrady U, Clarke J, et al.. Long-term persistence of primary genotypic resistance after HIV-1 seroconversion. J Acquir Immune Defic Syndr. 2004;37:1570–1573.
67. Rhee SY, Taylor J, Fessel WJ, et al.. HIV-1 protease mutations and protease inhibitor cross-resistance. Antimicrob Agents Chemother. 2010;54:4253–4261.
68. Wensing AM, van Maarseveen NM, Nijhuis M. Fifteen years of HIV protease inhibitors: raising the barrier to resistance. Antivir Res. 2010;85:59–74.
69. Guidelines for the Use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents. Department of Health and Human Services. Available at: http://aidsinfo.nih.gov/contentfiles/lvguidelines/AdultandAdolescentGL.pdf
. Accessed November 17, 2014.
70. Gunthard HF, Aberg JA, Eron JJ, et al.. Antiretroviral treatment of adult HIV infection: 2014 recommendations of the International Antiviral Society-USA Panel. JAMA. 2014;312:410–425.
71. Young B, Fransen S, Greenberg KS, et al.. Transmission of integrase strand-transfer inhibitor multidrug-resistant HIV-1: case report and response to raltegravir-containing antiretroviral therapy. Antivir Ther. 2011;16:253–256.
72. Boyd SD, Maldarelli F, Sereti I, et al.. Transmitted raltegravir resistance in an HIV-1 CRF_AG-infected patient. Antivir Ther. 2011;16:257–261.
73. Stekler JD, McKernan J, Milne R, et al.. Lack of resistance to integrase inhibitors among antiretroviral-naive subjects with primary HIV-1 infection, 2007–2013. Antivir Ther. 2014;20:77–80.
74. Bhaskaran K, Pillay D, Walker AS, et al.. Do patients who are infected with drug-resistant HIV have a different CD4 cell decline after seroconversion? An exploratory analysis in the UK register of HIV Seroconverters. AIDS. 2004;18:1471–1473.
75. Booth CL, Garcia-Diaz AM, Youle MS, et al.. Prevalence and predictors of antiretroviral drug resistance in newly diagnosed HIV-1 infection. J Antimicrob Chemother. 2007;59:517–524.
76. Sungkanuparph S, Oyomopito R, Sirivichayakul S, et al.. HIV-1 drug resistance mutations among antiretroviral-naive HIV-1-infected patients in Asia: results from the TREAT Asia Studies to Evaluate Resistance-Monitoring Study. Clin Infect Dis. 2011;52:1053–1057.
77. Banez Ocfemia M, Saduval N, Oster A. Transmitted HIV-1 drug resistance among men who have sex with men, 11 US jurisdictions, 2008-2011. CROI 2014. Paper presented at: 21st Conference on Retroviruses and Opportunistic Infections (CROI); March 3-6, 2014; Boston, MA. Abstract 579.
78. 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.
79. Morris SR, Little SJ, Cunningham T, et al.. Evaluation of an HIV nucleic acid testing program with automated internet and voicemail systems to deliver results. Ann Intern Med. 2010;152:778–785.
80. Rosenberg ES, Altfeld M, Poon SH, et al.. Immune control of HIV-1 after early treatment of acute infection. Nature. 2000;407:523–526.