adherence. In our study, deep sequencing provided additional genotypic information in all adherence strata. (Table S1, Table S2) Deep sequencing, for example, confirmed virus susceptibility to integrase strand-transfer inhibitors in 4 out of 5 subjects who developed virological failure to raltegravir-including regimens, indicating that raltegravir remained a suitable option for subsequent salvage regimens. Similarly, deep sequencing con- firmed that subjects 5, 6 and 7 could still be treated with a number of different PIs and suggested higher virus susceptibility to tipranavir than to darunavir in subject 7. (Table S2) The absence of resistance mutations by deep sequencing, however, should be interpreted with caution; the loss of most baseline mutants in subject 7 during antiretroviral therapy interruption shows that deep sequencing can also miss clinically relevant resistance mutations present ,1% in the viral population. Detection of low-frequency resistance mutations associated with hypersusceptibility could also improve predicted susceptibility to certain antiretrovirals. For example, the incorporation of a minority T215F mutation in subject 2 changed the HIVdb predicted susceptibility to tenofovir from ‘‘resistant’’ to ‘‘interme- diate’’ at virological failure. Therefore, ultrasensitive genotyping also has the potential to refine genotype interpretation rules towards increased virus susceptibility. Our findings extend those of Le et al. [15], who showed that deep sequencing detected low-frequency mutations unrecognized by Sanger sequencing in 19 out of 22 antiretroviral–experienced individuals experiencing virological failure in routine HIV care between 2004-2007. Additional minority mutants increased a subject’s genotypic resistance to one or more antiretrovirals in 17 of 22 individuals (77%), correlated with the failing drugs in 21% subjects, and with historical antiretroviral use in 79% subjects. In Le’s study, however, samples were collected before etravirine, darunavir or raltegravir became available, so the effect additional minority mutants on HIV susceptibility to these drugs could not be evaluated. The main limitations of our study are its small sample size, its retrospective observational nature and the fact that adherence was self-reported. This study makes two arguable assumptions: First, that a mutant detected above 1% is clinically relevant. This threshold is clearly above the PCR and 454 sequencing error found in our own and other studies [14,25] and has been shown to predict virological outcomes of first-line NNRTI therapy in treatment–na¨ıve individu- als.[2] Moreover, potential PCR-derived recombination should not affect our findings because we did not evaluate mutational linkage. The second assumption is that minority mutants have the same weight in genotypic susceptibility interpretation algorithms as if they were present at higher levels. While biologically plausible, formal studies have not been developed to assess this assumption. In conclusion, in this subset of heavily pre-treated individuals, deep sequencing showed technical non-inferiority to population sequencing. However, although additional mutations improved the assessment of genotypic resistance to etravirine, deep sequencing did not consistently provide additional information on darunavir, tipranavir or raltegravir susceptibility relative to population sequencing. Further studies should extend our findings and address the clinical impact of ultrasensitive genotyping in HIV-1 infected individuals experiencing virological failure to their first or second- line antiretroviral therapy. Proper throughput escalation, sample References 454 in Heavily Pre-Treated HIV+ Subjects multiplexing and adequate sequence coverage may potentially turn ultrasensitive genotyping into a feasible strategy for HIV drug resistance management in the clinical setting. Supporting Information Table S1 Additional genotypic information provided by deep HIV-1 sequencing and changes in predicted phenotypic suscep- tibility, relative to population sequencing a, b, c. a Only mutations with a score $ 5 in the Stanford HIV Drug Resistance Database were included in the analysis. b ART: antiretroviral therapy; DS: deep sequencing; PI: protease inhibitor; NRTI: nucleoside reverse transcriptase inhibitor; NNRTI: non-nucleoside reverse transcrip- tase inhibitor; InSTI: Integrase Strand Transfer Inhibitor; ZDV: zidovudine; ABC: abacavir; d4T: stavudine; ddI: didanosine; TDF: tenofovir; EFV: efavirenz; NVP: nevirapine; ETR: etravir- ine; ATV: atazanavir; IDVr: indinavir/ritonavir; FAPVr: fosam- prenavir/ritonavir; SQVr: saquinavir/ritonavir; LPVr: lopinavir/ ritonavir; DRVr: darunavir/ritonavir; TPVr: tipranavir/ritonavir; ANRS: algorithm of the French ANRS (Agence Nationale de Recherche sur le SIDA) AC11 Resistance group, version 18, July 2009, France; HIVdb: HIV db program version 6.0.8, implemented at the Stanford HIV Drug Resistance Database, Stanford University, USA; REGA: Algorithm of the Rega Institute version 8.0.2, University of Leuven, Belgium. c Predicted antiretroviral suscep- tibility: S: susceptible; I: intermediate; R: Resistant. (DOC) Table S2 Predicted antiretroviral susceptibility according to Sanger or quantitative deep 454 sequencing. Color code: Green = Sensitive; Yellow = Intermediate; Red = Resistant. Changes in predicted phenotypic susceptibility between Sanger and 454 sequencing sequencing are highlighted in black boxes. DLV: delavirdine; EFV: efavirenz; ETR: etravirine; NVP: neviapine; 3TC: lamivudine; ABC: abacavir; AZT: zidovudine; d4T: stavudine; ddI: didanosine; FTC: emtricitabine; TDF: tenofovir dippivoxil fumarate; ATV: atazanavir; ATVr: ritonavir-boosted atazanavir; DRVr: ritonavir-boosted darunavir; FPVr: ritonavir- boosted fosamprenavir; IDVr: ritonavir-boosted indinavir; LPVr: ritonavir-boosted lopinavir; NFV: nelfinavir; SQVr: ritonavir- boosted saquinavir; TPVr: ritonavir-boosted tipranavir; RA: ralegravirL; ELV: elvitegravir. (XLS) Acknowledgments Roger Paredes wishes to thank Dr. Peter Millard, from the Centro de Sau´de San Lucas, Universidade Cato´lica de Moc¸ambique in Beira, for critical review of the manuscript. This work was presented in part at the 17th Conference on Retroviruses and Opportunistic Infections (CROI), San Francisco, 17 February 2010, [abstract # 567]. Author Contributions Conceived and designed the experiments: LR BC RP. Performed the experiments: FMC CP RP. Analyzed the data: FMC AT RP. Contributed reagents/materials/analysis tools: FMC AT FG RD DD MA-T LR BC RP. Wrote the paper: FMC CP RP. Contributed to the study design: FG RD DD MA-T. 1. Lodwick R, Costagliola D, Reiss P, Torti C, Teira R, et al. (2010) Triple-class virologic failure in HIV-infected patients undergoing antiretroviral therapy for up to 10 years. Arch Intern Med 170: 410–419. 2. Simen BB, Simons JF, Hullsiek KH, Novak RM, Macarthur RD, et al. (2009) Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes. J Infect Dis 199: 693–701. 3. Paredes R, Lalama CM, Ribaudo HJ, Schackman BR, Shikuma C, et al. (2010) Pre-existing minority drug-resistant HIV-1 variants, adherence, and risk of antiretroviral treatment failure. J Infect Dis 201: 662–671. PLoS ONE | www.plosone.org 4 May 2011 | Volume 6 | Issue 5 | e19461