JAIDS Journal of Acquired Immune Deficiency Syndromes:
Letters to the Editor
*YRG CARE, Chennai, India
†Department of Medicine, Brown University, Providence, RI
‡Division of Infectious Diseases, Stanford University, Stanford, CA
§University of California San Diego, LaJolla, CA
‖Veterans Affairs Healthcare System, San Diego, CA
Work by author D.M.S. was supported by grants from the National Institutes of Health: AI69432, AI043638, MH62512, MH083552, AI077304, AI36214, AI047745, AI74621, AI080353, and the James B. Pendleton Charitable Trust. Drs. Kantor and Katzenstein are supported by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health grant number RO1AI66922.
The authors have no conflicts of interest to disclose.
To the Editors:
In HIV-1 subtype C–infected populations in south India, we searched for novel mutations associated with failing antiretroviral therapy that included nucleoside reverse transcriptase (RT) inhibitors. HIV-1 RT sequences were generated from treated and untreated groups, and each nucleotide position was analyzed with appropriate corrections for multiple testing. We found that nonsynonymous mutations at positions 208 and 228 were strongly associated with the presence of thymidine analogue mutations in the treated group and were not present at all in the naive group. The role of these substitutions on treatment outcomes and the evolution of drug resistance in HIV-1 subtype C–infected populations warrant further investigation.
The emergence of HIV-1 drug resistance within a HIV-infected individual depends on the genetic background of the virus and the selection pressure of the therapy. A variety of mutations in HIV RT have been associated with nucleoside reverse transcriptase inhibitor (NRTI) exposure, which give rise to a range of drug susceptibilities and viral replication capacity. The genetic variability of HIV-1 is the result of several factors including the high rate of replication, recombination, the low fidelity of HIV-1 RT, and the lack of proof-reading activities during the RNA-dependent DNA synthesis.1–3 In India, subtype C is the most prevalent genetic background of circulating HIV-1 and RT inhibitors [zidovudine (AZT)/stavudine (D4T) + lamivudine (3TC) + nevirapine (NVP)/efavirenz (EFV)] are the most commonly prescribed first-line drugs. Resistance to AZT and D4T results from the sequential accumulation of thymidine analog resistance mutations (TAMs) at RT codons 41, 67, 70, 210, 215, and 219. Data from several studies suggest that TAMs mostly occur in 2 distinct clusters. The TAM-1 cluster includes mutations that occur with T215Y (including M41L, L210W, and sometimes D67N), and the TAM-2 cluster includes mutations that occur with K70R (including D67N, T215F, and K219Q).4,5 Most of these observations have occurred in the setting of drug resistance that has developed in the viral genetic background of HIV-1 clade B, and it is unclear if other mutations can arise in the background of HIV-1 subtype C. To this end, we investigated HIV-1 RT sequences generated from antiretroviral-naive and antiretroviral-experienced patients who were infected with HIV subtype C in southern India to determine mutational patterns associated with treatment status.
Y.R. Gaitonde Centre for AIDS Research and Education is a nonprofit medical and research institution in Chennai, India, that provides medical care to >15,000 HIV-infected individuals. The patients attending this center are treated according to WHO treatment guidelines. Patients were seen every 3 months or as clinically indicated. CD4 cell count monitoring was performed every 3 to 6 months. HIV-1 plasma viral load monitoring was not standard of care due to cost factor. We sequenced the RT region (codons 1–240) of 100 treatment naive and 350 patients who were failing their initial HAART regimen (treatment experienced). Genotyping was performed using validated home-brew assay (RT-nested polymerase chain reaction), as previously described.6 Sequences were aligned (ClustalX) to an Indian subtype C reference (C.IN.AF067155) and examined for HIV-1 subtype in REGA v2.7 The sequence data were analyzed using the Stanford HIV Drug resistance database available at http://hivdb.stanford.eduin January 2011. Nucleotide and residue positions in sequences collected from antiretroviral-naive and antiretroviral-experienced patients were compared using the χ2 test and with continuity correction. Logistic regression was then used to examine associations between mutations that were statistically different between the 2 groups and the presence of established RT resistance mutations, as determined by the Stanford HIV Resistance Database.8 Bonferronni correction was applied to all tests to correct for multiple comparisons, and a Bonferronni corrected P value <0.05 was considered statistically significant. Mutations present as mixtures were excluded from the analysis.
In univariate analysis, mutations in the HIV-1 RT coding region at codons 36 (81% vs. 76%; P < 0.01), 48 (84% vs 59%; P<0.01), 60 (94% vs. 79%; P = 0.01), and 173 (66% vs. 33%; P < 0.01) were higher among treatment-naive patients, whereas substitution at codons 179 (15% vs. 2%; P < 0.01) and 207 (79% vs. 63%; P = 0.02) were higher among treatment-experienced patients. Substitutions at codons 208 (7% vs. 0%; P = 0.14) and 228 (9% vs. 0%; P = 0.04) were observed only among treatment-experienced patients (Table 1). A nonsynonymous substitution at position L228H was associated with the presence of K70R (OR: 4.0; P < 0.01) and K219Q/E (OR: 5.02; P < 0.01). Whereas, a nonsynonymous substitution in codon H208Y was strongly associated with the presence of TAMs: M41L (OR: 6.46; P < 0.01), T215Y (OR: 5.31; P < 0.01), and D67N (OR: 7.24: P < 0.01).
When antiretroviral therapy fails to fully suppress HIV RNA replication, new viral variants can emerge that have decreased susceptibility to the antiretroviral agents. These new variants may escape from the drugs by accumulating mutations. Concerning the RT coding region, many studies have identified new mutations that are associated with NRTI treatment,9–11 but their exact role in the development of NRTI resistance remains unclear. The emergence of TAMs in treated patients occurs in an orderly manner, and their accumulation is associated with increasing levels of resistance. The magnitude of thymidine analogue resistance conferred by TAMs can also be modulated by other nucleoside analogue mutations.4 Such is the case for the M184V mutation commonly seen in regimens containing lamivudine or emtricitabine which causes high-level resistance to lamivudine and emtricitabine, moderate resistance to didanosine and abacavir, and increases the susceptibility to zidovudine, stavudine, and tenofovir.12 Another example is the fact that the prevalence of H208Y is more often found in the setting of multiple TAMs, which indicates its emergence with prolonged selective pressure from NRTI and its likelihood of being an accessory mutation.5 Although limited by sample size in the treatment-naive group, RT nonsynonymous substitutions at positions 208 and 228 were strongly associated with the presence of TAMs in the viral genetic background of HIV-1 subtype C. The role of these substitutions on treatment outcomes and the evolution of drug resistance in HIV-1 subtype C–infected populations warrants further investigation.
We are grateful to the clinical and laboratory staff at YRG Centre for AIDS Research and Education, VHS, Chennai, India, for their facilitation of the study.
1. Preston BD, Poiesz BJ, Loeb LA. Fidelity of HIV- 1 reverse transcriptase. Science. 1988;242:1168–1171.
2. Wei X, Ghosh SK, Taylor ME, et al.. Viral dynamics in human immunodeficiency virus type 1 infection. Nature. 1995;373:117–122.
3. Hu WS, Temin HM. Genetic consequences of packaging two RNA genomes in one retroviral particle: pseudodiploidy and high rate of genetic recombination. Proc Natl Acad Sci U S A. 1990;87:1556–1560.
4. Boucher CA, O'Sullivan E, Mulder JW, et al.. Ordered appearance of zidovudine resistance mutations during treatment of 18 human immunodeficiency virus-positive subjects. J Infect Dis. 1992;165:105–110.
5. Sturmer M, Staszewski S, Doerr HW, et al.. Correlation of phenotypic zidovudine resistance with mutational patterns in the reverse transcriptase of human immunodeficiency virus type 1: interpretation of established mutations and characterization of new polymorphisms at codons 208, 211, and 214. Antimicrob Agents Chemother. 2003;47:54–61.
6. Saravanan S, Vidya M, Balakrishanan P, et al.. Evaluation of two human immunodeficiency virus-1 genotyping systems: ViroSeq 2.0 and an in-house method. J Virol Methods. 2009;159:211–216.
7. 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.
8. Liu TF, Shafer RW. Web resources for HIV type 1 genotypic-resistance test interpretation. Clin Infect Dis. 2006;42:1608–1618.
9. Chen L, Perlina A, Lee CJ. Positive selection detection in 40,000 human immunodeficiency virus (HIV) type 1 sequences automatically identifies drug resistance and positive fitness mutations in HIV protease and reverse transcriptase. J Virol. 2004;78:3722–3732.
10. Ceccherini-Silberstein F, Gago F, Santoro M, et al.. High sequence conservation of human immunodeficiency virus type 1 reverse transcriptase under drug pressure despite the continuous appearance of mutations. J Virol. 2005;79:10718–10729.
11. Gonzales MJ, Wu TD, Taylor J, et al.. Extended spectrum of HIV-1 reverse transcriptase mutations in patients receiving multiple nucleoside analog inhibitors. AIDS. 2003;17:791–799.
12. Shafer RW. Genotypic testing for human immunodeficiency virus type 1 drug resistance. Clin Microbiol Rev. 2002;15:247–277.