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JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e318201a904
Clinical Science

Impact of HIV-1 Group O Genetic Diversity on Genotypic Resistance Interpretation by Algorithms Designed for HIV-1 Group M

Depatureaux, Agnès MD*†; Charpentier, Charlotte PharmD, PhD*; Leoz, Marie MSc*; Unal, Guillemette MSc*; Damond, Florence PharmD, PhD‡; Kfutwah, Anfunbom PhD§; Vessière, Aurélia PhD*§; Simon, François MD, PhD‖; Plantier, Jean-Christophe PharmD, PhD*†

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Author Information

From the *Laboratoire associé au Centre National de Référence du VIH, CHU de Rouen, Rouen, France; †Equipe d'Accueil 2656, Institut Hospitalo-Universitaire, Faculté de Médecine-Pharmacie, Université de Rouen, Rouen, France; ‡Laboratoire de Virologie, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France; §Centre Pasteur du Cameroun, Yaoundé, Cameroon; and ∥Laboratoire de Virologie, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.

Received for publication May 4, 2010; accepted October 13, 2010.

Supported by the Institut de Veille Sanitaire (InVS), the Agence Nationale de Recherches sur le SIDA et les hépatites virales (ANRS) and Rouen University Hospital.

The authors have no conflicts of interest to disclose.

Correspondence to: Prof Jean-Christophe Plantier, PharmD, PhD, Laboratoire de Virologie, Institut de Biologie Clinique, Hôpital Charles Nicolle, CHU de Rouen, 1 rue de Germont, 76031, Rouen, France (e-mail: jean-christophe.plantier@univ-rouen.fr).

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Abstract

Background: HIV-1 group O (HIV-O) is characterized by a high genetic divergence from HIV-1 group M viruses. Little is known about the therapeutic impact of this diversity. The aim of this study was to assess in a large series of samples (1) the genotypic impact of natural polymorphism of the HIV-O reverse transcriptase and protease genes; and (2) the predictive value of resistance interpretation algorithms developed for HIV-1 group M when used for highly mutated HIV-O viruses.

Methods: Sixty-eight antiretroviral-naive and 9 highly antiretroviral-experienced HIV-O-infected patients were included. The viruses were sequenced and resistance-associated mutations were identified using 3 different algorithms (Agence Nationale de Recherches sur le SIDA et les hépatites virales, Rega, Stanford).

Results: All HIV-O samples naturally exhibited the A98G and V179E resistance mutations in the reverse transcriptase region; 54% of samples presented the Y181C mutation, conferring resistance to nonnucleoside reverse transcriptase inhibitors. Twelve minor resistance mutations, present in more than 75% of the protease sequences, led to the different algorithms giving discrepant results for nelfinavir and saquinavir susceptibility. A marked virological response was observed in 8 of the 9 antiretroviral-experienced patients, despite the prediction of limited activity of the combination for 5 to 8 patients according to the algorithm used.

Conclusions: The high level of natural polymorphism in HIV-O genes, and the important discrepancies between genotypic resistance interpretation and the virological response, emphasize the need for resistance algorithm rules better adapted to HIV-O.

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INTRODUCTION

Most cases of HIV type 1 group O (HIV-O) infection are detected in Cameroon, where the prevalence is estimated to be about 1% of all HIV infections.1,2 Cases of HIV-O infections have also been described in countries with links to this region. In France, about 120 HIV-O-infected patients have been identified (unpublished data) since the first case reported in 1992,3 and 0.1% of new diagnoses of HIV infection involve HIV-O.4 HIV-O variants are genotypically divergent from the HIV-1 group M (major) virus, leading to their denomination: “outlier”. HIV-O displays important intragroup genetic diversity, and consequently, unlike HIV-1 group M viruses, are not divided by phylogenetic analyses into subtypes, but into 3 clades-A, B, and C-with numerous divergent strains outside these clades.5,6

The large intragroup genetic diversity has consequences for the diagnosis and monitoring of HIV-O infections.7,8 HIV-O virological monitoring is currently performed by in-house real-time polymerase chain reaction techniques9 or with commercial kits recently validated for HIV-O quantification (Abbott Realtime HIV-1, Abbott Molecular, Rungis, France; Cobas AmpliPrep-Cobas TaqMan HIV-1 Version 2.0, Roche, Meylan, France).10 Little is known about the impact of this genetic diversity on the in vivo virological response to antiretroviral (ARV)-based therapies.11,12 Furthermore, very few studies have addressed virological failure in HIV-O infection and its management.12,13 Most of HIV-O isolates are considered to be naturally resistant to nonnucleoside reverse transcriptase inhibitors (NNRTIs) due to the presence of the Y181C resistance mutation.14,15 Data related to the virological response to nucleoside reverse transcriptase inhibitors (NRTIs)-containing and protease inhibitors (PIs)-containing regimens are limited particularly as concerns the recent and most used ARV drugs.11,12 HIV-O susceptibility to the newest ARV drug classes has nevertheless been studied. Enfuvirtide (ENF), an HIV type 1 (HIV-1) fusion inhibitor, seems to be active in vitro and in vivo against HIV-O despite the systematic natural presence of the N42D mutation16-18; also, we have reported genotypic data indicating HIV-O susceptibility to integrase inhibitors in most cases.19

The large genetic differences between HIV-1 group M and HIV-O, and the paucity of studies assessing virological response to ARV-based therapies, raise questions about the predictive value for HIV-O infection, of resistance interpretation algorithms developed for HIV-1 group M virus.

In this study, we used a large series of samples to assess the genotypic impact of natural polymorphism of HIV-O reverse transcriptase (RT), and protease genes in ARV-naive patients. Also, in nine highly ARV-experienced HIV-O-infected patients, we compared the virological response to a salvage ARV-based regimen with the predictions of susceptibility to ARV, using 3 current resistance interpretation algorithms [those developed by the Agence Nationale de Recherches sur le SIDA et les hépatites virales (ANRS), Stanford, and the Institute Rega].

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PATIENTS AND METHODS

Patients

Two groups of patients were studied. In the first group, ARV-naive patients with a confirmed serological diagnosis of HIV-O infection were included to assess HIV-O natural polymorphism. Sixty-eight patients, from the French network (RES-O) created for identification and survey of HIV-O variants (n = 42), and from the Centre Pasteur du Cameroun in Yaoundé (n = 26), were included. This study included viruses belonging to the different HIV-O clades representative of the genetic diversity within this group.

The second group of patients was comprised of 9, highly ARV-experienced, HIV-O-infected patients, in virological failure and initiating a salvage ARV-based regimen.

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Sequencing of the Protease, Reverse Transcriptase, gp41, and Integrase Genes

Natural polymorphism of protease and RT genes in the first group of patients was assessed by sequencing reverse transcriptase-polymerase chain reaction products as previously described.2 HIV-O sequences obtained were compared with the HIV-1 group M reference strain HIVHXB2.

Direct sequencing of the protease, RT, gp41 and integrase genes of viruses from the 9 highly ARV-experienced patients were performed at baseline of the salvage ARV-based therapy. All 4 regions were amplified and sequenced as previously described.2,18,19 Direct sequencing of regions of interest was also performed in case of viral rebound.

Drug resistance mutations described for HIV-1 group M were identified using the latest versions of 3 different algorithms: ANRS version 18 (http://www.hivfrenchresistance.org), Stanford HIV Drug Resistance Database (HIVdb) version 6.0.7 (http://hivdb.stanford.edu), and Institute Rega (Rega) version 8.0.1 (http://www.rega.kuleuven.be). Major PI resistance-associated mutations were identified according to the 2009 International AIDS Society-USA list (www.iasusa.org/resistance_mutations/mutations_figures.pdf).

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RESULTS

HIV-O Natural Polymorphism in RT Gene

Among the 68 samples from HIV-O-infected ARV-naive patients, 67 were successfully amplified and then sequenced until codon 230 of RT gene. Consequently, positions 234, 236, 238, 318, 333, and 348 included in HIVdb, and Rega algorithms were then excluded from the analysis.

At positions associated with resistance to NRTI, all HIV-O sequenced samples (67 of 67, 100%) harbored the uncommon mutations V118C and L210Y, and 3% (2 of 67) exhibited the T69N mutation (Fig. 1A). At all other positions associated with resistance (n = 20), the residues in HIV-O were the same as those in the HIV-1 group M subtype B reference sequence.

Figure 1
Figure 1
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We used the 67 sequences of our series to generate a consensus sequence that included residues present in more than 50% of the sequences. It was thus representative of the majority of the HIV-O viral sequences and was used to assess predictions of the 3 algorithms of resistance interpretation. The HIV-O consensus was interpreted as being susceptible to all NRTI by all 3 algorithms (Table 1).

Table 1
Table 1
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We then considered positions associated with resistance to NNRTI (Fig. 1B). Thirty-six of the 67 HIV-O samples (54%) harbored the resistance mutation Y181C. The mutations K101R, K103R, V179E, associated with resistance to the NNRTI drug class according to the HIVdb and/or Rega algorithms, were detected in 4%, 28%, and 100% of the HIV-O sequences, respectively. The A98G, V90I, and V106I mutations, all included in the set of mutations associated with resistance to etravirine (ETR), were detected in 100%, 3%, and 18% of the HIV-O sequences, respectively. The E138A mutation, associated with possible resistance to ETR according to the ANRS algorithm, and the uncommon mutations E138T and E138V were detected in 3% (n = 2), 4%, and 5% of the sequences, respectively.

Due to the presence of these mutations, the consensus HIV-O sequence was interpreted as follows: (1) resistant to nevirapine by all 3 algorithms; (2) resistant to efavirenz by the ANRS and Rega algorithms and as having intermediate resistance to EFV by the HIVdb algorithm; and (3) as having intermediate resistance to ETR according to the HIVdb and Rega algorithms, and as susceptible to ETR by the ANRS algorithm (Table 1). As concerns ETR, the most recent drug, 54% (n = 36), 52% (n = 35), and 16% (n = 11) of the HIV-O sequences were interpreted as possibly resistant according to the HIVdb, Rega, and ANRS algorithms, respectively. Furthermore, 1 HIV-O sample in the series was interpreted as fully resistant to ETR according to the Rega algorithm.

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HIV-O Natural Polymorphism in the Protease Gene

All 68 samples were successfully sequenced for the whole protease gene. A major resistance mutation, the M46I substitution, was detected in 1 HIV-O sample, which also contained a viral mixture with the wild-type amino acid. The following minor resistance mutations, defined by at least 1 of the 3 algorithms, were found in more than 75% of the sequences: L10V (75%), I15V (91%), E35N (97%), M36I (99%), K43T (75%), Q58E (97%), I62V (100%), I64V (84%), H69R (88%), A71V (94%), L89I (88%), and I93L (100%) (Fig. 1C). Other minor resistance mutations were also observed at lower frequencies: L10I (25%), V11I (4%), G16E (12%), L33I/V (7%), M36L (1%), H69K (12%), K70E (51%), A71I (3%), V77I (3%), and L89M (12%). In addition, 5 uncommon mutations at resistance-associated positions were detected in more than 75% of virus: I13A (100%), K20C (100%), E34N (77%), R41K (85%), and L63T (85%).

All 3 algorithms interpreted the consensus HIV-O protease sequence as susceptible to 5 drugs of the PI drug class: atazanavir, darunavir, fosamprenavir, indinavir, and lopinavir. There were discrepancies between the different algorithms for the genotypic resistance interpretation of the HIV-O consensus for nelfinavir and saquinavir (Table 1). The HIV-O protease consensus sequence was interpreted as having intermediate resistance to tipranavir by the HIVdb and Rega algorithms but as resistant by the ANRS algorithm.

The 3 algorithms were not concordant about the interpretation of the M46I mutant (n = 1) in our series for all PI molecules except for darunavir to which the isolate was interpreted as susceptible by all (data not shown).

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Prediction and In Vivo Virological Response to a Salvage ARV-Based Therapy in HIV-O-Infected Patients

We studied 9 highly ARV-experienced HIV-O-infected patients initiating a salvage regimen (Table 2). These patients had received ARV drugs for a median duration of 8 years (range = 2-12) and had been exposed to a median of 6 ARV drugs (range = 4-12). The fusion inhibitor enfuvirtide was prescribed to all 9 patients, and 3 also received raltegravir, a member of the new drug class of integrase inhibitors active against HIV-O,19,20 2 received darunavir, and 1 etravirine. The median baseline HIV-O viral load was 4.10 log10 copies per milliliter (range = 3.30-5.10), and the median baseline CD4 cell count was 54 cells per microliter (range = 14-444).

Table 2
Table 2
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Baseline genotypic resistance tests showed that patients carried highly mutated virus, with a median of 3 mutations associated with resistance to NRTI (range = 0-8), 5 mutations associated with resistance to NNRTI (range = 3-7), and 16 mutations associated with resistance to PI (range = 11-20), including 4 major resistance mutations (range = 1-6) (Table 3). In addition, a median of 2, 0, and 5 uncommon mutations were observed at positions associated with resistance to NRTI, NNRTI, and PI drug classes, respectively. At the start of the salvage regimen, all 9 patients carried virus harboring the N42D mutation in gp41, associated with resistance to enfuvirtide according to the ANRS algorithm, and with possible resistance or susceptibility according to the Rega and HIVdb algorithms, respectively. No resistance mutation was detected at baseline of the salvage regimen in the integrase gene in the 3 patients receiving raltegravir.

Table 3
Table 3
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There were discrepancies between the different algorithms for baseline sequences for the prediction of antiviral activity of all ARV drugs, except for lamivudine and nevirapine (Fig. 2). Overall, the median number of active ARV drugs in the salvage regimen predicted by the ANRS, HIVdb, and Rega algorithms was 2 [interquartile range, (IQR) = 2-3], 2 (IQR = 2-3), and 1 (IQR = 1-2), respectively (Table 2). The numbers of patients administered a salvage regimen including fewer than 3 predicted active drugs were 5 (with the ANRS algorithm), 6 (with HIVdb), and 8 (with Rega). The numbers of patients with 3 predicted active drugs in their regimen were 4, 3, and 1 according to the ANRS, HIVdb, and Rega algorithms, respectively.

Figure 2
Figure 2
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An early and marked decrease in HIV-O viral load was observed in all 9 patients within a median of 2 months after the initiation of the salvage regimen. In 8 patients, HIV-O viral load reached undetectable level (<50 copies/mL) with a median increase in CD4 cell count of 73 cells per microliter (Table 2). Further follow-up was available for only 8 patients, the ninth having very recently initiated the salvage regimen.

In 1 patient (BCF101), all 3 algorithms were concordant, predicting no or only 1 active ARV drug in the regimen, and all correctly predicted the virological outcome: despite an initial decrease in viral load, it did not decreased below the detection threshold, and rebound only 4 months after initiation of the salvage regimen.

A sustained virological response was observed in 5 of the 9 patients. For 3 of them (BCF13, BCF161, and RBF125), 0 to 2 of the ARV drugs used were predicted to be active by the 3 algorithms. For the 2 remaining patients (RBF132 and RBF133), the ANRS and HIVdb algorithms predicted at least 3 active ARV drugs and the Rega algorithm a maximum of 2.

Patient RBF148 displayed an early virological response, despite a baseline prediction of 0 to 2 active drugs according to the algorithm, followed by virological failure at month 12 associated with the selection of the I84V resistance mutation in the protease gene and of the N43S resistance mutation in gp41 (data not shown).

For patient BCF183, all 3 algorithms predicted at least 3 active ARV drugs in the regimen. An early virological response was indeed observed but was followed by a viral rebound at month 19. No genotypic resistance testing or plasma drug level analysis during follow-up was available for this patient preventing further assessment of this therapeutic failure.

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DISCUSSION

We report here a wide range of natural polymorphism in the RT and protease regions in a large series of HIV-O samples issued from 68 ARV-naive patients, extending similar findings previously reported for smaller series.5,11-13,21

The uncommon mutations, V118C and L210Y, were found naturally present in 100% of the RT sequences. These mutations may be specific of HIV-O, consistent with the prevalence described in the literature and in the Stanford HIV drug resistance database (http://hivdb.stanford.edu/pages/algs/HIVseq.html). The phenotypic characteristics of the V118C and L210Y mutants are currently unknown and should be determined to assess their impact on viral susceptibility.

About the first generation of NNRTI, our data showed concordance between genotypic natural resistance and previous phenotypic and enzymatic data.14,15,22 In particular, the Y181C resistance mutation was found in 54% of our samples; we recently showed that there was a statistically significant link between the presence of this Y181C mutation and HIV-O clade A.23 In addition, the NNRTI resistance-associated mutations A98G and V179E were found in 100% of HIV-O samples but rarely in other subtypes (A98G: 1% of subtype C virus; V179E: 7% of subtype G and 1% of CRF02_AG according to Stanford database); they both, therefore, seem to be characteristic of HIV-O. In our study, the presence of these 2 mutations affects the genotypic interpretation for susceptibility to ETR with a prevalence of possible resistance ranging from 16% to 54% according to the algorithms. Further phenotypic studies of diverse HIV-O genotypic profiles will be needed to allow better assessments of specific phenotypic susceptibility.

We found more than 15 differences with the HIV-1 group M sequence in the HIV-O protease gene at resistance-associated positions; this is twice as many as observed for HIV-1 group M non-B subtypes (n = 6 to 8 mutations according to the subtype).24 Some of these variants found in HIV-O are also found in other HIV-1 subtypes but K20C, E34N, and Q58E seem to be specific for HIV-O. Overall, the HIV-1 group M reference sequence (HIV-1HXB2) and HIV-O reference sequence (HIV-OANT70) differ at 17 of the 46 (37%) resistance-associated positions in the protease gene. For comparison, there are 28 differences (61%) between the HIV type 2 reference sequence (HIV-2ROD) and HIV-1HXB2. Furthermore, HIV-OANT70 and HIV-2ROD reference sequences shared 6 resistance-associated mutations (M36I, Q58E, I62V, A71V, L89I, I93L) and 1 uncommon mutation (I13A).25-27 Thus, as for HIV-2,27-29 HIV-O natural polymorphism is likely to have consequences for susceptibility to the different PI.

This high level of natural polymorphism has several possible implications. First, it could favor a specific genotypic pathway of HIV-O in response to therapeutic selection pressure; this is observed with HIV-2 and also with HIV-1 subtype C, for which the frequency of the A98S NNRTI resistance mutation is higher28-30 and leading to specific rules in the ANRS algorithm.30,31 Second, their presence may affect viral enzyme conformation, as described by Chen et al32 in crystallographic analyses of protease mutants. This is particularly pertinent for the HIV-O protease because its sequence is different from that of HIV-1 group M at nearly 40% of the positions. This possibility needs to be investigated by analyses of the 3-dimensional structure of the HIV-O protease. Third, although there are small differences between the interpretations provided by the algorithms also when used with HIV-1 group M sequences,33 the extensive natural polymorphism of HIV-O viruses seems to lead to much larger divergence in interpretation. Indeed, we report substantial discrepancies in interpretation, particularly between susceptible, intermediate resistance and full resistance for etravirine and enfuvirtide. However, we have demonstrated that for enfuvirtide, HIV-O shows a phenotypic susceptibility comparable with that of HIV-1 group M, despite a genotypically predicted resistance due to the natural presence of the N42D mutation.18

Thus, these discrepancies and the extensive polymorphism are such that genotypic prediction based on current tools generated from HIV-1 group M sequences seems not to be well adapted for HIV-O variants. Our analysis of a series of 9 patients harboring highly mutated viruses supports this view: the salvage regimen was predicted not to be effective, especially by the Rega algorithm, whereas available biological data indicate an early virological response in 8 patients that was sustained in 5. Although the HIVdb and ANRS algorithms generally predicted inefficacy less strongly, an early virological response was observed in patients receiving a maximum of 2 active ARV drugs. Some molecules interpreted as having an intermediary level of resistance can display a residual antiviral activity that might explain the partial efficacy and some of virological responses. However, our study shows that the management of virological failure in HIV-O-infected patients using the current algorithms is difficult.

In conclusion, HIV-O is characterized by a wide range of natural polymorphism leading to a genotypic impact on susceptibility to NNRTI and probably, to a lesser extent, to PI. The large number of differences in resistance-associated mutations between HIV-1 group M virus and HIV-O, and the discrepancies between genotypic interpretations using resistance algorithms developed for HIV-1 group M virus and in vivo virological responses, show that further studies are required to develop resistance algorithms rules better adapted to these divergent variants.

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ACKNOWLEDGMENTS

We thank the Institut de Veille Sanitaire (InVS), the ANRS, and Rouen University Hospital for financial support. We thank all physicians and biologists involved in the RES-O network for surveillance of HIV-O variants in France.

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ADDITIONAL STUDY AUTHORS

Magali Bouvier, Jean-Marie Chennebault, Constance Delaugerre, Laurence Gérard, Geneviève Giraudeau, Isabelle Gueit, Houria Ichou, Tessa Lambolez, Hélène LeGuillou-Guillemette, Gwenael Le Moal.

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Keywords:

HIV-1 group O; natural polymorphism; genotypic resistance; resistance interpretation algorithms

© 2011 Lippincott Williams & Wilkins, Inc.

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