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In-depth analysis of HIV-1 drug resistance mutations in HIV-infected individuals failing first-line regimens in West and Central Africa

Villabona-Arenas, Christian Julian; Vidal, Nicole; Guichet, Emilande; Serrano, Laetitia; Delaporte, Eric; Gascuel, Olivier; Peeters, Martine

Author Information
doi: 10.1097/QAD.0000000000001233



Antiretroviral therapy (ART) has been rapidly scaled up over the last decade, and today almost 40% of HIV-infected individuals in sub-Saharan Africa are estimated to be on ART. This rapid ART rollout was possible because resource-limited countries (RLCs) adopted the 2010 WHO guidelines, which recommended standardized regimes and laboratory monitoring when available [1]. Nonetheless, new WHO guidelines recommend virological load to monitor treatment outcome, which has proven useful to deliver high-quality treatment in high-income countries [2–5].

Scaling up the quality of ART in sub-Saharan Africa is challenging because of the high costs of tests, lack of laboratory infrastructure, logistic constraints and need for trained staff [6,7–9]. However, there is growing evidence that a long-term failing treatment contributes to the development of resistance and increases the risk of transmitted drug resistance (TDR) [10–16]. Therefore, continuous expansion of ART increases the need to rapidly detect cases in which first-line treatment has failed, despite ongoing debate concerning the utility of routine virological monitoring [17,18].

Another peculiarity in RLCs is the high diversity of HIV-1 genetic variants, in particular of circulating recombinant forms (CRFs). It is now well established for subtype C that certain mutations differ from what is observed in individuals infected with subtype B under similar drug pressure [19–21], but our knowledge on the selection of variant-specific drug resistance mutations (DRMs) remains incomplete.

A better understanding of the emergence of DRM is important to evaluate the long-term effectiveness of the standardized regimens. Here, we thoroughly studied the impact of ART rollout without virological monitoring on the frequency of both known DRMs and additional selected mutations, using comprehensive genetic data from West and Central African countries. We also examined whether differences exist between the genetic variants that predominate in these regions.


Study design and HIV-infected individuals

Partial p51 reverse transcriptase sequences from reverse transcriptase inhibitor (RTI)-naive individuals and RTI-experienced individuals on first-line treatment failure were retrieved from cross-sectional studies on transmitted and acquired drug resistance in the database of UMI233 [10,22–45]. These studies were done in West and Central Africa. Experienced individuals received the same first-line regimen: zidovudine (ZDV)/stavudine (d4T) + lamivudine (3TC) + neviparine (NVP)/efavirenz (EFV). Virological failure was defined as HIV-1 RNA plasma viral load measurement more than 1000 copies/ml, as recommended by WHO for virological failure in RLCs [46].

HIV-1 subtype/circulating recombinant form designation in reverse transcriptase

A reference dataset containing each HIV-1 group-M genetic variant was retrieved from the Los Alamos curated database to confirm the subtype and CRF designations of our sequences. Alignments and maximum likelihood phylogenetic analyses were achieved using Muscle v3.8.31 and PhyML v3.0, respectively [47–49]. Clustering with particular reference sequences (approximate likelihood ratio ≥0.90) and recombination analyses with Simplot v3.5.1 were used for subtype/CRF assignment [50]. Sequences with unique mosaic structures composed of regions from multiple subtypes/CRFs were designated as unique recombinant forms (URFs).

Analysis of antiretroviral drug resistance mutations

Sequences were analyzed for DRMs using the HIVdb algorithm v7.0.1 (Drug Resistance Database from Stanford University, The resistance profile for each drug was categorized as high-level resistance, intermediate resistance and susceptible. The mutations from the International Antiviral Society-United States of America (IAS-USA) list of 2015 were used as a reference of DRM to RTI [51].

Statistical analysis

We selected the subtypes/CRFs from the RTI-experienced group with more than 60 occurrences to assess if DRMs were more frequently observed in some of them. We compared the prevalence of amino acid substitutions in RTI-experienced and RTI-naive individuals to identify additional nonpolymorphic RTI-selected mutations. Mutations that had a statistically significant association with treatment were defined as being nonpolymorphic when they occurred more frequently in RTI-experienced individuals (≥1%) than in RTI-naive ones (≤0.05%); mutations were independently selected (due to convergent evolution under drug pressure) when they were applicable to three or more countries and three or more major subtypes/CRFs (A, C, D, F, G, CRF01_AE and CRF02_AG); this criterion is similar to that outlined by Shahriar et al.[52]. We used the RTI-experienced dataset to evaluate if time on treatment affected the occurrences of DRMs. Here, the ART duration was discretized into three categories: 24 or less, 24–48 and more than 48 months. In all analyses, we distinguished between different substitutions for the same amino acid position.

Moreover, Fisher exact tests were conducted to evaluate the significance of the associations. The null hypothesis of no association was rejected if the P value of the test was less than a significance level of 0.05 after adjusting for multiple comparisons. We used the Benjamini–Hochberg correction that controls the expected proportion of false-positives among all rejected hypotheses and performs well in scenarios of independence or positive dependency [53]. We also used Bonferroni and Holm procedure corrections that sought to control the familywise error rate; these procedures reduce the probability of spurious findings, but are overly conservative by not accounting for the correlation structure of the test statistics [54].


Dataset characteristics

A total of 3736 sequences were included in the analysis; 1599 sequences were obtained from RTI-experienced individuals failing first-line ART (ZDV/d4T + 3TC + NVP/EFV) and 2137 were obtained from RTI-naive individuals. The datasets of RTI-experienced individuals were reduced to 1303 by removing sequences that were susceptible to the complete first-line regimen: This was done to diminish the effects of nonadherent individuals. Similarly, the number of sequences from RTI-naive individuals was reduced to 2039 sequences to diminish the effect of TDR, and this was accomplished by removing strains with resistance to any of the drugs.

Sixty-five percent (n = 2176) of the sequences were from Central African countries (Burundi, Cameroon, Chad, Democratic Republic of Congo and Equatorial Guinea), and 35% (n = 1166) were from West African countries (Benin, Burkina Faso, Cote d’Ivoire, Senegal and Togo) (Table 1). HIV-1 strains were highly diverse and included eight subtypes, 18 CRFs and numerous URFs. The dominant subtypes/CRF in the RTI-experienced group were CRF02_AG (45.7%, n = 595), A (6.7%, n = 83), CRF06_cpx (6.3%, n = 82), C (5.0%, n = 65) and G (4.9%, n = 64), whereas in the RTI-naive group they were C (27.0%, n = 550), CRF02_AG (26.5%, n = 541), A (9.0%, n = 184), CRF06_cpx (5.7%, n = 116) and D (3.4%, n = 70). The disparate proportion of treated versus naive populations for each subtype and CRFs do not reflect a particular epidemiological setting, but rather the random outcome of aggregating data from independent studies. URFs accounted for 17.8% (n = 232) and 16.6% (n = 338) of sequences from RTI-experienced and RTI-naive individuals, respectively.

Table 1
Table 1:
Dataset characteristics.

Resistance in the reverse transcriptase inhibitor–experienced group

Among the 1367 RTI-experienced individuals included, 88.9% were resistant to 3TC (n = 1158), 26.9% were resistant to ZDV (n = 351), 27.2% were resistant to d4T (n = 354), 97.5% were resistant to NVP (n = 1270) and 75.2% were resistant to EFV (n = 980). ZDV and d4T levels of resistance were similar because nearly the same number of mutations confers resistance to both drugs. Likewise, EFV and NVP select for the same DRMs, but differential penalty scores for the mutations to these drugs result in different drug resistance interpretations (e.g. mutation Y181C/I/V scores 60 for NVP but 30 for EFV, whereas both K103S and G190A score 60 for NVP and 45 for EFV): 22.6% (n = 294) of the sequences were associated with an intermediate level of resistance to EFV, whereas the same sequences showed high-level resistance to NVP. Resistance to other RTI drugs currently approved by the Food and Drug Administration, USA, was also observed: 20.9% (n = 273) to abacavir, 19.5% (n = 254) to didanosine, 8.4% (n = 110) to tenofovir (TDF), 6.1% (n = 80) to etravirine and 16.3% (n = 213) to rilpivirine.

All RTI mutations from the IAS-USA list were present in the dataset, except 227C (Fig. 1 and Table S1, The most frequent nucleoside reverse transcriptase inhibitor (NRTI) mutation was M184V/I (88.9%), with M184V (86.9%) being the predominant mutation. Thymidine analog mutations (TAMs) were most frequent at position T215Y/F (33.6%). The Q151M mutation was only detected in 27 individuals (2.1%). Insertions at codon 69 were observed in four sequences (0.3%). The K103N mutation was the most common non-nucleoside reverse transcriptase inhibitors (NNRTI) (49.7%) mutation, followed by Y181C (31.9%).

Fig. 1
Fig. 1:
Frequency of mutations.

Emergence of additional variation

We compared the frequencies of each amino acid at each sequence position between RTI-experienced individuals and RTI-naive individuals to identify which mutations were associated with treatment. We validated our approach after confirming that only positions and/or substitutions from the IAS-USA list with very low frequency in the ART-experienced group (≤0.8%) and/or multiple occurrences in the ART-naive group did not have a statistically significant association with ART in our dataset. These DRMs were K65E/N, K70E, F77 K101P, E138G/K/R, V179, Y181I, Y188/C/H, F227C and M230I; V179D, V179T, V106I and E138A were present in the ART-naive at frequencies 0.2, 0.4, 1.0 and 2.6%, respectively. Moreover, some of these substitutions (K70E, E138A/G/K/Q/R, V179D/F/T/L, F227C and M230I) were not associated with resistance to the drugs taken by the RTI-experienced individuals under study [51].

In addition to the known DRMs, a total of 62 mutations were significantly more frequently observed in RTI-experienced individuals (Fisher exact test), but only 14 of them qualified as independently selected mutations as outlined in methods (Table 2). Among these 14 mutations, nine are not included in the IAS-USA list but are included in at least one of the three expert lists of DRMs (HIVdb: Stanford University, Stanford, California, USA; ANRS: French National Agency for AIDS Research, and REGA: Rega Institute for Medical Research and University Hospitals Leuven, Leuven, Belgium): D67G (2.5%), T69D (1.9%), T69N (4.1%), L74I (1.5%), V75M (1.8%), I132L (2.2%), D218E (1.7%), F227L (3.6%) and K238T (5.5%) (Fig. 1) [55–58]. The majority of these mutations are scored in at least one of the lists with the exception of I132L and D218E, which are only listed as poorly characterized RTI-DRMs in HIVdb. Mutations T69D/N, L74I and V75M are present in the three lists.

Table 2
Table 2:
Mutations selected by first-line nonnucleoside reverse transcriptase inhibitor–based antiretroviral therapy.

We thus identified five additional selected mutations associated with RTI-experienced individuals, which are not taken into account by any of the expert lists of DRMs: I94L (1.1%), L109I (2.4%), V111L (1.1%), T139R (2.4%) and T165L (2.6%). I94L and T165L were frequently observed along TAMs, whereas L109I and T138R were associated with both TAMs and NNRTI-DRM. The frequency of V111L was not associated with any known mutation (Table S2, The ‘RTI-experienced versus RTI-naive’ archive from HIVdb substitutions informs that these mutations are very rare in RTI-naive individuals (≤0.06%, except V111I 0.4%, but mostly on subtype B and CRF01_AE) but have been documented in RTI-experienced individuals from different subtypes (subtypes A, B, C, D, F, G, CRF01_AE and CRF02_AG), although in lower frequencies: 0.3% I94L, 0.6% L109I, 1.0% V111L, 0.6% T139R and 0.7% T165L (HIVdb was last accessed in February 2016).

Resistance and time on antiretroviral therapy

The median time on ART was 28 months (interquartile range 22–55). Mutations M184V, all TAMS, certain NNRTIs (A98G, K101H, V108I, P225H and the additional selected mutation K238T from the Stanford and REGA lists) and two newly identified selected mutations (I94L and L109I) were significantly more frequently observed in the groups treated for longer periods of time (P < 0.05) (Table 3). K65R was the only mutation significantly more frequently observed in the category of patients 24 months or less on ART.

Table 3
Table 3:
Reverse transcriptase inhibitor–selected mutations associated with time on treatmenta.

Mutation M184V, which alone confers high level of resistance to 3TC, was the NRTI-resistant mutation observed in highest proportion (82.4, 90.4 and 96.7% for ≤24, >24–48 and >48 months, respectively) and is among the first NRTI mutations to emerge; consequently, sequences had on average at least one mutation of this class (Fig. 2). TAMs develop more slowly than other NRTI. However, the proportion of sequences with three or more TAM resistance mutations increased when patients were treated for longer periods of time (Fig. 2). The frequency of any TAM profile did not have a statistically significant association with time on treatment.

Fig. 2
Fig. 2:
Distribution of the number of known reverse transcriptase inhibitor resistance mutations by sequence and time category.

NNRTI mutations are drugs with low genetic barriers to resistance. Consistently, sequences from RTI-experienced individuals had on average two mutations conferring resistance to NNRTIs, but results differed again depending on time under treatment: the longer the treatment, the higher the proportion of sequences with more mutations (Fig. 2). Common NNRTI DRMs (e.g. K103N and Y181C) did not have a statistically significant association with time on treatment. They were equally frequent regardless of time category and further supported the rapid selection of these mutations.

Resistance and HIV-1 subtype/circulating recombinant form

We evaluated whether DRMs from the IAS-USA list and the 14 additional selected mutations were more or less frequently observed in certain HIV-1 variants. We only considered subtypes/CRFs for which more than 60 sequences were available in the RTI-experienced group: subtypes A, C, G, CRF02_AG and CRF06_cpx. The frequency of one NRTI and two NNRTI mutations differed according to subtype/CRF (Fig. 3 and Table S3, Our study confirmed higher frequency of NNRTI mutation V106M in subtype C (12.3%). We observed that (NRTI) G190A was less frequent in CRF02_AG (14%) and M41L was more frequent in CRF06_cpx (37%). We also observed higher frequencies of K65R in subtype C, but the latter was not statistically significant in our study (P = 0.076 after Benjamini–Hochberg correction).

Fig. 3
Fig. 3:
Reverse transcriptase inhibitor resistance mutations associated with subtype or circulating recombinant forms.An asterisk indicates a frequency value that differed significantly from the corresponding values for other subtypes/circulating recombinant forms.


We retrieved and analyzed more than 3500 partial reverse transcriptase sequences from RTI-naive and RTI-experienced individuals from 10 countries in West and Central Africa. All RTI-experienced individuals were on a standardized first-line RTI-based regimen without virological monitoring. ART failure was primarily detected during cross-sectional studies, which aimed to document ART efficacy at the country or program level, and the majority of these individuals did not have clinical signs of failure [28]. Drug-resistant profiles evidenced that the drugs 3TC and the NNRTIs from the regimen were no longer effective for a considerable proportion of patients. Moreover, multiresistance was revealed, involving drugs not included in the administered first-line regimens.

Using a representative dataset of individuals failing a first-line treatment, we confirmed the accumulation and increased frequency of DRMs over time, stressing the impact of expanding ART without scaling virological monitoring. Moreover, following a thorough statistical analysis, we identified five selected mutations that are not included in any expert list of DRM and nine mutations from the HIVdb, ANRS and/or REGA lists. These mutations had a statistically significant association with treatment, they were common in the RTI-experienced group (≥1%), rare in the RTI-naive group (≤0.05%) and independently selected (present in at least three countries and at least three main subtypes/CRFs). Finally, we found one major NRTI and two major NNRTI mutations that differed significantly in frequency across subtypes/CRFs.

We argue that our approach for identifying treatment-selected mutations was satisfactory given that it identified most positions and substitutions from the IAS-USA list and additional mutations from other expert lists of DRM. The accurate identification of additional treatment-related mutations in databases is challenging, because misclassification can result from TDR, population stratification, sample size and the many statistical comparisons required [59]. In this respect, we excluded isolates from RTI-naive persons with evidence of drug resistance prior to the analyses and verified the phylogenetically independent selection of additional mutations by confirming that they were present in different countries and subtypes/CRFs. We used sound significance thresholds for individual comparisons following controls of the false discovery rate and the familywise error rate, so as to compensate for the number of inferences being made, and the corrected P values of the additional selected mutations suggest that there is a low chance that our results are hampered by false positives.

Following our statistical approach, we did not find novel substitutions associated with resistance at known positions from any expert lists of DRM, but we observed the emergence of five additional independently selected mutations at different positions of the reverse transcriptase that are not documented in any of these lists: I94L, L109I, V111I, T139R and T165L. In the HIVdb database, these mutations are very rare in RTI-naive individuals but have been documented at low levels in RTI-experienced individuals from different subtypes and treatments. T165L has been pointed out as an NRTI-DRM, I94L and T139R as NNRTI-DRMs, and L109I has been pointed out as an undifferentiated RTI-DRM [52,60]. T139R and T165L have also been associated with reduced susceptibility to NNRTIs in vitro[61]. These observations come from worldwide data, which clarifies that these mutations arose independently [60]. Although these mutations are unlikely to strongly impact HIV-1 drug resistance, which in this study was already high, more studies are necessary to unveil the association between novel treatment-selected mutations and established DRM especially with regard to fitness. In our study, mutations I94L and L109I were significantly associated with time on treatment and in higher frequencies than previously reported. Therefore, the emergence of additional variation may be a consequence of long periods of time in treatment and the absence of adequate individual monitoring.

In high-income countries, routine virological monitoring has allowed the prompt detection of treatment failure and use of individual-based fully active ART regimens [2–4]. The overall picture appears to indicate a decrease of drug resistance, as more individuals on therapy become completely suppressed [5]. For example, Scherrer et al.[62] showed that in Switzerland the prevalence of drug resistance steadily decreased over 15 years as a result of individual virological monitoring and the use of highly effective first-line regimens. In contrast, major questions remain on the long-term and population-level impact of managing ART without adequate individual monitoring and the use of drugs with low genetic barriers to resistance in RLC. In addition to the rapid selection of two NNRTI-DRM (K103N and Y181C), which confer intermediate or high degree level of resistance to NVP and EFV, we evidenced a trend of increased DRM accumulation with time on ART. We clearly showed that the emergence of certain NNRTI mutations, all TAMs and mutation M184V were positively associated with time on treatment. The selection of multiple NNRTI-DRM and the accumulation of TAMs over time, associated with cross-resistance to all RTIs [63,64], reduces the clinical utility of several of the currently approved RTIs and, consequently, of drug options for second-line and subsequent regimens. For example, second-generation NNRTIs, which are only available as part of third-line regimens, may lose their ability to perform well because of the previous accumulation of mutations. Also, Ciaffi et al.[28] pointed out that late detection of treatment failure might lead to patients with high viral load, which has been significantly associated with shorter timeframe to second-line failure and may deserve special treatment strategies. The consequences of a delay until modification of a regimen may differ according to the original regimen combination. The administration of a regimen containing a protease inhibitor drug, as compared with a regimen that contains a NNRTI, may result in a lower prevalence of DRMs after virological failure [65–67]. The integrase inhibitor Dolutegravir (DTG) has shown a high barrier to resistance, and, in this context, the WHO now recommends its use as an alternative first-line option [68–70]. Nonetheless, even with the use of highly potent antiretroviral drugs, clinical monitoring would continue to be a major barrier to optimal management as treatment failure is often detected late [71]. Therefore, both adequate regimens (such as first-line DTG-based regimens) and virological monitoring are necessary to better control resistance and to guarantee the use of RTIs in a durable manner.

Graham et al.[72] evidenced the continuous shed of resistant virus and the steady accumulation of mutations in the female genital tract after virological failure, stressing that undetected treatment failure increases the risk for TDR. Treatment-naive individuals with resistant virus have also been shown to drive TDR [73,74], and baseline resistance has been significantly associated with a shorter timeframe to virological failure in individuals receiving standard first-line regimens [75]. The impact on drug susceptibility by NNRTI-TDR has been shown to be substantial in both high-income and resource-limited settings [16,76]. We also documented high prevalence of K103N and increased frequency of TAMs, which upon transmission persist longer in the absence of drug pressure [74,77]. Consequently, the scale-up of viral load testing to promptly detect virological failure will prevent the emergence of complex drug resistance and will contribute to a reduction of TDR.

Previous reports showed that certain DRMs are differentially selected by standard regimens according to HIV-1 subtype [20,78]. Moreover, infections with subtype C have a shorter timeframe to secondary virological failure when compared with subtype B [79]. Similar to other studies, we also observed a higher frequency of V106M in subtype C [80,81]. We also observed an increased presence of K65R in subtype C, but in contrast to the study from Theys et al.[82], the difference was NS in our study. However, our number of subtype C samples was lower. Overall, given the high prevalence of this subtype for certain counties of sub-Saharan Africa, it will be important to continue surveillance of the preferential selection of DRMs by this subtype.

Higher frequencies of infections caused by recombinant viruses have been observed over the last decades, and the cocirculation of multiple variants increases the emergence of more diverse and complex recombinant forms [83–86]. Given so, the effect of CRF-specific responses on ART may be diminished in regions where many variants cocirculate. Here, using a representative sample from West and Central African countries, we documented 18 CRFs and a substantial proportion of URFs in both the RTI-naive (16.6%) and experienced groups (17.8%). We documented two mutations differentially associated with CRF02_AG and CRF06_cpx that are less studied but have a high prevalence in some of these countries. However, these mutations only differ in proportions and therefore have no major impact on actual predictions of ART outcome. Nonetheless, it cannot be excluded that the use of other drug regimens can have different impacts on certain CRFs. In particular, the long-term use of the actual recommended TDF-based first-line regimens has to be evaluated. Higher TDF resistance rates were already observed in sub-Saharan countries where many people continue to be on ART with suboptimal monitoring [19,87–90].

In conclusion, the absence of adequate monitoring in West and Central African countries leads to lack of awareness of both virological failure (with substantial acquired resistance to drugs from first-line standard regimens and to drugs not yet taken by the patients) and the selection of rare mutations (though more studies are necessary to attribute their selection to resistance or fitness). Failing to recognize virological failure under NNRTI-based regimens leads to a rapid selection of resistance to this drug class and to the accumulation of known RTI-DRM over time. The accumulation of mutations limits future treatment options and increases the risk of transmission of HIV-1 drug-resistant strains; these observations stress the importance of rapidly scaling-up virological tests and using more potent first-line drugs to preserve the long-term use of ART treatment programs in RLC. In addition, to confirm a higher frequency of V106M in subtype C, we did not find CRF-specific mutations associated with drug resistance. Therefore, recombinants with high prevalence in West and Central African countries, in particular CRF02_AG, do not differentially impact prediction of treatment outcomes. This validates the global robustness of the actual drugs resistance algorithms.


Role of each of the authors: C.J.V.A. designed the project, consolidated and analyzed the data, wrote the R-scripts, prepared the figures and tables, interpreted the data and wrote the manuscript. N.V. processed HIV samples, generated sequence data and determined HIV subtype. E.G. and L.S. processed HIV samples, generated sequence data and determined HIV subtype. E.D. conceived the project, provided supervision, interpreted the data and participated in the writing of the manuscript. O.G. designed the project, provided supervision, performed statistical analysis, interpreted the data and participated in the writing of the manuscript. M.P. conceived and designed the project, provided supervision, interpreted the data and participated in the writing of the manuscript.

This work was supported by IRD (Institut de recherche pour le développement). CJVA was supported by a fellowship from the Labex EpiGenMed, via the National Research Agency, Programme for Future Investment “ANR-10-LABX-12-01” and University of Montpellier.

Conflicts of interest

There are no conflicts of interest.


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antiretroviral therapy; drug resistance; HIV-1; mutation; sub-Saharan Africa; treatment failure

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