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Meta-Analysis
. 2015 Apr 7;12(4):e1001810.
doi: 10.1371/journal.pmed.1001810. eCollection 2015 Apr.

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

Soo-Yon Rhee  1 , Jose Luis Blanco  2 , Michael R Jordan  3 , Jonathan Taylor  4 , Philippe Lemey  5 , Vici Varghese  6 , Raph L Hamers  7 , Silvia Bertagnolio  8 , Tobias F Rinke de Wit  7 , Avelin F Aghokeng  9 , Jan Albert  10 , Radko Avi  11 , Santiago Avila-Rios  12 , Pascal O Bessong  13 , James I Brooks  14 , Charles A B Boucher  15 , Zabrina L Brumme  16 , Michael P Busch  17 , Hermann Bussmann  18 , Marie-Laure Chaix  19 , Bum Sik Chin  20 , Toni T D'Aquin  21 , Cillian F De Gascun  22 , Anne Derache  23 , Diane Descamps  24 , Alaka K Deshpande  25 , Cyrille F Djoko  26 , Susan H Eshleman  27 , Herve Fleury  28 , Pierre Frange  29 , Seiichiro Fujisaki  30 , P Richard Harrigan  31 , Junko Hattori  32 , Africa Holguin  33 , Gillian M Hunt  34 , Hiroshi Ichimura  35 , Pontiano Kaleebu  36 , David Katzenstein  6 , Sasisopin Kiertiburanakul  37 , Jerome H Kim  38 , Sung Soon Kim  39 , Yanpeng Li  40 , Irja Lutsar  11 , Lynn Morris  34 , Nicaise Ndembi  41 , Kee Peng Ng  42 , Ramesh S Paranjape  43 , Martine Peeters  44 , Mario Poljak  45 , Matt A Price  46 , Manon L Ragonnet-Cronin  47 , Gustavo Reyes-Terán  12 , Morgane Rolland  38 , Sunee Sirivichayakul  48 , Davey M Smith  49 , Marcelo A Soares  50 , Vincent V Soriano  51 , Deogratius Ssemwanga  36 , Maja Stanojevic  52 , Mariane A Stefani  53 , Wataru Sugiura  32 , Somnuek Sungkanuparph  37 , Amilcar Tanuri  50 , Kok Keng Tee  42 , Hong-Ha M Truong  54 , David A M C van de Vijver  15 , Nicole Vidal  55 , Chunfu Yang  56 , Rongge Yang  40 , Gonzalo Yebra  33 , John P A Ioannidis  57 , Anne-Mieke Vandamme  58 , Robert W Shafer  6
Affiliations
Meta-Analysis

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

Soo-Yon Rhee et al. PLoS Med. .

Erratum in

  • Correction: 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.
    Rhee SY, Blanco JL, Jordan MR, Taylor J, Lemey P, Varghese V, Hamers RL, Bertagnolio S, de Wit TF, Aghokeng AF, Albert J, Avi R, Avila-Rios S, Bessong PO, Brooks JI, Boucher CA, Brumme ZL, Busch MP, Bussmann H, Chaix ML, Chin BS, D'Aquin TT, De Gascun CF, Derache A, Descamps D, Deshpande AK, Djoko CF, Eshleman SH, Fleury H, Frange P, Fujisaki S, Harrigan PR, Hattori J, Holguin A, Hunt GM, Ichimura H, Kaleebu P, Katzenstein D, Kiertiburanakul S, Kim JH, Kim SS, Li Y, Lutsar I, Morris L, Ndembi N, Kee PN, Paranjape RS, Peeters M, Poljak M, Price MA, Ragonnet-Cronin ML, Reyes-Terán G, Rolland M, Sirivichayakul S, Smith DM, Soares MA, Soriano VV, Ssemwanga D, Stanojevic M, Stefani MA, Sugiura W, Sungkanuparph S, Tanuri A, Tee KK, Truong HM, van de Vijver DA, Vidal N, Yang C, Yang R, Yebra G, Ioannidis JP, Vandamme AM, Shafer RW. Rhee SY, et al. PLoS Med. 2015 Jun 1;12(6):e1001845. doi: 10.1371/journal.pmed.1001845. eCollection 2015 Jun. PLoS Med. 2015. PMID: 26030872 Free PMC article. No abstract available.

Abstract

Background: Regional and subtype-specific mutational patterns of HIV-1 transmitted drug resistance (TDR) are essential for informing first-line antiretroviral (ARV) therapy guidelines and designing diagnostic assays for use in regions where standard genotypic resistance testing is not affordable. We sought to understand the molecular epidemiology of TDR and to identify the HIV-1 drug-resistance mutations responsible for TDR in different regions and virus subtypes.

Methods and findings: We reviewed all GenBank submissions of HIV-1 reverse transcriptase sequences with or without protease and identified 287 studies published between March 1, 2000, and December 31, 2013, with more than 25 recently or chronically infected ARV-naïve individuals. These studies comprised 50,870 individuals from 111 countries. Each set of study sequences was analyzed for phylogenetic clustering and the presence of 93 surveillance drug-resistance mutations (SDRMs). The median overall TDR prevalence in sub-Saharan Africa (SSA), south/southeast Asia (SSEA), upper-income Asian countries, Latin America/Caribbean, Europe, and North America was 2.8%, 2.9%, 5.6%, 7.6%, 9.4%, and 11.5%, respectively. In SSA, there was a yearly 1.09-fold (95% CI: 1.05-1.14) increase in odds of TDR since national ARV scale-up attributable to an increase in non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance. The odds of NNRTI-associated TDR also increased in Latin America/Caribbean (odds ratio [OR] = 1.16; 95% CI: 1.06-1.25), North America (OR = 1.19; 95% CI: 1.12-1.26), Europe (OR = 1.07; 95% CI: 1.01-1.13), and upper-income Asian countries (OR = 1.33; 95% CI: 1.12-1.55). In SSEA, there was no significant change in the odds of TDR since national ARV scale-up (OR = 0.97; 95% CI: 0.92-1.02). An analysis limited to sequences with mixtures at less than 0.5% of their nucleotide positions—a proxy for recent infection—yielded trends comparable to those obtained using the complete dataset. Four NNRTI SDRMs—K101E, K103N, Y181C, and G190A—accounted for >80% of NNRTI-associated TDR in all regions and subtypes. Sixteen nucleoside reverse transcriptase inhibitor (NRTI) SDRMs accounted for >69% of NRTI-associated TDR in all regions and subtypes. In SSA and SSEA, 89% of NNRTI SDRMs were associated with high-level resistance to nevirapine or efavirenz, whereas only 27% of NRTI SDRMs were associated with high-level resistance to zidovudine, lamivudine, tenofovir, or abacavir. Of 763 viruses with TDR in SSA and SSEA, 725 (95%) were genetically dissimilar; 38 (5%) formed 19 sequence pairs. Inherent limitations of this study are that some cohorts may not represent the broader regional population and that studies were heterogeneous with respect to duration of infection prior to sampling.

Conclusions: Most TDR strains in SSA and SSEA arose independently, suggesting that ARV regimens with a high genetic barrier to resistance combined with improved patient adherence may mitigate TDR increases by reducing the generation of new ARV-resistant strains. A small number of NNRTI-resistance mutations were responsible for most cases of high-level resistance, suggesting that inexpensive point-mutation assays to detect these mutations may be useful for pre-therapy screening in regions with high levels of TDR. In the context of a public health approach to ARV therapy, a reliable point-of-care genotypic resistance test could identify which patients should receive standard first-line therapy and which should receive a protease-inhibitor-containing regimen.

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Conflict of interest statement

JHK and MR are employees of the Walter Reed Army Institute of Research, however, the views expressed herein are those of the authors and do not represent the official views of the Departments of the Army or Defense. DD has received honoraria and travel grants from Viiv Healthcare, Janssen-Cilag, Gilead-Sciences, MSD and BMS for participation to advisory boards and international conferences. SHE collaborates on research studies with investigators from Abbott Laboratories (distributor of the ViroSeq HIV-1 Genotyping System). Abbott Laboratories has provided reagents and performed testing for some collaborative studies. PF has received paid employment for educational presentation (Bristol-Myers Squibb, Janssen-Cilag), travel grants and honoraria for speaking or participation at meetings (Bristol-Myers Squibb, MSD, Gilead, Astellas). WS has received honoraria for speaking from Viiv, MSD, Janssen and Torii. PRH has received grants from, served as an ad hoc advisor to, or spoke at various events sponsored by: Pfizer, Glaxo-Smith Kline, Abbott, Merck, Tobira Therapeutics, Virco and Quest Diagnostics. MAP was supported in part from the United States Agency for International Development (USAID), however, the contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. SB is a staff member of the World Health Organization and the contents are the responsibility of the authors and do not necessarily reflect the views of the World Health Organization. JPAI is a member of the Editorial Board of PLOS Medicine. All other authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow chart showing the derivation of study sets meeting meta-analysis inclusion criteria: studies of representative ARV-naïve populations of 25 or more individuals with published RT sequences with or without protease sequences.
Fig 2
Fig 2. A snapshot of an interactive map plotting the prevalence of transmitted drug resistance in 111 countries from 287 studies between 2000 and 2013 (http://hivdb.stanford.edu/surveillance/map/).
Each study is represented by a circle. The size of the circle is proportional to the number of individuals in the study. The circle color indicates the prevalence of overall TDR in the study: white (<2.5%), pale yellow (2.5% to 4.9%), orange (5.0% to 9.9%), and red (≥10.0%). Each study can also be located on a sidebar, which lists each publication, percent overall TDR, number of individuals, and the country (or countries) where the study was conducted. Clicking on a sidebar row or a study circle in the interactive version of the map at http://hivdb.stanford.edu/surveillance/map/ generates a pop-up box with additional information including a link to the appropriate PubMed reference, the TDR prevalence by ARV class, the median year of virus sampling, the source of virus isolation, the mechanism of participant recruitment, and the virus subtype distribution (a pop-up box of the study Bila13 is shown as an example). The complete set of data associated with a study can be reviewed by clicking on the “Resistance (%)” link either on the sidebar or within the study circle pop-up menu.
Fig 3
Fig 3. Temporal trends in the yearly proportion of individuals having one or more surveillance drug-resistance mutations in sub-Saharan Africa.
The x-axes represent the number of years since ARV scale-up for each isolate. The diameter of each circle is proportional to the number of samples sequenced that year. The fitted line shows the fixed effect of years since ARV scale-up in generalized linear mixed model regression.
Fig 4
Fig 4. Temporal trends in the yearly proportion of individuals having one or more surveillance drug-resistance mutations in low- and middle-income countries of south and southeast Asia.
The x-axes represent the number of years since ARV scale-up for each isolate. The diameter of each circle is proportional to the number of samples sequenced that year. The fitted line shows the fixed effect of years since ARV scale-up in generalized linear mixed model regression.
Fig 5
Fig 5. Temporal trends in the yearly proportion of individuals having one or more surveillance drug-resistance mutations in Latin America/Caribbean.
The x-axes represent the calendar year of the sample. The diameter of each circle is proportional to the number of samples sequenced that year. The fitted line shows the fixed effect of sample year in generalized linear mixed model regression.
Fig 6
Fig 6. Temporal trends in the yearly proportion of individuals having one or more surveillance drug-resistance mutations in North America.
The x-axes represent the calendar year of the sample. The diameter of each circle is proportional to the number of samples sequenced that year. The fitted line shows the fixed effect of sample year in generalized linear mixed model regression.
Fig 7
Fig 7. Temporal trends in the yearly proportion of individuals having one or more surveillance drug-resistance mutations in upper-income Asian countries.
The x-axes represent the calendar year of the sample. The diameter of each circle is proportional to the number of samples sequenced that year. The fitted line shows the fixed effect of sample year in generalized linear mixed model regression.
Fig 8
Fig 8. Temporal trends in the yearly proportion of individuals having one or more surveillance drug-resistance mutations in Europe (and Israel).
The x-axes represent the calendar year of the sample. The diameter of each circle is proportional to the number of samples sequenced that year. The fitted line shows the fixed effect of sample year in generalized linear mixed model regression.
Fig 9
Fig 9. The prevalence of each NRTI-associated surveillance drug-resistance mutation in this meta-analysis versus in NRTI-experienced individuals in the same regions according to HIVDB.
The Spearman’s rank correlation coefficient (rho) and the p-value are shown in each plot. The number of isolates from NRTI-experienced individuals were 4,522, 2,218, 4,164, and 13,522 for SSA, SSEA, Latin America/Caribbean, and the pooled upper-income countries (UIC; Europe, North America, and upper-income Asian countries), respectively.
Fig 10
Fig 10. The prevalence of each NNRTI-associated surveillance drug-resistance mutation in this meta-analysis versus in NNRTI-experienced individuals in the same regions according to HIVDB.
The Spearman’s rank correlation coefficient (rho) and the p-value are shown in each plot. The number of isolates from NNRTI-experienced individuals were 4,959, 1,994, 3,677, and 8,927 for SSA, SSEA, Latin America/Caribbean, and the pooled upper-income countries (UIC; Europe, North America, and upper-income Asian countries), respectively.
Fig 11
Fig 11. The prevalence of each PI-associated surveillance drug-resistance mutation in this meta-analysis versus in PI-experienced individuals in the same regions according to HIVDB.
The Spearman’s rank correlation coefficient (rho) and the p-value are shown in each plot. The number of isolates from PI-experienced individuals were 717, 103, 4,107, and 9,985 for SSA, SSEA, Latin America/Caribbean, and the pooled upper-income countries (UIC; Europe, North America, and upper-income Asian countries), respectively.
Fig 12
Fig 12. Estimated levels of predicted genotypic drug resistance for viruses with and without surveillance drug-resistance mutations.
HIVDB genotypic resistance interpretation program predictions of NRTI, NNRTI, and PI resistance for all virus samples using NRTI, NNRTI, and PI SDRMs, respectively (A). HIVDB program predictions of NRTI, NNRTI, and PI resistance in all samples without an SDRM (B). NRTIs: zidovudine (AZT), abacavir (ABC), lamivudine (3TC), and tenofovir (TDF); NNRTIs: nevirapine (NVP), efavirenz (EFV), rilpivirine (RPV), and etravirine (ETR); PIs: lopinavir (LPVr), atazanavir (ATVr), and darunavir (DRVr). UIC, upper-income countries.
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