Manon Ragonnet-Cronin

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

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.

Automated analysis of phylogenetic clusters

BackgroundAs sequence data sets used for the investigation of pathogen transmission patterns increase in size, automated tools and standardized methods for cluster analysis have become necessary. We have developed an automated Cluster Picker which identifies monophyletic clades meeting user-input criteria for bootstrap support and maximum genetic distance within large phylogenetic trees. A second tool, the Cluster Matcher, automates the process of linking genetic data to epidemiological or clinical data, and matches clusters between runs of the Cluster Picker.ResultsWe explore the effect of different bootstrap and genetic distance thresholds on clusters identified in a data set of publicly available HIV sequences, and compare these results to those of a previously published tool for cluster identification. To demonstrate their utility, we then use the Cluster Picker and Cluster Matcher together to investigate how clusters in the data set changed over time. We find that clusters containing sequences from more than one UK location at the first time point (multiple origin) were significantly more likely to grow than those representing only a single location.ConclusionsThe Cluster Picker and Cluster Matcher can rapidly process phylogenetic trees containing tens of thousands of sequences. Together these tools will facilitate comparisons of pathogen transmission dynamics between studies and countries.

HIV-1 Modulates the tRNA Pool to Improve Translation Efficiency

Abstract Despite its poorly adapted codon usage, HIV-1 replicates and is expressed extremely well in human host cells. HIV-1 has recently been shown to package non-lysyl transfer RNAs (tRNAs) in addition to the tRNALys needed for priming reverse transcription and integration of the HIV-1 genome. By comparing the codon usage of HIV-1 genes with that of its human host, we found that tRNAs decoding codons that are highly used by HIV-1 but avoided by its host are overrepresented in HIV-1 virions. In particular, tRNAs decoding A-ending codons, required for the expression of HIVs A-rich genome, are highly enriched. Because the affinity of Gag-Pol for all tRNAs is nonspecific, HIV packaging is most likely passive and reflects the tRNA pool at the time of viral particle formation. Codon usage of HIV-1 early genes is similar to that of highly expressed host genes, but codon usage of HIV-1 late genes was better adapted to the selectively enriched tRNA pool, suggesting that alterations in the tRNA pool are induced late in viral infection. If HIV-1 genes are adapting to an altered tRNA pool, codon adaptation of HIV-1 may be better than previously thought.

Longitudinal phylogenetic surveillance identifies distinct patterns of cluster dynamics.

Objective:Through the application of simple, accessible, molecular epidemiology tools, we aimed to resolve the phylogenetic relationships that best predicted patterns of cluster growth using longitudinal population level drug resistance genotype data. Methods:Analysis was performed on 971 specimens from drug naïve, first time HIV positive subjects collected in British Columbia between 2002 and 2005. A 1240bp fragment of the pol gene was amplified and sequenced with relationships among subtype B sequences inferred using Neighbour-Joining analysis. Apparent clusters of infections having both a mean within group distance <0.031 and bootstrap value >80% were systematically identified. The entire 2002-2005 dataset was then re-analyze to evaluate the relationship of subsequent infections to those identified in 2002. BED testing was used to identify recent infections (<156 days). Results:Among the 2002 infections, 136 of 300 sequences sorted into 52 clusters ranging in size from 2 to 9 members. Aboriginal ethnicity and intravenous drug use were correlated, and both were linked to cluster membership in 2002. Although cluster growth between 2002 and 2005 was correlated with the size of the original cluster, more related infections were found in clusters seeded from nonclustered infections. Finally, all large growth clusters were seeded from infections that were much more likely to be recent. Conclusions:This population level phylogenetic analysis suggests that a greater increase in cluster size is associated with recently infected individuals, which may represent the leading edge of the epidemic. The most impressive increase in cluster size is seen originating from initially nonclustered infections. In contrast, smaller existing clusters likely describe historical patterns of transmission and do not substantially contribute to the ongoing epidemic. Application of this method for cross-sectional analysis of existing sequences from defined geographic regions may be useful in predicting trends in HIV transmission.

Phylogenetic analyses reveal HIV-1 infections between men misclassified as heterosexual transmissions.

Objective:HIV-1 subtype B infections are associated with MSM in the UK. Yet, around 13% of subtype B infections are found in those reporting heterosexual contact as transmission route. Using phylogenetics, we explored possible misclassification of sexual exposure among men diagnosed with HIV in the UK. Design:Viral gene sequences linked to patient-derived information were used to identify phylogenetic transmission chains. Methods:A total of 22 481 HIV-1 subtype B pol gene sequences sampled between 1996 and 2008 were analysed. Dated phylogenies were reconstructed and transmission clusters identified as clades of at least two sequences with a maximum genetic distance of 4.5%, a branch support of at least 95% and spanning 5 years. The characteristics of clusters containing at least one heterosexually acquired infection were analysed. Results:Twenty-nine percent of the linked heterosexuals clustered exclusively with MSM. These were more likely to be men than women. Estimated misclassification of homosexually acquired infections ranged between 1 and 11% of the reported male heterosexuals diagnosed with HIV. Black African heterosexual men were more often phylogenetically linked to MSM than other ethnic group, with an estimated misclassification range between 1 and 21%. Conclusion:Overall, a small proportion of self-reported heterosexual men diagnosed with HIV could have been infected homosexually. However, up to one in five black African heterosexual men chose not to disclose sex with men at HIV diagnosis and preferred to be identified as heterosexual. Phylogenetic analyses can enhance surveillance-based risk information and inform national programmes for monitoring and preventing HIV infections.

Genetic Diversity as a Marker for Timing Infection in HIV-Infected Patients: Evaluation of a 6-Month Window and Comparison With BED

BACKGROUND It has been reported that the increase in human immunodeficiency virus (HIV) sequence diversity in drug resistance surveillance specimens may be used to classify the duration of HIV infection as <1 or >1 year. We describe a mixed base classifier (MBC) optimized to categorize the duration of subtype B infections as <6 or >6 months on the basis of sequences for drug resistance surveillance specimens and compared MBC findings with those of serologic methods. METHODS The behavior of the MBC was examined across a range of thresholds for calling mixed bases. MBC performance was then evaluated using either complete pol sequences or sites reflecting evolutionary pressures (HLA selection sites, sites that increased in entropy over the course of infection, and codon positions). RESULTS The MBC performance was optimal when secondary peaks on the sequencing chromatogram accounted for at least 15% of the area of primary peaks. A cutoff of <0.45% mixed bases in the pol region best identified recent infections (sensitivity = 82.7%, specificity = 78.8%), with improvement achieved by analyzing only sites that increased in entropy. CONCLUSIONS In an extended data set of 1354 specimens classified by BED, the optimized MBC performed significantly better than a simple MBC (agreement, 68.98% vs 67.13%). If further validated, the MBC may prove beneficial for detecting recent infection and estimating the incidence of HIV infection.

Transmission of Non-B HIV Subtypes in the United Kingdom Is Increasingly Driven by Large Non-Heterosexual Transmission Clusters

Background. The United Kingdom human immunodeficiency virus (HIV) epidemic was historically dominated by HIV subtype B transmission among men who have sex with men (MSM). Now 50% of diagnoses and prevalent infections are among heterosexual individuals and mainly involve non-B subtypes. Between 2002 and 2010, the prevalence of non-B diagnoses among MSM increased from 5.4% to 17%, and this study focused on the drivers of this change. Methods. Growth between 2007 and 2009 in transmission clusters among 14 000 subtype A1, C, D, and G sequences from the United Kingdom HIV Drug Resistance Database was analysed by risk group. Results. Of 1148 clusters containing at least 2 sequences in 2007, >75% were pairs and >90% were heterosexual. Most clusters (71.4%) did not grow during the study period. Growth was significantly lower for small clusters and higher for clusters of ≥7 sequences, with the highest growth observed for clusters comprising sequences from MSM and people who inject drugs (PWID). Risk group (P < .0001), cluster size (P < .0001), and subtype (P < .01) were predictive of growth in a generalized linear model. Discussion. Despite the increase in non-B subtypes associated with heterosexual transmission, MSM and PWID are at risk for non-B infections. Crossover of subtype C from heterosexuals to MSM has led to the expansion of this subtype within the United Kingdom.

Analysis of the history and spread of HIV-1 in Uganda using phylodynamics

HIV prevalence has decreased in Uganda since the 1990s, but remains substantial within high-risk groups. Here, we reconstruct the history and spread of HIV subtypes A1 and D in Uganda and explore the transmission dynamics in high-risk populations. We analysed HIV pol sequences from female sex workers in Kampala (n = 42), Lake Victoria fisher-folk (n = 46) and a rural clinical cohort (n = 74), together with publicly available sequences from adjacent regions in Uganda (n = 412) and newly generated sequences from samples taken in Kampala in 1986 (n = 12). Of the sequences from the three Ugandan populations, 60 (37.1 %) were classified as subtype D, 54 (33.3 %) as subtype A1, 31 (19.1 %) as A1/D recombinants, six (3.7 %) as subtype C, one (0.6 %) as subtype G and 10 (6.2 %) as other recombinants. Among the A1/D recombinants we identified a new candidate circulating recombinant form. Phylodynamic and phylogeographic analyses using BEAST indicated that the Ugandan epidemics originated in 1960 (1950–1968) for subtype A1 and 1973 (1970–1977) for D, in rural south-western Uganda with subsequent spread to Kampala. They also showed extensive interconnection with adjacent countries. The sequence analysis shows both epidemics grew exponentially during the 1970s–1980s and decreased from 1992, which agrees with HIV prevalence reports in Uganda. Inclusion of sequences from the 1980s indicated the origin of both epidemics was more recent than expected and substantially narrowed the confidence intervals in comparison to previous estimates. We identified three transmission clusters and ten pairs, none of them including patients from different populations, suggesting active transmission within a structured transmission network.

Lack of Effectiveness of Antiretroviral Therapy in Preventing HIV Infection in Serodiscordant Couples in Uganda: An Observational Study.

Background We examined the real-world effectiveness of ART as an HIV prevention tool among HIV serodiscordant couples in a programmatic setting in a low-income country. Methods We enrolled individuals from HIV serodiscordant couples aged ≥18 years of age in Jinja, Uganda from June 2009 – June 2011. In one group of couples the HIV positive partner was receiving ART as they met clinical eligibility criteria (a CD4 cell count ≤250 cells/ μL or WHO Stage III/IV disease). In the second group the infected partner was not yet ART-eligible. We measured HIV incidence by testing the uninfected partner every three months. We conducted genetic linkage studies to determine the source of new infections in seroconverting participants. Results A total of 586 couples were enrolled of which 249 (42%) of the HIV positive participants were receiving ART at enrollment, and an additional 99 (17%) initiated ART during the study. The median duration of follow-up was 1.5 years. We found 9 new infections among partners of participants who had been receiving ART for at least three months and 8 new infections in partners of participants who had not received ART or received it for less than three months, for incidence rates of 2.09 per 100 person-years (PYRs) and 2.30 per 100 PYRs, respectively. The incidence rate ratio for ART-use was 0.91 (95% confidence interval 0.31-2.70; p=0.999). The hazard ratio for HIV seroconversion associated with ART-use by the positive partner was 1.07 (95% CI 0.41-2.80). A total of 5/7 (71%) of the transmissions on ART and 6/7 (86%) of those not on ART were genetically linked. Conclusion Overall HIV incidence was low in comparison to previous studies of serodiscordant couples. However, ART-use was not associated with a reduced risk of HIV transmission in this study.

Using nearly full-genome HIV sequence data improves phylogeny reconstruction in a simulated epidemic

HIV molecular epidemiology studies analyse viral pol gene sequences due to their availability, but whole genome sequencing allows to use other genes. We aimed to determine what gene(s) provide(s) the best approximation to the real phylogeny by analysing a simulated epidemic (created as part of the PANGEA_HIV project) with a known transmission tree. We sub-sampled a simulated dataset of 4662 sequences into different combinations of genes (gag-pol-env, gag-pol, gag, pol, env and partial pol) and sampling depths (100%, 60%, 20% and 5%), generating 100 replicates for each case. We built maximum-likelihood trees for each combination using RAxML (GTR + Γ), and compared their topologies to the corresponding true tree’s using CompareTree. The accuracy of the trees was significantly proportional to the length of the sequences used, with the gag-pol-env datasets showing the best performance and gag and partial pol sequences showing the worst. The lowest sampling depths (20% and 5%) greatly reduced the accuracy of tree reconstruction and showed high variability among replicates, especially when using the shortest gene datasets. In conclusion, using longer sequences derived from nearly whole genomes will improve the reliability of phylogenetic reconstruction. With low sample coverage, results can be highly variable, particularly when based on short sequences.