Despite important resources directed at prevention strategies, the pattern of HIV-1 transmission chains leading to its spread in specific populations over time remains mostly ill defined and poorly documented. Phylogenetic studies suggest that chains or groups of transmission associated with recent HIV-1 infection play an important role in HIV-1 spread [1–6].
Through the improved control of viral replication, modern anti-HIV therapies have decreased the prevalence of drug-resistant HIV among the chronically infected [7–10]. However, in most countries with access to HAART, the transmission rate of HIV drug resistance has not decreased in recent years [11–16]. This paradox is confirmed by studies showing that individuals with acute or recent HIV-1 infection contribute substantially to the transmission of resistant strains [5,16,17]. Persistence of drug-resistant HIV strains in a given population of newly diagnosed HIV-infected individuals requires preserved transmission efficiency together with risk behaviors and sexual networks. The impact of clustered transmission events on the frequency of transmitted drug resistance has not been fully evaluated in a well defined and relatively closed population. Understanding the pattern of HIV transmission chains will help to optimize prevention and control, as well as recommendations for resistance screening and prophylactic treatment following exposure to HIV.
Since 2000, the Geneva resistance database has contained all sequences from newly HIV-diagnosed individuals resident in the county of Geneva, as well as all resistance tests performed in chronically HIV-infected persons in Geneva who are referred to a single reference laboratory. Thus, this database offers the opportunity to study HIV transmission in a well defined population. We performed this study to monitor the rate of transmitted HIV drug resistance and to characterize the phylogenetic relationships of HIV strains recovered in newly diagnosed individuals.
On the basis of the mandatory reporting of all newly diagnosed cases of HIV infection in Switzerland , all individuals resident in the Geneva area (population, 447 584 persons) with a new HIV infection diagnosed between January 2000 and May 2008 were included in the study. Recent infection was defined as the presence of an evolving serology or a documented negative screening test followed by a positive test within a 12-month period. Cases with a screening test positive for the first time, but who did not fulfill the previous criteria, were defined as infections of unknown duration.
All individuals resident in Geneva with genotypic resistance testing performed during the same period and not included in the newly diagnosed dataset were selected from our centralized database (IDNS™; SmartGene, Zug, Switzerland). All were infected before 2000 and were followed either at our university medical center or by private practitioners; they are defined as chronically infected. For individuals with multiple resistance testing, only the first sequence (2000–2008) was considered. All sequences were anonymized prior to analysis.
Analyses of drug-resistant HIV-1
Population-based sequence analysis of the reverse transcriptase and protease gene was performed on an Applied Biosystems (ABI) sequencer as previously described . Prevalence of mutations associated with transmitted drug-resistant (TDR) HIV-1 was analyzed using the list of mutations for the surveillance of TDR . Samples were typed by using REGA HIV-1 subtyping tool version 2.0 (http://www.bioafrica.net/subtypetool). Clinical charts of all newly diagnosed individuals with detected drug resistance were reviewed to preclude previous antiretroviral therapy exposure.
Identification of transmission clusters
Phylogenetic analyses of concatenated protease and reverse transcriptase regions (895 bp) were performed on a total of 637 sequences from newly diagnosed individuals and from 1058 individuals with chronic infection by using a maximum likelihood tree produced with PhyML  using the GTR model from a multiple alignment created with muscle . Clusters were defined as those clades with a support bootstrap value of 980/1000 or more; no ancestor with a value of 980/1000 or more; and at least two ancestors (the root having 0). This last condition excludes the root and its immediate child, both with values of 100, because these values are artifacts of rerooting the tree.
Comparisons between recent and chronic infections were analyzed using the chi-squared test for categorical variables or Fisher's exact test if assumptions to apply the chi-squared were violated, and the Mann–Whitney nonparametric test for continuous variables. TDR proportions were assorted with their 95% confidence interval (95% CI) using a normal distribution assumption due to the large sample size. The chi-squared test for trend was used to assess if there was a linear trend of TDR between 2000 and 2008. Logistic regression was performed to assess the impact of predictive factors on the likelihood to belong to a transmission cluster. Factors tested in the model were the overall TDR, year of diagnosis, sex, HIV subtype, age (as continuous), CD4 cell count (four categories based on quartiles), HIV-1 RNA level in log10 copies/ml (four categories based on quartiles). We used a backward stepwise procedure to select the independent variables using P < 0.20 at entry and P < 0.05 to keep the variables in the model, except for year and sex, which were forced into the model. Goodness of fit was assessed using the Hosmer–Lemeshow test. The amount of variation in the dependent variable explained by the model was indicated by the Cox and Snell R square and the Nagelkerke R square. Data were analyzed using SPSS 15.0 (SPSS Inc, Chicago, Illinois, USA). All P-values were two-sided and the level of significance was set at P < 0.05.
On the basis of the mandatory reporting of all newly diagnosed cases of HIV infection in the county of Geneva , we estimate that our study population represents at least 85% of all newly diagnosed HIV infections in the Geneva area from January 2000 to May 2008, and 95% of cases for the last 3 years studied. All 718 newly diagnosed individuals identified in our centralized reference laboratory between January 2000 and May 2008 were enrolled in the study. Stored samples with HIV-1 RNA more than 500 copies/ml were obtained for 656 (91%), and reverse transcriptase and protease sequences were successfully amplified in 637 (97%).
Overall, 64% of cases were male with a median age of 35 years [interquartile range (IQR), 28–41]. The median HIV-1 RNA was 4.82 log10 copies/ml (IQR, 4.15–5. 40) and the median CD4 cell count was 343 cells/μl (IQR, 184–523). On the basis of the pol gene, non-B HIV-1 subtypes were identified in 51.5%, with CRF02_AG (18.2%), C (10.2%), and CRF01_AE (5.3%) being the most predominant. Patient characteristics according to information on recent infection or infection of unknown duration are reported in Table 1.
HIV-1 drug resistance in newly diagnosed individuals
Over the 8 years surveyed, the average rate of TDR was 8.5% (95% CI, 6.3%–10.6%) for any antiretroviral drug, 6.3% (4.4%–8.2%) for nucleoside reverse transcriptase inhibitors (NRTIs), 3.5% (2.0%–4.9%) for nonnucleoside reverse transcriptase inhibitors (NNRTIs), and 1.9% (0.8%–2.9%) for protease inhibitors. Evidence of resistance to two classes of drugs was detected in 2.4%, whereas 0.3% harbored resistance to three classes (Table 1). The rate of drug resistance was higher in individuals with recent infection compared with those with an infection of unknown duration (15.3 vs. 6.7%; P = 0.002). The differences in resistance rates between the two populations were not equivalent across all drug classes, and were significantly different for NRTIs (10.7 vs. 5.1%; P = 0.02) and protease inhibitors (6.1 vs. 0.8%, P = 0.001), but not for NNRTIs (5.3 vs. 3.0%; P = 0.15). Figure 1 reports the rate of TDR between January 2000 and May 2008. We found that the overall resistance rate increased significantly between 2000 and 2008 (P-trend = 0.015). A significantly increased rate was found for NNRTI resistance (P-trend = 0.002), but not for NRTIs (P-trend = 0.14) and protease inhibitors (P-trend = 0.43).
Of the 54 patients with mutations associated with drug resistance, 39% harbored one mutation, 30% two to three mutations, and 19% more than three mutations. The most common mutation was T215Y/F/C/D/N/S/E/V (39%), followed by 103N (37%) and M184I/V (17%). Mutations associated with multidrug resistance within a class were detected in six individuals (Q151M in one and 69 insertion in five).
Among newly diagnosed infections (N = 637), individuals with resistance mutations were more often male (79.2% vs. 62.6%; P = 0.02), older (median of 38 vs. 34 years; P = 0.001), with a higher CD4 cell count (median of 452 vs. 339 cells/μl; P = 0.02), and with a lower HIV-1 RNA level (median of 4.61 vs. 4.82 log10 HIV-1 RNA copies/ml, P = 0.02). Frequency of resistance to any drugs was significantly higher in individuals infected with subtype B compared with those infected with non-B subtypes (12.0 vs. 5.2%, respectively; P = 0.002). Significant differences in resistance frequency between B and non-B infections were observed for NRTIs (10.0 vs. 2.7%, respectively; P < 0.0001) and protease inhibitors (3.2 vs. 0.6%, respectively; P = 0.015), but not for NNRTIs (3.6 vs. 3.4%, respectively; P = 0.89).
Phylogenetic analysis was performed on a total of 637 reverse transcriptase–protease concatenated sequences from newly diagnosed individuals and from 1058 individuals with chronic infection (Fig. 2). This tree revealed 123 transmission clusters including 330 individuals. The size of clusters varied from two to 13 (two, 71.5%; three, 16.3% and more than four, 12.2%). The largest transmission cluster observed included 13 individuals who were all chronically infected with a CRF-11-like strain (of a total of 19 CRF-11 sequences identified). This strain is known to circulate in intravenous drug users in the western region of Switzerland . Three subclusters were detected indicating multiple and distinct transmission chains. Each of these clusters was very strongly supported (bootstrap values ≥ 98%). Additional large clusters (> five individuals) with lower bootstrap values (70–97%) that could be related to common lineage were not identified. All clusters were confirmed by maximum likelihood analysis after excluding from the alignment 39 major drug resistance-associated sites  to prevent false clustering due to convergent evolution (data not shown). The mean pairwise sequence distance for all confirmed clusters was 0.028 (SD, 0.025) substitutions per site. A supplementary figure illustrating the maximum likelihood tree is available at http://cegg.unige.ch/hiv.
Among the 637 newly diagnosed individuals, 222 (34.9%) were part of 105 different transmission clusters (Fig. 3a). When further analyzed according to the duration of infection, transmission clusters were more frequent in those with recent infection (<1 year) than in newly diagnosed individuals with an unknown duration of infection (52.7 vs. 30.2%, respectively; P < 0.0001). Among the 222 newly diagnosed individuals included in transmission clusters, 147 (66.2%) were part of transmission clusters composed of only newly diagnosed individuals, 42 (18.9%) of clusters composed of chronic infection individuals and at least two newly diagnosed individuals, and only 33 (14.9%) were part of clusters consisting only of individuals with chronic infection. The median time between the diagnostic dates of individuals included in clusters composed only of newly diagnosed individuals was 7.1 months (IQR, 0.9–26.8 months). Among the 1058 individuals with chronic infection, phylogenetically related clusters were infrequent as only 108 (10.2%) individuals were identified as part of a cluster.
Transmission clusters and HIV-1 resistance
Transmission clusters were more frequently identified in newly diagnosed individuals infected with drug-resistant strains than in individuals infected with sensitive ones (59.3 vs. 32.6%, respectively; P < 0.0001). Among the newly diagnosed individuals with TDR, 27 of 32 (84.4%) were part of transmission clusters composed only of newly diagnosed individuals, two (6.3%) of clusters composed of chronic infection individuals and at least two newly diagnosed individuals, and only three (9.4%) were part of clusters consisting only of individuals with chronic infection (Fig. 3b). The transmission cluster size was larger in individuals infected with drug-resistant strains when compared with those infected with sensitive strains, with 36% and 18% of clusters including three or more individuals, respectively.
Consistent with the earlier-mentioned observations, the prevalence of TDR was significantly higher in individuals who were part of clustered transmissions than in nonclustered ones (14.4 vs. 5.3%, respectively; P < 0.0001). Higher resistance rates in clustered transmissions were observed for NRTIs (9.9 vs. 4.3%, respectively; P = 0.006), NNRTIs (5.4 vs. 2.4%, respectively; P = 0.048), and protease inhibitors (4.1% vs. 0.7%, respectively; P = 0.005).
Several predictive factors that might affect the risk to belong to a transmission cluster were explored. Table 2 presents the results of the univariate and multivariate logistic regression model assessing the likelihood of belonging to a clustered transmission group. The final model containing all predictors of interest was statistically significant (chi-squared = 38.9; P < 0.0001) and explained as a whole between 7.1 and 9.7% of the variance in transmission clusters, and correctly classified 68.2% of cases. As shown in Table 2, the strongest predictor of the likelihood to belong to transmission clusters was the detection of drug-resistant strains with an odds ratio of 3.02 (1.54–5.92). The others significant predictors were a high CD4 cell count (odds ratio, 2.37) and a high viral load (odds ratio, 1.83).
Mutations associated with resistance were compared between all individuals of each transmission cluster. Complete concordance was found for seven of 14 clusters including 19 individuals. In three cases, discrepancies were related to one individual without resistance mutations in a cluster containing three, four and seven newly diagnosed individuals with resistance mutations (184V, 215D and 103N, respectively). In the latter, the mutation 103N was not detected at the time of diagnosis, but available follow-up resistance testing revealed this mutation 1 year later (see below). The remaining four discrepancies were related to one newly diagnosed individual with resistance mutation (103N in three, and 41L and 184V in one) included in clusters of chronic infection without detected resistance mutations at the time of sample analysis.
Large clusters of transmitted drug resistance
On the basis of the present analysis, two large clusters of TDR containing only newly diagnosed individuals were identified. The first cluster included five newly diagnosed individuals, all men who had sex with men. Of these, recent infections were documented in four within a short period of time (13 months). Mutations associated with resistance-included reverse transcriptase 69 insertion (69I), 70R and protease 90M. This subtype B strain is considered multiresistant according to reference algorithms [http://www.hivfrenchresistance.org]. A complete concordance of resistance mutations was found between all individuals.
The second cluster included seven newly diagnosed intravenous drug users; recent infections were documented in four within an 8-month period. The 103N mutation and a 35insertion (T) in protease were detected on this subtype C strain. Although not detected in the initial sample, the index case later developed the 103N mutation while on NNRTI therapy. This allowed a more precise timing of the first transmission event in this cluster. This particular 35insertion in protease has been rarely reported and the phenotypic consequences still need to be fully characterized. Interestingly, a blast search of this protease sequence on the NCBI database revealed a sequence with 98.6% homology reported in Portugal in 2002 where the index case was reported to be infected.
We performed a phylogenetic analysis of HIV strains representing almost all newly infected individuals in a restricted geographical area. Thirty-five per cent of newly diagnosed individuals, and more than 50% of recently acquired HIV infections, can be linked to individuals infected with a closely related strain. Two-thirds of these linked infections were part of clusters composed only of newly diagnosed cases. In comparison, only 10% of chronically infected individuals were clustered. These results suggest that many new infections were due to recently HIV-infected individuals, still unaware of their infection, which are an unexpected source of drug-resistant strains, given that nearly half of TDR was linked to transmission clusters composed only of newly diagnosed individuals.
Overall, 8.5% of all newly infected patients had HIV strains with mutations conferring antiretroviral resistance to at least one drug, although they had not been previously exposed to antiretroviral treatment. Transmitted resistance increased significantly over the 8-year study period. Most importantly, the average resistance rate was even higher (15%) in the subgroup of those in whom the HIV infection could be dated as having occurred within the preceding year, a consequence of the frequent chains of transmissions observed in this population. Although the prevalence of newly transmitted drug-resistant HIV-1 can vary widely with location, risk behavior, sampling time after infection, and years of infection [11–15,25–27], the prevalence of TDR observed in our cohort is consistent with recent reports in developed countries .
It is generally assumed that treated individuals with chronic infection are the main source of TDR. Advances in treatment, including improved virological control and rapid management of treatment failure, are expected to lower rates of resistance and, consequently, diminish TDR. Some studies have correlated the rate of treatment failure with the subsequent rate of TDR [1,7,29–31]. Other studies have linked more precisely the TDR rate to the patterns of drug resistance in the population exposed to antiretroviral therapy [2,32]. These observations have been supported by mathematical models, predicting significant associations between the TDR rate, treatment guidelines, and the replication fitness of drug-resistant strains [33,34].
In a recent study from the Swiss HIV Cohort Study, the prevalence of drug resistance among antiretroviral therapy-exposed individuals decreased from 50% in 1999 to 37% in 2007 . This needs to be related to the increased rate of TDR observed in our study, similar to other reports [12,13,27,35], which supports the view that drug-exposed individuals with chronic infection are not the only ones responsible for TDR. Reconstruction of the HIV transmission networks using phylogenetic analysis reveals that nearly 60% of newly diagnosed individuals with TDR belong to transmission clusters, and that 84% of these clusters include only newly diagnosed individuals. This demonstrates that newly diagnosed, untreated individuals are a significant source of resistant strains, thus suggesting an important self-fueling mechanism for TDR. These results are in contrast with a Canadian study on recently infected individuals showing a lower prevalence of mutations associated with resistance to NRTIs and protease inhibitors in clustered transmissions as compared with nonclustered infections . However, in a recent analysis of the same cohort, the authors found that clustering was associated with an increased transmission of NNRTI resistance . Indeed, the population studied differs and the low number of TDR observed precludes the possibility of drawing any strong conclusion. Similar to other reports [36,37], our data highlight that drug-resistant variants do not harbor a reduced transmissibility.
Unlike acquired drug resistance in chronically drug-experienced individuals, transmitted drug-resistant mutations can persist over time in the absence of drug pressure [38,39]. We found a high concordance of resistance mutations in most transmission clusters composed of newly diagnosed individuals, and a detailed analysis of each large cluster revealed that the same strain caused multiple transmission events occurring years apart. The discrepancies in resistance mutations observed within a cluster may have been related to the potential reversion of a particular mutation (that is 184V, 215D) in some individuals with an infection of unknown duration. Thus, transmission of resistance from untreated individuals who may not be aware of their infection remains a cause for concern. This implies that prevention measures should be targeted to these individuals and that the widespread use of HIV screening might be beneficial in this respect.
Several limitations of the study should be acknowledged. First, the representativeness of the populations studied is crucial. Although the present study included approximately 90% of the chronically infected population with detectable HIV RNA during 2000–2008, many persons remain undiagnosed and unaware of their HIV status; this population was estimated at around 25% by the Swiss Federal Office of Public Health . In addition, chronically infected individuals with undetectable HIV RNA during the 2000–2008 period of time could not be genotyped and are therefore not included in our analysis. Nonetheless, some of these individuals may have been the source of infection before 2000. Second, despite its ability to detect relationships between HIV strains, phylogenetic analysis alone cannot be considered as a substitute for other epidemiological information, such as sexual behavior.
In conclusion, our findings highlight the role of newly diagnosed individuals, not yet exposed to antiretroviral drugs, in replenishing the pool of individuals infected with drug-resistant HIV.
We thank the patients for their participation, the physicians for patient care, and Chantal Gaille and Wanda Caveng for their excellent technical contribution.
The members of the Swiss HIV Cohort Study and the Swiss Mother and Child HIV Study are: C. Aebi, M. Battegay, E. Bernasconi, J. Böni, P. Brazzola, HC Bucher, Ph. Bürgisser, A. Calmy, S. Cattacin, M. Cavassini, J.-J. Cheseaux, G. Drack, R. Dubs, M. Egger, L. Elzi, M. Fischer, M. Flepp, A. Fontana, P. Francioli (President of the SHCS, Centre Hospitalier Universitaire Vaudois, CH-1011- Lausanne), HJ. Furrer, C. Fux, A. Gayet-Ageron, S. Gerber, M. Gorgievski, H. Günthard, Th. Gyr, H. Hirsch, B. Hirschel, I. Hösli, M. Hüsler, L. Kaiser, Ch. Kahlert, U. Karrer, C. Kind, Th. Klimkait, B. Ledergerber, G. Martinetti, B. Martinez, N. Müller, D. Nadal, F. Paccaud, G. Pantaleo, L. Raio, A. Rauch, S. Regenass, M. Rickenbach, C. Rudin (Chairman of the MoChiV Substudy, Basel UKBB, Römergasse 8, CH-4058 Basel), P. Schmid, D. Schultze, J. Schüpbach, R. Speck, P. Taffé, A. Telenti, A. Trkola, P. Vernazza, R. Weber, C.-A. Wyler, S. Yerly.
S.Y. designed and conducted the study, analyzed the data and drafted the manuscript. T.J. performed phylogenetic analyses under the supervision of E.Z., B.H. and L.K. designed the study and drafted the manuscript. A.G.-A. performed the statistical analysis. E.B.ElA. collected clinical data. H.F.G. and V.vW. collected sequences data and drafted the manuscript. All authors have reviewed the latest version of the manuscript and have approved its content.
This study was financed by the Swiss HIV Cohort Study (SHCS) (Swiss National Science Foundation (SBF) grant #3345-062041). Further support was provided by the Fond Benoit from Geneva's University Hospitals, the SNF grant #3247B0-112594/1, SHCS projects 470, 528 and 569, the SHCS Research Foundation, and a research grant of the Union Bank of Switzerland in the name of a donor (H.F.G.).
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