Understanding HIV-1 subtype distribution and epidemiology can assist preventive measures and clinical decisions. Sequence variation may affect antiviral drug resistance development, disease progression, evolutionary rates and transmission routes.

The prevalence and the origin of HIV-1 subtype B, the most prevalent circulating clade among the long-term residents in Europe, have been studied extensively. However the spatial diffusion of the epidemic from the perspective of the virus has not previously been traced. In the current study we inferred the migration history of HIV-1 subtype B by way of a phylogeography of viral sequences sampled from 16 European countries and Israel. Migration events were inferred from viral phylogenies by character reconstruction using parsimony. With regard to the spatial dispersal of the HIV subtype B sequences across viral phylogenies, in most of the countries in Europe the epidemic was introduced by multiple sources and subsequently spread within local networks. Poland provides an exception where most of the infections were the result of a single point introduction. According to the significant migratory pathways, we show that there are considerable differences across Europe. Specifically, Greece, Portugal, Serbia and Spain, provide sources shedding HIV-1; Austria, Belgium and Luxembourg, on the other hand, are migratory targets, while for Denmark, Germany, Italy, Israel, Norway, the Netherlands, Sweden, Switzerland and the UK we inferred significant bidirectional migration. For Poland no significant migratory pathways were inferred. Subtype B phylogeographies provide a new insight about the geographical distribution of viral lineages, as well as the significant pathways of virus dispersal across Europe, suggesting that intervention strategies should also address tourists, travellers and migrants.

Background: Preexposure prophylaxis (PrEP) with tenofovir and emtricitabine can prevent new HIV-1 infections, but there is a concern that use of PrEP could increase HIV drug resistance resulting in loss of treatment options. We compared standardized outcomes from three independent mathematical models simulating the impact of PrEP on HIV transmission and drug resistance in sub-Saharan African countries. Methods: All models assume that people using PrEP receive an HIV test every 3–6 months. The models vary in structure and parameter choices for PrEP coverage, effectiveness of PrEP (at different adherence levels) and the rate with which HIV drug resistance emerges and is transmitted. Results: The models predict that the use of PrEP in conjunction with antiretroviral therapy will result in a lower prevalence of HIV than when only antiretroviral therapy is used. With or without PrEP, all models suggest that HIV drug resistance will increase over the next 20 years due to antiretroviral therapy. PrEP will increase the absolute prevalence of drug resistance in the total population by less than 0.5% and amongst infected individuals by at most 7%. Twenty years after the introduction of PrEP, the majority of drug-resistant infections is due to antiretroviral therapy (50–63% across models), whereas 40–50% will be due to transmission of drug resistance, and less than 4% to the use of PrEP. Conclusion: HIV drug resistance resulting from antiretroviral therapy is predicted to far exceed that resulting from PrEP. Concern over drug resistance should not be a reason to limit the use of PrEP.

One out of ten newly diagnosed patients in Europe was infected with a virus carrying a drug resistant mutation. We analysed the patterns over time for transmitted drug resistance mutations (TDRM) using data from the European Spread program.

Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford's HIVdb) to predict virological outcome at 12, 24, and 48 weeks. Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8–16) weeks (2152 TCEs), 24 (16–32) weeks (2570 TCEs), and 48 (44–52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92±1.17, compared to Rega and ANRS, with 2.22±1.09 and 2.23±1.05, respectively. However, similar odds ratio's were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5–1.7] for HIVdb, 1.7 [1.5–1.8] for ANRS, and 1.7 [1.9–1.6] for Rega. Odds ratio's increased over time, but remained comparable (odds ratio's ranging between 1.9–2.1 at 24 weeks and 1.9–2.2 at 48 weeks). The Area under the curve of the ROC did not differ between the systems at all time points; p = 0.60 at week 12, p = 0.71 at week 24, and p = 0.97 at week 48. Three commonly used HIV drug resistance interpretation systems ANRS, Rega and HIVdb predict virological response at 12, 24, and 48 weeks, after change of treatment to the same extent.