Bayesian phylogeographical tree of 229 HIV-1 A <i>pol</i> sequences with branches colored on the basis of the most probable location of the descendent nodes.

<p>The correspondences between the locations and colors are shown in the panel (left), and the posterior probabilities >0.8 are indicated on the internal nodes of the tree. The scale axis below the tree shows the number of years before the last sampling time.</p

Significant non-zero HIV-1 A1 migration rates worldwide.

<p>Only the rates supported by a BF of >3 are shown in red, yellow indicates the probable rate. The relative strength of the statistical support is indicated by the color of the lines (from dark red, <i>id est</i> weak to light red <i>id est</i> strong). The map was reconstructed using SPREAD program. This figure is similar but not identical to the original image, and is therefore for illustrative purposes only.</p

Migration pattern of HIV-1 subtype A1 circulation based on phylogeographic dataset.

<p>The bubblegram shows the frequency of gene flow (migrations) to/from different geographic areas. The surface of each circle is proportional to the percentage of observed migrations in the Maximum Likelihood genealogy. Migrations were inferred with a modified version of the Slatkin and Maddison algorithm. BY, Byelorussia; CA, Central Africa; EA, East Africa; EE, Estonia; GA, Greece/Albania; IT, Italy; KU, Kazakhstan/Uzbekistan; LL, Latvia/Lithuania; MU, Moldavia/Ukraine; RU, Russia.</p

Skyline plot obtained by analyzing the data set of Italian patients (n = 53).

<p>Ordinate: the number of effective infections at time t (Ne(t)); abscissa: time (in years before the present). The thick solid line represents the median value and the grey area the 95% HPD of the Ne(t) estimates. The vertical lines indicate the 95% lower HPD (dotted) and the mean tMRCA estimate (bold) of the tree root.</p

Clusters characteristics and Time of Most Recent Common Ancestor (TMRCA) stratified according to country of origin.

<p>Clusters characteristics and Time of Most Recent Common Ancestor (TMRCA) stratified according to country of origin.</p

Rumanian cluster details.

<p>Time-line scale is displayed underneath the tree. Node labels indicate posterior probability values.</p

Study population and epidemiological network: risk factor and gender according to the most frequent citizenship of patients (n = 306).

1<p>Risk factor was known for 153 Italians, 18 South Americans and 14 Rumanians.</p>2<p>HETs: heterosexuals.</p>3<p>HO-BISEXs: homo-bisexuals.</p>4<p>Gender was known for 248 Italians, 32 South Americans and 25 Rumanians.</p

Trends and Predictors of Transmitted Drug Resistance (TDR) and Clusters with TDR in a Local Belgian HIV-1 Epidemic

<div><p>We aimed to study epidemic trends and predictors for transmitted drug resistance (TDR) in our region, its clinical impact and its association with transmission clusters. We included 778 patients from the AIDS Reference Center in Leuven (Belgium) diagnosed from 1998 to 2012. Resistance testing was performed using population-based sequencing and TDR was estimated using the WHO-2009 surveillance list. Phylogenetic analysis was performed using maximum likelihood and Bayesian techniques. The cohort was predominantly Belgian (58.4%), men who have sex with men (MSM) (42.8%), and chronically infected (86.5%). The overall TDR prevalence was 9.6% (95% confidence interval (CI): 7.7–11.9), 6.5% (CI: 5.0–8.5) for nucleoside reverse transcriptase inhibitors (NRTI), 2.2% (CI: 1.4–3.5) for non-NRTI (NNRTI), and 2.2% (CI: 1.4–3.5) for protease inhibitors. A significant parabolic trend of NNRTI-TDR was found (p = 0.019). Factors significantly associated with TDR in univariate analysis were male gender, Belgian origin, MSM, recent infection, transmission clusters and subtype B, while multivariate and Bayesian network analysis singled out subtype B as the most predictive factor of TDR. Subtype B was related with transmission clusters with TDR that included 42.6% of the TDR patients. Thanks to resistance testing, 83% of the patients with TDR who started therapy had undetectable viral load whereas half of the patients would likely have received a suboptimal therapy without this test. In conclusion, TDR remained stable and a NNRTI up-and-down trend was observed. While the presence of clusters with TDR is worrying, we could not identify an independent, non-sequence based predictor for TDR or transmission clusters with TDR that could help with guidelines or public health measures.</p></div

Cellular HIV-1 DNA load in drug-resistant and non-resistant samples.

<p>Dot plot of log₁₀ cellular HIV-1 STS DNA load per million PBMC among 161 newly-diagnosed individuals with major drug-resistant mutations (n = 19) and with no major drug-resistant mutations (n = 142). The results indicate a trend towards higher cellular HIV-1 STS DNA load (<i>P</i> = 0.14) in resistant samples (median 3.64, IQR 2.63–4.25) compared to non-resistant samples (median 3.27, IQR 2.90–3.73).</p

Characteristics of study subjects.

<p>^<i>a</i>IQR, interquartile range.</p><p>^<i>b</i>Data available for 130 patients.</p