Risk behaviors among patients exposed to HIV-positive contact or contact with unknown HIV status (N = 351).^*

<p>*Nine (2.5%) patients were excluded from this table because they reported that their source contact was HIV-negative or because data about their source contact was missing.</p>†<p>Four (1%) patients reported that his/her contact was HIV negative: one reported a regular source contact, one reported an anonymous source contact, and data for source contact type was missing for two of these patients.</p>‡<p>Information about source contact was missing for five (1.5%) patients.</p

Presentations for nPEP by exposure risk level.^†

†<p>Risk levels (inappropriate, appropriate and high risk) here and elsewhere in this manuscript specifically describe a determination of whether nPEP should be provided <i>from a public health perspective</i>. Individual providers should make case-by-case determinations for their patients informed by the CDC guidance for nPEP provision.</p>a<p>Inappropriate risk  = 1) evaluated >72 hours; 2) risk event did not include receptive or insertive anal or vaginal intercourse or intravenous drug use (IDU); 3) used a condom; OR 4) source contact was known to be HIV-negative.</p>b<p>Appropriate risk  =  patients 1) evaluated for PEP ≤72 hours; 2) risk event included receptive or insertive anal or vaginal intercourse or intravenous drug use (IDU); 3) did not report using a condom or experienced condom malfunction; and 4) source contact was known to be HIV-positive or was of unknown HIV status.</p>c<p>High risk  =  patients appropriate for nPEP and also: 1) were identified as MSM; and 2) engaged in RAI.</p>‡<p>Same as age categories used in CDC HIV Surveillance Report Volume 17, Number 4.</p

HIV Web Study.

<p>HIV Web Study.</p

Sites and countries by year.

<p>Sites and countries by year.</p

Adobe Connect Lecture Screenshot.

<p>Adobe Connect Lecture Screenshot.</p

Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

<div><p>The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties of variants expected to appear in the future.</p></div

Relationship between in-host volatility and population-level diversity of Env features.

<p><b>(A)</b> Volatilities measured in samples collected in Iowa City during Period1 or Period3 are compared with the diversity of each feature (calculated by the standard deviation of the feature value) in Iowa City during Period3 or Period1, respectively. <b>(B)</b> Comparison between in-host volatility and diversification of gp120 features between Period1 and Period3 in Iowa City. Volatility was calculated using the 20 samples of the MOTIVATE trial. SP, signal peptide; V, variable loop; C, constant region. Comparison between volatility and diversity of features in Iowa City during Period3 is shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s012" target="_blank">S12 Fig</a>. Data underlying this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s016" target="_blank">S2 Data</a>.</p

Antigenic and segmental features of Env show conserved levels of in-host variance.

<p><b>(A)</b> Binding of the indicated probes was measured to Envs isolated from plasma samples of 60 HIV-infected individuals (2–8 Envs per sample). Values represent the variance in binding efficiency among Envs isolated from the same plasma sample, as calculated by the coefficient of variation (CoV). The CoVs are color-coded according to their values (darker shades of blue represent greater variance). <b>(B)</b> Mean CoVs of each feature for the 60 patients examined in panel A. Error bars represent the standard error of the mean (SEM). <b>(C)</b> The protein sequence of each Env was used to calculate the indicated features of the five variable loops of gp120, including amino acid length, mean hydropathy score, and the density of charge and potential N-linked glycosylation sites (PNGSs) (calculated as a fraction of the loop length). The CoV of features among Envs from the same plasma sample was calculated and averaged for all 60 patients. Data underlying this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s006" target="_blank">S6 Fig</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s016" target="_blank">S2 Data</a>.</p

Longitudinal divergence of Env features and associated asymmetry of increments.

<p><b>(A)</b> Evolution of variance in 18 longitudinally monitored patients. Divergence of all 11 features is shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s010" target="_blank">S10 Fig</a>. <b>(B)</b> Correlation between predicted and measured antigenic variance that developed at each genetic distance section (in the range of 0.01 to 0.09 units). <b>(C)</b> Changes in length of the V5 loop in longitudinally monitored patients with increasing genetic distance from the reference isolate. Evolution was examined separately for patients in which the V5 loop of the reference Env(s) was short (9 amino acids, red), intermediate (11 amino acids, yellow), or long (15 amino acids, blue). A least-squares regression line was fit to each dataset, which describes the mean change in feature value per genetic distance unit; the slope of the line (<i>μ</i>) is indicated. <b>(D)</b> Changes in binding efficiency of mAb PG16 in longitudinally monitored patients from different reference states. Data are colored according to the value of their reference state. The vertical colored bars by the <i>y</i>-axis represent the range of values of the reference isolates. Data underlying this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s016" target="_blank">S2 Data</a>.</p

The volatility index is a conserved property of each feature.

<p><b>(A)</b> Schematic of the approach used to measure the volatility index of a feature in a given plasma sample. The squared pairwise phenotypic distance between each Env pair in the plasma sample is calculated and divided by the genetic distance (based on amino acid sequence) that separates them. The ratio is averaged for all Env pairs in the sample to generate the feature volatility index for that plasma sample. <b>(B)</b> Volatility indices of antigenicity features measured in 60 patients. Calculated values were first log₁₀-transformed. For averaging of the indices, all values smaller than –6 were assigned a value of –6. <b>(C)</b> Correlation between the median volatility index of antigenic features measured in 60 patients from Iowa City and 43 samples (from 15 patients) collected in Seattle. The ideal correlation (y = x) is shown by a blue line. <b>(D)</b> Mean volatility indices measured for segmental features using 60 samples from Iowa City. Amino acid positions of segments are numbered according to the HXBc2 convention [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.ref088" target="_blank">88</a>]. Volatilities of all gp120 and gp41 segments are shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s008" target="_blank">S8A Fig</a>. <b>(E, F)</b> Correlations between the mean volatility indices of segmental features measured using the above Env panels and a panel of Envs isolated from plasma samples of 20 patients collected for the MOTIVATE trial. Two-tailed <i>p</i>-values for the Spearman correlation test of each feature type are indicated (*, <i>p</i> ≤ 0.01; **, <i>p</i> ≤ 0.001). <b>(G)</b> Effect of Env sample size on differences between hosts in measured volatilities. We calculated the hydropathy volatility of the 23 segments of Env in Iowa City samples containing two, three, or more than three Envs and in MOTIVATE trial samples (average of nine Envs tested per sample). Each dot represents the standard deviation among patient volatilities for a given feature. Groups are compared using Wilcoxon signed-rank test. <b>(H)</b> The cumulative mean volatility of V1 loop hydropathy is shown for the above groups. For each group, ten random paths of calculation are shown, which represent different orders of cumulative averaging of volatility values. Error bars represent the SEM. Spearman rank correlation coefficient, r_S; <i>p</i>-value, two-tailed test; ns, not statistically significant. Data underlying this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001549#pbio.2001549.s016" target="_blank">S2 Data</a>.</p