Procedure for simulation of clinical trial on mock network.

<p>The clinical trial depicted here has a treatment efficacy of 25% (3 participants on treatment, shown in white, versus 4 on placebo, shown in gray) with an edge removal rate of 40% (2 out of 5 edges connected to infected treatment cases). (a) First, the network is constructed using a synonymous sequence divergence cut-off. (b) Next, trial status is assigned: treatment in white, placebo in gray, and community members in black. Treatment nodes are infected at a reduced rate reflecting treatment efficacy. (c) Edges are then removed from treatment nodes at a given rate to represent the reduction in transmission due to delay in infection and reduction in forward transmission. The metrics are then calculated. The Network Statistic metric is 6 for treatment and 9 for placebo; the Number Infected Statistic is 3 for treatment and 4 for placebo. (d) Finally, a permutation test to determine significance is performed on the modified network by randomizing the assignment of treatment and placebo. Community nodes remain unaltered by the permutation.</p

Statistical power of the Network Statistic as a function of the edge removal rate and the number of community nodes.

<p>Treatment efficacies of 10%, 20%, and 30% are shown. ‘All community nodes’ corresponds with the power of the Network Statistic in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027775#pone-0027775-g003" target="_blank">Figure 3</a>.</p

Histogram depicting the distribution of degree across nodes in the San Diego network.

<p>Histogram depicting the distribution of degree across nodes in the San Diego network.</p

Statistical power of the Network Statistic on simulated clinical trials on simulated networks.

<p>Scale-free networks are shown in blue, random networks in green, and the San Diego network in gray. The San Diego network values correspond with the power of the Network Statistic in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027775#pone-0027775-g003" target="_blank">Figure 3</a>.</p

Statistical power of the Network Statistic on simulated clinical trials as function of the edge removal rate on the San Diego network.

<p>The Network Statistic values are blue dots. The power of the Number Infected Statistic for a given efficacy is a solid red line.</p

Comparative performance of MEME and FEL on 16 empirical alignments (see Results and Text S1 for an extended discussion of each individual case).

<p> () reports the number of sequences (codons) in the alignment. () refers sites found by MEME to be positively (negatively) selected (). () denote sites found by FEL to be positively (negatively) selected (). references sites that are classified as neutrally evolving by FEL. Values in parentheses for the column show the mean p-values for FEL and MEME on this set of sites, respectively. Values reported in the rightmost column count the number of sites where MEME fits significantly better than FEL, based on a 2-degrees of freedom LRT (). Abbreviations: IAV = Influenza A virus, JEV = Japanese encephalitis virus.</p

Individual sites of the vertebrate rhodopsin alignment used to illustrate similarities and differences between FEL and MEME.

<p>Branches that have experienced substitutions, based on most likely joint maximum likelihood ancestral reconstructions at a given site, are labeled as count of synonymous substitutions:count of non-synonymous substitutions. The thickness of each branch is proportional to the minimal number of single nucleotide substitutions mapped to the branch. Branches are colored according to the magnitude of the empirical Bayes factor (EBF) for the event of positive selection: red – evidence for positive selection, teal – evidence for neutral evolution or negative selection, black –Ê no information. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002764#s2" target="_blank">Methods</a> for more detail. All three sites were identified as experiencing positive diversifying selection by MEME. FEL reported site 54 as positively selected, site 273 as neutral, and site 210 as negatively selected.</p

Comparative performance of FEL and MEME on simulated data where varies along phylogenetic lineages.

<p>Power to detect sites under selection () are reported for FEL and MEME (in <b>boldface</b>) for each unique combination of negative selection (), positive selection (), and proportion of branches under positive selection () parameters.</p

Using HIV Networks to Inform Real Time Prevention Interventions

<div><p>Objective</p><p>To reconstruct the local HIV-1 transmission network from 1996 to 2011 and use network data to evaluate and guide efforts to interrupt transmission.</p><p>Design</p><p>HIV-1 <i>pol</i> sequence data were analyzed to infer the local transmission network.</p><p>Methods</p><p>We analyzed HIV-1 <i>pol</i> sequence data to infer a partial local transmission network among 478 recently HIV-1 infected persons and 170 of their sexual and social contacts in San Diego, California. A transmission network score (TNS) was developed to estimate the risk of HIV transmission from a newly diagnosed individual to a new partner and target prevention interventions.</p><p>Results</p><p>HIV-1 <i>pol</i> sequences from 339 individuals (52.3%) were highly similar to sequences from at least one other participant (i.e., clustered). A high TNS (top 25%) was significantly correlated with baseline risk behaviors (number of unique sexual partners and insertive unprotected anal intercourse (p = 0.014 and p = 0.0455, respectively) and predicted risk of transmission (p<0.0001). Retrospective analysis of antiretroviral therapy (ART) use, and simulations of ART targeted to individuals with the highest TNS, showed significantly reduced network level HIV transmission (p<0.05).</p><p>Conclusions</p><p>Sequence data from an HIV-1 screening program focused on recently infected persons and their social and sexual contacts enabled the characterization of a highly connected transmission network. The network-based risk score (TNS) was highly correlated with transmission risk behaviors and outcomes, and can be used identify and target effective prevention interventions, like ART, to those at a greater risk for HIV-1 transmission.</p></div

Baseline characteristics of study participants.

<p>SDPIC  =  San Diego Primary Infection Cohort, Others  =  non-SDPIC participants, MSM  =  men who have sex with men, ART  =  antiretroviral therapy.</p><p>*Date of infection and start of ART were not estimated for non-SDPIC participants.</p