17 research outputs found

    Short-Term Dynamic and Local Epidemiological Trends in the South American HIV-1B Epidemic

    No full text
    <div><p>The human displacement and sexual behavior are the main factors driving the HIV-1 pandemic to the current profile. The intrinsic structure of the HIV transmission among different individuals has valuable importance for the understanding of the epidemic and for the public health response. The aim of this study was to characterize the HIV-1 subtype B (HIV-1B) epidemic in South America through the identification of transmission links and infer trends about geographical patterns and median time of transmission between individuals. Sequences of the protease and reverse transcriptase coding regions from 4,810 individuals were selected from GenBank. Maximum likelihood phylogenies were inferred and submitted to ClusterPicker to identify transmission links. Bayesian analyses were applied only for clusters including ≥5 dated samples in order to estimate the median maximum inter-transmission interval. This study analyzed sequences sampled from 12 South American countries, from individuals of different exposure categories, under different antiretroviral profiles, and from a wide period of time (1989–2013). Continentally, Brazil, Argentina and Venezuela were revealed important sites for the spread of HIV-1B among countries inside South America. Of note, from all the clusters identified about 70% of the HIV-1B infections are primarily occurring among individuals living in the same geographic region. In addition, these transmissions seem to occur early after the infection of an individual, taking in average 2.39 years (95% CI 1.48–3.30) to succeed. Homosexual/Bisexual individuals transmit the virus as quickly as almost half time of that estimated for the general population sampled here. Public health services can be broadly benefitted from this kind of information whether to focus on specific programs of response to the epidemic whether as guiding of prevention campaigns to specific risk groups.</p></div

    Number of transmission clusters and clustered sequences among 4,810 HIV-1 Subtype B codon-stripped <i>pol</i> sequences from South America.

    No full text
    <p>(A) Number of transmission clusters identified using Cluster Picker with a SH-aLRT support threshold of ≥90 and under different within-maximum genetic distances. (B) Number of clustered sequences under different within-cluster genetic distances.</p

    Geographical type of HIV-1 Subtype B transmissions among clusters identified within South America for the codon-stripped dataset (901bp).

    No full text
    <p>Geographical type of HIV-1 Subtype B transmissions among clusters identified within South America for the codon-stripped dataset (901bp).</p

    Geographic distribution and proportion of HIV-1 subtype B <i>pol</i> sequences from South American countries.

    No full text
    <p>Map shows locations of the HIV-1 subtype B sequences included in the dataset. The proportion (green bars) and the total number of sequences analyzed from each location is indicated. A compilation of all sequences from the Brazilian set and its respective state of sampling is indicated at the table included in the figure. Black dots indicate the cities sampled in this study. Sequences from Venezuela were sampled at Caracas (n = 213), from Colombia were sampled at Medellín (n = 32) and Bogotá (n = 7), the unique sequence from Bolivia was sampled at La Paz, sequences from Argentina were sampled at Mendoza (n = 2) and Buenos Aires (n = 1238), and sequences from Uruguay were sampled at Montevideo. For the rest of the countries the sequences had no identification of sampling region. Gray-shaded areas indicate regions not included in this study.</p

    HIV-1 subtype C migration rates and routine traffic of people amongst the three states of the South Brazilian region.

    No full text
    <p>Capitals are represented by blue circles proportional to the incidence of AIDS cases per 100,000 inhabitants in each city in 2009 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035649#pone.0035649-Brazilian1" target="_blank">[1]</a>. The arrows are colored according to the routine traffic between states <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035649#pone.0035649-Brazilian2" target="_blank">[41]</a>, from green (less) to red (more) passing through yellow, and their thickness is proportional to HIV-1 subtype C migration rate between state capitals as measured by BayesTraits. HIV-1 subtype C migration rate from Porto Alegre to Curitiba was more than 100 fold smaller than any of the others and is not represented in this picture.</p

    Bayesian MCC tree for HIV-1 subtype C <i>pol</i> (PR/RT) sequences circulating in Brazil.

    No full text
    <p>Branches are colored according to the most probable location state of their descendent nodes. The legend for the colors is shown on the left. Brackets indicate the monophyletic clade formed by subtype C sequences sampled from Brazil, and the position of subtype C reference sequences of African origin used to root the tree. The box highlights the position of the sub-cluster BR-PA. The state posterior probability is indicated only at key nodes. Horizontal branch lengths are drawn to scale with the bar at the bottom indicating years.</p

    Map of Brazil showing the five country regions.

    No full text
    <p>An expanded map of the South region with the states (Paraná, Santa Catarina and Rio Grande do Sul) and state capitals (Curitiba, Florianópolis and Porto Alegre) is shown to the right. The number and sampling dates of sequences from the three southern state capitals included in the present study are indicated.</p

    Bayesian MCMC test of phylogenetic isolation of Brazilian HIV-1 subtype C sequences by geographic region.

    No full text
    a<p>Expected AI or PS value under the null hypothesis of no phylogenetic clustering of isolates by sampling location. PA: Porto Alegre. FL: Florianópolis. CU: Curitiba.</p
    corecore