Comprehensive Characterization of HIV-1 Molecular Epidemiology and Demographic History in the Brazilian Region Most Heavily Affected by AIDS.

UNLABELLED: The high incidence of AIDS cases and the dominance of HIV-1 subtype C infections are two features that distinguish the HIV-1 epidemic in the two southernmost Brazilian states (Rio Grande do Sul [RS] and Santa Catarina [SC]) from the epidemic in other parts of the country. Nevertheless, previous studies on HIV molecular epidemiology were conducted mainly in capital cities, and a more comprehensive understanding of factors driving this unique epidemic in Brazil is necessary. Blood samples were collected from individuals in 13 municipalities in the Brazilian southern region. HIV-1 env and pol genes were submitted to phylogenetic analyses for assignment of subtype, and viral population phylodynamics were reconstructed by applying Skygrid and logistic coalescent models in a Bayesian analysis. A high prevalence of subtype C was observed in all sampled locations; however, an increased frequency of recombinant strains was found in RS, with evidence for new circulating forms (CRFs). In the SC state, subtype B and C epidemics were associated with distinct exposure groups. Although logistic models estimated similar growth rates for HIV-1 subtype C (HIV-1C) and HIV-1B, a Skygrid plot reveals that the former epidemic has been expanding for a longer time. Our results highlight a consistent expansion of HIV-1C in south Brazil, and we also discuss how heterosexual and men who have sex with men (MSM) transmission chains might have impacted the current prevalence of HIV-1 subtypes in this region. IMPORTANCE: The AIDS epidemic in south Brazil is expanding rapidly, but the circumstances driving this condition are not well known. A high prevalence of HIV-1 subtype C was reported in the capital cities of this region, in contrast to the subtype B dominance in the rest of the country. This study sought to comparatively investigate the HIV-1 subtype B and C epidemics by sampling individuals from several cities in the two states with the highest AIDS incidences in Brazil. Our analyses showed distinct epidemic growth curves for the two epidemics, and we also found evidence suggesting that separate transmission chains may be impacting the viral phylodynamics and the emergence of new recombinant forms.

Contribution of Epidemiological Predictors in Unraveling the Phylogeographic History of HIV-1 Subtype C in Brazil.

UNLABELLED: The phylogeographic history of the Brazilian HIV-1 subtype C (HIV-1C) epidemic is still unclear. Previous studies have mainly focused on the capital cities of Brazilian federal states, and the fact that HIV-1C infections increase at a higher rate than subtype B infections in Brazil calls for a better understanding of the process of spatial spread. A comprehensive sequence data set sampled across 22 Brazilian locations was assembled and analyzed. A Bayesian phylogeographic generalized linear model approach was used to reconstruct the spatiotemporal history of HIV-1C in Brazil, considering several potential explanatory predictors of the viral diffusion process. Analyses were performed on several subsampled data sets in order to mitigate potential sample biases. We reveal a central role for the city of Porto Alegre, the capital of the southernmost state, in the Brazilian HIV-1C epidemic (HIV-1C_BR), and the northward expansion of HIV-1C_BR could be linked to source populations with higher HIV-1 burdens and larger proportions of HIV-1C infections. The results presented here bring new insights to the continuing discussion about the HIV-1C epidemic in Brazil and raise an alternative hypothesis for its spatiotemporal history. The current work also highlights how sampling bias can confound phylogeographic analyses and demonstrates the importance of incorporating external information to protect against this. IMPORTANCE: Subtype C is responsible for the largest HIV infection burden worldwide, but our understanding of its transmission dynamics remains incomplete. Brazil witnessed a relatively recent introduction of HIV-1C compared to HIV-1B, but it swiftly spread throughout the south, where it now circulates as the dominant variant. The northward spread has been comparatively slow, and HIV-1B still prevails in that region. While epidemiological data and viral genetic analyses have both independently shed light on the dynamics of spread in isolation, their combination has not yet been explored. Here, we complement publically available sequences and new genetic data from 13 cities with epidemiological data to reconstruct the history of HIV-1C spread in Brazil. The combined approach results in more robust reconstructions and can protect against sampling bias. We found evidence for an alternative view of the HIV-1C spatiotemporal history in Brazil that, contrary to previous explanations, integrates seamlessly with other observational data.

Inferring population dynamics of HIV-1 subtype C epidemics in Eastern Africa and Southern Brazil applying different Bayesian phylodynamics approaches.

The subtype C Eastern Africa clade (CEA), a particularly successful HIV-1 subtype C lineage, has seeded several sub-epidemics in Eastern African countries and Southern Brazil during the 1960s and 1970s. Here, we characterized the past population dynamics of the major CEA sub-epidemics in Eastern Africa and Brazil by using Bayesian phylodynamic approaches based on coalescent and birth-death models. All phylodynamic models support similar epidemic dynamics and exponential growth rates until roughly the mid-1980s for all the CEA sub-epidemics. Divergent growth patterns, however, were supported afterwards. The Bayesian skygrid coalescent model (BSKG) and the birth-death skyline model (BDSKY) supported longer exponential growth phases than the Bayesian skyline coalescent model (BSKL). The BDSKY model uncovers patterns of a recent decline for the CEA sub-epidemics in Burundi/Rwanda and Tanzania (Re < 1) and a recent growth for Southern Brazil (Re > 1); whereas coalescent models infer an epidemic stabilization. To the contrary, the BSKG model captured a decline of Ethiopian CEA sub-epidemic between the mid-1990s and mid-2000s that was not uncovered by the BDSKY model. These results underscore that the joint use of different phylodynamic approaches may yield complementary insights into the past HIV population dynamics.