HIV-1 subtype A gag variability and epitope evolution

Objective: The aim of this study was to examine the course of time-dependent evolution of HIV-1 subtype A on a global level, especially with respect to the dynamics of immunogenic HIV gag epitopes.Methods: We used a total of 1,893 HIV-1 subtype A gag sequences representing a timeline from 1985 through 2010, and 19 different countries in Africa, Europe and Asia. The phylogenetic relationship of subtype A gag and its epidemic dynamics was analysed through a Maximum Likelihood tree and Bayesian Skyline plot, genomic variability was measured in terms of G → A substitutions and Shannon entropy, and the time-dependent evolution of HIV subtype A gag epitopes was examined. Finally, to confirm observations on globally reported HIV subtype A sequences, we analysed the gag epitope data from our Kenyan, Pakistani, and Afghan cohorts, where both cohort-specific gene epitope variability and HLA restriction profiles of gag epitopes were examined. Results: The most recent common ancestor of the HIV subtype A epidemic was estimated to be 1956 ± 1. A period of exponential growth began about 1980 and lasted for approximately 7 years, stabilized for 15 years, declined for 2-3 years, then stabilized again from about 2004. During the course of evolution, a gradual increase in genomic variability was observed that peaked in 2005-2010. We observed that the number of point mutations and novel epitopes in gag also peaked concurrently during 2005-2010. Conclusion: It appears that as the HIV subtype A epidemic spread globally, changing population immunogenetic pressures may have played a role in steering immune-evolution of this subtype in new directions. This trend is apparent in the genomic variability and epitope diversity of HIV-1 subtype A gag sequences

Estimating the rate of intersubtype recombination in early HIV-1 group M strains

West Central Africa has been implicated as the epicenter of the HIV-1 epidemic, and almost all group M subtypes can be found there. Previous analysis of early HIV-1 group M sequences from Kinshasa in the Democratic Republic of Congo, formerly Zaire, revealed that isolates from a number of individuals fall in different positions in phylogenetic trees constructed from sequences from opposite ends of the genome as a result of recombination between viruses of different subtypes. Here, we use discrete ancestral trait mapping to develop a procedure for quantifying HIV-1 group M intersubtype recombination across phylogenies, using individuals' gag (p17) and env (gp41) subtypes. The method was applied to previously described HIV-1 group M sequences from samples obtained in Kinshasa early in the global radiation of HIV. Nine different p17 and gp41 intersubtype recombinant combinations were present in the data set. The mean number of excess ancestral subtype transitions (NEST) required to map individuals' p17 subtypes onto the gp14 phylogeny samples, compared to the number required to map them onto the p17 phylogenies, and vice versa, indicated that excess subtype transitions occurred at a rate of approximately 7 × 10(−3) to 8 × 10(−3) per lineage per year as a result of intersubtype recombination. Our results imply that intersubtype recombination may have occurred in approximately 20% of lineages evolving over a period of 30 years and confirm intersubtype recombination as a substantial force in generating HIV-1 group M diversity

Genetic Analysis of Human Immunodefiency Virus Type I Strains in Kenya: A Comparison Using Phylogenetic Analysis and a Combinatorial Melting Assay

We surveyed human immunodeficiency virus (HIV) subtype distribution from peripheral blood mononuclear cells (PBMCs) collected in 1995 from 24 HIV-1-infected Kenyan residents (specimens from predominantly male truck drivers and female sex workers near Mombasa and Nairobi). Processed lysates from the PBMC samples were used for env amplification, directly sequenced, and analyzed by phylogenetic analysis. Envelope amplification products were also used for analysis in a polymerase chain reaction (PCR)-based assay, called the combinatorial melting assay (COMA). Results of the two tests were compared for assignment of subtype for this Kenyan cohort. The COMA, a PCR capture technique with colorimetric signal detection, was used with HIV reference subtype strains as well as regional (East Africa) HIV strains for subtype identification. Performance of the COMA was at 100% concordance (24 of 24) as compared with DNA sequencing analysis. Phylogenetic analysis showed 17 isolates to be subtype A, 3 subtype D, and 4 subtype C viruses. This may represent an increase in subtype C presence in Kenya compared with previously documented reports. The COMA can offer advantages for rapid HIV-1 subtype screening of large populations, with the use of previously identified regional strains to enhance the identification of local strains. When more detailed genetic information is desired, DNA sequencing and analysis may be required

Divergent HIV and Simian Immunodeficiency Virus Surveillance, Zaire

Recent HIV infection or divergent HIV or simian immunodeficiency virus (SIV) strains may be responsible for Western blot–indeterminate results on 70 serum samples from Zairian hospital employees that were reactive in an enzyme immunoassay. Using universal polymerase chain reaction HIV-1, HIV-2, and SIV primers, we detected 1 (1.4%) HIV-1 sequence. Except for 1 sample, no molecular evidence for unusual HIV- or SIV-like strains in this sampling was found

HIV Genetic Diversity in Cameroon: Possible Public Health Importance

To monitor the evolving molecular epidemiology and genetic diversity of HIV in a country where many distinct strains cocirculate, we performed genetic analyses on sequences from 75 HIV-1-infected Cameroonians: 74 were group M and 1 was group O. Of the group M sequences, 74 were classified into the following env gp41 subtypes or recombinant forms: CRF02 (n = 54), CRF09 (n = 2), CRF13 (n = 2), A (n = 5), CRF11 (n = 4), CRF06 (n = 1), G (n = 2), F2 (n = 2), and E (n = 1, CRF01), and 1 was a JG recombinant. Comparison of phylogenies for 70 matched gp41 and protease sequences showed inconsistent classifications for 18 (26%) strains. Our data show that recombination is rampant in Cameroon with recombinant viruses continuing to recombine, adding to the complexity of circulating HIV strains. This expanding genetic diversity raises public health concerns for the ability of diagnostic assays to detect these unique HIV mosaic variants and for the development of broadly effective HIV vaccines.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63150/1/aid.2006.22.812.pd

Central African Hunters Exposed to Simian Immunodeficiency Virus

HIV-seronegative Cameroonians with exposure to nonhuman primates were tested for simian immunodeficiency virus (SIV) infection. Seroreactivity was correlated with exposure risk (p<0.001). One person had strong humoral and weak cellular immune reactivity to SIVcol peptides. Humans are exposed to and possibly infected with SIV, which has major public health implications

HIV-1 Transmission Clustering and Phylodynamics Highlight the Important Role of Young Men Who Have Sex with Men.

More persons living with HIV reside in the Southern United States than in any other region, yet little is known about HIV molecular epidemiology in the South. We used cluster and phylodynamic analyses to evaluate HIV transmission patterns in middle Tennessee. We performed cross-sectional analyses of HIV-1 pol sequences and clinical data collected from 2001 to 2015 among persons attending the Vanderbilt Comprehensive Care Clinic. Transmission clusters were identified using maximum likelihood phylogenetics and patristic distance differences. Demographic, risk behavior, and clinical factors were assessed evaluating "active" clusters (clusters including sequences sampled 2011-2015) and associations estimated with logistic regression. Transmission risk ratios for men who have sex with men (MSM) were estimated with phylodynamic models. Among 2915 persons (96% subtype-B sequences), 963 (33%) were members of 292 clusters (distance ≤1.5%, size range 2-39). Most clusters (62%, n = 690 persons) were active, either being newly identified (n = 80) or showing expansion on existing clusters (n = 101). Correlates of active clustering among persons with sequences collected during 2011-2015 included MSM risk and ≤30 years of age. Active clusters were significantly more concentrated in MSM and younger persons than historical clusters. Young MSM (YMSM) (≤26.4 years) had high estimated transmission risk [risk ratio = 4.04 (2.85-5.65) relative to older MSM] and were much more likely to transmit to YMSM. In this Tennessee cohort, transmission clusters over time were more concentrated by MSM and younger age, with high transmission risk among and between YMSM, highlighting the importance of interventions among this group. Detecting active clusters could help direct interventions to disrupt ongoing transmission chains

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies