A Challenge to the Ancient Origin of SIVagm Based on African Green Monkey Mitochondrial Genomes

While the circumstances surrounding the origin and spread of HIV are becoming clearer, the particulars of the origin of simian immunodeficiency virus (SIV) are still unknown. Specifically, the age of SIV, whether it is an ancient or recent infection, has not been resolved. Although many instances of cross-species transmission of SIV have been documented, the similarity between the African green monkey (AGM) and SIVagm phylogenies has long been held as suggestive of ancient codivergence between SIVs and their primate hosts. Here, we present well-resolved phylogenies based on full-length AGM mitochondrial genomes and seven previously published SIVagm genomes; these allowed us to perform the first rigorous phylogenetic test to our knowledge of the hypothesis that SIVagm codiverged with the AGMs. Using the Shimodaira–Hasegawa test, we show that the AGM mitochondrial genomes and SIVagm did not evolve along the same topology. Furthermore, we demonstrate that the SIVagm topology can be explained by a pattern of west-to-east transmission of the virus across existing AGM geographic ranges. Using a relaxed molecular clock, we also provide a date for the most recent common ancestor of the AGMs at approximately 3 million years ago. This study substantially weakens the theory of ancient SIV infection followed by codivergence with its primate hosts

HIV transmission networks among transgender women in Los Angeles County, CA, USA: a phylogenetic analysis of surveillance data.

BackgroundTransgender women are among the groups at highest risk for HIV infection, with a prevalence of 27·7% in the USA; and despite this known high risk, undiagnosed infection is common in this population. We set out to identify transgender women and their partners in a molecular transmission network to prioritise public health activities.MethodsSince 2006, HIV protease and reverse transcriptase gene (pol) sequences from drug resistance testing have been reported to the Los Angeles County Department of Public Health and linked to demographic data, gender, and HIV transmission risk factor data for each case in the enhanced HIV/AIDS Reporting System. We reconstructed a molecular transmission network by use of HIV-TRAnsmission Cluster Engine (with a pairwise genetic distance threshold of 0·015 substitutions per site) from the earliest pol sequences from 22 398 unique individuals, including 412 (2%) self-identified transgender women. We examined the possible predictors of clustering with multivariate logistic regression. We characterised the genetically linked partners of transgender women and calculated assortativity (the tendency for people to link to other people with the same attributes) for each transmission risk group.Findings8133 (36·3%) of 22 398 individuals clustered in the network across 1722 molecular transmission clusters. Transgender women who indicated a sexual risk factor clustered at the highest frequency in the network, with 147 (43%) of 345 being linked to at least one other person (adjusted odds ratio [aOR] 2·0, p=0·0002). Transgender women were assortative in the network (assortativity 0·06, p<0·001), indicating that they tended to link to other transgender women. Transgender women were more likely than expected to link to other transgender women (OR 4·65, p<0·001) and cisgender men who did not identify as men who have sex with men (MSM; OR 1·53, p<0·001). Transgender women were less likely than expected to link to MSM (OR 0·75, p<0·001), despite the high prevalence of HIV among MSM. Transgender women were distributed across 126 clusters, and cisgender individuals linked to one transgender woman were 9·2 times more likely to link to a second transgender woman than other individuals in the surveillance database. Reconstruction of the transmission network is limited by sample availability, but sequences were available for more than 40% of diagnoses.InterpretationClustering of transgender women and the observed tendency for linkage with cisgender men who did not identify as MSM, shows the potential to use molecular epidemiology both to identify clusters that are likely to include undiagnosed transgender women with HIV and to improve the targeting of public health prevention and treatment services to transgender women.FundingCalifornia HIV and AIDS Research Program and National Institutes of Health-National Institute of Allergy and Infectious Diseases

Characteristics of a cohort of high-risk men who have sex with men on pre-exposure prophylaxis reporting transgender sexual partners.

Transgender people continue to be at high-risk for HIV acquisition, but little is known about the characteristics of their sexual partners. To address this gap, we examined sociodemographic and sexual characteristics of cisgender men who have sex with men (MSM) on pre-exposure prophylaxis (PrEP) reporting transgender sexual partners.A cohort of 392 MSM in southern California in a randomized clinical trial for PrEP adherence were followed from 2013 to 2016. Multivariable generalized estimating equation and logistic models identified characteristics of MSM reporting transgender sexual partners and PrEP adherence.Only 14 (4%) MSM reported having transgender sexual partners. MSM were more likely to report transgender partners if they were African American, had incident chlamydia, reported injection drug-using sexual partners, or received items for sex. Most associations remained significant in the multivariable model: African American (adjusted odds ratio [AOR] 11.20, P = .01), incident chlamydia (AOR 3.71, P = .04), and receiving items for sex (AOR 5.29, P = .04). There were no significant differences in PrEP adherence between MSM reporting transgender partners and their counterpart.MSM who report transgender sexual partners share characteristics associated with individuals with high HIV prevalence. Identifying this group distinct from larger cohorts of MSM could offer new HIV prevention opportunities for this group of MSM and the transgender community

Natural selection favoring more transmissible HIV detected in United States molecular transmission network.

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters

Analysis of Hepatitis B Virus Genotype D in Greenland Suggests the Presence of a Novel Quasi-Subgenotype

A disproportionate number of Greenland's Inuit population are chronically infected with Hepatitis B virus (HBV; 5-10%). HBV genotypes B and D are most prevalent in the circumpolar Arctic. Here, we report 39 novel HBV/D sequences from individuals residing in southwestern Greenland. We performed phylodynamic analyses with ancient HBV DNA calibrators to investigate the origin and relationship of these taxa to other HBV sequences. We inferred a substitution rate of 1.4 × 10-5 [95% HPD 8.8 × 10-6, 2.0 × 10-5] and a time to the most recent common ancestor of 629 CE [95% HPD 37-1138 CE]. The Greenland taxa form a sister clade to HBV/D2 sequences, specifically New Caledonian and Indigenous Taiwanese sequences. The Greenland sequences share amino acid signatures with subgenotypes D1 and D2 and ~97% sequence identity. Our results suggest the classification of these novel sequences does not fit within the current nomenclature. Thus, we propose these taxa be considered a novel quasi-subgenotype

HIV-TRACE (Transmission Cluster Engine):A tool for large scale molecular epidemiology of HIV-1 and other rapidly evolving pathogens

In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoe-leather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, that is, on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from www.github.com/veg/hivtrace, along with the accompanying result visualization module from www.github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens

Random-effects substitution models for phylogenetics via scalable gradient approximations

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches

The Re-Emergence of H1N1 Influenza Virus in 1977: A Cautionary Tale for Estimating Divergence Times Using Biologically Unrealistic Sampling Dates

In 1977, H1N1 influenza A virus reappeared after a 20-year absence. Genetic analysis indicated that this strain was missing decades of nucleotide sequence evolution, suggesting an accidental release of a frozen laboratory strain into the general population. Recently, this strain and its descendants were included in an analysis attempting to date the origin of pandemic influenza virus without accounting for the missing decades of evolution. Here, we investigated the effect of using viral isolates with biologically unrealistic sampling dates on estimates of divergence dates. Not accounting for missing sequence evolution produced biased results and increased the variance of date estimates of the most recent common ancestor of the re-emergent lineages and across the entire phylogeny. Reanalysis of the H1N1 sequences excluding isolates with unrealistic sampling dates indicates that the 1977 re-emergent lineage was circulating for approximately one year before detection, making it difficult to determine the geographic source of reintroduction. We suggest that a new method is needed to account for viral isolates with unrealistic sampling dates