Evolutionary models that make use of site-specific parameters have recently been criticized on the grounds that parameter estimates obtained under such models can be unreliable and lack theoretical guarantees of convergence. We present a simulation study providing empirical evidence that a simple version of the models in question does exhibit sensible convergence behavior and that additional taxa, despite not being independent of each other, lead to improved parameter estimates. Although it would be desirable to have theoretical guarantees of this, we argue that such guarantees would not be sufficient to justify the use of these models in practice. Instead, we emphasize the importance of taking the variance of parameter estimates into account rather than blindly trusting point estimates – this is standardly done by using the models to construct statistical hypothesis tests, which are then validated empirically via simulation studies.

Over the past two decades, comparative sequence analysis using codon-substitution models has been honed into a powerful and popular approach for detecting signatures of natural selection from molecular data. A substantial body of work has focused on developing a class of “branch-site” models which permit selective pressures on sequences, quantified by the ω ratio, to vary among both codon sites and individual branches in the phylogeny. We develop and present a method in this class, adaptive branch-site random effects likelihood (aBSREL), whose key innovation is variable parametric complexity chosen with an information theoretic criterion. By applying models of different complexity to different branches in the phylogeny, aBSREL delivers statistical performance matching or exceeding best-in-class existing approaches, while running an order of magnitude faster. Based on simulated data analysis, we offer guidelines for what extent and strength of diversifying positive selection can be detected reliably and suggest that there is a natural limit on the optimal parametric complexity for “branch-site” models. An aBSREL analysis of 8,893 Euteleostomes gene alignments demonstrates that over 80% of branches in typical gene phylogenies can be adequately modeled with a single ω ratio model, that is, current models are unnecessarily complicated. However, there are a relatively small number of key branches, whose identities are derived from the data using a model selection procedure, for which it is essential to accurately model evolutionary complexity.

Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes. This leaves the distribution of selection parameters essentially unconstrained, and also allows sites experiencing positive and purifying selection to be identified orders of magnitude faster than by existing methods. We demonstrate that popular random effects likelihood methods can produce misleading results when sites assigned to the same site class experience different levels of positive or purifying selection—an unavoidable scenario when using a small number of site classes. Our Fast Unconstrained Bayesian AppRoximation (FUBAR) is unaffected by this problem, while achieving higher power than existing unconstrained (fixed effects likelihood) methods. The speed advantage of FUBAR allows us to analyze larger data sets than other methods: We illustrate this on a large influenza hemagglutinin data set (3,142 sequences). FUBAR is available as a batch file within the latest HyPhy distribution (http://www.hyphy.org), as well as on the Datamonkey web server (http://www.datamonkey.org/).

Relaxation of selective strength, manifested as a reduction in the efficiency or intensity of natural selection, can drive evolutionary innovation and presage lineage extinction or loss of function. Mechanisms through which selection can be relaxed range from the removal of an existing selective constraint to a reduction in effective population size. Standard methods for estimating the strength and extent of purifying or positive selection from molecular sequence data are not suitable for detecting relaxed selection, because they lack power and can mistake an increase in the intensity of positive selection for relaxation of both purifying and positive selection. Here, we present a general hypothesis testing framework (RELAX) for detecting relaxed selection in a codon-based phylogenetic framework. Given two subsets of branches in a phylogeny, RELAX can determine whether selective strength was relaxed or intensified in one of these subsets relative to the other. We establish the validity of our test via simulations and show that it can distinguish between increased positive selection and a relaxation of selective strength. We also demonstrate the power of RELAX in a variety of biological scenarios where relaxation of selection has been hypothesized or demonstrated previously. We find that obligate and facultative γ-proteobacteria endosymbionts of insects are under relaxed selection compared with their free-living relatives and obligate endosymbionts are under relaxed selection compared with facultative endosymbionts. Selective strength is also relaxed in asexual Daphnia pulex lineages, compared with sexual lineages. Endogenous, nonfunctional, bornavirus-like elements are found to be under relaxed selection compared with exogenous Borna viruses. Finally, selection on the short-wavelength sensitive, SWS1, opsin genes in echolocating and nonecholocating bats is relaxed only in lineages in which this gene underwent pseudogenization; however, selection on the functional medium/long-wavelength sensitive opsin, M/LWS1, is found to be relaxed in all echolocating bats compared with nonecholocating bats.

Understanding when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We used a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated before the time of the most recent common ancestor of all sequenced SARS-CoV-2 genomes. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province, China. By characterizing the likely dynamics of the virus before it was discovered, we show that more than two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

Herpesviruses have been infecting and codiverging with their vertebrate hosts for hundreds of millions of years. The primate simplex viruses exemplify this pattern of virus–host codivergence, at a minimum, as far back as the most recent common ancestor of New World monkeys, Old World monkeys, and apes. Humans are the only primate species known to be infected with two distinct herpes simplex viruses: HSV-1 and HSV-2. Human herpes simplex viruses are ubiquitous, with over two-thirds of the human population infected by at least one virus. Here, we investigated whether the additional human simplex virus is the result of ancient viral lineage duplication or cross-species transmission. We found that standard phylogenetic models of nucleotide substitution are inadequate for distinguishing among these competing hypotheses; the extent of synonymous substitutions causes a substantial underestimation of the lengths of some of the branches in the phylogeny, consistent with observations in other viruses (e.g., avian influenza, Ebola, and coronaviruses). To more accurately estimate ancient viral divergence times, we applied a branch-site random effects likelihood model of molecular evolution that allows the strength of natural selection to vary across both the viral phylogeny and the gene alignment. This selection-informed model favored a scenario in which HSV-1 is the result of ancient codivergence and HSV-2 arose from a cross-species transmission event from the ancestor of modern chimpanzees to an extinct Homo precursor of modern humans, around 1.6 Ma. These results provide a new framework for understanding human herpes simplex virus evolution and demonstrate the importance of using selection-informed models of sequence evolution when investigating viral origin hypotheses.

Objectives

Curative strategies using agents to perturb the HIV reservoir have demonstrated only modest activity, whereas increases in viremia after standard vaccination have been described. We investigated whether vaccination against non-HIV pathogens can induce HIV transcription and thereby play a role in future eradication strategies.

Design

A randomized controlled trial (NCT00329251) was performed to compare the effects of clinical vaccines with placebo on HIV transcription and immune activation.

Methods

Twenty-six HIV-infected individuals on suppressive antiretroviral therapy were randomized to receive a vaccination schedule (n = 13) or placebo (n = 13). Cell-associated RNA and DNA were extracted from peripheral blood mononuclear cells, and HIV was quantified by droplet digital PCR using primers for gag and 2-LTR (for HIV DNA), unspliced gag RNA (gag usRNA), multispliced tat-rev RNA (tat-rev msRNA) and polyA mRNA.

Results

Significant increases in gag usRNA after influenza/hepatitis B vaccination (P = 0.02) and in gag usRNA (P = 0.04) and polyA mRNA (P = 0.04) after pneumococcus/hepatitis B vaccination were seen in vaccinees but not controls. HIV DNA and plasma HIV RNA did not change in either group. Increases in CD4 and CD8 T-cell activation markers (P = 0.08 and P < 0.001, respectively) and HIV-specific CD8 responses (P = 0.04 for p24 gag, P = 0.01 for p17 gag and P = 0.04 for total gag) were seen in vaccinees but not controls.

Conclusion

In this study, vaccination was associated with increases in HIV cell-associated RNA and HIV-specific responses during antiretroviral therapy. Using standard vaccines to stimulate HIV transcription may therefore be a useful component of future eradication strategies.

As in many urban areas in the United States, the largest burden of the HIV epidemic in San Diego is borne by men who have sex with men (MSM). Using data from well-characterized HIV transmitting and non-transmitting partner pairs of MSM in San Diego, we calculated the population attributable risk (PAR) of HIV transmissions for different co-infections common among MSM in this area. We found that over a third of HIV transmissions could be potentially attributed to genital shedding of cytomegalovirus (CMV) (111 transmission events), compared to 21% potentially attributed to bacterial sexually transmitted infections (STI) (62 events) and 17% to herpes simplex virus type-2 (HSV-2) (51 events). Although our study cannot infer causality between the described associations and is limited in sample size, these results suggest that interventions aimed at reducing CMV shedding might be an attractive HIV prevention strategy in populations with high prevalence of CMV co-infection.

Background

Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks.

Methods and findings

The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men.

Conclusions

We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the importance and complementarity of both epidemiological and molecular genetic surveillance for characterizing regional HIV-1 epidemics.