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The scale of the HIV-1 epidemic underscores the need for a vaccine. The multitude of circulating HIV-1 strains together with HIV-1's high evolvability hints that HIV-1 could adapt to a future vaccine. Here, we wanted to investigate the effect of vaccination on the evolution of the virus post-breakthrough infection. We analyzed 2,635 HIV-1 env sequences sampled up to a year post-diagnosis from 110 vaccine and placebo participants who became infected in the RV144 vaccine efficacy trial. We showed that the Env signature sites that were previously identified to distinguish vaccine and placebo participants were maintained over time. In addition, fewer sites were under diversifying selection in the vaccine group than in the placebo group. These results indicate that HIV-1 would possibly adapt to a vaccine upon its roll-out., (© The Author(s) 2021. Published by Oxford University Press.)

The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no comprehensive nonparametric framework for characterizing and comparing patterns of phenotypic evolution. Here, we build on a recently introduced approach for using the phylogenetic spectral density profile (SDP) to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for nonparametric analysis of phylogenetic trait data. We show how to construct the SDP of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the SDP to successfully cluster phylogenetic trait data into meaningful groups and to characterize the phenotypic patterning within those groups. We furthermore demonstrate how the SDP is a powerful tool for visualizing phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical data sets: a comprehensive data set of traits involved in song, plumage, and resource-use in tanagers, and a high-dimensional data set of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework., (© The Author(s) 2019. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Phylogenetics is a powerful tool for understanding the diversification dynamics of viral pathogens. Here we present an extension of the spectral density profile of the modified graph Laplacian, which facilitates the characterization of within-host molecular evolution of viruses and the direct comparison of diversification dynamics between hosts. This approach is non-parametric and therefore fast and model-free. We used simulations of within-host evolutionary scenarios to evaluate the efficiency of our approach and to demonstrate the significance of interpreting a viral phylogeny by its spectral density profile in terms of diversification dynamics. The key features that are captured by the profile are positive selection on the viral gene (or genome), temporal changes in substitution rates, mutational fitness, and time between sampling. Using sequences from individuals infected with HIV-1, we showed the utility of this approach for characterizing within-host diversification dynamics, for comparing dynamics between hosts, and for charting disease progression in infected individuals sampled over multiple years. We furthermore propose a heuristic test for assessing founder heterogeneity, which allows us to classify infections with single and multiple HIV-1 founder viruses. This non-parametric approach can be a valuable complement to existing parametric approaches., (© The Author(s) 2019. Published by Oxford University Press.)

Understanding the relative influence of various abiotic and biotic variables on diversification dynamics is a major goal of macroevolutionary studies. Recently, phylogenetic approaches have been developed that make it possible to estimate the role of various environmental variables on diversification using time-calibrated species trees, paleoenvironmental data, and maximum-likelihood techniques. These approaches have been effectively employed to estimate how speciation and extinction rates vary with key abiotic variables, such as temperature and sea level, and we can anticipate that they will be increasingly used in the future. Here we compile a series of biotic and abiotic paleodatasets that can be used as explanatory variables in these models and use simulations to assess the statistical properties of the approach when applied to these paleodatasets. We demonstrate that environment-dependent models perform well in recovering environment-dependent speciation and extinction parameters, as well as in correctly identifying the simulated environmental model when speciation is environment-dependent. We explore how the strength of the environment-dependency, tree size, missing taxa, and characteristics of the paleoenvironmental curves influence the performance of the models. Finally, using these models, we infer environment-dependent diversification in two empirical phylogenies: temperature-dependence in Cetacea and $\delta^{13}C$-dependence in Ruminantia. We illustrate how to evaluate the relative importance of abiotic and biotic variables in these two clades and interpret these results in light of macroevolutionary hypotheses. Given the important role paleoenvironments are presumed to have played in species evolution, our statistical assessment of how environment-dependent models behave is crucial for their utility in macroevolutionary analysis.

Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profile of a phylogenetic tree from its Laplacian graph. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications., (© The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

A major focus of comparative neuroanatomy has been on whether the mammalian brain evolves in a concerted or a mosaic fashion. Workers have examined variation in the volume of different brain regions across taxa to test the degree to which selection is constrained by the timing of events in neural development. Whether a conserved neurogenetic program in the mammalian brain constrains the distribution of different cell types, however, has not yet been investigated. Here we tested for evidence of evolutionary constraints on the densities of different cell types in the primary visual cortex (V1) and the hippocampus in 37 primate and 21 carnivore species. Cellular densities in V1 and the hippocampus scale isometrically with respect to one another in carnivores, as predicted by the concerted evolution hypothesis. In primates, however, cellular distributions in the hippocampus and primary visual cortex show no correlations, which supports the hypothesis of mosaic brain evolution. We therefore provide evidence for the presence of constraints controlling the adult densities of different cell types in disparate regions of the mammalian brain, but also for specializations along the primate lineage. We propose that adaptations to modularity at the cellular level may carry a deep phylogenetic signal., (© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.)