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A fascinating, far-reaching study of how our species' innate capacity for culture altered the course of our social and evolutionary history.

Statistical characterisations of traits evolving on phylogenies combine the contributions of unique and shared influences on those traits, potentially confusing the interpretation of historical events of macroevolution. The Fabric model, introduced in 2022, identifies historical events of directional shifts in traits (e.g. becoming larger/smaller, faster/slower over evolutionary time) and of changes in macroevolutionary ‘evolvability’ or the realised historical ability of a trait to explore its trait-space. Here we extend the model to accommodate situations in which the trait is correlated with one or more covarying traits. The Fabric-regression model identifies a unique component of variance in the trait that is free of influences from correlated traits, while simultaneously estimating directional and evolvability effects. We show in a dataset of 1504 Mammalian species that inferences about historical directional shifts in brain size and in its evolvability, having accounted for body size, differ qualitatively from inferences about brain size alone, including finding many new effects not visible in the whole trait. A class of fundamental macroevolutionary questions awaits testing on the variation uniquely attributable to traits, and the ability to accommodate statistically one or more covariates opens the possibility of bringing the formal methods of causal inference to phylogenetic-comparative studies. Here, the authors extend the Fabric model to accommodate correlation between a trait and covarying traits, demonstrating how inferences about the evolution of brain size change when accounting for body size across 1504 mammalian species.

Macroevolution posed difficulties for Darwin and later theorists because species’ phenotypes frequently change abruptly, or experience long periods of stasis, both counter to the theory of incremental change or gradualism. We introduce a statistical model that accommodates this uneven evolutionary landscape by estimating two kinds of historical change: directional changes that shift the mean phenotype along the branches of a phylogenetic tree, and evolvability changes that alter a clade’s ability to explore its trait-space. In mammals, we find that both processes make substantial independent contributions to explaining macroevolution, and are rarely linked. ‘Watershed’ moments of increased evolvability greatly outnumber reductions in evolutionary potentials, and large or abrupt phenotypic shifts are explicable statistically as biased random walks, allowing macroevolutionary theory to engage with the language and concepts of gradualist microevolution. Our findings recast macroevolutionary phenomena, illustrating the necessity of accounting for a variety of evolutionary processes simultaneously. ‘Macroevolution posed difficulties for Darwin and later theorists because species frequently change abruptly, or experience long periods of stasis, both counter to the theory of incremental change or gradualism. Here, the authors propose a macroevolutionary statistical model that accommodates this uneven evolutionary landscape, and shows how even abrupt macroevolutionary changes are compatible with gradualist microevolutionary processes.’

Unlike most other biological species, humans can use cultural innovations to occupy a range of environments, raising the intriguing question of whether human migrations move relatively independently of habitat or show preferences for familiar ones. The Bantu expansion that swept out of West Central Africa beginning ∼5,000 y ago is one of the most influential cultural events of its kind, eventually spreading over a vast geographical area a new way of life in which farming played an increasingly important role. We use a new dated phylogeny of ∼400 Bantu languages to show that migrating Bantu-speaking populations did not expand from their ancestral homeland in a “random walk” but, rather, followed emerging savannah corridors, with rainforest habitats repeatedly imposing temporal barriers to movement. When populations did move from savannah into rainforest, rates of migration were slowed, delaying the occupation of the rainforest by on average 300 y, comparedwith similar migratory movements exclusively within savannah or within rainforest by established rainforest populations. Despite unmatched abilities to produce innovations culturally, unfamiliar habitats significantly alter the route and pace of human dispersals.

We describe a Bayesian method for investigating correlated evolution of discrete binary traits on phylogenetic trees. The method fits a continuous‐time Markov model to a pair of traits, seeking the best fitting models that describe their joint evolution on a phylogeny. We employ the methodology of reversible‐jump (RJ) Markov chain Monte Carlo to search among the large number of possible models, some of which conform to independent evolution of the two traits, others to correlated evolution. The RJ Markov chain visits these models in proportion to their posterior probabilities, thereby directly estimating the support for the hypothesis of correlated evolution. In addition, the RJ Markov chain simultaneously estimates the posterior distributions of the rate parameters of the model of trait evolution. These posterior distributions can be used to test among alternative evolutionary scenarios to explain the observed data. All results are integrated over a sample of phylogenetic trees to account for phylogenetic uncertainty. We implement the method in a program called RJ Discrete and illustrate it by analyzing the question of whether mating system and advertisement of estrus by females have coevolved in the Old World monkeys and great apes.

We have previously shown that the ‘low limit’ number words (from one to five) have exceptionally slow rates of lexical replacement when measured across the Indo-European (IE) languages. Here, we replicate this finding within the Bantu and Austronesian language families, and with new data for the IE languages. Number words can remain stable for 10 000 to over 100 000 years, or around 3.5–20 times longer than average rates of lexical replacement among the Swadesh list of ‘fundamental vocabulary’ items. Ordinal evidence suggests that number words also have slow rates of lexical replacement in the Pama–Nyungan language family of Australia. We offer three hypotheses to explain these slow rates of replacement: (i) that the abstract linguistic-symbolic processing of ‘number’ links to evolutionarily conserved brain regions associated with numerosity; (ii) that number words are unambiguous and therefore have lower ‘mutation rates’; and (iii) that the number words occupy a region of the phonetic space that is relatively full and therefore resist change because alternatives are unlikely to be as ‘good’ as the original word. This article is part of a discussion meeting issue ‘The origins of numerical abilities’.

Phylogenetic trees describe the pattern of descent amongst a group of species. With the rapid accumulation of DNA sequence data, more and more phylogenies are being constructed based upon sequence comparisons. The combination of these phylogenies with powerful new statistical approaches for the analysis of biological evolution is challenging widely held beliefs about the history and evolution of life on Earth.

Hypoxia in the tumor microenvironment hinders antitumor immunity. Increasing tumor oxygenation may promote T cell infiltration and tumor control by immune checkpoint blockade (ICB). We found that a radiosensitizer, myo-inositol trispyrophosphate (ITPP), caused oxygen unloading from hemoglobin in CT26 and 4T1 tumors as indicated by photoacoustic imaging (PAI). This change in hypoxia detected by PAI was correlated with strong positive correlations with CD8+ and CD4+ FoxP3- effector T cell (Teff), and negative correlations with monocyte frequencies, indicating that ITPP promoted more immunogenic tumor microenvironments in both models. Combination ITPP and ICB improved tumor control and survival in both models. Therefore, imaging ITPP-modulated tumor hypoxia with PAI was related to ICB treatment response in these studies. Future combination immunotherapy regimens may benefit from monitoring hypoxia using molecular imaging with PAI.

Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods (BayesMultiState) is available from the authors.

Immune checkpoint inhibitors have revolutionized cancer therapy, yet the efficacy of these treatments is often limited by the heterogeneous and hypoxic tumor microenvironment (TME) of solid tumors. In the TME, programmed death-ligand 1 (PD-L1) expression on cancer cells is mainly regulated by Interferon-gamma (IFN-γ), which induces T cell exhaustion and enables tumor immune evasion. In this study, we demonstrate that acidosis, a common characteristic of solid tumors, significantly increases IFN-γ-induced PD-L1 expression on aggressive cancer cells, thus promoting immune escape. Using preclinical models, we found that acidosis enhances the genomic expression and phosphorylation of signal transducer and activator of transcription 1 (STAT1), and the translation of STAT1 mRNA by eukaryotic initiation factor 4F (elF4F), resulting in an increased PD-L1 expression. We observed this effect in murine and human anti-PD-L1-responsive tumor cell lines, but not in anti-PD-L1-nonresponsive tumor cell lines. In vivo studies fully validated our in vitro findings and revealed that neutralizing the acidic extracellular tumor pH by sodium bicarbonate treatment suppresses IFN-γ-induced PD-L1 expression and promotes immune cell infiltration in responsive tumors and thus reduces tumor growth. However, this effect was not observed in anti-PD-L1-nonresponsive tumors. In vivo experiments in tumor-bearing IFN-γ −/− mice validated the dependency on immune cell-derived IFN-γ for acidosis-mediated cancer cell PD-L1 induction and tumor immune escape. Thus, acidosis and IFN-γ-induced elevation of PD-L1 expression on cancer cells represent a previously unknown immune escape mechanism that may serve as a novel biomarker for anti-PD-L1/PD-1 treatment response. These findings have important implications for the development of new strategies to enhance the efficacy of immunotherapy in cancer patients.