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Evolution

Evolution: explore 3 source-linked works published from 2026 to 2026, with original documents and citations.

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Evolutionary diversification of invertase paralogs couples carbon metabolism and sexual reproduction in fission yeasts.

Dynamic patterns of gene gain and loss play a major role in the diversification of eukaryotes, reflecting adaptation to a broad range of ecological contexts. Reconstructing the evolutionary history of genes provides a powerful framework for understanding how functional innovation shapes life-history traits. Here we report a comprehensive analysis of gene gain and loss across the fission yeast clade, whose evolutionary trajectory remains elusive. Reductive evolution of metabolic genes is a major contributor to species diversification, as observed in other fungal taxa. Notably, we uncovered an evolutionary scenario in which an ancestral gene duplication was followed by lineage-specific loss of one or the other paralog, except in S. pombe, which retained both. We demonstrate that these paralogs encode catalytically-active invertases, named Inv1 and Inv2, with distinct enzymatic properties, localization, regulation, and physiological roles. Inv1 is a secreted enzyme subject to glucose catabolite repression and is the sole invertase required for sucrose assimilation, resembling canonical yeast invertases. In contrast, Inv2 is intracellular, constitutively expressed, and required for inducing sexual differentiation in response to nutrient availability. Overall, these findings reveal an unexpected role for carbon metabolism in modulating the haploid-diploid cycle of fission yeasts, suggesting that diversification of core metabolic functions may contribute to adaptation to environments with distinct sugar compositions.

Evolution↗

Evolution of maize recombination landscape during domestication.

Despite the plethora of knowledge about the benefits of meiotic recombination and numerous theoretical studies examining how recombination rates evolve, there is a general lack of empirical support and consensus across species. To fill this knowledge gap, we characterized the evolution of recombination landscape in maize during its domestication from teosinte and related the observed changes to established theoretical frameworks. Through examining recombination in experimental populations of maize and teosinte and the population genomics approach of identifying historical recombination events using ancestral recombination graph inference to generate saturated maize and teosinte recombination maps, we found that during domestication, maize experienced a 12% increase in its genome-wide recombination rate. Furthermore, maize evolved higher recombination rates on the long arms of chromosomes in regions closer to centromeres, where recombination is generally very low. The repatterning of crossover events came from changes in global crossover positioning rather than alterations in cis-acting chromatin factors. Consequently, we found evidence of selection acting on trans-acting recombination modifiers affecting crossover interference and controlling the interference-dependent class I crossover pathway. We show that CO repatterning was likely beneficial for maize fitness, as significant recombination rate increases were predominantly in gene-rich regions, which harbor domestication-related variation. This work suggests genomic and mechanistic processes leading to the evolution of meiotic recombination landscape in response to directional selection pressure and provides evidence for the evolutionary advantage of recombination.

Zea mays↗

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗
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