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Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade‐off with cell growth. Here, we utilize genome‐scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth‐secretion trade‐off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model‐designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux‐rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model‐designed selection environments open new opportunities for predictive evolution. Synopsis EvolveX, a new algorithm enabling model‐guided design of chemical environments for targeted adaptive evolution, is applied to evolve a wine yeast strain for increased aroma secretion. EvolveX predicts environment‐dependence of trait‐fitness correlations using genome‐scale metabolic models. Multi‐omics analysis shows agreement with the model‐predicted metabolic changes. EvolveX enables devising adaptive evolution strategies for improving traits uncorrelated with cell fitness. Graphical Abstract EvolveX, a new algorithm enabling model‐guided design of chemical environments for targeted adaptive evolution, is applied to evolve a wine yeast strain for increased aroma secretion.

Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

A discrepancy between the phenotype of an individual and that which would confer optimal responses in terms of fitness in an environment is termed ‘mismatch’. Phenotype results from developmental plasticity, conditioned partly by evolutionary history of the species and partly by aspects of the developmental environment. We discuss two categories of such mismatch with reference primarily to nutrition and in the context of evolutionary medicine. The categories operate over very different timescales. A developmental mismatch occurs when the phenotype induced during development encounters a different environment post-development. This may be the result of wider environmental changes, such as nutritional transition between generations, or because maternal malnutrition or placental dysfunction give inaccurate information about the organism's likely future environment. An evolutionary mismatch occurs when there is an evolutionarily novel environment. Developmental plasticity may involve immediate adaptive responses (IARs) to preserve survival if an environmental challenge is severe, and/or predictive adaptive responses (PARs) if the challenge does not threaten survival, but there is a fitness advantage in developing a phenotype that will be better adapted later. PARs can have long-term adverse health consequences if there is a developmental mismatch. For contemporary humans, maternal constraint of fetal growth makes PARs likely even if there is no obvious IAR, and this, coupled with the pervasive nutritionally dense modern environment, can explain the widespread observations of developmental mismatch, particularly in populations undergoing nutritional transition. Both developmental and evolutionary mismatch have important public health consequences and implications for where policy interventions may be most effective. This article is part of the theme issue ‘Developing differences: early-life effects and evolutionary medicine'.

Sexually reproducing organisms usually invest equally in male and female offspring. Deviations from this pattern have led researchers to new discoveries in the study of parent–offspring conflict, genomic conflict, and cooperative breeding. Some social insect species exhibit the unusual population-level pattern of split sex ratio, wherein some colonies specialize in the production of future queens and others specialize in the production of males. Theoretical work predicted that worker control of sex ratio and variation in relatedness asymmetry among colonies would cause each colony to specialize in the production of one sex. While some empirical tests supported theoretical predictions, others deviated from them, leaving many questions about how split sex ratio emerges. One factor yet to be investigated is whether colony sex ratio may be influenced by the genotypes of queens or workers. Here, we sequence the genomes of 138 Formica glacialis workers from 34 male-producing and 34 gyne-producing colonies to determine whether split sex ratio is under genetic control. We identify a supergene spanning 5.5 Mbp that is closely associated with sex allocation in this system. Strikingly, this supergene is adjacent to another supergene spanning 5 Mbp that is associated with variation in colony queen number. We identify a similar pattern in a second related species, Formica podzolica. The discovery that split sex ratio is determined, at least in part, by a supergene in two species opens future research on the evolutionary drivers of split sex ratio.

Developmental plasticity describes situations where a specific input during an individual's development produces a lasting alteration in phenotype. Some instances of developmental plasticity may be adaptive, meaning that the tendency to produce the phenotype conditional on having experienced the developmental input has been under positive selection. We discuss the necessary assumptions and predictions of hypotheses concerning adaptive developmental plasticity (ADP) and develop guidelines for how to test empirically whether a particular example is adaptive. Central to our analysis is the distinction between two kinds of ADP: informational, where the developmental input provides information about the future environment, and somatic state-based, where the developmental input enduringly alters some aspect of the individual's somatic state. Both types are likely to exist in nature, but evolve under different conditions. In all cases of ADP, the expected fitness of individuals who experience the input and develop the phenotype should be higher than that of those who experience the input and do not develop the phenotype, while the expected fitness of those who do not experience the input and do not develop the phenotype should be higher than those who do not experience the input and do develop the phenotype. We describe ancillary predictions that are specific to just one of the two types of ADP and thus distinguish between them.

This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors—and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising—about predictive processing—with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

With the continuous expansion of saline soils under climate change, understanding the eco-evolutionary tradeoff between the microbial mitigation of carbon limitation and the maintenance of functional traits in saline soils represents a significant knowledge gap in predicting future soil health and ecological function. Through shotgun metagenomic sequencing of coastal soils along a salinity gradient, we show contrasting eco-evolutionary directions of soil bacteria and archaea that manifest in changes to genome size and the functional potential of the soil microbiome. In salt environments with high carbon requirements, bacteria exhibit reduced genome sizes associated with a depletion of metabolic genes, while archaea display larger genomes and enrichment of salt-resistance, metabolic, and carbon-acquisition genes. This suggests that bacteria conserve energy through genome streamlining when facing salt stress, while archaea invest in carbon-acquisition pathways to broaden their resource usage. These findings suggest divergent directions in eco-evolutionary adaptations to soil saline stress amongst microbial clades and serve as a foundation for understanding the response of soil microbiomes to escalating climate change. From metagenomic sequencing of coastal soils along a salinity gradient, this study shows contrasting eco-evolutionary strategies for relieving carbon limitation under salt stress in bacteria and archaea. The findings suggest that bacteria conserve energy through genome streamlining when facing salt stress, while archaea invest in carbon-acquisition pathways to broaden their resource usage.

Humans, and many other species, are host to diverse symbionts. It is often suggested that the mutual benefits of host-microbe relationships can alone explain cooperative evolution. Here, we evaluate this hypothesis with evolutionary modelling. Our model predicts that mutual benefits are insufficient to drive cooperation in systems like the human microbiome, because of competition between symbionts. However, cooperation can emerge if hosts can exert control over symbionts, so long as there are constraints that limit symbiont counter evolution. We test our model with genomic data of two bacterial traits monitored by animal immune systems. In both cases, bacteria have evolved as predicted under host control, tending to lose flagella and maintain butyrate production when host-associated. Moreover, an analysis of bacteria that retain flagella supports the evolution of host control, via toll-like receptor 5, which limits symbiont counter evolution. Our work puts host control mechanisms, including the immune system, at the centre of microbiome evolution. Humans, and many other species, carry a large set of beneficial microbes. Here, the authors present new theory and data to argue that these vital relationships only work when hosts can control their microbiome and suppress wayward symbionts.

Adaptive therapy is a dynamic cancer treatment protocol that updates (or \"adapts\") treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: 1) the necessary components of mathematical models of adaptive therapy 2) design and validation of dosing protocols, and 3) challenges and opportunities in clinical translation.

Hybridization has been recognized to play important roles in evolution, however studies of the genetic consequence are still lagging behind in vertebrates due to the lack of appropriate experimental systems. Fish of the genus Xiphophorus are proposed to have evolved with multiple ancient and ongoing hybridization events. They have served as an informative research model in evolutionary biology and in biomedical research on human disease for more than a century. Here, we provide the complete genomic resource including annotations for all described 26 Xiphophorus species and three undescribed taxa and resolve all uncertain phylogenetic relationships. We investigate the molecular evolution of genes related to cancers such as melanoma and for the genetic control of puberty timing, focusing on genes that are predicted to be involved in pre-and postzygotic isolation and thus affect hybridization. We discovered dramatic size-variation of some gene families. These persisted despite reticulate evolution, rapid speciation and short divergence time. Finally, we clarify the hybridization history in the entire genus settling disputed hybridization history of two Southern swordtails. Our comparative genomic analyses revealed hybridization ancestries that are manifested in the mosaic fused genomes and show that hybridization often preceded speciation. The phylogenetic and hybridization history of Xiphophorus fish remains contentious, despite their long-standing role as models in evolutionary biology and human disease research. This study presents a complete genome resource that resolves the previously conflicting phylogeny and evolutionary history of the group, revealing that hybridizations preceded speciation.