Brachypodium is a powerful model system for investigating grass genome evolution, yet genomic resources remain concentrated in the three annual species, whereas perennial species are less sampled. Here, we present chromosome-level assemblies for two perennial species, Brachypodium mexicanum and B. arbuscula, which represent the earliest-diverging lineages of the genus and the earliest-diverging lineage of the core perennial clade, respectively. Synteny-based phylogenomics indicate that B. mexicanum is a meso-allotetraploid composed of two closely related but temporally distinct x=10 subgenomes, here designed as P and U, each carrying subgenome-specific chromosome rearrangements. We further show that the unusually large B. mexicanum genome, in contrast to the reduced genomes of most other Brachypodium species, is primarily due to transposable elements distributed across all chromosomal regions. By contrast, the diploid genome of the earliest-diverging core perennial, B. arbuscula, contains few transposable elements, whereas the most recent diverged diploid perennial B. sylvaticum shows evidence of a secondary TEs proliferation. Comparisons of lineage-specific and functionally enriched orthogroups among B. mexicanum, core perennial species and annual species suggest that ancestral hybridization between annual and perennial lineages may have contributed to the origin of allotetraploid B. mexicanum. These assemblies provide a framework for testing how polyploidy, descending dysploidy, transposable-element turnover, and life-history evolution jointly shaped genome architecture in Brachypodium.

Pleiotropy, the phenomenon where a single mutation influences multiple phenotypic traits, creates genetic correlations that can constrain evolutionary trajectories. Yet genetic correlations differ in their persistence: some remain stable over long evolutionary timescales, whereas others change rapidly across generations or environments. One explanation is that similar values of genetic correlation, rG, can arise from different pleiotropic architectures: broadly aligned effects across many loci, or disproportionate covariance contributions from a few large effect loci. Motivated by the distinction between vertical and horizontal pleiotropy, here, we develop a bivariate marker effect framework for recombinant mapping populations that separates candidate large covariance contributors from the polygenic background correlation, rD. We define rD as the correlation among marker effects after trimming markers with unusually large covariance contributions. rD is a trait-pair summary of how consistently small and moderate effect markers align across the genome; high rD is expected when many perturbations propagate through shared developmental, physiological, causal, or geometric structure. Applying this framework to high-dimensional yeast single-cell morphology, we show that trait pairs with similar rG can differ substantially in rD, and that a small number of candidate outlier regions can strongly influence some marker effect correlations. We then test whether rD predicts the environmental stability of genetic correlations under geldanamycin-mediated Hsp90 perturbation. Trait pairs with stronger rD show smaller absolute changes in rG. These results suggest that genetic correlations supported by a strong polygenic marker effect background are more environmentally stable than correlations shaped primarily by a few large covariance contributors.

Genome analyses reveal that gene duplication in eukaryotes is pervasive, providing a primary source for the emergence of new genes. Nevertheless, the mechanisms influencing the probability of early duplicate retention and the emergence of functional biases,such as the enrichment of tandem duplicates in environmental responses, remain unclear. Here, to elucidate the mechanisms and factors determining gene retention, we study a frequently overlooked molecular feature-within-line expression variation, termed expression variability. We demonstrate that, on average, genes with duplicates exhibit higher expression variability than singletons. Furthermore, small-scale duplications (SSDs) and whole-genome duplications (WGDs) display contrasting functional outcomes and time-dependent profiles in expression variability. These findings suggest a potential overarching mechanism that facilitates gene expression divergence, functional gains of environmental responses, and duplicate retention following SSDs.

Phenology and the timing of development are often under selection. However, the relative contributions of genotype, environment, and prior developmental transitions to variance in the phenology of wild plants is largely unknown. Individual components of phenology (e.g., germination) might be loosely related with the timing of maturation due to variation in prior developmental transitions. Given widespread evidence that genetic variation in life history is adaptive, we investigated to what degree experimentally measured genetic variation in Arabidopsis phenology predicts phenology of plants in the wild. As a proxy of phenology, we obtained collection dates from nature of 227 naturally inbred Arabidopsis thaliana accessions from across Eurasia. We compared this phenology in nature with experimental data on the descendant inbred lines that we synthesized from two new and 155 published controlled experiments. We tested whether the genetic variation in flowering and germination timing from experiments predicted the phenology of the same lines in nature. We found that genetic variation in phenology from controlled experiments significantly predicts day of collection from wild individuals, as a proxy for date of flowering, across Eurasia. However, local variation in collection dates within a region was not explained by genetic variance in phenology in experiments, suggesting high plasticity across small-scale environmental gradients or complex interactions between the timing of different developmental transitions. While experiments have shown phenology is under selection, understanding the subtle environmental and stochastic effects on phenology may help to clarify the heritability and evolution of phenological traits in nature.

The extraordinary diversity and adaptive fit of organisms to their environment depends fundamentally on the availability of variation. While most population genetic frameworks assume that random mutations produce isotropic phenotypic variation, the distribution of variation available to natural selection is more restricted, as the distribution of phenotypic variation is affected by a range of factors in developmental systems. Here, we revisit the concept of developmental bias - the observation that the generation of phenotypic variation is biased due to the structure, character, composition, or dynamics of the developmental system - and argue that a more rigorous investigation into the role of developmental bias in the genotype-to-phenotype map will produce fundamental insights into evolutionary processes, with potentially important consequences on the relation between micro- and macro-evolution. We discuss the hierarchical relationships between different types of variational biases, including mutation bias and developmental bias, and their roles in shaping the realized phenotypic space. Furthermore, we highlight the challenges in studying variational bias and propose potential approaches to identify developmental bias using modern tools.

Projected atmospheric CO2 rise, coupled with intensification of drought in many regions, impacts the physiology of C3 plants beyond photosynthesis and carbon metabolism. The interaction between CO2 and drought affects the concentrations of many nutrients in crops, often resulting in excessive agrochemical use and less nutritious food production. To address these challenges, we investigated nutrient dynamics in Brachypodium distachyon, a model crop plant, under ambient and elevated CO2, factorially combined with well-watered or drought treatments. Integrative analyses of plant physiology, transcriptomics, and machine learning-enabled non-targeted metabolomics revealed that plant elemental composition and metabolomic responses to elevated CO2 strongly depend on water availability and differ between shoots and roots. Elevated CO2 and drought significantly impaired nitrogen status, with root nitrate uptake being more negatively affected than ammonium uptake. However, elevated CO2 increased iron partitioning in shoots under drought, potentially driven by enhanced carbon availability facilitating chelator synthesis for iron translocation. The high accumulation of sphingolipids in roots under combined stresses suggests a protective role against ionome imbalances. These findings highlight how climate stressors interact to shape plant nutrient dynamics, providing insights that can guide agricultural practices and breeding strategies to optimize nutrient management and foster sustainable agriculture under changing climate.

Plant domestication may create trade-offs between growth and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. Trade-offs can also occur when traits optimized for favorable conditions perform less efficiently under stress. Deciphering these mechanisms is crucial for maintaining growth in changing environments. We examine one key aspect of vegetative growth, leaf elongation, in six species of grasses. We use a machine learning-enabled pipeline to extract cell dimensions and positions from leaf microscope images to study cell kinematics. We find that domesticated plants generally have longer leaves, larger division zones, and higher cell production rates. While no clear trade-off is observed between domestication and drought response in final leaf length, a trade-off occurs in development; wild species exhibit a smaller decrease in the elongation zone size under drought compared with domesticated species. This pattern points to compensatory mechanisms, such as extended elongation duration or increased cell production, mitigating drought effects in domesticated plants. These nuanced trade-offs associated with domestication highlight the importance of robustly phenotyping developmental and physiological traits, possibly informing breeding strategies to enhance crop resilience in future climates.

Climate change is driving earlier spring leaf-out across temperate regions, but the genetic mechanisms and environmental interactions underlying this variability are poorly understood. We conducted a controlled growth chamber experiment using excised northern red oak (Quercus rubra ) branches, testing the influence of temperature and photoperiod on leaf development. Two genotypes of red oak were exposed to four different warming and daylength treatments, and gene expression was analyzed across stages of bud development. Results revealed significant phenotypic differences between genotypes and across treatments, confirming that leaf-out timing is both genetically determined and environmentally responsive. Our analysis identified several key genes involved in dormancy break and photoperiod sensitivity, including orthologs to genes identified in Populus species, suggesting conserved pathways across tree species. These genes were differentially expressed in response to environmental factors, highlighting the polygenic nature of phenological timing. Notably, modules associated with temperature and photoperiod showed overlap with dormancy break pathways, indicating shared regulatory networks. This study provides a foundational dataset for understanding phenology in red oak and offers insights into how genetic and environmental factors shape leaf development in temperate trees, setting the stage for further functional genomic research.

Gene expression is a quantitative trait under the control of genetic and environmental factors and their interaction, so-called genotype and environment (G × E). Understanding the mechanisms driving G × E is fundamental for ensuring stable crop performance across environments and for predicting the response of natural populations to climate change. Gene expression is regulated through complex molecular networks, yet the interactions between genotype and environment in gene regulation are rarely considered, particularly at the genome scale. Current frameworks and experimental designs often lack power to explicitly test network rewiring or to systematically compare regulatory networks. Here, we leverage a highly replicated RNA-sequencing dataset to model genome-scale gene expression variation between two natural accessions of the model grass Brachypodium distachyon and their response to soil drying. We first identified genotypic, environmental, and G × E effects on physiological, metabolic, and gene expression traits. We identify patterns of conservation-or variation-in gene coexpression networks and link these coexpression features to physiological traits. We further develop predictions of gene-gene interactions using causal inference and screen for interactions specific to-or with higher affinity in-a single genotype, treatment, or their interaction, G × E. Our analyses identify variation in candidate gene regulatory networks that may shape the evolution of environmental response in B. distachyon. We highlight the environmentally dependent regulatory control of several metabolic traits shown previously to play a role in drought acclimation. The framework presented here provides a scalable approach for more complex comparisons, particularly with the growing availability of large datasets from technologies such as single-cell transcriptomics.

Rising atmospheric CO2 and intensified drought are reshaping nutrient dynamics in C-3 plants, with implications for ecosystem function and food security. To investigate how these stressors jointly affect nutrient homeostasis, we examined Brachypodium distachyon, a model for C-3 cereal grasses, grown under ambient (400 ppm) or elevated (800 ppm) CO2, factorially combined with well-watered or drought treatments. Integrative analyses of physiology, ionomics, transcriptomics, and non-targeted metabolomics revealed that plant elemental composition and metabolomic responses to elevated CO2 strongly depend on water availability. The CO2 fertilization effect on biomass was abolished under drought, coinciding with reduced nitrogen content, altered carbon-to-nitrogen ratios, and nutrient-specific translocation changes. These shifts were partly linked to reduced stomatal conductance and transpiration but also reflected active regulation. Nitrogen status declined, accompanied by greater repression of root nitrate transporter genes than ammonium transporters and increased accumulation of the polyamine spermidine. Under combined stress, foliar iron increased alongside elevated expression of chelator synthesis genes and accumulation of S-adenosylmethionine, suggesting enhanced support for Fe homeostasis. Lipid metabolism was reprogrammed, notably via root sphingolipid accumulation, potentially contributing to ionome stabilization. Together, these findings highlight coordinated molecular and metabolic strategies governing nutrient regulation under interacting climate-related stressors.

Microbiome breeding through host-mediated selection is a technique to artificially select for microbiomes conferring beneficial properties to plants. Using a systematic selection protocol that maximises the heritability of microbiome effects, transmission fidelity, and microbiome stability through multiple selection cycles, we previously developed root-associated microbial communities conferring sodium and aluminium tolerance to Brachypodium distachyon, a model for cereal crops. Here, we explore the physiological mechanisms underlying our selected microbiomes’ effect on plant fitness and analyse how our selection protocol shaped the composition and structure of these microbiomes. We analysed the effects of our selected microbiomes on plant fitness and tissue-nutrient concentration, then used 16S rRNA amplicon sequencing to examine microbial community composition and co-occurrence network patterns. Our sodium-selected microbiomes reduced leaf sodium concentration by 50

Gene expression is a quantitative trait under the control of genetic and environmental factors and their interaction, so-called GxE. Understanding the mechanisms driving GxE is fundamental for ensuring stable crop performance across environments, and for predicting the response of natural populations to climate change. Gene expression is regulated through complex molecular networks, however environmental and genotypic effects on genome-wide regulatory networks are rarely considered. In this study, we model genome-scale gene expression variation between two natural accessions of the model grass Brachypodium distachyon and their response to soil drying. We identified genotypic, environmental, and GxE responses in physiological, metabolic, and gene expression traits. We then identified gene regulation conservation and variation among conditions and genotypes, simplified as co-expression clusters found unique in or conserved across library types. Putative gene regulatory interactions are inferred as network edges with a graphical modelling approach, resulting in hypotheses about gene-gene interactions specific to -- or with higher affinity in -- one genotype, one environmental treatment, or in one genotype under treatment. We further find that some gene-gene interactions are conserved across conditions such differential expression is apparently transmitted to the target gene. These variably detected edges cluster together in co-expression modules, suggestive of different constraints or selection strength acting on specific pathways. We further applied our graphical modeling approach to identify putative, environmentally dependent regulatory mechanisms of leaf glucose content as an exemplar metabolite. Our study highlights an approach to identify variable features of gene regulatory networks and thereby identify key components for later genomic intervention to elucidate function or modulate environmental response. Our results also suggest possible targets of evolutionary change in gene regulatory networks associated with environmental plasticity. ### Competing Interest Statement The authors have declared no competing interest.

Organisms experience a constantly changing environment and must adjust their development to maximize fitness. These “life histories” are fantastically diverse and have fascinated biologists for decades. Recent work published in Cell reveals the complex genetic mechanisms that drive life-history variation within and among species in the Brassicaceae plant family.

Genome analyses reveal that gene duplication in eukaryotes is pervasive, providing a primary source for the emergence of new genes. The mechanisms governing the retention of duplicated genes, particularly in the early stages post-duplication, remains ambiguous. Patterns of divergence between duplicated genes vary, leading to biases in the functions of genes retained following duplication. For example, genes that arise from tandem duplication tend to be involved in environmental responses. However, the mechanisms that cause such functional bias remain elusive. Here, to better understand the mechanisms and factors promoting gene retention of certain functional categories, we study a frequently overlooked aspect \---|expression variability\---|as measured by within-line expression variation. We find that, on average, genes with duplicates exhibit higher expression variability than singletons. We further find that patterns of duplicate retention are likely driven by the immediate increase in expression variability following small-scale duplications (SSDs) and prolonged evolutionary processes after whole-genome duplications (WGDs). These findings suggest a potential overarching mechanism that facilitates gene expression divergence and, consequently, promotes gene retention. ### Competing Interest Statement The authors have declared no competing interest.

The extraordinary diversity and adaptive fit of organisms to their environment depends fundamentally on the availability of variation. While most population genetic frameworks assume that random mutations produce isotropic phenotypic variation, the distribution of variation available to natural selection is more restricted, as the distribution of phenotypic variation is affected by a range of factors in developmental systems. Here, we revisit the concept of developmental bias - the observation that the generation of phenotypic variation is biased due to the structure, character, composition, or dynamics of the developmental system - and argue that a more rigorous investigation into the role of developmental bias in the genotype-to-phenotype map will produce fundamental insights into evolutionary processes, with potentially important consequences on the relation between micro- and macro-evolution. We discuss the hierarchical relationships between different types of variational biases, including mutation bias and developmental bias, and their roles in shaping the realized phenotypic space. Furthermore, we highlight the challenges in studying variational bias and propose potential approaches to identify developmental bias using modern tools.

Scientists must have an integrative understanding of ecology and evolution across spatial and temporal scales to predict how species will respond to global change. Although comprehensively investigating these processes in nature is challenging, the infrastructure and data from long-term ecological research networks can support cross-disciplinary investigations. We propose using these networks to advance our understanding of fundamental evolutionary processes and responses to global change. For ecologists, we outline how long-term ecological experiments can be expanded for evolutionary inquiry, and for evolutionary biologists, we illustrate how observed long-term ecological patterns may motivate new evolutionary questions. We advocate for collaborative, multi-site investigations and discuss barriers to conducting evolutionary work at network sites. Ultimately, these networks offer valuable information and opportunities to improve predictions of species' responses to global change.

Plant domestication is thought to create trade-offs between high yield and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. However, trade-offs can also occur when traits (such as yield in agriculture) optimized for favorable conditions perform less efficiently in stressful conditions. Deciphering the mechanisms driving these trade-offs is crucial for maintaining yield in changing environments. We examine leaf growth, a key trait influencing carbon assimilation, in eight species of grasses. We use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics, finding that domesticated plants generally have longer leaves, larger division zones and higher cell production rates. We found no clear evidence of trade-off between domestication and drought response in final leaf length. However, a trade-off is observed in development as wild species exhibited a smaller decrease in elongation zone size under drought than their domesticated counterparts. These nuanced trade-offs associated with domestication highlight the importance of examining physiological traits and mechanisms in greater detail, possibly informing breeding strategies to enhance crop resilience in the face of climate change. ### Competing Interest Statement The authors have declared no competing interest.

Genetic correlation represents an important class of evolutionary constraint, which is itself evolvable. Empirical studies have found mixed results on whether genetic correlations change rapidly or slowly. This uncertainty challenges our ability to predict the outcome of selection. Despite the tremendous diversity and complexity of life forms, there are certain forms of life that are never observed. This might be because of developmental biases that restrict how organisms can evolve, or because they have low fitness in any environment yet available on Earth. Given that both developmental bias and selection can generate similar phenotypes, it is difficult to distinguish between the two causes of evolutionary stasis among related taxa. For example, remarkably invariant traits are observed spanning million years, such as wing shape in Drosophila wherein qualitative differences are rare within genera. Here, we ask whether the absence of certain combinations of traits, as indicated by genetic correlation, reflects developmental bias. However, much confusion and controversy remain over definitions of developmental bias, and probing it is challenging. We thus present a novel approach aiming to dissect genetic correlations and estimate the relative contribution of developmental bias in maintaining genetic correlations. We do so by leveraging a common but under-utilized type of data: genetic crosses. Through empirical analyses, we find that our approach can distinguish whether genetically correlated traits are developmentally constrained to covary. We also find that our developmental bias metric is an indicator of genetic correlation stability across conditions. Our framework presents a feasible way to dissect the mechanisms underlying genetic correlation and pleiotropy. ### Competing Interest Statement The authors have declared no competing interest. Data and code have been deposited in Github ()

How do selection and standing genetic variation shape population divergence across landscapes? Henry and Stinchcombe estimated selection gradients on traits in the ivy-leaved morning glory (Ipomoea hederacea) in the field and compared them with the G-matrix and population divergence for four populations in North America. The authors show that population divergence and genetic covariances are largely unaligned with the selection gradient at the species' range edge. These findings raise the question of whether limited evolvability or multivariate genetic variation of populations at range edges prevent species from range expansion, which is important for understanding the role of genetic constraint in population divergence and predicting local adaptation in the face of climate change.