Summary (1) Rationale Quantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) Methods We computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key results ECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusion The ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.

Physiologically relevant drought stress is difficult to apply consistently, and the heterogeneity in experimental design, growth conditions, and sampling schemes makes it challenging to compare water deficit studies in plants. Here, we reanalyzed hundreds of drought gene expression experiments across diverse model and crop species and quantified the variability across studies. We found that drought studies are surprisingly incomparable, even when accounting for differences in genotype, environment, drought severity, and method of drying. Many studies, including most Arabidopsis (Arabidopsis thaliana) work, lack high-quality phenotypic and physiological datasets to accompany gene expression, making it challenging to assess the severity or consistency of water deficit stress events. To help address this, we developed supervised learning classifiers that can distinguish RNAseq samples that likely experienced drought stress. While not a substitute for direct measurements, these classifiers may aid in interpreting existing datasets and assessing drought severity in studies lacking physiological metadata. Together, our analyses highlight the importance of paired physiological data to quantify stress severity for reproducibility and future data analyses.

Water is essential for life on Earth, yet some plants can survive the near-complete loss of cellular water, a trait known as desiccation tolerance. Although rare in vegetative tissues of vascular plants, desiccation tolerance is scattered across the plant kingdom, where it likely enabled early terrestrialization and supports survival in environments with prolonged or intermittent drying. Large-scale multi-omics, comparative, and functional studies over the past decade suggest that desiccation tolerance evolved primarily through the repurposing of ancient stress-resilience mechanisms, giving rise to convergent phenotypes where cells are protected and stabilized during extreme dehydration. In photosynthetic lineages, desiccation is further compounded by light-dependent photooxidative stress, requiring specialized mechanisms that safeguard the photosynthetic apparatus during both drying and recovery. Here, we synthesize our current understanding of the evolutionary origins and core mechanisms underlying plant desiccation tolerance and place them within a broader comparative context across kingdoms. We highlight how genome-scale datasets suggest desiccation tolerance is an emergent, systems-level phenotype coordinated across biological scales. Finally, we discuss the translational opportunities and constraints of desiccation tolerance in agriculture, medicine, biotechnology, and related fields.

Understanding and predicting complex traits in plants remains a fundamental challenge due to the emergent nature of most phenotypes and their dependence on genetic, regulatory, and environmental interactions. Accurate prediction of traits and identification of underlying genetic elements has broad applications for plant breeding, systems biology, and biotechnology. Here, we tested if multi-omic datasets could improve predictive accuracy of 129 diverse maize phenotypes across nine environments using genomic markers, field based transcriptomic data from two locations, and drone-derived phenomic data of vegetative indices. We trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs. Multi-omics models consistently outperformed single-omics models for most traits, with genomic and transcriptomic inputs contributing distinct biological features. Phenomic features alone yielded the lowest predictive power but improved predictions for specific trait categories like root architecture. Transcriptomic datasets enabled cross-environment prediction, demonstrating that gene expression patterns from one field site could accurately predict traits measured in another. Environment-specific expression of benchmark flowering time genes highlighted the value of transcriptomics in capturing genotype-by-environment (G×E) interactions not detectable through genomic data alone. These findings demonstrate that integrating transcriptomic and phenomic data with genotypes enhances trait prediction, improves model generalizability across environments, and provides deeper insight into the genetic and regulatory architecture of agriculturally important traits in maize.

In recent years, a deluge of big and diverse datasets from hundreds of plant species, coupled with spectacular innovations in artificial intelligence (AI) and generative AI (GenAI), has altered the landscape of plant science. These developments are increasingly democratizing the field, reducing the entry barriers to complex data analysis and enabling a new wave of innovative research while introducing new challenges. Therefore, in this era, it is critical that we train the next generation of plant scientists to be AI-literate, ie, not only proficient in using AI but also vigilant about its pitfalls and biases. In this perspective, we call for six strategic shifts necessary for training the next generation of plant scientists. We argue that while maintaining a core focus on subject expertise, educators should simultaneously emphasize development of new AI-forward pedagogical and evaluation frameworks that reward interdisciplinary and critical thinking, human-driven knowledge synthesis, self-directed learning, and conceptual understanding of workflows. For effective critique and sound interpretations based on biological reality, plant scientists must be explicitly trained in recognizing biases underlying GenAI models. Finally, we highlight the structural barriers hindering the equitable and ethical use of GenAI, where awareness and resolution are critical for sustainable growth of the field. Through the above conceptual framework and numerous plant-science-focused illustrative activities, examples, and resources meant for students and educators alike, this Perspective defines high-level emphasis areas for GenAI-enabled scientific training, aimed at creating a more effective, engaged, and adaptive community of plant scientists.

Abiotic stresses, including drought, salt, heat, cold, flooding, and low nitrogen, are harmful to agriculture and increasing in frequency due to climate change. Plants can experience multiple stresses within a single season, which elicit shared or overlapping responses. We searched for core stress-responsive genes in maize across stressors through meta-analysis of public RNA-seq data. Using nearly 1,900 RNA-seq samples with both set operations and random forest classification, we identified a core set of 744 stress-responsive genes across the six stressors. These are enriched in transcription factors, including the stress-responsive families AP2/ERF-ERF, NAC, bZIP, HSF, and C2C2-CO-like. Co-expression network analysis demonstrated that core transcription factors are co-expressed with stress-specific genes, supporting their role in regulating both generalized and stress-specific responses. This provides a valuable resource for understanding stress tolerance mechanisms and guiding future efforts to enhance maize resilience under climate change.

Global crop production faces increasing threats from the rise in frequency, duration, and intensity of drought and heat stress events due to climate change. Most staple food crops, including wheat, rice, soybean, and corn that provide over half of the world's caloric intake, are not well adapted to withstand heat or drought. Efforts to breed or engineer stress-tolerant crops have had limited success due to the complexity of tolerance mechanisms and the variability of agricultural environments. Effective solutions require a shift towards fundamental research that incorporates realistic agricultural settings and focuses on practical outcomes for farmers. This review explores the genetic and environmental factors affecting heat and drought tolerance in major crops, examines the physiological and molecular mechanisms underlying these stress responses, and evaluates the limitations of current breeding programs and models. It also discusses emerging technologies and approaches that could enhance crop resilience, such as synthetic biology, advanced breeding techniques, and high-throughput phenotyping. Finally, this review emphasizes the need for interdisciplinary research and collaboration with stakeholders to translate fundamental research into practical agricultural solutions.

Myrothamnus flabellifolia is a dioecious resurrection plant endemic to southern Africa that has become an important model for understanding desiccation tolerance. Despite its ecological and medicinal significance, genomic and transcriptomic resources for the species are limited. We generated a chromosome-level, haplotype-resolved reference genome assembly and annotation for M. flabellifolia and conducted transcriptomic profiling across a natural dehydration-rehydration time course in the field. Genome architecture and sex determination were characterized, and co-expression network and cis-regulatory element (CRE) enrichment analyses were used to investigate dynamic responses to desiccation. The 1.28-Gb genome exhibits unusually consistent chromatin architecture with unique chromosome organization across highly divergent haplotypes. We identified an XY sexual system with a small sex-determining region on Chromosome 8. Transcriptomic responses varied with dehydration severity, pointing to early suppression of growth, progressive activation of protective mechanisms, and subsequent return to homeostasis upon rehydration. Late embryogenesis abundant and early light-induced protein transcripts were dynamically regulated and showed enrichment of abscisic acid and stress-responsive CREs pointing toward conserved responses. Together, this study provides foundational resources for understanding the genomic architecture and reproductive biology of M. flabellifolia and offers new insights into the mechanisms of desiccation tolerance.

KEY MESSAGE:NDR1 and AHA5 coordinate drought stress and immune responses via stomatal regulation, uncovering a molecular link between abiotic and biotic stress adaptation in Arabidopsis. Plant stress responses have overlapping molecular and physiological signatures. Not surprisingly, many of these are also shared with numerous other processes, including growth and development, as well as abiotic and biotic signaling. NON-RACE-SPECIFIC DISEASE RESISTANCE1 (NDR1) is a key component of plant immune signaling, required for defense against the bacterial pathogen Pseudomonas syringae. In this study, we have identified that NDR1 contributes to stomatal-based processes following exposure to biotic (flg22 (flagellin peptide) and elf26 (EF-Tu-related elicitor)) and abiotic (ABA, drought, etc.) elicitors. Interestingly, we found that NDR1 is part of a signaling cascade that confers tolerance to water loss-a required component of drought stress responses in plants, a role that couples stress signaling in an abscisic acid-dependent manner. As a definition of its broader connectivity to this response, we identified that NDR1 physically associates with the PM-localized H+-ATPases AHA1, AHA2, and AHA5, an association that is required for proper regulation of H+-ATPase activity and stomatal guard cell dynamics. Using a comprehensive whole-transcriptome analysis, we further show that NDR1 is required for multiple, genetically overlapping physiological processes, including response to water withholding. In total, we demonstrate that NDR1 functions in signaling processes associated with both biotic and abiotic stress response pathways, a function we hypothesize illustrates NDR1's role in the maintenance of cellular homeostasis during stress response activation.

Tef [Eragrostis tef (Zucc.) Trotter] is the major staple crop for millions of people in Ethiopia and Eritrea and is believed to have been domesticated several thousand years ago. Tef has the smallest grains of all the cereals, which directly impacts its productivity and presents numerous challenges to its cultivation. In this study, we assessed the natural variation in seed size of 189 tef and 11 accessions of its wild progenitor Indian lovegrass (Eragrostis pilosa (L.) P. Beauv.) and explored the mineral distribution of representative accessions. Our findings revealed significant natural variation in seed size and mineral concentration among both the tef and E. pilosa accessions. We observed significant variation in seed length, seed width, and seed area among the accessions of both Eragrostis spp. we analyzed. Using representative accessions of both species, we also found significant variation in 1000-grain weight. The observed variation in seed size attributes prompted us to use comparative genomics to identify seed size regulating genes based on the well-studied and closely related monocot cereal rice [Oryza sativa (L.)]. Using this approach, we identified putative orthologous genes in the tef genome that belong to a number of key pathways known to regulate seed size in rice. Phylogenetic analysis of putative tef orthologs of ubiquitin-proteasome, G-protein, MAPK, and brassinosteroid (BR)-family genes indicate significant similarity to seed size regulating genes in rice and other cereals. Because tef is known to be more nutrient-dense than other more common cereals such as rice, wheat, and maize, we also studied the mineral concentration of selected accessions using ICP-OES and explored their distribution within the seeds using synchrotron-based X-ray fluorescence (SXRF) microscopy. The findings showed significant variation in seed mineral concentration and mineral distribution among the selected accessions of both Eragrostis spp. This study highlights the natural variation in seed size attributes, mineral concentration, and distribution, while establishing the basis for understanding the genetic mechanisms regulating these traits. We hope our findings will lead to a better understanding of the evolution of tef at the genetic level and for the development of elite tef cultivars to improve seed size, yield, and quality of the grains.

Desiccation tolerance has evolved repeatedly in plants as an adaptation to survive extreme environments. Plants use similar biophysical and cellular mechanisms to survive life without water, but convergence at the molecular, gene and regulatory levels remains to be tested. Here we explore the evolutionary mechanisms underlying the recurrent evolution of desiccation tolerance across grasses. We observed substantial convergence in gene duplication and expression patterns associated with desiccation. Syntenic genes of shared origin are activated across species, indicative of parallel evolution. In other cases, similar metabolic pathways are induced but using different gene sets, pointing towards phenotypic convergence. Species-specific mechanisms supplement these shared core mechanisms, underlining the complexity and diversity of evolutionary adaptations to drought. Our findings provide insight into the evolutionary processes driving desiccation tolerance and highlight the roles of parallel and convergent evolution in response to environmental challenges. Marks et al. explore the repeated evolution of desiccation tolerance in grasses. Their analysis of diverse resurrection grasses reveals significant genetic convergence and parallel evolution, suggesting a shared foundation for adapting to extreme drought.

Desiccation tolerance evolved recurrently across diverse plant lineages to enable survival in water-limited conditions. Many resurrection plants are polyploid, and several groups have hypothesized that polyploidy contributed to the evolution of desiccation tolerance. However, due to the vast phylogenetic distance between resurrection plant lineages, the rarity of desiccation tolerance, and the prevalence of polyploidy in plants, this hypothesis has been difficult to test. Here, we surveyed natural variation in morphological, reproductive, and desiccation tolerance traits across several cytotypes of a single species to test for links between polyploidy and increased resilience. We sampled multiple natural populations of the resurrection grass Microchloa caffra across an environmental gradient ranging from mesic to xeric in South Africa. We describe two distinct ecotypes of M. caffra that occupy different extremes of the environmental gradient and exhibit consistent differences in ploidy, morphological, reproductive, and desiccation tolerance traits in both field and common growth conditions. Interestingly, plants with more polyploid genomes exhibited consistently higher recovery from desiccation, were less reproductive, and were larger than plants with smaller genomes and lower ploidy. These data indicate that selective pressures in increasingly xeric sites may play a role in maintaining and increasing desiccation tolerance and are mediated by changes in ploidy.

Physiologically relevant drought stress is difficult to apply consistently, and the heterogeneity in experimental design, growth conditions, and sampling schemes make it challenging to compare water deficit studies in plants. Here, we re-analyzed hundreds of drought gene expression experiments across diverse model and crop species and quantified the variability across studies. We found that drought studies are surprisingly uncomparable, even when accounting for differences in genotype, environment, drought severity, and method of drying. Many studies, including most Arabidopsis work, lack high-quality phenotypic and physiological datasets to accompany gene expression, making it impossible to assess the severity or in some cases the occurrence of water deficit stress events. From these datasets, we developed supervised learning classifiers that can accurately predict if RNA-seq samples have experienced a physiologically relevant drought stress, and suggest this can be used as a quality control for future studies. Together, our analyses highlight the need for more community standardization, and the importance of paired physiology data to quantify stress severity for reproducibility and future data analyses.

Interdisciplinarity is used to integrate and synthesize new research directions between scientific domains, but it is not the only means by which to generate novelty by bringing diverse perspectives together. Internationality draws upon cultural and linguistic diversity that can potentially impact interdisciplinarity as well. We created an interdisciplinary class originally intended to bridge computational and plant science that eventually became international in scope, including students from the United States and Mexico. We administered a survey over 4 years designed to evaluate student expertise. The first year of the survey included only US students and demonstrated that biology and computational student groups have distinct expertise but can learn the skills of the other group over the course of a semester. Modeling of survey responses shows that biological and computational science expertise is equally distributed between US and Mexico student groups, but that nonetheless, these groups can be predicted based on survey responses due to subspecialization within each domain. Unlike interdisciplinarity, differences arising from internationality are mostly static and do not change with educational intervention and include unique skills such as working across languages. We end by discussing a distinct form of interdisciplinarity that arises through internationality and the implications of globalizing research and education efforts.

Desiccation tolerance, or the ability to survive extreme dehydration, has evolved recurrently across the tree of life. While our understanding of the mechanisms underlying desiccation tolerance continues to expand, the compartmentalization of findings by study system impedes progress. Here, we analyzed 5,963 papers related to desiccation and examined model systems, research topics, citation networks, and disciplinary siloing over time. Our results show significant siloing, with plant science dominating the field, and relatively isolated clustering of plants, animal, microbial, and fungal literature. Topic modeling identified 46 distinct research topics, highlighting both commonalities and divergences across the knowledge of desiccation tolerance in different systems. We observed a rich diversity of model desiccation tolerant species within the community, contrasting the single species model for most biology research areas. To address citation gaps, we developed a rule-based algorithm to recommend new invitees to a niche conference, DesWorks, enhancing the integration of diverse research areas. The algorithm, which considers co-citation, co-authorship, research topics, and geographic data, successfully identified candidates with novel expertise that was unrepresented in previous conferences. Our findings underscore the importance of interdisciplinary collaboration in advancing desiccation tolerance research and provide a framework for using bibliometric tools to foster scientific integration. ### Competing Interest Statement The authors have declared no competing interest.

Premise:The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural- or ecological-based model species were rejected, in favor of building knowledge in a species that would facilitate genome-enabled research. Methods:Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. Results:The identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. k-nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. Discussion:Our data-driven results highlight that the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis on Arabidopsis and prioritize plant diversity.

Desiccation tolerance evolved multiple times across the land plants for survival in water-limited habitats. We find that desiccation tolerance evolved convergently in grasses by independent duplication of the same gene family and activation of conserved, ancestral protective pathways.

Sour cherry (Prunus cerasus L.) is a valuable fruit crop in the Rosaceae family and a hybrid between progenitors closely related to extant Prunus fruticosa (ground cherry) and Prunus avium (sweet cherry). Here we report a chromosome-scale genome assembly for sour cherry cultivar Montmorency, the predominant cultivar grown in the USA. We also generated a draft assembly of P. fruticosa to use alongside a published P. avium sequence for syntelog-based subgenome assignments for 'Montmorency' and provide compelling evidence P. fruticosa is also an allotetraploid. Using hierarchal k-mer clustering and phylogenomics, we show 'Montmorency' is trigenomic, containing two distinct subgenomes inherited from a P. fruticosa-like ancestor (A and A') and two copies of the same subgenome inherited from a P. avium-like ancestor (BB). The genome composition of 'Montmorency' is AA'BB and little-to-no recombination has occurred between progenitor subgenomes (A/A' and B). In Prunus, two known classes of genes are important to breeding strategies: the self-incompatibility loci (S-alleles), which determine compatible crosses, successful fertilization, and fruit set, and the Dormancy Associated MADS-box genes (DAMs), which strongly affect dormancy transitions and flowering time. The S-alleles and DAMs in 'Montmorency' and P. fruticosa were manually annotated and support subgenome assignments. Lastly, the hybridization event 'Montmorency' is descended from was estimated to have occurred less than 1.61 million years ago, making sour cherry a relatively recent allotetraploid. The 'Montmorency' genome highlights the evolutionary complexity of the genus Prunus and will inform future breeding strategies for sour cherry, comparative genomics in the Rosaceae, and questions regarding neopolyploidy.

Drought tolerance is a highly complex trait controlled by numerous interconnected pathways with substantial variation within and across plant species. This complexity makes it difficult to distill individual genetic loci underlying tolerance, and to identify core or conserved drought-responsive pathways. Here, we collected drought physiology and gene expression datasets across diverse genotypes of the C4 cereals sorghum and maize and searched for signatures defining water-deficit responses. Differential gene expression identified few overlapping drought-associated genes across sorghum genotypes, but using a predictive modeling approach, we found a shared core drought response across development, genotype, and stress severity. Our model had similar robustness when applied to datasets in maize, reflecting a conserved drought response between sorghum and maize. The top predictors are enriched in functions associated with various abiotic stress-responsive pathways as well as core cellular functions. These conserved drought response genes were less likely to contain deleterious mutations than other gene sets, suggesting that core drought-responsive genes are under evolutionary and functional constraints. Our findings support a broad evolutionary conservation of drought responses in C4 grasses regardless of innate stress tolerance, which could have important implications for developing climate resilient cereals.

The field of plant science has grown dramatically in the past two decades, but global disparities and systemic inequalities persist. Here, we analyzed ~300,000 papers published over the past two decades to quantify disparities across nations, genders, and taxonomy in the plant science literature. Our analyses reveal striking geographical biases—affluent nations dominate the publishing landscape and vast areas of the globe have virtually no footprint in the literature. Authors in Northern America are cited nearly twice as many times as authors based in Sub-Saharan Africa and Latin America, despite publishing in journals with similar impact factors. Gender imbalances are similarly stark and show remarkably little improvement over time. Some of the most affluent nations have extremely male biased publication records, despite supposed improvements in gender equality. In addition, we find that most studies focus on economically important crop and model species, and a wealth of biodiversity is underrepresented in the literature. Taken together, our analyses reveal a problematic system of publication, with persistent imbalances that poorly capture the global wealth of scientific knowledge and biological diversity. We conclude by highlighting disparities that can be addressed immediately and offer suggestions for long-term solutions to improve equity in the plant sciences.