The shape of plants can be sensitive to dehydration. Here, we focus on herbaceous plants whose petiole cross-section is U-shaped and contains a lot of water. Among a large range of plants showing the same behaviour, we examine Spathiphyllum that exhibits a pronounced, sudden but reversible drooping under dehydration. We show that it is the consequence of a high-amplitude hinge mechanism located at the base of its long petioles, similar to a carpenter’s tape folding under sufficient load. Mechanical testing demonstrated that small-amplitude bending rigidity decreases by only a factor of three during dehydration, due to tissue shrinkage rather than material softening. The petiole is composed of water-rich parenchyma tissue: drooping occurs abruptly at 35%–40% of mass loss, remaining reversible unless dehydration is prolonged. Inspired by these observations, we introduce a biomimetic hinge which offers a programmable bending stiffness and nonlinear behaviour under load, with applications in computing mechanical metamaterials.

Linker histone H1 is crucial for chromatin organization and gene expression in Arabidopsis thaliana, influencing development and stress responses. To explore its role in diurnal gene regulation, we examined H1-deficient plants and found that H1 is essential for maintaining rhythmic gene expression. Genes losing synchronization often contained NAC transcription factor binding sites, indicating H1 may affect their accessibility. Nuclear imaging revealed that H1 subtly modulates nuclear size and chromatin distribution across the photoperiod. Epigenetic analysis showed typical diurnal changes – declines in H3K4me3 and active RNA Pol II in the evening and increases in H3K27me3. In H1 mutants, these patterns persisted but with elevated H3K4me3 and RNA Pol II (Ser2P) levels at night and in the morning. These results suggest that H1 fine-tunes chromatin and transcriptional rhythms, contributing to the temporal coordination of gene activity in response to environmental and developmental signals.

Pangenome graphs are revolutionising evolutionary and population genomics by moving beyond linear reference genomes to represent the full spectrum of sequence diversity within and across species. This review traces the field’s progression from reference-augmented graphs to assembly-based, alignment-first approaches that capture complex structural variation with reduced bias. We examine key strategies for graph construction, genotyping and implementing graph-aware tools in functional genomics, including transcriptomics and epigenomics. While much of the work to date has focused on humans, diverse and structurally complex plant genomes pose unique challenges that require further methodological innovation. Key bottlenecks – including visualisation, scalability and integration with multi-omic data – persist. By outlining trade-offs among current tools and emphasising the need for rigorous evaluation frameworks, we argue that progress will depend on community-driven efforts to unify graph construction, genotyping and interpretation. Despite technical hurdles, pangenome graphs offer a powerful foundation for more inclusive evolutionary and population genomics.

Plants are under constant genetic siege. From viruses and bacteria to transposable elements within their genomes, cells must contend with foreign genetic material. Besides these natural threats, modern biotechnology adds complexity by introducing transgenes to plants. While the integration of such DNA can enhance genetic diversity and confer desirable traits, its foreign origin is typically recognised by the plant cell as a signal of invasion and therefore targeted by the repressive mechanisms. Epigenetic silencing is a central strategy and involves the methylation of DNA and histones. A critical trigger of this silencing is the generation of small interfering RNAs (siRNAs). Although the role of siRNAs in maintaining epigenetic silencing is well established, the initial steps that lead to their production remain incompletely understood. This review discusses the key discoveries on how plant cells recognise foreign nucleic acids and initiate epigenetic silencing, contributing to our broader understanding of genome integrity and defence.

Root water transport has been viewed as primarily limited by the radial component, with the axial pathway considered highly conductive and non-limiting. This is supported by theoretical estimates of axial conductance using the Hagen–Poiseuille equation. However, increasing evidence indicates that actual axial conductance is often nearly an order of magnitude lower than predicted, challenging assumptions that it does not limit water uptake. In this review, we discuss how recent model inversion approaches, guided by root hydraulic conductance measurements, have revealed that water transport can be co-limited by radial and axial conductance. We explore possible explanations for this co-limitation, with particular attention to root topology. Finally, we highlight how drought-induced adjustments in xylem vessel traits can reduce axial conductance, contributing to water conservation and cavitation resistance, thereby supporting drought adaptation strategies. Leveraging this overlooked limitation opens new avenues for breeding crops with improved water-use efficiency and resilience to drought .

Understanding how climate change impacts berry ripening physiology is essential for selecting genotypes that balance sugars and acids under warming conditions. In this context, we used a portable near-infrared spectrometer in the vineyard, to monitor sugar and acid evolution in individual berries from 10 grapevine varieties over two years. Spectra were periodically acquired on the same berries all along ripening, and a subset of these berries was also collected for sugars and organic acids quantification by HPLC, to train partial least square regression models. Prediction models for glucose, fructose, and malic acid concentrations were characterized by validation R2 of 0.71, 0.64, and 0.55, respectively. We further used these models to follow sugar accumulation in individual berries and observed that single berries ripen two times faster than found in samples composed of multiple berries. Our results pave avenues toward precise quantitative approaches on sugar and acid fluxes in berry ripening studies.

Sustainable phosphorus fertilization is a growing challenge in agriculture. Phosphorus is necessary for plant growth, but it is typically only bioavailable in its orthophosphate form. Phosphate fertilizers contribute to environmental damage as they leach into aquatic ecosystems. Therefore, it is imperative to develop new fertilization techniques such as controlled-release small-scale phosphate fertilizers. However, iteratively optimizing various new fertilizers using a comparable method is difficult. Here, we use three-dimensional bioprinting as a high-throughput screening platform to evaluate cellular phosphate uptake of various phosphate sources, including triple super phosphate, diammonium phosphate and struvite, which are composed of different chemistries and scales. As a result, we identified ideal phosphate fertilizer sources for the development of controlled-release phosphate fertilizers. Then, we evaluated whether plant growth and root architecture responded differently to the ideal controlled-release fertilizers. This study demonstrates the utility of this screening platform in developing a controlled-release phosphate fertilizer that effectively provides phosphate to plants at the microparticle scale.

Environmental stresses, such as drought and salt, adversely affect plant growth and crop productivity. While many studies have focused on established components of stress signaling pathways, research on unknown elements remains limited. In this study, we collected RNA sequencing (RNA-Seq) data from Oryza sativa registered in public databases and conducted a meta-analysis integrating multiple studies. We analyzed 105 paired RNA-Seq datasets from resistant or susceptible O. sativa cultivars under salt and drought conditions to identify novel stress-responsive genes with common expression changes across these datasets. A meta-analysis identified 10 genes specifically upregulated in resistant cultivars and 12 specifically upregulated in susceptible cultivars under both drought and salt conditions. Furthermore, by comparing previously identified stress-responsive genes in Arabidopsis thaliana, we explored genes potentially involved in stress response mechanisms that are conserved across plant species. The genes identified in this data-driven study may serve as targets for future research and genome editing.

This perspective paper examines biodesign pedagogy in higher education, focusing on the integration of plant sciences with design and technology. We propose a dual framework for teaching biodesign: nature-driven and socially driven approaches. The nature-driven approach draws inspiration from biological strategies or biotechnologies to address environmental and societal challenges, while the socially driven approach begins with identifying societal problems and exploring biological solutions. Drawing on seven years of teaching experience, we highlight student-led projects that illustrate each approach, including eco-friendly textiles derived from plant fibres and genetically engineered crops designed for sustainable urban agriculture. Our findings underscore the potential of biodesign to bridge STEM and creative disciplines, fostering interdisciplinary collaboration, enhancing scientific literacy and equipping students to tackle complex real-world challenges.

Near-infrared spectra (NIRS) from plant tissues can be used to predict traits owing to their relationship to internal biochemical states, shaped by both environmental and genetic components. Here, we tested the use of NIRS as predictors of budbreak the following year. We measured NIRS on leaf and bud tissue, collected at several dates during the growing season, of 240 dessert apple cultivars in 2021 and 2022. NIRS collected in 2021 and budbreak of 2022 were used to train partial least squares (PLSR) models, then tested using NIRS of 2022 to predict budbreak in 2023. A GWAS using these predictions identified a QTL, previously associated to budbreak in apple, indicating a significant genetic component was maintained in the predictions. Our results demonstrate the potential of NIRS to predict future developmental stages, such as budbreak, by detecting the metabolic states that precede them and could aid in genetic studies of difficult-to-measure traits.

Plants respond to stresses like drought and heat through complex gene regulatory networks (GRNs). To improve resilience, understanding these is crucial, but large-scale GRNs (>100 genes) are difficult to model using ordinary differential equations (ODEs) due to the high number of parameters that have to be estimated. Here we solve this problem by introducing BADDADAN, which uses machine learning to identify gene modules—groups of co-expressed and/or co-regulated genes—and constructs an ODE model that predicts gene module dynamics under stress. By integrating time-series gene expression data with prior co-expression data it finds modules that are both coherent and interpretable. We demonstrate BADDADAN on heat and drought datasets of A. thaliana, modelling over 1,000 genes, recovering known mechanistic insights, and proposing new hypotheses. By combining machine learning with mechanistic modelling, BADDADAN deepens our understanding of stress-related GRNs in plants and potentially other organisms.

Scientific progress relies on reproducibility, replicability, and robustness of research outcomes. After briefly discussing these terms and their significance for reliable scientific discovery, we argue for the importance of investigating robustness of outcomes to experimental protocol variations. We highlight challenges in achieving robust, replicable results in multi-step plant science experiments, using split-root assays in Arabidopsis thaliana as a case study. These experiments are important for unraveling the contributions of local, systemic and long-distance signalling in plant responses and play a central role in nutrient foraging research. The complexity of these experiments allows for extensive variation in protocols. We investigate what variations do or do not result in similar outcomes and provide concrete recommendations for enhancing the replicability and robustness of these and other complex experiments by extending the level of detail in research protocols.

Recent advancements in data science and artificial intelligence have significantly transformed plant sciences, particularly through the integration of image recognition and deep learning technologies. These innovations have profoundly impacted various aspects of plant research, including species identification, disease detection, cellular signaling analysis, and growth monitoring. This review summarizes the latest computational tools and methodologies used in these areas. We emphasize the importance of data acquisition and preprocessing, discussing techniques such as high-resolution imaging and unmanned aerial vehicle (UAV) photography, along with image enhancement methods like cropping and scaling. Additionally, we review feature extraction techniques like colour histograms and texture analysis, which are essential for plant identification and health assessment. Finally, we discuss emerging trends, challenges, and future directions, offering insights into the applications of these technologies in advancing plant science research and practical implementations.

Transformer-based large language models are receiving considerable attention because of their ability to analyse scientific literature. Small language models (SLMs), however, also have potential in this area as they have smaller compute footprints and allow users to keep data in-house. Here, we quantitatively evaluate the ability of SLMs to: (i) score references according to project-specific relevance and (ii) extract and structuring data from unstructured sources (scientific abstracts). By comparing SLMs’ outputs against those of a human on hundreds of abstracts, we found that (i) SLMs can effectively filter literature and extract structured information relatively accurately (error rates as low as 10%), but not with perfect yield (as low as 50% in some cases), (ii) that there are tradeoffs between accuracy, model size and computing requirements and (iii) that clearly written abstracts are needed to support accurate data extraction. We recommend advanced prompt engineering techniques, full-text resources and model distillation as future directions.

Plants exhibit diverse morphological, anatomical and physiological responses to hypoxia stress from soil waterlogging, yet coordination between these responses is not fully understood. Here, we present a mechanistic model to simulate how rooting depth, root aerenchyma -porous tissue arising from localized cell death-, and root barriers to radial oxygen loss (ROL) interact to influence waterlogging survival. Our model revealed an interaction between rooting depth and the relative effectiveness of aerenchyma and ROL barriers for prolonging waterlogging survival. As the formation of shallow roots increases waterlogging survival time, the positive effect of aerenchyma becomes more apparent with increased rooting depth. While ROL barriers further increased survival in combination with aerenchyma in deep-rooted plants, ROL barriers had little positive effect in the absence of aerenchyma. Furthermore, as ROL barriers limit root-to-soil oxygen diffusion bidirectionally, our model revealed optimality in the timing of ROL formation. These findings highlight the importance of coordination between morphological and anatomical responses in waterlogging resilience of plants.

Organ morphogenesis is a complex process and numerous factors must be considered while choosing a method for its quantitative investigation. Few methods facilitate in vivo imaging. These are sequential replica methods combined with scanning electron microscopy and sequential confocal microscopy imaging. The latter is now the most used method to study spatiotemporal changes of organ geometry, growth and involvement of molecular factors in regulating organ development. The time-lapse confocal imaging combined with quantitative analysis of the spatiotemporal pattern of auxin efflux proteins (PIN-FORMED) was used to investigate growth and morphogenesis of Arabidopsis gynoecium and enabled detailed insight into gynoecium development. Yet time-lapse imaging of the gynoecium, concealed within a flower bud, presents challenges in ensuring high-quality data during all the stages of such investigations (sample preparation, maintenance of growing organ during the relatively long time of its development, laser exposure time, etc.). Analysis of vast quantitative data was facilitated by MorphoGraphX.

Yield is impacted by the environmental conditions that plants are exposed to. Controlled environmental agriculture provides growers with an opportunity to fine-tune environmental conditions for optimising yield and crop quality. However, space and time constraints will limit the number of experimental conditions that can be tested, which will, in turn, limit the resolution to which environmental conditions can be optimised. Here we present an innovative experimental approach that utilises the existing heterogeneity in light quantity and quality across a vertical farm to evaluate hundreds of environmental conditions concurrently. Using an observational study design, we identify features in light quality that are most predictive of biomass in different kinds of microgreens (kale, radish and sunflower) that may inform future iterations of lighting technology development for vertical farms.

Expanding crop diversity is essential to address the imminent challenges of agriculture. This is especially true for organic farming, which relies on locally adapted species and varieties. Recently, participatory research approaches have emerged as effective means to support this endeavour. In this study, we collaborated with several stakeholders in the Lyon region, France, to evaluate three minor species related to common wheat (Triticum aestivum subsp. aestivum): einkorn (Triticum monococcum subsp. monococcum), emmer (Triticum turgidum subsp. dicoccum) and spelt (Triticum aestivum subsp. spelta (L.) Thell). First, we assessed the agronomic characteristics of each species, highlighting a distinction of einkorn that was associated with high tillering, high protein content, a long phenological cycle, small kernels and low relative yields. Second, we compared intra-species variabilities, revealing greater variation in emmer and spelt. Lastly, outcomes of the participatory approach, including testing adaptive methods and fostering collective learning, may interest other participatory research groups.

In the past 50 years, the formalism of L-systems has been successfully used and developed to model the growth of filamentous and branching biological forms. These simulations take place in classical 2-D or 3-D Euclidean spaces. However, various biological forms actually grow in curved, non-Euclidean, spaces. This is, for example, the case of vein networks growing within curved leaf blades, of unicellular filaments, such as pollen tubes, growing on curved surfaces to fertilise distant ovules, of teeth patterns growing on folded epithelia of animals, of diffusion of chemical or mechanical signals at the surface of plant or animal tissues, etc. To model these forms growing in curved spaces, we thus extended the formalism of L-systems to non-Euclidean spaces. In a first step, we show that this extension can be carried out by integrating concepts of differential geometry in the notion of turtle geometry. We then illustrate how this extension can be applied to model and program the development of both mathematical and biological forms on curved surfaces embedded in our Euclidean space. We provide various examples applied to plant development. We finally show that this approach can be extended to more abstract spaces, called abstract Riemannian spaces, that are not embedded into any higher-dimensional space, while being intrinsically curved. We suggest that this abstract extension can be used to provide a new approach for effective modelling of growth of branching systems within non-uniform substrates and illustrate this idea on a few conceptual examples.

Nutation is one of the most striking and ubiquitous examples of the rhythmic nature of plant development. Although the consensus is that this wide oscillatory motion is driven by growth, its internal mechanisms remain to be fully elucidated. In this work, we study the specific case of nutation in compound leaves of the Averrhoa carambola plant. We quantify the macroscopic growth kinematics with time lapse imaging, image analysis and modelling. Our results highlight a distinct spatial region along the rachis—situated between the growth and mature zones—where the differential growth driving nutation is localised. This region coincides with the basal edge of the growth zone, where the average growth rate drops. We further show that this specific spatiotemporal growth pattern implies localised contraction events within the plant tissue.