in silico Plants

Accurately modeling carbon (C) allocation is essential for predicting crop yield and the performance of new cultivars in various environments. Most crop models allocate C empirically, using fixed partitioning tables or harvest indices that prescribe allocation without representing the underlying physiology, limiting their predictive power under novel conditions. A mechanistic alternative, in which C allocation emerges from local utilization and transport, could instead respond dynamically to environmental changes, source-sink perturbations, and organ-level trait modifications. To achieve this design, we integrated a utilization-transport-resistance (UTR) allocation model into the Soybean-BioCro crop growth modeling framework. We calibrated and validated the model using organ biomass data from two soybean cultivars grown at two CO2 levels over eight seasons, achieving accuracy comparable to partitioning-based models while predicting more reasonable carbon allocation fractions. Further, the UTR-BioCro model predicted leaf and stem total nonstructural carbohydrate concentrations with reasonable accuracy compared to experimental measurements across the 2022 growing season. A local sensitivity analysis of the model parameters indicated that the onset of reproductive growth influenced yield more strongly than utilization or transport parameters suggesting the timing of this transition as a potential target for crop improvement. Finally, the UTR-BioCro model reproduced yield responses to source-sink perturbations including shading and pod removal, and captured the qualitative response to defoliation without requiring scenario-specific tuning as most partitioning approaches require. By grounding C allocation in physiological mechanisms, this work provides a foundation for predicting crop responses across diverse environments and engineered traits, supporting crop improvement for a changing environment.

Auxins are a family of plant hormones involved in various processes across plant tissues and species. The Nuclear Auxin Pathway (NAP) consists of interacting transcription factors (ARFs) and repressors (Aux/IAAs), which govern an individual cells response to changes in auxin concentration. These components are present in all land plants, and many species possess multiple copies of each signalling component. We present a general framework for ODE-based models of NAP submodules with the flexibility to model the promotion and repression of target genes by any combination of transcriptional regulators. We analyse published data and show that auxin treatment in Arabidopsis thaliana roots triggers a range of characteristically distinct temporal response profiles--for both target genes and the signalling components themselves. Using our modelling framework, we recapitulate aspects of this behaviour by presenting examples of real and theoretical NAP subnetworks, and by analysing the effect that these network dynamics have on auxin-mediated transcriptional responses. This work demonstrates the utility of our modelling framework as a general-purpose tool for understanding the function of certain protein-protein and protein-DNA interactions through their effects on the NAP. This exploration of the rich dynamics of more complex signalling pathways promises to advance our understanding of the NAP.

Mechanistic models of plant regulatory networks typically require extensive parameterization, limiting their generalisation and scalability. Here we present a parameter-free, topology-driven model of shoot branching that predicts phenotypic outcomes from network structure alone. We constructed a signed, directed causal network by distilling regulatory relationships from the published literature spanning many laboratories, species, years, data types, and methodological frameworks. This extracted the essential logic of the system, consistent with developmental-biological reasoning and anchored in empirical evidence. Using PSoup, which automatically translates network topology into algebraic equations, the model propagates information across the network and predicts the qualitative direction of change relative to a defined baseline, mirroring the comparative framework of biological experiments. The pipeline, from network construction through automated equation generation to prediction, is transparent and reproducible. Trained against branching phenotype data with 78 diverse perturbations spanning genetic mutations and hormone treatments, the model achieved 86% accuracy in predicting branching direction. On an independent test set of 84 perturbations measuring bud release and gene expression at nodes not used during training, accuracy reached 75%. The approach highlighted deficiencies in our understanding of the topology of the network around SMXL 6/7/8 and ABA nodes. Other errors came mainly from modelling choices, such as the threshold for scoring a node as changed relative to baseline. Beyond shoot branching, this work demonstrates a general strategy for synthesizing biological knowledge into validated predictive networks, providing a foundation for both applied breeding and the advancement of fundamental biology.

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

Root phenotypic plasticity is often proposed to improve crop performance under stress, yet it remains unclear how much plasticity is beneficial and whether adaptive responses require changes across many traits or adjustments in few specific traits. Using public data of 6,500 field-grown maize and barley plants, this study examined the extent and distribution of root plasticity, and when it is associated with yield stability. We quantified root plasticity across nine anatomical and architectural traits using complementary statistical models and applied a feature-discovery framework to identify the drought-associated optimal integrated phenotypes and determine whether plasticity toward these phenotypes improved yield stability. More plasticity did not mean greater yield stability. Neither the number of plastic traits nor the magnitude of plastic responses predicted yield stability. Rather, we identified species-specific high-yielding, stable integrated phenotypes defined by distinct trait configurations. Critically, genotypes whose plastic responses moved their root phenotype toward these targets achieved greater yield stability, whereas movement away from them was associated with lower stability. Root plasticity is adaptive when it shifts root phenotypes towards an optimal integrated phenotype. These findings show that the value of plasticity depends on the trajectory of phenotypic change rather than its magnitude alone.

Concurrent exposure to heat and drought stress compromises legume productivity, yet their combined effects are rarely quantified systematically. We compiled a database of 18 studies covering seven legume species. From these, we extracted 929 physiological, biochemical, and yield-related traits and calculated actual-to-additive ratios to classify heat-drought interactions as antagonistic (ratio < 1), additive (ratio = 1), or synergistic (ratio > 1). Additive heat-drought relationships accounted for 59 % of all classifiable observations, 37% relationships were antagonistic, and 4% synergistic. The relationship varied with species, genotype, trait, and experimental conditions highlighting the complexity of combined abiotic stress effects. The results challenge the common assumption that concurrent stresses invariably exacerbate damage and underscore the need for more realistic, quantitatively defined stress treatments as well as frameworks that integrate trait-level responses into predictive models of crop growth and development. Our synthesis provides a quantitative foundation to understand legume phenotypes under the increasingly frequent co-occurrence of heat and drought stress and identifies research areas where further work is needed to improve insight into combined stress responses. HighlightsO_LICombined heat and drought responses were mainly additive or antagonistic. C_LIO_LIEvidence is biased toward few legumes and controlled environments. C_LIO_LIField-based, multi-species studies are needed to identify adaptive traits. C_LI

Soybean, a widely cultivated leguminous crop valued for its protein, amino acids, and oil, faces the challenge of maintaining protein levels, which have an inverse correlation with yield. Reducing leaf chlorophyll levels could increase seed protein levels without compromising yield; however, this is yet to be tested. Therefore, to understand the impacts of low chlorophyll mutations on soybean yield and seed composition, we screened and compared 25 low chlorophyll soybean mutants to their 11 dark green parents. PI548210 (Lincoln mutant) demonstrates a higher concentration of protein without affecting yield compared to its dark green parent PI548362 (Lincoln), suggesting it as a good candidate for further large-scale field trials. PI547555 (Y11/y11, Clark mutant) demonstrates a lower concentration of oil without impacting yield, alongside lower gross photosynthesis, but with chlorophyll levels in the pod and seed tissues that are comparable to its dark green parent PI548533 (Clark). These findings are consistent with the oil concentration of the soybean being influenced by pod and seed photosynthesis, which is correlated with pod height and row spacing. Chlorophyll levels in the leaf do not necessarily correlate with those in the pod and seed of low chlorophyll mutants, possibly due to substantially lower expression of chlorophyll synthesis genes in the pod and seed. SIGNIFICANCEO_LIPI548210 (Lincoln mutant), one of twenty-five low chlorophyll soybean mutants, demonstrates a higher concentration of soybean protein without affecting yield compared to its dark green parent (Figure 1 and Table 1). C_LIO_LIPI547555 (Y11/y11, Clark mutant), a low chlorophyll soybean mutant, demonstrates a reduced concentration of soybean oil without impacting yield, alongside lower gross photosynthesis in pod and seed tissues compared to its dark green parent (Figures 3 and Table 2). These findings suggest that the oil concentration of the soybean is influenced by pod and seed photosynthesis, which is in turn influenced by pod height and row spacing (Figure 2). C_LIO_LIChlorophyll levels in the leaf do not necessarily correlate with those in the pod and seed of low chlorophyll mutants, possibly due to substantially lower expression of chlorophyll synthesis genes in the pod and seed (Figure 5-6). C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/744892v1_fig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@4282dcorg.highwire.dtl.DTLVardef@9d565forg.highwire.dtl.DTLVardef@1918292org.highwire.dtl.DTLVardef@1359b1_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Two low chlorophyll mutants are as healthy as their dark green parents. Lincoln and its low chlorophyll mutant, left; Clark and its low chlorophyll mutant, known as Y11/y11, right. It can be seen by eye that the plants have low chlorophyll (light green/yellow leaves) but a similar growth habit to their dark green parents. See Supplemental Figures 1-4 for contrast, where low chlorophyll mutants are stunted in growth compared to their dark green parents. C_FIG O_TBL View this table: org.highwire.dtl.DTLVardef@657ec9org.highwire.dtl.DTLVardef@166e75borg.highwire.dtl.DTLVardef@df23c7org.highwire.dtl.DTLVardef@1a60124org.highwire.dtl.DTLVardef@194ed96_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 1.C_FLOATNO O_TABLECAPTIONComparison of seed yield, weight, seed composition between low chlorophyll mutants and their dark green parents. ANOVA is used with linear mixed model (random effect = block, fixed effect = variety). Least squares mean is used to compare. For yield and seed composition, N=4 blocks. For leaf chlorophyll (SPAD), N=40. Yield is average yield per plant (g). n.s. = not significant. C_TABLECAPTION C_TBL O_FIG O_LINKSMALLFIG WIDTH=179 HEIGHT=200 SRC="FIGDIR/small/744892v1_fig3.gif" ALT="Figure 3"> View larger version (26K): org.highwire.dtl.DTLVardef@7a368aorg.highwire.dtl.DTLVardef@192b8f0org.highwire.dtl.DTLVardef@1abb738org.highwire.dtl.DTLVardef@89e978_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO Light response curve of low chlorophyll mutant (Y11/y11, PI547555) and its parent (Clark, PI548533). Rates of net and gross photosynthesis of low chlorophyll (white) and dark green parents (black) pods under field conditions. Each dot represents a value (n=4) {+/-}SE. We assumed that the seeds greatly inhibited the transmittance of light through the pod and used photosynthetic photon flux density for a single-side. C_FIG O_TBL View this table: org.highwire.dtl.DTLVardef@3f0528org.highwire.dtl.DTLVardef@16ba712org.highwire.dtl.DTLVardef@a5ab2aorg.highwire.dtl.DTLVardef@889254org.highwire.dtl.DTLVardef@3efa4f_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONPod photosynthetic parameters for low chlorophyll mutant (Y11/y11, PI547555) and its parent (Clark, PI548533). Photosynthesis was measured 1 September through 15 September 2021 at the University of Illinois Energy Farm in Urbana, IL, USA. The statistical analysis was done using ANOVA with linear mixed model (alpha=0.05). N=4 {+/-} SEM for Clark and N=3 {+/-} SEM for Y11. C_TABLECAPTION C_TBL O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/744892v1_fig2.gif" ALT="Figure 2"> View larger version (23K): org.highwire.dtl.DTLVardef@a36c26org.highwire.dtl.DTLVardef@1116c8forg.highwire.dtl.DTLVardef@ee5e61org.highwire.dtl.DTLVardef@1766712_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Low chlorophyll mutant (Y11/y11, PI547555) and its parent (Clark, PI548533) differ in concentration of seed oil, which interacts with height of pod and row spacing. The box plots show the median (central line), the lower and upper quartiles (box) and the minimum and maximum values (whiskers). The statistical analysis was done using ANOVA with linear mixed model (n=3 blocks, alpha=0.05). Least squares mean is used to compare. N.s., non- significant in the analysis. A. Concentration of oil in low chlorophyll mutant seeds from the upper canopy decreased by 4% compared to the dark green parent (18.2% vs 19%) while there was no difference between them in the seeds from the lower canopy (20.2% vs 20.6%). B. Schematic layout of 2013 field setting showing two different row spacings. C. Concentration of oil in low chlorophyll mutant decreased by 2% in 38cm spacing (21.4% vs 22%) while there was no difference in 19cm spacing (21.3% vs 21.7%) in 2013 field. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/744892v1_fig5.gif" ALT="Figure 5"> View larger version (22K): org.highwire.dtl.DTLVardef@68e508org.highwire.dtl.DTLVardef@94a6ccorg.highwire.dtl.DTLVardef@152a187org.highwire.dtl.DTLVardef@1eae137_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 5C_FLOATNO (greenhouse). Correlation between the level of leaf chlorophyll (x-axis: SPAD reading) and the level of immature pod or seed chlorophyll (y-axis, mg/g DW). Line represents the linear regression model. R-squared is a coefficient of determination, the percentage of the response variable variation that is explained by the linear model. Pod is labeled by the fresh weight of seeds it contained. A. Level of chlorophyll of 25-100mg pod (n=18). B. Level of chlorophyll of 100-200mg pod (n=17) . C. Level of chlorophyll of 25-100mg seed (n=17). D. Level of chlorophyll of 100-200mg seed (n=20). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=180 SRC="FIGDIR/small/744892v1_fig6.gif" ALT="Figure 6"> View larger version (28K): org.highwire.dtl.DTLVardef@167fd88org.highwire.dtl.DTLVardef@361472org.highwire.dtl.DTLVardef@786325org.highwire.dtl.DTLVardef@1b53855_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO Levels of gene expression in chlorophyll synthesis pathway. A. CHL common pathway genes; Glutamyl-tRNA reductase (GluTR). Glutamate 1- semialdehyde aminotransferase (GSA-AT). ALA dehydratase (ALAD). Uroporphyrinogen III synthase (UROS). Uroporphyrinogen III decarboxylase (UROD). Protoporphyrinogen IX oxidase (PPO). B. Mg branch; Mg-chelatase (Mgch). Magnesium-protoporphyrin IX monomethyl ester cyclase (MPEC). Protochlorophyllide reductase (POR). 3,8-divinyl protochlorophyllide a 8-vinyl-reductase (4VCR). Heme pathway; Ferrochelatase (FECH). Heme oxygenase (HO). Phytochromobilin synthase (HY). Data come from Severin et al (2010). RPKM, reads per kilobase per million mapped reads. DAF, days after flowering. The source seed is experimental line A81-356022 which was generated by introgressing G. soja (PI468916) into G. max (A81-356022). C_FIG

Tillering plasticity is a key adaptive trait in sorghum influencing resource use efficiency via a plants ability to adjust branching to neighbour density. Neighbour detection through red:far-red (R:FR) light sensing regulates this plasticity. While molecular pathways regulating tiller outgrowth are partly known, the genetic architecture underlying density-responsive tillering has not been resolved in any grass species. A sorghum diversity panel (n = 895) was evaluated over two growing seasons (2023 and 2024) with plant spacing ranging from 5 to 60 cm. A linear mixed model incorporating neighbour distance and tiller counts estimated genotype-specific response. GWAS was conducted on isolated plants (no neighbours within 60 cm) and on estimated responsiveness to neighbours. GWAS identified 52 baseline tillering QTLs and 50 for spacing responsiveness, with 10 overlapping, suggesting shared genetic control. Comparison with 41 R:FR pathway candidate genes revealed enrichment in responsiveness QTLs (5/50, 10%) versus baseline (0/52, 0%) (Fishers exact test, P = 0.025). Our model identified 40 unique density-responsive tillering QTL regions. Reducing genotype response to neighbour absence could be a selection target to develop water-efficient sorghum varieties where controlled architecture may be more valuable than natural plasticity.

Plant breeding is a resource-intensive process that requires repeated cultivation and selection across multiple generations to develop varieties with desirable traits, yet computational tools capable of supporting this process remain limited. Here, we present DigiAra, an AI-based framework for designing Arabidopsis thaliana mutants with targeted traits, particularly enhanced microbial resistance. DigiAra implements an S3 pipeline--simulation, scoring, and screening: it simulates the transcriptional effects of genetic perturbations and microbial infections, scores the predicted responses in terms of relevant traits through biological pathway analysis, and screens candidate perturbations at multiple levels. In doing so, DigiAra enables the computational exploration of the genome-wide effects of genetic perturbations and diverse microbial infections in Arabidopsis. To develop DigiAra, we address two fundamental challenges. Methodologically, we introduce a hybrid architecture that integrates local gene-level interaction modeling with global transcriptional-state modeling to predict perturbation-induced changes in the Arabidopsis transcriptional state. From a data perspective, we establish a standardized pipeline for curating, harmonizing, and processing an integrated Arabidopsis-microbe transcriptional dataset comprising 495 samples from 26 projects. As a result, DigiAra accurately predicts gene-expression changes induced by unobserved genetic perturbations and microbial infections, achieving a Pearson correlation of 0.49. Moreover, it recapitulates the general non-self response (GNSR), a 24-gene program reflecting broad transcriptional reprogramming across bacterial perturbations. In an independent study, the predicted pattern-triggered immunity pathway scores further correlate with bacterial load, with a Pearson correlation of 0.57. Lastly, we deploy DigiAra to identify 27 gene knockouts through genome-wide screening that are predicted to enhance resistance to Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) while limiting growth compromise, 9 of which are supported by published studies. Together, these results establish DigiAra as an effective framework for the computational design of Arabidopsis mutants. We have made our implementation openly available at https://github.com/youlab2025/DigiAra.

1. Plants produce a great chemodiversity, which is the diversity of specialized metabolites (SMs). These SMs are produced in complex metabolic pathways and play an important role in inter-species interactions. There are numerous hypotheses about the evolutionary processes which brought about and maintain chemodiversity. Some have been partially tested in lab and field studies. However, some of their assumptions and predictions are better tested by quantitative modeling, and so far no quantitative model has investigated the role of metabolic pathways. 2. To close this gap, we developed an individual-based model for metabolic pathway evolution. It models enzymes creating metabolites with various modifications. Enzymes undergo inheritance and mutation. We used the model to compare the screening and interaction diversity hypotheses. 3. The screening hypothesis predicts promiscuous enzymes, genetic drift, the presence of many non-beneficial metabolites, and high metabolite richness. The interaction diversity hypothesis predicts specialized enzymes, selection, the almost exclusive presence of beneficial metabolites, and situation- dependent metabolite richness. We found that the patterns predicted by the screening hypothesis did not occur, while those predicted by the interaction diversity hypothesis did. 4. This provides reason to favor the interaction diversity hypothesis over the screening hypothesis when connecting empirical results to their evolutionary context

Accelerating photoprotective regulation to improve carbon assimilation is a promising strategy to increase crop productivity. Although rapid non-photochemical quenching (NPQ) relaxation has been validated as a target through metabolic engineering, it remains unclear whether conventional breeding has improved this trait. Here, we investigated whether more than a century of soybean breeding enhanced NPQ relaxation alongside light-saturated carbon assimilation and seed traits. We evaluated a historical panel of 24 soybean genotypes across vegetative and reproductive developmental stages by integrating NPQ relaxation, gas exchange parameters, xanthophyll-cycle pigment profiles, expression of key photoprotective genes (VDE, PsbS, and ZEP), seed number and seed weight. NPQ relaxation parameters were not consistently associated with genotype release year, seed number, or seed weight at either developmental stage. The only exception was the amplitude of the rapidly relaxing NPQ component (AqE), which was negatively correlated with all three variables during the reproductive stage. In contrast, genotype release year was positively associated with maximum net CO2 assimilation rate (Amax), maximum carboxylation rate of Rubisco (Vcmax), maximum electron transport rate (Jmax), seed number, and seed weight, while Amax and Vcmax were positively correlated with seed number and seed weight. These findings indicate that the greater photosynthetic capacity of modern genotypes was not accompanied by faster photoprotective response. Thus, photoprotective regulation has not kept pace with gains in photosynthetic capacity under field conditions. We conclude that rapid NPQ relaxation remains an important target for synchronizing photoprotection with the high photosynthetic capacity of modern soybean lines.

Improving nitrogen use efficiency in maize (Zea mays) requires understanding how distinct root cell types and regulatory networks process fertilizer inputs. Given the current limited understanding of fertilizer-induced, cell-type-resolved maize roots and regulatory networks, computational biology frameworks are needed to model and predict how nutrient inputs are translated into transcriptional responses. Here, we integrated fertilizer-induced maize root bulk RNA-seq with reference atlases of single-cell RNA-seq and scATAC-seq to construct and predict a cell-specific regulome of the maize root under inorganic and mixed amendments. We demonstrate that inorganic fertilization induced stress associated and management pathways. Regulome analysis identified transcription factors (TF) from the AP2/ERF, NAC, HSF, and WRKY superfamilies that were preferentially active across root tissues. Deconvolution of the regulome onto single-cell atlases predicted core TF activity to the vascular cylinder and pith across both regimes, while mature cortex regulatory programs diverged. Construction of a gene regulatory network revealed that shared TF-target edges maintained the same regulatory orientation across fertilizer regimes. However, a small number of stress related TFs, including WRKY24, DREB1A, and NAC61, underwent a directional change between fertilization treatments. In silico knockout analysis predicted the activation targets for six of the seven regulators in their resident vascular/pith tissues, indicating the network behaves as a coherent, perturbable system. Additionally, soil metagenomic analysis showed that host soil microbial functions overlap with differentially expressed genes (DEGs) in shared functional categories, linking host regulome dynamics to rhizosphere processes. These findings and predictions suggest that the maize root regulome is spatially organized and dynamically reprogrammed by master regulators, predicting high-priority candidate nodes for engineering improved nutrient use efficiency.

Genome-wide association studies (GWAS) play a crucial role in unraveling the genetic foundations of complex traits in plants but are also hampered by the application of heterogeneous tools, incompatible file formats and disparate computational environments. Existing GWAS frameworks are often restricted to a single linear reference genome, limiting the capacity for the analysis of structural variations and presence/absence variations (PAV) within plant populations. These issues pose obstacles to reproducibility, scalability, and comprehensive investigations. Here, we present PlantOmicsGWAS, an open-source Python framework for reproducible plant genome-wide association analysis and genomic prediction. It integrates reference indexing, FASTQ quality control, alignment, variant calling, VCF normalization, PLINK conversion, linkage disequilibrium analysis, population-structure estimation, association testing, marker scoring, genomic prediction, and visualization within a unified Linux and HPC workflow. The framework supports conventional linear-reference analyses and includes an optional pangenome-oriented module for working with multiple assemblies and graph-derived variation. Using a Vitis benchmark dataset containing 120 accessions and 118,247 graph-derived variants, PlantOmicsGWAS reduced manual workflow fragmentation and generated standardized association outputs. This tool provides a modular and extensible platform for plant GWAS and pan-GWAS workflows while retaining compatibility with established command-line tools and common genotype formats. The GWAS workflow described herein is adaptable to a range of sequencing methods and plant genomes, bridging research on crop related issues across various biological levels, from the individual organism to entire populations. PlantOmicsGWAS implements Bayesian sparse linear mixed modeling (BSLMM) through GEMMA for multi-trait association discovery, while also supporting FaST-LMM, regression-based approaches, and machine-learning algorithms (Random Forest, XGBoost) as benchmarking alternatives. The PlantOmicsGWAS, a versatile toolkit is available at GitHub https://github.com/plantomicsgwas1-boop/PlantOmicsGwas_V1 and on Linux and HPC platform (https://pypi.org/project/PlantOmicsGwas/1.0.2/).

Leaf reflectance spectra are emerging as a viable substitute for gas-exchange measurements of photosynthetic capacity, with a community benchmark reporting that a spectrum accurately recovers most Farquhar-von Caemmerer-Berry (FvCB) parameters. This study re-scores the recovery under dataset-blocked, species-blocked, and leave-one-dataset-out designs, measuring the split-half reliability of each curated parameter. We constructed a convolutional encoder that maps a spectrum to the four parameters through a fixed, differentiable FvCB decoder trained on measured assimilation. A conspecific of 97.4% of held-out leaves were present in the training set, and accuracy is lost along the dataset axis but not along the species axis. Under blocked evaluation, a spectrum constrains a single capacity axis. Jmax25 retains only 17% of its recovery when Vcmax25 is held constant, and the Jmax25:Vcmax25 ratio is not predicted above a median null. The curated values of TPU25 are not reproducible, whereas those of Rday25 are well determined, but its recovery fails due to the loss. The published study measures interpolation rather than transfer, and spectra constrain less of the FvCB parameter space than assumed, including the carboxylation to electron transport balance. Routing predictions through explicit biochemistry makes identifiability measurable, although it does not improve prediction accuracy.

Chloroplasts comprise photosynthesis and other important processes. Plants protect chloroplasts from stresses including Cd accumulation. Common terrestrial plants, excluders apply a set of mechanisms to restrict Cd penetration to chloroplasts. Removal of accumulated Cd from chloroplasts should also be a beneficial strategy. However, we do not know whether excluder plant species have ability to remove Cd from chloroplasts. We used barley as a common excluder plant species. To barley plants, we applied a model with two stable isotopes 111Cd and 114Cd to distinguish Cd accumulated earlier and later. A portion of Cd absorbed by roots continued translocation to shoot for some days after the external source of Cd was changed from one isotope to another. Chloroplasts acquired new portions of Cd and lost part of Cd accumulated earlier; a total Cd content remained rather unchanged. Cd loss from thylakoids was detected in vivo and in vitro. Cd loss from stroma and envelope was observed in vivo but not in vitro. Therefore, barley has at least two distinct mechanisms for Cd removal from chloroplasts: one from thylakoids and another from stroma. We hypothesized diverse chlorophagy pathways as a potential mechanism for Cd removal from chloroplasts. Cd accumulation by chloroplasts was mainly light-independent. In chloroplasts, Cd accumulated in vivo was tightly bound and mainly located in thylakoids. In vitro, chloroplasts from Cd-treated plants accumulated much less Cd than chloroplasts from untreated plants in a previous study. This implies reorganization of transport across chloroplast envelope membranes. HighlightsO_LICd was removed from thylakoids both in vivo and in vitro C_LIO_LICd was removed from stroma and envelope in vivo but not in vitro C_LIO_LIIn chloroplasts, Cd accumulated in vivo was tightly bound C_LIO_LICd accumulation by chloroplasts was mainly light-independent C_LIO_LIRoot barrier slowed down Cd translocation to shoot but not halted it C_LI

Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address these challenges by using inexpensive, scalable, high-dimensional phenotypes collected early in development, although the robustness of such predictions across breeding cycles remains uncertain. Here, we compared genomic selection and phenomic selection across two breeding cycles of Thinopyrum intermedium (intermediate wheatgrass; IWG; Kernza(R)), comprising approximately 2,280 individuals from maternal half-sib families evaluated across multiple field sites and years. We constructed relationship matrices from genomic markers and early-life stage phenomic data, including seed and leaf color (HSV), CropReporter multispectral reflectance and indices, and cycle-specific hyperspectral reflectance sensors. Genomic models provided the strongest predictions on average across all field traits in both cycles. Among phenomic predictors, leaf HSV was consistently the most informative, whereas CropReporter and hyperspectral data showed lower and more trait-dependent performance and seed HSV provided little predictive value. Genomic, leaf HSV, and CropReporter models transferred across breeding cycles with little apparent loss of predictive ability relative to within-cycle validation, demonstrating that their predictive signals were not restricted to a single breeding cycle. Early-life stage leaf HSV emerged as a practical, accessible tool for germplasm thinning and early-stage prioritization in perennial breeding programs. Despite limited similarity among relationship matrices, multi-relationship-matrix models rarely improved prediction beyond the stronger constituent single-relationship-matrix model. Together, these results show that early-life stage phenomic data provide reproducible information about agronomic performance expressed years later, but that predictor complexity and data integration do not guarantee improved prediction.

Plant functional genomics requires the integration of sequence, expression, evolutionary, regulatory and literature evidence. However, the corresponding analyses are often distributed across disparate programs, scripts and databases, creating substantial barriers to task organization and result interpretation. Here, we present PlantAI, a multi-agent system that integrates bioinformatics analysis, project-level process tracking and knowledge-assisted interpretation. A Main Agent coordinates two complementary routes: an analysis route that invokes bioinformatics tools for RNA-seq and gene-family analyses, and a knowledge route that uses PlantAI-RAG for knowledge retrieval and evidence synthesis. PlantAI-RAG currently contains 31,207 plant-science literature records, comprising approximately 3.82 million normalized entities and 8.25 million literature-supported relation assertions. In an evaluation using plant-science questions, it achieved a Gold evidence-assertion recall of 86.7%, while strict accuracy ranged from 77% to 82% across three independent evaluator models. We further demonstrate an end-to-end task using 24 rice RNA-seq libraries collected under salt stress, spanning transcriptome analysis, candidate-family screening, HXK/HKL family analysis and knowledge-assisted interpretation, and prioritize OsHXK8 for experimental validation. By preserving analysis artifacts, run manifests, logs and environment records, PlantAI supports result verification and repeat execution while linking project-derived results to traceable literature evidence. Together, these capabilities provide an integrated and auditable framework to support plant functional genomics research.

Photorespiration significantly influences terrestrial carbon fluxes, yet empirical measurements of its variability across tree species and temperature conditions remain limited, constraining predictions of vegetation and climate models. We quantified apparent photorespiratory CO2 loss (Lapp) and its temperature response for seven temperate broadleaf tree species in northern Europe, using in situ O2-shift measurements in Uppsala, Sweden during peak summer. Apparent loss was derived as the difference between net CO2 assimilation under ambient (Anet) and O2-free conditions at three leaf temperatures (25, 30, and 35 {degrees}C), spanning typical and heat-wave scenarios. Apparent photorespiratory CO2 loss showed pronounced interspecific variation and increased with temperature, while net photosynthesis remained relatively stable. The ratio of apparent loss to net photosynthesis ({phi} = Lapp/Anet) rose sharply with temperature, reaching species-mean values up to 0.94 at 35 {degrees}C, indicating that photorespiration can represent nearly the entirety of net carbon gain under heat stress even when leaves remain net CO2 sinks. Suppression of photorespiration under N2 and associated changes in leaf temperature systematically reallocated photosynthetic electron transport: the fraction of ambient electron transport rate (ETR) allocated to net CO2 assimilation declined with temperature, whereas the complementary fraction allocated to apparent photorespiratory loss and other O2-dependent sinks increased, with ETR-based apparent loss and its proportional expression rising steeply across the 25-35 {degrees}C range. Together, these in situ flux and partitioning measurements reveal high variability and strong temperature sensitivity in apparent photorespiration among temperate trees. Compared to crop-based parameterisations, the {phi} values we report for temperate trees are substantially higher and more temperature-dependent, providing species-specific constraints that can improve Farquhar-von Caemmerer-Berry-type vegetation model representations of photorespiration in forest ecosystems.

Root hairs (RH) are excellent model systems for studying cell size and polarity since they elongate several hundred-fold their original size. Their tip growth is regulated by both intrinsic and environmental signals and is associated with the existence of a highly controlled cytoplasmic tip Ca{superscript 2} gradient, whose disruption impairs RH development. The molecular mechanisms underlying the Ca2+ homeostasis fine tuning and the Ca2+ organellar contributions to the cytoplasmic pool remain unclear. In the model plant Arabidopsis thaliana, many efflux routes are present, including those that employ Ca2+-pumps from the Autoinhibited Ca2+-ATPase (ACA) family. Here, we identified that the ER localized ACA2, and to a lower extent ACA7, are crucial ACAs required to control RH growth. By using genetically encoded Ca2+ biosensors we showed that Ca2+-dynamics are compromised in the aca2-2 mutant, having lower cytosolic Ca2+ concentration [Ca2+]cyt and growth rate, showing an altered homeostatic calcium setpoint compared to Col-0. Accordingly, the ACA2 mutation changed the dynamics of [Ca2+]cyt oscillations coupled to growth rate, inducing longer periods and more regular oscillations in the dominant high-frequency range (around 22 s), and slower oscillations (around 1 min) in the low-frequency range. Finally, expression of ACA2 with changes in four putative Ca2+ binding residues (ACA2{Delta}Ca2+) failed to rescue the RH growth phenotype in the aca2-2 mutant. Collectively, our findings indicate that ER-localized ACA2 and possibly ACA7 are crucial for modulating cytoplasmic Ca2+ signals, possibly composing a critical part of a negative feedback loop, and their absence leads to impairments in RH cell elongation.

Mechanistic interpretation of photosynthetic energy-transfer models requires more than reproduction of experimental observables: a model intended to support mechanistic claims should also respond consistently when its proposed functional organizations are removed. Here, we evaluate whether a calibrated PSI-LHCI transport surrogate encodes physically meaningful principles by applying a 2x2 factorial knockout framework that independently removes site-energy heterogeneity and coupling-strength heterogeneity in a 155-pigment network. All perturbations were evaluated using the same Full-model calibration without parameter refitting. Although the calibrated model reproduced a high excitation-trapping yield, eliminating either energetic or coupling heterogeneity unexpectedly improved its apparent transport performance. A strict zero-coupling control confirmed that coupling itself remained necessary for network-mediated reaction-center access, whereas the supplied organization of coupling strengths was not supported by the surrogate. An audit of the model inputs further identified peripheral localization of all lowest-energy states and effective coupling scales far above those used in structure-based chlorophyll Hamiltonians. These findings do not imply that native PSI favors flat energy landscapes or uniform couplings. Instead, they show that endpoint agreement alone does not validate a mechanistic interpretation of a pigment-network model. Factorial knockout analysis provides a falsification-oriented framework for separating physical necessity from proposed organization, diagnosing numerical compensation, and identifying the constraints required for more predictive models of PSI-LHCI energy transfer.