Plant Communications

Abscisic acid (ABA) is involved in Cd tolerance in Arabidopsis, but the underlying mechanisms are unclear. In this study, we revealed that the ABI5-WRKY45-LSU1 axis confers the tolerance of Arabidopsis to Cd stress. Under Cd stress, the biosynthesis of ABA is increased, and the expression of transcription factor ABI5 is upregulated. Accordingly, the abi5-8 mutants show increased Cd sensitivity. ABI5 directly binds the ABRE element in the WRKY45 promoter to activate its transcription. Overexpression of WRKY45 rescues the Cd-hypersensitive phenotype of the abi5-8 mutant, placing WRKY45 downstream of ABI5. Transcriptome analyses identified LSU1 as a potential WRKY45 target. qRT-PCR, DUAL-LUC and EMSA experiments verified that WRKY45 binds the W-box cis-element in the LSU1 promoter to activate its expression. Overexpression of LSU1 enhances Cd tolerance by promoting the biosynthesis of non-protein thiols (NPT), glutathione (GSH), and phytochelatins (PC). Moreover, overexpression of LSU1 suppresses Cd sensitivity in the wrky45 mutant, confirming LSU1 acts downstream of WRKY45. On the other hand, we found that ATP sulfurylase 1 (APS1) interacts with LSU1 based in vitro and in vivo evidences. LSU1 stabilizes APS1, slows its degradation, and enhances APS1 activity, thus leading to increased NPT, GSH, and PC accumulation and improved Cd detoxification. Notably, overexpressing LSU1 did not rescue the Cd sensitivity of the aps1-1 mutant, indicating that LSU1 acts upstream of and depends on APS1. In short, we demonstrated a novel ABI5-WRKY45-LSU1 axis that regulates Cd tolerance through sulfur assimilation and phytochelatin synthesis. HighlightsO_LICadmium stress triggers ABA biosynthesis and ABI5 expression; ABI5 directly binds to ABRE motifs in the WRKY45 promoter and activates its transcription. C_LIO_LIWRKY45 transcriptionally activates LSU1, and LSU1 interacts with APS1 to stabilize it and elevate ATP sulfurylase activity, acting in an APS1-dependent manner. C_LIO_LIThe ABI5-WRKY45-LSU1 module enhances Arabidopsis Cd tolerance by boosting sulfur assimilation and GSH/PC-mediated Cd detoxification, rather than reducing Cd uptake. C_LI

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in human-centric models, plant genomics, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenomeRice (OGR), a genomic foundation model for rice (Oryza sativa) engineered by a Mixture of Experts (MoE) transformer architecture with 1.25-billion-parameters. OGR was pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. A comprehensive benchmark, including short-sequence motif identification, long-range regulatory modeling, single-nucleotide resolution prediction, selective sweep detection and subspecies classification, demonstrated that OGR significantly outperforms existing state-of-the-art plant or all-life genome models in 11 categories. The model was also further used for several downstream applications, such as introgression between indica and japonica subspecies using embedding-based supervised classification, agronomy trait-associated functional loci through attention-derived importance signals, and gene expression prediction of DNA sequences etc. These results indicate OGR being a promising foundational computational infrastructure for functional genomics and precision breeding of rice.

Early sowing of maize (Zea mays L.) is increasingly required to mitigate summer drought under climate change, making the acquisition of chilling tolerance a major agronomic challenge. Here, we investigated the molecular and physiological bases of cold tolerance using two maize near-isogenic lines (NILs) differing at two major chilling tolerance quantitative trait loci (QTLs) located on chromosome 4. Plants were exposed to low temperature (14{degrees}C day/10{degrees}C night) for 20 days and analyzed using an integrated multi-omics approach combining transcriptomics, soluble and cell wall proteomics, and metabolomics (primary and specialized metabolites), together with physiological measurements. Univariate and multivariate analyses revealed significant chilling-induced variability across all molecular layers, affecting [~]0.2% of genes, [~]6% of proteins, and a subset of specialized metabolites, while primary metabolites were largely stable. Integrative statistical analyses demonstrated that the soluble and cell wall proteomes contributed most strongly to the genotype effect, highlighting protein-level regulation as a major determinant of chilling tolerance. A restricted 5.15 Mb divergence region on chromosome 4 was sufficient to drive contrasting physiological responses, including differences in photosynthetic charge separation efficiency and leaf development, favoring the chilling-tolerant NIL. Notably, several components of the benzoxazinoid pathway located within the divergence region, including BX1 and associated specialized metabolites (BZX-like glucoside, DIBOA-glucoside-2, HBOA-glucoside-2), were specifically associated with chilling tolerance, suggesting a role in stress signaling and hormonal crosstalk. Overall, this study demonstrates that integrative multi-omics analyses provide a powerful framework to resolve genotype-specific regulatory mechanisms underlying chilling tolerance in maize and to identify candidate molecular targets for breeding. HighlightsO_LIFirst organ-resolved multi-omics dissection of chilling responses in maize NILs. C_LIO_LIA 5.1Mb divergence on chromosome 4 drives major physiological and molecular differences. C_LIO_LIChilling tolerance is linked to more robust photochemical homeostasis and sustained leaf development. C_LIO_LISoluble and cell-wall proteomes dominate the genotype-discriminating -omics signal. C_LIO_LIBenzoxazinoids and defense-related transcriptional modules are differentially activated. C_LIO_LICell wall remodeling enzymes and apoplastic peroxidases emerge as key tolerance players. C_LI

RNA-seq datasets from medicinal yews are crucial for studying paclitaxel biosynthesis. However, cross-study data analyses are hindered by pronounced batch effects. Here, we compiled 45 RNA-seq samples from three studies across four tissues (bark, leaf, root, stem) and assessed 35 preprocessing pipelines combining six normalization strategies with five batch-effect correction approaches. Unsupervised clustering (HCA, k-means, Grade-of-Membership), evaluated using Jaccard and Adjusted Rand indices, revealed significant variability in batch effect removal. Supervised classification of tissue and project labels (Random Forest and linear/radial SVM) demonstrated improved accuracy in tissue type prediction, highlighting the effectiveness of correction methods. The processed data facilitated the identification of 189 putative ABC transporters across samples, six of which showing a strong correlation to the gene encoding 10-deacetylbaccatin-III-10{beta}-O-acetyltransferase, a key biosynthetic enzyme in the taxol pathway. High expression levels in leaf and bark further support their role in taxane intermediates trafficking in taxol biosynthesis. Structural analysis and molecular docking further supported the selection of these candidates, and the agreement between transcriptomic ranking and docking-based prioritization suggests that these transporters may participate in taxane intermediate recognition, trafficking, or export. These findings demonstrate the importance of normalization and batch effect correction in RNA-seq analysis to advance gene discovery in Taxus species and, more broadly, in plant research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/723993v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@1469162org.highwire.dtl.DTLVardef@1f2c4deorg.highwire.dtl.DTLVardef@15ad821org.highwire.dtl.DTLVardef@123676d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Iron (Fe) is an essential micronutrient for plant growth, and the hormone auxin is a key regulator of developmental processes, including root gravitropism. Here, we investigated the molecular mechanisms underlying the crosstalk between iron nutrition and auxin-mediated root growth in Arabidopsis thaliana. Phenotypic analysis revealed that iron deficiency strongly shaped root system architecture and root gravitropism, and these phenotypes were exacerbated in the iron uptake mutant irt1-1. Genetic analysis revealed that iron deficiency did not aggravate the gravitropic defect of the pin2 mutant, eir1-4, suggesting that iron availability modulates root gravitropism through a PIN2-dependent pathway. Further transcriptomic analysis confirmed that iron deficiency significantly altered the expression of numerous genes related to the auxin pathway, providing molecular evidence for the observed physiological connection. Collectively, this study revealed that iron availability regulates root gravitropic growth by modulating PIN-mediated auxin transport and distribution, providing insights into how plants integrate nutritional cues with developmental programs. Graphical abstract A brief descriptionIron modulates auxin transport and root tip distribution by regulating PIN2 protein, thereby mediating root gravitropism in Arabidopsis. Public summaryO_LIIron nutrition specifically regulates root gravitropism and architecture in Arabidopsis. C_LIO_LIIron deficiency disrupts local auxin homeostasis in root tips and impairs asymmetric distribution in response to gravity. C_LIO_LIIron deficiency stress significantly reduces the abundance of PIN2 protein in root tip cells and disrupts its polar localization pattern on the plasma membrane, thereby precisely modulating polar auxin transport by interfering with the vesicle trafficking and recycling efficiency of PIN2. C_LIO_LIRNA-seq results showed that iron deficiency induced differential expression of multiple auxin-related genes, indicating that iron nutrition affects root development through the auxin pathway. C_LI

The combination of target capture sequencing (TCS) with low-coverage whole genome sequencing (lcWGS), an approach known as Hyb-Seq, has allowed the integration of natural history collections into the genomics revolution, transforming biodiversity research. To implement Hyb-Seq, a collection of genomic targets, often nuclear orthologs, is needed to design probes for TCS. In flowering plants, the universal Angiosperms353 probe set has been proven resolutive at multiple evolutionary scales, with caveats. Malpighiales is known to be one of the most challenging flowering plant orders to resolve. Within this order, the clusioid clade ([~]2.2K species, 94 genera, five families) is no exception. To resolve phylogenetic relationships in this recalcitrant clade, we design a custom probe set, the Clusioids626 kit, composed of 39,936 120-mer probes targeting 626 nuclear orthologs ([~]6.6M nucleotides). This probe set includes all Angiosperms353 targets and 273 clusioid-specific ones, carefully chosen taking copy-number, length evenness, and phylo-informativeness into account. We test our probe set on 70 accessions representing all families and tribes in the clusioid clade. On average, 50.4% of TCS reads mapped to our targets, recovering a median of [~]600 orthologs. Relationships for all clusioid families are fully resolved for our nuclear targets. Additionally, 105 plastid coding DNA sequences were retrieved from the lcWGS fraction. A strong cyto-nuclear conflict was detected. The Clusioids626 kit performs better than the universal Angiosperms353 enrichment panel alone. Our kit design workflow can be extended into other lineages for which a universal probe set exists but more resolution is needed.

Chromatin organization regulates genome stability and gene expression by controlling DNA accessibility to transcription factors and regulatory complexes. DNA-protein interactions are commonly investigated using chromatin immunoprecipitation (ChIP), which relies on specific antibodies often involving technically demanding protocols. CRISPR-Cas technologies have enabled sequence-specific targeting of genomic loci using catalytically inactive Cas9 (dCas9), but most CRISPR-based chromatin capture approaches in plants require transient or stable transformation to express the CRISPR machinery, limiting their applicability across species, tissues and physiological contexts. Here, we present GRASP (Genomic Region Affinity Sequestration by CRISPR-Purification), a transformation-independent strategy for sequence-specific chromatin isolation operating directly on purified plant nuclei. In GRASP, dCas9-gRNA ribonucleoprotein complexes are used to capture predefined genomic regions from chromatin under native conditions, bypassing the need for transgene expression. Using grapevine and tomato as model systems, we demonstrate efficient and highly specific enrichment of target loci, including telomeric repeats as well as low-copy and single-copy genomic regions, with qPCR and NGS validation. These results establish GRASP as a robust and broadly applicable platform for locus-specific chromatin isolation in plants. Beyond sequence-specific DNA isolation, GRASP establishes a versatile platform for potential downstream analyses of locus-associated chromatin components, including protein complexes, distal DNA-DNA interactions and chromatin-associated RNAs, providing new opportunities to investigate regulatory architecture in plant genomes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/712347v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@13758e8org.highwire.dtl.DTLVardef@adfd82org.highwire.dtl.DTLVardef@de81f4org.highwire.dtl.DTLVardef@25c2d3_HPS_FORMAT_FIGEXP M_FIG C_FIG

O_LIPlants produce diverse bouquets of specialized metabolites (SMs), yet only a fraction of the vast phytochemical space has been explored to date. Comparative analysis of SM profiles can reveal hotspots of biochemical novelty, while systematic profiling across taxonomic levels does presently not cover large plant families. C_LIO_LITo study core and accessory SM profiles in the Brassicaceae plant family, we fingerprinted 14 species by Liquid-Chromatography Mass-Spectrometry (LCMS/MS). We develop standardized experimental and computational workflows integrating in silico annotation tools to study consensus compound class and substructure distributions of SMs. Furthermore, we investigate the congruence of chemotaxonomy and species phylogeny across an extended panel of 17 species. C_LIO_LIUnique metabolite profiles were outstanding in Camelina sativa, Capsella rubella, and B. vulgaris, with the largest unique terpenoid profile annotated in C. sativa, accounting for 33.5% and 55.6% in positive and negative ionization mode, respectively. Substructure motifs were found to overlap with compound class predictions, highlighted for triterpenoids in Camelinodae. Furthermore, dual-tissue chemotaxonomic clustering resembled relationships of Brassica subgenomes across tissues. C_LIO_LIWe anticipate that our systematic approach can serve as a blueprint for investigating biochemical diversity in other plant lineages and can boost the characterization of plant natural product pathways. C_LI

Plant architecture is a major determinant of yield potential in cereal crops, where tiller number directly influences spike production and grain yield. The transcriptional regulators Ideal Plant Architecture 1 (IPA1) and Teosinte Branched1 (TB1) constitute a conserved genetic module controlling axillary bud activity and branching in grasses; however, their functional contribution to wheat architecture remains largely unexplored. Here, we employed CRISPR/Cas9-mediated genome editing to investigate the roles of TaIPA1 and TaTB1 in regulating tillering in hexaploid wheat (Triticum aestivum L.). Comparative genomic analysis identified conserved IPA1 orthologs across the wheat A, B, and D sub-genomes, with strong conservation of the SQUAMOSA-binding protein domain. Sequencing analysis confirmed targeted mutations, including nucleotide substitutions and insertions that generated frameshift mutations and premature stop codons. Genome-edited lines exhibited enhanced tillering compared with wild-type plants. Several TaIPA1 mutant lines produced up to two-fold higher tiller numbers, while TaTB1 knockout lines showed earlier tiller initiation and [~]50% increased tillering. Notably, enhanced tillering was associated with increased grain weight without affecting spikelet number per spike. Together, these results demonstrate that the conserved TaIPA1-TaTB1 regulatory module plays a pivotal role in shaping wheat plant architecture. Targeted manipulation of this pathway using CRISPR/Cas9 provides a promising strategy for optimizing tillering and developing high-yielding wheat ideotypes.

Grain legumes such as pea (Pisum sativum L.) accumulate large amounts of seed storage proteins without nitrogen fertilization due to their symbiosis with nitrogen-fixing bacteria, making them a key source of plant-based proteins. Seed growth and the accumulation of seed storage proteins are tightly regulated by complex gene networks; however, the mechanisms governing these processes in pea remain poorly understood. In this study, we generated a comprehensive seed expression atlas covering six developmental stages in pea (cv Cameor), including the key transition stage from embryogenesis to early seed filling, providing a detailed temporal resolution of transcriptional dynamics throughout seed development in this species. Co-expression network analysis highlighted several candidate transcription factors potentially involved in the transition towards seed filling. Among them, we characterized the seed-specific NF-YB transcription factor PsLEC1-like (PsL1L), the major LEC1-type factor expressed during early pea seed development. Functional analyses using TILLING mutants demonstrated that loss of PsL1L function reduces seed size and seed nitrogen content and impairs early embryo growth from the end of embryogenesis. Finally, we show that the expression of the B3-domain transcription factor PsFUS3, but not that of PsLEC2 or PsABI3, is reduced in the loss-of-function l1l mutant, suggesting that PsL1L acts upstream of PsFUS3 to control seed size.

Polyploidization plays a fundamental role in plant evolution and crop domestication. However, due to the high similarity of genomic sequences between some homologous or homeologous chromosomes, the assembly of some polyploid genomes is extremely difficult, frequently resulting in erroneous assemblies, such as sequence chimeras and sequence collapse. The genus Brachypodium is an important model system for the study of polyploidy in grasses. However, high-quality reference genomes are still lacking for its complex polyploid perennial species. In this study, we developed a bioinformatic pipeline for the accurate assembly of high-quality reference genomes at the chromosomal level for two representative perennial Brachypodium species with conflicting collapsed segments, the allotetraploid B. phoenicoides (2n = 4x = 28) and the autohexaploid B. boissieri (2n = 6x = 48). We developed an innovative methodology (CollapsedChrom) that uses depth-of-read profiling and relies on prior karyotypic information to systematically detect and rescue collapsed regions. This depth-sensitive curation strategy successfully recovered 328.9 Mb and 195.8 Mb of previously collapsed sequences in the genomes of B. phoenicoides and B. boissieri, respectively. Comprehensive quality assessments demonstrated the high quality of our final assemblies. Our chromosomal-level assemblies fully capture the genomic architectures of these species. These robust genomic resources overcome long-standing challenges in polyploid assembly and provide an essential foundation for future research on the evolutionary dynamics, subgenomic interactions, and functional biology of complex polyploid plant genomes.

AbstractEarly sowing is a key strategy to improve maize productivity and resilience under climate change, but it exposes plants to prolonged chilling stress that can severely compromise seedling establishment. While previous genetic studies have focused on germination or very early stages, tolerance to long-term chilling during the autotrophic transition remains poorly characterized. Here, we combined genome-wide association studies (GWAS) and transcriptome analysis on QTL near-isogenic lines (NILs) to dissect the genetic architecture of early vigor under chilling in maize. We identified a major genomic region on chromosome 4 (LD_COL4), harboring two QTLs within a 2.7 Mb interval, that were consistently associated with early vigor under long-term chilling conditions. Transcriptomic analysis of contrasted NILs revealed a cluster of differentially expressed genes co-localizing with LD_COL4, pointing to two strong candidate genes, Zm00001d048582, an ortholog of the Arabidopsis OPS gene that regulates the brassinosteroid (BR) signaling pathway upstream of the key transcription factors BES1 and BZR1, and Zm00001d048612, a brassinosteroid-signaling kinase (BSK). Multiple orthologs of BES1/BZR1 modulators were differentially expressed between genotypes under chilling, supporting the involvment of brassinosteroid signaling in this response. These findings highlight both genes as promising targets for marker-assisted breeding and gene editing to improve maize adaptation to early sowing.

IRT1 is the major root iron transporter responsible for broad spectrum metal absorption in Arabidopsis root epidermal cells. Non-iron metal substrates of IRT1 were recently shown to regulate IRT1 cell surface levels by endocytosis and vacuolar degradation. TurboID-based proximity labeling was recently developed to detect protein:protein interactions and thus shed light on the intricate regulation of proteins in living cells. Although TurboID-based proximity labeling technology has been successfully established in mammals, its application in plant systems remains limited and inexistent for highly hydrophobic multispann transmembrane proteins. Here, we established TurboID for proximity labeling of IRT1 and identified 494 IRT1-specific proximal proteins, including the previously reported FYVE1 IRT1 interacting protein. To showcase the biological relevance of identified IRT1 proximal proteins, we characterized further the NHX5 Na+(K+)/H+ antiporter and the RGLG2 E3 ubiquitin ligase. We validated both IRT1 proximal proteins as IRT1 partners using several orthogonal assays. We also highlight the contribution of NHX-type antiporters and RGLG-type E3 ligases in plants responses to non-iron metal nutrition and IRT1 endocytosis. Overall, our work showcases the power of TurboID to identify new interacting proteins for plant transporters, expanding the application of this technology to proteins notoriously difficult to work with.

Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS -- a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress -- which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Chol, cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, {beta}-aminobutyric acid, {gamma}-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.

Ginsenosides are the primary bioactive compounds in Panax notoginseng, but the transcriptional mechanisms governing their tissue- and age-dependent accumulation remain elusive. Here, we integrated targeted metabolomics with transcriptome profiling across four tissues (root, stem, leaf, and flower) and three developmental stages (1-3 years) to investigate the spatiotemporal regulation of ginsenoside biosynthesis. We observed distinct tissue- and age-specific accumulation patterns: roots exhibited a progressive increase in total ginsenoside content during the second and third years, while flowers preferentially accumulated rare protopanaxadiol-type ginsenosides such as Rg3-2. Transcriptomic analysis revealed extensive differential gene expression across tissues and stages, particularly in roots during late development. Clustering and transcription factor (TF) enrichment analyses identified multiple tissue-associated regulatory modules. Four TFs--AT3G12130, SPL9, MYB33, and SPL1--emerged as core candidates based on coordinated expression, promoter motif enrichment, and functional annotation of predicted target genes. Motif analysis further linked these TFs to key biosynthetic genes involved in triterpenoid oxidation and glycosylation, including CYP716A53v2 and CYP716A47. Together, these findings suggest that tissue-specific ginsenoside accumulation in P. notoginseng is associated with coordinated transcriptional regulation of biosynthetic enzymes. This study provides a transcriptomic framework for understanding spatial regulation in ginsenoside biosynthesis and identifies candidate regulators for future functional validation and metabolic engineering.

Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L (SKIP19L), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.

The evolutionary transition of the green plant lineage (Viridiplantae) from aquatic environments to terrestrial habitats required unprecedented adaptations of cellular metabolism to severe environmental stressors, including desiccation, high irradiance, and rapid temperature fluctuations. Redox regulation, mediated by oxidation and reduction of reactive cysteine residues (RCys), plays a crucial role in translating environmental fluctuations into rapid cellular responses. Although comparative genomics has revealed expansions in multiple cellular systems preceding terrestrialization, the evolutionary history of redox-regulated protein networks remains elusive. This work integrated large-scale phylogenomic reconstructions across 37 Viridiplantae species with five independent Arabidopsis thaliana redox proteomics datasets to trace the evolutionary trajectory of RCys. The analysis showed that the ancestral core, consisting of plastid-localized regulatory cysteines, was already established at the base of the green lineage. Furthermore, an expansion driven by gains of RCys via amino acid replacements within pre-existing proteins occurred in the common ancestor of Zygnematophyceae and land plants. These findings suggest that a targeted incorporation of thiol-based regulatory switches provided early land plant ancestors with enhanced protein functional plasticity necessary to cope with the challenges of terrestrial environments. HighlightsO_LIThe foundational plastid-localized redox core was established at the root of Viridiplantae. C_LIO_LINovel regulatory switches were integrated into conserved machinery via amino acid replacement. C_LIO_LIA punctuated burst of redox innovation at Zygnematophyceae and Embryophyta last common ancestor preceded plant terrestrialization. C_LIO_LIRedox acquisition rates declined sharply following the successful colonization of land. C_LI

Polyploid genome assembly presents unique challenges due to extensive heterozygosity and complex haplotype structure. We report a haplotype-resolved, chromosome-scale assembly of Regen-SY27x, a genotype of autotetraploid alfalfa (Medicago sativa), which is widely used for genetic modification because of its excellent regenerative capacity in tissue culture. Using PacBio HiFi long reads, Omni-C scaffolding, and linkage map guided phasing, we generated a 3.2 GB assembly comprising four haplotypes with high contiguity and completeness. Kmer-based validation confirmed accurate haplotype separation, while linkage map integration and dotplot analysis identified and corrected chimeric scaffolds. Gene annotation yielded 221,688 protein-coding genes, with more than 99% assigned to pseudochromosomes. Repetitive elements accounted for 62.7% of the genome, dominated by long terminal repeat retrotransposons and a high fraction of Helitrons. The spatial enrichment of Helitrons within gene-dense distal chromosome arms underscores their pivotal role as key drivers of genomic innovation and gene family expansion. We identified 3,696 nucleotide-binding leucine-rich repeat R genes, with Toll/interleukin-1 receptor-like and Rx-type subclasses forming large tandem clusters across haplotypes. Comparative analyses revealed strong macrosyntenic conservation among Regen-SY27x and the publicly available Chinese alfalfa genomes but extensive structural variation both within Regen-SY27x haplotypes and between Regen-SY27x and the Chinese genotypes with tens of thousands of duplications, inversions, and translocations detected. These results demonstrate that a single autotetraploid individual captures extensive structural diversity, but individuals from different populations vary greatly. The Regen-SY27x assembly provides a foundational genomic resource for investigating polyploid genome evolution and identifying genetic variation relevant to biological and agronomic improvement in alfalfa. Article SummaryThis study presents the first chromosome-scale, haplotype-resolved genome assembly of the US alfalfa germplasm, Regen-SY27x, a key alfalfa genotype used widely for genetic engineering. We integrated HiFi long reads, Omni-CTM scaffolding, and linkage map-guided phasing to reconstruct all four haplotypes of this complex autotetraploid. Our results identified 221,688 protein-coding genes and reveal immense intra-individual structural variations dominated by small duplications. This high-quality reference serves as a foundational tool for the alfalfa community, enabling researchers to link complex structural diversity with agronomic traits and further enhance the biotechnological potential of this essential forage crop.

Oryza granulata Nees et Arn. ex Watt, a diploid wild rice (GG genome), possesses exceptional shade tolerance and is a key genetic resource for rice improvement. However, previous genome assemblies lacked continuity and completeness. Here we present a chromosome-scale reference genome of O. granulata using PacBio SMRT (113x), Hi-C (95x), and Illumina sequencing. The final assembly is ~764.24 Mb, with a scaffold N50 of ~59.32 Mb, and ~96.47% of the sequence anchored to 12 chromosomes. BUSCO completeness is ~98.6%. We annotated ~42,064 protein-coding genes, of which ~95.39% were functionally annotated, along with ~73.46% repetitive elements. The genome assembly and raw sequencing data are available at NGDC (PRJCA061980), NGDC GSA (CRA068332), and NGDC GWH (GWHISVE00000000.1). This high-quality genome will serve as a fundamental resource for evolutionary genomics, conservation biology, and breeding of shade-tolerant rice cultivars.

Genotype-by-environment interactions are central to crop adaptation and yield stability, yet they remain difficult to model for robust prediction across heterogeneous environments. Although enviromic profiling has improved the characterization of dynamic field conditions, most existing genomic prediction methods adopt a late-fusion strategy that encodes genomic and environmental information independently before global integration, thereby limiting their ability to resolve fine-scale, context-dependent G x E effects. Here, we developed GE-BiCross, a hierarchical bidirectional cross-attention framework for maize prediction. GE-BiCross incorporates a dual-path feature extraction module to disentangle independent and cooperative effects, a tokenized bidirectional cross-attention module to enable reciprocal genotype-environment interaction learning, and a mixture-of-experts module to adaptively capture heterogeneous response patterns across environments. Using a large-scale dataset of approximately 360,000 observations from 4,923 maize hybrids evaluated in 241 environments, GE-BiCross consistently outperformed conventional genomic prediction, machine learning, and deep learning baselines across six agronomic traits. The greatest improvements were observed for environmentally responsive and genetically complex traits. In particular, GE-BiCross achieved an R2 of 0.672 for grain yield and 0.880 for grain moisture, significantly surpassing all comparison models. Ablation analyses demonstrated that the three core modules make distinct and complementary contributions to predictive performance.These results show that deep, bidirectional integration of genomic and enviromic information can substantially improve modeling of complex G x E interactions, providing a powerful framework for interpretable genomic prediction and climate-smart crop breeding.