Plant Communications

Abiotic stresses are primary constraints on global crop productivity, reducing yields by up to 80%. While traditional phenotypic sensing detects stress only after physiological symptoms emerge and often fails to discriminate specific stressor types, transcriptomic profiling offers a high-dimensional solution, capturing rapid and sensitive molecular shifts. In this study, we developed AbiOmics, the first end-to-end machine learning pipeline specifically designed to identify and discriminate among multiple stressors. This approach represents a previously undocumented method for stress specification using large-scale transcriptomic big data. We identified 320 stress-specific marker genes using a curated collection of 1,243 transcriptomes of Arabidopsis samples treated with four major abiotic stresses, salt, cold, heat, and drought. A single-layer perceptron model trained on these features achieved 91% accuracy during five-fold cross-validation and 93% accuracy on an independent test set. The model demonstrated an unprecedented capacity to generalize to multi-stress conditions, identifying concurrent signatures in combinatorial salt-and-heat treatments. By integrating marker identification with SHAP-based biological interpretation, AbiOmics provides a rigorously validated diagnostic tool superior to conventional sensing. This framework establishes a high-confidence labeling strategy for AI-driven crop management and precision breeding to mitigate climate change impacts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/707868v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@573cb5org.highwire.dtl.DTLVardef@152a0b0org.highwire.dtl.DTLVardef@1b389a5org.highwire.dtl.DTLVardef@11c60d_HPS_FORMAT_FIGEXP M_FIG C_FIG

High-throughput sequencing technologies have enabled the generation of high-quality reference genomes for numerous rice cultivars. However, inferring gene functions, associated phenotypes, and causal variants from these sequences remains challenging. The Rice Annotation Project Database (RAP-DB; https://rapdb.dna.affrc.go.jp) is a curated genomic resource that provides comprehensive gene annotations for the reference genome of Oryza sativa ssp. japonica cv. Nipponbare. Since its major update in 2013, gene models and functional annotations have been continuously revised through expert manual curation of newly published literature related to rice genes. As of March 2025, a total of 6,631 transcripts corresponding to 6,371 loci have been curated based on 4,699 peer-reviewed publications. These curated genes are functionally characterized and are frequently associated with agronomic traits, including yield components, stress tolerance, and disease resistance. To support molecular breeding, RAP-DB now provides a curated catalogue of 904 agronomically important loci, including gene symbols, functional descriptions, and associated traits, together with more than 1,000 functionally characterized alleles compiled from the literature. In addition to in-house expert curation, RAP-DB integrates community-curated datasets for major gene families, such as WRKY transcription factors, S-domain receptor-like kinases, and leucine-rich repeat-containing receptors, thereby expanding coverage of key regulatory and defense-related genes. RAP-DB also incorporates reanalyzed RNA sequencing expression profiles alongside microarray-based expression data and co-expression networks, offering gene-centric views of expression patterns across tissues, conditions, and developmental stages. Furthermore, RAP-DB is linked to genome-wide variation datasets from diverse rice varieties through the TASUKE+ genome browser, enabling exploration of allelic diversity across varieties. To enhance annotation quality and long-term sustainability, AI-assisted literature screening and a web-based feedback system have been introduced, allowing users to submit corrections to gene models and report newly characterized genes or relevant publications. Together, these developments strengthen RAP-DB as a primary, literature-based gene annotation resource and provide a practical foundation for molecular breeding in 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

Plant proteomes contain evolutionarily conserved peptides with poorly conserved primary sequences, often hindering their identification and classification into families. Homology-based approaches and conventional annotation pipelines frequently fail to detect these family members, particularly in poorly characterized, but agronomically relevant plant species. CLE peptides (CLAVATA3/EMBRYO SURROUNDING REGION-related peptides) constitute a large and evolutionarily conserved family of plant signaling molecules, yet their characterization remains incomplete. Beyond a limited number of well-studied members, a substantial number of CLE peptides remain uncharacterized due to functional redundancy and the intrinsic features of CLE genes, which encode short pre-propeptides with only a small 12-residue conserved motif. Here, we present a novel framework leveraging state-of-the-art Protein Language Models (pLMs) to discover CLE peptides directly from 13 plant proteomes. By coupling sequence embeddings trained on large evolutionary datasets (ESM2 and ProtT5) with supervised machine learning, our dual-model approach captures deep semantic features of the CLE family that are missed by traditional alignment methods. The pipeline demonstrated robust generalization, achieving high classification accuracy (98.9-99.4%) on a held-out set of CLE peptides not used during training. Consequently, we identified a set of high-confidence, previously unannotated CLE candidates prioritized through a stringent consensus-based filtering strategy. This work demonstrates how AI-driven proteome analysis can overcome the limitations of homology-based methods and provides a scalable strategy for uncovering previously unidentified peptide-mediated signaling molecules across plant lineages. HighlightLeveraging Protein Language Models, our AI framework uncovers "hidden" signaling peptides missed by standard tools, revealing the elusive diversity of CLE regulators across plant proteomes.

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

Genetic studies leveraging natural variation in Arabidopsis species have improved our understanding of evolutionary genetic processes underlying ecologically important and adaptive traits. Integrating the thorough functional knowledge accumulated in A. thaliana with the extensive natural variation in outcrossing Arabidopsis species is a powerful approach to study the basis of adaptation in natural evolutionary and ecological contexts. Here we present an integrated genomics database of sequenced genomes from several studies in A. lyrata (1018 genomes in total) and A. arenosa (736 genomes in total), spanning the geographic ranges of these two ploidy-variable, outcrossing taxa. We provide a searchable genome browser with population data mapped to respective reference genomes, an interactive geographic map of population structure clusters, and an efficient way to subsample the full dataset of genetic variation, available at arabidopsislyrata.org. To demonstrate its utility, we perform a genome-wide association study on a latitudinal cline of A. lyrata and find strong associations of several loci with latitude, including variants in key regulators of photoperiodic growth. This resource provides access to genetic diversity data in a single repository, enabling further studies of comparative genetics and local adaptation, as well as of individual genes of interest.

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.

Common bean (Phaseolus vulgaris L.) is one of the most important grain legumes for direct human consumption. Currently, 60% of its production is estimated to be at risk due to drought. However, the genetic basis of common beans drought resistance is poorly understood. To this end, we assessed the genetic architecture of drought-responsive changes in a whole genome-sequenced population of 218 common bean accessions. Using multi-omics-based trait evaluation, including lipidomics, photosynthetic and agronomic traits, followed by multi-omics genome-wide association studies (moGWAS), yielded in the detection of a myriad of moQTL for photosynthesis and yield, as well as the levels of various lipids. QTL associated with glycolipids, which are integral to photosynthesis, since they constitute the major membrane components of chloroplasts, were identified. In addition, we molecularly validated several lipid-related candidate genes via P. vulgaris hairy root transformation as well as transient expression in tobacco. In particular, a lipoxygenase and an allene oxide synthase were identified as explaining the variation in triacylglycerol by oxylipin production. These data provide a blueprint for multi-omics-assisted improvement of crop water 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.

Isoflavone synthase (IFS), a cytochrome P450 monooxygenase of the CYP93C subfamily, catalyzes the conversion of flavanones into isoflavones, the first committed step in the biosynthesis of isoflavonoid phytoalexins. In pea (Pisum sativum L.), the phytoalexin pisatin plays a pivotal role in defense against pathogens. However, the molecular basis underlying IFS function in pea remains poorly understood. In this study, we performed a comprehensive genome-wide identification and characterization of IFS genes in pea. Three IFS candidates, PsIFS7A, PsIFS7B, and PsIFS7C, were identified that reside on chromosome 7, each harboring all conserved cytochrome P450 signature motifs. PsIFS genes exhibited predominant expression in root tissue, with transcript levels induced rapidly upon Aphanomyces euteiches infection. Enzymatic assays confirmed their catalytic activity in converting the flavanones naringenin and liquiritigenin into the isoflavones genistein and daidzein, respectively, both in vitro and in planta systems. Furthermore, all three PsIFS genes were found in close proximity to quantitative trait loci (QTL) associated with Aphanomyces root rot resistance. Together, these findings provide novel insights into the IFS gene family in pea and lay a foundation for metabolic engineering or molecular breeding strategies to enhance disease resistance through targeted modulation of pisatin biosynthesis.

With the rapid accumulation of diverse omics datasets, achieving efficient management and integrative analysis of plant multi-omics data remains a major challenge. Conventional solutions rely on constructing web-based databases, which often demand substantial programming expertise and long-term financial support. To address these limitations, we developed the Plant Multi-omics Database Construction System (PlantMDCS)-a locally deployable, user-friendly, and collaborative platform that unifies database construction and downstream multi-omics analysis within a graphical environment. PlantMDCS adopts a decoupled front-end/back-end architecture. The back end serves as the core engine for data management and computation, and is responsible for the storage, preprocessing, integration, and hierarchical association of multi-omics data. Once initialized, the front end supports the complete research workflow, including data import, querying, integrative analysis and visualization. All operations can be performed without programming, while local resource usage is dominated by disk storage required for user-provided datasets rather than sustained computational overhead. Benchmarking across plant species ranging from Arabidopsis to hexaploid wheat demonstrated that database construction can be completed within minutes, independent of genome size or data complexity. PlantMDCS is designed for local deployment to ensure data security, while allowing multi-user collaboration within local networks and supporting controlled remote access for teams distributed across different regions. Overall, PlantMDCS offers a secure and sustainable framework that integrates data management and analysis within a unified system. This design shifts multi-omics research away from fragmented file-based processing toward persistent, database-driven exploration, thereby enhancing analytical efficiency and reproducibility.

Pathogen pressure threatens legume crop productivity worldwide. Nucleotide-binding leucine-rich repeat (NLR) immune receptors serve as crucial plant resistance genes, recognizing pathogens and triggering immunity. However, the extent and patterns of NLR expression in different tissues and organs, notably across evolutionary time, remain largely uncharacterized. To investigate tissue-specificity of NLR expression in the Fabaceae (legumes), we conducted comparative analyses integrating phylogenomics and transcriptomics in root and shoot tissues across different legume species. The NLR repertoires of 28 legumes were grouped into five monophyletic clades: coiled-coil NLR (CC-NLR), Toll/interleukin-1 receptor NLR (TIR-NLR), G10-subclade CC NLR (CCG10-NLR), RESISTANCE TO POWDERY MILDEW 8-like CC NLR (CCR-NLR), and TIR-NB-ARC-like {beta}-propeller WD40/tetratricopeptide repeats (TNPs). Most legume NLRs belonged to CC-NLR and TIR-NLR clades, followed by CCG10-NLR, CCR-NLR, and TNP clades. In seven of these species, comparative analysis of NLR expression in leaves versus roots revealed that over half ([~]57%) of expressed NLR genes showed predominant expression in one tissue: 34% in roots (451/1336), and 23% in leaves (311/1336). We identified 324 root-specific NLRs, 171 leaf-specific NLRs, and 841 non-specific NLRs, with an average tissue specificity per species of 32%. The closely related species grass pea (Lathyrus sativus) and pea (Pisum sativum) were an exception, showing higher levels of leaf-specific rather than root-specific NLR expression. We also identified conserved tissue expression patterns across legume species, resulting in a comprehensive resource describing tissue expression bias, enrichment, and specificity for 113 phylogenetic NLR subclasses. These legume NLR repertoires will support comparative studies between species and inform precision-breeding programs considering tissue expression patterns.

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.

Poplar seed fibers cause environmental and health concerns, yet their developmental mechanisms remain poorly understood. Here, we constructed a high-resolution spatiotemporal transcriptomic atlas of female poplar capsules by integrating single-nucleus and spatial transcriptomics. We delineated the developmental trajectory of seed fibers, confirming their origin from placenta cells, and identified three functionally distinct fiber cell subtypes involved in initiation, metabolic support, and elongation. Weighted gene co-expression network analysis (WGCNA) identified several hub transcription factors, including PtoMYB, PtoHDT1, PtoEIF6 and PtoPDF2, that may serve as key regulators of fiber development. Our study provides a cellular-resolution framework for understanding trichome development in woody perennials and offers candidate targets for functional characterization toward breeding low-fluff poplar cultivars. HighlightsO_LIA spatiotemporal transcriptomic atlas of poplar capsule development is constructed at single-cell resolution C_LIO_LIFiber cells originate from placenta cells and comprise three functionally distinct subtypes C_LIO_LIProvides molecular targets for breeding low-fluff poplar cultivars to mitigate environmental pollution C_LI

Alkalinity stress significantly restricts global plant productivity, yet the genetic basis for plant tolerance remains largely uncharacterized. In this study, a genome-wide association study was performed using 218 diverse natural Arabidopsis thaliana ecotypes to identify the top 73 SNPs associated with alkalinity tolerance, measured by relative root length in hydroponic growth media containing NaHCO3 at pH 8.0. Prominent association peaks were localized near genes involved in lipid metabolism (GGL20), protein degradation (AT3G17570), and vesicle-mediated protein sorting (VPS13B and AT5G57210). Expression level and protein polymorphisms in these genes were associated with alkalinity tolerance. T-DNA mutants of GGL20, AT3G17570, and the chromatin-modifying gene AFR1 showed alkaline hypersensitivity, reduced root length, iron content, and rosette size, and elevated hydrogen peroxide. Conversely, mutants of the DNA repair gene ETG1 exhibited greater tolerance than wild type in hydroponics, solid media, and soil assays, confirming their role in alkalinity tolerance. Transcriptome and network analyses revealed that alkalinity responses significantly overlap with iron deficiency pathways, identifying hub genes involved in ribosome assembly and translation control. These findings provide a comprehensive map of the genetic and transcriptional landscape of alkalinity adaptation and offer promising candidate genes for engineering crops resilient to alkaline soil conditions.

BackgroundThe genomic region spanning 1.1-1.3 Mb on rice chromosome 6 is a recognized structural variation (SV) hotspot linked to Rice Black-Streaked Dwarf Virus (RBSDV) resistance. However, the precise molecular mechanism has remained elusive due to the inherent "reference bias" of the japonica-based genome, which lacks the critical causative sequences. MethodsLeveraging a neuro-symbolic-driven analysis of gap-free Telomere-to-Telomere (T2T) pangenome datasets and the LGEMP engine, we conducted a high-resolution comparative study between indica (9311) and japonica (Nippon bare). This approach allowed us to treat genomic variations as 3D structural building blocks rather than linear strings. ResultsWe identified a 3.3 kb large-scale insertion uniquely present at the 1.21 Mb locus in 9311. This SV, likely mediated by transposable elements, exhibits extreme sequence divergence (24% identity). We demonstrate that this insertion acts as a topological modifier, driving a dramatic functional shift: while the japonica allele encodes a basic DUF590 transporter, the indica allele has undergone de novo evolution into a complete CC-NBS-LRR (NLR) immune receptor. Transcriptomic profiling confirmed the generation of six novel isoforms (T01-T06) enabled by the SVs structural re-organization. Validation across 16 representative T2T assemblies confirms this 3.3 kb SV as an indica-specific "evolutionary patch," effectively filling the "missing heritability" gap in rice viral immunity. ConclusionOur findings uncover a novel mechanism of gene birth through structural re-organization at high-diversity hotspots. By integrating T2T pangenomics with AI-driven inference, this study provides a definitive molecular marker for the precision breeding of virus-resistant crops and redefines our understanding of subspecies-specific adaptation..

Together with biosynthesis and transport, inactivation regulates the concentration of indole-3-acetic acid (IAA), a key auxinic compound with a myriad of functions in plant development. Main inactive IAA metabolites are categorised into oxidised forms and ester- or amide-linked conjugates. DIOXYGENASE FOR AUXIN OXIDATION1 (DAO1) and DAO2, 2-oxoglutarate and iron-dependent dioxygenases, contribute to IAA oxidative inactivation in collaboration with group II GRETCHEN HAGEN3 (GH3) IAA-amido synthetases, while a group of UDP-glycosyltransferases (UGTs) conjugate IAA to sugars. To study the IAA inactivation routes, we generated combinatorial mutants between all group II GH3s (gh3oct) and DAO1 or DAO2, as well as between the DAOs and main UGTs. In vivo [13C6]IAA feeding experiments traced the metabolic fate of the exogenously applied IAA, supporting the main IAA inactivation pathway, in which DAO acts downstream of GH3s. They also indicated that UGT-mediated IAA glycosylation is more important than previously assumed for modulating IAA levels and plant development. Our metabolic and transcriptomic data further revealed that gh3oct may still produce some GH3 activity, explaining previous reported phenotypic inconsistencies. Our data additionally suggest that other not yet identified metabolic activities might play a role in IAA overproducing plants, and that the premature downregulation of flowering time integrators like FLOWERING LOCUS C (FLC) likely underlies the early flowering of gh3oct and gh3oct dao1 plants.