Bacterial wilt, caused by Ralstonia solanacearum, is a destructive disease with no effective chemical control, severely affecting global crop production. This study applied BSR-seq on 581 recombinant inbred lines (RILs), combined with linkage mapping, to identify resistance quantitative trait loci (QTL). Illumina sequencing yielded 189.6 Gb of data, identifying 70,035 high-quality SNPs from 55,840 genes. Two resistance loci were mapped on chromosome 12: a novel 1.11 Mb QTL and an adjacent 1.03 Mb region. Five CC-NBS-LRR-type resistance candidate genes were identified. The AhRRS6 alleles were cloned, and three allele-specific SNP markers were developed and validated across peanut breeding varieties. Additionally, 3,851 differentially expressed genes were detected, including key resistance-related genes. Transgenic AhRRS6y conferred strong resistance to R. solanacearum, while AhRRS6x caused susceptibility in both Nicotiana benthamiana and Arabidopsis thaliana. These alleles differentially regulated genes in HR, ETI, and PTI pathways, particularly affecting NbPDF1.2 and NbNDR1. AhRRS6y expression reduced oxidative damage, indicated by lower malondialdehyde and higher ascorbate peroxidase activity. This work provides critical genetic resources for breeding bacterial wilt-resistant peanut varieties and enhances the mechanistic understanding of plant immune responses.

Super-pangenomes expand species-level pan-genomes to genus-wide frameworks, which integrate cultivated and wild genomes to capture hidden diversity. Super-pangenomes reduce reference bias, discover structural variations, rare alleles, and regulatory elements which drive stress adaptation and trait evolution. By leveraging QTL/GWAS and panomics, super-pangenomes fast-track breeding of stress-smart and high-yielding crops. This review highlights advances, challenges, and prospects, which position super-pangenomes as a “surprise package” for sustainable, food-secure agriculture.

Land plants underpin civilization and planetary health, yet their genomic diversity remains largely uncharted. Current resources are unstandardized and scarce, lacking reference genomes for 95% of genera, 70% of families, and 51% of orders, impeding evolutionary and functional insight. We thus propose the PLANeT initiative, an international effort to generate high-quality, standardized genomes across the plant tree of life. Integrating artificial intelligence (AI) with genomics, we will decode conserved principles to advance fundamental plant biology, biodiversity conservation, crop improvement, and natural product discovery. Engaging around 100 labs to train 1,000 scientists, we will tackle pivotal questions for a sustainable future.

Mung bean (Vigna radiata) is a globally important legume crop valued for its short growing cycle, nitrogen-fixing capacity and high nutritional value, particularly in developing countries. Here we report a comprehensive graph-based pan-genome assembled from 11 genetically diverse global accessions. The framework captures 75,268 gene families (50.86% core, 35.19% dispensable and 13.95% private) and 66,862 nonredundant structural variants. Integrating these structural variants and single nucleotide polymorphisms, genome-wide association studies across five environments identified candidate genes for 20 agronomic traits, underscoring the pivotal roles of these variants in driving mung bean domestication and improvement. Mechanistically, we demonstrate that a 68-bp promoter insertion in VrTIFY6B and a 136-bp promoter deletion in VrPGIP1 regulate flavonoid content and confer bruchid resistance, respectively. These genomic resources and actionable functional variants provide a powerful toolkit to accelerate mung bean improvement through marker-assisted breeding, genomic selection and genome editing to address global food security.

With the escalating impacts of climate change, drought stress (DS) is significantly decreasing water accessibility and availability, causing substantial direct and indirect economic repercussions within agricultural systems. Addressing the needs of a growing global population necessitates urgent advancements in breeding DS-tolerant crops while maintaining high yields. This urgency demands a fast and adaptable defensive strategy to mitigate the adverse effects of DS on crop productivity. Accelerating such developments requires leveraging advanced omics-assisted breeding (e.g., genomics, transcriptomics, proteomics, and metabolomics), genetic engineering (e.g., transgenic technology, and genome editing), machine learning, precise phenotyping, crop wild relatives, and speed breeding. These fast-forward methods present highly promising avenues for the design of future crops that can withstand DS pressures. In summary, we propose an innovative approach termed the “OGS trio,” which encompasses omics integration, genetic engineering, and speed breeding. This trio stands composed to transform efforts against DS, offering significant potential for developing drought-tolerant crops to achieve and food security amidst climate change.

Crops increasingly face overlapping stresses such as heat, drought, salinity, and pathogens that conventional breeding or genome editing rarely overcome in combination. To address this, we propose CRISPR-enabled horizontal gene transfer (CRISPR-HGT) as a programmable framework that recreates the evolutionary process by which plants historically acquired adaptive microbial genes. Microbial genes, refined under extreme environments, provide a naturally preadapted resource for multi-trait resilience. By integrating tools such as Cas12a, CasΦ, RNA-targeting, and dCas-based epigenome editors with AI-guided microbial gene discovery, CRISPR-HGT enables modular and inducible stress regulation. This approach shifts genome editing from allelic modification to evolution-guided design. We outline a conceptual pipeline spanning microbial gene mining to adaptive field deployment, highlighting the ecological, biosafety, and regulatory dimensions, from the European Union's cautious oversight to the UK's product-based framework. CRISPR-HGT thus introduces an evolution-informed paradigm for engineering crops that anticipate stress and sustain yield under climate uncertainty.

Abstract Passion fruit (Passiflora edulis) is a highly nutritious horticultural crop cultivated widely across tropical and subtropical regions. Despite decades of breeding efforts that have led to the release of a few high‐yielding cultivars, on‐farm productivity remains suboptimal, and several existing cultivars are showing signs of declining vigor. To ensure the development of cultivars with stable and enhanced yields under both optimal and stress‐prone conditions, there is a growing impetus to improve breeding efficiency. Integrating advanced genomics technologies into conventional breeding pipelines offers a promising path forward. Over the past decade, substantial genomic resources have been developed, including genome‐wide markers, marker‐trait associations, reference genomes, and resequencing datasets. Some of these tools are already being deployed in breeding programs to enhance yield and consumer‐preferred traits. Emerging approaches such as genomic selection, speed breeding, and high‐throughput phenotyping hold further potential to accelerate genetic gains. Realizing the full benefits of these tools will require strategic utilization of diverse and targeted genetic resources, coupled with streamlined cultivar delivery systems. Addressing the technical and operational bottlenecks that hinder the translation of genomic advances to field‐ready cultivars will be key to securing the future of passion fruit improvement.

Climate change-driven heat and drought stresses during reproductive stages significantly threaten wheat productivity. To investigate the genetic and physiological basis of combined heat-drought (HD) tolerance, we evaluated 345 wheat genotypes under three environments of HD stresses, non-stress glasshouse conditions and a late-sowing field trial. HD stresses caused significant reductions in chlorophyll content, flag leaf area, biomass, seed-setting rate and grain weight-related traits. Notably, HD-tolerant lines maintained higher grain weight, grain number and chlorophyll retention, with less than half the reductions observed in sensitive genotypes. A genome-wide association study using a 40K single-nucleotide polymorphism (SNP) array identified 124 candidate SNPs (cSNPs) associated with 51 traits across three environments with 78 cSNPs associated with HD tolerance. In total, 24 cSNP blocks exhibited pleiotropic associations with multiple traits under those three environments. Tight genomic co-localisations were detected between chlorophyll content (SPAD or CCM200 values), flag leaf width, seed-setting rate and grain yield components (thousand grain weight, grain number per spike), with superior haplotypes identified, supporting their utility in selections. Stay-green traits appeared to contribute significantly to yield stability under HD stresses. Those results provide valuable genomic and physiological insights into wheat HD tolerance for future targeted wheat breeding.

Insect pest control in crop production incurs substantial economic costs annually on a global scale. Although broad-spectrum chemical pesticides were once considered the most effective solution, their overreliance has led to adverse effects on beneficial insects, human health, and the environment, as well as the development of pesticide-resistant insect populations. Consequently, there is an urgent need for alternative pest management strategies that minimize pesticide use and reduce unintended impacts on natural enemies, thereby maintaining ecological balance. Host plant resistance plays a pivotal role in integrated pest management (IPM). However, developing pest-resistant varieties through conventional breeding methods can be time-consuming and challenging due to the involvement of multiple quantitative traits controlled by various genetic loci. One promising biotechnological approach is the development of fusion proteins, engineered molecules that combine the functional properties of two or more distinct proteins. These fusion proteins effectively target specific insect pests while minimizing environmental impact. Importantly, they overcome key limitations of single-gene constructs, including narrow target range and rapid resistance development. Numerous fusion proteins have been successfully developed and deployed in various crop plants, demonstrating their versatility and broad-spectrum activity against major insect pests. This review discusses fusion protein technologies and their application in developing transgenic crops with enhanced resistance to insect pests.

Peanut (Arachis hypogaea L.) is a globally significant leguminous oil crop. Here we present telomere-to-telomere genome assemblies for two diploid and four tetraploid peanut varieties, resulting in high-quality reference genomes, showing that the complex activities of transposable elements, chromosomal rearrangements and centromere expansions within subgenomes collectively contribute to the asymmetrical evolution of the tetraploid genome, and unique structural variants in the four tetraploid peanut varieties provide clear evidence of domestication. Population analyses of 521 peanut accessions revealed asymmetric selection events between subgenomes during breeding, and genome-wide association studies identified candidate genes linked to oil content, seed size and weight, kernel dehydration rate, and arachidic acid content. In addition, transcriptomic and metabolomic analyses revealed enhanced activity in lipidomic and anthocyanin biosynthetic pathways during seed development. These comprehensive findings provide insights into genome organization, evolutionary dynamics and phenotypic differentiation across peanut varieties that could inform future peanut breeding and improvement strategies.

Lipoxygenases (LOXs) play vital roles in plant growth and defense. In this study, through genomic and molecular analyses, we discover a major LOX gene (LOX-A4) differentially expressed in Triticum urartu (Tu), the diploid progenitor of A subgenome in polyploid wheat. Compared to Tu accessions carrying wild type gene (LOX-A4W), those bearing mutant allele (LOX-A4m) show better growth but lower stress tolerance. These differences concur with a wider geographical distribution of LOX-A4m accessions than LOX-A4W materials in the Fertile Crescent. Interestingly, only mutant LOX-A4 alleles are detected in 3,516 worldwide tetraploid and hexaploid wheat lines; restoring LOX-A4W expression in common wheat inhibits growth but enhances stress tolerance. Furthermore, genome-wide identity-by-state analysis reveals that polyploid wheat A subgenome is more related to the A genome in 13 LOX-A4m Tu accessions. Thus, our work provides evidence that LOX gene variation shapes plant gene pools and their contributions to polyploid genome formation via regulating growth-defense trade-offs.

Chickpea (Cicer arietinum L.) is an important legume crop predominantly cultivated in arid and semi-arid regions, where drought limits yield. This review outlines recent advancements in drought tolerance research in chickpea, integrating genetic, molecular, environmental, and physiological approaches. The idiosyncratic nature of drought is emphasized, highlighting the need to align plant phenotypes with specific drought types across phenological scales. Advances in genomics, including genomic selection and marker-assisted selection, have accelerated the breeding for adaptation to drought. CRISPR-Cas9 and other modern genome-editing technologies are enabling precise modifications of drought-responsive genes, offering new insights. High-throughput phenotyping and data-driven predictive models further enhance the identification and selection of superior genotypes. The integration of traditional breeding methods with modern technologies addresses challenges posed by the idiosyncratic nature of drought, the interaction between drought and other stresses (e.g., heat), the polygenic nature of drought tolerance, the narrow genetic diversity in cultivated chickpeas, and incomplete conceptual models of plant phenotypes in crop stands. This review underscores the importance of multidisciplinary collaboration in developing drought-tolerant chickpeas.

Global warming poses a critical threat to wheat (Triticum aestivum L.) production, particularly during sensitive reproductive stages. This review synthesizes current understanding of how heat stress affects wheat and the adaptive mechanisms that confer tolerance, with emphasis on recent advances in genomics and biotechnology. Heat stress impairs morphological, physiological, biochemical, and molecular processes, reducing photosynthetic efficiency, accelerating senescence, disrupting assimilate partitioning, and damaging cellular structures. Adaptive responses include optimized water relations, antioxidant defences, osmolyte accumulation, heat shock protein induction, and hormonal regulation, all coordinated by complex gene networks. Advances in genetic dissection through quantitative trait locus (QTL) mapping, genome-wide association studies, and candidate gene discovery have identified loci and alleles linked to thermotolerance. Multi-omics integration has uncovered regulatory pathways, transcription factors, and epigenetic mechanisms, including stress memory, that underpin resilience. Emerging tools such as genome sequencing, pangenomics, genomic prediction, haplotype-informed breeding, and CRISPR-based editing are accelerating the translation of these discoveries into improved cultivars. In addition to summarizing these advances, this review highlights key challenges, from harmonizing heat stress phenotyping and validating causal variants to integrating multi-omics into breeding pipelines, and proposes targeted strategies to bridge the gap between discovery and deployment to enable the development of climate-ready wheat cultivars.

Genetic variation underlying phenotypic diversity between wild and domesticated species has been extensively studied, the contribution of higher-order chromatin architecture to these processes remains less explored. Advances in Hi-C and related genomic technologies have revealed that plant genomes exhibit complex three-dimensional (3D) genome organization, hierarchically structured into A/B compartments, and topologically associated domains (TADs). TADs represent self-interacting genomic regions that can constrain or regulate without directly determining transcriptional outcomes. Alterations to TAD organization or boundary have been associated with changes in chromatin interactions and gene regulatory potential in specific developmental or environmental contexts. In plants, emerging evidence indicates that TAD structure can be genetically and environmentally modulated, despite the absence of canonical architectural proteins such as CTCF. Both environmental stress and genetic perturbations have been shown to remodel chromatin organization, with context-dependent changes in gene expression. Such plasticity in chromatin dynamics that contribute to adaptive responses raises a potential link between 3D genomic structure and cryptic genetic variations (CGVs). CGVs remain phenotypically silent under normal conditions but can be revealed under environmental or genetic perturbations, representing an additional layer of regulatory potential in plant genomes. Here, we propose that stress-induced chromatin organization, including changes in TAD organization and chromatin compartmentalization, may influence accessibility and expression of CGVs in a context-dependent manner. While a direct mechanistic link between TADs and CGVs remains largely unexplored. Here, we reviewed recent findings from model plants and major crops to highlight how variation in 3D genome organization can contribute to transcriptional plasticity, stress responses, and lineage-specific regulatory evolution. By integrating 3D genomics, chromatin accessibility, and multi-omics data, we outline a conceptual framework for generating hypotheses and open questions on how TAD-associated chromatin dynamics and CGVs together may shape transcriptional plasticity, stress responses, and long-term adaptive evolution in plants with implications for future crop improvement strategies.

The WUSCHEL (WUS) transcription factor, long recognized as a master regulator of stem cell maintenance in the shoot apical meristem (SAM), has expanded in significance as a multifaceted tool in plant biotechnology. With an emphasis on its new uses in crop regeneration, somatic embryogenesis (SE), stress tolerance, and developmental regulation in cereals, legumes, and other plant species, this review summarizes recent developments on WUS function outside of Arabidopsis. We emphasize how insights from WUS biology can be translated into practical strategies to improve yield, adaptability, and resilience, while also enhancing in vitro tissue culture systems. The objective of this review is to establish WUS as a crucial molecular target for future crop genetic improvement and sustainable farming methods by highlighting the current knowledge gaps and suggesting future directions.

Genomic selection (GS) has revolutionized breeding programmes by enabling the prediction of phenotypes based on genetic data. However, GS often only explains a portion of the phenotypic variation. This review explores the potential of integrating various data types beyond genomics to enhance the prediction ability of phenotypes. We categorize data integration strategies into five categories: eliminate, facilitate, aggregate, incorporate, and modulate. Eliminating refers to removing the effect of non-genomic data on the phenotype, such as environmental data. Facilitating methods leverage non-genomic data to improve the accuracy of GS models. Aggregating approaches combine different data types for analysis, potentially revealing variation components not captured by individual data sources. Incorporation focuses on explicitly modelling interactions between data types. Modulating methods transform data into formats suitable for advanced models such as deep learning convolutional neural networks (CNNs). The review discusses the advantages and limitations of each strategy, providing a comprehensive overview of the current state of the field. We conclude by emphasizing the prospects of multi-data phenotypic prediction towards the development of a holistic prediction approach that facilitates a more comprehensive understanding of complex biological systems and significantly enhances prediction accuracy.