A natural gene drive system confers reproductive isolation in rice.

Hybrid sterility restricts the utilization of superior heterosis of indica-japonica inter-subspecific hybrids. In this study, we report the identification of RHS12, a major locus controlling male gamete sterility in indica-japonica hybrid rice. We show that RHS12 consists of two genes (iORF3/DUYAO and iORF4/JIEYAO) that confer preferential transmission of the RHS12-i type male gamete into the progeny, thereby forming a natural gene drive. DUYAO encodes a mitochondrion-targeted protein that interacts with OsCOX11 to trigger cytotoxicity and cell death, whereas JIEYAO encodes a protein that reroutes DUYAO to the autophagosome for degradation via direct physical interaction, thereby detoxifying DUYAO. Evolutionary trajectory analysis reveals that this system likely formed de novo in the AA genome Oryza clade and contributed to reproductive isolation (RI) between different lineages of rice. Our combined results provide mechanistic insights into the genetic basis of RI as well as insights for strategic designs of hybrid rice breeding.

Rewiring of the Fruit Metabolome in Tomato Breeding.

Humans heavily rely on dozens of domesticated plant species that have been further improved through intensive breeding. To evaluate how breeding changed the tomato fruit metabolome, we have generated and analyzed a dataset encompassing genomes, transcriptomes, and metabolomes from hundreds of tomato genotypes. The combined results illustrate how breeding globally altered fruit metabolite content. Selection for alleles of genes associated with larger fruits altered metabolite profiles as a consequence of linkage with nearby genes. Selection of five major loci reduced the accumulation of anti-nutritional steroidal glycoalkaloids in ripened fruits, rendering the fruit more edible. Breeding for pink tomatoes modified the content of over 100 metabolites. The introgression of resistance genes from wild relatives in cultivars also resulted in major and unexpected metabolic changes. The study reveals a multi-omics view of the metabolic breeding history of tomato, as well as provides insights into metabolome-assisted breeding and plant biology.

Plant pangenomes for crop improvement, biodiversity and evolution.

Plant genome sequences catalogue genes and the genetic elements that regulate their expression. Such inventories further research aims as diverse as mapping the molecular basis of trait diversity in domesticated plants or inquiries into the origin of evolutionary innovations in flowering plants millions of years ago. The transformative technological progress of DNA sequencing in the past two decades has enabled researchers to sequence ever more genomes with greater ease. Pangenomes - complete sequences of multiple individuals of a species or higher taxonomic unit - have now entered the geneticists' toolkit. The genomes of crop plants and their wild relatives are being studied with translational applications in breeding in mind. But pangenomes are applicable also in ecological and evolutionary studies, as they help classify and monitor biodiversity across the tree of life, deepen our understanding of how plant species diverged and show how plants adapt to changing environments or new selection pressures exerted by human beings.

Targeted genome-modification tools and their advanced applications in crop breeding.

Crop improvement by genome editing involves the targeted alteration of genes to improve plant traits, such as stress tolerance, disease resistance or nutritional content. Techniques for the targeted modification of genomes have evolved from generating random mutations to precise base substitutions, followed by insertions, substitutions and deletions of small DNA fragments, and are finally starting to achieve precision manipulation of large DNA segments. Recent developments in base editing, prime editing and other CRISPR-associated systems have laid a solid technological foundation to enable plant basic research and precise molecular breeding. In this Review, we systematically outline the technological principles underlying precise and targeted genome-modification methods. We also review methods for the delivery of genome-editing reagents in plants and outline emerging crop-breeding strategies based on targeted genome modification. Finally, we consider potential future developments in precise genome-editing technologies, delivery methods and crop-breeding approaches, as well as regulatory policies for genome-editing products.

Genome editing of a rice CDP-DAG synthase confers multipathogen resistance.

The discovery and application of genome editing introduced a new era of plant breeding by giving researchers efficient tools for the precise engineering of crop genomes1. Here we demonstrate the power of genome editing for engineering broad-spectrum disease resistance in rice (Oryza sativa). We first isolated a lesion mimic mutant (LMM) from a mutagenized rice population. We then demonstrated that a 29-base-pair deletion in a gene we named RESISTANCE TO BLAST1 (RBL1) caused broad-spectrum disease resistance and showed that this mutation caused an approximately 20-fold reduction in yield. RBL1 encodes a cytidine diphosphate diacylglycerol synthase that is required for phospholipid biosynthesis2. Mutation of RBL1 results in reduced levels of phosphatidylinositol and its derivative phosphatidylinositol 4,5-bisphosphate (PtdIns(4,5)P2). In rice, PtdIns(4,5)P2 is enriched in cellular structures that are specifically associated with effector secretion and fungal infection, suggesting that it has a role as a disease-susceptibility factor3. By using targeted genome editing, we obtained an allele of RBL1, named RBL1Δ12, which confers broad-spectrum disease resistance but does not decrease yield in a model rice variety, as assessed in small-scale field trials. Our study has demonstrated the benefits of editing an LMM gene, a strategy relevant to diverse LMM genes and crops.

The giant diploid faba genome unlocks variation in a global protein crop.

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia faba L.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.

New Horizons for Dissecting Epistasis in Crop Quantitative Trait Variation.

Uncovering the genes, variants, and interactions underlying crop diversity is a frontier in plant genetics. Phenotypic variation often does not reflect the cumulative effect of individual gene mutations. This deviation is due to epistasis, in which interactions between alleles are often unpredictable and quantitative in effect. Recent advances in genomics and genome-editing technologies are elevating the study of epistasis in crops. Using the traits and developmental pathways that were major targets in domestication and breeding, we highlight how epistasis is central in guiding the behavior of the genetic variation that shapes quantitative trait variation. We outline new strategies that illuminate how quantitative epistasis from modified gene dosage defines background dependencies. Advancing our understanding of epistasis in crops can reveal new principles and approaches to engineering targeted improvements in agriculture.

Unraveling the diversity and cultural heritage of fruit crops through paleogenomics.

Abundant and plentiful fruit crops are threatened by the loss of diverse legacy cultivars which are being replaced by a limited set of high-yielding ones. This article delves into the potential of paleogenomics that utilizes ancient DNA analysis to revive lost diversity. By focusing on grapevines, date palms, and tomatoes, recent studies showcase the effectiveness of paleogenomic techniques in identifying and understanding genetic traits crucial for crop resilience, disease resistance, and nutritional value. The approach not only tracks landrace dispersal and introgression but also sheds light on domestication events. In the face of major future environmental challenges, integrating paleogenomics with modern breeding strategies emerges as a promising avenue to significantly bolster fruit crop sustainability.

Living with Two Genomes: Grafting and Its Implications for Plant Genome-to-Genome Interactions, Phenotypic Variation, and Evolution.

Plant genomes interact when genetically distinct individuals join, or are joined, together. Individuals can fuse in three contexts: artificial grafts, natural grafts, and host-parasite interactions. Artificial grafts have been studied for decades and are important platforms for studying the movement of RNA, DNA, and protein. Yet several mysteries about artificial grafts remain, including the factors that contribute to graft incompatibility, the prevalence of genetic and epigenetic modifications caused by exchanges between graft partners, and the long-term effects of these modifications on phenotype. Host-parasite interactions also lead to the exchange of materials, and RNA exchange actively contributes to an ongoing arms race between parasite virulence and host resistance. Little is known about natural grafts except that they can be frequent and may provide opportunities for evolutionary innovation through genome exchange. In this review, we survey our current understanding about these three mechanisms of contact, the genomic interactions that result, and the potential evolutionary implications.

Genomic signatures of strawberry domestication and diversification.

Cultivated strawberry (Fragaria × ananassa) has a brief history of less than 300 yr, beginning with the hybridization of octoploids Fragaria chiloensis and Fragaria virginiana. Here we explored the genomic signatures of early domestication and subsequent diversification for different climates using whole-genome sequences of 289 wild, heirloom, and modern varieties from two major breeding programs in the United States. Four nonadmixed wild octoploid populations were identified, with recurrent introgression among the sympatric populations. The proportion of F. virginiana ancestry increased by 20% in modern varieties over initial hybrids, and the proportion of F. chiloensis subsp. pacifica rose from 0% to 3.4%. Effective population size rapidly declined during early breeding. Meanwhile, divergent selection for distinct environments reshaped wild allelic origins in 21 out of 28 chromosomes. Overlapping divergent selective sweeps in natural and domesticated populations revealed 16 convergent genomic signatures that may be important for climatic adaptation. Despite 20 breeding cycles since initial hybridization, more than half of loci underlying yield and fruit size are still not under artificial selection. These insights add clarity to the domestication and breeding history of what is now the most widely cultivated fruit in the world.

An artificially evolved gene for herbicide-resistant rice breeding.

Discovering and engineering herbicide-resistant genes is a crucial challenge in crop breeding. This study focuses on the 4-hydroxyphenylpyruvate dioxygenase Inhibitor Sensitive 1-Like (HSL) protein, prevalent in higher plants and exhibiting weak catalytic activity against many ß-triketone herbicides (ß-THs). The crystal structures of maize HSL1A complexed with ß-THs were elucidated, identifying four essential herbicide-binding residues and explaining the weak activity of HSL1A against the herbicides. Utilizing an artificial evolution approach, we developed a series of rice HSL1 mutants targeting the four residues. Then, these mutants were systematically evaluated, identifying the M10 variant as the most effective in modifying ß-THs. The initial active conformation of substrate binding in HSL1 was also revealed from these mutants. Furthermore, overexpression of M10 in rice significantly enhanced resistance to ß-THs, resulting in a notable 32-fold increase in resistance to methyl-benquitrione. In conclusion, the artificially evolved M10 gene shows great potential for the development of herbicide-resistant crops.

GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.

Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).

Multiple wheat genomes reveal global variation in modern breeding.

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome1, and the lack of genome-assembly data for multiple wheat lines2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm16, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

Dominance complementation of parental heading date alleles of Hd1, Ghd7, DTH8, and PRR37 confers transgressive late maturation in hybrid rice.

Rice (Oryza sativa L.) is a short-day plant whose heading date is largely determined by photoperiod sensitivity (PS). Many parental lines used in hybrid rice breeding have weak PS, but their F1 progenies have strong PS and exhibit an undesirable transgressive late-maturing phenotype. However, the genetic basis for this phenomenon is unclear. Therefore, effective methods are needed for selecting parents to create F1 hybrid varieties with the desired PS. In this study, we used bulked segregant analysis with F1 Ningyou 1179 (strong PS) and its F2 population, and through analyzing both parental haplotypes and PS data for 918 hybrid rice varieties, to identify the genetic basis of transgressive late maturation which is dependent on dominance complementation effects of Hd1, Ghd7, DTH8, and PRR37 from both parents rather than from a single parental genotype. We designed a molecular marker-assisted selection system to identify the genotypes of Hd1, Ghd7, DTH8, and PRR37 in parental lines to predict PS in F1 plants prior to crossing. Furthermore, we used CRISPR/Cas9 technique to knock out Hd1 in Ning A (sterile line) and Ning B (maintainer line) and obtained an hd1-NY material with weak PS while retaining the elite agronomic traits of NY. Our findings clarified the genetic basis of transgressive late maturation in hybrid rice and developed effective methods for parental selection and gene editing to facilitate the breeding of hybrid varieties with the desired PS for improving their adaptability.

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

Pollinator-assisted plant phenotyping, selection, and breeding for crop resilience to abiotic stresses.

Food security is threatened by climate change, with heat and drought being the main stresses affecting crop physiology and ecosystem services, such as plant-pollinator interactions. We hypothesize that tracking and ranking pollinators' preferences for flowers under environmental pressure could be used as a marker of plant quality for agricultural breeding to increase crop stress tolerance. Despite increasing relevance of flowers as the most stress sensitive organs, phenotyping platforms aim at identifying traits of resilience by assessing the plant physiological status through remote sensing-assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype-to-phenotype prediction. However, as the transport of photoassimilates from leaves (sources) to flowers (sinks) is reduced in low-resilient plants, flowers are better indicators than leaves of plant well-being. Indeed, the chemical composition and amount of pollen and nectar that flowers produce, which ultimately serve as food resources for pollinators, change in response to environmental cues. Therefore, pollinators' preferences could be used as a measure of functional source-to-sink relationships for breeding decisions. To achieve this challenging goal, we propose to develop a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo-positioning system. Pollinator-assisted selection can be validated by metabolic, transcriptomic, and ionomic traits, and mapping of candidate genes, linking floral and leaf traits, pollinator preferences, plant resilience, and crop productivity. This radical new approach can change the current paradigm of plant phenotyping and find new paths for crop redomestication and breeding assisted by ecological decisions.

Wild grass-derived alleles represent a genetic architecture for the resilience of modern common wheat to stresses.

This review explores the integration of wild grass-derived alleles into modern bread wheat breeding to tackle the challenges of climate change and increasing food demand. With a focus on synthetic hexaploid wheat, this review highlights the potential of genetic variability in wheat wild relatives, particularly Aegilops tauschii, for improving resilience to multifactorial stresses like drought, heat, and salinity. The evolutionary journey of wheat (Triticum spp.) from diploid to hexaploid species is examined, revealing significant genetic contributions from wild grasses. We also emphasize the importance of understanding incomplete lineage sorting in the genomic evolution of wheat. Grasping this information is crucial as it can guide breeders in selecting the appropriate alleles from the gene pool of wild relatives to incorporate into modern wheat varieties. This approach improves the precision of phylogenetic relationships and increases the overall effectiveness of breeding strategies. This review also addresses the challenges in utilizing the wheat wild genetic resources, such as the linkage drag and cross-compatibility issues. Finally, we culminate the review with future perspectives, advocating for a combined approach of high-throughput phenotyping tools and advanced genomic techniques to comprehensively understand the genetic and regulatory architectures of wheat under stress conditions, paving the way for more precise and efficient breeding strategies.

Green Revolution dwarfing Rht genes negatively affected wheat floral traits related to cross-pollination efficiency.

Hybrid breeding is a promising strategy to quickly improve wheat yield and stability. Due to the usefulness of the Rht 'Green Revolution' dwarfing alleles, it is important to gain a better understanding of their impact on traits related to hybrid development. Traits associated with cross-pollination efficiency were studied using Near Isogenic Lines carrying the different sets of alleles in Rht genes: Rht1 (semi-dwarf), Rht2 (semi-dwarf), Rht1 + 2 (dwarf), Rht3 (extreme dwarf), Rht2 + 3 (extreme dwarf), and rht (tall) during four growing seasons. Results showed that the extreme dwarfing alleles Rht2 + 3, Rht3, and Rht1 + 2 presented the greatest effects in all the traits analyzed. Plant height showed reductions up to 64% (Rht2 + 3) compared to rht. Decreases up to 20.2% in anther length and 33% in filament length (Rht2 + 3) were observed. Anthers extrusion decreased from 40% (rht) to 20% (Rht1 and Rht2), 11% (Rht3), 8.3% (Rht1 + 2), and 6.5% (Rht2 + 3). Positive correlations were detected between plant height and anther extrusion, anther, and anther filament lengths, suggesting the negative effect of dwarfing alleles. Moreover, the magnitude of these negative impacts depends on the combination of the alleles: Rht2 + 3 > Rht3/Rht1 + 2 > Rht2/Rht1 > rht (tall). Reductions were consistent across genotypes and environments with interactions due to magnitude effects. Our results indicate that Rht alleles are involved in multiple traits of interest for hybrid wheat production and the need to select alternative sources for reduced height/lodging resistance for hybrid breeding programs.

Combining Traditional Mutagenesis with New High-Throughput Sequencing and Genome Editing to Reveal Hidden Variation in Polyploid Wheat.

Induced mutations have been used to generate novel variation for breeding purposes since the early 1900s. However, the combination of this old technology with the new capabilities of high-throughput sequencing has resulted in powerful reverse genetic approaches in polyploid crops. Sequencing genomes or exomes of large mutant populations can generate extensive databases of mutations for most genes. These mutant collections, together with genome editing, are being used in polyploid species to combine mutations in all copies of a gene (homoeologs), and to expose phenotypic variation that was previously hidden by functional redundancy among homoeologs. This redundancy is more extensive in recently formed polyploids such as wheat, which can now benefit from the deployment of useful recessive mutations previously identified in its diploid relatives. Sequenced mutant populations and genome editing have changed the paradigm of what is possible in functional genetic analysis of wheat.