Kernel rate (KR), the ratio of kernel to ear weight, serves as a key metric of assimilate partitioning efficiency and determines single-ear yield stability under high planting densities. Despite its critical role in modern intensive agriculture, the genetic architecture of KR remains largely elusive. Here, we performed a genome-wide association study (GWAS) for KR and its component traits—ear weight (EW), kernel weight (KW), and cob weight (CW)—using a diverse panel of 502 maize inbred lines across seven environments. Phenotypic profiling revealed substantial variation and high heritability. Leveraging 10.77 million high-quality SNPs, we identified 202 significant associations, which were refined into 106 quantitative trait loci (QTL), each explaining 5

Abstract The domestication of maize from teosinte involved dramatic remodeling of the ear, yet the cellular and genetic bases of this transformation remain unclear. Here, we generate a single-nucleus and spatial transcriptome atlas of developing maize and teosinte ears. Comparative analysis reveals divergence in cob-associated cell types, with enhanced cytokinin signaling and reduced growth-inhibitory signals collectively driving cob thickening and enlargement in maize. We further demonstrate that domestication expanded the spatial expression domain of key transcription factors in maize meristem cells, enhancing the potential for increasing kernel number. Additionally, we verified a major domestication gene, ZmSPD1 , in which two nonsynonymous SNPs differentiate maize from teosinte and alter jasmonic acid (JA) levels in the ear, thereby suppressing spikelet abortion to effectively double kernel production. These findings provide a cell-resolved mechanistic framework for how cob architecture and kernel number were shaped during maize domestication, offering new insights into the formation of key agronomic traits.

B73 and Mo17 are foundational inbred lines in maize genetics and breeding. Despite their extensive phenotypic and genomic characterization, the assembly and functional dynamics of their rhizosphere microbiomes remain poorly understood. Here, we compared the structure and function of rhizosphere microbial communities in these two lines across four growth stages and investigated the role of root exudates in microbiome modulation. The results demonstrate that Mo17 exhibited higher microbial diversity and community evenness, with functional profiles enriched in energy metabolism and carbon-nitrogen cycling. In contrast, B73 harbored a more tightly cooperative microbial network, functionally biased toward biosynthesis and cellular maintenance. The microbial communities were most different during the early vegetative growth stage. As maize developed, the rhizosphere functional profile shifted from high energy supply during vegetative growth toward ecological stability during the reproductive stage, aligning with the host’s physiological trajectory. Crucially, the exogenous application of a key metabolite identified in Mo17 exudates to B73 soil effectively reconfigured the B73 rhizosphere community. This intervention shifted the microbiome toward a Mo17-like profile. Together, our findings highlight that maize genotypes influence plant growth by modulating the structure and function of the rhizosphere microbial community, and demonstrate that key metabolites can mediate this process.

BackgroundPhenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize.ResultsHere, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies.ConclusionThe findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.

Chromatin interactions establish spatial proximity between distant regulatory elements and their target genes, significantly influencing gene expression, and phenotypic traits. In this study, we present a plant chromatin interaction prediction model called PlantCTCIP based on Convolutional Neural Networks and Transformer. PlantCTCIP demonstrated superior performance compared to the conventional models. Specifically, PlantCTCIP improved the average AUC of chromatin interaction predictions by 14.56% across the four species in PPI (proximal promoter interaction) mode. Similarly, PlantCTCIP improved the average AUC of chromatin interaction predictions by 9.6% in the PDI (distal promoter interaction) mode. We constructed genome-wide chromatin interaction maps for four plants (maize, rice, cotton and wheat) through PlantCTCIP, further used the Hi-C experiment to validate correctness of the predicted PPIs and PDIs. Some key motifs that influence chromatin interactions are identified, and they are significantly enriched in expression quantitative trait loci (eQTLs) and open chromatin regions. We also analysed the enrichment and species specificity of the transcription factors (TF) and synergistic network of TFs that affect PPIs and PDIs of four crops. Using cloned genes (ZmRAVL1, ZmRPG, ZmRap2.7 and GaFZ) of maize and cotton as examples, PlantCTCIP can assist in identifying target genes regulated by distal elements and mining functional sites combined with chromatin conformation capture (3C) experiments. This research helps to analyse the regulatory mechanism of gene expression and provides novel perspectives for intelligent design breeding of diverse crops. PlantCTCIP is available at http://www.plantctcip.com.

Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, yet conventionally measured plant characteristics have limited value as NUE proxies. Here we show that artificial intelligence (AI) can uncover previously unrecognized phenotypic variation associated with NUE, revealing genetic variation that is largely missed by conventional phenotypes. We trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy. The learned features were defined as deep phenotypes. Compared with conventional phenotypes, deep phenotypes showed greater phenotypic variation and higher heritability, enabling the identification of 523 significant loci compared with 21 for conventional phenotypes. We next investigated candidate genes underlying these loci and used these findings to interpret the learned features. Lower CNN layers primarily reflected visual patterns overlapping with conventional phenotypes, whereas deeper layers encoded additional features associated with N-responsive genetic variation. To validate candidate genes identified by the AI framework, we functionally characterized Liguleless2 (LG2), a basic-leucine zipper (bZIP) transcription factor, and demonstrated that lg2 mutants exhibit enhanced root architecture and increased N uptake efficiency. Field trials of 200 hybrids across diverse N environments further supported the AI findings, with each beneficial allele increasing ear weight by an average of 18 g per plot under low-N conditions. These results show how integrating AI and biology can uncover biologically relevant variation underlying complex traits such as NUE and enhance the interpretability of AI models.

ABSTRACT The artificial intelligence (AI)‐driven generation of genetic sequences holds transformative potential for addressing global challenges in agriculture, medicine, and bioenergy. Traditional approaches including hybridization, mutagenesis, and CRISPR‐based editing enable targeted modification of endogenous DNA, yet remain constrained by natural sequence diversity. We here introduce PlantGFM, an application of the Hyena operator within a plant‐oriented genomic foundation model, which was pre‐trained on 10.84 billion nucleotides from 12 plant species and supports long‐context (64 kb) prediction and sequence generation within a unified architecture. After fine‐tuning on 10 annotated plant genomes, PlantGFM matched or exceeded the performance of specialized gene prediction tools. Beyond reproducing natural genes, it enables de novo design of novel candidates through the emergence capability of AI. Seven candidates selected through an AI–Human Knowledge fusion screening pipeline all showed transcriptional activity in Nicotiana benthamiana, two with stable protein expression—representing the first demonstration of DNA–RNA–protein expression of Large Language Model‐generated sequences in plants. As a proof of concept, PlantGFM also exhibits emergent abilities in generating plant NLR genes. Our findings establish the feasibility of LLM technology for de novo plant gene design, providing a foundation for plant synthetic biology and AI‐assisted breeding.

Maize primary metabolism drives complex agronomic traits, yet its genetic regulation remains difficult to resolve. Here we integrated genomic, transcriptomic and metabolomic data from 1,404 maize progenies derived from 24 diverse founders to dissect the genetic architecture of primary metabolism. We constructed a high-confidence regulatory network that resolved causal genes underlying metabolic quantitative trait loci and successfully identified targets for improving maize nutritional quality. This systems-level framework further prioritized ZmAVT1A-1, encoding a putative amino acid transporter, as a key regulator of amino acid accumulation. Natural variation and transgenic analyses showed that ZmAVT1A-1 modulates nitrogen partitioning between vegetative tissues and kernels, revealing pleiotropic effects on agronomic traits. These findings illustrated the intricate trade-offs inherent in metabolic regulation. Together, our study provides a comprehensive multiomics resource for decoding metabolic networks and underscores the necessity of a systems approach to navigate the pleiotropic nature of crop improvement targets.

Understanding the genetic basis of trait divergence between maize and its wild progenitor teosinte is critical for elucidating crop domestication processes and accelerating maize improvement. This divergence is governed by multiple loci, including genes with pleiotropic effects and complex genetic interactions that collectively contribute to morphological variation. Using a maize–teosinte (Mo17–mexicana) introgression population, we identified 93 additive quantitative trait loci (QTLs) and nine epistatic interaction pairs affecting 20 agronomic traits, revealing a polygenic architecture underlying the domestication and improvement of these traits. The constructed QTL–trait network, together with identified genome-wide selection signals, revealed extensive genetic interconnections, with correlated traits sharing common loci, indicating a shared genetic basis for morphological divergence. Selection feature analysis further demonstrated that domestication, improvement, and mexicana introgression targeted key genomic regions associated with agronomic traits. Furthermore, we cloned an ear length QTL, EL3-2, which encodes a ULTRAPETALA (ULT) transcriptional regulator. EL3-2 may function as a central pleiotropic regulator of domestication by modulating maize–teosinte divergence and regulating complex gene-expression networks across multiple developmental stages of maize. This study reveals a complex genetic basis for maize–teosinte morphological divergence and highlights the role of pleiotropic regulators in crop domestication. Our findings provide new insights that bridge evolutionary genomics and breeding for complex traits in maize.

In maize hybrid breeding, synergic multi-trait selection of elite hybrids in specific target environments remains a major challenge. Enviromic data and functional gene knowledge are rapidly increasing; however, they have not been effectively integrated into crop breeding decisions. Here, we present TOPlus, a multi-trait hybrid prioritization framework comprising predictive and selective modules. The predictive module uses environmental information to improve phenotype prediction for untested genotypes and environments, whereas the selective module incorporates functional gene priors and multiple predicted traits to prioritize hybrids for target environments. Across 17 agronomic traits, TOPlus improved average cross-environment prediction accuracy by 12% over JGRA and 3% over EADW+GW. Incorporating functional gene priors further improved hybrid prioritization: hybrids selected by TOPlus showed 5.90-19.64% higher yield than those selected by the original TOP method while maintaining comparable performance for other traits. The TOPlus algorithm internally clustered the functional genes into environmentally stable and plastic gene groups, with stable genes associated with core plant developmental processes and plastic genes enriched in stress-response and environmental-adaptation pathways. Independent validation in commercial hybrid panels demonstrated that TOPlus supports region-level suitability assessment and extrapolative deployment across diverse agroecological zones for specific candidate maize hybrid varieties. Overall, by integrating enviromic data and functional gene priors within an interpretable framework, TOPlus provides a biologically grounded and data-driven approach for cross-environment prediction and multi-trait synergic selection of hybrids for specific regions in maize breeding.

Rational design of a regulatory protein decouples the ability to tolerate cold from the means to acquire phosphorus, effectively enhancing crop yield under cold stress. Rational design of a regulatory protein decouples the ability to tolerate cold from the means to acquire phosphorus, effectively enhancing crop yield under cold stress.

Sweet corn is a globally important dual-purpose crop for both food and fresh vegetables. The plant architecture and ear-related traits directly determine its yield potential and field ecological adaptability. To elucidate the genetic architecture of these traits and identify superior alleles for breeding, we conducted a genome-wide association study (GWAS) on 11 agronomic traits using 30,597 high-quality SNP markers in a panel of 101 elite sweet corn inbred lines. Population genetic structure was analyzed using sparse non-negative matrix factorization (sNMF) and discriminant analysis of principal components (DAPC) algorithms, revealing three main clusters and six subpopulations. The clustering pattern was highly consistent with germplasm origin. Association mapping with the fixed and random Circulating Probability Unification (FarmCPU) model identified 16 significant marker-trait associations (MTAs), distributed across seven target agronomic traits. The phenotypic variance explained (PVE) by individual loci ranged from 8.0% to 16.0%. Among these, five stable MTAs across environments, a novel ERN locus (SNP25518) specific to sweet corn, and most association intervals overlapped with previously reported quantitative trait loci (QTLs). Within the ±0.15 Mb (defined by LD decay) flanking windows around the significant SNP loci, a total of 236 candidate genes were annotated, which are primarily involved in hormone signaling, carbon and nitrogen metabolism, cell division, and plant growth and development. In summary, this study dissected the genetic basis of key agronomic traits in sweet corn and provides a foundation for marker-assisted selection and functional validation.

Raffinose family oligosaccharides are key endogenous factors influencing seed germination, with raffinose as the sole member in maize. However, the mechanisms by which raffinose regulates germination remain largely unexplored. A metabolite-based genome-wide association study using a maize CUBIC population identifies six candidate genes involved in raffinose metabolism in maize kernel. Among these, ZmAGA1, encoding an alkaline α-galactosidase, is localized in the cytosol and exhibits high expression levels in the embryo during seed germination. CRISPR-Cas9-mediated knockout of ZmAGA1 leads to delayed germination, reduced germination rates, and impaired seedling growth. Integrative spatial metabolomics and single-nucleus transcriptomics analyses at 18, 24, 36, and 48 h after imbibition in germinating seeds reveal that ZmAGA1 not only mediates raffinose catabolism and spatiotemporal sugar dynamics, but also positively correlates with ZmαAMY2 and ZmαAMY3 expression in aleurone cells and elevates α-amylase activity. Moreover, ZmAGA1 physically interacts with the energy-sensing kinase ZmSnRK1α in the cytosol, and both genes are co-expressed across embryonic tissues. In zmaga1 protoplasts, ZmSnRK1α shifts toward nuclear enrichment, where it interacts with the transcription factor ZmMYBS1, which directly activates ZmαAMY3 transcription. Consistently, ZmαAMY3 transcript levels are reduced in zmaga1 and zmsnrk1α protoplasts. These findings support a ZmAGA1–ZmSnRK1α–ZmMYBS1 cascade linking raffinose hydrolysis to α-amylase activation during seed germination. Our integrative spatial-omics analyses reveal that ZmAGA1 facilitates seed germination by modulating sugar metabolism through the hydrolysis of raffinose and via ZmSnRK1α-dependent regulation of starch mobilization, with both processes providing essential energy for seed germination and early seedling establishment in maize.

Single-cell technologies are transforming plant biology, yet broadly transferable nuclei isolation remains a key bottleneck for snRNA-seq. We developed a reproducible, cost-efficient Percoll-based workflow that is applicable to multiple maize tissues and nine additional plant species. In maize, nuclei from root, shoot, leaf, and embryo consistently concentrated at the 80

Protein malnutrition remains a major global health challenge, particularly in regions where cereal grains dominate daily diets and access to diverse protein sources is limited. Cereals such as rice, wheat and maize provide most of the world's calories, yet their grain proteins are often low in essential amino acids and poorly balanced for human nutrition. Improving both the quantity and quality of cereal protein therefore represents a critical opportunity to enhance human health while reducing reliance on environmentally intensive animal-based foods. In this Review, we synthesize recent advances in understanding how grain protein content and composition are regulated in cereals, and why protein enhancement has historically been constrained by trade-offs with starch accumulation and yield. We discuss how domestication and modern breeding reshaped carbon and nitrogen allocation in cereal grains, creating a starch-dominant optimum that limits protein concentration. Drawing on genetic studies from rice, maize and wheat, we highlight emerging strategies that improve nitrogen acquisition, amino acid transport, storage protein composition and endosperm buffering capacity, enabling partial decoupling of protein accumulation from yield penalties. Finally, we place cereal protein biofortification within a broader nutritional and environmental context. Enhancing protein density and amino acid balance in staple cereals can improve dietary adequacy for vulnerable populations while lowering greenhouse gas emissions per unit of nutrition. Together, these insights position cereal protein biofortification as a scalable and equitable pathway towards healthier diets and more sustainable food systems under global climate and population pressures.

Achieving optimal flowering time is a pivotal factor in precision plant breeding. Here, in maize, we characterized an EMS-induced additive delayed-flowering mutant, edf1. edf1 and its heterozygotes are late-flowering under both long- and short-day photoperiods in the field, and harbor a splice-donor mutation in ALTERED PHLOEM DEVELOPMENT (APL). This mutation leads to the accumulation of the minor APL-β transcript, a splice variant expressed across a large natural maize population. Expression of the major splice variant APL-α is associated with flowering initiation; APL knockdown delays flowering and results in taller plants with increased biomass and plot yield. APL-α binds to and activates the promoter of ZCN8 to trigger flowering, while APL-β has impaired DNA-binding activity and antagonizes APL-α function. Base editing of the APL MYB domain in an elite rice cultivar and Arabidopsis results in delayed flowering, providing a feasible approach to regulate flowering time for the molecular breeding of staple crops.

Global agriculture faces critical challenges due to the overreliance on chemical pesticides, driving an urgent need for eco-friendly biopesticides and biostimulants (BioP&S). Plant-derived peptides, evolved as natural regulators of growth, development, and stress adaptation, offer immense potential as biodegradable and biocompatible alternatives. However, their commercialization remains constrained by limited exploration of the diversity and activity, high production costs, incomplete ecological risk evaluations, and undefined application scenarios. This Perspective overviews emerging discoveries and proposes integrated frameworks for plant peptide identification, molecular design, biomanufacturing, and ecological impact assessments integrated with germplasm development and field application systems. To overcome existing bottlenecks, we discuss the integrative potential of emerging technologies that synergistically combine artificial intelligence for high-throughput peptide discovery and de novo structural refinement, nanotechnology for enhancing environmental resilience and targeted delivery, and synthetic biology for developing industrial biomanufacturing platforms. We emphasize the need to align phytopeptide BioP&S with compatible germplasm resources, stage-specific crop requirements, and complementary chemical pesticides to maximize their efficacy, cost-effectiveness, and trait-specific agronomic performance by integrating with precision agriculture systems. Future advancements will rely on interdisciplinary innovations and policy support to unlock their full potential in enhancing crop resilience, productivity, and quality while ensuring ecological sustainability.

Global food security requires sustainable strategies to improve crop yield and nutrition. Although thiamine pyrophosphate (TPP), the active form of vitamin B1, plays a central role in energy metabolism, redox homeostasis, and carbon assimilation, its contribution to crop yield and quality remains largely unexplored. Here, we show that ZmTPK2, a thiamine pyrophosphokinase encoded by the major ear length QTL qKB6.2a, is a key regulator of maize yield. We reveal that ZmTPK2-dependent TPP homeostasis synchronizes three cornerstones of plant metabolism: mitochondrial tricarboxylic acid (TCA) cycle activity, chloroplast-mediated carbon fixation, and nitrogen utilization. Both overexpression and knockout of ZmTPK2 disrupt yield and grain quality, revealing that optimal TPP levels are required for productivity. Exogenous TPP supplementation increases grain yield in maize, rice, and rapeseed up to 9.8%. These findings identify TPP metabolism as a key regulatory pathway for metabolic engineering, biofortification, and global food security solutions.