Summary: Stalk rot caused by Fusarium verticillioides (Fv) is one of the most destructive diseases in maize production. The defence response of root system to Fv invasion is important for plant growth and development. Dissection of root cell type‐specific response to Fv infection and its underlying transcription regulatory networks will aid in understanding the defence mechanism of maize roots to Fv invasion. Here, we reported the transcriptomes of 29 217 single cells derived from root tips of two maize inbred lines inoculated with Fv and mock condition, and identified seven major cell types with 21 transcriptionally distinct cell clusters. Through the weighted gene co‐expression network analysis, we identified 12 Fv‐responsive regulatory modules from 4049 differentially expressed genes (DEGs) that were activated or repressed by Fv infection in these seven cell types. Using a machining‐learning approach, we constructed six cell type‐specific immune regulatory networks by integrating Fv‐induced DEGs from the cell type‐specific transcriptomes, 16 known maize disease‐resistant genes, five experimentally validated genes (ZmWOX5b, ZmPIN1a, ZmPAL6, ZmCCoAOMT2, and ZmCOMT), and 42 QTL or QTN predicted genes that are associated with Fv resistance. Taken together, this study provides not only a global view of maize cell fate determination during root development but also insights into the immune regulatory networks in major cell types of maize root tips at single‐cell resolution, thus laying the foundation for dissecting molecular mechanisms underlying disease resistance in maize.

With global warming, drought stress is becoming increasingly severe, causing serious impacts on crop yield and quality. In order to survive under adverse conditions such as drought stress, plants have evolved a certain mechanism to cope. The tolerance to drought stress is mainly improved through the synergistic effect of regulatory pathways, such as transcription factors, phytohormone, stomatal movement, osmotic substances, sRNA, and antioxidant systems. This study summarizes the research progress on plant drought resistance, in order to provide a reference for improving plant drought resistance and cultivating drought-resistant varieties through genetic engineering technology.

High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait–marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.

Maize is an important cereal crop but is sensitive to heat stress, which significantly restricts its grain yield. To explore the molecular mechanism of maize heat tolerance, a heat-tolerant hybrid ZD309 and its parental lines (H39_1 and M189) were subjected to heat stress, followed by transcriptomic and metabolomic analyses. After six-day-heat treatment, the growth of ZD309 and its parental lines were suppressed, showing dwarf stature and rolled leaf compared with the control plants. ZD309 exhibited vigorous growth; however, M189 displayed superior heat tolerance. By transcriptomic and metabolomic analysis, hundreds to thousands of differentially expressed genes (DEGs) and metabolites (DEMs) were identified. Notably, the female parent H39 shares more DEGs and DEMs with the hybrid ZD309, indicating more genetic gain derived from the female instead of the male. A total of 299 heat shock genes detected among three genotypes were greatly aggregated in sugar transmembrane transporter activity, plasma membrane, photosynthesis, protein processing in the endoplasmic reticulum, cysteine, and methionine metabolism. A total of 150 heat-responsive metabolites detected among three genotypes were highly accumulated, including jasmonic acid, amino acids, sugar, flavonoids, coumarin, and organic acids. Integrating transcriptomic and metabolomic assays revealed that plant hormone signal transduction, cysteine, and methionine metabolism, and α-linolenic acid metabolism play crucial roles in heat tolerance in maize. Our research will be facilitated to identify essential heat tolerance genes in maize, thereby contributing to breeding heat resistance maize varieties.

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI’s role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding.

Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement.