Search Results (598)

Potato (Solanum tuberosum L.) is one of the most important food crops in the world, ranking fourth after rice, maize, and wheat. Potatoes are exposed to biotic and abiotic environmental factors, which lead to economic losses and increase the possibility of food security threats in many countries. Traditional potato breeding faces several challenges, primarily due to its genetic complexity and the time-consuming nature of the process. Therefore, gene editing—CRISPR-Cas technology—allows for more precise and rapid changes to the potato genome, which can speed up the breeding process and lead to more effective varieties. In this review, we consider CRISPR-Cas technology as a potential tool for plant breeding strategies to ensure global food security. This review summarizes in detail current and potential technological breakthroughs that open new opportunities for the use of CRISPR-Cas technology for potato breeding, as well as for increasing resistance to abiotic and biotic stresses, and improving potato tuber quality. In addition, the review discusses the challenges and future perspectives of the CRISPR-Cas system in the prospects of the development of potato production and the regulation of gene-edited crops in different countries around the world. Full article

This study measured eight key phenotypic traits across 259 fresh maize inbred lines, including plant height and spike length. A total of 82 single nucleotide polymorphisms (SNPs) significantly associated with these phenotypes were identified by applying a mixed linear model to calculate the best linear unbiased prediction (BLUP) values and integrating genome-wide genotypic data through genome-wide association analysis (GWAS). A further analysis of significant SNPs contributed to the identification of 63 candidate genes with functional annotations. Notably, 11 major candidate genes were identified from multi-trait association loci, all of which exhibited highly significant P-values (<0.0001) and explained between 7.21% and 12.78% of phenotypic variation. These 11 genes, located on chromosomes 1, 3, 4, 5, 6, and 9, were functionally involved in signaling, metabolic regulation, structural maintenance, and stress response, and are likely to play crucial roles in the growth and physiological processes of fresh maize inbred lines. The functional genes identified in this study have significant implications for the development of molecular markers, the optimization of breeding strategies, and the enhancement of quality in fresh maize. Full article

Corn smut, caused by Ustilago maydis, significantly threatens maize production. This study evaluated 199 maize inbred lines at the seedling stage under greenhouse conditions for resistance to U. maydis, identifying 39 highly resistant lines. A genome-wide association study (GWAS) using the mrMLM model detected 19 significant single-nucleotide polymorphism (SNP) loci. Based on a linkage disequilibrium (LD) decay distance of 260 kb, 226 candidate genes were identified. Utilizing the significant loci chr1_244281660 and chr5_220156746, two kompetitive allele-specific PCR (KASP) markers were successfully developed. A PCR-based sequence-specific oligonucleotide probe hybridization technique applied to the 199 experimental lines and 60 validation lines confirmed polymorphism for both markers, with selection efficiencies of 48.12% and 43.33%, respectively. The tested materials were derived from foundational inbred lines of domestic and foreign origin. Analysis of 39 highly resistant lines showed that the advantageous alleles carrying thymine/cytosine (T/C) predominated at frequencies of 94.87% and 53.84%, respectively. The genotype TTCC conferred high resistance, while CCTT was highly susceptible. The resistance exhibited high heritability and significant gene-by-environment interaction. This work systematically dissects the genetic basis of common smut resistance in maize, identifies favorable alleles, and provides a novel KASP marker-based strategy for developing disease-resistant germplasm. Full article

Kernel test weight (KTW) is one of the important assessment indexes of maize quality grade and one of the important influencing factors of yield. This study analyzed 12 traits related to KTW in 321 maize inbred lines using multivariate methods. The principal component analysis (PCA) indicated that the four PCs covered 78.176% of the information of the 12 traits in 321 maize inbred lines. Cluster analysis categorized the maize lines into six groups, identifying 16 elite inbred lines with the highest KTW. A stepwise regression model for KWT evaluation was developed using four PCA traits: starch content, amylopectin content, 100-kernel weight, and kernel circumference. The findings of this study serve as a valuable reference point for the genetic improvement of maize germplasm re-sources in kernel test weight and the creation of high kernel test weight maize resources. Full article

Enhancing quality traits is a primary objective in silage maize breeding programs. The use of genome-wide association studies (GWAS) for quality traits, in combination with the integration of genetic resources, presents an opportunity to identify crucial genomic regions and candidate genes influencing silage maize quality. In this study, a GWAS was conducted on 580 inbred lines of silage maize, and a meta-analysis was performed on 477 quantitative trait loci (QTLs) from 34 studies. The analysis identified 27 significant single nucleotide polymorphisms (SNPs) and 87 consensus QTLs (cQTLs), with 7 cQTLs associated with multiple quality traits. By integrating the SNPs identified through association mapping, one SNP was found to overlap with the cQTL interval related to crude protein, neutral detergent fiber, and starch content. Furthermore, enrichment analysis predicted 300 and 5669 candidate genes through GWAS and meta-analysis, respectively, highlighting pathways such as cellular metabolism, the biosynthesis of secondary metabolites, ribosome function, carbon metabolism, protein processing in the endoplasmic reticulum, and amino acid biosynthesis. The examination of 13 candidate genes from three co-located regions revealed Zm00001d050977 as a cytochrome P450 family gene, while the other 2 genes primarily encode proteins involved in stress responses and other biological pathways. In conclusion, this research presents a methodology combining GWAS and meta-analysis to identify genomic regions and potential genes influencing quality traits in silage maize. These findings serve as a foundation for the identification of significant QTLs and candidate genes crucial for improving silage maize quality. Full article

The rapid evolution of pathogens and the limited genetic diversity of hosts are two major factors contributing to the plant pathogenic phenomenon known as the loss of disease resistance in maize (Zea mays L.). It has emerged as a significant biological stressor threatening the global food supplies and security. Based on previous cross-species homologous gene screening assays conducted in the laboratory, this study identified the maize disease-resistance candidate gene ZmNLR-7 to investigate the maize immune regulation mechanism against Bipolaris maydis. Subcellular localization assays confirmed that the ZmNLR-7 protein is localized in the plasma membrane and nucleus, and phylogenetic analysis revealed that it contains a conserved NB-ARC domain. Analysis of tissue expression patterns revealed that ZmNLR-7 was expressed in all maize tissues, with the highest expression level (5.11 times) exhibited in the leaves, and that its transcription level peaked at 11.92 times 48 h post Bipolaris maydis infection. Upon inoculating the ZmNLR-7 EMS mutants with Bipolaris maydis, the disease index was increased to 33.89 and 43.33, respectively, and the lesion expansion rate was higher than that in the wild type, indicating enhanced susceptibility to southern corn leaf blight. Physiological index measurements revealed a disturbance of ROS metabolism in ZmNLR-7 EMS mutants, with SOD activity decreased by approximately 30% and 55%, and POD activity decreased by 18% and 22%. Moreover, H₂O₂ content decreased, while lipid peroxide MDA accumulation increased. Transcriptomic analysis revealed a significant inhibition of the expression of the key genes NPR1 and ACS6 in the SA/ET signaling pathway and a decrease in the expression of disease-related genes ERF1 and PR1. This study established a new paradigm for the study of NLR protein-mediated plant immune mechanisms and provided target genes for molecular breeding of disease resistance in maize. Overall, these findings provide the first evidence that ZmNLR-7 confers resistance to southern corn leaf blight in maize by synergistically regulating ROS homeostasis, SA/ET signal transduction, and downstream defense gene expression networks. Full article

Nitrogen use efficiency remains the primary bottleneck for sustainable maize production. This study elucidates the functional mechanisms of the amino acid transporter ZmAAP1 in nitrogen absorption and stress resilience. Through systematic evolutionary analysis of 55 maize inbred lines, we discovered that the ZmAAP1 gene family exhibits distinct chromosomal localization (Chr7 and Chr9) and functional domain diversification (e.g., group 10-specific motifs 11/12), indicating species-specific adaptive evolution. Integrative analysis of promoter cis-elements and multi-omics data confirmed the root-preferential expression of ZmAAP1 under drought stress, mediated via the ABA-DRE signaling pathway. To validate its biological role, we generated transgenic maize lines expressing Arabidopsis thaliana AtAAP1 via Agrobacterium-mediated transformation. Three generations of genetic stability screening confirmed the stable genomic integration and root-specific accumulation of the AtAAP1 protein (Southern blot/Western blot). Field trials demonstrated that low-N conditions enhanced the following transgenic traits: the chlorophyll content increased by 13.5%, and the aboveground biomass improved by 7.2%. Under high-N regimes, the gene-pyramided hybrid ZD958 (AAP1 + AAP1) achieved a 12.3% yield advantage over conventional varieties. Our findings reveal ZmAAP1’s dual role in root development and long-distance nitrogen transport, establishing it as a pivotal target for molecular breeding. This study provides actionable genetic resources for enhancing NUE in maize production systems. Full article

Plastid division regulatory genes play a crucial role in the morphogenesis of chloroplasts and amyloplasts. Chloroplasts are the main sites for photosynthesis and metabolic reactions, while amyloplasts are the organelles responsible for forming and storing starch granules. The proper division of chloroplasts and amyloplasts is essential for plant growth and yield maintenance. Therefore, this study aimed to examine the J175 (FtsZ2-2) gene, cloned from an ethyl methanesulphonate (EMS) mutant involved in chloroplast and amyloplast division in maize, through map-based cloning. We found that J175 encodes a cell division protein, FtsZ (filamentous temperature-sensitive Z). The FtsZ family of proteins is widely distributed in plants and may be related to the division of chloroplasts and amyloplasts. The J175 protein is localized in plastids, and its gene is expressed across various tissues. From the seedling stage, the leaves of the j175 mutant exhibited white stripes, while the division of chloroplasts was inhibited, leading to a significant increase in volume and a reduction in their number. Measurement of the photosynthetic rate showed a significant decrease in the photosynthetic efficiency of j175. Additionally, the division of amyloplasts in j175 grains at different stages was impeded, resulting in irregular polygonal starch granules. RNA-seq analyses of leaves and kernels also showed that multiple genes affecting plastid division, such as FtsZ1, ARC3, ARC6, PDV1-1, PDV2, and MinE1, were significantly downregulated. This study demonstrates that the maize gene j175 is essential for maintaining the division of chloroplasts and amyloplasts and ensuring normal plant growth, and provides an important gene resource for the molecular breeding of maize. Full article

Maize is an essential staple food, and its genetic diversity plays a central role in breeding programs aimed at developing climate-adapted cultivars. Constructing a representative core germplasm set is necessary for the efficient conservation and utilization of maize genetic resources. In this study, we analyzed 588 cultivated maize accessions using agronomic traits such as plant morphology and yield traits such as ear characteristics and single-nucleotide polymorphisms (SNPs) to assess molecular diversity and population structure and to construct a core collection. Nineteen phenotypic traits were evaluated, revealing high genetic diversity and significant correlations among most quantitative traits. The optimal sampling strategy was identified as “Mahalanobis distance + 20% + deviation sampling + flexible method.” Whole-genome genotyping was conducted using the Maize6H-60K liquid phase chip. Population structure analysis, principal component analysis, and cluster analysis divided the 588 accessions into six subgroups. A core collection of 172 accessions was selected based on both phenotypic and genotypic data. These were further evaluated for salt–alkali tolerance during germination, and cluster analysis classified them into five groups. Sixty-five accessions demonstrated salt–alkali tolerance, including 18 with high resistance. This core collection serves as a valuable foundation for germplasm conservation and utilization strategies. Full article

Maize (Zea mays L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements. Full article

MYB transcription factors constitute a diverse and functionally versatile family, playing central roles in regulating plant responses to a range of abiotic stressors. Based on previous research, we identified and characterized a maize MYB transcription factor gene, ZmMYBR24, which is involved in responses to salt, alkali, and low-temperature stress. This study aimed to investigate the function and mechanism of ZmMYBR24 in response to salt, alkali, and low-temperature stresses. We hypothesized that ZmMYBR24 regulates biosynthetic pathways to influence maize resistance to multiple abiotic stresses. The results indicate that ZmMYBR24 expression was markedly upregulated (p < 0.01) and the fold-change in gene expression ranged from 1.54 to 25.69 when plants were exposed to these combined stresses. Phenotypically, the zmmybr24 mutant line exhibited more pronounced inhibition of seedling and root growth under stress compared to the wild-type B73 line. Based on a correlation expression pattern analysis and mutant line evaluation, ZmMYBR24 was confirmed to be a positive regulatory transcription factor for multiple types of abiotic stress resistance. An RNA-seq analysis of both lines revealed differentially expressed genes (DEGs), with gene ontology (GO) and KEGG enrichment analyses indicating that ZmMYBR24 may mediate stress responses by modulating the expression of genes involved in flavonoid biosynthesis. Notable differences were observed in the expression of pathway-associated genes between the mutant and wild-type plants. A haplotype analysis across 80 inbred maize lines revealed 16 ZmMYBR24 coding region haplotypes—comprising 25 SNPs and 17 InDels—with HAP12 emerging as a superior haplotype. These results demonstrate that ZmMYBR24 enhances maize yields by regulating the flavonoid biosynthesis pathway in response to adverse climatic conditions including salt, alkaline conditions, and low temperatures. Collectively, these findings offer novel insights into the molecular mechanisms underlying maize adaptation to combined abiotic stresses and lay the groundwork for breeding programs targeting multi-stress resistance. Full article

Maize is a vital global crop, serving as a fundamental component of global food security. To support sustainable maize production, the accurate classification of maize seeds—particularly distinguishing haploid from diploid types—is essential for enhancing breeding efficiency. Conventional methods relying on manual inspection or simple machine learning are prone to errors and unsuitable for large-scale data. To overcome these limitations, we propose Slot-Maize, a novel deep learning architecture that integrates Convolutional Neural Networks (CNN), Slot Attention, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) layers. The Slot-Maize model was evaluated using two datasets: the Maize Seed Dataset and the Maize Variety Dataset. The Slot Attention module improves feature representation by focusing on object-centric regions within seed images. The GRU captures short-term sequential patterns in extracted features, while the LSTM models long-range dependencies, enhancing temporal understanding. Furthermore, Grad-CAM was utilized as an explainable AI technique to enhance the interpretability of the model’s decisions. The model demonstrated an accuracy of 96.97% on the Maize Seed Dataset and 92.30% on the Maize Variety Dataset, outperforming existing methods in both cases. These results demonstrate the model’s robustness, generalizability, and potential to accelerate automated maize breeding workflows. In conclusion, the Slot-Maize model provides a robust and interpretable solution for automated maize seed classification, representing a significant advancement in agricultural technology. By combining accuracy with explainability, Slot-Maize provides a reliable tool for precision agriculture. Full article

Background: Phytoremediation is an efficient approach for remediating heavy metal-contaminated soils. Heavy metal-associated isoprenylated plant proteins (HIPPs)—crucial for metal ion homeostasis—are unique to vascular plants, featuring a heavy metal-associated (HMA) domain and an isoprenylated CaaX motif. However, ZmHIPP genes have not been systematically or functionally characterized in maize. Methods: This study characterizes ZmHIPP at the genome-wide level, including phylogenetic classification, motif/gene structure, chromosome location, gene duplication events, promoter elements, and tissue expression patterns. Cadmium (Cd) responses were evaluated by specific ZmHIPP expression and Cd accumulation in shoots and roots under Cd treatment. Results: A total of 66 ZmHIPPs were distributed unevenly across ten chromosomes, classified into five phylogenetic groups phylogenetically. Gene collinearity revealed 26 pairs of segmental duplications in ZmHIPPs. Numerous synteny genes were detected in rice and sorghum, but none in Arabidopsis, suggesting high conservation of HIPP genes in crop evolution. Transcriptomic analysis revealed tissue-specific expression patterns of ZmHIPP members in maize. Cis-acting element analysis linked several binding elements to abscisic acid, MeJA response, and MYB and MYC transcription factors. Under Cd stress, 53 out of 66 ZmHIPP genes were significantly induced, exhibiting three expression patterns. Cd exposure confirmed that the expression of ZmHIPP11, ZmHIPP30, and ZmHIPP48 was generally higher in shoots than roots, while ZmHIPP02 and ZmHIPP57 exhibited the opposite. Cd accumulation was higher in roots than shoots, peaking at 72 h (96 mg/kg) in shoots and exceeding 1000 mg/kg in roots after 120 h. Conclusions: This study not only provides fundamental genetic and molecular insights into HIPP function in maize but also identifies specific ZmHIPP genes as promising genetic resources for breeding Cd-tolerant maize, aiding in phytoremediation of Cd-contaminated soils. Full article

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods—random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)—were employed to systematically analyze the relevant traits of maize seedlings’ drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R² = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies. Full article

Drought severely limits crop yield every year, making it critical to clarify the genetic basis of drought tolerance for breeding of improved varieties. As drought tolerance is a complex quantitative trait, we analyzed three phenotypic groups: (1) agronomic traits under well-watered (WW) and water-deficit (WD) conditions, (2) stress tolerance indices of these traits, and (3) phenotypic plasticity, using a multi-parent doubled haploid (DH) population assessed in multi-environment trials. Genome-wide association studies (GWAS) identified 130, 171, and 71 quantitative trait loci (QTL) for the three groups of phenotypes, respectively. Only one QTL was shared among all trait groups, 25 between stress indices and agronomic traits, while the majority of QTL were specific to their group. Functional annotation of candidate genes revealed distinct pathways of the three phenotypic groups. Candidate genes under WD conditions were enriched for stress response and epigenetic regulation, while under WW conditions for protein synthesis and transport, RNA metabolism, and developmental regulation. Stress tolerance indices were enriched for transport of amino/organic acids, epigenetic regulation, and stress response, whereas plasticity showed enrichment for environmental adaptability. Transcriptome analysis of 26 potential candidate genes showed tissue-specific drought responses in leaves, ears, and tassels. Collectively, these results indicated both shared and independent genetic mechanisms underlying drought tolerance, providing novel insights into the complex phenotypes related to drought tolerance and guiding further strategies for molecular breeding in maize. Full article