As a unique and important germplasm resource in China, the Taigu male-sterile wheat has made significant contributions to wheat breeding. To further explore its potential application, this study aimed to establish an effective seed-stage identification system for the Taigu genic male sterility trait (Ms2) using molecular biology techniques, employing DsRed (encoding red fluorescent protein) and RUBY (a pigment biosynthesis-related gene) as reporter systems. Additionally, the single-copy semi-dwarf gene Rht-D1b was introduced to develop semi-dwarf, visually identifiable male sterile wheat. Through characterization of transgenic wheat plants and their progeny, the red fluorescent protein was efficiently expressed and stably inherited in wheat embryos. Although RUBY expression was undetected, effective identification of male sterility could still be achieved through red fluorescence. Sterile seeds with red fluorescence and fertile seeds without fluorescence exhibited a 1∶1 segregation ratio. T₁ plants derived from red fluorescent seeds were all semi-dwarf and sterile, while those from non-fluorescent seeds were non-dwarf and fertile, indicating tight linkage among DsRed, Ms2, and Rht-D1b. The constructed reporter system not only enables fertility sorting at the seed stage via fluorescence but also addresses the excessive dwarfing issue in current dwarf-sterile wheat lines by incorporating the semi-dwarfing gene. This approach offers a novel tool for hybrid wheat production and recurrent selection breeding.

To clarify the correlation between the diseased spikelet rate, the relative abundance of Fusarium species, and accumulation of DON toxin in Fusarium head blight, 128 wheat ears from naturally occurring wheat fields were collected in 11 environments in 5 provinces of Jiiangsu, Hubei, Anhui, Henan and Shaanxi in 2023. We calculated the diseased spikelet rate in the samples, classified the disease severity, and used high-throughput sequencing and LC/MS technology to determine the relative abundance of Fusarium species and DON toxin content, and analyzed the their correlation. The results showed that there was a significantly positive correlation between the diseased spikelet rate and relative abundance of Fusarium species and DON toxin content (P<0.001). However, the relative abundance of fusarium species in samples with severity level 4 was significantly higher than that of level 0 and level 1; the average content of DON toxin in samples with severity level 4 was significantly higher than that of other levels (P<0.05), and there was no significant difference in the relative abundance of Fusarium species and average content of DON toxin among samples between severity levels 1, 2, and 3; the diseased spikelet rate is more suitable than the disease severity for evaluating the relative abundance of Fusarium species and DON toxin content in wheat spikes. The results of multiple linear regression analysis showed that the combined effect of diseased spikelet rate and relative abundance of Fusarium species on DON toxin accumulation was greater than that of a single factor (R²=0.364), and both factors may play important roles in the construction of DON toxin content prediction model.

Wheat is one of the most critical food crops, and the identification and functional analysis of its key genes are crucial for wheat variety improvement and molecular breeding. The MYB transcription factor family represents one of the largest groups of transcription factor families in plants, playing a pivotal role in plant growth and development, metabolism, and response to both biotic and abiotic stresses. The study of MYB transcription factors has become a research hotspot in wheat functional genomics. However, compared to model plants such as Arabidopsis and rice, research on wheat MYB transcription factors remains relatively limited. With the advancement of high-throughput sequencing technology and improvement of wheat genome data, a total of 719 MYB transcription factors have been identified in the wheat genome, with the R2R3-type MYBs being the most predominant. Previous studies have demonstrated that members of the MYB transcription factor family play significant roles throughout different stages of wheat growth and development, regulating the expression of stress-responsive genes under drought, salinity, nutrient deficiency, and pathogen or pest attacks. Most MYB genes act as positive regulators of stress resistance, while some display negative regulatory effects. These findings suggest that MYB family members contribute to complex regulatory patterns in wheat's stress adaptation mechanisms. This review summarizes recent progress in the identification, classification, and biological functions of wheat MYB transcription factors. Additionally, we suggested that it is necessary to improve the evolutionary classification of MYB family in wheat, explore the target genes regulated by MYB, and identify their function and mechanism in order to apply them in wheat molecular breeding.

To investigate the effectiveness of natural/chemical inhibitors in the drip-irrigated wheat field, six treatments were set up to study the effects of nitrogen (N) fertilizer applied with natural or chemical inhibitors on photosynthetic performance, biomass, nutrient uptake and yield of wheat. It were no N application (CK), N fertilizer applied alone (U), N fertilizer applied with chemical nitrification inhibitor (U+CP), N fertilizer applied with natural nitrification inhibitor (U+MHPP), N fertilizer applied with chemical nitrification and urease inhibitors (U+CP+NBPT), and N fertilizer applied with natural nitrification and urease inhibitors (U+MHPP+DATS). The results showed that SPAD value of wheat leaves, biomass of various organs, and uptake of N, phosphorus (P), and potassium (K) of wheat plants were increased under the inhibitor treatments compared with the U treatment, and especially in nutrient accumulation of the cob+glumes+grains. Nutrients uptake rate by cob+glumes+grains at maturity of wheat plants were significantly increased 13.17%-19.38% and 11.59%-15.68% for N uptake, 14.26%-21.95% and 9.84%-18.74% for P uptake , and 12.92%-24.61% and 9.91%-22.41% for K uptake in 2022 and 2023 years respectively. Wheat yield was increased but not significant and nitrogen use efficiency (NUE) was significantly increased by 20.10%-36.14% under all inhibitor treatments as compared to the U treatment. Photosynthetic capacity, biomass, nutrient uptake, and yield under the natural inhibitor treatments (U+MHPP and U+MHPP+DATS) were slightly lower than that under the chemical inhibitor treatments (U+CP and U+CP+NBPT) with no significance. In conclusion, the addition of natural or chemical inhibitors to N fertilizer can promote the accumulation of photosynthetic products and nutrient uptake in wheat plants, and significantly improve NUE in wheat fields; the application effect natural of inhibitors was weaker than that of chemical inhibitors but the difference was not significant, and the effect of combined application of either chemical or natural inhibitors was better than that of applying an individual inhibitor.

To evaluate the feasibility of using deep learning for efficient and accurate wheat spike counting, ten major winter wheat cultivars from the Huang-Huai wheat region (Fanmai 8, Zhoumai 36, Zhongmai 895, Malan 1, Xinmai 26, Yumai 49, Jimai 22, Zhongmai 578, Zhengmai 1860, and Zhongmai 255) were selected as materials. Three planting densities (1.2 million, 2.4 million, and 3.6 million plants·hm^-2) were tested, and four deep learning algorithms (YOLO v5, YOLO v6, YOLO v8, and YOLO v10) were employed to construct real-time video-based wheat spike detection and counting models. The models were validated using field-grown Zhongmai 578 populations (3 million plants·hm^-2). The results showed that the initial loss functions and convergence rates varied among models, with all models improving in spike detection performance as iterations increased. In terms of training speed and inference efficiency, YOLO v6 and YOLO v10 were faster but exhibited lower detection accuracy compared to YOLO v5 and YOLO v8. Although YOLO v5 and YOLO v8 required longer processing time, YOLO v8 achieved the best performance in recall (90.90%), F1-score (93.00%), mean average precision (97.20%), and overall accuracy (88.00%). The correlation (r²) between YOLO v8’s spike counts and manual counts decreased with increasing planting density, yielding values of 0.92, 0.81, and 0.79 for the three densities, respectively. Field validation demonstrated that YOLO v8 outperformed other models in stability and precision across different grain-filling stages, with the highest r² (0.90) for real-time video-based spike counting. The model also showed robustness against variations in planting density, cultivar, and spike growth stage, maintaining high performance in complex field environments. These findings suggest that YOLO v8 is a reliable algorithm for wheat spike counting, suitable for yield prediction, breeding, and cultivation management applications.

To provide excellent germplasm resources for wheat breeding against powdery mildew (a major disease affecting wheat production), this study evaluated the adult plant resistance (APR) of 96 wheat varieties (lines) from diverse sources to powdery mildew under natural infection and artificial inoculation in Yunnan and Zhejiang. Molecular markers for 13 known Pm genes were used to screen the materials. Results showed that 67 varieties (lines) exhibited resistance, including 28 highly resistant (HR) materials (29.17%). Resistance genes were detected in 62 materials, with Pm4b having the highest detection frequency, followed by Pm21. In terms of gene composition, materials carrying a single resistance gene predominated (58.1%), while only one material combined five different Pm genes. Notably, resistance genes Pm2, Pm4a, Pm4b, Pm8, Pm18, Pm34, Pm35, and Pm52 were detected in some susceptible materials, indicating their resistance may have been overcome by the pathogen. In contrast, Pm21, Pm24, Pm26, Pm57, and PmTm4 retained stable resistance.

Wheat yellow mosaic disease, caused by wheat yellow mosaic virus (WYMV) and transmitted via the soil-borne vector Polymyxa graminis, is one of the most devastating soil-borne viral diseases affecting wheat production in China. In recent years, its incidence in the Huang-Huai wheat-growing region has shown an escalating trend, posing significant challenges for disease control. This study aimed to screen elite germplasms and genetic resources resistant to wheat yellow mosaic disease by conducting phenotypic evaluations and molecular marker detection for major resistance genes. Field resistance assessments were performed on 158 wheat varieties (lines) across three environments over two consecutive years in the Huang-Huai region, and genotype screening was carried out using molecular markers linked to major resistance genes/QTLs. Phenotypic analysis revealed that 26, 35 and 10 accessions were consistently classified as immune, highly resistant and moderately resistant to WYMV across all three environments, accounting for 16.5%, 22.2% and 6.3% of the total, respectively. In contrast, 72(45.5%) and 15(9.5%) varieties (lines) were moderately susceptible and highly susceptible, respectively. Molecular marker detection identified 39(24.7%), 48(30.4%) and 3(1.9%) accessions carrying the resistance genes/QTLs QYm.nau-2D, Ym2 and QYm.njau-5A.1, respectively. Notably, accessions individually carrying QYm.nau-2D, QYm.njau-5A.1 or the combination of QYm.nau-2D and Ym2 exhibited enhanced disease resistance. Based on integrated phenotypic and genotypic analyses, 61 immune or highly resistant wheat varieties (lines), including Yunong 907, Yunong 908, Zhoumai 30, were identified as valuable genetic resources for breeding and deployment of WYMV-resistant wheat varieties. These materials provide a foundation for improving disease-resistant germplasms and exploring novel resistance genes.

In order to effectively reduce the impact of Fusarium head blight (FHB) on wheat production in southwestern China and explore resistance germplasm resources and genes against FHB, the study was conducted from 2022 to 2024 on agronomic traits such as thousand grain weight, plant height and grain number per ear of 80 wheat varieties (lines) grown in Guizhou. Single flower drip inoculation method was used to identify the phenotype resistance of FHB in the field, combined with 11 pairs of molecular markers closely linked to Fhb1, Fhb2, Fhb4, Fhb5, Fhb7 and QFhs.crc-2DL for detection. The molecular detection results showed that 59 varieties (lines) carried disease resistance genes, among which the frequencies of Fhb1, Fhb2, Fhb4, Fhb5, Fhb7 and QFhs.crc-2DL were 37.50%, 28.75%, 22.50%, 40.00%, 2.50% and 23.75%, respectively. A total of 37 varieties (lines) simultaneously carried 2 to 4 disease resistance genes, with the combinations of Fhb1+Fhb2+Fhb5 and Fhb1+Fhb5 exhibiting detection frequencies of 8.75% and 6.25%, respectively. After two years of resistance identification, a total of 11 resistant varieties (lines) were screened. Comprehensive analysis revealed that three varieties (lines), namely Qian 0938-3, Qian 15168 and Qian 13 Xia 163F₈-5, performed good disease resistance and excellent agronomic traits, which can be used as germplasm resources for genetic improvement of wheat resistance to FHB.

To analyze the genetic rules of agronomic traits related to wheat yield, in this study, 319 wheat materials and 16K SNP array data were utilized. Through the method of genome-wide association study (GWAS), seven yield-related traits, namely plant height, spike length, number of spikelets per spike, 1 000-grain weight, grain length, grain width, and grain length-to-width ratio, were analyzed in five environments. The results showed that all the seven traits exhibited extensive phenotypic variations, with the coefficient of variation ranging from 5.97% to 15.77%, and the broad-sense heritability ranging from 82.42% to 96.62%. The results of the genome-wide association analysis indicated that a total of 98 significant and stable SNP loci were detected in five environments, explaining 2.9% to 12.1% of the phenotypic variations. Among them, three SNP loci, namely 1D-325966494, 2D-645942075, and 7D-166300030, showed pleiotropy. Through haplotype analysis, two excellent haplotypes that significantly reduced the plant height were obtained, which were located in specific regions of chromosomes 2A and 3A. One candidate gene TraesCS2A02G457400 related to plant height was identified, and its function has not been clarified yet.

Plant height is a key trait in the growth and development of wheat and has an important impact on wheat yield. To further identify quantitative trait loci (QTL) associated with plant height in wheat, this study utilized an F₂ population derived from a cross between a dwarf mutant 20DH2-2 and a tall parent 21Hs-7. Genetic analysis of plant height was conducted using a mixed dominant gene plus multi-gene inheritance model. A genetic linkage map was constructed using the screening results of 620 simple sequence repeat (SSR) molecular markers, and QTL mapping analysis was performed in combination with the plant height phenotypic data of the F₂ individuals. Results showed that the optimal genetic model for plant height was 2MG-EA (two equal additive major genes). Through bulked segregant analysis (BSA), 26 polymorphic SSR markers were identified, constructing a genetic linkage map spanning 531.62 cM. Five plant height-related QTLs were detected on chromosomes 2A (two QTLs), 4A, 5A, and 6A, with logarithm of odds (LOD scores) ranging from 2.60 to 8.19 and explaining 7.97%~22.88% of phenotypic variation. Specifically, QPH-nwafu-2A.2, QPH-nwafu-4A, QPH-nwafu-5A, and QPH-nwafu-6A tightly linked to molecular markers BQ838266 (4.01 cM), Xwmc262 (3.00 cM), Xwmc524 (3.94 cM), and Xbarc107 (0.69 cM), respectively.