Background: Low temperature is a crucial stress factor of wheat (Triticum aestivum L.) and adversely impacts on plant growth and grain yield. Multi-million tons of grain production are lost annually because crops lack the resistance to survive in winter. Particularlly, winter wheat yields was severely damaged under extreme cold conditions. However, studies about the transcriptional and metabolic mechanisms underlying cold stresses in wheat are limited so far. Results: In this study, 14,466 differentially expressed genes (DEGs) were obtained between wild-type and cold-sensitive mutants, of which 5278 DEGs were acquired after cold treatment. 88 differential accumulated metabolites (DAMs) were detected, including P-coumaroyl putrescine of alkaloids, D-proline betaine of mino acids and derivativ, Chlorogenic acid of the Phenolic acids. The comprehensive analysis of metabolomics and transcriptome showed that the cold resistance of wheat was closely related to 13 metabolites and 14 key enzymes in the flavonol biosynthesis pathway. The 7 enhanced energy metabolites and 8 up-regulation key enzymes were also compactly involved in the sucrose and amino acid biosynthesis pathway. Moreover, quantitative real-time PCR (qRT-PCR) revealed that twelve key genes were differentially expressed under cold, indicating that candidate genes POD, Tacr7, UGTs, and GSTU6 which were related to cold resistance of wheat. Conclusions: In this study, we obtained the differentially expressed genes and differential accumulated metabolites in wheat under cold stress. Using the DEGs and DAMs, we plotted regulatory pathway maps of the flavonol biosynthesis pathway, sucrose and amino acid biosynthesis pathway related to cold resistance of wheat. It was found that candidate genes POD, Tacr7, UGTs and GSTU6 are related to cold resistance of wheat. This study provided valuable molecular information and new genetic engineering clues for the further study on plant resistance to cold stress.

Sharp eyespot, caused by necrotrophic fungus Rhizoctonia cerealis, is a serious fungal disease in wheat (Triticum aestivum). Certain wall-associated receptor kinases (WAK) mediate resistance to diseases caused by biotrophic/hemibiotrophic pathogens in several plant species. Yet, none of wheat WAK genes with positive effect on the innate immune responses to R. cerealis has been reported. In this study, we identified a WAK gene TaWAK7D, located on chromosome 7D, and showed its positive regulatory role in the defense response to R. cerealis infection in wheat. RNA-seq and qRT-PCR analyses showed that TaWAK7D transcript abundance was elevated in wheat after R. cerealis inoculation and the induction in the stem was the highest among the tested organs. Additionally, TaWAK7D transcript levels were significantly elevated by pectin and chitin treatments. The knock-down of TaWAK7D transcript impaired resistance to R. cerealis and repressed the expression of five pathogenesis-related genes in wheat. The green fluorescent protein signal distribution assays indicated that TaWAK7D localized on the plasma membrane in wheat protoplasts. Thus, TaWAK7D, which is induced by R. cerealis, pectin and chitin stimuli, positively participates in defense responses to R. cerealis through modulating the expression of several pathogenesis-related genes in wheat.

High-molecular-weight glutenin subunits (HMW-GS) play important roles in the elasticity of dough made from wheat. The HMW-GS null line is useful for studying the contribution of HMW-GS to the end-use quality of wheat. In a previous work, we cloned the Glu-1Ebx gene from Thinopyrum bessarabicum and introduced it into the wheat cultivar, Bobwhite. In addition to lines expressing the Glu-1Ebx gene, we also obtained a transgenic line (LH-11) with all the HMW-GS genes silenced. The HMW-GS deletion was stably inherited as a dominant and conformed to Mendel’s laws. Expression levels of HMW-GS were determined by RT-PCR and epigenetic changes in methylation patterns and small RNAs were analyzed. Glutenins and gliadins were separated and quantitated by reversed-phase ultra-performance liquid chromatography. Measurement of glutenin macropolymer, and analysis of agronomic traits and end-use quality were also performed. DNA methylation and the presence of small double-stranded RNA may be the causes of post-transcriptional gene silencing in LH-11. The accumulation rate and final content of glutenin macropolymer (GMP) in LH-11 were significantly lower than in wild-type (WT) Bobwhite. The total protein content was not significantly affected as the total gliadin content increased in LH-11 compared to WT. Deletion of HMW-GS also changed the content of different gliadin fractions. The ratio of ω-gliadin increased, whereas α/β- and γ-gliadins declined in LH-11. The wet gluten content, sedimentation value, development time and stability time of LH-11 were remarkably lower than that of Bobwhite. Bread cannot be made using the flour of LH-11. Post-transcriptional gene silencing through epigenetic changes and RNA inhibition appear to be the causes for the gene expression deficiency in the transgenic line LH-11. The silencing of HMW-GW in LH-11 significantly reduced the dough properties, GMP content, wet gluten content, sedimentation value, development time and stability time of flour made from this wheat cultivar. The HMW-GS null line may provide a potential material for biscuit-making because of its low dough strength.

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture.

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.

Genetic dissection kernel weight-related traits is of great significance for improving wheat yield potential. As one of the three major yield components of wheat, thousand kernel weight (TKW) was mainly affected by grain length (GL) and grain width (GW). To uncover the key loci for these traits, we carried out a quantitative trait loci (QTL) analysis of an F₆ recombinant inbred lines (RILs) population derived from a cross of Henong 5290 (small grain) and 06Dn23 (big grain) with a 50 K single nucleotide polymorphism (SNP) array. A total of 17 stable and big effect QTL, including 5 for TKW, 8 for GL and 4 for GW, were detected on the chromosomes 1B, 2A, 2B, 2D, 4B, 5A, 6A and 6D, respectively. Among these, there were two co-located loci for three traits that were mapped on the chromosome 4BS and 6AL. The QTL on 6AL was the most stable locus and explained 15.4–24.8%, 4.1–8.8% and 15.7–24.4% of TKW, GW and GL variance, respectively. In addition, two more major QTL of GL were located on chromosome arm 2BL and 2DL, accounting for 9.7–17.8% and 13.6–19.8% of phenotypic variance, respectively. In this study, we found one novel co-located QTL associated with GL and TKW in 2DL, QGl.haaf-2DL.2/QTkw.haaf-2DL.2, which could explain 13.6–19.8% and 9.8–10.7% phenotypic variance, respectively. Genetic regions and linked markers of these stable QTL will help to further refine mapping of the corresponding loci and marker-assisted selection (MAS) breeding for wheat grain yield potential improvement.