Chang Liu

Freezing stress in spring often causes the death and abnormal development of young ears of wheat, leading to a significant reduction in grain production. However, the mechanisms of young wheat ears responding to freezing are largely unclear. In this study, the role of the ascorbic acid–glutathione cycle (AsA–GSH cycle) in alleviating freezing-caused oxidative damage in young wheat ears at the anther connective tissue formation phase (ACFP) was investigated. The results showed that the release rate of reactive oxygen species (ROS) and the relative electrolyte conductivity in young ears of Jimai22 (JM22, freezing-tolerant) were significantly lower than those in young ears of Xumai33 (XM33, freezing-sensitive) under freezing. The level of the GSH pool (231.8~392.3 μg/g FW) was strikingly higher than that of the AsA pool (98.86~123.4 μg/g FW) in young wheat ears at the ACFP. Freezing significantly increased the level of the AsA pool and the activities of ascorbate peroxidase (APX) and monodehydroascorbate reductase (MDHAR) in the young ears of both varieties. The level of the GSH pool increased in the young ears of XM33 under freezing but decreased in the young ears of JM22. The young ears of JM22 showed higher activities of glutathione reductase (GR), glutathione-S-transferase (GST) and glutathione peroxidase (GPX) than the young ears of XM33 under freezing. Collectively, these results suggest that the AsA–GSH cycle plays a positive role in alleviating freezing-induced oxidative damage in young wheat ears. Furthermore, the ability of utilizing GSH as a substrate to scavenge ROS is an important factor affecting the freezing tolerance of young wheat ears. In addition, abscisic acid (ABA), salicylic acid (SA), 3-indolebutyric acid (IBA) and cis-zeatin (cZ) may be involved in regulating the AsA–GSH cycle metabolism in young wheat ears under freezing.

Late spring frost is an important meteorological factor threatening the safe production of winter wheat in China. The young ear is the most vulnerable organ of the wheat plant to spring frost. To gain an insight into the mechanisms underpinning young wheat ears’ tolerance to freezing, we performed a comparative proteome analysis of wheat varieties Xumai33 (XM33, freezing-sensitive) and Jimai22 (JM22, freezing-tolerant) under normal and freezing conditions using label-free quantitative proteomic techniques during the anther connective tissue formation phase (ACFP). Under freezing stress, 392 and 103 differently expressed proteins (DEPs) were identified in the young ears of XM33 and JM22, respectively, and among these, 30 proteins were common in both varieties. A functional characterization analysis revealed that these DEPs were associated with antioxidant capacity, cell wall modification, protein folding, dehydration response, and plant–pathogen interactions. The young ears of JM22 showed significantly higher expression levels of antioxidant enzymes, heat shock proteins, and dehydrin under normal conditions compared to those of XM33, which might help to prepare the young ears of JM22 for freezing stress. Our results lead to new insights into understanding the mechanisms in young wheat ears’ response to freezing stress and provide pivotal potential candidate proteins required for improving young wheat ears’ tolerance to spring frost.

Heat stress is an important factor affecting soybean yield. Brassinosteroids (BRs) play a crucial role in plant growth, development, and defense. In the present study, the regulatory effects of 24-epibrassinolide (EBR, one of the bioactive BRs) on heat tolerance in soybeans, and its underlying physiological mechanisms were investigated. The results show that foliar spraying with EBR significantly alleviate heat stress-induced water loss and oxidative damage in soybean leaves. The activities of antioxidant enzymes (superoxide dismutase, catalase, and peroxidase) and the contents of antioxidant substances (ascorbic acid and reduced glutathione) were markedly increased in EBR-treated leaves compared with water-treated leaves, which contributed to maintaining reactive oxygen species homeostasis and relieving oxidative injury under heat stress. However, EBR-treated leaves showed a significant decrease in free proline and total soluble sugar content under heat stress compared to water-treated leaves. In addition, EBR treatment showed obviously higher photosystem II activity under heat stress, and higher net photosynthetic rate and biomass accumulation after recovery from heat stress compared to water treatment. Collectively, these results indicated that EBR could significantly improve the capacity of antioxidant defense systems to protect photosynthetic apparatus under heat stress, thereby effectively alleviating heat stress-induced growth inhibition in soybean plants.

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R²), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R² = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.