Proteomics and phosphoproteomics reveal the different drought-responsive mechanisms of priming with (Z)-3-hexenyl acetate in two tea cultivars.

Drought is an important abiotic stress that constrains the quality and quantity of tea plants. The green leaf volatiles Z-3-hexenyl acetate (Z-3-HAC) have been reported to play an essential role in stress responses. However, the underlying mechanisms of drought tolerance in tea plants remain elusive. This study investigated the physiological, proteomic, and phosphoproteomic profiling of two tea plant varieties of Longjingchangye (LJCY) and Zhongcha 108 (ZC108) with contrasting drought tolerance characteristics under drought stress. Physiological data showed that spraying Z-3-HAC exhibited higher activities of superoxide dismutase (SOD) and catalase (CAT) in both LJCY and ZC108 but lower content of malondialdehyde (MDA) in LJCY under drought stress. The proteomic and phosphoproteomic analysis suggested that the drought tolerance mechanism of Z-3-HAC in LJCY and ZC108 was different. Proteomic analyses revealed that Z-3-HAC enhanced the drought tolerance of LJCY by fructose metabolism while enhancing the drought tolerance of ZC108 by promoting glucan biosynthesis and galactose metabolism. Furthermore, the differential abundance phosphoproteins (DAPPs) related to intracellular protein transmembrane transport and protein transmembrane transport were enriched in LJCY, and the regulation of response to osmotic stress and regulation of mRNA processing were enriched in ZC108. In addition, protein-phosphoprotein interactions (PPI) analyses suggested that energy metabolism and starch and sucrose metabolic processes might play critical roles in LJCY and ZC108, respectively. These results will help to understand the mechanisms by which Z-3-HAC enhances the drought resistance of tea plants at the protein level. SIGNIFICANT: Green leaf volatiles (GLVs) are important volatile organic compounds that play essential roles in plant defense against biotic and abiotic stresses. To understand the mechanisms of Z-3-HAC in improving the drought tolerance of tea plants, two contrasting drought tolerance varieties (LJCY and ZC108) were comparatively evaluated by proteomics and phosphoproteomics. This analysis evidenced changes in the abundance of proteins involved in energy metabolism and starch and sucrose metabolic processes in LJCY and ZC108, respectively. These proteins may elucidate new molecular aspects of the drought resistance mechanism of Z-3-HAC, providing a theoretical basis for drought resistance breeding of tea plants.

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality.

BACKGROUND: Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field. RESULTS: We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2  = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2  = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10. CONCLUSION: The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.