Integrated metabolomic and transcriptomic analyses reveal the molecular mechanism of amino acid transport between source and sink during tea shoot development.

KEY MESSAGE: The weighted gene co-expression network analysis and antisense oligonucleotide-mediated transient gene silencing revealed that CsAAP6 plays an important role in amino acid transport during tea shoot development. Nitrogen transport from source to sink is crucial for tea shoot growth and quality formation. Amino acid represents the major transport form of reduced nitrogen in the phloem between source and sink, but the molecular mechanism of amino acid transport from source leaves to new shoots is not yet clear. Therefore, the composition of metabolites in phloem exudates collected by the EDTA-facilitated method was analyzed through widely targeted metabolomics. A total of 326 metabolites were identified in the phloem exudates with the richest variety of amino acids and their derivatives (93), accounting for approximately 39.13% of the total metabolites. Moreover, through targeted metabolomics, it was found that the content of glutamine, glutamic acid, and theanine was the most abundant, and gradually increased with the development of new shoots. Meanwhile, transcriptome analysis suggested that the expression of amino acid transport genes changed significantly. The WGCNA analysis identified that the expression levels of CsAVT1, CsLHTL8, and CsAAP6 genes located in the MEterquoise module were positively correlated with the content of amino acids such as glutamine, glutamic acid, and theanine in phloem exudates. Reducing the CsAAP6 in mature leaves resulted in a significant decrease in the content of glutamic acid, aspartic acid, alanine, leucine, asparagine, glutamine, and arginine in the phloem exudates, indicating that CsAAP6 played an important role in the source to sink transport of amino acids in the phloem. The research results will provide the theoretical basis and genetic resources for the improvement of nitrogen use efficiency and tea quality.

Zno nanoparticles: improving photosynthesis, shoot development, and phyllosphere microbiome composition in tea plants.

BACKGROUND: Nanotechnology holds revolutionary potential in the field of agriculture, with zinc oxide nanoparticles (ZnO NPs) demonstrating advantages in promoting crop growth. Enhanced photosynthetic efficiency is closely linked to improved vigor and superior quality in tea plants, complemented by the beneficial role of phyllosphere microorganisms in maintaining plant health. However, the effects of ZnO NPs on the photosynthesis of tea plants, the sprouting of new shoots, and the community of phyllosphere microorganisms have not been fully investigated. RESULTS: This study investigated the photosynthetic physiological parameters of tea plants under the influence of ZnO NPs, the content of key photosynthetic enzymes such as RubisCO, chlorophyll content, chlorophyll fluorescence parameters, transcriptomic and extensive targeted metabolomic profiles of leaves and new shoots, mineral element composition in these tissues, and the epiphytic and endophytic microbial communities within the phyllosphere. The results indicated that ZnO NPs could enhance the photosynthesis of tea plants, upregulate the expression of some genes related to photosynthesis, increase the accumulation of photosynthetic products, promote the development of new shoots, and alter the content of various mineral elements in the leaves and new shoots of tea plants. Furthermore, the application of ZnO NPs was observed to favorably influence the microbial community structure within the phyllosphere of tea plants. This shift in microbial community dynamics suggests a potential for ZnO NPs to contribute to plant health and productivity by modulating the phyllosphere microbiome. CONCLUSION: This study demonstrates that ZnO NPs have a positive impact on the photosynthesis of tea plants, the sprouting of new shoots, and the community of phyllosphere microorganisms, which can improve the growth condition of tea plants. These findings provide new scientific evidence for the application of ZnO NPs in sustainable agricultural development and contribute to advancing research in nanobiotechnology aimed at enhancing crop yield and quality.

Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology.

BACKGROUND: Gray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ-6) was simulated. Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short-term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions. RESULTS: The visible red-light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS-LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA-LSTM model excelled, achieving an accuracy of 82.9%. CONCLUSION: This study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.

A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML) algorithms, this study presents an innovative approach for classifying the quality grade of fresh tea leaves. This approach leverages an integration of image recognition and deep learning (DL) algorithm to accurately classify tea leaves' grades by identifying distinct bud and leaf combinations. The method begins by acquiring separate images of orderly scattered and randomly stacked fresh tea leaves. These images undergo data augmentation techniques, such as rotation, flipping, and contrast adjustment, to form the scattered and stacked tea leaves datasets. Subsequently, the YOLOv8x model was enhanced by Space pyramid pooling improvements (SPPCSPC) and the concentration-based attention module (CBAM). The established YOLOv8x-SPPCSPC-CBAM model is evaluated by comparing it with popular DL models, including Faster R-CNN, YOLOv5x, and YOLOv8x. The experimental findings reveal that the YOLOv8x-SPPCSPC-CBAM model delivers the most impressive results. For the scattered tea leaves, the mean average precision, precision, recall, and number of images processed per second rates of 98.2%, 95.8%, 96.7%, and 2.77, respectively, while for stacked tea leaves, they are 99.1%, 99.1%, 97.7% and 2.35, respectively. This study provides a robust framework for accurately classifying the quality grade of fresh tea leaves.

Effect of Shading on Physiological Attributes and Proteomic Analysis of Tea during Low Temperatures.

Shading is an important technique to protect tea plantations under abiotic stresses. In this study, we analyzed the effect of shading (SD60% shade vs. SD0% no-shade) on the physiological attributes and proteomic analysis of tea leaves in November and December during low temperatures. The results revealed that shading protected the tea plants, including their soil plant analysis development (SPAD), photochemical efficiency (Fv/Fm), and nitrogen content (N), in November and December. The proteomics analysis of tea leaves was determined using tandem mass tags (TMT) technology and a total of 7263 proteins were accumulated. Further, statistical analysis and the fold change of significant proteins (FC < 0.67 and FC > 1.5 p < 0.05) revealed 14 DAPs, 11 increased and 3 decreased, in November (nCK_vs_nSD60), 20 DAPs, 7 increased and 13 decreased, in December (dCK_vs_dSD60), and 12 DAPs, 3 increased and 9 decreased, in both November and December (nCK_vs_nSD60). These differentially accumulated proteins (DAPs) were dehydrins (DHNs), late-embryogenesis abundant (LEA), thaumatin-like proteins (TLPs), glutathione S-transferase (GSTs), gibberellin-regulated proteins (GAs), proline-rich proteins (PRPs), cold and drought proteins (CORA-like), and early light-induced protein 1, which were found in the cytoplasm, nucleus, chloroplast, extra cell, and plasma membrane, and functioned in catalytic, cellular, stimulus-response, and metabolic pathways. In conclusion, the proliferation of key proteins was triggered by translation and posttranslational modifications, which might sustain membrane permeability in tea cellular compartments and could be responsible for tea protection under shading during low temperatures. This study aimed to investigate the impact of the conventional breeding technique (shading) and modern molecular technologies (proteomics) on tea plants, for the development and protection of new tea cultivars.