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.

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.

Improved phyllosphere microbiome composition of tea plant with the application of small peptides in combination with rhamnolipid.

BACKGROUND: Small peptides play a crucial role in plant growth and adaptation to the environment. Exogenous small peptides are often applied together with surfactants as foliar fertilizers, but the impact of small peptides and surfactants on the tea phyllosphere microbiome remains unknown. RESULTS: In this study, we investigated the effects of small peptides and different surfactants on the tea phyllosphere microbiome using 16S and ITS sequencing. Our results showed that the use of small peptides reduced the bacterial diversity of the tea phyllosphere microbiome and increased the fungal diversity, while the use of surfactants influenced the diversity of bacteria and fungi. Furthermore, the addition of rhamnolipid to small peptides significantly improved the tea phyllosphere microbiome community structure, making beneficial microorganisms such as Pseudomonas, Chryseobacterium, Meyerozyma, and Vishniacozyma dominant populations. CONCLUSION: Our study suggests that the combined use of small peptides and surfactants can significantly modify the tea phyllosphere microbiome community structure, particularly for beneficial microorganisms closely related to tea plant health. Thus, this preliminary study offers initial insights that could guide the application of small peptides and surfactants in agricultural production, particularly with respect to their potential for modulating the phyllosphere microbiome community in tea plant management.

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.

A deep learning model for rapid classification of tea coal disease.

BACKGROUND: The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification. RESULTS: Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging. CONCLUSIONS: This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

Enzymatic fermentation of rapeseed cake significantly improved the soil environment of tea rhizosphere.

BACKGROUND: Rapeseed cake is an important agricultural waste. After enzymatic fermentation, rapeseed cake not only has specific microbial diversity but also contains a lot of fatty acids, organic acids, amino acids and their derivatives, which has potential value as a high-quality organic fertilizer. However, the effects of fermented rapeseed cake on tea rhizosphere microorganisms and soil metabolites have not been reported. In this study, we aimed to elucidate the effect of enzymatic rapeseed cake fertilizer on the soil of tea tree, and to reveal the correlation between rhizosphere soil microorganisms and nutrients/metabolites. RESULTS: The results showed that: (1) The application of enzymatic rapeseed cake increased the contents of soil organic matter (OM), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), and available phosphorus (AP); increased the activities of soil urease (S-UE), soil catalase (S-CAT), soil acid phosphatase (S-ACP) and soil sucrase (S-SC); (2) The application of enzymatic rapeseed cake increased the relative abundance of beneficial rhizosphere microorganisms such as Chaetomium, Inocybe, Pseudoxanthomonas, Pseudomonas, Sphingomonas, and Stenotrophomonas; (3) The application of enzymatic rapeseed cake increased the contents of sugar, organic acid, and fatty acid in soil, and the key metabolic pathways were concentrated in sugar and fatty acid metabolisms; (4) The application of enzymatic rapeseed cake promoted the metabolism of sugar, organic acid, and fatty acid in soil by key rhizosphere microorganisms; enzymes and microorganisms jointly regulated the metabolic pathways of sugar and fatty acids in soil. CONCLUSIONS: Enzymatic rapeseed cake fertilizer improved the nutrient status and microbial structure of tea rhizosphere soil, which was beneficial for enhancing soil productivity in tea plantations. These findings provide new insights into the use of enzymatic rapeseed cake as an efficient organic fertilizer and expand its potential for application in tea plantations.

Enhancing the Adaptability of Tea Plants (Camellia sinensis L.) to High-Temperature Stress with Small Peptides and Biosurfactants.

Tea plants are highly susceptible to the adverse effects of a high-temperature climate, which can cause reduced yield and quality and even lead to plant death in severe cases. Therefore, reducing the damage caused by high-temperature stress and maintaining the photosynthetic capacity of tea plants is a critical technical challenge. In this study, we investigated the impact of small oligopeptides (small peptides) and surfactants on the high-temperature-stress tolerance of tea plants. Our findings demonstrated that the use of small peptides and surfactants enhances the antioxidant capacity of tea plants and protects their photosynthetic system. They also induce an increase in gibberellin (GA) content and a decrease in jasmonic acid (JA), strigolactone (SL), auxin (IAA), and cytokinin (CTK) content. At the same time, small peptides regulate the metabolic pathways of diterpenoid biosynthesis. Additionally, small peptides and surfactants induce an increase in L-Carnosine and N-Glycyl-L-Leucine content and a decrease in (5-L-Glutamyl)-L-Amino Acid content, and they also regulate the metabolic pathways of Beta-Alanine metabolism, Thiamine metabolism, and Glutathione metabolism. In summary, small peptides and surfactants enhance the ability of tea plants to resist high-temperature stress.

Different changes of bacterial diversity and soil metabolites in tea plants-legume intercropping systems.

As an essential agroforestry, intercropping legumes can improve the physical, chemical, and biological fertility of the soil in tea plantations. However, the effects of intercropping different legume species on soil properties, bacterial communities, and metabolites remain elusive. In this study, the 0-20 cm and 20-40 cm soils of three planting patterns (T1: tea plants/mung bean intercropping, T2: tea plants/adzuki bean intercropping, T3: tea plants/mung bean and adzuki bean intercropping) were sampled to explore the diversity of the bacterial community and soil metabolites. The findings showed that, as compared to monocropping, intercropping systems had greater concentrations of organic matter (OM) and dissolved organic carbon (DOC). Notably, pH values were significantly lower, and soil nutrients increased in intercropping systems compared with monoculture in 20-40 cm soils, especially in T3. In addition, intercropping resulted in an increased relative abundance of Proteobacteria but a decreased relative abundance of Actinobacteria. 4-methyl-Tetradecane, acetamide, and diethyl carbamic acid were key metabolites mediating the root-microbe interactions, especially in tea plants/adzuki intercropping and tea plants/mung bean, adzuki bean mixed intercropping soils. Co-occurrence network analysis showed that arabinofuranose, abundant in tea plants and adzuki bean intercropping soils, showed the most remarkable correlation with the soil bacterial taxa. Our findings demonstrate that intercropping with adzuki beans is better at enhancing the diversity of soil bacteria and soil metabolites and is more weed-suppressing than other tea plants/legume intercropping systems.

Metabolic Variations among Three New Tea Varieties Cultivated in Shandong, China.

Cultivar identification is a necessary step in tea breeding programs. Rapid identification methods would greatly improve these breeding processes. To preliminarily identify the three new Lucha tea varieties (LC6, LC7, and LC17) cultivated in Shandong, we measured their main agronomic characters and biochemical components. Then, we analyzed the metabolic profiles of these tea varieties and Fuding Dabaicha (FD) using a UPLC-ESI-MS/MS system. Their biochemical components indicated that the Lucha varieties had excellent varietal characteristics, with higher amino acid contents. Furthermore, secondary metabolism changed a lot in the Lucha tea varieties compared with that in the FD, with their accumulations of flavonoids and phenolic acids showing significant differences. These differential flavonoids were dominated by flavones and flavanone, flavonols, flavonoid carbonosides, and flavanols monomer. Flavanols especially, including epicatechin glucoside, epicatechin-3-(3″-O-methyl)gallate, epigallocatechin-3-O-(3,5-O-dimethyl)gallate, and epitheaflavic acid-3-O-Gallate, showed higher levels in the Lucha varieties. The phenolic acids containing caffeoyl groups showed higher levels in the Lucha varieties than those in the FD, while those containing galloyl groups showed a reverse pattern. Nitrogen metabolism, including amino acids, also showed obvious differences between the Lucha varieties and FD. The differential amino acids were mainly higher in the Lucha varieties, including 5-L-glutamyl-L-amino acid, N-monomethyl-L-arginine, and N-α-acetyl-L-ornithine. By using these approaches, we found that LC6, LC7, and LC17 were excellent varieties with a high yield and high quality for making green teas in Shandong.

Effect of shading on physiological attributes and comparative transcriptome analysis of Camellia sinensis cultivar reveals tolerance mechanisms to low temperatures.

Tea is a vital beverage crop all over the world, including in China. Low temperatures restrict its growth, development, and terrestrial distribution, and cold event variability worsens cold damage. However, the physiological and molecular mechanisms of Camellia sinensis under shade in winter remain unclear. In our study, tea leaves were utilized for physiological attributes and transcriptome analysis in November and December in three shading groups and no-shade control plants. When compared to the no-shade control plants, the shading group protected tea leaves from cold damage, increased photochemical efficiency (Fv/Fm) and soil plant analysis development (SPAD), and sustained chlorophyll a, chlorophyll b, chlorophyll, and carotenoid contents by physiological mean. Then, transcriptome analysis revealed 20,807 differentially expressed genes (DEGs) and transcription factors (TFs) in November and December. A comparative study of transcriptome resulted in 3,523 DEGs and many TFs under SD0% vs. SD30%, SD0% vs. SD60%, and SD0% vs. SD75% of shading in November and December. Statistically, 114 DEGs were downregulated and 72 were upregulated under SD0% vs. SD30%. SD0% vs. SD60% resulted in 154 DEGs, with 60 downregulated and 94 upregulated. Similarly, there were 505 DEGs of which 244 were downregulated and 263 were upregulated under SD0% vs. SD75% of shading throughout November. However, 279 DEGs were downregulated and 105 were upregulated under SD0% vs. SD30%. SD0% vs. SD60% resulted in 296 DEGs, with 172 downregulated and 124 upregulated. Finally, 2,173 DEGs were regulated in December, with 1,428 downregulated and 745 upregulated under SD0% vs. SD75%. These indicate that the number of downregulated DEGs in December was higher than the number of upregulated DEGs in November during low temperatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes were highly regulated in the photosynthesis, plant hormone signal transduction, and mitogen-activated protein kinase (MAPK) signaling pathways. However, qRT-PCR and RNA-seq relative expression of photosynthetic (DEGs) Lhcb2 in both November and December, plant hormone (DEGs) BRI1 and JAZ in November and IAA and ERF1 in December, and key DEGs of MAPK signal transduction FLS2, CHIB, and MPK4 in November and RBOH, MKK4_5, and MEKK1 in December in three shading groups and no-shade control plants responded to tea cold tolerance. The enhanced expression of light-harvesting photosystem I gene Lhca5, light-harvesting photosystem II gene Lhcb2, and mitogen-activated protein kinases MEKK1 and MPK4/6 enhance the cold-tolerance mechanism of C. sinensis. These comprehensive transcriptomic findings are significant for furthering our understanding of the genes and underlying regulatory mechanisms of shade-mediated low-temperature stress tolerance in horticultural crops.

Low temperature response index for monitoring freezing injury of tea plant.

Freezing damage has been a common natural disaster for tea plantations. Quantitative detection of low temperature stress is significant for evaluating the degree of freezing injury to tea plants. Traditionally, the determination of physicochemical parameters of tea leaves and the investigation of freezing damage phenotype are the main approaches to detect the low temperature stress. However, these methods are time-consuming and laborious. In this study, different low temperature treatments were carried out on tea plants. The low temperature response index (LTRI) was established by measuring seven low temperature-induced components of tea leaves. The hyperspectral data of tea leaves was obtained by hyperspectral imaging and the feature bands were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The LTRI and seven indexes of tea plant were modeled by partial least squares (PLS), support vector machine (SVM), random forests (RF), back propagation (BP) machine learning methods and convolutional neural networks (CNN), long short-term memory (LSTM) deep learning methods. The results indicated that: (1) the best prediction model for the seven indicators was LTRI-UVE-CNN (R2 = 0.890, RMSEP=0.325, RPD=2.904); (2) the feature bands screened by UVE algorithm were more abundant, and the later modeling effect was better than CARS and SPA algorithm; (3) comparing the effects of the six modeling algorithms, the overall modeling effect of the CNN model was better than other models. It can be concluded that out of all the combined models in this paper, the LTRI-UVE-CNN was a promising model for predicting the degree of low temperature stress in tea plants.

Glycine-Induced Phosphorylation Plays a Pivotal Role in Energy Metabolism in Roots and Amino Acid Metabolism in Leaves of Tea Plant.

Phosphorylation is the most extensive post-translational modification of proteins and thus regulates plant growth. However, the regulatory mechanism of phosphorylation modification on the growth of tea plants caused by organic nitrogen is still unclear. In order to explore the phosphorylation modification mechanism of tea plants in response to organic nitrogen, we used glycine as the only nitrogen source and determined and analyzed the phosphorylated proteins in tea plants by phosphoproteomic analysis. The results showed that the phosphorylation modification induced by glycine-supply played important roles in the regulation of energy metabolism in tea roots and amino acid metabolism in tea leaves. In roots, glycine-supply induced dephosphorylation of proteins, such as fructose-bisphosphate aldolase cytoplasmic isozyme, glyceraldehyde-3-phosphate dehydrogenase, and phosphoenolpyruvate carboxylase, resulted in increased intensity of glycolysis and decreased intensity of tricarboxylic acid cycle. In leaves, the glycine-supply changed the phosphorylation levels of glycine dehydrogenase, aminomethyltransferase, glutamine synthetase, and ferredoxin-dependent glutamate synthase, which accelerated the decomposition of glycine and enhanced the ability of ammonia assimilation. In addition, glycine-supply could improve the tea quality by increasing the intensity of amino acids, such as theanine and alanine. This research clarified the important regulatory mechanism of amino acid nitrogen on tea plant growth and development through protein phosphorylation.

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.

Hyperspectral machine-learning model for screening tea germplasm resources with drought tolerance.

Drought tolerance and quality stability are important indicators to evaluate the stress tolerance of tea germplasm resources. The traditional screening method of drought resistant germplasm is mainly to evaluate by detecting physiological and biochemical indicators of tea plants under drought stresses. However, the methods are not only time consuming but also destructive. In this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators. The results showed that: (1) the information contents of malondialdehyde, soluble sugar and total polyphenol were 0.21, 0.209 and 0.227 respectively, and the drought tolerance coefficient (DTC) index of each tea variety was between 0.069 and 0.81; (2) the comprehensive drought tolerance of different varieties were (from strong to weak): QN36, SCZ, ZC108, JX, JGY, XY10, QN1, MS9, QN38, and QN21; (3) by using SVM, RF and PLSR to model DTC (drought tolerance coefficient) data, the best prediction model was selected as MSC-2D-UVE-SVM (R2 = 0.77, RMSE = 0.073, MAPE = 0.16) for drought tolerance of tea germplasm resources, named Tea-DTC model. Therefore, the Tea-DTC model based on hyperspectral machine-learning technology can be used as a new screening method for evaluating tea germplasm resources with drought tolerance.

The Role of Calmodulin Binding Transcription Activator in Plants under Different Stressors: Physiological, Biochemical, Molecular Mechanisms of Camellia sinensis and Its Current Progress of CAMTAs.

Low temperatures have a negative effect on plant development. Plants that are exposed to cold temperatures undergo a cascade of physiological, biochemical, and molecular changes that activate several genes, transcription factors, and regulatory pathways. In this review, the physiological, biochemical, and molecular mechanisms of Camellia sinensis have been discussed. Calmodulin binding transcription activator (CAMTAs) by molecular means including transcription is one of the novel genes for plants' adaptation to different abiotic stresses, including low temperatures. Therefore, the role of CAMTAs in different plants has been discussed. The number of CAMTAs genes discussed here are playing a significant role in plants' adaptation to abiotic stress. The illustrated diagrams representing the mode of action of calcium (Ca2+) with CAMTAs have also been discussed. In short, Ca2+ channels or Ca2+ pumps trigger and induce the Ca2+ signatures in plant cells during abiotic stressors, including low temperatures. Ca2+ signatures act with CAMTAs in plant cells and are ultimately decoded by Ca2+sensors. To the best of our knowledge, this is the first review reporting CAMAT's current progress and potential role in C. sinensis, and this study opens a new road for researchers adapting tea plants to abiotic stress.

Pea-Tea Intercropping Improves Tea Quality through Regulating Amino Acid Metabolism and Flavonoid Biosynthesis.

Pea-tea intercropping is an excellent cultivation method that can improve tea quality. However, the underlying mechanism is still unclear. The present study was aimed at elucidating the mechanism of the effect of pea-tea intercropping on tea quality through a high-throughput method. Transcriptome and metabolome analyses were conducted to identify the changes in gene expression and metabolites changes intercropping, respectively. In addition, the amino acids and catechins were detected using the LC-MS method and quantified absolutely. The results showed that total polyphenols and catechins decreased but amino acids increased in pea intercropped tea shoots. Correspondingly, genes related to amino acid metabolism and flavonoid biosynthesis were differentially expressed. For amino acid metabolism, 11 differentially expressed genes were identified, including 5 upregulated and 6 downregulated genes. Meanwhile, three genes involved in carbohydrate transport and metabolism were upregulated in pea intercropped tea plants. These genes were also involved in amino acid metabolism. For flavonoid biosynthesis, two downregulated genes were identified, which were the flavonol synthase and anthocyanidin synthase genes and followed a similar pattern to changes in catechins and polyphenols. These advances have opened new horizons for understanding the biochemical mechanisms of amino acids and flavonoids in improving tea quality in the pea-tea intercropping cultivation model.

Tea-Soybean Intercropping Improves Tea Quality and Nutrition Uptake by Inducing Changes of Rhizosphere Bacterial Communities.

The positive aspects of the tea plant/legume intercropping system draw attention to the Chinese tea industry for its benefit for soil fertility improvement with low fertilizer input. However, limited information exists as to the roles of intercropped legumes in the rhizosphere microbiome and tea quality. Hereby, soybean was selected as the intercropped plant to investigate its effect on bacterial communities, nutrient competition, tea plant development, and tea quality. Our data showed that intercropped soybean boosted the uptake of nitrogen in tea plants and enhanced the growth of young tea shoots. Nutrient competition for phosphorus and potassium in soil existed between soybeans and tea plants. Moreover, tea/soybean intercropping improved tea quality, manifested by a significantly increased content of non-ester type catechins (C, EGC, EC), total catechins and theanine, and decreased content of ester type catechins (EGCG). Significant differences in rhizobacterial composition were also observed under different systems. At the genus level, the relative abundance of beneficial bacteria, such as Bradyrhizobium, Saccharimonadales and Mycobacterium, was significantly increased with the intercropping system, while the relative abundance of denitrifying bacteria, Pseudogulbenkiania, was markedly decreased. Correlation analysis showed that Pseudogulbenkiania, SBR1031, and Burkholderiaceae clustered together showing a similar correlation with soil physicochemical and tea quality characteristics; however, other differential bacteria showed the opposite pattern. In conclusion, tea/soybean intercropping improves tea quality and nutrition uptake by increasing the relative abundance of beneficial rhizosphere bacteria and decreasing denitrifying bacteria. This study strengthens our understanding of how intercropping system regulate the soil bacterial community to maintain the health of soils in tea plantations and provides the basis for replacing chemical fertilizers and improving the ecosystem in tea plantations.

Enzymes and microorganisms jointly promote the fermentation of rapeseed cake.

Rapeseed cake is a by-product of rapeseed oil separation. The nutritional components of rapeseed cake mainly include a variety of carbohydrates, proteins, and minerals. In order to improve the conversion rate of rapeseed cake, we studied the physicochemical properties, the structure of microbial communities, and the composition of metabolites in rapeseed cake after enzymatic fermentation. The results showed that the addition of enzymatic preparation increased microbial diversity. The relative abundance of Bacillus, Lysinibacillus, Empedobacter, Debaryomyces, Hyphopichia, and Komagataella in enzymatic fermentation was significantly higher than that in natural fermentation. Unlike natural fermentation, microbial diversity during enzymatic fermentation is specific, which improves the efficiency of fermentation. Otherwise, enzymatic fermentation promotes the conversion of macromolecular substances in rapeseed cake, which increases small metabolites, such as fatty acids, organic acids, amino acids and their derivatives. The metabolite enrichment pathway is mostly concentrated in sugar metabolism and fatty acid metabolism. In conclusion, after adding enzymatic preparation, enzymes and microorganisms jointly promote the transformation of macromolecules during the fermentation of rapeseed cake, which laid a good foundation for further utilization of rapeseed cake.