Dataset Information

Drought resistance of Camellia oleifera under drought stress: Changes in physiology and growth characteristics.

ABSTRACT: To investigate the growth, physiological changes and mechanism of drought resistance of Camellia oleifera GWu-2 under drought stress conditions, changes in the main growth and physiological indices of GWu-2 under different water gradients were studied. Factor analysis was used to study the differences between indicators under different water gradients, and correlation analysis was implemented to analyze the relationship between different factors. We observed that the growth state, enzyme secretion, stomatal morphology and leaf osmotic adjustment substances were significantly affected by drought stress. In particular, increases in leaf abscisic acid (ABA), indole acetic acid (IAA) and methyl jasmonate (MeJA) contents under drought stress were negatively correlated with the stomatal opening degree, and the ratio of ZR/GA3 was significantly correlated with the growth and physiological indicators of GWu-2, indicating that different hormones respond differently to drought stress and have different functions in the growth regulation and drought resistance of GWu-2. We concluded that the drought resistance mechanism of GWu-2 was controlled by maintaining root growth to obtain the necessary water, increasing the contents of osmotic substances of leaves to maintain water holding capacity, reducing the transpiration of water by increasing leaf ABA, IAA and MeJA content to close stomata and reducing the damage caused by drought by increasing the activity of superoxide dismutase (SOD).

SUBMITTER: He X

PROVIDER: S-EPMC7347177 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Drought resistance of Camellia oleifera under drought stress: Changes in physiology and growth characteristics.

He Xiaosan X Xu Linchu L Pan Chang C Gong Chun C Wang Yujuan Y Liu Xinliang X Yu Yuanchun Y

PloS one 20200709 7

To investigate the growth, physiological changes and mechanism of drought resistance of Camellia oleifera GWu-2 under drought stress conditions, changes in the main growth and physiological indices of GWu-2 under different water gradients were studied. Factor analysis was used to study the differences between indicators under different water gradients, and correlation analysis was implemented to analyze the relationship between different factors. We observed that the growth state, enzyme secreti ...[more]

PMID: 32645115

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Project description:BackgroundDrought stress is one of the major natural challenges in the main tea-producing regions of China. The tea plant (Camellia sinensis) is a traditional beverage plant whose growth status directly affects tea quality. Recent studies have revealed that microRNAs (miRNAs) play key functions in plant growth and development. Although some miRNAs have been identified in C. sinensis, little is known about their roles in the drought stress response of tea plants.ResultsPhysiological characterization of Camellia sinensis 'Tieguanyin' under drought stress showed that the malondialdehyde concentration and electrical conductivity of leaves of drought-stressed plants increased when the chlorophyll concentration decreased under severe drought stress. We sequenced four small-RNA (sRNA) libraries constructed from leaves of plants subjected to four different treatments, normal water supply (CK); mild drought stress (T1); moderate drought stress (T2) and severe drought stress (T3). A total of 299 known mature miRNA sequences and 46 novel miRNAs were identified. Gene Ontology enrichment analysis revealed that most of the differentially expressed-miRNA target genes were related to regulation of transcription. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the most highly enriched pathways under drought stress were D-alanine metabolism, sulfur metabolism, and mineral absorption pathways. Real-time quantitative PCR (qPCR) was used to validate the expression patterns of 21 miRNAs (2 up-regulated and 19 down-regulated under drought stress). The observed co-regulation of the miR166 family and their targets ATHB-14-like and ATHB-15-like indicate the presence of negative feedback regulation in miRNA pathways.ConclusionsAnalyses of drought-responsive miRNAs in tea plants showed that most of differentially expressed-miRNA target genes were related to regulation of transcription. The results of study revealed that the expressions of phase-specific miRNAs vary with morphological, physiological, and biochemical changes. These findings will be useful for research on drought resistance and provide insights into the mechanisms of drought adaptation and resistance in C. sinensis.

Project description:BackgroundCamellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types.MethodsThis study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R2 and and time consumption.ResultsWhen using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera.

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Dataset Information

Drought resistance of Camellia oleifera under drought stress: Changes in physiology and growth characteristics.

Publications

Drought resistance of Camellia oleifera under drought stress: Changes in physiology and growth characteristics.

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