Untargeted metabolism approach reveals difference of varieties of bud and relation among characteristics of grafting seedlings in Camellia oleifera

Wei, Long; Huang, Gongsheng; Yao, Xiaohua; Lv, Leyan; Yu, Chunlian; Wang, Kailiang

doi:10.3389/fpls.2022.1024353

Cited by 5 publications

(4 citation statements)

References 84 publications

(75 reference statements)

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“…This indicates that using only species as the selection criteria for oil tea rootstocks may pose a risk to yield quality after oil tea grafting. At the same time, it also indicates that the interaction between rootstocks and scions in Camellia oleifera is not only the regulation of root growth by scions [50][51], but also re ected in the in uence of fruit characteristics. Due to the signi cant impact of rootstocks on the physiological characteristics and other aspects of the growth and development of grafted plants, the reduction of scion growth is one of the most interesting phenomena.…”

Section: Discussionmentioning

confidence: 86%

Prediction of Fruit Traits of Camellia oleifera after Grafting Based on Deep Neural Networks

Zhou,

Long,

et al. 2023

Preprint

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Background: Camellia 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. Methods: This study used Deep Neural Network (DNN) methods to analyze the impact of 108 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, oil content, fruit height, fruit diameter, fresh fruit weight, peel 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 (MAE) and time consumption. Results: The results show that when using 32 neurons in two hidden layer, the artificial neural network model had a Mean Absolute Percentage Error (MAPE) of less than or equal to 16.30% on the verification set and less than or equal to 17.69% 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 increase rates ranging from 0% to 28.06% and 0% to 9.23%. 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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Section: Discussionmentioning

confidence: 86%

Prediction of Fruit Traits of Camellia oleifera after Grafting Based on Deep Neural Networks

Zhou,

Long,

et al. 2023

Preprint

Self Cite

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“…This indicates that using only species as the selection criteria for oil tea rootstocks may pose a risk to yield quality after oil tea grafting. At the same time, it also indicates that the interaction between rootstocks and scions in C.oleifera is not only the regulation of root growth by scions [ 67 68 ], but also reflected in the influence of fruit characteristics. Due to the significant impact of rootstocks on the physiological characteristics and other aspects of the growth and development of grafted plants, the reduction of scion growth is one of the most interesting phenomena.…”

Section: Discussionmentioning

confidence: 99%

“…It has an impact on the stability of the product. Research has found a significant rootstock and scion interaction in the grafted seedlings of C.oleifera [ 14 – 17 ], and scions control root growth after grafting [ 14 ], and the effect of scions on nutrient absorption [ 18 ]. There are significant differences in tree potential and growth among different varieties [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Yang,

Zhou,

et al. 2024

Plant Methods

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Background Camellia 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. Methods This 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. Results When 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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“…It measures a wide range of low‐molecular weight metabolites, including steroids, fatty acids, carbohydrates, and amino acids (Abadie et al, 2022; Chen et al, 2022). Finding the metabolites that correspond to each biological phenotype and then providing an understanding of the process involved are two important goals of metabolomics, bearing in mind that each plant variety has a unique metabolite type and correlation network relationship (Long et al, 2022). Despite its importance in understanding the associated biological processes, the possibility of metabolomics to acquire a full description of the processes may be limited due to the lack of an experimental database and advanced statistical and bioinformatics software requirements (Kind and Fiehn, 2006; Chen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Untargeted metabolomic in basil cell cultures – a case study of Ocimum basilicum L. var. minimum Alef.

Jakovljević,

Kruszka,

Waligórski

et al. 2024

Physiologia Plantarum

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AimsDue to the lack of experimental databases, together with the chemical complexity and the dynamic nature of plants' metabolome, most of the metabolites in complex biological materials (like plant in vitro tissue cultures) are not‐annotated, unidentified metabolites. In this study, a method for further metabolite characterization and classification based on the UPLC‐HESI‐HRMS/MS approach for small‐leaved basil (Ocimum basilicum L. var. minimum Alef.) callus and cell suspension culture is presented.Key resultsA total of 2168 metabolic features were detected, out of which the database for exact mass metabolic profiling for 1949 metabolites is presented here since there is no available database dedicated to O. basilicum. We further focused on secondary metabolites (particularly phenolic compounds). The presence of 60 different phenolic compounds belonging mainly to the groups of flavonoids, glycosides, terpenoids, and phenolic acids is confirmed. By comparing relative abundances of phenolic compounds from callus culture and cell suspension culture, both grown on two types of media, via svd‐PCА, univariate analysis, post‐hoc tests, and heatmapping of metabolites, we provided a practical example of how resources presented here can be further applied in tissue culture‐based basil metabolomics studies.UtilityThis study represents the first approach toward routine targeted investigation of secondary metabolites in basil in vitro cultures and provides various opportunities for new‐generation analyses.

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Untargeted metabolism approach reveals difference of varieties of bud and relation among characteristics of grafting seedlings in Camellia oleifera

Cited by 5 publications

References 84 publications

Prediction of Fruit Traits of Camellia oleifera after Grafting Based on Deep Neural Networks

Prediction of Fruit Traits of Camellia oleifera after Grafting Based on Deep Neural Networks

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Untargeted metabolomic in basil cell cultures – a case study of Ocimum basilicum L. var. minimum Alef.

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