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

Comparative metabolomics and transcriptomics analyses provide insights into branching changes induced by heterografting in Pinus massoniana seedlings

The investigation of dwarfing rootstocks for the establishment of high-generation seed orchards is a prospective avenue of research. In this investigation, Pinus massoniana, Pinus yunnanensis var. pygmaea (P. pygmaea), and P. elliottii seedlings were used as rootstocks for grafting with P. massoniana scions. Grafting P. massoniana onto P. pygmaea rootstock resulted in observable phenotypic alterations in lateral branches, apical buds, and needle length. Certain characteristic metabolites of rootstocks, such as fatty acyls, pregnenolones, steroids, and steroid derivatives, were found to be highly expressed in scions after grafting. RNA-seq analysis revealed MYB-related, SBP, and bHLH demonstrating a significant positive correlation, while C2H2 and Orphans exhibited negative correlations with the differential intensity of metabolites related to lipids and lipid-like molecules. This study offers valuable insights for the establishment of rootstock breeding programs.

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

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.

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

AIMS

Due 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 RESULTS

A 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.

UTILITY

This 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.