Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method

Abstract

Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion–iPBS (CRED–iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED–iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.

Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms

“…MSE measures the proximity between the predicted and actual values, as expressed by Equation (2). Additionally, the root MAD characterizes the overall distribution of prediction errors, as outlined in Equation (3) [20,26,27]. The dataset was randomly divided into two sets using the caret package in the R software (version 4.3.2): the training set (70%) and the testing set (30%).…”

“…Frequently employed for acquiring knowledge and constructing models optimized for specific tasks, ML algorithms undergo a learning process from data. Their objective is to provide predictions for a designated target variable using knowledge acquired from the data's properties [20,26,27]. Presently, the determination of drought tolerance, susceptibility levels, and the estimation of observed parameters in plants can be reliably accomplished using ML systems in the fastest, most cost-effective, and practical manner through the applied methods and analyses.…”

Influence of exogenous polyamines on the secondary somatic embryogenesis of cork oak ( Quercus suber L.)

“…Polyamines are small aliphatic amines with low molecular weight, organic polycations, and ubiquitous in all plant cells [ 23 , 24 ]. All of the three polyamines have been involved in the regulation of cell division and differentiation and in the morphogenesis [ 25 , 26 ]. These polycations have been shown to fluctuate as somatic embryogenesis proceeds, acting in root formation, cell division, floral initiation and development, secondary metabolism, fruit development, senescence, biotic and abiotic stress responses [ 11 , 26 , 27 ].…”

“…All of the three polyamines have been involved in the regulation of cell division and differentiation and in the morphogenesis [ 25 , 26 ]. These polycations have been shown to fluctuate as somatic embryogenesis proceeds, acting in root formation, cell division, floral initiation and development, secondary metabolism, fruit development, senescence, biotic and abiotic stress responses [ 11 , 26 , 27 ]. In addition, polyamines have been found to regulate the apoptosis [ 28 ].…”

“…In addition, polyamines have been found to regulate the apoptosis [ 28 ]. During zygotic embryo development and somatic embryogenesis processes, polyamines have been used as developmental biomarkers [ 20 , 21 , 26 , 29 ], while few data describing variations in polyamines during the development of angiosperm seeds are available [ 24 ].…”