RESUMEN
The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO3 and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO3 and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L-1). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study's results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L-1 of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L-1 of AgNO3. Taking an epigenetic approach, AgNO3 at a concentration of 2 mg L-1 demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO3 and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R2 score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R2 value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO3 and Ag-NPs.
RESUMEN
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.
RESUMEN
BACKGROUND: Turkey is one of the important gene centers for many crop species. In this research, some ancient wheats such as tetraploid and diploid hulled wheats together with hexaploid tir wheats (Triticum aestivum ssp. leucospermum Korn.) landraces mainly adapted to harsh winter conditions of Eastern Anatolian region of Turkey were characterized at agro-morphological and molecular level. Totally 50 hulled wheat population from Kastamonu, Konya and Kayseri provinces and 15 tir wheats from Kars provinces of Turkey were in-situ collected for characterization in 2013. Some quantitative and qualitative traits of each population were determined. RESULTS: Twenty three hulled wheat population collected from Kastamonu province were distinguished into nine emmer and 14 einkorn wheats at morphological level. Additionally, Konya, Kayseri and Kars population were characterized as einkorn, emmer and tir wheat, respectively. Among the evaluated traits, protein ratios of hulled wheats were strikingly higher than registered cultivars. All the populations were also examined by molecular level by using fluorescently labelled 11 polymorphic SSRs primers. The primers exhibited 104 bands, ranging from 6 to 16 with a mean value 9.45 per loci. The clustering analysis separated the germplasm into two clusters which were also divided into two subclusters based on genetic similarity coefficient. Sixty-five population and five checks were analyzed to estimate mean number of alleles (N), expected and observed heterozygoties (He and Ho), polymorphism information content (PIC), Wright fix index (F), genetic deviation from Hardy-Weinberg expectation (Fit-Fis) and genetic variation (Fst) were determined as 9.45, 0.71, 0.07, 0.67, 0.90, 0.39, 0.87 and 0.39, respectively. A clear genetic deviation from Hardy - Weinberg expectation was observed among population in particular. These results showed considerable genetic variation among landraces rather than within population. CONCLUSIONS: These molecular information has revealed genetically diverse einkorn, emmer wheat and tir wheat population could be used as parents for further breeding studies in both Turkey and abroad. Furthermore, the molecular analysis has also generally discriminated the germplasm into ploidy level.