Evaluation of ethyl methanesulfonate-induced in vitro mutagenesis, polymorphism and genomic instability in wheat (Triticum aestivum L.)

Türkoğlu, Aras; Tosun, Metin; Haliloğlu, Kamil

doi:10.1007/s12892-022-00172-2

Cited by 10 publications

(12 citation statements)

References 51 publications

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“…The study by Temel et al [ 50 ] propounds that DNA methylation is optimally elucidated within tissue culture research through the prism of the CRED methodology. The amalgamation of iPBS and CRED methodologies [ 14 , 15 , 16 , 17 , 18 ] constitutes a boon for tissue culture investigations, empowering the delineation of both genetic and epigenetic transformations. In the current research, the synergy of iPBS and CRED was harnessed to unravel the polyamine-induced perturbations in the genetic and epigenetic landscapes of wheat.…”

Section: Discussionmentioning

confidence: 99%

“…In the realm of plant biology, the spectrum of epigenetic variations encompasses diverse phenomena, such as point mutations, deletions, transposable element activations, rearrangements, and changes in ploidy [ 9 , 10 ], as well as alterations to the DNA structure itself [ 11 ]. Various molecular markers, including single nucleotide polymorphisms (SNPs), microsatellites (SSRs), simple sequence repeat polymorphisms (ISSRs), fragment length polymorphisms (RFLPs), and random amplified polymorphic DNA (RAPD) markers [ 12 ], alongside the inter-primer binding site (iPBS) retrotransposon [ 13 , 14 ], are currently harnessed for detecting polymorphic states and DNA methylation patterns [ 15 , 16 , 17 , 18 ]. Within the context of epigenetic modulation, DNA methylation emerges as a pivotal process, universally governing gene expression and the repression of transposable elements.…”

Section: Introductionmentioning

confidence: 99%

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

Eren,

Türkoğlu,

Haliloğlu

et al. 2023

Plants

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Eren,

Türkoğlu,

Haliloğlu

et al. 2023

Plants

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“…When compared to the control, the RAPD profiles revealed polymorphism when a band that was predicted to be present did not exist and a novel band did appear. Changes in the average polymorphism were calculated for each treatment group (changing concentrations of AgNO 3 and Ag-NPs), and the results were reported as a percentage in comparison to the value obtained from the control (which was set to 100%) [ 45 , 46 , 104 , 105 ]. A quantitative metric known as genomic template stability (GTS%) was estimated for RAPD using the formula GTS = (1 − a/n) × 100.…”

Section: Methodsmentioning

confidence: 99%

“…In this calculation, a represents the mean number of polymorphic bands in each treated template, and n represents the total number of bands in the control [ 38 , 103 ]. With the use of the formula Polymorphism = a/n × 100 [ 45 , 46 , 104 , 105 ], we were able to calculate the average polymorphism values (in percent) for each concentration.…”

Section: Methodsmentioning

confidence: 99%

“…In most cases, traditional statistical methods have been employed to analyze and interpret the output variables. These methodologies often utilize variance analysis and linear regression models to determine the degree of correlation between independent input factors and dependent output variables [ 45 , 46 ]. Complex factors and non-linear features present some of the most significant challenges for researchers working in the field of plant tissue culture [ 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation

Türkoğlu,

Haliloğlu,

Demirel

et al. 2023

Plants

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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.

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Mutagenesis: Exploring Genetic Diversity of Industrial Crop Plants

Mir,

Faheem,

Sial

et al. 2024

Interdisciplinary Biotechnological Advances

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Evaluation of ethyl methanesulfonate-induced in vitro mutagenesis, polymorphism and genomic instability in wheat (Triticum aestivum L.)

Cited by 10 publications

References 51 publications

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

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

Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation

Mutagenesis: Exploring Genetic Diversity of Industrial Crop Plants

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