Harnessing Current Knowledge of DNA N6-Methyladenosine From Model Plants for Non-model Crops.

Changfei Guan,Adeel Riaz,Jingrong Liu,Pingxian Zhang,Shuyuan Liu,Sadaruddin Chachar

doi:10.3389/fgene.2021.668317

Abstract

Epigenetic modifications alter the gene activity and function by causing change in the chromosomal architecture through DNA methylation/demethylation, or histone modifications without causing any change in DNA sequence. In plants, DNA cytosine methylation (5mC) is vital for various pathways such as, gene regulation, transposon suppression, DNA repair, replication, transcription, and recombination. Thanks to recent advances in high throughput sequencing (HTS) technologies for epigenomic “Big Data” generation, accumulated studies have revealed the occurrence of another novel DNA methylation mark, N6-methyladenosine (6mA), which is highly present on gene bodies mainly activates gene expression in model plants such as eudicot Arabidopsis (Arabidopsis thaliana) and monocot rice (Oryza sativa). However, in non-model crops, the occurrence and importance of 6mA remains largely less known, with only limited reports in few species, such as Rosaceae (wild strawberry), and soybean (Glycine max). Given the aforementioned vital roles of 6mA in plants, hereinafter, we summarize the latest advances of DNA 6mA modification, and investigate the historical, known and vital functions of 6mA in plants. We also consider advanced artificial-intelligence biotechnologies that improve extraction and prediction of 6mA concepts. In this Review, we discuss the potential challenges that may hinder exploitation of 6mA, and give future goals of 6mA from model plants to non-model crops.

Highlights

Hereditary changes in activity and function of a gene caused by the direct alteration of DNA sequence like deletions, insertions, point mutations and translocations is known as Genetics
The support vector machine approach (SVM) to identify 6mA sites in rice genome with 83% accuracy, in which the DNA sequences are effectively formulated and encoded through the use of chemical property and nucleotide frequency dependent on the SVM approach A simple and lightweight deep learning model approach for identifying 6mA from rice genome, its evaluation is based on five metrics such as sensitivity, accuracy, specificity, area under the curve (AUC) and Matthews correlation coefficient (MCC) A high-quality computational method to identify and predict 6mA sites in the rice genome
We are known that 6mA has been identified as an important epigenetic mark in both mammals and model plants as the detection approaches have begun to become increasingly sensitive (Liang et al, 2020)

Summary

INTRODUCTION

Hereditary changes in activity and function of a gene caused by the direct alteration of DNA sequence like deletions, insertions, point mutations and translocations is known as Genetics. While combined SMRT sequencing and 6mA-IP-Seq have been used to detect genome wide 6mA levels in Arabidopsis and rice (Liang et al, 2018b; Zhang et al, 2018; Zhou et al, 2018), and they reveals more efficient information for dynamic distribution and pattern of 6mA compared to these 6mA studies in non-model plants. Liang et al measured the genome-wide distribution and levels of 6mA in gDNA of Arabidopsis, applying different approaches, including DNA dot blot, using a specific 6mA antibody, SMRT sequencing and LC-MS/MS, which could detect the strand specific 6mA sites at single nucleotide resolution (Liang et al, 2018b). The p6mA outperformed using different datasets (Wang et al, 2020). Lv et al (2020) in a recent study developed a computational approach for identifying 5hmC, 6mA, and 4mC sites in different species

Method name

Findings

CONCLUDING REMARKS AND FUTURE PERSPECTIVE