Different DNA methylation patterns detected by the Amplified Methylation Polymorphism Polymerase Chain Reaction (AMP PCR) technique among various cell types of bulls

DNA methylation: an emerging paradigm of gene regulation under drought stress in plants

Drought is an enormous threat to global crop production. In order to ensure food security for the burgeoning population, we must develop drought tolerant crop varieties. This necessitates the identification of drought-responsive genes and understanding the mechanisms involved in their regulation. DNA methylation is a widely studied mechanism of epigenetic regulation of gene expression, which is known to play vital role in conferring tolerance to various biotic and abiotic stress factors. The recent advances in next-generation sequencing (NGS) technologies, has allowed unprecedented access to genome-wide methylation marks, with single base resolution. The most important roles of DNA methylation have been studied in terms of gene body methylation (gbM), which is associated with regulation of both transcript abundance and its stability. The availability of mutants for the various genes encoding enzymes involved in methylation of DNA has allowed ascertainment of the biological significance of methylation. Even though a vast number of reports have emerged in the recent past, where both genome-wide methylation landscape and locus specific changes in DNA methylation have been studied, a conclusive picture with regards to the biological role of DNA methylation is still lacking. Compounding this, is the lack of sufficient evidence supporting the heritability of these epigenetic changes. Amongst the various epigenetic variations, the DNA methylation changes are observed to be the most stable. This review describes the drought-induced changes in DNA methylation identified across different plant species. We also briefly describe the stress memory contributed by these changes. The identification of heritable, drought-induced methylation marks would broaden the scope of crop improvement in the future.

Assessment of natural DNA methylation variation and its association with economically important traits in dolichos bean (Lablab purpureus L. Var. Lignosus) using AMP-PCR assay

As a prelude to exploit DNA methylation-induced variation, we hypothesized the existence of substantial natural DNA methylation variation and its association with economically important traits in dolichos bean, and tested it using amplified methylation polymorphism-polymerase chain reaction (AMP-PCR) assay. DNA methylation patterns such as internal, external, full and non-methylation were amplified in a set of 64 genotypes using 26 customized randomly amplified polymorphic DNA (RAPD) primers containing 5'CCGG3' sequence. The 64 genotypes included 60 germplasm accessions (GA), two advanced breeding lines (ABLs) and two released varieties. The ABLs and released varieties are referred to as improved germplasm accessions (IGA) in this study. The association of DNA methylation patterns with economically important traits such as days to 50% flowering, raceme length, fresh pods plant^-1, fresh pod yield plant^-1 and 100-fresh seed weight was explored. At least 50 genotypes were polymorphic for DNA methylation patterns at 10 loci generated by seven of the 26 RAPD primers. The GA and IGA differed significantly for total, full and external methylation and the frequency of methylation was higher in GA compared to that in IGA. The genotypes with external methylation produced longer racemes than those with full, internal and non-methylation in that order at polymorphic RAPD-11-242 locus. High pod yielding genotypes had significantly lower frequency of full methylation than low yielding ones. On the contrary, the genotypes that produced heavier fresh seeds harboured higher frequencies of total and externally methylated loci than those that produced lighter fresh seeds.

DNA methylation changes stimulated by drought stress in ABA-deficient maize mutant vp10

Plants are constantly challenged with several biotic and abiotic stresses, and the adaptation to these stresses requires molecular and morphological changes. Epigenetic regulation provides effective control that enables plants to tolerate stress, which results in improved survivability. The distinct role of abscisic acid (ABA) in controlling numerous stress-responsive genes and enhancing respiration metabolism is well known; however, whether DNA methylation is associated with the regulation of ABA-dependent gene expression remains unclear. This study was conducted to identify the changes in DNA methylation induced by drought stress in ABA-deficient maize mutant vp10 using the amplified methylation polymorphism-polymerase chain reaction (AMP-PCR) technique. Differentially methylated DNA fragments were mapped to intragenic regions of zinc finger, amino acid catabolic enzymes, and other genes implicated in DNA repair and plant survival, in addition to several demethylated transposable elements.

Genomic imprinting in farm animals

The mouse is the first species in which genomic imprinting was studied. Imprinting research in farm species has lagged behind owing to a lack of sequencing and genetic background information, as well as long generation intervals and high costs in tissue collection. Since the creation of Dolly, the first cloned mammal from an adult sheep, studies on genomic imprinting in domestic species have accelerated because animals from cloning and other assisted reproductive technologies exhibit phenotypes of imprinting disruptions. Although this review focuses on new developments in farm animals, most of the imprinting mechanism information was derived from the mouse.

CpGIMethPred: computational model for predicting methylation status of CpG islands in human genome

DNA methylation is an inheritable chemical modification of cytosine, and represents one of the most important epigenetic events. Computational prediction of the DNA methylation status can be employed to speed up the genome-wide methylation profiling, and to identify the key features that are correlated with various methylation patterns. Here, we develop CpGIMethPred, the support vector machine-based models to predict the methylation status of the CpG islands in the human genome under normal conditions. The features for prediction include those that have been previously demonstrated effective (CpG island specific attributes, DNA sequence composition patterns, DNA structure patterns, distribution patterns of conserved transcription factor binding sites and conserved elements, and histone methylation status) as well as those that have not been extensively explored but are likely to contribute additional information from a biological point of view (nucleosome positioning propensities, gene functions, and histone acetylation status). Statistical tests are performed to identify the features that are significantly correlated with the methylation status of the CpG islands, and principal component analysis is then performed to decorrelate the selected features. Data from the Human Epigenome Project (HEP) are used to train, validate and test the predictive models. Specifically, the models are trained and validated by using the DNA methylation data obtained in the CD4 lymphocytes, and are then tested for generalizability using the DNA methylation data obtained in the other 11 normal tissues and cell types. Our experiments have shown that (1) an eight-dimensional feature space that is selected via the principal component analysis and that combines all categories of information is effective for predicting the CpG island methylation status, (2) by incorporating the information regarding the nucleosome positioning, gene functions, and histone acetylation, the models can achieve higher specificity and accuracy than the existing models while maintaining a comparable sensitivity measure, (3) the histone modification (methylation and acetylation) information contributes significantly to the prediction, without which the performance of the models deteriorate, and, (4) the predictive models generalize well to different tissues and cell types. The developed program CpGIMethPred is freely available at http://users.ece.gatech.edu/~hzheng7/CGIMetPred.zip.