scholarly journals Widespread natural variation of DNA methylation within angiosperms

2016 ◽  
Author(s):  
Chad E. Niederhuth ◽  
Adam J. Bewick ◽  
Lexiang Ji ◽  
Magdy S. Alabady ◽  
Kyung Do Kim ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Life Histories ◽  
Natural Variation ◽  
Gene Body ◽  
Sequencing Data ◽  
Gene Body Methylation ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Angiosperm Species ◽  
Methylation Patterns

AbstractTo understand the variation in genomic patterning of DNA methylation we compared methylomes of 34 diverse angiosperm species. By analyzing whole-genome bisulfite sequencing data in a phylogenetic context it becomes clear that there is extensive variation throughout angiosperms in gene body DNA methylation, euchromatic silencing of transposons and repeats, as well as silencing of heterochromatic transposons. The Brassicaceae have reduced CHG methylation levels and also reduced or loss of CG gene body methylation. The Poaceae are characterized by a lack or reduction of heterochromatic CHH methylation and enrichment of CHH methylation in genic regions. Reduced CHH methylation levels are found in clonally propagated species, suggesting that these methods of propagation may alter the epigenomic landscape over time. These results show that DNA methylation patterns are broadly a reflection of the evolutionary and life histories of plant species.

scholarly journals DNA methylation is not a driver of gene expression reprogramming in young honey bee workers

2021 ◽  
Author(s):  
Carlos A. M. Cardoso-Junior ◽  
Boris Yagound ◽  
Isobel Ronai ◽  
Emily J. Remnant ◽  
Klaus Hartfelder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Honey Bee ◽  
Differential Gene Expression ◽  
Social Contexts ◽  
Gene Body ◽  
Gene Body Methylation ◽  
Differential Gene ◽  
Honey Bee Workers ◽  
Methylation Patterns

AbstractIntragenic DNA methylation, also called gene body methylation, is an evolutionarily-conserved epigenetic mechanism in animals and plants. In social insects, gene body methylation is thought to contribute to behavioral plasticity, for example between foragers and nurse workers, by modulating gene expression. However, recent studies have suggested that the majority of DNA methylation is sequence-specific, and therefore cannot act as a flexible mediator between environmental cues and gene expression. To address this paradox, we examined whole-genome methylation patterns in the brains and ovaries of young honey bee workers that had been subjected to divergent social contexts: the presence or absence of the queen. Although these social contexts are known to bring about extreme changes in behavioral and reproductive traits through differential gene expression, we found no significant differences between the methylomes of workers from queenright and queenless colonies. In contrast, thousands of regions were differentially methylated between colonies, and these differences were not associated with differential gene expression in a subset of genes examined. Methylation patterns were highly similar between brain and ovary tissues and only differed in nine regions. These results strongly indicate that DNA methylation is not a driver of differential gene expression between tissues or behavioral morphs. Finally, despite the lack of difference in methylation patterns, queen presence affected the expression of all four DNA methyltransferase genes, suggesting that these enzymes have roles beyond DNA methylation. Therefore, the functional role of DNA methylation in social insect genomes remains an open question.

scholarly journals Focal disruption of DNA methylation dynamics at enhancers in IDH-mutant AML cells

Leukemia ◽  
2021 ◽  
Author(s):  
Elisabeth R. Wilson ◽  
Nichole M. Helton ◽  
Sharon E. Heath ◽  
Robert S. Fulton ◽  
Jacqueline E. Payton ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Myeloid Leukemia ◽  
Bisulfite Sequencing ◽  
Sequencing Data ◽  
Genome Wide ◽  
Cd34 Cells ◽  
Recurrent Mutations ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Acute Myeloid

AbstractRecurrent mutations in IDH1 or IDH2 in acute myeloid leukemia (AML) are associated with increased DNA methylation, but the genome-wide patterns of this hypermethylation phenotype have not been comprehensively studied in AML samples. We analyzed whole-genome bisulfite sequencing data from 15 primary AML samples with IDH1 or IDH2 mutations, which identified ~4000 focal regions that were uniquely hypermethylated in IDHmut samples vs. normal CD34+ cells and other AMLs. These regions had modest hypermethylation in AMLs with biallelic TET2 mutations, and levels of 5-hydroxymethylation that were diminished in IDH and TET-mutant samples, indicating that this hypermethylation results from inhibition of TET-mediated demethylation. Focal hypermethylation in IDHmut AMLs occurred at regions with low methylation in CD34+ cells, implying that DNA methylation and demethylation are active at these loci. AML samples containing IDH and DNMT3AR882 mutations were significantly less hypermethylated, suggesting that IDHmut-associated hypermethylation is mediated by DNMT3A. IDHmut-specific hypermethylation was highly enriched for enhancers that form direct interactions with genes involved in normal hematopoiesis and AML, including MYC and ETV6. These results suggest that focal hypermethylation in IDH-mutant AML occurs by altering the balance between DNA methylation and demethylation, and that disruption of these pathways at enhancers may contribute to AML pathogenesis.

scholarly journals SNP-guided identification of monoallelic DNA-methylation events from enrichment-based sequencing data

2014 ◽  
Author(s):  
Sandra Steyaert ◽  
Wim Van Criekinge ◽  
Ayla De Paepe ◽  
Simon Denil ◽  
Klaas Mensaert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Genetic Disorders ◽  
Data Sets ◽  
Sequencing Data ◽  
Promoter Regions ◽  
Sequencing Technologies ◽  
Genome Bisulfite Sequencing ◽  
Significant Enrichment ◽  
Bisulfite Sequencing Data

Monoallelic gene expression is typically initiated early in the development of an organism. Dysregulation of monoallelic gene expression has already been linked to several non-Mendelian inherited genetic disorders. In humans, DNA-methylation is deemed to be an important regulator of monoallelic gene expression, but only few examples are known. One important reason is that current, cost-affordable truly genome-wide methods to assess DNA-methylation are based on sequencing post enrichment. Here, we present a new methodology that combines methylomic data from MethylCap-seq with associated SNP profiles to identify monoallelically methylated loci. Using the Hardy-Weinberg theorem for each SNP locus, it could be established whether the observed frequency of samples featured by biallelic methylation was lower than randomly expected. Applied on 334 MethylCap-seq samples of very diverse origin, this resulted in the identification of 80 genomic regions featured by monoallelic DNA-methylation. Of these 80 loci, 49 are located in genic regions of which 25 have already been linked to imprinting. Further analysis revealed statistically significant enrichment of these loci in promoter regions, further establishing the relevance and usefulness of the method. Additional validation of the found loci was done using 14 whole-genome bisulfite sequencing data sets. Importantly, the developed approach can be easily applied to other enrichment-based sequencing technologies, such as the ChIP-seq-based identification of monoallelic histone modifications.

Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1679-1692
Author(s):  
Jiang Zhu ◽  
Mu Su ◽  
Yue Gu ◽  
Xingda Zhang ◽  
Wenhua Lv ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Throughput ◽  
Bisulfite Sequencing ◽  
The Cancer Genome Atlas ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Allele Specific ◽  
New Perspective

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

scholarly journals MethFlowVM: a virtual machine for the integral analysis of bisulfite sequencing data

2016 ◽  
Author(s):  
Ricardo Lebron ◽  
Guillermo Barturen ◽  
Cristina Gomez-Martin ◽  
Jose L Oliver ◽  
Michael Hackenberg
Keyword(s):  
Dna Methylation ◽  
Virtual Machine ◽  
Genome Stability ◽  
Regulation Of Gene Expression ◽  
Sequencing Data ◽  
Methylation Analysis ◽  
User Friendliness ◽  
Bisulfite Sequencing Data ◽  
Methylation Patterns ◽  
Privacy Issues

The analysis of whole genome DNA methylation patterns is an important first step towards the understanding on how DNA methylation is involved in the regulation of gene expression and genome stability. Previously, we published MethylExtract, a program for DNA methylation profiling and genotyping from the same sample. Over the last years we developed it further into a methylation analysis pipeline that allows to take full advantage of novel genome assembly models. The result is a new pipeline termed MethFlow which permits both, profiling of methylation levels and differential methylation analysis. Frequently DNA methylation research is carried out in the biomedical field, where privacy issues play an important role. Therefore we implemented the pipeline into a virtual machine termed MethFlowVM which shares with a web-server its user-friendliness however, the decisive advantage is that the sequencing data does not leave the user desktop or server and therefore no privacy issues do exist. The virtual machine is available at: http://bioinfo2.ugr.es:8080/MethFlow/

scholarly journals Knockdown of Dnmt1 links Gene body DNA methylation to regulation of gene expression and maternal-zygotic transition in the wasp Nasonia

2021 ◽  
Author(s):  
Deanna Arsala ◽  
Xin Wu ◽  
Soojin V. Yi ◽  
Jeremy A. Lynch
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Regulation Of Gene Expression ◽  
Gene Body ◽  
Nasonia Vitripennis ◽  
Gene Body Methylation ◽  
Genome Bisulfite Sequencing ◽  
Genome Activation ◽  
Zygotic Genome

AbstractGene body methylation (GBM) is an ancestral form of DNA methylation whose role in development has remained unclear. Unlike vertebrates, DNA methylation is found exclusively in gene bodies in the wasp Nasonia vitripennis, which provides a unique opportunity to interpret the role of GBM in development. We confirmed that parental RNAi (pRNAi) knockdown of a DNMT1 ortholog (Nv-Dnmt1a) in Nasonia leads to embryonic lethality and failures in cellularization and morphogenesis. Using whole-genome bisulfite sequencing, we found a widespread loss of GBM in Nv-Dnmt1a pRNAi embryos. Using RNAseq, we found that methylated genes that lost GBM in the pRNAi samples were exclusively downregulated during zygotic genome activation. Unexpectedly, nearly all affected unmethylated genes were up-regulated after pRNAi. Lack of proper clearance of mRNAs and abnormal activation drive this up-regulation, indicating critical roles for Nv-Dnmt1a and GBM in the maternal-zygotic transition (MZT) in the wasp, despite their absence in Drosophila.

AKSmooth: Enhancing low-coverage bisulfite sequencing data via kernel-based smoothing

2014 ◽  
Vol 12 (06) ◽  
pp. 1442005
Author(s):  
Junfang Chen ◽  
Pavlo Lutsik ◽  
Ruslan Akulenko ◽  
Jörn Walter ◽  
Volkhard Helms
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Human Colon ◽  
Sequencing Data ◽  
Human Colon Cancer ◽  
High Coverage ◽  
Comprehensive Picture ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Low Coverage

Whole-genome bisulfite sequencing (WGBS) is an approach of growing importance. It is the only approach that provides a comprehensive picture of the genome-wide DNA methylation profile. However, obtaining a sufficient amount of genome and read coverage typically requires high sequencing costs. Bioinformatics tools can reduce this cost burden by improving the quality of sequencing data. We have developed a statistical method Ajusted Local Kernel Smoother (AKSmooth) that can accurately and efficiently reconstruct the single CpG methylation estimate across the entire methylome using low-coverage bisulfite sequencing (Bi-Seq) data. We demonstrate the AKSmooth performance on the low-coverage (~ 4×) DNA methylation profiles of three human colon cancer samples and matched controls. Under the best set of parameters, AKSmooth-curated data showed high concordance with the gold standard high-coverage sample (Pearson 0.90), outperforming the popular analogous method. In addition, AKSmooth showed computational efficiency with runtime benchmark over 4.5 times better than the reference tool. To summarize, AKSmooth is a simple and efficient tool that can provide an accurate human colon methylome estimation profile from low-coverage WGBS data. The proposed method is implemented in R and is available at https://github.com/Junfang/AKSmooth .

scholarly journals Identification of the expressome by machine learning on omics data

2019 ◽  
Vol 116 (36) ◽  
pp. 18119-18125 ◽  
Author(s):  
Ryan C. Sartor ◽  
Jaclyn Noshay ◽  
Nathan M. Springer ◽  
Steven P. Briggs
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Inbred Lines ◽  
Training Data ◽  
Gene Body ◽  
Gene Body Methylation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Methylation Patterns

Accurate annotation of plant genomes remains complex due to the presence of many pseudogenes arising from whole-genome duplication-generated redundancy or the capture and movement of gene fragments by transposable elements. Machine learning on genome-wide epigenetic marks, informed by transcriptomic and proteomic training data, could be used to improve annotations through classification of all putative protein-coding genes as either constitutively silent or able to be expressed. Expressed genes were subclassified as able to express both mRNAs and proteins or only RNAs, and CG gene body methylation was associated only with the former subclass. More than 60,000 protein-coding genes have been annotated in the reference genome of maize inbred B73. About two-thirds of these genes are transcribed and are designated the filtered gene set (FGS). Classification of genes by our trained random forest algorithm was accurate and relied only on histone modifications or DNA methylation patterns within the gene body; promoter methylation was unimportant. Other inbred lines are known to transcribe significantly different sets of genes, indicating that the FGS is specific to B73. We accurately classified the sets of transcribed genes in additional inbred lines, arising from inbred-specific DNA methylation patterns. This approach highlights the potential of using chromatin information to improve annotations of functional genes.

scholarly journals DNA methylation is maintained with high fidelity in the honey bee germline and exhibits global non-functional fluctuations during somatic development

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Keith D. Harris ◽  
James P. B. Lloyd ◽  
Katherine Domb ◽  
Daniel Zilberman ◽  
Assaf Zemach
Keyword(s):  
Dna Methylation ◽  
Honey Bee ◽  
Dynamic Range ◽  
Model Organism ◽  
Mammalian Brain ◽  
Gene Body ◽  
Gene Body Methylation ◽  
Somatic Development ◽  
Gene Expression Alterations ◽  
Methylation Patterns

Abstract Background DNA methylation of active genes, also known as gene body methylation, is found in many animal and plant genomes. Despite this, the transcriptional and developmental role of such methylation remains poorly understood. Here, we explore the dynamic range of DNA methylation in honey bee, a model organism for gene body methylation. Results Our data show that CG methylation in gene bodies globally fluctuates during honey bee development. However, these changes cause no gene expression alterations. Intriguingly, despite the global alterations, tissue-specific CG methylation patterns of complete genes or exons are rare, implying robust maintenance of genic methylation during development. Additionally, we show that CG methylation maintenance fluctuates in somatic cells, while reaching maximum fidelity in sperm cells. Finally, unlike universally present CG methylation, we discovered non-CG methylation specifically in bee heads that resembles such methylation in mammalian brain tissue. Conclusions Based on these results, we propose that gene body CG methylation can oscillate during development if it is kept to a level adequate to preserve function. Additionally, our data suggest that heightened non-CG methylation is a conserved regulator of animal nervous systems.

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