General DNA Methylation Patterns and Environmentally-Induced Differential Methylation in the Eastern Oyster (Crassostrea virginica)

AbstractEpigenetic modification, specifically DNA methylation, is one possible mechanism for intergenerational plasticity. Before inheritance of methylation patterns can be characterized, we need a better understanding of how environmental change modifies the parental epigenome. To examine the influence of experimental ocean acidification on eastern oyster (Crassostrea virginica) gonad tissue, oysters were cultured in the laboratory under control (491 ± 49 μatm) or high (2550 ± 211 μatm) pCO2 conditions for four weeks. DNA from reproductive tissue was isolated from five oysters per treatment, then subjected to bisulfite treatment and DNA sequencing. Irrespective of treatment, DNA methylation was primarily found in gene bodies with approximately 22% of CpGs (2.7% of total cytosines) in the C. virginica genome predicted to be methylated. In response to elevated pCO2, we found 598 differentially methylated loci primarily overlapping with gene bodies. A majority of differentially methylated loci were in exons (61.5%) with less intron overlap (31.9%). While there was no evidence of a significant tendency for the genes with differentially methylated loci to be associated with distinct biological processes, the concentration of these loci in gene bodies, including genes involved in protein ubiquitination and biomineralization suggests DNA methylation may be important for transcriptional control in response to ocean acidification. Changes in gonad methylation also indicate potential for these methylation patterns to be inherited by offspring. Understanding how experimental ocean acidification conditions modify the oyster epigenome, and if these modifications are inherited, allows for a better understanding of how ecosystems will respond to environmental change.

Download Full-text

Effects of Florida Red Tides on histone variant expression and DNA methylation in the Eastern oyster Crassostrea virginica

Aquatic Toxicology ◽

10.1016/j.aquatox.2017.03.006 ◽

2017 ◽

Vol 186 ◽

pp. 196-204 ◽

Cited By ~ 14

Author(s):

Rodrigo Gonzalez-Romero ◽

Victoria Suarez-Ulloa ◽

Javier Rodriguez-Casariego ◽

Daniel Garcia-Souto ◽

Gabriel Diaz ◽

...

Keyword(s):

Dna Methylation ◽

Crassostrea Virginica ◽

Eastern Oyster ◽

Histone Variant ◽

Red Tides

Download Full-text

Genome-Wide DNA Methylation Analysis Shows Enrichment of Differential Methylation in “Open Seas” and Enhancers and Reveals Hypomethylation in DNMT3A Mutated Cytogenetically Normal AML (CN-AML)

Blood ◽

10.1182/blood.v120.21.653.653 ◽

2012 ◽

Vol 120 (21) ◽

pp. 653-653 ◽

Cited By ~ 2

Author(s):

Ying Qu ◽

Andreas Lennartsson ◽

Verena I. Gaidzik ◽

Stefan Deneberg ◽

Sofia Bengtzén ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Cpg Island ◽

Cpg Islands ◽

Differential Methylation ◽

Methylation Analysis ◽

Cpg Sites ◽

Genome Wide ◽

Genomic Regions ◽

Methylation Patterns

Abstract Abstract 653 DNA methylation is involved in multiple biologic processes including normal cell differentiation and tumorigenesis. In AML, methylation patterns have been shown to differ significantly from normal hematopoietic cells. Most studies of DNA methylation in AML have previously focused on CpG islands within the promoter of genes, representing only a very small proportion of the DNA methylome. In this study, we performed genome-wide methylation analysis of 62 AML patients with CN-AML and CD34 positive cells from healthy controls by Illumina HumanMethylation450K Array covering 450.000 CpG sites in CpG islands as well as genomic regions far from CpG islands. Differentially methylated CpG sites (DMS) between CN-AML and normal hematopoietic cells were calculated and the most significant enrichment of DMS was found in regions more than 4kb from CpG Islands, in the so called open sea where hypomethylation was the dominant form of aberrant methylation. In contrast, CpG islands were not enriched for DMS and DMS in CpG islands were dominated by hypermethylation. DMS successively further away from CpG islands in CpG island shores (up to 2kb from CpG Island) and shelves (from 2kb to 4kb from Island) showed increasing degree of hypomethylation in AML cells. Among regions defined by their relation to gene structures, CpG dinucleotide located in theoretic enhancers were found to be the most enriched for DMS (Chi χ2<0.0001) with the majority of DMS showing decreased methylation compared to CD34 normal controls. To address the relation to gene expression, GEP (gene expression profiling) by microarray was carried out on 32 of the CN-AML patients. Totally, 339723 CpG sites covering 18879 genes were addressed on both platforms. CpG methylation in CpG islands showed the most pronounced anti-correlation (spearman ρ =-0.4145) with gene expression level, followed by CpG island shores (mean spearman rho for both sides' shore ρ=-0.2350). As transcription factors (TFs) have shown to be crucial for AML development, we especially studied differential methylation of an unbiased selection of 1638 TFs. The most enriched differential methylation between CN-AML and normal CD34 positive cells were found in TFs known to be involved in hematopoiesis and with Wilms tumor protein-1 (WT1), activator protein 1 (AP-1) and runt-related transcription factor 1 (RUNX1) being the most differentially methylated TFs. The differential methylation in WT 1 and RUNX1 was located in intragenic regions which were confirmed by pyro-sequencing. AML cases were characterized with respect to mutations in FLT3, NPM1, IDH1, IDH2 and DNMT3A. Correlation analysis between genome wide methylation patterns and mutational status showed statistically significant hypomethylation of CpG Island (p<0.0001) and to a lesser extent CpG island shores (p<0.001) and the presence of DNMT3A mutations. This links DNMT3A mutations for the first time to a hypomethylated phenotype. Further analyses correlating methylation patterns to other clinical data such as clinical outcome are ongoing. In conclusion, our study revealed that non-CpG island regions and in particular enhancers are the most aberrantly methylated genomic regions in AML and that WT 1 and RUNX1 are the most differentially methylated TFs. Furthermore, our data suggests a hypomethylated phenotype in DNMT3A mutated AML. Disclosures: No relevant conflicts of interest to declare.

Download Full-text

An analytical pipeline for DNA Methylation Array Biomarker Studies

10.1101/2021.07.14.452293 ◽

2021 ◽

Author(s):

Jennifer Lu ◽

Darren Korbie ◽

Matt Trau

Keyword(s):

Dna Methylation ◽

Feature Selection ◽

Differential Methylation ◽

The Cancer Genome Atlas ◽

Methylation Array ◽

Epigenetic Biomarkers ◽

Cancer Genome Atlas ◽

Dna Methylation Array ◽

Methylation Patterns ◽

Selection Of

DNA methylation is one of the most commonly studied epigenetic biomarkers, due to its role in disease and development. The Illumina Infinium methylation arrays still remains the most common method to interrogate methylation across the human genome, due to its capabilities of screening over 480, 000 loci simultaneously. As such, initiatives such as The Cancer Genome Atlas (TCGA) have utilized this technology to examine the methylation profile of over 20,000 cancer samples. There is a growing body of methods for pre-processing, normalisation and analysis of array-based DNA methylation data. However, the shape and sampling distribution of probe-wise methylation that could influence the way data should be examined was rarely discussed. Therefore, this article introduces a pipeline that predicts the shape and distribution of normalised methylation patterns prior to selection of the most optimal inferential statistics screen for differential methylation. Additionally, we put forward an alternative pipeline, which employed feature selection, and demonstrate its ability to select for biomarkers with outstanding differences in methylation, which does not require the predetermination of the shape or distribution of the data of interest. Availability: The Distribution test and the feature selection pipelines are available for download at: https://github.com/uqjlu8/DistributionTest Keywords: DNA methylation, Biomarkers, Cancers, Data Distribution, TCGA, 450K

Download Full-text

Differential DNA methylation patterns of homeobox genes in proximal and distal colon epithelial cells

Physiological Genomics ◽

10.1152/physiolgenomics.00046.2015 ◽

2016 ◽

Vol 48 (4) ◽

pp. 257-273 ◽

Cited By ~ 4

Author(s):

Alan Barnicle ◽

Cathal Seoighe ◽

Aaron Golden ◽

John M. Greally ◽

Laurence J. Egan

Keyword(s):

Colorectal Cancer ◽

Dna Methylation ◽

Epithelial Cells ◽

Distal Colon ◽

Human Colon ◽

Differential Methylation ◽

Cell Type ◽

Cell Type Specific ◽

Inflammatory Bowel ◽

Methylation Patterns

Region and cell-type specific differences in the molecular make up of colon epithelial cells have been reported. Those differences may underlie the region-specific characteristics of common colon epithelial diseases such as colorectal cancer and inflammatory bowel disease. DNA methylation is a cell-type specific epigenetic mark, essential for transcriptional regulation, silencing of repetitive DNA and genomic imprinting. Little is known about any region-specific variations in methylation patterns in human colon epithelial cells. Using purified epithelial cells and whole biopsies ( n = 19) from human subjects, we generated epigenome-wide DNA methylation data (using the HELP-tagging assay), comparing the methylation signatures of the proximal and distal colon. We identified a total of 125 differentially methylated sites (DMS) mapping to transcription start sites of protein-coding genes, most notably several members of the homeobox ( HOX) family of genes. Patterns of differential methylation were validated with MassArray EpiTYPER. We also examined DNA methylation in whole biopsies, applying a computational technique to deconvolve variation in methylation within cell types and variation in cell-type composition across biopsies. Including inferred epithelial proportions as a covariate in differential methylation analysis applied to the whole biopsies resulted in greater overlap with the results obtained from purified epithelial cells compared with when the covariate was not included. Results obtained from both approaches highlight region-specific methylation patterns of HOX genes in colonic epithelium. Regional variation in methylation patterns has implications for the study of diseases that exhibit regional expression patterns in the human colon, such as inflammatory bowel disease and colorectal cancer.

Download Full-text

HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2015-0076 ◽

2016 ◽

Vol 15 (1) ◽

Cited By ~ 13

Author(s):

Shuying Sun ◽

Xiaoqing Yu

Keyword(s):

Dna Methylation ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Differential Methylation ◽

Sequencing Data ◽

Exact Test ◽

Fisher's Exact Test ◽

Fisher’S Exact Test ◽

Methylation Patterns

AbstractDNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher’s exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher’s exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.

Download Full-text

DNA Methylation Profiling Reveals Correlation of Differential Methylation Patterns with Gene Expression in Human Epilepsy

Journal of Molecular Neuroscience ◽

10.1007/s12031-016-0735-6 ◽

2016 ◽

Vol 59 (1) ◽

pp. 68-77 ◽

Cited By ~ 21

Author(s):

Liang Wang ◽

Xinwei Fu ◽

Xi Peng ◽

Zheng Xiao ◽

Zhonggui Li ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Differential Methylation ◽

Human Epilepsy ◽

Dna Methylation Profiling ◽

Methylation Patterns ◽

Methylation Profiling

Download Full-text

DNA Methylation in Placentas of Interspecies Mouse Hybrids

Genetics ◽

10.1093/genetics/165.1.223 ◽

2003 ◽

Vol 165 (1) ◽

pp. 223-228

Author(s):

Sabine Schütt ◽

Andrea R Florl ◽

Wei Shi ◽

Myriam Hemberger ◽

Annie Orth ◽

...

Keyword(s):

Dna Methylation ◽

Molecular Basis ◽

Interspecific Hybrids ◽

Genetic System ◽

Differential Methylation ◽

Late Gestation ◽

Epigenetic Changes ◽

Repeat Sequences ◽

Mouse Placenta ◽

Methylation Patterns

Abstract Interspecific hybridization in the genus Mus results in several hybrid dysgenesis effects, such as male sterility and X-linked placental dysplasia (IHPD). The genetic or molecular basis for the placental phenotypes is at present not clear. However, an extremely complex genetic system that has been hypothesized to be caused by major epigenetic changes on the X chromosome has been shown to be active. We have investigated DNA methylation of several single genes, Atrx, Esx1, Mecp2, Pem, Psx1, Vbp1, Pou3f4, and Cdx2, and, in addition, of LINE-1 and IAP repeat sequences, in placentas and tissues of fetal day 18 mouse interspecific hybrids. Our results show some tendency toward hypomethylation in the late gestation mouse placenta. However, no differential methylation was observed in hyper- and hypoplastic hybrid placentas when compared with normal-sized littermate placentas or intraspecific Mus musculus placentas of the same developmental stage. Thus, our results strongly suggest that generalized changes in methylation patterns do not occur in trophoblast cells of such hybrids.

Download Full-text

THU0022 DIFFERENTIAL DNA METHYLATION AS A PREDICTOR OF TOCILIZUMAB RESPONSE IN RHEUMATOID ARTHRITIS PATIENTS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.4394 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 224.1-224

Author(s):

N. Nair ◽

D. Plant ◽

J. Isaacs ◽

A. Morgan ◽

K. Hyrich ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Dna Methylation ◽

Disease Activity ◽

Whole Blood ◽

Differential Methylation ◽

Poor Responders ◽

Research Support ◽

Response Status ◽

Pre Treatment ◽

Methylation Patterns

Background:Tocilizumab (TCZ) is a biological disease-modifying antirheumatic drug that blocks IL-6 signalling and is effective in ameliorating disease activity in rheumatoid arthritis (RA). However, approximately 50% of patients do not respond adequately to TCZ and some patients report adverse events. Considering there is growing evidence that DNA methylation is implicated in RA susceptibility and response to some biologics (1, 2), we investigated DNA methylation as a candidate biomarker for response to TCZ in RA.Objectives:To identify differential DNA methylation signatures in whole blood associated with TCZ response in patients with RA.Methods:Epigenome-wide DNA methylation patterns were measured using the Infinium EPIC BeadChip (Illumina) in whole blood-derived DNA samples from patients with RA. DNA was extracted from blood samples taken pre-treatment and following 3 months on therapy, and response was determined at 6 months using the Clinical Disease Activity Index (CDAI). Patients who had good response (n=10) or poor response (n=10) to TCZ by 6 months were selected. Samples from secondary poor responders (n=10) (patients who had an improvement of CDAI and were in remission at 3 months, followed by a worsening of CDAI at 6 months) were also analysed. Differentially methylated positions and regions (DMPs/DMRs) were identified using linear regression, adjusting for gender, age, cell composition, smoking status, and glucocorticoid use. Gene Set Enrichment Analysis (GSEA) was used to identify significant pathways associated with response and Functional Epigenetic Module analysis of interactome hotspots in regions of differential methylation.Results:20 DMPs were significantly associated with response status at 6 months in the pre-treatment samples. Another 21 DMPs were associated with response in the 3 month samples. Within good responders, 10 DMPs showed significant change in methylation level between pre-treatment and the 3 month samples (unadjusted P-value <10-6). One DMP, cg03121467, was significantly less methylated in good responders compared to poor responders in the pre-treatment samples. This DMP is close toEPB41L4Aand thought to have a role in β–catenin signalling. GSEA of DMRs in non- and secondary non- responders identified histone acetyltransferase pathways and included theKAT2Agene, which is a repressor of NF-κB. Additional analysis of interaction hotspots of differential methylation identified significant interactions withSTAMBPandPTPN12associated with response status.Conclusion:These preliminary results provide evidence that DNA methylation patterns may predict response to TCZ. Validation of these findings in other larger data sets is required.References:[1]Liu,Y., Aryee,M.J., Padyukov,L., Fallin,M.D., Hesselberg,E., Runarsson,A., Reinius,L., Acevedo,N., Taub,M., Ronninger,M.,et al.(2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.Nat. Biotechnol.,31, 142–147.[2]Plant,D., Webster,A., Nair,N., Oliver,J., Smith,S.L., Eyre,S., Hyrich,K.L., Wilson,A.G., Morgan,A.W., Isaacs,J.D.,et al.(2016) Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis.Arthritis Rheumatol. (Hoboken, N.J.),68, 1353–60.Disclosure of Interests:Nisha Nair: None declared, Darren Plant: None declared, John Isaacs Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Gilead, Janssen, Merck, Pfizer, Roche, Ann Morgan Grant/research support from: I have received a grant from Roche Products Ltd to establish a registry for GCA patients treated with tocilizumab., Consultant of: I have undertaken consultancy work for Roche, Chugai, Regeneron, Sanofi and GSK in the area of GCA therapeutics., Speakers bureau: I have presented on tocilizumab therapy for GCA and glucocorticoid toxicity on behalf of Roche products ltd., Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Anne Barton Consultant of: AbbVie, Anthony G Wilson: None declared

Download Full-text