Genome-Wide De Novo Methylation in Epithelial Ovarian Cancer

2011 ◽  
Vol 21 (2) ◽  
pp. 269-279 ◽  
Author(s):  
Rachel Michaelson-Cohen ◽  
Ilana Keshet ◽  
Ravid Straussman ◽  
Merav Hecht ◽  
Howard Cedar ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cell Differentiation ◽  
Gene Silencing ◽  
De Novo ◽  
Cpg Islands ◽  
Aberrant Methylation ◽  
Genome Wide ◽  
A Genome ◽  
Treatment Responsiveness ◽  
De Novo Methylation

Background:DNA methylation regulates gene expression during development. The methylation pattern is established at the time of implantation. CpG islands are genome regions usually protected from methylation; however, selected islands are methylated later. Many undergo methylation in cancer, causing epigenetic gene silencing. Aberrant methylation occurs early in tumorigenesis, in a specific pattern, inhibiting differentiation.Although methylation of specific genes in ovarian tumors has been demonstrated in numerous studies, they represent only a fraction of all methylated genes in tumorigenesis.Objectives:To explore the hypermethylation design in ovarian cancer compared with the methylation profile of normal ovaries, on a genome-wide scale, thus shedding light on the role of gene silencing in ovarian carcinogenesis.Identifying genes that undergo de novo methylation in ovarian cancer may assist in creating biomarkers for disease diagnosis, prognosis, and treatment responsiveness.Methods:DNA was collected from human epithelial ovarian cancers and normal ovaries. Methylation was detected by immunoprecipitation using 5-methyl-cytosine-antibodies. DNA was hybridized to a CpG island microarray containing 237,220 gene promoter probes. Results were analyzed by hybridization intensity, validated by bisulfite analysis.Results:A total of 367 CpG islands were specifically methylated in cancer cells. There was enrichment of methylated genes in functional categories related to cell differentiation and proliferation inhibition. It seems that their silencing enables tumor proliferation.Conclusions:This study provides new perspectives on methylation in ovarian carcinoma, genome-wide. It illustrates how methylation of CpG islands causes silencing of genes that have a role in cell differentiation and functioning. It creates potential biomarkers for diagnosis, prognosis, and treatment responsiveness.

QSER1 protects DNA methylation valleys from de novo methylation

Science ◽  
2021 ◽  
Vol 372 (6538) ◽  
pp. eabd0875 ◽  
Author(s):  
Gary Dixon ◽  
Heng Pan ◽  
Dapeng Yang ◽  
Bess P. Rosen ◽  
Therande Jashari ◽  
...  
Keyword(s):  
Dna Methylation ◽  
De Novo ◽  
Embryonic Stem ◽  
Central Question ◽  
Mammalian Development ◽  
Uncharacterized Gene ◽  
Pathological Conditions ◽  
Genome Wide ◽  
A Genome ◽  
De Novo Methylation

DNA methylation is essential to mammalian development, and dysregulation can cause serious pathological conditions. Key enzymes responsible for deposition and removal of DNA methylation are known, but how they cooperate to regulate the methylation landscape remains a central question. Using a knockin DNA methylation reporter, we performed a genome-wide CRISPR-Cas9 screen in human embryonic stem cells to discover DNA methylation regulators. The top screen hit was an uncharacterized gene, QSER1, which proved to be a key guardian of bivalent promoters and poised enhancers of developmental genes, especially those residing in DNA methylation valleys (or canyons). We further demonstrate genetic and biochemical interactions of QSER1 and TET1, supporting their cooperation to safeguard transcriptional and developmental programs from DNMT3-mediated de novo methylation.

scholarly journals Role of transcription complexes in the formation of the basal methylation pattern in early development

2018 ◽  
Vol 115 (41) ◽  
pp. 10387-10391 ◽  
Author(s):  
Razi Greenfield ◽  
Amalia Tabib ◽  
Ilana Keshet ◽  
Joshua Moss ◽  
Ofra Sabag ◽  
...  
Keyword(s):  
De Novo ◽  
Cpg Islands ◽  
Embryonic Stem ◽  
Methylation Pattern ◽  
Entire Genome ◽  
Genome Wide ◽  
Topological Pattern ◽  
Histone H3k4 ◽  
Transcription Complexes ◽  
De Novo Methylation

Following erasure in the blastocyst, the entire genome undergoes de novo methylation at the time of implantation, with CpG islands being protected from this process. This bimodal pattern is then preserved throughout development and the lifetime of the organism. Using mouse embryonic stem cells as a model system, we demonstrate that the binding of an RNA polymerase complex on DNA before de novo methylation is predictive of it being protected from this modification, and tethering experiments demonstrate that the presence of this complex is, in fact, sufficient to prevent methylation at these sites. This protection is most likely mediated by the recruitment of enzyme complexes that methylate histone H3K4 over a local region and, in this way, prevent access to the de novo methylation complex. The topological pattern of H3K4me3 that is formed while the DNA is as yet unmethylated provides a strikingly accurate template for modeling the genome-wide basal methylation pattern of the organism. These results have far-reaching consequences for understanding the relationship between RNA transcription and DNA methylation.

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

2020 ◽  
Vol 15 ◽  
Author(s):  
Dicle Yalcin ◽  
Hasan H. Otu
Keyword(s):  
Model Building ◽  
De Novo ◽  
Cpg Islands ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Global Methylation ◽  
Sequence Features ◽  
A Genome ◽  
Combined Features ◽  
Epigenetic Repression

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

scholarly journals A genome-wide study of de novo deletions identifies a candidate locus for non-syndromic isolated cleft lip/palate risk

BMC Genetics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 24 ◽  
Author(s):  
Samuel G Younkin ◽  
Robert B Scharpf ◽  
Holger Schwender ◽  
Margaret M Parker ◽  
Alan F Scott ◽  
...  
Keyword(s):  
Cleft Lip ◽  
De Novo ◽  
Candidate Locus ◽  
Genome Wide ◽  
A Genome ◽  
Cleft Lip Palate ◽  
Genome Wide Study

TRANSCRIPTIONAL REGULATION OF HYAL-2 HYALURONIDASE BY DE NOVO METHYLATION OF CpG ISLANDS IN BRAIN

Hyaluronan ◽  
2002 ◽  
pp. 187-194
Author(s):  
Günter Lepperdinger ◽  
Birgit Strobl ◽  
Johannes Müllcgger ◽  
Günther Kreil
Keyword(s):  
Transcriptional Regulation ◽  
De Novo ◽  
Cpg Islands ◽  
De Novo Methylation

scholarly journals Gene Silencing Triggers Polycomb Repressive Complex 2 Recruitment to CpG Islands Genome Wide

2014 ◽  
Vol 55 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Eva Madi Riising ◽  
Itys Comet ◽  
Benjamin Leblanc ◽  
Xudong Wu ◽  
Jens Vilstrup Johansen ◽  
...  
Keyword(s):  
Gene Silencing ◽  
Cpg Islands ◽  
Polycomb Repressive Complex ◽  
Polycomb Repressive Complex 2 ◽  
Genome Wide

scholarly journals Genome assembly of the JD17 soybean provides a new reference genome for Comparative genomics

2021 ◽  
Author(s):  
Xinxin Yi ◽  
Jing Liu ◽  
Shengcai Chen ◽  
Hao Wu ◽  
Min Liu ◽  
...  
Keyword(s):  
Nitrogen Fixation ◽  
Genome Assembly ◽  
Reference Genome ◽  
De Novo ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
High Quality ◽  
Genome Wide ◽  
A Genome ◽  
Cultivated Soybean

Cultivated soybean (Glycine max) is an important source for protein and oil. Many elite cultivars with different traits have been developed for different conditions. Each soybean strain has its own genetic diversity, and the availability of more high-quality soybean genomes can enhance comparative genomic analysis for identifying genetic underpinnings for its unique traits. In this study, we constructed a high-quality de novo assembly of an elite soybean cultivar Jidou 17 (JD17) with chromsome contiguity and high accuracy. We annotated 52,840 gene models and reconstructed 74,054 high-quality full-length transcripts. We performed a genome-wide comparative analysis based on the reference genome of JD17 with three published soybeans (WM82, ZH13 and W05) , which identified five large inversions and two large translocations specific to JD17, 20,984 - 46,912 PAVs spanning 13.1 - 46.9 Mb in size, and 5 - 53 large PAV clusters larger than 500kb. 1,695,741 - 3,664,629 SNPs and 446,689 - 800,489 Indels were identified and annotated between JD17 and them. Symbiotic nitrogen fixation (SNF) genes were identified and the effects from these variants were further evaluated. It was found that the coding sequences of 9 nitrogen fixation-related genes were greatly affected. The high-quality genome assembly of JD17 can serve as a valuable reference for soybean functional genomics research.

scholarly journals sPepFinder expedites genome-wide identification of small proteins in bacteria

2020 ◽  
Author(s):  
Lei Li ◽  
Yanjie Chao
Keyword(s):  
De Novo ◽  
Bacterial Species ◽  
Computational Prediction ◽  
Ribosome Profiling ◽  
Support Vector ◽  
Initiation Rate ◽  
E Coli ◽  
Small Proteins ◽  
Genome Wide ◽  
A Genome

ABSTRACTSmall proteins shorter than 50 amino acids have been long overlooked. A number of small proteins have been identified in several model bacteria using experimental approaches and assigned important functions in diverse cellular processes. The recent development of ribosome profiling technologies has allowed a genome-wide identification of small proteins and small ORFs (smORFs), but our incomplete understanding of small proteins hinders de novo computational prediction of smORFs in non-model bacterial species. Here, we have identified several sequence features for smORFs by a systematic analysis of all the known small proteins in E. coli, among which the translation initiation rate is the strongest determinant. By integrating these features into a support vector machine learning model, we have developed a novel sPepFinder algorithm that can predict conserved smORFs in bacterial genomes with a high accuracy of 92.8%. De novo prediction in E. coli has revealed several novel smORFs with evidence of translation supported by ribosome profiling. Further application of sPepFinder in 549 bacterial species has led to the identification of > 100,000 novel smORFs, many of which are conserved at the amino acid and nucleotide levels under purifying selection. Overall, we have established sPepFinder as a valuable tool to identify novel smORFs in both model and non-model bacterial organisms, and provided a large resource of small proteins for functional characterizations.

scholarly journals A Genome-Wide RNA Interference Screen Identifies a Differential Role of the Mediator CDK8 Module Subunits for GATA/ RUNX-Activated Transcription in Drosophila

2010 ◽  
Vol 30 (11) ◽  
pp. 2837-2848 ◽  
Author(s):  
Vanessa Gobert ◽  
Dani Osman ◽  
Stéphanie Bras ◽  
Benoit Augé ◽  
Muriel Boube ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Rna Interference ◽  
Cell Fate ◽  
Gata Factor ◽  
Hematopoietic System ◽  
Crystal Cell ◽  
Genome Wide ◽  
A Genome

ABSTRACT Transcription factors of the RUNX and GATA families play key roles in the control of cell fate choice and differentiation, notably in the hematopoietic system. During Drosophila hematopoiesis, the RUNX factor Lozenge and the GATA factor Serpent cooperate to induce crystal cell differentiation. We used Serpent/Lozenge-activated transcription as a paradigm to identify modulators of GATA/RUNX activity by a genome-wide RNA interference screen in cultured Drosophila blood cells. Among the 129 factors identified, several belong to the Mediator complex. Mediator is organized in three modules plus a regulatory “CDK8 module,” composed of Med12, Med13, CycC, and Cdk8, which has long been thought to behave as a single functional entity. Interestingly, our data demonstrate that Med12 and Med13 but not CycC or Cdk8 are essential for Serpent/Lozenge-induced transactivation in cell culture. Furthermore, our in vivo analysis of crystal cell development show that, while the four CDK8 module subunits control the emergence and the proliferation of this lineage, only Med12 and Med13 regulate its differentiation. We thus propose that Med12/Med13 acts as a coactivator for Serpent/Lozenge during crystal cell differentiation independently of CycC/Cdk8. More generally, we suggest that the set of conserved factors identified herein may regulate GATA/RUNX activity in mammals.

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