scholarly journals Chromatin modifications and genomic contexts linked to dynamic DNA methylation patterns across human cell types

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Haidan Yan ◽  
Dongwei Zhang ◽  
Hongbo Liu ◽  
Yanjun Wei ◽  
Jie Lv ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Human Cell ◽  
Cell Types ◽  
Chromatin Modifications ◽  
Methylation Patterns

scholarly journals Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression

EBioMedicine ◽  
2019 ◽  
Vol 43 ◽  
pp. 411-423 ◽  
Author(s):  
Ewoud Ewing ◽  
Lara Kular ◽  
Sunjay J. Fernandes ◽  
Nestoras Karathanasis ◽  
Vincenzo Lagani ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Dna Methylation ◽  
Immune Cell ◽  
Cell Types ◽  
Combining Evidence ◽  
Methylation Patterns

scholarly journals Promoter DNA Methylation Patterns of Differentiated Cells Are Largely Programmed at the Progenitor Stage

2010 ◽  
Vol 21 (12) ◽  
pp. 2066-2077 ◽  
Author(s):  
Anita L. Sørensen ◽  
Bente Marie Jacobsen ◽  
Andrew H. Reiner ◽  
Ingrid S. Andersen ◽  
Philippe Collas
Keyword(s):  
Dna Methylation ◽  
Cell Types ◽  
Molecular Evidence ◽  
Mesenchymal Progenitors ◽  
H3k27 Methylation ◽  
Differentiated Cells ◽  
Promoter Dna Methylation ◽  
Promoter Dna ◽  
A Minor ◽  
Methylation Patterns

Mesenchymal stem cells (MSCs) isolated from various tissues share common phenotypic and functional properties. However, intrinsic molecular evidence supporting these observations has been lacking. Here, we unravel overlapping genome-wide promoter DNA methylation patterns between MSCs from adipose tissue, bone marrow, and skeletal muscle, whereas hematopoietic progenitors are more epigenetically distant from MSCs as a whole. Commonly hypermethylated genes are enriched in signaling, metabolic, and developmental functions, whereas genes hypermethylated only in MSCs are associated with early development functions. We find that most lineage-specification promoters are DNA hypomethylated and harbor a combination of trimethylated H3K4 and H3K27, whereas early developmental genes are DNA hypermethylated with or without H3K27 methylation. Promoter DNA methylation patterns of differentiated cells are largely established at the progenitor stage; yet, differentiation segregates a minor fraction of the commonly hypermethylated promoters, generating greater epigenetic divergence between differentiated cell types than between their undifferentiated counterparts. We also show an effect of promoter CpG content on methylation dynamics upon differentiation and distinct methylation profiles on transcriptionally active and inactive promoters. We infer that methylation state of lineage-specific promoters in MSCs is not a primary determinant of differentiation capacity. Our results support the view of a common origin of mesenchymal progenitors.

scholarly journals Global Modulation in DNA Epigenetics during Pro-Inflammatory Macrophage Activation

2019 ◽  
Author(s):  
Nikhil Jain ◽  
Tamar Shahal ◽  
Tslil Gabrieli ◽  
Noa Gilat ◽  
Dmitry Torchinsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Single Molecule ◽  
Transcriptional Control ◽  
Excision Repair ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Inflammatory Activation ◽  
Methylation Patterns

AbstractDNA methylation patterns create distinct gene expression profiles. These patterns are maintained after cell division, thus enabling the differentiation and maintenance of multiple cell types from the same genome sequence. The advantage of this mechanism for transcriptional control is that chemical-encoding allows to rapidly establish new epigenetic patterns “on-demand” through enzymatic methylation and de-methylation of DNA. Here we show that this feature is associated with the fast response of macrophages during their pro-inflammatory activation. By using a combination of mass spectroscopy and single-molecule imaging to quantify global epigenetic changes in the genomes of primary macrophages, we followed three distinct DNA marks (methylated, hydroxymethylated and unmethylated), involved in establishing new DNA methylation patterns during pro-inflammatory activation. The observed epigenetic modulation together with gene expression data generated for the involved enzymatic machinery, may suggest that de-methylation upon LPS-activation starts with oxidation of methylated CpGs, followed by excision-repair of these oxidized bases and their replacement with unmodified cytosine.

scholarly journals Exploring DNA Methylation Diversity in the Honey Bee Brain by Ultra-Deep Amplicon Sequencing

Epigenomes ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 10
Author(s):  
Robert Kucharski ◽  
Ryszard Maleszka
Keyword(s):  
Dna Methylation ◽  
Honey Bee ◽  
Illumina Miseq ◽  
Amplicon Sequencing ◽  
Cell Types ◽  
Significant Loss ◽  
Frequent Patterns ◽  
Bisulphite Sequencing ◽  
Special Challenge ◽  
Methylation Patterns

Understanding methylation dynamics in organs or tissues containing many different cell types is a challenging task that cannot be efficiently addressed by the low-depth bisulphite sequencing of DNA extracted from such sources. Here we explored the feasibility of ultra-deep bisulphite sequencing of long amplicons to reveal the brain methylation patterns in three selected honey bee genes analysed across five distinct conditions on the Illumina MiSeq platform. By combing 15 libraries in one run we achieved a very high sequencing depth of 240,000–340,000 reads per amplicon, suggesting that most of the cell types in the honey bee brain, containing approximately 1 million neurons, are represented in this dataset. We found a small number of gene-specific patterns for each condition in individuals of different ages and performing distinct tasks with 80–90% of those were represented by no more than a dozen patterns. One possibility is that such a small number of frequent patterns is the result of differentially methylated epialleles, whereas the rare and less frequent patterns reflect activity-dependent modifications. The condition-specific methylation differences within each gene appear to be position-dependent with some CpGs showing significant changes and others remaining stable in a methylated or non-methylated state. Interestingly, no significant loss of methylation was detected in very old individuals. Our findings imply that these diverse patterns represent a special challenge in the analyses of DNA methylation in complex tissues and organs that cannot be investigated by low-depth genome-wide bisulphite sequencing. We conclude that ultra-deep sequencing of gene-specific amplicons combined with genotyping of differentially methylated epialleles is an effective way to facilitate more advanced neuro-epigenomic studies in honey bees and other insects.

scholarly journals Comparative analysis of genome-wide DNA methylation identifies patterns that associate with conserved transcriptional programs in osteosarcoma

2020 ◽  
Author(s):  
Lauren J. Mills ◽  
Milcah C. Scott ◽  
Pankti Shah ◽  
Anne R. Cunanan ◽  
Archana Deshpande ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Large Scale ◽  
Immune Cell ◽  
Cell Types ◽  
Structural Variations ◽  
Canine Osteosarcoma ◽  
Genome Wide ◽  
Highly Correlated ◽  
Murine Osteosarcoma ◽  
Methylation Patterns

AbstractOsteosarcoma is an aggressive tumor of the bone that primarily affects young adults and adolescents. Osteosarcoma is characterized by genomic chaos and heterogeneity. While inactivation of tumor suppressor p53 TP53 is nearly universal other high frequency mutations or structural variations have not been identified. Despite this genomic heterogeneity, key conserved transcriptional programs associated with survival have been identified across human, canine and induced murine osteosarcoma. The epigenomic landscape, including DNA methylation, plays a key role in establishing transcriptional programs in all cell types. The role of epigenetic dysregulation has been studied in a variety of cancers but has yet to be explored at scale in osteosarcoma. Here we examined genome-wide DNA methylation patterns in 24 human and 44 canine osteosarcoma samples identifying groups of highly correlated DNA methylation marks in human and canine osteosarcoma samples. We also link specific DNA methylation patterns to key transcriptional programs in both human and canine osteosarcoma. Building on previous work, we built a DNA methylation-based measure for the presence and abundance of various immune cell types in osteosarcoma. Finally, we determined that the underlying state of the tumor, and not changes in cell composition, were the main driver of differences in DNA methylation across the human and canine samples.SignificanceThis is the first large scale study of DNA methylation in osteosarcoma and lays the ground work for the exploration of DNA methylation programs that help establish conserved transcriptional programs in the context of different genomic landscapes.

scholarly journals A Novel Method to Identify Epigenetic Subclones with Increased Fitness from Genomic DNA Methylation Data of Lymphoma and Leukemia Patients

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2604-2604
Author(s):  
Jerry Fong ◽  
John R. Edwards ◽  
Jacob R. Gardner ◽  
Amanda F Cashen ◽  
Kilian Q. Weinberger ◽  
...  
Keyword(s):  
Dna Methylation ◽  
T Lymphocytes ◽  
Cancer Progression ◽  
Bisulfite Sequencing ◽  
B Cell Lymphoma ◽  
Cell Types ◽  
Computational Method ◽  
Gene Set Enrichment Analysis ◽  
Methylation Data ◽  
Methylation Patterns

Abstract DNA methylation has been functionally implicated in X-inactivation, genomic imprinting, and silencing of transposable elements. DNA methylation also has a complex regulatory relationship with gene expression. Canonically, methylation around the promoters of tumor-suppressor genes induces gene-silencing, thereby representing a hit in the two-hit hypothesis for the development of cancer. Unfortunately, profiling studies conducted to determine how aberrant methylation may contribute to cancer progression are confounded by heterogeneity in the original clinical sample. Thus, though studies in patients with Acute Myeloid Leukemia (AML), Diffuse Large B-cell Lymphoma (DLBCL), and Chronic Lymphocytic Leukemia (CLL) have found that variation in detected methylation values from patients at diagnosis correlates with prognosis following therapy, they do not address which subclonal methylation events contribute to cancer progression. To address this concern, we developed a novel computational method to deconvolve the bisulfite sequencing data from a sample into its major methylation profiles and their respective prevalence in the sample. Our method, based on a modified Hidden Markov Model, effectively models the autocorrelations found in methylation data and outperforms existing algorithms. Our method was validated across a wide range of mixture simulations, where bisulfite sequencing reads from various different cell types were subsampled to form test samples that could be deconvolved. We were able to accurately (98%) distinguish distinct methylation patterns corresponding to the expected underlying subpopulations, such as for CD14 and CD22 in mixtures of germinal center B-cells and monocytes and for CD4 and CD8A in mixtures of CD4+ T-lymphocytes and CD8+ T-lymphocytes. These patterns also recapitulated differentially methylated regions (DMRs) identified by an independent DMR-caller. Given that our method does not rely on cell-type specific parameters and is therefore robust to all samples, to further validate and demonstrate the applicability of our method, we conducted Agilent Methyl-Seq on 5 primary DLBCL samples procured by the Lymphoma Core at the Siteman Cancer Center. As a positive control, our method identified differential methylation profiles at loci expected to differ from underlying CD19+ and CD4+ cells, which comprise a large majority of each sample. Our method also identified distinct methylation profiles not found in reference profiles from normal cell-types, suggesting these methylation profiles may be specific to DLBCL. To further validate these findings, we used single-cell bisulfite-sequencing at ten loci to demonstrate that the methylation profiles predicted by our method from the original sample are found in individual cells. We found several methylation patterns that only existed in a subset of CD19+ cells, which may represent distinct epigenetic subclones of DLBCL. Using our novel computational method, we next profiled the subclonal epigenetic architecture of publicly available (dbGaP) paired samples from patients with AML (n=137) at diagnosis and following therapy. We were able to not only identify subclonal methylation profiles that were specific to cancer but also find profiles at higher prevalence in patients at relapse compared to diagnosis. These methylation profiles, which were enriched for genes in cancer pathways as seen by Gene Set Enrichment Analysis, may confer fitness advantages for a cancer subclone to expand. We are currently conducting additional analyses to characterize the epigenetic regulatory circuits that contribute to our observed increase in subclonal fitness. In summary, we have developed a robust method to identify subclonal methylation changes that may contribute to cancer progression and prognosis, as seen in AML, and may lead to new avenues for improving treatment for patients with leukemia or lymphoma. Disclosures No relevant conflicts of interest to declare.

O37: Histone lysine methylation patterns in human cell types are arranged in distinct three-dimensional nuclear zones

2005 ◽  
Vol 48 (4) ◽  
pp. 538-539
Author(s):  
Heiner Albiez ◽  
Roman Zinner ◽  
Joachim Walter ◽  
Antoine H. Peters ◽  
Thomas Cremer ◽  
...  
Keyword(s):  
Human Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Lysine Methylation ◽  
Histone Lysine Methylation ◽  
Histone Lysine ◽  
Methylation Patterns

Genome-Wide DNA Methylation Patterns Reveal Specific Signatures for HSC, CMP and Erythroblasts.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 391-391
Author(s):  
Amber Hogart ◽  
Subramanian S. Ajay ◽  
Hatice Ozel Abaan ◽  
Stacie M. Anderson ◽  
Elliott H. Margulies ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bone Marrow ◽  
Cell Surface ◽  
Gene Silencing ◽  
Blood Cells ◽  
Genomic Dna ◽  
Cell Types ◽  
Methylated Dna ◽  
Genome Wide ◽  
Methylation Patterns

Abstract Abstract 391 DNA methylation is a reversible epigenetic modification that is required for proper mammalian development and is proposed to contribute to the pathogenesis of hematologic diseases including leukemia and bone marrow failure syndromes. Elucidating the pathways and genes regulated by DNA methylation during hematopoiesis may reveal new therapeutic targets for disease. Because the phenotype and activity of hematopoietic stem cells (HSC) and hematopoietic progenitor cells of many different lineages have been defined by both in vitro and in vivo assays, hematopoiesis is an excellent model for investigating epigenomic changes during differentiation. HSCs have the ability to self-renew and to generate blood cells of all lineages, which allows them to repopulate recipients after stem cell transplantation. The common myeloid progenitor (CMP) gives rise to all myeloid cell types including neutrophils, monocytes, platelets, and red blood cells, but cannot self renew or repopulate. In contrast to the multipotent HSC and CMP, erythroblasts (ERY) are terminally committed cells that become mature enucleated red blood cells. These three cell types represent unique stages of lineage commitment with distinct transcriptional programs, and potentially unique epigenomic signatures. In contrast to human HSC, which are defined by the absence of several cell surface markers, mouse HSC have the cell surface phenotype of lineage marker negative (Lin-) c-kit+ Sca-1+ and can be positively selected. For this reason we chose the mouse model for genome-wide methylation profiling. Murine HSC and CMP (Lin- c-kit+ Sca-1-) cells were enriched from adult mouse bone marrow with flow cytometry. Erythroblasts (CD71+/Ter119+) were positively selected from E13.5 mouse fetal livers. Genomic DNA isolated from each enriched cell population was sheared to 200-300 bp fragments. MBD2, one of five endogenous mammalian methyl CpG binding domain proteins, binds methylated DNA sequences with broad affinity. Methylated DNA fragments were enriched from the genomic DNA using a tagged, recombinant MBD2 pulldown kit (Active Motif). After pulldown, enrichment of known methylated sequences regulating the imprints of Snrpn and Rasgrf was validated by qPCR. Two biological replicates of HSC, CMP, and ERY methylated sequences and negative control supernatant fractions were submitted for high-throughput sequencing with the Illumina Genome Analyzer platform. Raw sequence data containing 32-46 × 106 reads of 36-50 base pairs were obtained for each sample. The Eland program was used to map 41-59% of reads to unique sequences in the mouse genome. Model-based Analysis of ChIP-Seq (MACS) was used to estimate the mean and variance of the sequence tag distribution across the genome and define peaks below the significance threshold of p<10-5. The number of methylation peaks decreased as cells differentiated, with 64,000 peaks identified in HSC (24,000 unique), 41,000 peaks in CMP (2000 unique), and 23,000 peaks in ERY (1000 unique). Approximately 20,000 peaks were common between all cell types with 57% of these peaks residing in RefSeq genes, 8% in regions adjacent to RefSeq genes (<10 kb), and 35% of methylation peaks in intergenic regions. Comparison of HSC expression data from Akashi et al (Blood 101: 383, 2003) to our HSC genic methylation peaks revealed that 2/3 of HSC genic peaks are within transcriptionally silent genes while 1/3 of HSC genic peaks are within expressed genes. Although DNA methylation is often associated with gene silencing, the important developmental gene Gata2 contains methylation peaks in HSC and CMP, cells that express Gata2, that are absent in ERY, where Gata2 is repressed. A Gata1-Fog1-Mbd2 complex has been described by Rodriguez et al (EMBO 24: 2354, 2005), therefore providing a link between DNA methylation and proteins known to bind at the Gata2 locus. Grass et al (Mol. Cell. Biol. 26:7056, 2006) determined that Gata2 is regulated by long-range interactions of GATA protein complexes, and consistent with this observation, distinct methylation patterns are observed up to 100 kb upstream of the Gata2 gene. Our genome-wide analysis supports an association of methylation with gene silencing but also suggests that DNA methylation is a dynamic epigenetic mark that influences hematopoietic differentiation. The changes in DNA methylation we observe around Gata2 may also contribute to long-range chromatin organization. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Virtual methylome dissection facilitated by single-cell analyses

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Liduo Yin ◽  
Yanting Luo ◽  
Xiguang Xu ◽  
Shiyu Wen ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Mixed Cell ◽  
Numerous Cell ◽  
Cell Type Specific ◽  
Methylation Patterns

Abstract Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

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