Tissue Specific DNA Methylation Patterns: Biochemistry of Formation and Possible Role

1986 ◽  
pp. 127-137
Author(s):  
Aharon Razin
Keyword(s):  
Dna Methylation ◽  
Tissue Specific ◽  
Methylation Patterns

scholarly journals Atlas of Age- and Tissue-specific DNA Methylation during Early Development of Barley (Hordeum vulgare)

2018 ◽  
Author(s):  
Moumouni Konate ◽  
Michael J. Wilkinson ◽  
Banjamin Mayne ◽  
Eileen Scott ◽  
Bettina Berger ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Hordeum Vulgare ◽  
Organ Function ◽  
Tissue Specific ◽  
Organ Differentiation ◽  
Differentiated Cells ◽  
Differentially Methylated Genes ◽  
Organ Specific ◽  
Development And Differentiation ◽  
Methylation Patterns

The barley (Hordeum vulgare) genome comprises over 32,000 genes, with differentiated cells expressing only a subset of genes; the remainder being silent. Mechanisms by which tissue-specific genes are regulated are not entirely understood, although DNA methylation is likely to be involved. DNA methylation patterns are not static during plant development, but it is still unclear whether different organs possess distinct methylation profiles. Methylation-sensitive GBS was used to generate DNA methylation profiles for roots, leaf-blades and leaf-sheaths from five barley varieties, using seedlings at the three-leaf stage. Differentially Methylated Markers (DMMs) were characterised by pairwise comparisons of roots, leaf-blades and leaf-sheaths of three different ages. While very many DMMs were found between roots and leaf parts, only a few existed between leaf-blades and leaf-sheaths, with differences decreasing with leaf rank. Organ-specific DMMs appeared to target mainly repeat regions, implying that organ differentiation partially relies on the spreading of DNA methylation from repeats to promoters of adjacent genes. Furthermore, the biological functions of differentially methylated genes in the different organs correlated with functional specialisation. Our results indicate that different organs do possess diagnostic methylation profiles and suggest that DNA methylation is important for both tissue development and differentiation and organ function.

The determination of tissue-specific DNA methylation patterns in forensic biofluids using bisulfite modification and pyrosequencing

2012 ◽  
Vol 33 (12) ◽  
pp. 1736-1745 ◽  
Author(s):  
Tania Madi ◽  
Kuppareddi Balamurugan ◽  
Robin Bombardi ◽  
George Duncan ◽  
Bruce McCord
Keyword(s):  
Dna Methylation ◽  
Tissue Specific ◽  
Methylation Patterns ◽  
Bisulfite Modification

scholarly journals The CLASSY family controls tissue-specific DNA methylation patterns in Arabidopsis

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Ming Zhou ◽  
Ceyda Coruh ◽  
Guanghui Xu ◽  
Laura M. Martins ◽  
Clara Bourbousse ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Global Scale ◽  
Epigenetic Landscape ◽  
Specific Expression ◽  
Tissue Specific ◽  
Dna Motif ◽  
Epigenetic Diversity ◽  
Aberrant Dna Methylation ◽  
Open Question ◽  
Methylation Patterns

AbstractDNA methylation shapes the epigenetic landscape of the genome, plays critical roles in regulating gene expression, and ensures transposon silencing. As is evidenced by the numerous defects associated with aberrant DNA methylation landscapes, establishing proper tissue-specific methylation patterns is critical. Yet, how such differences arise remains a largely open question in both plants and animals. Here we demonstrate that CLASSY1-4 (CLSY1-4), four locus-specific regulators of DNA methylation, also control tissue-specific methylation patterns, with the most striking pattern observed in ovules where CLSY3 and CLSY4 control DNA methylation at loci with a highly conserved DNA motif. On a more global scale, we demonstrate that specific clsy mutants are sufficient to shift the epigenetic landscape between tissues. Together, these findings reveal substantial epigenetic diversity between tissues and assign these changes to specific CLSY proteins, elucidating how locus-specific targeting combined with tissue-specific expression enables the CLSYs to generate epigenetic diversity during plant development.

Tissue-specific DNA methylation patterns of the rat glial fibrillary acidic protein gene

1994 ◽  
Vol 39 (6) ◽  
pp. 694-707 ◽  
Author(s):  
D. F. Condorelli ◽  
V. G. Nicoletti ◽  
V. Barresi ◽  
A. Caruso ◽  
S. Conticello ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Glial Fibrillary Acidic Protein ◽  
Protein Gene ◽  
Acidic Protein ◽  
Tissue Specific ◽  
Methylation Patterns

scholarly journals Adaptation of the targeted capture Methyl-Seq platform for the mouse genome identifies novel tissue-specific DNA methylation patterns of genes involved in neurodevelopment

Epigenetics ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. 581-596 ◽  
Author(s):  
Benjamin Hing ◽  
Enrique Ramos ◽  
Patricia Braun ◽  
Melissa McKane ◽  
Dubravka Jancic ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mouse Genome ◽  
Tissue Specific ◽  
Targeted Capture ◽  
Methylation Patterns

The Potential Use of DNA Methylation Patterns from Peripheral Blood Leukocytes as a Surrogate for Stress Axis Tissues in Mature Brahman Cows

2021 ◽  
Vol 99 (Supplement_2) ◽  
pp. 1-2
Author(s):  
Emilie C Baker ◽  
Audrey L Earnhardt ◽  
Kubra Z Cilkiz ◽  
Brittni P Littlejohn ◽  
Haley C Collins ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Adrenal Cortex ◽  
Adrenal Medulla ◽  
Peripheral Blood ◽  
Pearson Correlation ◽  
Specific Gene ◽  
Peripheral Blood Leukocytes ◽  
Blood Leukocytes ◽  
Tissue Specific ◽  
Methylation Patterns

Abstract DNA methylation (DNAm) patterns are tissue specific and aid in tissue specific gene expression changes. The use of DNAm patterns from peripheral blood leukocytes (PBL) as a surrogate for patterns in other tissues is common, especially in longitudinal studies when sampling of tissues is not plausible. Thus, the objective of this study was to investigate the suitability of using DNAm patterns of PBL as a surrogate for the DNAm patterns in neuroendocrine tissues responsible for stress responses and energy metabolism. Samples from the paraventricular region of the hypothalamus, anterior pituitary gland, adrenal cortex, and the adrenal medulla were harvested from 5-yr-old Brahman cows (n = 8) and DNA was extracted from each sample. Methylation was assessed using reduced representation sodium bisulfite sequencing and differentially methylated regions (DMR) between the PBL DNA and tissue DNA were identified using EdgeR from Bioconductor, R. Analysis revealed over 15,000 DMRs located within promoter regions of genes in each tissue, with the majority of the sites having increased methylation in the PBL (Table 1). To further evaluate the use of PBL DNA as a surrogate, Pearson correlation values were calculated for genes (n = 20) pertinent to each respective tissue using the mean methylation of the specific gene in the PBL and in the tissue (Table 2). Three correlations were significant (P ≤ 0.05), two of which were negative. The sizable differences indicate that DNA methylation patterns from PBL do not compare well to patterns from hypothalamic, pituitary, adrenal cortex, and adrenal medulla tissues from 5-yr-old Brahman cows. This is especially the case for the majority of the specific genes examined in this study. Whether DNAm in the surrogate PBL will shift in a direction similar to that of specific tissues of Brahman cows exposed to stressful stimuli during developmental periods remains to be determined.

scholarly journals Development of Tissue-Specific Age Predictors Using DNA Methylation Data

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 888 ◽  
Author(s):  
Choi ◽  
Joe ◽  
Nam
Keyword(s):  
Dna Methylation ◽  
Aging Process ◽  
Prediction Models ◽  
Cpg Island ◽  
Absolute Error ◽  
Methylation Array ◽  
Tissue Specific ◽  
Epigenetic Aging ◽  
Common Group ◽  
Methylation Patterns

DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples. The constructed models predict the chronological age with good performance (mean absolute error of 5.11 years on average) and show better performance in the independent test than previous multi-tissue age predictors. We also compared tissue-common and tissue-specific aging markers and found that they had different characteristics. Firstly, the tissue-common group tended to contain more positive aging markers with methylation values that increased during the aging process, whereas the tissue-specific group tended to contain more negative aging markers. Secondly, many of the tissue-common markers were located in Cytosine-phosphate-Guanine (CpG) island regions, whereas the tissue-specific markers were located in CpG shore regions. Lastly, the tissue-common CpG markers tended to be located in more evolutionarily conserved regions. In conclusion, our prediction models identified CpG markers that capture both tissue-common and tissue-specific characteristics during the aging process.

Tissue-Specific DNA Methylation Patterns in Forensic Samples Detected by Pyrosequencing®

2015 ◽  
pp. 397-409 ◽  
Author(s):  
Joana Antunes ◽  
Kuppareddi Balamurugan ◽  
George Duncan ◽  
Bruce McCord
Keyword(s):  
Dna Methylation ◽  
Tissue Specific ◽  
Forensic Samples ◽  
Methylation Patterns

scholarly journals Comparative whole genome DNA methylation profiling across cattle tissues reveals global and tissue-specific methylation patterns

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Yang Zhou ◽  
Shuli Liu ◽  
Yan Hu ◽  
Lingzhao Fang ◽  
Yahui Gao ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Whole Genome ◽  
Tissue Specific ◽  
Dna Methylation Profiling ◽  
Methylation Patterns ◽  
Methylation Profiling
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