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Identification of DNA-Methylated CpG Islands Associated With Gene Silencing in the Adult Body Tissues of the Ogye Chicken Using RNA-Seq and Reduced Representation Bisulfite Sequencing.

ABSTRACT: DNA methylation is an epigenetic mark that plays an essential role in regulating gene expression. CpG islands are DNA methylations regions in promoters known to regulate gene expression through transcriptional silencing of the corresponding gene. DNA methylation at CpG islands is crucial for gene expression and tissue-specific processes. At the current time, a limited number of studies have reported on gene expression associated with DNA methylation in diverse adult tissues at the genome-wide level. Expression levels are rarely affected by DNA methylation in normal adult tissues; however, statistical differences in gene expression level correlated with DNA methylation have recently been revealed. In this study, we examined 20 pairs of DNA methylomes and transcriptomes from RNA-seq and reduced representation bisulfite sequencing (RRBS) data using adult Ogye chicken tissues. A total of 3,133 CpG islands were identified from 20 tissue data in a single chicken sample which could affect downstream genes. Analyzing these CpG island and gene pairs, 121 significant units were statistically correlated. Among them, six genes (CLDN3, DECR2, EVA1B, NME4, NTSR1, and XPNPEP2) were highly significantly changed by altered DNA methylation. Finally, our data demonstrated how DNA methylation correlated to gene expression in normal adult tissues. Our source codes can be found at https://github.com/wjlim/correlation-between-rna-seq-and-RRBS.

SUBMITTER: Lim WJ

PROVIDER: S-EPMC6476954 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMammalian CpG islands (CGIs) normally escape DNA methylation in all adult tissues and developmental stages. However, in our previous study we unexpectedly identified many methylated CGIs in human peripheral blood leukocytes. Methylated CpG dinucleotides convert to TpG dinucleotides through deaminization of their cytosine bases more frequently than hypomethylated CpG dinucleotides. Therefore, we wondered how methylated CGIs in germline or non-germline cells maintain their CpG-rich sequences. It is known that events such as germline hypomethylation, CpG selection, biased gene conversion (BGC), and frequent CpG fixation can contribute to the maintenance of CpG-rich sequences in methylated CGIs in germline or non-germline cells. However, it has not been investigated which of the processes maintain CpG-rich sequences of methylated CGIs in each genomic position.ResultsIn this study, we comprehensively examined the contribution of the processes described above to the maintenance of CpG-rich sequences in methylated CGIs in germline and non-germline cells which were classified by genomic positions. Approximately 60-80% of CGIs with high methylation in H1 cell line (H1-HM) in all the genomic positions showed a low average CpG→TpG/CpA substitution rate. In contrast, fewer than half the numbers of CGIs with H1-HM in all the genomic positions showed a low average CpG→TpG/CpA substitution rate and low levels of methylation in sperm cells (SPM-LM). Furthermore, a small fraction of CGIs with a low average CpG→TpG/CpA substitution rate and high levels of methylation in sperm cells (SPM-HM) showed CpG selection. On the other hand, independent of the positions in genes, most CGIs with SPM-HM showed a slightly higher average TpG/CpA→CpG substitution rate compared with those with SPM-LM.ConclusionsRelatively high numbers (approximately 60-80%) of CGIs with H1-HM in all the genomic positions preserve their CpG-rich sequences by a low CpG→TpG/CpA substitution rate caused mainly by their SPM-LM, and for those with SPM-HM partly by CpG selection and TpG/CpA→CpG fixation. BGC has little contribution to the maintenance of CpG-rich sequences of CGIs with SPM-HM which were classified by genomic positions.

Project description:BackgroundHypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).ResultsMLDA was programmed in R (version 2.7.0) and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing.ConclusionMLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

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Identification of DNA-Methylated CpG Islands Associated With Gene Silencing in the Adult Body Tissues of the Ogye Chicken Using RNA-Seq and Reduced Representation Bisulfite Sequencing.

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