Dataset Information

Comparative analysis of CpG islands among HBV genotypes.

ABSTRACT: DNA methylation is being increasingly recognized to play a role in regulation of hepatitis B virus (HBV) gene expression. The aim of this study was to compare the CpG island distribution among different HBV genotypes. We analyzed 176 full-length HBV genomic sequences obtained from the GenBank database, belonging to genotypes A through J, to identify the CpG islands in the HBV genomes. Our results showed that while 79 out of 176 sequences contained three conventional CpG islands (I-III) as previously described, 83 HBV sequences harbored only two of the three known islands. Novel CpG islands were identified in the remaining 14 HBV isolates and named as CpG island IV, V, and VI. Among the eight known HBV genotypes and two putative genotypes, while HBV genomes containing three CpG islands were predominant in genotypes A, B, D, E, and I; genotypes C, F, G, and H tended to contain only two CpG islands (II and III). In conclusion, the CpG islands, which are potential targets for DNA methylation mediated by the host functions, differ among HBV genotypes, and these genotype-specific differences in CpG island distribution could provide new insights into the understanding of epigenetic regulation of HBV gene expression and hepatitis B disease outcome.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC3579858 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Comparative analysis of CpG islands among HBV genotypes.

Zhang Yongmei Y Li Chenxiao C Zhang Yijun Y Zhu Haoxiang H Kang Yaoyue Y Liu Hongyan H Wang Jinyu J Qin Yanli Y Mao Richeng R Xie Yi Y Huang Yuxian Y Zhang Jiming J

PloS one 20130222 2

DNA methylation is being increasingly recognized to play a role in regulation of hepatitis B virus (HBV) gene expression. The aim of this study was to compare the CpG island distribution among different HBV genotypes. We analyzed 176 full-length HBV genomic sequences obtained from the GenBank database, belonging to genotypes A through J, to identify the CpG islands in the HBV genomes. Our results showed that while 79 out of 176 sequences contained three conventional CpG islands (I-III) as previo ...[more]

PMID: 23451072

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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:Hypermethylation 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).MLDA 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.MLDA 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.

Project description:BackgroundEmerging infectious disease-causing RNA viruses, such as the SARS-CoV-2 and Ebola viruses, are thought to rely on bats as natural reservoir hosts. Since these zoonotic viruses pose a great threat to humans, it is important to characterize the bat genome from multiple perspectives. Unsupervised machine learning methods for extracting novel information from big sequence data without prior knowledge or particular models are highly desirable for obtaining unexpected insights. We previously established a batch-learning self-organizing map (BLSOM) of the oligonucleotide composition that reveals novel genome characteristics from big sequence data.ResultsIn this study, using the oligonucleotide BLSOM, we conducted a comparative genomic study of humans and six bat species. BLSOM is an explainable-type machine learning algorithm that reveals the diagnostic oligonucleotides contributing to sequence clustering (self-organization). When unsupervised machine learning reveals unexpected and/or characteristic features, these features can be studied in more detail via the much simpler and more direct standard distribution map method. Based on this combined strategy, we identified the Mb-level enrichment of CG dinucleotide (Mb-level CpG islands) around the termini of bat long-scaffold sequences. In addition, a class of CG-containing oligonucleotides were enriched in the centromeric and pericentromeric regions of human chromosomes. Oligonucleotides longer than tetranucleotides often represent binding motifs for a wide variety of proteins (e.g., transcription factor binding sequences (TFBSs)). By analyzing the penta- and hexanucleotide composition, we observed the evident enrichment of a wide range of hexanucleotide TFBSs in centromeric and pericentromeric heterochromatin regions on all human chromosomes.ConclusionFunction of transcription factors (TFs) beyond their known regulation of gene expression (e.g., TF-mediated looping interactions between two different genomic regions) has received wide attention. The Mb-level TFBS and CpG islands are thought to be involved in the large-scale nuclear organization, such as centromere and telomere clustering. TFBSs, which are enriched in centromeric and pericentromeric heterochromatin regions, are thought to play an important role in the formation of nuclear 3D structures. Our machine learning-based analysis will help us to understand the differential features of nuclear 3D structures in the human and bat genomes.

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Comparative analysis of CpG islands among HBV genotypes.

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Comparative analysis of CpG islands among HBV genotypes.

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