Placental ischemia disrupts DNA methylation patterns of distal regulatory regions in rat
Ontology highlight
ABSTRACT: We carry out whole-genome bisulfite sequencing (WGBS) of rat placental DNA to investigate epigenome-wide alterations in a reduced uterine perfusion pressure (RUPP) PE model. The Hidden Markov Model (HMM) was applied to identify functional epigenetic structures including partially methylated domains (PMDs), low methylated regions (LMRs) and unmethylated regions (UMRs).
Project description:Using WGBS we investigated blood DNA methylation profiles of Cooinda the Alpine dingo and determined putative regulatory elements (unmethylated regions, UMRs, and lowly methylated regions, LMRs).
Project description:DNA methylation is essential for embryonic and neuronal differentiation, but the function of most genomic DNA methylation marks are poorly understood. Generally the human genome is highly methylated (>70%) except for CpG islands and gene promoters. However, it was recently shown that the IMR90 human fetal lung fibroblast cells have large regions of the genome with partially methylated domains (PMDs, <70% average methylation), in contrast to the rest of the genome which is in highly methylated domains (HMDs, >70% average methylation). Using bisulfite conversion followed by high-throughput sequencing (MethylC-seq), we discovered that human SH-SY5Y neuronal cells also contain PMDs. We developed a novel hidden Markov model (HMM) to computationally map the genomic locations of PMDs in both cell types and found that autosomal PMDs can be over 9 Mb in length and cover 41% of the IMR90 genome and 19% of the SH-SY5Y genome. Genomic regions marked by cell line specific PMDs contain genes that are expressed in a tissue-specific manner, with PMDs being a mark of repressed transcription. Genes contained within N-HMDs (neuronal HMDs, defined as a PMD in IMR90 but HMD in SH-SY5Y) were significantly enriched for calcium signaling, synaptic transmission and neuron differentiation functions. Autism candidate genes were enriched within PMDs and the largest PMD observed in SH-SY5Y cells marked a 10 Mb cluster of cadherin genes with strong genetic association to autism. Our results suggest that these large-scale methylation domain maps could be relevant to interpreting and directing future investigations into the elusive etiology of autism. Examined DNA methylation in a human neuronal cell line and cerebral cortex
Project description:Using WGBS we investigated blood DNA methylation profiles of German Shepherd and determined putative regulatory elements (unmethlated regions (UMRs) and lowly methylated regions (LMRs).
Project description:Using WGBS we investigated blood DNA methylation profiles of Canis lupus basenji and determined putative regulatory elements (unmethlated regions, UMRs, and lowly methylated regions, LMRs).
Project description:Using WGBS we investigated blood DNA methylation profiles of Canis lupus dingo and determined putative regulatory elements (unmethlated regions (UMRs) and lowly methylated regions (LMRs).
Project description:DNA methylation is essential for embryonic and neuronal differentiation, but the function of most genomic DNA methylation marks are poorly understood. Generally the human genome is highly methylated (>70%) except for CpG islands and gene promoters. However, it was recently shown that the IMR90 human fetal lung fibroblast cells have large regions of the genome with partially methylated domains (PMDs, <70% average methylation), in contrast to the rest of the genome which is in highly methylated domains (HMDs, >70% average methylation). Using bisulfite conversion followed by high-throughput sequencing (MethylC-seq), we discovered that human SH-SY5Y neuronal cells also contain PMDs. We developed a novel hidden Markov model (HMM) to computationally map the genomic locations of PMDs in both cell types and found that autosomal PMDs can be over 9 Mb in length and cover 41% of the IMR90 genome and 19% of the SH-SY5Y genome. Genomic regions marked by cell line specific PMDs contain genes that are expressed in a tissue-specific manner, with PMDs being a mark of repressed transcription. Genes contained within N-HMDs (neuronal HMDs, defined as a PMD in IMR90 but HMD in SH-SY5Y) were significantly enriched for calcium signaling, synaptic transmission and neuron differentiation functions. Autism candidate genes were enriched within PMDs and the largest PMD observed in SH-SY5Y cells marked a 10 Mb cluster of cadherin genes with strong genetic association to autism. Our results suggest that these large-scale methylation domain maps could be relevant to interpreting and directing future investigations into the elusive etiology of autism.
Project description:In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data using a training set of empirical hybridizations of infected and uninfected samples. We have expanded up our previous implementation by incorporating a hidden Markov model (HMM) to detect recombination. We trained our HMM on a set of nonrecombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM identified 95% of the 62 inter-species recombinant breakpoints in the validation set and only two false positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of identifying recombination in viruses based on diagnostic microarray hybridization patterns.
Project description:Embryonic stem cells can be differentiated in vitro to produce a variety of somatic cell types. We have employed the mDIP method combined with hybridization to a tiling microarray to obtain a genome-wide methylation analysis of all UnMethylated Regions(UMRs). We show that this differentiation is accompanied by an intrinsic process of extensive aberrant CpG island de novo methylation that includes developmental and cancer target genes. CpG-methylated genomic DNA was enriched using a methyl-DNA immunoprecipitation (mDIP) assay. DNA from the input and bound (enriched) DNA for each sample were labeled and hybridized on the array to define the methylation state of each region.
Project description:<p>Aberrant DNA methylation changes are known to occur during prostate cancer progression beginning with precursor lesions. Utilizing fifty nanograms of genomic DNA in Methylplex-Next Generation Sequencing (M-NGS) we mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2). Peaks were located from mapped reads obtained in each sequencing run using a Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(<a href="http://www.sph.umich.edu/csg/qin/HPeak">http://www.sph.umich.edu/csg/qin/HPeak</a>). The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. Promoter CGI methylation gradually increased from -12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues and approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues. We observed distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. Overall Design: We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were sequenced twice on Illumina next generation sequencing platform to access overall repeatability of M-NGS.</p>