Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer

ABSTRACT: Aberrant DNA methylation is common in cancer. To associate DNA methylation with gene function, we performed RNAseq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients. To quantify 5mC and 5hmC level in each CG site at genome-wide level, we performed BS-seq and TAB-seq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients, respectively. mRNA profiles of tumor and matched normal tissues from two ccRCC patients were generated by deep sequencing, using Hiseq 2000. Single-nucleotide-resolution, whole-genome, 5mC and 5hmC profiles of tumor and matched normal tissues from two ccRCC (clear cell renal cell carcinoma) patients were generated by deep sequencing, using Hiseq 2000.

ORGANISM(S): Homo sapiens

SUBMITTER: Jing Zhang

PROVIDER: E-GEOD-63183 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer.

Chen Ke K Zhang Jing J Guo Zhongqiang Z Ma Qin Q Xu Zhengzheng Z Zhou Yuanyuan Y Xu Ziying Z Li Zhongwu Z Liu Yiqiang Y Ye Xiongjun X Li Xuesong X Yuan Bifeng B Ke Yuwen Y He Chuan C Zhou Liqun L Liu Jiang J Ci Weimin W

Cell research 20151218 1

Both 5-methylcytosine (5mC) and its oxidized form 5-hydroxymethylcytosine (5hmC) have been proposed to be involved in tumorigenesis. Because the readout of the broadly used 5mC mapping method, bisulfite sequencing (BS-seq), is the sum of 5mC and 5hmC levels, the 5mC/5hmC patterns and relationship of these two modifications remain poorly understood. By profiling real 5mC (BS-seq corrected by Tet-assisted BS-seq, TAB-seq) and 5hmC (TAB-seq) levels simultaneously at single-nucleotide resolution, we ...[more]

PMID: 26680004

Similar Datasets

Project description:Methylation is a repressive modification of DNA prevalent throughout mammalian genomes yet mostly absent at CG rich stretches referred to as CGI. Here we identify their building principles by parallel genomic targeting of sequence libraries. Iterative insertions generated over 3,000 variants of genome-derived and artificial sequences at the same genomic site. Single molecule profiling of the methylation status of this collection allowed modeling the contribution of CG content and DNA binding factors towards the unmethylated state. It made the surprising prediction that the majority of CGs within endogenous islands are susceptible to methylation changes modulated by the presence of transcription factors, which is indeed confirmed by genome-wide methylation dynamics during multiple cellular differentiations. Our model further predicts blocks of constitutively unmethylated CGs independent from TF binding, which have a median size of ~300bp but are only present in half of all islands. Their constitutively unmethylated state is a hallmark of untransformed cells but their increased methylation is a specific and predictive feature of cancer. This study quantifies the two principal mechanisms governing methylation patterns in mammalian genomes. It provides a framework to interpret methylation data across normal and cancer samples and refines the concept of CpG islands. Methylation is a repressive modification of DNA prevalent throughout mammalian genomes yet mostly absent at CG rich stretches referred to as CGI. Here we identify their building principles by parallel genomic targeting of sequence libraries. Iterative insertions generated over 3,000 variants of genome-derived and artificial sequences at the same genomic site. Single molecule profiling of the methylation status of this collection allowed modeling the contribution of CG content and DNA binding factors towards the unmethylated state. It made the surprising prediction that the majority of CGs within endogenous islands are susceptible to methylation changes modulated by the presence of transcription factors, which is indeed confirmed by genome-wide methylation dynamics during multiple cellular differentiations. Our model further predicts blocks of constitutively unmethylated CGs independent from TF binding, which have a median size of ~300bp but are only present in half of all islands. Their constitutively unmethylated state is a hallmark of untransformed cells but their increased methylation is a specific and predictive feature of cancer. This study quantifies the two principal mechanisms governing methylation patterns in mammalian genomes. It provides a framework to interpret methylation data across normal and cancer samples and refines the concept of CpG islands. Libraries of DNA sequences were constructed either by mouse genome (129S6) or E.coli genome (NC_010473.1) subrepresentation or custom synthesis. DNA fragments were inserted into the genome of mouse embryonic stem cells by recombination mediated casette exchange (RMCE) at the B-globin locus. Methylation status of the inserted DNA sequences was profiled by bisulfite sequencing using a pair of universal primers flanking the fragments.

Dataset Information

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer

Publications

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets