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Multigenerational adverse effects from the environment such as nutrition and chemicals are among important concerns in environmental health issues. Previously, we have found that arsenite exposure of only F0 females during their pregnancy increases hepatic tumors in the F2 males in C3H mice. In the current study, we investigated the association of DNA methylation with the hepatic tumor increase in the F2 males of the arsenite group. Reduced-representation bisulfite sequencing analysis newly identified that DNA methylation levels of regions around the transcriptional start sites of Tmem54 and Cd74 were decreased and the expression of these genes were significantly increased in the hepatic tumors of F2 males of the arsenite group. The associations between DNA methylation in these regions and gene expression changes were confirmed by treatment of murine hepatoma cell lines and hepatic stellate cell line with 5-aza-2'-deoxycytidine. Overexpression of Cd74 in Hepa1c1c7 cells increased Trib3 expression and suppressed the expression of tumor suppressor genes Id3 and Atoh8. Human database analysis using the Cancer Genome Atlas indicated that TMEM54, CD74, and TRIB3 were significantly increased and that ATOH8 was decreased in hepatocellular carcinoma. The data also showed that high expression of TMEM54 and TRIB3 and low expression of ATOH8 were associated with poor survival. These results suggested that an increase in Tmem54 and Cd74 expression via DNA methylation reduction was involved in the tumor increase in the F2 male offspring by gestational arsenite exposure of F0 females. This study also suggested that genes downstream of Cd74 were involved in tumorigenesis.
Pubmed ID: 31215104
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsCollection of curated, non-redundant genomic DNA, transcript RNA, and protein sequences produced by NCBI. Provides a reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis, expression studies, and comparative analyses. Accessed through the Nucleotide and Protein databases.
View all literature mentionsAn R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing.
View all literature mentionsComprehensive differentially methylated regions (DMR) analysis based on bimodal normal distribution model and weighted cost function for regional methylation analysis optimization.
View all literature mentionsA commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis.
View all literature mentionsSoftware tool to automate quality and adapter trimming as well as quality control, with some added functionality to remove biased methylation positions for RRBS sequence files for directional, non-directional or paired-end sequencing. Wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for Reduced Representation Bisulfite Sequencing data.
View all literature mentionsCell line Hepa-1c1c7 is a Cancer cell line with a species of origin Mus musculus (Mouse)
View all literature mentionslaboratory mouse with name C3H/HeN from MGI.
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