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      Tumor hypoxia causes DNA hypermethylation by reducing TET activity

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          Summary

          Hypermethylation of tumor suppressor gene (TSG) promoters confers growth advantages to cancer cells, but how these changes arise is poorly understood. Here, we report that tumor hypoxia reduces the activity of oxygen-dependent TET enzymes, which catalyze DNA de-methylation through 5-methylcytosine oxidation. This occurs independently of hypoxia-associated alterations in TET expression, proliferation, metabolism, HIF activity or reactive oxygen, but directly depends on oxygen shortage. Hypoxia-induced loss of TET activity increases hypermethylation at gene promoters in vitro. Also in patients, TSG promoters are markedly more methylated in hypoxic tumors, independently of proliferation, stromal cell infiltration and tumor characteristics. Our data suggest cellular selection of hypermethylation events, with almost half of them being ascribable to hypoxia across tumor types. Accordingly, increased hypoxia after vessel pruning in murine breast tumors increases hypermethylation, while restored tumor oxygenation by vessel normalization abrogates this effect. Tumor hypoxia thus acts as a novel regulator underlying DNA methylation.

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          Most cited references24

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          Is Open Access

          Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

          A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
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            CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer.

            Aberrant DNA methylation of CpG islands has been widely observed in human colorectal tumors and is associated with gene silencing when it occurs in promoter areas. A subset of colorectal tumors has an exceptionally high frequency of methylation of some CpG islands, leading to the suggestion of a distinct trait referred to as 'CpG island methylator phenotype', or 'CIMP'. However, the existence of CIMP has been challenged. To resolve this continuing controversy, we conducted a systematic, stepwise screen of 195 CpG island methylation markers using MethyLight technology, involving 295 primary human colorectal tumors and 16,785 separate quantitative analyses. We found that CIMP-positive (CIMP+) tumors convincingly represent a distinct subset, encompassing almost all cases of tumors with BRAF mutation (odds ratio = 203). Sporadic cases of mismatch repair deficiency occur almost exclusively as a consequence of CIMP-associated methylation of MLH1 . We propose a robust new marker panel to classify CIMP+ tumors.
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              Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution.

              5-Methylcytosine can be converted to 5-hydroxymethylcytosine (5hmC) in mammalian DNA by the ten-eleven translocation (TET) enzymes. We introduce oxidative bisulfite sequencing (oxBS-Seq), the first method for quantitative mapping of 5hmC in genomic DNA at single-nucleotide resolution. Selective chemical oxidation of 5hmC to 5-formylcytosine (5fC) enables bisulfite conversion of 5fC to uracil. We demonstrate the utility of oxBS-Seq to map and quantify 5hmC at CpG islands (CGIs) in mouse embryonic stem (ES) cells and identify 800 5hmC-containing CGIs that have on average 3.3% hydroxymethylation. High levels of 5hmC were found in CGIs associated with transcriptional regulators and in long interspersed nuclear elements, suggesting that these regions might undergo epigenetic reprogramming in ES cells. Our results open new questions on 5hmC dynamics and sequence-specific targeting by TETs.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                17 August 2016
                17 August 2016
                01 September 2016
                17 February 2017
                : 537
                : 7618
                : 63-68
                Affiliations
                [1 ]Vesalius Research Center, VIB, Leuven, Belgium
                [2 ]Laboratory of Translational Genetics, Department of Oncology, KU Leuven, Leuven, Belgium
                [3 ]Department für Chemie und Pharmazie, Ludwig-Maximilians-Universität, München, Germany
                [4 ]Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
                [5 ]Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
                [6 ]Gynecologic Oncology, University Hospitals Leuven, Department of Oncology, KU Leuven, Leuven, Belgium
                [7 ]Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
                [8 ]Department of Development and Regeneration, and Stem Cell Institute Leuven, KU Leuven, Leuven, Belgium
                [9 ]Laboratory of Molecular Oncology and Angiogenesis, Department of Oncology, KU Leuven, Leuven, Belgium
                Author notes
                Correspondence and requests for materials should be addressed to BT ( bernard.thienpont@ 123456vib-kuleuven.be ) or DL ( diether.lambrechts@ 123456vib-kuleuven.be ).
                Article
                EMS69189
                10.1038/nature19081
                5133388
                27533040
                eb717273-128e-444d-83f6-4b5b104fe6de

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