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      VHL inactivation without hypoxia is sufficient to achieve genome hypermethylation

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          Abstract

          VHL inactivation is a key oncogenic event for renal carcinomas. In normoxia, VHL suppresses HIF1a-mediated transcriptional response, which is characteristic to hypoxia. It has previously been shown that hypoxic conditions inhibit TET-dependent hydroxymethylation of cytosines and cause DNA hypermethylation at gene promoters. In this work, we performed VHL inactivation by CRISPR/Cas9 and studied its effects on gene expression and DNA methylation. We showed that even without hypoxia, VHL inactivation leads to hypermethylation of the genome. Hypermethylated cytosines were evenly distributed throughout the genome with a slight preference for AP-1 (JUN and FOS) binding sites. Hypermethylated cytosines tended to be enriched within the binding sites of transcription factors that showed increased gene expression after VHL inactivation. We also observed promoter hypermethylation associated with decreased gene expression for several regulators of transcription and DNA methylation including SALL3.

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          Tumor hypoxia causes DNA hypermethylation by reducing TET activity

          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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            Hypoxia-inducible factor determines sensitivity to inhibitors of mTOR in kidney cancer.

            Inhibitors of the kinase mammalian target of rapamycin (mTOR) have shown sporadic activity in cancer trials, leading to confusion about the appropriate clinical setting for their use. Here we show that loss of the Von Hippel-Lindau tumor suppressor gene (VHL) sensitizes kidney cancer cells to the mTOR inhibitor CCI-779 in vitro and in mouse models. Growth arrest caused by CCI-779 correlates with a block in translation of mRNA encoding hypoxia-inducible factor (HIF1A), and is rescued by expression of a VHL-resistant HIF1A cDNA lacking the 5' untranslated region. VHL-deficient tumors show increased uptake of the positron emission tomography (PET) tracer fluorodeoxyglucose (FDG) in an mTOR-dependent manner. Our findings provide preclinical rationale for prospective, biomarker-driven clinical studies of mTOR inhibitors in kidney cancer and suggest that FDG-PET scans may have use as a pharmacodynamic marker in this setting.
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              Deep and wide digging for binding motifs in ChIP-Seq data.

              ChIP-Seq data are a new challenge for motif discovery. Such a data typically consists of thousands of DNA segments with base-specific coverage values. We present a new version of our DNA motif discovery software ChIPMunk adapted for ChIP-Seq data. ChIPMunk is an iterative algorithm that combines greedy optimization with bootstrapping and uses coverage profiles as motif positional preferences. ChIPMunk does not require truncation of long DNA segments and it is practical for processing up to tens of thousands of data sequences. Comparison with traditional (MEME) or ChIP-Seq-oriented (HMS) motif discovery tools shows that ChIPMunk identifies the correct motifs with the same or better quality but works dramatically faster. ChIPMunk is freely available within the ru_genetika Java package: http://line.imb.ac.ru/ChIPMunk. Web-based version is also available. ivan.kulakovskiy@gmail.com Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                artem.v.artemov@gmail.com
                prokhortchouk@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 July 2018
                13 July 2018
                2018
                : 8
                : 10667
                Affiliations
                ISNI 0000 0001 2192 9124, GRID grid.4886.2, Institute of Bioengineering, Research Center of Biotechnology RAS, ; Moscow, Russia
                Article
                28795
                10.1038/s41598-018-28795-y
                6045645
                30006568
                7f0d490b-7a27-400d-bc18-c826078c1447
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 January 2018
                : 19 June 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100006769, Russian Science Foundation (RSF);
                Award ID: 14-14-01202
                Award ID: 14-14-01202
                Award ID: 14-14-01202
                Award ID: 14-14-01202
                Award ID: 14-14-01202
                Award Recipient :
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