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      HPV16 E6 and E7 upregulate the histone lysine demethylase KDM2B through the c-MYC/miR-146a-5p axys

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Widespread microRNA repression by Myc contributes to tumorigenesis.

            The c-Myc oncogenic transcription factor (Myc) is pathologically activated in many human malignancies. Myc is known to directly upregulate a pro-tumorigenic group of microRNAs (miRNAs) known as the miR-17-92 cluster. Through the analysis of human and mouse models of B cell lymphoma, we show here that Myc regulates a much broader set of miRNAs than previously anticipated. Unexpectedly, the predominant consequence of activation of Myc is widespread repression of miRNA expression. Chromatin immunoprecipitation reveals that much of this repression is likely to be a direct result of Myc binding to miRNA promoters. We further show that enforced expression of repressed miRNAs diminishes the tumorigenic potential of lymphoma cells. These results demonstrate that extensive reprogramming of the miRNA transcriptome by Myc contributes to tumorigenesis.
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              Transcriptional activation of miR-34a contributes to p53-mediated apoptosis.

              p53 is a potent tumor suppressor, whose biological effects are largely due to its function as a transcriptional regulator. Here we report that, in addition to regulating the expression of hundreds of protein-coding genes, p53 also modulates the levels of microRNAs (miRNAs). Specifically, p53 can induce expression of microRNA-34a (miR-34a) in cultured cells as well as in irradiated mice, by binding to a perfect p53 binding site located within the gene that gives rise to miR-34a. Processing of the primary transcript into mature miR-34a involves the excision of a 30 kb intron. Notably, inactivation of miR-34a strongly attenuates p53-mediated apoptosis in cells exposed to genotoxic stress, whereas overexpression of miR-34a mildly increases apoptosis. Hence, miR-34a is a direct proapoptotic transcriptional target of p53 that can mediate some of p53's biological effects. Perturbation of miR-34a expression, as occurs in some human cancers, may thus contribute to tumorigenesis by attenuating p53-dependent apoptosis.
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                Author and article information

                Journal
                Oncogene
                Oncogene
                Springer Nature
                0950-9232
                1476-5594
                January 16 2018
                :
                :
                Article
                10.1038/s41388-017-0083-1
                29335520
                4e1f5925-e4ad-4219-82a1-845c001e5f85
                © 2018

                http://www.springer.com/tdm

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