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Upregulated by p53 in HCT116 cells appear at the top of this ranking (e.g., CDKN1A, DDB2 and GDF15, ranked two, four and 62, respectively) (Figure 3–figure supplement 2A). Having said that, some direct targets `basally repressed’ by p53, including GJB5, show an inverse correlation with WT p53 status. Collectivelly, the direct p53 targets Pyrroloquinolinequinone disodium salt web identified by GRO-seq are enriched toward the major on the ranking, which can be revealed in a Gene set enrichment analysis (GSEA) (Figure 3–figure supplement 2A). In contrast, genes induced only inside the microarray platform (i.e., mainly indirect targets) usually do not show robust enrichment in a GSEA analysis. When the relative basal transcription involving HCT116 p53 ++ and p53 — cells is plotted against the relative mRNA expression in p53 WT vs p53 mutant cell lines, it really is apparent that numerous `basally activated’ genes are extra extremely expressed in p53 WT cells (green dots within the upper right quadrant in Figure 3–figure supplement 2B). Finally, the differential pattern of basal expression amongst p53 targets can also be observed in an analysis of 256 breast tumors for which p53 status was determined, exactly where CDKN1A, DDB2 and GDF15 (but not GJB5) show larger expression in the p53 WT tumors (Figure 3–figure supplement 2C). Altogether, these final results reveal a qualitative difference amongst p53 target genes with regards to their sensitivity to basal p53-MDM2 complexes as depicted in Figure 3–figure supplement 2D. Even though indirect effects can not be fully ruled out, the fact that we are able to PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/21352867 detect p53 and MDM2 binding to the p53REs near these gene loci recommend direct action. Of note, early in vitro transcription studies demonstrated that MDM2 represses transcription when tethered to DNA independently of p53, which may possibly present the molecular mechanism behind our observations (Thut et al., 1997) (`Discussion’).GRO-seq reveals gene-specific regulatory mechanisms affecting crucial survival and apoptotic genesMany investigation efforts have already been devoted to the characterization of molecular mechanisms conferring gene-specific regulation inside the p53 network, as these mechanisms may be exploited to manipulate the cellular response to p53 activation. Most investigation has focused on factors that differentially modulate p53 binding or transactivation of survival vs apoptotic genes (Vousden and Prives, 2009). GRO-seq identified a plethora of gene-specific regulatory options affecting p53 targets, but our analysis failed to reveal a universal discriminator amongst survival and death genes within the network. When direct p53 target genes with well-established pro-survival (i.e., cell cycle arrest, survival, DNA repair and damaging regulation of p53) and pro-death (i.e., extrinsic and intrinsic apoptotic signaling) functions are ranked determined by their transcriptional output in Nutlin-treated p53 ++ cells, it can be evident that crucial pro-survival genes like CDKN1A, GDF15, BTG2 and MDM2 are transcribed at muchAllen et al. eLife 2014;3:e02200. DOI: 10.7554eLife.12 ofResearch articleGenes and chromosomes Human biology and medicinehigher rates than any apoptotic gene (Figure 4A). As an example, 20-fold more RNA is produced from the CDKN1A locus than in the BBC3 locus encoding the BH3-only protein PUMA. By far the most potently transcribed apoptotic gene is TP53I3 (PIG3), but its transcriptional output is still 3.4-fold reduce than CDKN1A. According to measurements of steady state RNA levels, it was observed that apoptotic genes for instance TP53I3 and FAS are induced with a slower kinetics than CD.

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