Dataset Information

Positive and negative feedback loops in the p53 and mRNA 3' processing pathways.

ABSTRACT: Although the p53 network has been intensively studied, genetic analyses long hinted at the existence of components that remained elusive. Recent studies have shown regulation of p53 at the mRNA level mediated via both the 5' and the 3' untranslated regions and affecting the stability and translation efficiency of the p53 mRNA. Here, we provide evidence of a feedback loop between p53 and the poly(A)-specific ribonuclease (PARN), in which PARN deadenylase keeps p53 levels low in nonstress conditions by destabilizing p53 mRNA, and the UV-induced increase in p53 activates PARN deadenylase, regulating gene expression during DNA damage response in a transactivation-independent manner. This model is innovative because it provides insights into p53 function and the mechanisms behind the regulation of mRNA 3' end processing in different cellular conditions.

SUBMITTER: Devany E

PROVIDER: S-EPMC3587245 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Positive and negative feedback loops in the p53 and mRNA 3' processing pathways.

Devany Emral E Zhang Xiaokan X Park Ji Yeon JY Tian Bin B Kleiman Frida Esther FE

Proceedings of the National Academy of Sciences of the United States of America 20130211 9

Although the p53 network has been intensively studied, genetic analyses long hinted at the existence of components that remained elusive. Recent studies have shown regulation of p53 at the mRNA level mediated via both the 5' and the 3' untranslated regions and affecting the stability and translation efficiency of the p53 mRNA. Here, we provide evidence of a feedback loop between p53 and the poly(A)-specific ribonuclease (PARN), in which PARN deadenylase keeps p53 levels low in nonstress conditio ...[more]

PMID: 23401530

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Project description:BackgroundThe search for molecular biomarkers of early-onset colorectal cancer (CRC) is an important but still quite challenging and unsolved task. Detection of CpG methylation in human DNA obtained from blood or stool has been proposed as a promising approach to a noninvasive early diagnosis of CRC. Thousands of abnormally methylated CpG positions in CRC genomes are often located in non-coding parts of genes. Novel bioinformatic methods are thus urgently needed for multi-omics data analysis to reveal causative biomarkers with a potential driver role in early stages of cancer.MethodsWe have developed a method for finding potential causal relationships between epigenetic changes (DNA methylations) in gene regulatory regions that affect transcription factor binding sites (TFBS) and gene expression changes. This method also considers the topology of the involved signal transduction pathways and searches for positive feedback loops that may cause the carcinogenic aberrations in gene expression. We call this method "Walking pathways", since it searches for potential rewiring mechanisms in cancer pathways due to dynamic changes in the DNA methylation status of important gene regulatory regions ("epigenomic walking").ResultsIn this paper, we analysed an extensive collection of full genome gene-expression data (RNA-seq) and DNA methylation data of genomic CpG islands (using Illumina methylation arrays) generated from a sample of tumor and normal gut epithelial tissues of 300 patients with colorectal cancer (at different stages of the disease) (data generated in the EU-supported SysCol project). Identification of potential epigenetic biomarkers of DNA methylation was performed using the fully automatic multi-omics analysis web service "My Genome Enhancer" (MGE) (my-genome-enhancer.com). MGE uses the database on gene regulation TRANSFAC®, the signal transduction pathways database TRANSPATH®, and software that employs AI (artificial intelligence) methods for the analysis of cancer-specific enhancers.ConclusionsThe identified biomarkers underwent experimental testing on an independent set of blood samples from patients with colorectal cancer. As a result, using advanced methods of statistics and machine learning, a minimum set of 6 biomarkers was selected, which together achieve the best cancer detection potential. The markers include hypermethylated positions in regulatory regions of the following genes: CALCA, ENO1, MYC, PDX1, TCF7, ZNF43.

Dataset Information

Positive and negative feedback loops in the p53 and mRNA 3' processing pathways.

Publications

Positive and negative feedback loops in the p53 and mRNA 3' processing pathways.

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