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Computational identification of tumour suppressor protein p53 target genes

Author(s): Siew-Chinn Fong, Fiona E.M. Paulin, Alastair M. Thompson
Affiliations: University of Dundee
Keywords: 'p53' 'tumour suppressor' 'response element' 'position weight matrices' 'bioinformatics'


The tumour suppressor protein p53 plays a major role in modulating cellular functions such as DNA repair, cell cycle arrest and apoptosis. Mutation in p53 gene is known to be one of the most frequently observed genetic alterations during tumorigenesis. It acts as a transcription factor and in response to cellular stress, e.g. hypoxia or nucleotide depletion, the p53 binds DNA in a sequence specific manner and induces the expression of target genes bearing p53 response elements (REs) within their promoter regions. The p53 response element consists of two repeated copies of 5’-PuPuPuC(A/T)(A/T)GPyPyPy-3’with a spacer of up to 13 bp in between. Hopefully, by identifying the p53 regulated genes, same group of functional genes could be clustered which provide better insight in understanding the gene interaction network.

Many studies have been carried out on p53 binding sites and in developing different algorithms to identify p53 RE in human genomes. However, the efficacy of predicting p53 target genes based on p53 RE sequence alone is not sufficient. Thus, we investigated these target genes to determine if additional elements adjacent to the p53 binding site may also have a role in modulating p53 binding. Genes experimentally found to be regulated by p53 were aligned with respect to their p53 REs and six conserved consensus motifs were found present at the upstream of the p53 REs. Each motif is represented by a Positional Weight Matrices (PWM). As a result, a program was written using Perl script based on the criteria of searching for a p53 binding site as well as the six upstream elements as an additional confirmation for the search result. The efficacy of this program in detecting a potential p53 target gene was tested using appropriate statistical tests and validated using lists of experimentally proven p53 target genes.

This program can be potentially use as a preliminary scanning tool to predict the presence of p53 target genes for high throughput experiments like DNA microarrays. Recently, it has also been used in predicting p53 as one of the potential regulators in the metabolomics pathway in a cancer cell line when treated with a non genotoxic drug.


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