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FP7

Promoter predictions — Result In Brief

Project ID: 276996
Funded under: FP7-PEOPLE
Country: Serbia

Bioinformatic modelling of gene expression regulation

Transcription is the process of making RNA from the DNA template in the cell. The EU-funded PROMOTER PREDICTIONS project studied the transcription regulation process in bacteria using bioinformatics and biophysical modelling.
Bioinformatic modelling of gene expression regulation
Transcription enables the synthesis of functional gene products and provides a major control point in this process. In bacteria, transcription is exhibited by the enzyme RNA polymerase (RNAP), which binds to promoters through a sigma factor and initiates transcription at transcription start sites (TSSs). Accurate knowledge of TTSs is the first necessary step in understanding transcription and its control. However, bioinformatic methods available for TSS detection are plagued by inaccuracy, due to a high number of false positive results.

The PROMOTER PREDICTIONS (Bioinformatic analysis of transcription regulation: a modelling approach) project significantly reduced false positive results by about 50 %, through systematic analysis of sigma 70 promoter elements in Escherichia coli (E. coli) bacteria. The researchers applied a novel mix-and-match model for promoter recognition and quantified specificity of RNAP for sigma 70 promoter elements through the corresponding weight matrices. These weight matrices helped produce aligned promoter elements with adequate transcription activity, and accurately identified strong promoters in a newly sequenced bacteriophage genome.

To further understand the reasons behind the remaining false positive results, the researchers computationally analysed kinetics of transcription initiation by RNAP in the E. coli genome. They found that a number of poised promoters exist in the genome but fail to result in functional transcription, due to slow opening of the two DNA strands. The development of a new method for detection of direct target genes of a given regulator led to a higher prediction accuracy.

To gain a better understanding of transcription regulatory circuits in bacteria, researchers modelled transcript processing of clustered, regularly interspaced, short palindromic repeats (CRISPR) within the CRISPR/Cas bacterial immune system. Transcript processing is a crucial step in control of expression of small RNA molecules that recognise invading viruses. Research results helped in the development of a novel synthetic gene circuit for large production of useful molecules from small substrate concentrations.

PROMOTER PREDICTIONS research findings were published in 9 peer-reviewed journals and presented at one regional and three international conferences. The project analysis significantly improved understanding of transcription initiation and accuracy of TSS detection. The methodologies developed could also be adopted in other research areas such as infectious diseases.

Related information

Keywords

Bioinformatic modelling ,transcription regulation, transcription start site, Escherichia coli, CRISPR/Cas
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