An integrative computational approach to effectively guide experimental identification of regulatory elements in promoters

Abstract

BackgroundTranscriptional activity of genes depends on many factors like DNA motifs, conformational characteristics of DNA, melting etc. and there are computational approaches for their identification. However, in real applications, the number of predicted, for example, DNA motifs may be considerably large. In cases when various computational programs are applied, systematic experimental knock out of each of the potential elements obviously becomes nonproductive. Hence, one needs an approach that is able to integrate many heterogeneous computational methods and upon that suggest selected regulatory elements for experimental verification.ResultsHere, we present an integrative bioinformatic approach aimed at the discovery of regulatory modules that can be effectively verified experimentally. It is based on combinatorial analysis of known and novel binding motifs, as well as of any other known features of promoters. The goal of this method is the identification of a collection of modules that are specific for an established dataset and at the same time are optimal for experimental verification. The method is particularly effective on small datasets, where most statistical approaches fail. We apply it to promoters that drive tumor-specific gene expression in tumor-colonizing Gram-negative bacteria. The method successfully identified a number of potential modules, which required only a few experiments to be verified. The resulting minimal functional bacterial promoter exhibited high specificity of expression in cancerous tissue.ConclusionsExperimental analysis of promoter structures guided by bioinformatics has proved to be efficient. The developed computational method is able to include heterogeneous features of promoters and suggest combinatorial modules for experimental testing. Expansibility and robustness of the methodology implemented in the approach ensures good results for a wide range of problems.

Isolation and Functional Characterization of a Constitutive Promoter in Upland Cotton (Gossypium hirsutum L.)

“…It has been confirmed that the Dof factor binding sites in subdomain B4 of the CaMV35S promoter are important and contribute to its promoter activity [46]. Although the above cis-elements are only bioinformatically predicted ones, some of them may be truly functional and they may serve as a bases for further experimental characterization and validation [47].…”

STAT1 Inhibits MiR‐181a Expression to Suppress Colorectal Cancer Cell Proliferation Through PTEN/Akt

“…To investigate the role of miR‐181a further in LoVo and SW480 cells, we focused on the mechanism of miR‐181a overexpression in these cells. Figure A is a schematic diagram of four potential STAT1 binding elements in the promoter region of the miR‐181a gene [Deyneko et al, ] predicted by Jaspar database (Fig. A).…”

Composing a Tumor Specific Bacterial Promoter

“…Therefore, no reasonable window size parameter as required for many programs could be suggested for our particular dataset. Therefore, we have developed a bioinformatics method that identifies combinations of heterogeneous features, like, DNA motifs, CpG islands, repeats, that are co-localized on a DNA sequence [ 10 ]. This method is based on a genetic algorithm and searches for a collection of motif combinations that exhibit high specificity for the positive dataset and localize separately on DNA sequences.…”

“…First bioinformatics analysis of promoters showing high expression in tumors revealed a so-called tusp motif apparently responsible for tumor specific activation [ 8 ]. However, further experimental data and advanced bioinformatics analysis of other groups of fragments with lower expression in tumors or with limited expression in spleen revealed that it was an oversimplification [ 9 , 10 ]. Therefore, it was required to thoroughly investigate the principles of the tumor specific transcriptional regulation and reveal contributing functional elements.…”