A mutation degree model for the identification of transcriptional regulatory elements

<p>Abstract</p> <p>Background</p> <p>Current approaches for identifying transcriptional regulatory elements are mainly via the combination of two properties, the evolutionary conservation and the overrepresentation of functional elements in the promoters of co-regulated genes. Despite the development of many motif detection algorithms, the discovery of conserved motifs in a wide range of phylogenetically related promoters is still a challenge, especially for the short motifs embedded in distantly related gene promoters or very closely related promoters, or in the situation that there are not enough orthologous genes available.</p> <p>Results</p> <p>A mutation degree model is proposed and a new word counting method is developed for the identification of transcriptional regulatory elements from a set of co-expressed genes. The new method comprises two parts: 1) identifying overrepresented oligo-nucleotides in promoters of co-expressed genes, 2) estimating the conservation of the oligo-nucleotides in promoters of phylogenetically related genes by the mutation degree model. Compared with the performance of other algorithms, our method shows the advantages of low false positive rate and higher specificity, especially the robustness to noisy data. Applying the method to co-expressed gene sets from Arabidopsis, most of known <it>cis</it>-elements were successfully detected. The tool and example are available at <url>http://mcube.nju.edu.cn/jwang/lab/soft/ocw/OCW.html</url>.</p> <p>Conclusions</p> <p>The mutation degree model proposed in this paper is adapted to phylogenetic data of different qualities, and to a wide range of evolutionary distances. The new word-counting method based on this model has the advantage of better performance in detecting short sequence of <it>cis</it>-elements from co-expressed genes of eukaryotes and is robust to less complete phylogenetic data.</p

Dissecting the regulatory strategies of NF-kB RelA target genes in the inflammatory response reveals differential transactivation logics

Nuclear factor κB (NF-κB) RelA is the potent transcriptional activator of inflammatory response genes. We stringently defined a list of direct RelA target genes by integrating physical (chromatin immunoprecipitation sequencing [ChIP-seq]) and functional (RNA sequencing [RNA-seq] in knockouts) datasets. We then dissected each gene’s regulatory strategy by testing RelA variants in a primary-cell genetic-complementation assay. All endogenous target genes require RelA to make DNA-base-specific contacts, and none are activatable by the DNA binding domain alone. However, endogenous target genes differ widely in how they employ the two transactivation domains. Through model-aided analysis of the dynamic time-course data, we reveal the gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and intrinsic affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent versus context-independent control of endogenous RelA-target genes. Although some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers are a part of the NF-κB RelA core response

Transcriptional and Proteomic Analysis of a Ferric Uptake Regulator (Fur) Mutant of Shewanella oneidensis: Possible Involvement of Fur in Energy Metabolism, Transcriptional Regulation, and Oxidative Stress

The iron-directed, coordinate regulation of genes depends on the fur (ferric uptake regulator) gene product, which acts as an iron-responsive, transcriptional repressor protein. To investigate the biological function of a fur homolog in the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1, a fur knockout strain (FUR1) was generated by suicide plasmid integration into this gene and characterized using phenotype assays, DNA microarrays containing 691 arrayed genes, and two-dimensional polyacrylamide gel electrophoresis. Physiological studies indicated that FUR1 was similar to the wild-type strain when they were compared for anaerobic growth and reduction of various electron acceptors. Transcription profiling, however, revealed that genes with predicted functions in electron transport, energy metabolism, transcriptional regulation, and oxidative stress protection were either repressed (ccoNQ, etrA, cytochrome b and c maturation-encoding genes, qor, yiaY, sodB, rpoH, phoB, and chvI) or induced (yggW, pdhC, prpC, aceE, fdhD, and ppc) in the fur mutant. Disruption of fur also resulted in derepression of genes (hxuC, alcC, fhuA, hemR, irgA, and ompW) putatively involved in iron uptake. This agreed with the finding that the fur mutant produced threefold-higher levels of siderophore than the wild-type strain under conditions of sufficient iron. Analysis of a subset of the FUR1 proteome (i.e., primarily soluble cytoplasmic and periplasmic proteins) indicated that 11 major protein species reproducibly showed significant (P < 0.05) differences in abundance relative to the wild type. Protein identification using mass spectrometry indicated that the expression of two of these proteins (SodB and AlcC) correlated with the microarray data. These results suggest a possible regulatory role of S. oneidensis MR-1 Fur in energy metabolism that extends the traditional model of Fur as a negative regulator of iron acquisition systems

Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences

An Sp1 Modulated Regulatory Region Unique to Higher Primates Regulates Human Androgen Receptor Promoter Activity in Prostate Cancer Cells

Funding: This work was supported by the Chief Scientist’s Office (CSO) of the Scottish Government (http://www.cso.scot.nhs.uk/): CWH (CZB-4-477) and IH (ETM/382).Peer reviewedPublisher PD