Identification of transcription factor binding sites in promoter databases

Transcription factors (TFs) are the proteins which regulates the expression of their target genes either in a positive or negative manner. TFs realize this task by binding to a specific DNA sequence contained in promoter regions, via their DNA binding motifs. Among ETS family TFs, Pea3 proteins are involved in the regulation of expression of genes, which are important for cell growth, development, differentiation, oncogenic transformation and apoptosis. In silico studies should be done to find out the novel target genes for this TF. Even though a few bioinformatics tools are available for this purpose, the user needs to go back and forth between different tools, and to repeat these steps for each of their candidate gene. Here we combined these tools and constituted a new tool which examines the affinity of any TF towards the selected target genes’ promoter sequences. The tool is tested on several genes, which are predicted to be regulated by Pea3 TF

A Statistical Analysis of the Robustness of Alternate Genetic Coding Tables

The rules that specify how the information contained in DNA is translated into amino acid “language†during protein synthesis are called “the genetic codeâ€Â, commonly called the “Standard†or “Universal†Genetic Code Table. As a matter of fact, this coding table is not at all “universalâ€Â: in addition to different genetic code tables used by different organisms, even within the same organism the nuclear and mitochondrial genes may be subject to two different coding tables. Results In an attempt to understand the advantages and disadvantages these coding tables may bring to an organism, we have decided to analyze various coding tables on genes subject to mutations, and have estimated how these genes “survive†over generations. We have used this as indicative of the “evolutionary†success of that particular coding table. We find that the “standard†genetic code is not actually the most robust of all coding tables, and interestingly, Flatworm Mitochondrial Code (FMC) appears to be the highest ranking coding table given our assumptions. Conclusions It is commonly hypothesized that the more robust a genetic code, the better suited it is for maintenance of the genome. Our study shows that, given the assumptions in our model, Standard Genetic Code is quite poor when compared to other alternate code tables in terms of robustness. This brings about the question of why Standard Code has been so widely accepted by a wider variety of organisms instead of FMC, which needs to be addressed for a thorough understanding of genetic code evolution

Selection shapes the robustness of ligand-binding amino acids

Abstract The phenotypes of biological systems are to some extent robust to genotypic changes. Such robustness exists on multiple levels of biological organization. We analyzed this robustness for two categories of amino acids in proteins. Specifically, we studied the codons of amino acids that bind or do not bind small molecular ligands. We asked to what extent codon changes caused by mutation or mistranslation may affect physicochemical amino acid properties or protein folding. We found that the codons of ligand-binding amino acids are on average more robust than those of non-binding amino acids. Because mistranslation is usually more frequent than mutation, we speculate that selection for error mitigation at the translational level stands behind this phenomenon. Our observations suggest that natural selection can affect the robustness of very small units of biological organization

Transcriptomic profile of Pea3 family members reveal regulatory codes for axon outgrowth and neuronal connection specificity

Abstract PEA3 transcription factor subfamily is present in a variety of tissues with branching morphogenesis, and play a particularly significant role in neural circuit formation and specificity. Many target genes in axon guidance and cell–cell adhesion pathways have been identified for Pea3 transcription factor (but not for Erm or Er81); however it was not so far clear whether all Pea3 subfamily members regulate same target genes, or whether there are unique targets for each subfamily member that help explain the exclusivity and specificity of these proteins in neuronal circuit formation. In this study, using transcriptomics and qPCR analyses in SH-SY5Y neuroblastoma cells, hypothalamic and hippocampal cell line, we have identified cell type-specific and subfamily member-specific targets for PEA3 transcription factor subfamily. While Pea3 upregulates transcription of Sema3D and represses Sema5B, for example, Erm and Er81 upregulate Sema5A and Er81 regulates Unc5C and Sema4G while repressing EFNB3 in SH-SY5Y neuroblastoma cells. We furthermore present a molecular model of how unique sites within the ETS domain of each family member can help recognize specific target motifs. Such cell-context and member-specific combinatorial expression profiles help identify cell–cell and cell-extracellular matrix communication networks and how they establish specific connections

A transcriptome based approach to predict candidate drug targets and drugs for Parkinson's disease using an in vitro 6-OHDA model

The most common treatment strategies for Parkinson's disease (PD) aim to slow down the neurodegeneration process or control the symptoms. In this study, using an in vitro PD model we carried out a transcriptome-based drug target prediction strategy. We identified novel drug target candidates by mapping genes upregulated in 6-OHDA-treated cells on a human protein-protein interaction network. Among the predicted targets, we show that AKR1C3 and CEBPB are promising in validating our bioinformatics approach since their known ligands, rutin and quercetin, respectively, act as neuroprotective drugs that effectively decrease cell death, and restore the expression profiles of key genes upregulated in 6-OHDA-treated cells. We also show that these two genes upregulated in our in vitro PD model are downregulated to basal levels upon drug administration. As a further validation of our methodology, we further confirm that the potential target genes identified with our bioinformatics approach are also upregulated in post-mortem transcriptome samples of PD patients from the literature. Therefore, we propose that this methodology predicts novel drug targets AKR1C3 and CEBPB, which are relevant to future clinical applications as potential drug repurposing targets for PD. Our systems-based computational approach to predict candidate drug targets can be employed in identifying novel drug targets in other diseases without a priori assumption