VEGFA SNPs (rs34357231 & rs35569394), Transcriptional Factor Binding Sites and Human Disease.

Purpose: The human vascular endothelial growth factor (VEGF)-A gene transcribes a signaling protein involved in the regulation of angiogenesis, vasculogenesis and endothelial cell growth. Two insertion/deletion (I/D) simple nucleotide polymorphisms (SNPs, rs34357231 & rs35569394) in the promoter region of the gene have been significantly associated with several human diseases. These SNPs were computationally examined with respect to changes in punitive transcriptional factor binding sites (TFBS) and these changes were discussed in relation to the diseases. Methods: The JASPAR CORE and ConSite databases were instrumental in identifying the TFBS. The Vector NTI Advance 11.5 computer program was employed in locating all the TFBS in the VEGFA gene from 2.7 kb upstream of the transcriptional start site to 1.6 kb past the 3’UTR. The JASPAR CORE database was also involved in computing each nucleotide occurrence (%) within the TFBS. Results: Regulatory SNPs (rSNPs) in the promoter region of the VEGFA gene alter the DNA landscape for potential transcriptional factors (TFs) to attach resulting in changes in TFBS. The VEGFA-deletion (D) allele of these SNPs has been found to be a risk factor for diabetic retinopathy, diabetic microvascular complications in patients with type 1 diabetes mellitus, breast cancer in north Indian patients, and bladder cancer. The changes in TFs associated with the TFBS are examined with respect to these human diseases. Conclusion: The VEGFA-insertion (I) allele provides punitive TFBS for the AR, EGR1 & 2, KLF5 and SP1 TFs whose BS do not occur with the VEGFA-D allele. These TFs have been linked to prostate cancer, cancer suppression and oncogenic processes and if not regulating the VEGFA gene may pose a risk for disease.

Computational DIO2 rSNP Analysis, Transcriptional Factor Binding Sites and Disease.

Purpose: The DIO2 gene transcribes the deiodinase type 2 enzyme that changes the thyroid prohormone, thyroxine (T4), to the biologically active triiodothyronine (T3) hormone. T3 plays a vital part in the regulation of energy balance and glucose metabolism. DIO2 single-nucleotide polymorphisms (SNPs) were computationally examined with respect to changes in punitive transcriptional factor binding sites (TFBS) and these changes were discussed in relation to human disease. Methods: The JASPAR CORE and ConSite databases were instrumental in identifying the TFBS. The Vector NTI Advance 11.5 computer program was employed in locating all the TFBS in the DIO2 gene from 2.4 kb upstream of the transcriptional start site to 508 bp past the 3’UTR. The JASPAR CORE database was also involved in computing each nucleotide occurrence (%) within the TFBS. Results: Regulatory SNPs (rSNPs) in the promoter region novel SNP (-2035bp), 5’UTR (rs12885300), intron one (rs225010, 225011 and rs225012), exon two [rs225014 (Thr92Ala)] and 3’ UTR (rs6574549, rs225015 and rs225017) of the DIO2 gene are in linkage disequilibrium. These rSNP alleles were found to alter the DNA landscape for potential transcriptional factors (TFs) to attach resulting in changes in TFBS. Conclusion: The alleles of each rSNP were found to generate unique TFBS resulting in potential changes in TF DIO2 regulation. These regulatory changes were discussed with respect to changes in human health resulting in disease or sickness.

The regulatory epicenter of miRNAs.

miRNAs are small non-coding RNAs with average length of ~21 bp. miRNA formation seems to be dependent upon multiple factors besides Drosha and Dicer, in a tissue/stage-specific manner, with interplay of several specific binding factors. In the present study, we have investigated transcription factor binding sites in and around the genomic sequences of precursor miRNAs and RNA-binding protein (RBP) sites in miRNA precursor sequences, analysed and tested in comprehensive manner. Here, we report that miRNA precursor regions are positionally enriched for binding of transcription factors as well as RBPs around the 3′ end of mature miRNA region in 5′ arm. The pattern and distribution of such regulatory sites appears to be a characteristic of precursor miRNA sequences when compared with non-miRNA sequences as negative dataset and tested statistically.When compared with 1 kb upstreamregions, a sudden sharp peak for binding sites arises in the enriched zone near the mature miRNA region. An expression-data-based correlation analysis was performed between such miRNAs and their corresponding transcription factors and RBPs for this region. Some specific groups of binding factors and associated miRNAs were identified. We also identified some of the overrepresented transcription factors and associated miRNAs with high expression correlation values which could be useful in cancer-related studies. The highly correlated groups were found to host experimentally validated composite regulatory modules, in which Lmo2-GATA1 appeared as the predominant one. For many of RBP–miRNAs associations, coexpression similarity was also evident among the associated miRNA common to given RBPs, supporting the Regulon model, suggesting a common role and common control of these miRNAs by the associated RBPs. Based on our findings, we propose that the observed characteristic distribution of regulatory sites in precursor miRNA sequence regions could be critical inmiRNA transcription, processing, stability and formation and are important for therapeutic studies. Our findings also support the recently proposed theory of self-sufficient mode of transcription by miRNAs, which states that miRNA transcription can be carried out in host-independent mode too.

Calibrating Thresholds to Improve the Detection Accuracy of Putative Transcription Factor Binding Sites

To understand the mechanism of transcriptional regulation, it is essential to detect promoters and regulatory elements. Various kinds of methods have been introduced to improve the prediction accuracy of regulatory elements. Since there are few experimentally validated regulatory elements, previous studies have used criteria based solely on the level of scores over background sequences. However, selecting the detection criteria for different prediction methods is not feasible. Here, we studied the calibration of thresholds to improve regulatory element prediction. We predicted a regulatory element using MATCH, which is a powerful tool for transcription factor binding site (TFBS) detection. To increase the prediction accuracy, we used a regulatory potential (RP) score measuring the similarity of patterns in alignments to those in known regulatory regions. Next, we calibrated the thresholds to find relevant scores, increasing the true positives while decreasing possible false positives. By applying various thresholds, we compared predicted regulatory elements with validated regulatory elements from the Open Regulatory Annotation (ORegAnno) database. The predicted regulators by the selected threshold were validated through enrichment analysis of muscle-specific gene sets from the Tissue-Specific Transcripts and Genes (T-STAG) database. We found 14 known muscle-specific regulators with a less than a 5% false discovery rate (FDR) in a single TFBS analysis, as well as known transcription factor combinations in our combinatorial TFBS analysis.