MicroRNAs Differentially Expressed in Postnatal Aortic Development Downregulate Elastin via 3′ UTR and Coding-Sequence Binding Sites

Elastin production is characteristically turned off during the maturation of elastin-rich organs such as the aorta. MicroRNAs (miRNAs) are small regulatory RNAs that down-regulate target mRNAs by binding to miRNA regulatory elements (MREs) typically located in the 3′ UTR. Here we show a striking up-regulation of miR-29 and miR-15 family miRNAs during murine aortic development with commensurate down-regulation of targets including elastin and other extracellular matrix (ECM) genes. There were a total of 14 MREs for miR-29 in the coding sequences (CDS) and 3′ UTR of elastin, which was highly significant, and up to 22 miR-29 MREs were found in the CDS of multiple ECM genes including several collagens. This overrepresentation was conserved throughout mammalian evolution. Luciferase reporter assays showed synergistic effects of miR-29 and miR-15 family miRNAs on 3′ UTR and coding-sequence elastin constructs. Our results demonstrate that multiple miR-29 and miR-15 family MREs are characteristic for some ECM genes and suggest that miR-29 and miR-15 family miRNAs are involved in the down-regulation of elastin in the adult aorta

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches