Altered microRNA and target gene expression related to Tetralogy of Fallot

MicroRNAs (miRNAs) play an important role in guiding development and maintaining function of the human heart. Dysregulation of miRNAs has been linked to various congenital heart diseases including Tetralogy of Fallot (TOF), which represents the most common cyanotic heart malformation in humans. Several studies have identified dysregulated miRNAs in right ventricular (RV) tissues of TOF patients. In this study, we profiled genome-wide the whole transcriptome and analyzed the relationship of miRNAs and mRNAs of RV tissues of a homogeneous group of 22 non-syndromic TOF patients. Observed profiles were compared to profiles obtained from right and left ventricular tissue of normal hearts. To reduce the commonly observed large list of predicted target genes of dysregulated miRNAs, we applied a stringent target prediction pipeline integrating probabilities for miRNA-mRNA interaction. The final list of disease-related miRNA-mRNA pairs comprises novel as well as known miRNAs including miR-1 and miR-133, which are essential to cardiac development and function by regulating KCNJ2, FBN2, SLC38A3 and TNNI1. Overall, our study provides additional insights into post-transcriptional gene regulation of malformed hearts of TOF patients

The Effect of Micrococcal Nuclease Digestion on Nucleosome Positioning Data

Eukaryotic genomes are packed into chromatin, whose basic repeating unit is the nucleosome. Nucleosome positioning is a widely researched area. A common experimental procedure to determine nucleosome positions involves the use of micrococcal nuclease (MNase). Here, we show that the cutting preference of MNase in combination with size selection generates a sequence-dependent bias in the resulting fragments. This strongly affects nucleosome positioning data and especially sequence-dependent models for nucleosome positioning. As a consequence we see a need to re-evaluate whether the DNA sequence is a major determinant of nucleosome positioning in vivo. More generally, our results show that data generated after MNase digestion of chromatin requires a matched control experiment in order to determine nucleosome positions

The Cardiac Transcription Network Modulated by Gata4, Mef2a, Nkx2.5, Srf, Histone Modifications, and MicroRNAs

The transcriptome, as the pool of all transcribed elements in a given cell, is regulated by the interaction between different molecular levels, involving epigenetic, transcriptional, and post-transcriptional mechanisms. However, many previous studies investigated each of these levels individually, and little is known about their interdependency. We present a systems biology study integrating mRNA profiles with DNA–binding events of key cardiac transcription factors (Gata4, Mef2a, Nkx2.5, and Srf), activating histone modifications (H3ac, H4ac, H3K4me2, and H3K4me3), and microRNA profiles obtained in wild-type and RNAi–mediated knockdown. Finally, we confirmed conclusions primarily obtained in cardiomyocyte cell culture in a time-course of cardiac maturation in mouse around birth. We provide insights into the combinatorial regulation by cardiac transcription factors and show that they can partially compensate each other's function. Genes regulated by multiple transcription factors are less likely differentially expressed in RNAi knockdown of one respective factor. In addition to the analysis of the individual transcription factors, we found that histone 3 acetylation correlates with Srf- and Gata4-dependent gene expression and is complementarily reduced in cardiac Srf knockdown. Further, we found that altered microRNA expression in Srf knockdown potentially explains up to 45% of indirect mRNA targets. Considering all three levels of regulation, we present an Srf-centered transcription network providing on a single-gene level insights into the regulatory circuits establishing respective mRNA profiles. In summary, we show the combinatorial contribution of four DNA–binding transcription factors in regulating the cardiac transcriptome and provide evidence that histone modifications and microRNAs modulate their functional consequence. This opens a new perspective to understand heart development and the complexity cardiovascular disorders

A structural keystone for drug design

3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structures of approved drugs. iii) A conformational library of ligands from the PDB, linking the realms of proteins and small molecules. The data collection contains structures of 30,000 proteins, 5,000 different ligands from 70,000 ligand-protein complexes, and 2,500 known drugs. Sets of protein structures can be refined by criteria like protein fold, family, metabolic pathway, resolution and textual annotation. The structures of organic compounds (drugs and ligands) can be searched considering chemical formula, trivial and trade names as well as medical classification codes for drugs (ATC). Retrieving structures by 2D-similarity has been implemented for all small molecules using Tanimoto coefficients. For the drug structures, 110,000 structural conformers have been calculated to account for structural flexibility. Two substances can be compared online by 3D-superimposition, where the pair of conformers that fits best is detected. Together, these web-accessible resources can be used to identify promising drug candidates. They have been used in-house to find alternatives to substances with a known binding activity but adverse side effects

A structural keystone for drug design

3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structures of approved drugs. iii) A conformational library of ligands from the PDB, linking the realms of proteins and small molecules