Victor Chang Cardiac Research Institute

About: Victor Chang Cardiac Research Institute is a nonprofit organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Mechanosensitive channels & Heart failure. The organization has 708 authors who have published 1599 publications receiving 70035 citations.

Evaluating the stability of disulfide bridges in proteins: a torsional potential energy surface for diethyl disulfide

Abstract: Disulfide bonds formed by the oxidation of cysteine residues in proteins are the major form of intra- and inter-molecular covalent linkages in the polypeptide chain. To better understand the conformational energetics of this linkage, we have used the MP2(full)/6-31G(d) method to generate a full potential energy surface (PES) for the torsion of the model compound diethyl disulfide (DEDS) around its three critical dihedral angles (χ2, χ3, χ2′). The use of ten degree increments for each of the parameters resulted in a continuous, fine-grained surface. This allowed us to accurately predict the relative stabilities of disulfide bonds in high resolution structures from the Protein Data Bank. The MP2(full) surface showed significant qualitative differences from the PES calculated using the Amber force field. In particular, a different ordering was seen for the relative energies of the local minima. Thus, Amber energies are not reliable for comparison of the relative stabilities of disulfide bonds. Surprisingly, ...

3D printing from cardiovascular CT: a practical guide and review.

Abstract: Current cardiovascular imaging techniques allow anatomical relationships and pathological conditions to be captured in three dimensions. Three-dimensional (3D) printing, or rapid prototyping, has also become readily available and made it possible to transform virtual reconstructions into physical 3D models. This technology has been utilised to demonstrate cardiovascular anatomy and disease in clinical, research and educational settings. In particular, 3D models have been generated from cardiovascular computed tomography (CT) imaging data for purposes such as surgical planning and teaching. This review summarises applications, limitations and practical steps required to create a 3D printed model from cardiovascular CT.

Platelet-derived growth factor (PDGF) signaling directs cardiomyocyte movement toward the midline during heart tube assembly

Abstract: Communication between neighboring tissues plays a central role in guiding organ morphogenesis. During heart tube assembly, interactions with the adjacent endoderm control the medial movement of cardiomyocytes, a process referred to as cardiac fusion. However, the molecular underpinnings of this endodermal-myocardial relationship remain unclear. Here, we show an essential role for platelet-derived growth factor receptor alpha (Pdgfra) in directing cardiac fusion. Mutation of pdgfra disrupts heart tube assembly in both zebrafish and mouse. Timelapse analysis of individual cardiomyocyte trajectories reveals misdirected cells in zebrafish pdgfra mutants, suggesting that PDGF signaling steers cardiomyocytes toward the midline during cardiac fusion. Intriguingly, the ligand pdgfaa is expressed in the endoderm medial to the pdgfra-expressing myocardial precursors. Ectopic expression of pdgfaa interferes with cardiac fusion, consistent with an instructive role for PDGF signaling. Together, these data uncover a novel mechanism through which endodermal-myocardial communication can guide the cell movements that initiate cardiac morphogenesis.

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies.

Abstract: Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.