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.

GPCRs in context: sexual dimorphism in the cardiovascular system.

Abstract: Cardiovascular disease (CVD) remains the largest cause of mortality worldwide, and there is a clear gender gap in disease occurrence, with men being predisposed to earlier onset of CVD, including atherosclerosis and hypertension, relative to women. Oestrogen may be a driving factor for female-specific cardioprotection, though androgens and sex chromosomes are also likely to contribute to sexual dimorphism in the cardiovascular system (CVS). Many GPCR-mediated processes are involved in cardiovascular homeostasis, and some exhibit clear sex divergence. Here, we focus on the G protein-coupled oestrogen receptor, endothelin receptors ETA and ETB and the eicosanoid G protein-coupled receptors (GPCRs), discussing the evidence and potential mechanisms leading to gender dimorphic responses in the vasculature. The use of animal models and pharmacological tools has been essential to understanding the role of these receptors in the CVS and will be key to further delineating their sex-specific effects. Ultimately, this may illuminate wider sex differences in cardiovascular pathology and physiology. Linked articles This article is part of a themed section on Molecular Pharmacology of GPCRs. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.21/issuetoc.

NK-2 Class Homeodomain Proteins: Conserved Regulators of Cardiogenesis

Abstract: Publisher Summary This chapter summarizes multiple functions of the best-characterized NK-2 class homeodomain proteins, Nkx2–5 and tin, during heart development, and the molecular mechanisms through which they execute their functions. NK-2 homeobox genes are essential for cardiogenesis in a wide range of species, reflecting the early deployment of these genes during the evolution of cardiac structures. As the complexity of the heart has increased during evolution, NK-2 genes have been utilized to control different processes. In vertebrates, Nkx2–5 represents a critical node in the complex transcriptional network governing the early specification and proliferation of the cardiac lineage in both the first and second heart fields of vertebrates, and during the subsequent molding of these progenitor cells into the mature organ. As outlined in this chapter, Nkx2–5 plays a role in almost all facets of heart development, including the regulation of cardiac progenitor cell number, the formation and patterning of the cardiac conduction system, septation, valve development, patterning of the venous returns, and the myogenic program. Importantly, Nkx2–5 does not act alone, but in concert with other highly-conserved transcription factors and signaling cascades to regulate cardiogenesis. Genome-wide expression profiling has begun to reveal Nkx2–5-dependent genes, and establishing which of these genes are direct targets remains the next challenge.

PDGFRα signaling in cardiac stem and stromal cells modulates quiescence, metabolism and self-renewal, and promotes anatomical and functional repair

Abstract: The interstitial and perivascular spaces of the heart contain stem-like cells including cardiac colony forming units - fibroblast (cCFU-F), a sub-fraction of SCA1+PDGFRα+CD31- (S+P+) immature stromal cells with the qualities of mesenchymal stem cells, that we have characterized previously. Here we explore the role of platelet-derived growth factor receptor α (PDGFRα) in cCFU-F and S+P+ cell niche regulation in homeostasis and repair. PDGFRα signaling modulated quiescence, metabolic state, mitogenic propensity and colony formation, as well as the rate and stability of self-renewal. Exogenously administered PDGF-AB ligand had anti-aging effects on cCFU-F, akin to those seen in heterochronic parabiotic mice. Post-myocardial infarction (MI), exogenous PDGF-AB stimulated S+P+ cell proliferation and conversion to myofibroblasts through a metabolic priming effect, yet significantly enhanced anatomical and functional repair via multiple cellular processes. Our study provides a rationale for a novel therapeutic approach to cardiac injury involving stimulating endogenous repair mechanisms via activation of cardiac stem and stromal cells.

Dynamics of Transforming Growth Factor (TGF)-β Superfamily Cytokine Induction During HIV-1 Infection Are Distinct From Other Innate Cytokines.

Abstract: Human immunodeficiency virus type 1 (HIV-1) infection triggers rapid induction of multiple innate cytokines including type I interferons, which play important roles in viral control and disease pathogenesis. The transforming growth factor (TGF)-β superfamily is a pleiotropic innate cytokine family, some members of which (activins and bone morphogenetic proteins (BMPs)) were recently demonstrated to exert antiviral activity against Zika and hepatitis B and C viruses but are poorly studied in HIV-1 infection. Here, we show that TGF-β1 is systemically induced with very rapid kinetics (as early as 1-4 days after viremic spread begins) in acute HIV-1 infection, likely due to release from platelets, and remains upregulated throughout infection. Contrastingly, no substantial systemic upregulation of activins A and B or BMP-2 was observed during acute infection, although plasma activin levels trended to be elevated during chronic infection. HIV-1 triggered production of type I interferons but not TGF-β superfamily cytokines from plasmacytoid dendritic cells (DCs) in vitro, putatively explaining their differing in vivo induction; whilst lipopolysaccharide (but not HIV-1) elicited activin A production from myeloid DCs. These findings underscore the need for better definition of the protective and pathogenic capacity of TGF-β superfamily cytokines, to enable appropriate modulation for therapeutic purposes.

Big data: the elements of good questions, open data, and powerful software.

Abstract: The field of bioinformatics has evolved in the last 15 years. Back in the days when we were students, we were taught the principle of hypothesis-driven scientific method—a process which involves formulating a testable hypothesis about a natural phenomenon, designing and carrying out an experiment with sufficient statistical power, and carefully analysing the experimental data to confirm or reject the initial hypothesis. This process is the bedrock of modern science, including the fields of biology and medicine. Good scientific findings always go hand-in-hand with good questions and well-designed experiments. Importantly, experimental data are generated to test a specific hypothesis or to address a specific question. In the field of molecular biology and genetics, genes are experimentally knocked out to assess their potential function. In the field of structural biology, X-ray crystallography is used to determine the structure of a protein. In clinical research, randomised controlled clinical trials are conducted to rigorously test the effect of a drug. In all these examples, data are generated experimentally to test a predefined hypothesis. Historically, bioinformatics largely plays a supportive role in this process by providing useful computational tools to analyse biological data. Bioinformatics software tools such as those designed for sequence alignment, phylogenetic tree inferences, molecular dynamics and statistical hypothesis testing for high throughput assays were all useful in helping scientists interpret the data generated from their experiments, often well-designed experiments. Nonetheless, the process largely still follows the standard hypothesis-driven scientific method in which experimental data are generated, collected and analysed for a specific purpose. The early development of biological databases, such as NCBI’s GenBank and Gene Expression Omnibus (GEO), are some early predecessors of big data bioinformatics, but they were primarily designed to be repositories of completed experiments and their data. This relatively linear scientific method seems to be slowly shifting in the big data era. We are increasingly witnessing consortium-based data collection projects which systematically generate a wide range of genome-wide data associated with cell lines, cultured cells, tissues and tumour samples. These data were not designed to answer one specific question by an individual researcher or research group; they were designed to act as reference data sets for all scientists such that they can use these data to address their specific questions. Using these open reference data, it is possible to explore specif ic hypotheses without individual ly conducting new experiments. For example, if we want to predict the effect of a DNA mutation in a particular gene in the human genome, we can now access data about this gene from various genetic databases online, and we can already make some reasonable predictions about the effect of this mutation without performing any experiment. This process significantly reduces the number of new experiments, allowing us to focus on formulating and testing more complex and interesting hypotheses. Big data can also be collected from other unconventional sources, such as unstructured data from short text messages in social media, photographs and data from commercially available wearable devices. These data may not be initially generated for any scientific project, but they can be repurposed for scientific studies as they tend to be abundant and widely available. Nonetheless, these data also tend to have variable This article is part of a Special Issue on ‘Big Data’ edited by Joshua WK Ho and Eleni Giannoulatou