School of Bioscience
This project is one of the three subprojects within the synergy project BioMine - Data-mining for biomarker discovery, selection, and validation. In this subproject we investigate how large-scale biomolecular data can be used to identify specific biomarkers for disease modelling
Of specific interest in this project are relevant biomarkers for monitoring the progression of cardiac hypertrophy and cardiomyopathy. The project is performed in close collaboration between the University of Skövde, AstraZeneca Gothenburg and Takara Bio Europe.
Recent progress in the stem cell field has opened up many promising novel research avenues. One of the most exciting ones is disease modeling, in which for example human induced pluripotent stem cells (hiPSCs) can be generated from patients with genetic disorders.
To elucidate disease mechanisms, human primary cells isolated from patients can also be examined, but many types of human cells, such as neural cells and cardiomyocytes, are unfortunately very difficult to obtain via tissue biopsies. Thus, for many diseases, efficient model systems for studies of disease mechanisms are currently lacking and hiPSC-based model systems have a high potential to fill this gap by delivering relevant human-based disease models.
Hypertrophic cardiomyopathy is a serious condition that can be either inherited through passing of mutated genes from your parents, or acquired as an effect due to another disease (e.g. diabetes). However, many times the cause of cardiomyopathy is unknown and appropriate human model systems to study the underlying mechanistic behind this disease are not available. This project focuses on identification of relevant biomarkers for modeling of cardiac disease such as cardiac hypertrophy and cardiac myopathy. These biomarkers are expected to be important for better understanding of the disease progression and prognosis and will reveal novel insights of the mechanisms of the disease.
The goal of this subproject is to develop a stem cell-based in vitro disease model, which can recapitulate important aspects of hypertrophic cardiomyopathy and enable modeling of cardiac associated diseases and screening of pharmaceuticals. In addition, identification of appropriate biomarkers to monitor the progression of the disease and the effect of pharmaceuticals in patients with different genetic background will support the development of personalized medicine. The anticipated outcome of the project includes;
School of Bioscience