Researchers from around the world have collaborated to develop a genomic computer system to show how some genetic mutations can cause different cardiovascular diseases.
Cardiomyopathies are a group of diseases that affect the structure and physiological function of the heart muscle. Around 0.2% of the global population have inherited cardiomyopathies, making it the most common form of genetic heart disease. The genetic variations are usually found in the regions that code for proteins within the sarcomere, including proteins called troponins.
It is understood that variations of these troponin proteins contribute up to 10% of all known sarcomeric protein cardiomyopathies. Within the genes that code for troponins, different variations at specific locations are linked to different types of cardiomyopathies. Each variation offers a different prognosis – some increase the risk of sudden cardiac death, while others lead to heart failure.
The application of clinical genomics to cardiomyopathies has historically proved challenging, but recently, an integrated, genomic computer system for precision cardiology was created.
The integrated computer system
Researchers from the Wellcome Sanger Institute, University of Cambridge, Massachusetts Institute of Technology and Lund University have collaborated to build a novel computer-based model to predict how genetic variations contribute to changes in troponins. The system integrated genomic data with biological and chemical information, which was then validated with world-wide data from over 980 patients suffering from inherited cardiomyopathies.
The main results were as follows:
- Intermolecular interactions across the troponins are dynamic and dependent on the calcium state.
- Troponin variations tend to cluster to hotspots in regions that share similar clinical phenotypes.
- Recurrent pathogenic variants occur at sites under negative selection.
- Arginine amino acids and CpG dinucleotides are mutation hotspots for troponin T, which is part of the troponin complex.
- Troponin T variations perturbed the protein structure and its flexibility, leading to variation-specific cardiovascular phenotypes.
- Cardiovascular death does not differ between troponins, but clinical phenotypes and outcomes do vary.
Overall, the research showed that genetic ‘hotspots’, characterised by specific troponin T variations, exist. Interestingly, it seemed that each hotspot corresponded to a specific type of inherited heart disease.
Therefore, this novel systems biology model provided a new perspective for understanding correlations between cardiomyopathies and troponin variations. In turn, this approach showed how troponin variations can affect phenotypic data and, ultimately, prognosis for patients.
The future of systems biology in cardiovascular disease
The model could prove invaluable as it has the possibility to provide beneficial information regarding patient care. This is because it enables cardiologists to work with patients to better assess the potential risk of developing heart disease. Each condition is likely to have different outcomes. Therefore, this unbiased analysis could provide the most effective personalised treatment plan available and improve clinical care.
Dr Lorenzo Monserrat, a previous CEO of Health in Code, explained:
“These kinds of studies are essential for the development of a really personalized medicine in inherited cardiovascular diseases, addressing not only diagnostic and preventive challenges, such as the decision to implant a defibrillator or not, but also for the development and appropriate selection of novel treatments.”
Further research is now required in order to assess whether new drugs could be developed to target some of the genetic hotspots that were found to be linked to cardiac disease. Also, extensive large-scale longitudinal clinical genomic studies are vital for the exploration of disease progression that is affected by different environmental and lifestyle factors. The role of individual variations in genetic backgrounds, modifier genes and epigenetic effects should also be explored to support future therapies and clinical decisions.
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