Long QT Syndrome (LQTS) is a severe arrhythmogenic condition characterised by the prolongation of the QT interval on the electrocardiogram. It is caused by genetic factors (congenital) or drugs (acquired) and sudden cardiac death can be the first manifestations of the disease. Advancements in genetic screenings have revealed profound links between genotype and phenotype for LQTS, improving diagnosis, risk stratification and therapy; However, it is still poorly understood why patients with identical pathogenic mutations have different clinical phenotypes, which factors are involved in this unpredictable disease severity and how we can protect these subjects from drug treatments that are safe in the general population. We do need improved and more physiological in vitro models to simulate arrhythmias in vitro, effective for drug testing, to identify, evaluate and study factors that shape the arrhythmogenic risk in vulnerable subjects. Here I propose a precision medicine approach that uses human pluripotent stem cells-derived cardiomyocytes (hPSC-CMs) from LQTS families (rare resources that include male, female, symptomatic and asymptomatic patients) to: i) demonstrate that the hiPSC technology can reproduce in vitro the clinical disease severity observed in symptomatic vs asymptomatic LQTS mutation carriers; ii) create an in vitro interdisciplinary pharmacological approach with proarrhythmic drugs which combines matched electrophysiological, contractile, molecular and genetic assays; iii) identify and evaluate the factors affecting the arrhythmogenic risk in predisposed subjects. This pipeline to assess arrhythmia susceptibility from patient-specific hiPSC-CMs can be applicable to other arrhythmogenic syndromes. The results of this project will contribute to reduce the use of animal models in preclinical research, to create safer, more effective drugs for humans and to promote the shift of new therapeutic approaches towards precision or personalised medicine.

Regular physical activity is known to be beneficial for health, can prevent cardiovascular disease and reduce overall mortality. However, high intensity exercise can promote the development of arrhythmias and cause sudden cardiac death. Recently the Prof.Peter Schwartz’s group discovered the phenomenon of exercise-induced Long QT Syndrome (exiLQTS). Affected athletes develop significant QT interval prolongation and repolarization abnormalities on the electrocardiogram during training periods and normalisation occurs during detraining. Clinically, these athletes resemble patients with congenital LQTS, who are known to be severely at risk for life-threatening arrhythmias, but no pathogenic genetic variants in main genes responsible for LQTS were found. Data on how exercise could trigger pathogenic QT prolongation and arrhythmias is limited and thus the possibilities to evaluate potential risks and prevent sudden cardiac death in these young athletes are currently absent. This project will use a precision medicine approach to reveal molecular culprits of exiLQTS by: i) demonstrating that the disease could be recapitulated in vitro using athlete-specific induced pluripotent stem cell-derived cardiomyocytes models; ii) creating a statistical model integrating genetic, electrophysiological, and molecular data; iii) discovering major factors responsible for exiLQTS development and identifying molecular targets for risk stratification and therapy. Results of the study will shed the light on mysterious exercise-related QT prolongation and will contribute to better risk assessment and clinical management of arrhythmias in young athletes.

BodyPass aims to break barriers between health sector and consumer goods sector and eliminate the current data silos. The main objective of BodyPass is to foster exchange, linking and re-use, as well as to integrate 3D data assets from the two sectors. For this, BodyPass has to adapt and create tools that allow a secure exchange of information between data owners, companies and subjects (patients and customers). 3D personal data is type of data that contains useful information for product design, online sale services, medical research and patient follow-up. Currently hospitals store and grow massive collections of 3D data that are not accessible by researchers, professionals and companies. About 2.7 petabytes a year stored in the EU26. In parallel to the advances made in the health sector, new 3D body-surface scanning technology has been developed for the goods consumer sector, namely apparel, animation and art. Moreover, new low-cost scanning technologies are expected to exponentially increase 3D data creation. It is estimated that currently one person is scanned every 15 minutes in the US and Europe. And increasing. The 3D data of the health sector contains the body shape information, not only internal body information. These data could be used by designers and manufacturers of the consumer goods sector. At the same time, although 3D body-surface scanners have been developed primarily for the clothing industry, 3D scanners’ low cost, non-invasive character, and ease of use make them appealing for widespread clinical applications and large-scale epidemiological surveys. However, companies and professionals of the consumer goods sector cannot access the 3D data of health sector. And vice versa. Even exchanging information between data owners in the same sector is a big problem today. It is necessary to overcome problems related with data privacy and the processing of huge 3D datasets.