Modeling regulatory mechanisms of cardiac dysfunction after preeclampsia

Abstract

Preeclampsia (PE), the most common pregnancy disease, is the main cause for maternal and fetal mortality and morbidity worldwide. It is characterized by a sudden increase in blood pressure in combination with signs of end organ damage, e.g. proteinuria. The mechanisms how pathological pregnancy leads to future cardiovascular morbidity are poorly understood, while there is a lack of comprehensive animal models describing more nuanced biological processes in the heart connected to immune dysregulation and endothelial dysfunction, important pathomechanisms leading to PE. We used well-established transgenic rat model for PE and RNA-sequencing to create expression profiles at different time points (non-pregnant, after-delivery and few weeks later, i.e. post-partum) of preeclamptic rats and healthy controls. We performed bioinformatical analysis to identify differentially expressed genes for different groups of comparisons (physiological and pathological changes) in 4 heart regions. We used gene set enrichment analysis and more advanced techniques for regulatory networks inference (Bayesian networks learning) to identify affected biological processes with comparison to existing heart failure models. We found that all heart regions of PE animals are still affected postpartum, while healthy controls recover after pregnancy. The data give more insights into the long-term consequences of pathological pregnancy and potential biomarkers for prediction.