Cortisol-responsive gene networks in cardiovascular disease

Project Summary:
Cardiovascular disease (CVD) is influenced by genetic, environmental and metabolic factors which govern both its onset and severity. One such factor is the activity of glucocorticoids such as cortisol which influences CVD risk factors such as hypertension, obesity and type II diabetes. The CORtisol NETwork (CORNET) consortium undertook a genome wide association meta-analysis to identify genetic variants that are associated with changes in levels of plasma cortisol (Bolton et al, 2014). Mendelian randomisation has then been used to demonstrate that these variants are causative for CVD, however the mechanism by which these variants exert an effect upon CVD risk is poorly understood. There is the potential to elucidate these mechanisms by examining gene expression in the tissues that are affected by CVD.
The Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET) sequenced and genotyped vascular and metabolic tissue from 600 CVD patients. This resource has the potential to be used to study gene regulatory risk variants for cardio-metabolic disease (Franzén et al, 2016). A recent investigation in the first 100 study participants illustrates that through the use of statistical learning algorithms, causal networks can be identified that link genetic variation to variation in expression of hundreds of genes to variation in clinical characteristics of patients (Talukdar et al, 2016). However, this requires the development of systematic methods to design therapeutic strategies that allow for the precision control of these networks in order to achieve translational benefit from this research.
This project will utilise a systems and synthetic biology approach to identify tissue-specific gene networks that are affected by risk variants for plasma cortisol through use of the CORNET and STARNET datasets. In addition, an in vitro model system allowing for real time control for these networks will be developed. As most variation in plasma cortisol is mediated through nuclear hormone receptors and therefore through changes in transcription, it is expected that this approach will be highly applicable to Glucocorticoid biology.



Aims:

- To identify tissue specific gene networks that are affected by genetic variation for changes in plasma cortisol levels and causally associated with cardiovascular disease phenotypes and type II diabetes. This will be achieved through use of the STARNET dataset and use of modern causal inference methods.

- To predict the effect of nuclear hormone receptor activity, known inhibitors of cortisol activity and other pharmacologic compounds on the activity of these networks by mining publicly available expression databases in GEO/ ArrayExpress.

- To genetically engineer an appropriate cell line to tag a master regulator of a selected network with a fluorescent reporter, and use optimal (feedback) control (Menolascina et al, 2014) to determine the best therapy (sequence of drugs and their concentrations) to control the activity of this regulator.

- To validate the predicated effect of the optimal treatment on network activity and cell phenotypic variables by a differential expression experiment in the same in vitro model.

References:

Bolton JL, et al. (2014). Genome wide association identifies common variants at the SERPINA6/SERPINA1 locus influencing plasma cortisol and corticosteroid binding globulin. PLoS Genet. 10(7), e1004474.
Franzén O, et al. (2016). Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science; 353(6301), 827-830.
Menolascina F, et al. (2014) In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol. 10(5), e1003625.
Talukdar H, et al. (2016). Cross-tissue regulatory gene networks in coronary artery disease. Cell Systems. 2(3), 196-208.