Precision Medicine Doctoral Training Programme

Cortisol-responsive gene networks in cardiovascular disease

Project Details

Supervisor(s): Dr Tom Michoel, Dr Filippo Menolascina & Prof Brian Walker
Centre/Institute: The Roslin Institute


The onset and severity of cardiovascular disease (CVD) is governed by a complex interplay between genetic and environmental risk factors. Excessive activity of glucocorticoids (e.g. cortisol, a stress-related steroid hormone) contributes to well-known CVD risk factors such as hypertension, obesity or diabetes, and has a direct influence on the development of vascular lesions. A genome-wide association meta-analysis in 12,500 individuals has identified genetic variants associated with plasma cortisol level [1], and Mendelian Randomisation has confirmed these variants to be causative for CVD [A. Crawford and B. Walker, unpublished data], but much remains unknown about the tissue-specific mechanisms by which genetic variation influencing plasma cortisol exerts its effect on CVD risk. By examining gene expression in tissues affected by CVD, such mechanisms can begin to be examined.

In the STARNET study, vascular and metabolic tissues (liver, visceral and subcutaneous adipose tissue, whole blood, skeletal muscle, internal mammary artery and atherosclerotic arterial wall) from 600 CVD patients undergoing surgical intervention were collected, genotyped and RNA-sequenced, providing an unprecedented resource to study the gene-regulatory effects of genetic risk variants for cardio-metabolic diseases [2]. 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 [3]. However, systematic methods to design therapeutic strategies by precisely controlling the activity of these networks in sub-categories of patients are still lacking.

This project proposes a systems and synthetic biology approach whereby tissue-specific gene networks affected by genetic risk variants for plasma cortisol level will be identified, and an in vitro model system for the real-time control of these networks will be set up. Glucocorticoid biology lends itself to this approach, given that most if not all effects of variations in cortisol action are mediated through nuclear hormone receptors (GR and MR) and therefore through altered transcription.


  1. To identify tissue-specific gene networks, with an initial focus on adipose tissue networks, affected by genetic variation for plasma cortisol and causally associated with cardiovascular disease phenotypes and type II diabetes from the STARNET data using modern causal inference methods.
  2. 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 datasets in GEO/ArrayExpress.
  3. To genetically engineer an appropriate cell line (by preference a human adipocyte line) to tag a master regulator of a selected network with a fluorescent reporter, and use optimal (feedback) control [4] to determine the best therapy (sequence of drugs and their concentrations) to control the activity of this regulator.
  4. To validate the predicted effect of the optimal treatment on network activity and cell phenotypic variables by a differential expression experiment in the same in vitro model. 

Training Outcomes

The student will be trained in a highly desirable set of interdisciplinary skills: bioinformatics of genomic and transcriptomic data, statistical machine learning for high-dimensional data, control engineering, and molecular biology techniques. Collaboration with medical scientists will ensure that quantitative skills training takes place in the context of a concrete biomedical research problem, and will train the student to communicate their work to people from a wide range of scientific backgrounds.


[1] Bolton JL, et al. Genome wide association identifies common variants at the SERPINA6/SERPINA1 locus influencing plasma cortisol and corticosteroid binding globulin. PLoS Genet 2014; 10(7):e1004474.

[2] Franzén O, et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 2016; 353(6301):827-830.

[3] Talukdar H, et al. Cross-tissue regulatory gene networks in coronary artery disease. Cell Systems 2016; 2(3):196-208.

[4] Menolascina F, et al. In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol 2014; 10(5):e1003625.

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  • Please note all applications for the Precision Medicine DTP should be submitted to University of Edinburgh, even those applying for a project at the University of Glasgow.
  • Applicants must apply to a specific project, ensure you include details of the project you are applying to in Section 4 of your application.  We encourage you to contact the primary supervisor prior to making your application.   
  • The deadline for 17/18 applications will be Monday 9th January 2017, with interviews expected to take place the weeks beginning 6th & 13th February 2017.