Characterising the feedback control mechanisms of the glucocorticoid receptor within the adrenal steroidogenic regulatory network

Modern problems in biomedicine have become very complex in the last decades. Recent scientific findings demonstrate that our bodies are better understood from a dynamic perspective, where several physiological processes are actively changing - and adapting - all the time. In fact, many health disorders are now better understood as disruptions of a certain "dynamic equilibrium", rather than as "static pictures" of normal physiology. Because of this, in addition to the powerful experimental techniques from molecular biology and biochemistry, novel tools borrowed from mathematics, physics and computer science are necessary to understand these dynamics and make sense of the disrupting mechanisms that lead to disease.

The dynamics of the Hypothalamic-Pituitary-Adrenal (HPA) axis is no exception. This homeostatic system controls our bodies' ability to respond to stress through the controlled, dynamic secretion of stress hormones that are rapidly synthesised in the adrenal glands. This occurs following stimuli from another hormone secreted by the pituitary gland. In normal, physiological conditions, both hormone levels fluctuate during the day in almost perfect synchrony. However, this behaviour can become altered during stress and in other pathological conditions, and is replaced by a "dissociated dynamic" state where fluctuations of these hormones are not synchronised anymore. Our current lack of understanding of the underlying regulatory mechanisms, and how their disruption leads to disease, is preventing clinicians delivering better treatments to a growing number of patients suffering from stress-related illness.

Recent research efforts have focused on understanding the regulatory mechanisms within the adrenal gland that control the synthesis of stress hormones following signals from the pituitary. During this fellowship, I will develop mathematical models of these mechanisms and predict how hormone dynamics is disrupted during disease. In fact, my current mathematical models suggest that a component of this network may act as an organising "hub" that integrates these hormone signals and is responsible for their dissociation.

To explore this, I will develop a mathematical framework to predict how hormone signals may control the expression of factors involved in steroidogenesis, including plausible biophysical mechanisms of gene regulation. Then, I will carry out experiments to test my model predictions and show how these hormones modulate the expression of steroidogenic genes. Lastly, I will integrate my models of gene regulation into larger, multiscale models of the adrenal gland and the HPA axis, and use them to generate predictions about a number of experiments, drug treatments, and physiological and pathological conditions. The long-term goal is to understand the biological mechanisms that govern the body's endogenous production of stress hormones, so that we can develop better therapies for patients suffering from adrenal insufficiency and stress-related disorders.

The Hypothalamic-Pituitary-Adrenal (HPA) axis modulates circulating levels of glucocorticoids (CORT), which are key hormone mediators of our body's response to stress. CORT is synthesised by the adrenal gland following stimulation by adrenocorticotropic hormone (ACTH) secreted by the pituitary, and both hormones normally exhibit tightly correlated fluctuations. However, under pathological conditions (e.g. inflammation, chronic stress, neurological dysfunction) or ageing, these pulsatile dynamics are altered and the tight synchrony between ACTH and CORT becomes significantly disrupted.

Uncovering the mechanisms through which the HPA axis maintains a "dynamic equilibrium" is critical to understand stress-related disorders affecting a large portion of the population. In this sense, my current models suggest that certain factors involved in CORT synthesis may be responsible for the dissociation of these hormone signals.

In this project, I will develop mathematical models of the regulatory mechanisms underlying CORT synthesis and predict how hormone dynamics is disrupted during disease. To test this, I will perform experiments to explore how ACTH and CORT modulate gene expression. This includes determining how transcription factors interact with gene promoters and synergise to modulate their expression. I will use these models to evaluate the plausible biophysical mechanisms through which these factors may regulate gene expression and disrupt hormone dynamics.

Lastly, I will develop multiscale models of the adrenal gland and the HPA axis, and use them to analyse a number of experiments, drug treatments, and relevant physiological and pathological conditions. Importantly, this will include designing novel CORT replacement therapies and understanding the effects of synthetic versions of CORT, which are widely used in the clinic and constitute some of the most frequently prescribed drugs in the UK to treat inflammation and stress-related illnesses.

Research team and collaborative network - I will be the first, immediate beneficiary of this multidisciplinary research project as it will provide me with continued training and development opportunities. This will be paramount for my career long-term goal of addressing challenging questions surrounding the HPA axis dynamics. My collaborators Professor Stafford Lightman, Professor Gordon Hager, and their groups will also see advancements towards the overall goals set within their labs. In particular, since I will develop my mathematical models at Dr. Jamie Walker's lab (University of Exeter), an MRC Career Development Award holder, I will contribute to both theoretical and experimental approaches to the HPA axis. In addition, my research will constitute a fruitful collaborative link among these three groups.

International biomedical research community - Researchers from multiple disciplines, but in particular endocrinology, mathematics, and computer science, will see the advantages of combining quantitative and experimental skills to address modern problems in biomedical research. This will be demonstrated by the predictive power of my biologically-informed mathematical models.

Industry - A business opportunity for developing a novel cortisol replacement therapy through innovative control of a healthcare appliance will be pursued. Though the necessary hardware for this opportunity already exists - a cortisol infusion pump is used in research, and wearable device technology is being commercialised - the algorithms necessary for controlling the pump with a level of efficiency needed for full commercialisation will greatly benefit from my mathematical models of glucocorticoid production. Thus, I will actively engage with biotech and pharma companies to explore the feasibility of this technology.

Patients, clinicians, and the general public - Of major importance for this fellowship, I will organise an outreach event that will involve representative groups of patients with HPA-related disorders and their families. This forum will also include endocrinologists and clinicians at the front line of treating stress-related illnesses. By providing information, reporting results, receiving feedback, and exchanging ideas, we all could greatly benefit from a better (dynamic) understanding of endocrine disorders.

The MRC - In line with the MRC strategic goals, I will put special care in emphasising how nowadays challenges in biomedicine are of such complexity that they demand multidisciplinary skills including quantitative approaches (mathematics, physics and computer science).

Health-care policy makers - Through the novel, dynamical understanding of stress-related disorders and the mathematically-based design of novel cortisol replacement therapies, my research will generate impact for clinicians, health-sector managers, and policy makers. Importantly, reducing the severity of side effects associated with cortisol replacement therapies will result in lower treatment costs, which will largely benefit health care service providers.