Hjelt grant holder 2019, Lund university
Can epigenetic factors explain the positive effect of exercise on human metabolism?
We all know that exercise is good for human health, but still metabolic diseases including type 2 diabetes increase worldwide, partly due to a sedentary lifestyle and unhealthy diets. With this study, we aim to find epigenetic mechanisms that can explain the beneficial effects of exercise and how it differs between individuals. Eventually, this may lead to new treatment strategies for type 2 diabetic patients.
Epigenetic factors include chemical modifications to our genome, that regulates what genes that should be turned on or off at a given time. While the DNA sequence is constant in all cell types, the epigenome is dynamic and gives the different tissues of the human body its own characteristics and also allow the body to respond to external influences. Thereby, epigenetic factors can be seen as a link between the environment and our genes, and understanding this regulation is important for understanding disease development. This knowledge will also be important for future drug development, with targets that could be modified to mimic or inhibit environmental influences on the epigenome and thereby turn on or off gene activity.
To start with, this project will focus on identifying epigenetic mechanisms responsible for the beneficial effects of exercise. For this, advanced mathematical methods will be developed to integrate genome-wide epigenetic factors with genetic factors and gene activity, in human skeletal muscle and adipose tissue from individuals participating in an exercise intervention. This information will be related to physiological parameters, such as age, BMI, glucose and insulin levels in the blood, and physical fitness. Secondly, this study aims to identify epigenetic markers in blood that mirrors the changes in muscle or fat after regular exercise, and that predict the response to exercise.
Overall, the proposed project has the potential to generate novel targets of importance for future prediction, prevention and treatment of metabolic disorders including type 2 diabetes.