Can we find disease fingerprints in our metabolome?

Background: The regulation of all processes taking place in a human organism is known as metabolism. Presence and concentration of metabolites, small molecules that take part in metabolism, is influenced by genetics and the environment. Many complex diseases also have a genetic and environmental background and influence on the concentration of metabolites, which may, therefore, be used as a biomarker for disease detection. Acquiring nuclear magnetic resonance (NMR) metabolomics data is relatively easy and has a low cost compared to other techniques used to characterize the metabolome. Yet, analysis of such NMR-based metabolomics data requires a sophisticated computational pipeline, for extracting information relevant to disease, like biomarkers.

Goal: The aim of this project is to write R code to analyse NMR data from complex biological samples. We aim to extract information from urinary NMR data regarding a disease of interest and if possible identify metabolites as biomarkers. We will use data obtained from CoLaus, a longitudinal study investigating the prevalence of cardiovascular disease risk factor in the population of Lausanne.

Tools: Using biclustering methods students will be asked to analyze urinary NMR metabolomics data, aiming to identify a group of individuals with a potentially common disease background. Moreover, students will use linear regression analysis to identify metabolites related to a disease of interest.

Supervisor: Mirjam Mattei

Project description: File:MetabolomeMirjam.pdf