WP3: Extraction & Metabolomics
WP3 Research Leaders
KUT: Kaunas University of Technology (Lithuania)
UPS: Université Paul Sabatier (France)
Metabolomics for the detection of new biological markers of liver cancer
Metabolites are the end products of cellular regulatory processes, forming a bond between molecular and phenotypic changes. Therefore, they reflect the physiological state of a cell, tissue or whole body at a given time. During pathological events (like viral infections), cellular homeostasis is disrupted; the body tries to maintain a basal internal environment by increasing or decreasing the levels of some endogenous metabolites that could be specific to a determined infectious agent and clinical status such as the hepatitis virus. The team in Laos (CILM) has collected more than 9000 serum samples from around 5,000 patients with viral hepatitis, which can be studied over time to identify predictive and dynamic markers of the evolution of the viral process. To gain insight into the mechanism of infection by the hepatitis virus in human, we will undertake a comparative metabolomics analysis between infected and uninfected sera, in two steps:
1. Extraction of metabolites (development of the best extraction process at KUT)
Samples (serum taken from patients) will be prepared for metabolomic analysis. The first step will consist in developing extraction processes. Serum will be mixed with different types of solvents in order to remove proteins (macromolecules) that could hide relevant metabolites. After a centrifugation step and evaporation of the supernatant, the residue-free macromolecules will be reconstituted in a suitable solvent for metabo-analysis.
2. Metabolomics (at UT3)
This complex mixture can be simplified by separating some metabolites from others with specialized equipment (gaz chromatography), the retention time of the metabolite serves as information regarding its identity. The precise molecular structure is then determined through mass spectrometry analysis for accurate quantification of selected biomarkers. The data matrix obtained will be then analysed using multivariate statistics tools to achieve differential analysis of the metabolomic fingerprints.