HP4 Environmental Metabolomics
204 Oregon Ballroom
1:20 PM - 5:20 PM, Thursday
() Application of FT-ICR-MS metabolomics for detecting hepatic neoplasia in marine flatfish.
Viant, M1, Cooper, H1, Stentiford, G2, Lyons, B2, Feist, S2, 1 University of Birmingham, Birmingham, United Kingdom2 Centre for Environment, Fisheries & Aquaculture Science, Weymouth, United Kingdom
ABSTRACT- Recent studies by CEFAS have shown that dab (Limanda limanda) populations residing in Cardigan Bay, UK have elevated levels of hepatic neoplasia (liver tumours) compared with frequencies considered to represent background incidence. The identification of elevated neoplasia, whether caused by pollutants, infectious agents or other environmental factors is considered to be a highly relevant indicator of ecosystem health and warrants further investigation. Key priorities include developing high-throughput techniques for classifying liver as either neoplastic or healthy, and understanding the underlying cause of disease. The metabolome, defined as the complement of all low molecular weight metabolites present in a cell, has recently been shown to provide an excellent description of molecular phenotype. Since the metabolomes of healthy and neoplastic livers would be expected to differ, their characterization could enable a rapid method for classifying the disease status of liver. Furthermore, the use of global and unbiased metabolic profiling technologies increases the likelihood of identifying discriminatory biomarkers of the disease and thus elucidating the mechanism of carcinogenesis. Fourier transformation-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) has recently emerged as a powerful tool for metabolomics studies because of unparalleled mass accuracy and resolution, facilitating the identification of metabolites within a complex mixture. Here we present the results of a preliminary metabolomics study of hepatic neoplasia in dab. Both healthy and diseased liver samples were extracted and analyzed by FT-ICR-MS using direct infusion and electrospray ionization methods. Complex metabolic fingerprints typically containing 600 to 800 peaks were recorded and then processed using custom-written computer code. Multivariate analyses, comprising principal components analysis and partial least squares regression, were used to identify several peaks in the mass spectra that discriminate healthy and neoplastic tissue. The identification of these metabolites is currently in progress.
Key words: cancer, metabolomics, mass spectrometry, limanda limanda