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Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple
Marc-Emmanuel Dumas1, Cécile Canlet, Laurent Debrauwer
1Biological Chemistry Section, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom. m.dumas@imperial.ac.uk
Journal of Proteome Research
|October 11, 2005
Summary
We developed a statistical method to combine data from multiple analytical platforms, like NMR and MS, for metabolic fingerprinting. This approach identifies biomarkers related to anabolic steroid dosage in cattle.
Area of Science:
- Analytical Chemistry
- Metabolomics
- Biotechnology
Background:
- Metabolic fingerprinting aids in understanding biological responses to external factors.
- Integrating data from diverse analytical platforms presents a significant challenge in '-omics' research.
- Anabolic steroids are used in cattle production, necessitating methods to detect their effects.
Purpose of the Study:
- To introduce a statistical approach for integrating data from multiple analytical platforms.
- To apply this approach to metabolic fingerprinting of cattle treated with anabolic steroids.
- To identify biomarkers associated with anabolic steroid dose-response.
Main Methods:
- Utilized (1)H-(13)C Heteronuclear Multiple Bond Connectivity nuclear magnetic resonance spectroscopy ((1)H-(13)C HMBC NMR).
- Employed Pyrolysis Metastable Atom Bombardment Time-of-Flight mass spectrometry (Py-MAB-TOF-MS).
- Applied Multiple Factor Analysis (MFA) to integrate NMR and MS data for a unified metabolic signature.
Main Results:
- Successfully integrated complementary data from NMR and MS platforms.
- Developed a unique metabolic signature characterizing anabolic steroid effects in cattle.
- Identified dose-response related biomarkers through the integrated analysis.
Conclusions:
- The proposed statistical approach effectively integrates multi-platform analytical data.
- Multiple Factor Analysis (MFA) provides a robust method for metabolic fingerprinting.
- This integrative approach offers significant benefits for metabonomics and other '-omics' biotechnologies.