Metabonomics and Biomarker Discovery:  LC−MS Metabolic Profiling and Constant Neutral Loss Scanning Combined with Multivariate Data Analysis for Mercapturic Acid Analysis

• Published in: Analytical chemistry (Washington) Vol. 78; no. 4; pp. 1296 - 1305
• Main Authors: Wagner, Silvia, Scholz, Karoline, Donegan, Michael, Burton, Lyle, Wingate, Julia, Völkel, Wolfgang
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 15.02.2006
• Subjects: Acetylcysteine - analysis; Acids; Algorithms; Analytical chemistry; Biomarkers - analysis; Chemistry; Chromatography, High Pressure Liquid - methods; Exact sciences and technology; Humans; Least-Squares Analysis; Mass spectrometry; Mass Spectrometry - methods; Metabolism; Multivariate Analysis; Principal components analysis; Spectrometric and optical methods; Performance evaluation; Human; Discriminant analysis; Data analysis; Coupled method; NMR spectrometry; Marker; Multivariate analysis; Algorithm; PLS regression; Paracetamol; Ion trap; Sensitivity; Model compound; Mass spectrometry; Chemometrics; Orthogonal signal; Principal component analysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac051705s

In the field of metabonomics, 1H NMR and full scan mass spectrometry methods have usually been combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to detect patterns in biofluids that correspond to specific effects, usually a toxic site effect of a compound. Confounders together with great interindividual variation complicate such analysis in humans, and therefore, metabonomic data are almost restricted to animals. In our study, a constant neutral loss (CNL) scan on a linear ion trap demonstrated increased sensitivity and specificity compared to a full scan approach and was performed to detect mercapturic acids (MA), a class of effect markers. The method was applied to human volunteers administered 50 and 500 mg of acetaminophen (AAP), a model compound known to form MAs. Using a new algorithm to prepare the CNL data for chemometrics, discrimination of control and postdose samples could be performed using PCA and PLS-DA. The loadings plots clearly revealed AAP-MA as a marker, even at low-dose levels. Orthogonal signal correction (OSC) was carried out to investigate background information that is not due to exposure. Surprisingly, the OSC data provided a classification of male and female subjects showing the performance of the new approach.