Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics |
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Authors: | Elin Pohjanen Elin Thysell Johan Lindberg Ina Schuppe-Koistinen Thomas Moritz Pär Jonsson Henrik Antti |
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Institution: | 1. Research Group for Chemometrics, Organic Chemistry, Department of Chemistry, Ume? University, SE-901 87, Ume?, Sweden 2. Molecular Toxicology, Safety Assessment, AstraZeneca R&D, SE-141 85, S?dert?lje, Sweden 3. Ume? Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-901 87, Ume?, Sweden
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Abstract: | A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity
is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely;
1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver
toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved
by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding
mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were
subjected to further statistical analysis following correction to account for the control variation over time. These dose
related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach,
named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites
involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster
analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis
and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected
metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation
to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods
the detected metabolites were subjected to identification by means of data base comparison as well as verification in the
raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations
in metabolomic studies. |
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Keywords: | Metabolomics Metabonomics Metabolite profiling GC/MS Curve resolution Hierarchical multivariate curve resolution Chemometrics Multivariate data analysis Cluster analysis Correlation networks Biomarkers |
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