Development of analytical methods for NMR spectra and application to a 13 C toxicology study

ArticleinMetabolomics 5(2):253-262 · June 2009with6 Reads
Impact Factor: 3.86 · DOI: 10.1007/s11306-008-0148-9

    Abstract

    Metabolomics offers the potential to assess the effects of toxicants on metabolite levels. To fully realize this potential,
    a robust analytical workflow for identifying and quantifying treatment-elicited changes in metabolite levels by nuclear magnetic
    resonance (NMR) spectrometry has been developed that isolates and aligns spectral regions across treatment and vehicle groups
    to facilitate analytical comparisons. The method excludes noise regions from the resulting reduced spectra, significantly
    reducing data size. Principal components analysis (PCA) identifies data clusters associated with experimental parameters.
    Cluster-centroid scores, derived from the principal components that separate treatment from vehicle samples, are used to reconstruct
    the mean spectral estimates for each treatment and vehicle group. Peak amplitudes are determined by scanning the reconstructed
    mean spectral estimates. Confidence levels from Mann–Whitney order statistics and amplitude change ratios are used to identify
    treatment-related changes in peak amplitudes. As a demonstration of the method, analysis of 13C NMR data from hepatic lipid extracts of immature, ovariectomized C57BL/6 mice treated with 30 μg/kg 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or sesame oil vehicle, sacrificed at 72, 120, or 168 h, identified 152 salient peaks. PCA clustering showed
    a prominent treatment effect at all three time points studied, and very little difference between time points of treated animals.
    Phenotypic differences between two animal cohorts were also observed. Based on spectral peak identification, hepatic lipid
    extracts from treated animals exhibited redistribution of unsaturated fatty acids, cholesterols, and triacylglycerols. This
    method identified significant changes in peaks without the loss of information associated with spectral binning, increasing
    the likelihood of identifying treatment-elicited metabolite changes.