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.
"Semiquantitative metabolomics screening. An integrated screening methodology for NMR spectral analysis, similar to that described previously (Jahns et al., 2009), was extended to examine spectra from multiple nuclides simultaneously. It is based upon successive steps of feature extraction, data fusion, modeled-variance standardization, PCA, and feature-change estimation. "
[Show abstract][Hide abstract] ABSTRACT: 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) elicits a broad spectrum of species-specific effects that have not yet been fully characterized. This study compares the temporal effects of TCDD on hepatic aqueous and lipid metabolite extracts from immature ovariectomized C57BL/6 mice and Sprague-Dawley rats using gas chromatography-mass spectrometry and nuclear magnetic resonance-based metabolomic approaches and integrates published gene expression data to identify species-specific pathways affected by treatment. TCDD elicited metabolite and gene expression changes associated with lipid metabolism and transport, choline metabolism, bile acid metabolism, glycolysis, and glycerophospholipid metabolism. Lipid metabolism is altered in mice resulting in increased hepatic triacylglycerol as well as mono- and polyunsaturated fatty acid (FA) levels. Mouse-specific changes included the induction of CD36 and other cell surface receptors as well as lipases- and FA-binding proteins consistent with hepatic triglyceride and FA accumulation. In contrast, there was minimal hepatic fat accumulation in rats and decreased CD36 expression. However, choline metabolism was altered in rats, as indicated by decreases in betaine and increases in phosphocholine with the concomitant induction of betaine-homocysteine methyltransferase and choline kinase gene expression. Results from these studies show that aryl hydrocarbon receptor-mediated differential gene expression could be linked to metabolite changes and species-specific alterations of biochemical pathways.
[Show abstract][Hide abstract] ABSTRACT: This chapter discusses the definition, historical evolution, methodologies, and applications of metabolomics in pesticide toxicology. Chemical toxicology, which includes pesticide toxicology, examines dose responses to toxic chemicals also referred to as chemical stimuli. Metabolomics is the systematic study of a metabolome, the entirety of metabolites, or a set of metabolites, forming an extensive network of metabolic reactions in which one metabolite from a specific pathway will affect one or more biochemical reactions or a comprehensive and quantitative analysis of all metabolites. Metabolomics research has provided valuable insights into many scientific interests. As a result of rapid technological advances, many more metabolomics results will abound in the near future. However, metabolomics has weaknesses. Because toxicological responses are under the regulation of a complex array of genes, proteins, and metabolites, combinations of different -omics approaches are required to understand biosystems. Most of the current research in these areas remains either diagnostic in nature or is limited to fingerprinting. Interpretation of metabolomic data integrated with other -omics data will provide truly systems biology-like information. Finally, it should be noted that only a limited number of organisms have been evaluated using metabolomics in relation to toxicometabolomics.
[Show abstract][Hide abstract] ABSTRACT: Spectroscopic profiling of biological samples is an integral part of metabolically driven top-down systems biology and can be used for identifying biomarkers of toxicity and disease. However, optimal biomarker information recovery and resonance assignment still pose significant challenges in NMR-based complex mixture analysis. The reduced signal overlap as achieved when projecting two-dimensional (2D) J-resolved (JRES) NMR spectra can be exploited to mitigate this problem and, here, full-resolution (1)H JRES projections have been evaluated as a tool for metabolic screening and biomarker identification. We show that the recoverable information content in JRES projections is intrinsically different from that in the conventional one-dimensional (1D) and Carr-Purcell-Meiboom-Gill (CPMG) spectra, because of the combined result of reduction of the over-representation of highly split multiplet peaks and relaxation editing. Principal component and correlation analyses of full-resolution JRES spectral data demonstrated that peak alignment is necessary. The application of statistical total correlation spectroscopy (STOCSY) to JRES projections improved the identification of previously overlapped small molecule resonances in JRES (1)H NMR spectra, compared to conventional 1D and CPMG spectra. These approaches are demonstrated using a galactosamine-induced hepatotoxicity study in rats and show that JRES projections have a useful and complementary role to standard one-dimensional experiments in complex mixture analysis for improved biomarker identification.
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