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Development of a NMR-based Metabolomics Analysis Methodology for Toxicology. Jahns, G.L.1, Reo, N.V.2, Kent, M.N.2, Burgoon, L.D.3, Zacharewski, T.R.3, DelRaso, N.4. 1BAE
Systems, San Diego, CA 92123 Metabolomics is the simultaneous measurement of
metabolites from endogenous and exogenous chemicals, which may be used
to identify putative biomarkers of exposure and toxicity. Currently,
most metabolomics studies focus on using pattern recognition techniques
to cluster spectrometric peaks, but most fail to statistically identify
peaks associated with exposure. We have developed a data analysis and
processing methodology for Nuclear Magnetic Resonance (NMR) spectrometry
to 1) identify and eliminate spectral regions with no signal, 2) statistically
characterize the significance of differentially expressed metabolite
signals, and 3) quantify the change in these signals. The method identifies
spectral regions with no signal by scanning spectra with a low-level
threshold. Detection Theory is used to produce probabilistic estimates
of the presence of a treatment effect, based on either a minimum Bayesian
risk cost or a constant false alarm rate. The treatment effect is then
quantified by either absolute or relative (fold) changes of the significant
bins. As an example, hepatic lipid extracts from mice dosed with 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) were analyzed using 13C NMR. Noise screening eliminated channels
with no signal in both control and treatment replicates, reducing active
bins from 1024 to 192. The Bayesian-cost significance metric further
reduced the data to 77 channels with a high probability of treatment
effect. We ranked these bins both by absolute and by fold change to
identify channels showing the largest effect. These results are valuable
as they stand, or can serve as a screened basis for further classification
and identification analysis. Funded by NIEHS RO1 ES013927. |
