Nicholas J. DelRaso

Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm’s ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a 1H NMR-based experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification.

Comparative Metabolomic and Genomic Analyses of TCDD-Elicited Metabolic Disruption in Mouse and Rat Liver

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.

Comparative Hepatotoxicity of Two Polychlorotrifluoroethylenes (3.1 Oils) and Two Chlorotrifluoroethylene (CTFE) Oligomers in Male Fischer 344 Rats

Polychlorotrifluoroethylene (3.1 oil) is a nonflammable hydraulic fluid composed of chlorotrifluoroethylene (CTFE) oligomers of different carbon chain lengths (C5 to C9), primarily six (trimer) and eight (tetramer) carbons. Four test groups of Fischer 344 rats (16 rats/group) were orally gavaged daily over a 2-week period at doses of 1.25 g/kg with 3.1 oil containing a 55:45 ratio of trimer and tetramer (3.1 oil-C6:C8), 3.1 oil composed of 95% trimer (3.1 oil-C6), pure tetramer, and pure trimer. Four rats per treatment group were terminated after 1, 3, 7, and 14 doses. Rats dosed with either 3.1 oil-C6:C8 or pure tetramer demonstrated significant weight losses, increased liver weights, increased rates of liver fatty acid beta-oxidation, pronounced hepatomegaly and altered hepatocellular architecture, and elevated serum liver-associated enzymes. Rats dosed with either 3.1 oil-C6 or only pure trimer demonstrated significant increase in liver weight and moderate liver histopathologic changes. Compositional analyses of the ratio percentage of trimer to tetramer present in 3.1 oil-C6:C8 (55:45) were found to be altered when measured in the liver (32:68). Differential CTFE oligomer toxicity was indicated by effects on liver, body weight, and peroxisomal beta-oxidation and may allow for less toxic formulations of 3.1 oil to be generated by reducing or eliminating the tetramer component.

Air Force Research Laboratory Integrated Omics Research

Integrated Omics research capabilities within the Air Force Research Laboratory began in 2003 with the initiation of a Defense Technology Objective project aimed to identify biomarkers of toxicity occurring within the warfighter as a preclinical indicator. Current methods for determining toxic exposures are not responsive enough or created available for deployment to prevent serious health effects. Using Integrated Omics (Genomics/Epigenetics, Proteomics, and Metabonomics) for biomarker discovery, we have identified specific molecular markers which, once validated, could be used for real-time or near-real-time monitoring of the human response to uncharacterized exposures. The determination and use of validated biomarker sets, when installed on a fieldable biomonitor system, could allow fast determination of subclinical organ damage in response to chemical exposures. Since initiation of this program, our group has applied Omics technologies for biomarker discovery in a number of toxicology and human performance projects, including jet fuel exposures and cognitive fatigue.

A Proposed Statistical Protocol for the Analysis of Metabolic Toxicological Data Derived from NMR Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy is a non-invasive method of acquiring a metabolic profile from biofluids. This metabolic information may provide keys to the early detection of exposure to a toxin. A typical NMR toxicology data set has low sample size and high dimensionality. Thus, traditional pattern recognition techniques are not always feasible. In this paper, we evaluate several common alternatives for isolating these biomarkers. The fold test, unpaired t-test, and paired t-test were performed on an NMR-derived toxicological data set and results were compared. The paired t-test method was preferred, due to its ability to attribute statistical significance, to take into consideration consistency of a single subject over a time course, and to mitigate the low sample, high dimensionality problem. We then grouped the resulting statistically salient potential biomarkers based on their significance patterns and compared results to several known metabolites affected by the tested toxin. Based on these results, we present a statistical protocol of sequential t-tests and clustering techniques for identifying putative biomarkers. We then present the results of this protocol applied to a specific real world toxicological data set.

Joint genomic and metabolomic analysis of toxic dose-response experiments

A methodology has been implemented for analyzing microarray and NMR spectral data obtained from the same set of toxic-exposure dose-response experiments. The NMR spectra additionally track the time course of exposure. Analyses consist of screening the data to eliminate variates with insignificant signal, normalization appropriate to the experimental design, principal components analysis, and nonlinear classification using a support vector machine. It is found that exposure at subtoxic levels can be detected.