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ABSTRACT: As metabolomic technology expands, validated techniques for analyzing highly dimensional categorical data are becoming increasingly
important. This manuscript presents a novel latent vector-based methodology for analyzing complex data sets with multiple
groups that include both high and low doses using orthogonal projections to latent structures (OPLS) coupled with hierarchical
clustering. This general methodology allows complex experimental designs (e.g., multiple dose and time combinations) to be
encoded and directly compared. Further, it allows for the inclusion of low dose samples that do not exhibit a strong enough
individual response to be modeled independently. A dose- and time-responsive metabolomic study was completed to evaluate and
demonstrate this methodology. Single doses (0.1–100mg/kg body weight) of α-naphthylisothiocyanate (ANIT), a common model
of hepatic cholestasis, were administered orally in corn oil to male Fischer 344 rats. Urine samples were collected pre-dose
and daily through day-4 post-dose. Blood samples were collected pre and post-dose to assess indices of clinical toxicity.
Urine samples were analyzed by 1H-NMR spectroscopy, and the spectra were adaptively binned to reduce dimensionality. The proposed methodology for NMR-based
urinary metabolomics was sensitive enough to detect ANIT-induced effects with respect to both dose and time at doses below
the threshold of clinical toxicity. A pattern of ANIT-dependent effects established at the highest dose was seen in the 50
and 20mg/kg dose groups, an effect not directly identifiable with individual principal component analysis (PCA). Coupling
the pattern found by the OPLS algorithm and hierarchical clustering revealed a relationship between the 100, 50 and 20mg/kg
dose groups, suggesting a characteristic effect of ANIT exposure. These studies demonstrate that the use of a metabolomics
approach with flexible binning of 1H spectra and appropriate application of multivariate analyses can reveal biologically relevant information about the temporal
metabolic perturbations caused by exposure and toxicity.
KeywordsNMR metabolomics–High dimension categorical data–Adaptive binning
Metabolomics 04/2012; 7(2):206-216. · 4.51 Impact Factor
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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 30μg/kg 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or sesame oil vehicle, sacrificed at 72, 120, or 168h, 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.
Metabolomics 04/2012; 5(2):253-262. · 4.51 Impact Factor
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ABSTRACT: 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.
KeywordsNMR–Metabolomics–Binning–Quantification–Dynamic programming
Metabolomics 04/2012; 7(2):179-190. · 4.51 Impact Factor
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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.
Toxicological Sciences 09/2011; 125(1):41-55. · 4.65 Impact Factor
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ABSTRACT: Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis.
We have developed a technique for creating realistic synthetic metabolomics validation sets based on NMR spectroscopic data. The validation sets are developed by characterizing the salient distributions in sets of empirical spectroscopic data. Using this technique, several validation sets are constructed with a variety of characteristics present in 'real' data. A case study is then presented to compare the relative accuracy of several alignment algorithms using the increased precision afforded by these synthetic data sets.
These data sets are available for download at http://birg.cs.wright.edu/nmr_synthetic_data_sets.
Bioinformatics 09/2009; 25(22):2992-3000. · 5.47 Impact Factor
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Bioinformatics. 01/2009; 25:2992-3000.
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ABSTRACT: In many metabolomics studies, NMR spectra are divided into bins of fixed width. This spectral quantification technique, known
as uniform binning, is used to reduce the number of variables for pattern recognition techniques and to mitigate effects from
variations in peak positions; however, shifts in peaks near the boundaries can cause dramatic quantitative changes in adjacent
bins due to non-overlapping boundaries. Here we describe a new Gaussian binning method that incorporates overlapping bins
to minimize these effects. A Gaussian kernel weights the signal contribution relative to distance from bin center, and the
overlap between bins is controlled by the kernel standard deviation. Sensitivity to peak shift was assessed for a series of
test spectra where the offset frequency was incremented in 0.5Hz steps. For a 4Hz shift within a bin width of 24Hz, the
error for uniform binning increased by 150%, while the error for Gaussian binning increased by 50%. Further, using a urinary
metabolomics data set (from a toxicity study) and principal component analysis (PCA), we showed that the information content
in the quantified features was equivalent for Gaussian and uniform binning methods. The separation between groups in the PCA
scores plot, measured by the J
2 quality metric, is as good or better for Gaussian binning versus uniform binning. The Gaussian method is shown to be robust
in regards to peak shift, while still retaining the information needed by classification and multivariate statistical techniques
for NMR-metabolomics data.
Metabolomics 08/2008; 4(3):261-272. · 4.51 Impact Factor
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Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007, October 14-17, 2007, Harvard Medical School, Boston, MA, USA; 01/2007
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ABSTRACT: An increasingly important issue in force protection is the toxicology associated with toxic chemical and mixture exposure at uncharacterized deployed sites. Current methods for determining or monitoring toxic exposures to the warfighter in their working or living environment are not adequate to prevent serious health effects. Deployed personnel may be exposed to toxic chemicals as a result of industrial accidents, intentional or unintentional activities of enemy or friendly forces or sabotage. Rapid risk assessment of these scenarios requires the development of new testing methods. In order to prevent serious injury to the deployed warfighter exposed to toxic substances and to minimize mission degradation due to environmentally related adverse health effects, novel human monitoring methodologies that provide near real-time detection of potential toxic injury must be developed. It is necessary to devise methodologies that will predict or identify exposure of personnel to low concentrations of harmful substances before they cause harm to an individual. It is also important to identify methodologies that are relatively non-invasive, which could include collection of urine, blood, saliva or epithelial cells from humans. Emerging biotechnologies, such as toxicogenomics, proteomics and metabonomics will be investigated for their effectiveness to identify toxic effects upon the warfighter before they can induce a reduction in health and/or operational performance or before they can induce a disease process that would not manifest for several years.
07/2006;
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ABSTRACT: The antioxidant capabilities of phosphatidylethanolamine plasmalogen (PlsEtn), in vivo, against lipid peroxidation were investigated via acute phosphine (PH(3)) administration in rats. Oxidative stress was assessed from measures of malondialdehyde and various enzyme activities, while NMR analyses of lipid and aqueous tissue extracts provided metabolic information in cerebellum, brainstem, and cortex. Brainstem had the highest basal [PlsEtn], and showed only moderate PH(3)-induced oxidative damage with no loss of ATP. The lowest basal [PlsEtn] was observed in cortex, where PH(3) caused a 51% decrease in [ATP]. The largest oxidative effect occurred in cerebellum, but [ATP] was unaffected. Myo-inositol+ethanolamine pretreatment attenuated all PH(3) effects. Specifically, the pretreatment attenuated the ATP decrease in cortex, and elevated brain [PlsEtn] in the cerebellum, nearly abolishing the cerebellar oxidative effects. Our data suggest a high basal [PlsEtn], or the capacity to synthesize new ethanolamine lipids (particularly PlsEtn) may protect against PH(3) toxicity.
Neurochemical Research 06/2006; 31(5):639-56. · 2.24 Impact Factor
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ABSTRACT: There are few studies of total body water (TBW) volume in children. Such studies are needed, as are new prediction equations for the clinical management of children with renal insufficiency and those receiving dialysis.
Mixed longitudinal data were from 124 white boys and 116 white girls 8 to 20 years of age. TBW volume was measured by deuterium nuclear magnetic resonance spectroscopy, and random effects models were used to determine patterns of change over time. Sex-specific TBW prediction equations were developed using regression analysis.
Boys had significantly greater (P < 0.05) mean TBW volumes than girls at all but 3 ages. TBW was significantly (P < 0.05) associated with age and maturation in the boys and the girls. In boys, mean TBW/WT varied from 0.55 to 0.59, while in the girls the mean declined from 0.53 to 0.49 by 16 years of age. Boys had significantly larger means for TBW/WT than girls, who had a significant, slight negative trend with age. The prediction equations were TBW = -25.87 + 0.23 (stature) + 0.37 (weight) for boys and TBW =-14.77 + 0.18 (stature) + 0.25 (weight) for girls.
Means are provided for TBW in white children from 8 to 20 years of age, whose average fatness affected the percentage of TBW in body weight. These updated TBW prediction equations perform better than those available from the past.
Kidney International 11/2005; 68(5):2317-22. · 6.61 Impact Factor
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ABSTRACT: Plasmalogens are ether-linked phospholipids highly abundant in nervous tissue. Previously we demonstrated that acute administration of myo-inositol (myo-Ins) + [2-(13)C] ethanolamine ([2-(13)C]Etn) significantly elevated phosphatidylethanolamine plasmalogen (PlsEtn) in rat whole brain. Current experiments investigated the effects of acute myo-Ins+[2-(13)C]Etn administration on [PlsEtn] and the biosynthesis of new Etn lipids using NMR spectroscopy in rat cerebral cortex, hippocampus, brainstem, midbrain and cerebellum. Treated rats received a single dose of myo-Ins + [2-(13)C]Etn and controls received saline rather than myoIns. Data reveal that the cerebellum is the brain region most affected by treatment, which resulted in a 22% increase in [PlsEtn] and 89% increase in newly synthesized Etn lipids relative to controls (P < 0.05). Furthermore, the cerebellar PlsEtn/phosphatidylethanolamine ratio and molar percentage of PlsEtn were significantly elevated by 12% and 8%, respectively (P < 0.05). These data suggest that myo-Ins influences Etn lipid metabolism in brain, particularly in the cerebellum where there is a stimulation in the biosynthesis of new Etn lipids with a preference towards PlsEtn.
Neurochemical Research 01/2005; 30(1):47-60. · 2.24 Impact Factor
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Fourth International IEEE Computer Society Computational Systems Bioinformatics Conference Workshops & Poster Abstracts (CSB 2005 Workshops), 8-11 August 2005, Stanford, CA, USA; 01/2005
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ABSTRACT: Plasmalogens are ether-linked phospholipids that are abundant in nervous tissues. Their biological role is unclear, but may involve membrane structure/function and antioxidant activities. This study further investigates a recent report that chronic administration of myo-inositol in rats increased brain phosphatidylethanolamine plasmalogen (PlsEtn). We examined the effects of myo-inositol administration on the incorporation of [2-(13)C]ethanolamine ([2-(13)C]Etn) into rat brain phospholipids using NMR spectroscopy. Rats received either acute myo-inositol (single dose) +/- [2-(13)C]Etn, or chronic myo-inositol (10-day treatment) + [2-(13)C]Etn. Controls received saline rather than myo-inositol. Acute myo-inositol produced a 68% increase in brain [myo-inositol] and an increase in the incorporation of [2-(13)C]Etn into phospholipids (P < .05). The PlsEtn/phosphatidylethanolamine ratio and the [PlsEtn] were increased by 27% and 30%, respectively. The PlsEtn content as a mole percentage of total phospholipids was elevated (P < or = .05). Acute administration of myo-inositol + ethanolamine illustrates a positive correlation between the brain [myo-inositol] and the biosynthesis of ethanolamine phospholipids, with preferential synthesis of PlsEtn.
Neurochemical Research 05/2004; 29(4):843-55. · 2.24 Impact Factor
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Nicholas V Reo
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ABSTRACT: Similar to genomics and proteomics which yield vast amounts of data about the expression of genes and proteins, metabolomics refers to the whole metabolic profile of the cell. The focus of this report concerns the use of nuclear magnetic resonance (NMR) spectroscopy for metabolic analyses and, in particular, its use in toxicology for examining the metabolic profile of biofluids. Examples from the literature will demonstrate how 1H NMR and pattern recognition methods are used to obtain the urinary metabolic profile, and how this profile is affected by exposure to various toxicants. These particular studies which focus on the metabolic profiles of biofluids, specifically urine, are referred to as metabonomics. NMR-based metabonomics provides a means to categorize organ-specific toxicity, monitor the onset and progression of toxicological effects, and identify biomarkers of toxicity. A future challenge, however, is to describe the cellular metabolome for purposes of understanding cellular functions (i.e., metabolomics). Thus the capabilities and advantages of multinuclear NMR to provide metabolic information in cells and tissues will also be discussed. Such information is essential if metabolomics is to provide a complementary dataset which together with genomics and proteomics can be used to construct computer network models to describe cellular functions.
Drug and Chemical Toxicology 12/2002; 25(4):375-82. · 1.08 Impact Factor
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ABSTRACT: Choline and ethanolamine are substrates for de novo synthesis of phosphatidylcholine (PtdC) and phosphatidylethanolamine (PtdE) through the CDP-choline and CDP-ethanolamine pathways. In liver, PtdE can also be converted to PtdC by PtdE N-methyltransferase (PEMT). We investigated these kinetics in rat liver during a 60 min infusion with (13)C-labeled choline and ethanolamine. NMR analyses of liver extracts provided concentrations and (13)C enrichments of phosphocholine (Pcho), phosphoethanolamine (Peth), PtdC, and PtdE. Kinetic models showed that the de novo and PEMT pathways are 'channeled' processes. The intermediary metabolites directly derived from exogenous choline and ethanolamine do not completely mix with the intracellular pools, but are preferentially used for phospholipid synthesis. Of the newly synthesized PtdC, about 70% was derived de novo and 30% was by PEMT. PtdC and PtdE de novo syntheses displayed different kinetics. A simple model assuming constant fluxes yielded a modest fit to the data; allowing upregulated fluxes significantly improved the fit. The ethanolamine-to-Peth flux exceeded choline-to-Pcho, and the rate of PtdE synthesis (1.04 micromol/h/g liver) was 2-3 times greater than that of PtdC de novo synthesis. The metabolic pathway information provided by these studies makes the NMR method superior to earlier radioisotope studies.
Biochimica et Biophysica Acta 03/2002; 1580(2-3):171-88. · 4.66 Impact Factor
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ABSTRACT: Perfluorodecanoic acid (PFDA) is a potent peroxisome proliferator that causes hepatotoxicity but lacks tumor-promoting activity in rats. We previously showed that a single dose of PFDA at 50 mg/kg (∼LD50) causes an elevation in liver phosphocholine (PCho) and other effects related to phospholipid metabolism. In this study, we examined metabolic effects in the dose range 2–50 mg/kg in rats. At doses ≤20 mg/kg, PFDA is significantly less hepatotoxic than the LD50, as manifested by electron microscopy and measurements of daily food consumption and body weight. At 50 mg/kg rat serum tumor necrosis factor (TNF)-α concentration was increased 8-fold, while at 15 mg/kg there was no apparent increase in this cytokine. This lower dose, however, induces metabolic effects similar to those seen at the LD50. Liver fatty acyl-CoA oxidase activity showed a dose-dependent increase from 5–25 mg/kg PFDA. Treatments at 15 and 50 mg/kg caused a significant increase in liver phosphatidylcholine (28 and 66%) and phosphatidylethanolamine (31 and 74%). Both doses caused a significant increase in liver PCho but did not affect liver ATP levels, as manifested in 31P nuclear magnetic resonance (NMR) spectra from rat livers in vivo. These data suggest that the increase in liver [PCho] observed following PFDA exposure in rats represents a specific metabolic response, rather than a broad-range hepatotoxic effect.
Toxicology.
