Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis

<p>Abstract</p> <p>Background</p> <p>Urine from male Sprague-Dawley rats 25, 40, and 80 days old was analyzed by NMR and UPLC/MS. The effects of data normalization procedures on principal component analysis (PCA) and quantitative analysis of NMR-based metabonomics data were investigated. Additionally, the effects of age on the metabolic profiles were examined by both NMR and UPLC/MS analyses.</p> <p>Results</p> <p>The data normalization factor was shown to have a great impact on the statistical and quantitative results indicating the need to carefully consider how to best normalize the data within a particular study and when comparing different studies. PCA applied to the data obtained from both NMR and UPLC/MS platforms reveals similar age-related differences. NMR indicated many metabolites associated with the Krebs cycle decrease while citrate and 2-oxoglutarate, also associated with the Krebs cycle, increase in older rats.</p> <p>Conclusion</p> <p>This study compared four different normalization methods for the NMR-based metabonomics spectra from an age-related study. It was shown that each method of normalization has a great effect on both the statistical and quantitative analyses. Each normalization method resulted in altered relative positions of significant PCA loadings for each sample spectra but it did not alter which chemical shifts had the highest loadings. The greater the normalization factor was related to age, the greater the separation between age groups was observed in subsequent PCA analyses. The normalization factor that showed the least age dependence was total NMR intensity, which was consistent with UPLC/MS data. Normalization by total intensity attempts to make corrections due to dietary and water intake of the individual animal, which is especially useful in metabonomics evaluations of urine. Additionally, metabonomics evaluations of age-related effects showed decreased concentrations of many Krebs cycle intermediates along with increased levels of oxidized antioxidants in urine of older rats, which is consistent with current theories on aging and its association with diminishing mitochondrial function and increasing levels of reactive oxygen species. Analysis of urine by both NMR and UPLC/MS provides a comprehensive and complementary means of examining metabolic events in aging rats.</p

Modeling Chemical Interaction Profiles: II. Molecular Docking, Spectral Data-Activity Relationship, and Structure-Activity Relationship Models for Potent and Weak Inhibitors of Cytochrome P450 CYP3A4 Isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the [U-13C6]-d-glucose tracer in mice

Previous genetic and proteomic studies identified altered activity of various enzymes such as those of fatty acid metabolism and glycogen synthesis after a single toxic dose of valproic acid (VPA) in rats. In this study, we demonstrate the effect of VPA on metabolite synthesis flux rates and the possible use of abnormal 13C labeled glucose-derived metabolites in plasma or urine as early markers of toxicity. Female CD-1 mice were injected subcutaneously with saline or 600 mg/kg) VPA. Twelve hours later, the mice were injected with an intraperitoneal load of 1 g/kg [U-13C]-d-glucose. 13C isotopomers of glycogen glucose and RNA ribose in liver, kidney and brain tissue, as well as glucose disposal via cholesterol and glucose in the plasma and urine were determined. The levels of all of the positional 13C isotopomers of glucose were similar in plasma, suggesting that a single VPA dose does not disturb glucose absorption, uptake or hepatic glucose metabolism. Three-hour urine samples showed an increase in the injected tracer indicating a decreased glucose re-absorption via kidney tubules. 13C labeled glucose deposited as liver glycogen or as ribose of RNA were decreased by VPA treatment; incorporation of 13C via acetyl-CoA into plasma cholesterol was significantly lower at 60 min. The severe decreases in glucose-derived carbon flux into plasma and kidney-bound cholesterol, liver glycogen and RNA ribose synthesis, as well as decreased glucose re-absorption and an increased disposal via urine all serve as early flux markers of VPA-induced adverse metabolic effects in the host

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis-7

<p><b>Copyright information:</b></p><p>Taken from "Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S3</p><p>BMC Bioinformatics 2007;8(Suppl 7):S3-S3.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099495.</p><p></p

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis-4

<p><b>Copyright information:</b></p><p>Taken from "Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S3</p><p>BMC Bioinformatics 2007;8(Suppl 7):S3-S3.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099495.</p><p></p>), and 80-day (yellow circles) old control SD rats at all timepoints. (B) The PC1 and PC2 loadings for the negative NMR PCA analysis

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis-2

<p><b>Copyright information:</b></p><p>Taken from "Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S3</p><p>BMC Bioinformatics 2007;8(Suppl 7):S3-S3.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099495.</p><p></p>D rats at all timepoints. (B) The PC1 and PC2 loadings for the negative NMR PCA analysis

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis-3

<p><b>Copyright information:</b></p><p>Taken from "Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S3</p><p>BMC Bioinformatics 2007;8(Suppl 7):S3-S3.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099495.</p><p></p> SD rats at all timepoints. (B) The PC1 and PC2 loadings for the negative NMR PCA analysis