Comprehensive Metabolic Profiling of Age-Related Mitochondrial Dysfunction in the High-Fat-Fed <i>ob</i>/<i>ob</i> Mouse Heart

The ectopic deposition of fat is thought to lead to lipotoxicity and has been associated with mitochondrial dysfunction and diabetic cardiomyopathy. We have measured mitochondrial respiratory capacities in the hearts of <i>ob</i>/<i>ob</i> and wild-type mice on either a regular chow (RCD) or high-fat (HFD) diet across four age groups to investigate the impact of diet and age on mitochondrial function alongside a comprehensive strategy for metabolic profiling of the tissue. Myocardial mitochondrial dysfunction was only evident in <i>ob</i>/<i>ob</i> mice on RCD at 14 months, but it was detectable at 3 months on the HFD. Liquid chromatography–mass spectrometry (LC–MS) was used to study the profiles of acylcarnitines and the accumulation of triglycerides, but neither class of lipid was associated with mitochondrial dysfunction. However, a targeted LC–MS/MS analysis of markers of oxidative stress demonstrated increases in GSSG/GSH and 8-oxoguanine, in addition to the accumulation of diacylglycerols, which are lipid species linked to lipotoxicity. Our results demonstrate that myocardial mitochondria in <i>ob</i>/<i>ob</i> mice on RCD maintained a similar respiratory capacity to that of wild type until a late stage in aging. However, on a HFD, unlike wild-type mice, <i>ob</i>/<i>ob</i> mice failed to increase mitochondrial respiration, which may be associated with a complex I defect following increased oxidative damage

A Metabolomics Investigation of Non-genotoxic Carcinogenicity in the Rat

Non-genotoxic carcinogens (NGCs) promote tumor growth by altering gene expression, which ultimately leads to cancer without directly causing a change in DNA sequence. As a result NGCs are not detected in mutagenesis assays. While there are proposed biomarkers of carcinogenic potential, the definitive identification of non-genotoxic carcinogens still rests with the rat and mouse long-term bioassay. Such assays are expensive and time-consuming and require a large number of animals, and their relevance to human health risk assessments is debatable. Metabolomics and lipidomics in combination with pathology and clinical chemistry were used to profile perturbations produced by 10 compounds that represented a range of rat non-genotoxic hepatocarcinogens (NGC), non-genotoxic non-hepatocarcinogens (non-NGC), and a genotoxic hepatocarcinogen. Each compound was administered at its maximum tolerated dose level for 7, 28, and 91 days to male Fisher 344 rats. Changes in liver metabolite concentration differentiated the treated groups across different time points. The most significant differences were driven by pharmacological mode of action, specifically by the peroxisome proliferator activated receptor alpha (PPAR-α) agonists. Despite these dominant effects, good predictions could be made when differentiating NGCs from non-NGCs. Predictive ability measured by leave one out cross validation was 87% and 77% after 28 days of dosing for NGCs and non-NGCs, respectively. Among the discriminatory metabolites we identified free fatty acids, phospholipids, and triacylglycerols, as well as precursors of eicosanoid and the products of reactive oxygen species linked to processes of inflammation, proliferation, and oxidative stress. Thus, metabolic profiling is able to identify changes due to the pharmacological mode of action of xenobiotics and contribute to early screening for non-genotoxic potential

A Metabolomics Investigation of Non-genotoxic Carcinogenicity in the Rat

Non-genotoxic carcinogens (NGCs) promote tumor growth by altering gene expression, which ultimately leads to cancer without directly causing a change in DNA sequence. As a result NGCs are not detected in mutagenesis assays. While there are proposed biomarkers of carcinogenic potential, the definitive identification of non-genotoxic carcinogens still rests with the rat and mouse long-term bioassay. Such assays are expensive and time-consuming and require a large number of animals, and their relevance to human health risk assessments is debatable. Metabolomics and lipidomics in combination with pathology and clinical chemistry were used to profile perturbations produced by 10 compounds that represented a range of rat non-genotoxic hepatocarcinogens (NGC), non-genotoxic non-hepatocarcinogens (non-NGC), and a genotoxic hepatocarcinogen. Each compound was administered at its maximum tolerated dose level for 7, 28, and 91 days to male Fisher 344 rats. Changes in liver metabolite concentration differentiated the treated groups across different time points. The most significant differences were driven by pharmacological mode of action, specifically by the peroxisome proliferator activated receptor alpha (PPAR-α) agonists. Despite these dominant effects, good predictions could be made when differentiating NGCs from non-NGCs. Predictive ability measured by leave one out cross validation was 87% and 77% after 28 days of dosing for NGCs and non-NGCs, respectively. Among the discriminatory metabolites we identified free fatty acids, phospholipids, and triacylglycerols, as well as precursors of eicosanoid and the products of reactive oxygen species linked to processes of inflammation, proliferation, and oxidative stress. Thus, metabolic profiling is able to identify changes due to the pharmacological mode of action of xenobiotics and contribute to early screening for non-genotoxic potential

A Metabolomics Investigation of Non-genotoxic Carcinogenicity in the Rat

Non-genotoxic carcinogens (NGCs) promote tumor growth by altering gene expression, which ultimately leads to cancer without directly causing a change in DNA sequence. As a result NGCs are not detected in mutagenesis assays. While there are proposed biomarkers of carcinogenic potential, the definitive identification of non-genotoxic carcinogens still rests with the rat and mouse long-term bioassay. Such assays are expensive and time-consuming and require a large number of animals, and their relevance to human health risk assessments is debatable. Metabolomics and lipidomics in combination with pathology and clinical chemistry were used to profile perturbations produced by 10 compounds that represented a range of rat non-genotoxic hepatocarcinogens (NGC), non-genotoxic non-hepatocarcinogens (non-NGC), and a genotoxic hepatocarcinogen. Each compound was administered at its maximum tolerated dose level for 7, 28, and 91 days to male Fisher 344 rats. Changes in liver metabolite concentration differentiated the treated groups across different time points. The most significant differences were driven by pharmacological mode of action, specifically by the peroxisome proliferator activated receptor alpha (PPAR-α) agonists. Despite these dominant effects, good predictions could be made when differentiating NGCs from non-NGCs. Predictive ability measured by leave one out cross validation was 87% and 77% after 28 days of dosing for NGCs and non-NGCs, respectively. Among the discriminatory metabolites we identified free fatty acids, phospholipids, and triacylglycerols, as well as precursors of eicosanoid and the products of reactive oxygen species linked to processes of inflammation, proliferation, and oxidative stress. Thus, metabolic profiling is able to identify changes due to the pharmacological mode of action of xenobiotics and contribute to early screening for non-genotoxic potential

Validation of microarray data.

<p>(<b>A</b>) Fold-change in expression of genes <i>UTS2</i>, <i>HSD11B1</i>, <i>OCLN</i>, <i>IGF1R</i> and <i>INSIG2</i> between athlete (n = 8) and control (n = 7) groups at time point T2, as evident by microarray analysis (Nexus Gene Expression and Genespring), and validated by quantitative real-time PCR (qRT-PCR). qRT-PCR data are normalized to the housekeeping gene and were statistically significant with <i>p</i><0.05 (n = 6 in each group). (<b>B</b>) Concentration of urotensin (pg/ml) in the plasma of control (n = 8) and athlete (n = 6) subjects at time-point T1.</p

¹H NMR spectra of control and athlete urine samples.

<p>¹H NMR spectra of a representative pre- (T1) and post-exercise (T2) urine sample from an athlete (<b>A</b>) and control (<b>B</b>) subject highlighting visible differences between the two spectra and the major resonances. Numbered biomolecules correspond to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092031#pone-0092031-t003" target="_blank">Table 3</a>.</p

Differential gene expression between control and athlete subjects after exercise.

<p>(<b>A</b>) Venn diagram representing genes differentially expressed between control and athlete groups at time points T1 (blue; 24 hrs before exercise; n = 4 for each group); T2 (yellow; immediately after exercise; n = 7 for controls and n = 8 for athlete group); and T3 (green; 24 hrs after exercise; n = 4 for each group). Fold change>1.5; False Discovery Rate (FDR)<0.1. Genes differentially regulated at two or three time points are shown in the corresponding intersecting segments. (<b>B</b>) Heatmap representing genes differentially expressed between individual control and athlete samples at time point T2 (immediately after exercise). Each column represents an individual subject. Red represents an up-regulation, and green a down-regulation in gene expression. Probes and samples are clustered by hierarchical clustering Fold change>1.5; FDR<0.1. (<b>C</b>) Top 10 canonical pathway maps representing genes differentially expressed between athlete and control groups at time-point T2 by Metacore analysis with corresponding <i>p</i>-values and FDR.</p

¹H NMR of urine metabolites.

1<p>H NMR identified urine metabolites and relative changes in concentration reported as fold change for athlete and control groups after exercise.</p><p>‡Signal not used for concentration calculation.</p><p>†Peak areas were unreported due to peak overlap.</p><p>♮Metabolite not detected.</p><p>*Significant difference between gr°ups.</p><p>♭Histidine concentrations were not calculated due to dynamic nature of His resonances.</p

Gene expression in control and athlete subjects.

<p>(<b>A</b>) Heatmap of the entire genome including all normalized and filtered probes in athlete and control groups across all time-points (T1: 24 hrs before exercise; T2: immediately after exercise; T3: 24 hrs after exercise). Red represents an up-regulation, and green a downregulation in gene expression. The probes were clustered by k-means clustering into 5 broad clusters. Representative Gene Ontology (GO) biological processes of each cluster are shown in their respective colours on the left. (<b>B</b>) Representative heatmap of probes found significantly different between athlete and control groups across all time-points (T1: 24 hrs before exercise; T2: immediately after exercise; T3: 24 hrs after exercise). The probes were clustered by hierarchical clustering. Fold change>1.5; False Discovery Rate (FDR)<0.1. (<b>C</b>) Top 10 networks representing genes differentially expressed between athlete and control groups across all time points by Metacore analysis, with corresponding <i>p</i>-values and FDR.</p

Physiological characteristics of all participants.

<p>Values are expressed as mean ± SE. “*” indicates statistical significance; p-value<0.01</p