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. 2014 Dec 24;14:14.
doi: 10.1186/s12899-014-0014-0.

Alteration in Circulating Metabolites During and After Heat Stress in the Conscious Rat: Potential Biomarkers of Exposure and Organ-Specific Injury

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Free PMC article

Alteration in Circulating Metabolites During and After Heat Stress in the Conscious Rat: Potential Biomarkers of Exposure and Organ-Specific Injury

Danielle L Ippolito et al. BMC Physiol. .
Free PMC article

Abstract

Background: Heat illness is a debilitating and potentially life-threatening condition. Limited data are available to identify individuals with heat illness at greatest risk for organ damage. We recently described the transcriptomic and proteomic responses to heat injury and recovery in multiple organs in an in vivo model of conscious rats heated to a maximum core temperature of 41.8°C (Tc,Max). In this study, we examined changes in plasma metabolic networks at Tc,Max, 24, or 48 hours after the heat stress stimulus.

Results: Circulating metabolites were identified by gas chromatography/mass spectrometry and liquid chromatography/tandem mass spectrometry. Bioinformatics analysis of the metabolomic data corroborated proteomics and transcriptomics data in the tissue at the pathway level, supporting modulations in metabolic networks including cell death or catabolism (pyrimidine and purine degradation, acetylation, sulfation, redox alterations and glutathione metabolism, and the urea cycle/creatinine metabolism), energetics (stasis in glycolysis and tricarboxylic acid cycle, β-oxidation), cholesterol and nitric oxide metabolism, and bile acids. Hierarchical clustering identified 15 biochemicals that differentiated animals with histopathological evidence of cardiac injury at 48 hours from uninjured animals. The metabolic networks perturbed in the plasma corroborated the tissue proteomics and transcriptomics pathway data, supporting a model of irreversible cell death and decrements in energetics as key indicators of cardiac damage in response to heat stress.

Conclusions: Integrating plasma metabolomics with tissue proteomics and transcriptomics supports a diagnostic approach to assessing individual susceptibility to organ injury and predicting recovery after heat stress.

Figures

Figure 1
Figure 1
Altered metabolic pathways and process networks in plasma of rats after heat stress. Biochemicals were grouped by KEGG super-pathway and sub-pathway. Pathways meeting significance thresholds were plotted by number of affected metabolites per total number identified in the plasma (at right). Significance thresholds were determined by ANOVA, by ratio of heat/control at Tc,Max, 24 or 48 hours. The 48 hour endpoint was further subdivided into injured or uninjured animals by cardiac histopathology.
Figure 2
Figure 2
Initial elevation in glutathione metabolism with recovery by 24–48 hours. (A) Biochemicals altered in heat exposure and disposition after recovery (fold-change, heat/control). (B) The glutathione metabolic pathway. Circled biochemicals represent changes observed after heat exposure. *P < 0.05 heat exposed versus control rat, two-way ANOVA with contrasts. AA, amino acid; GCS, γ-glutamyl cysteine synthase; GGT, γ-glutamyl transferase; GS, glutathione synthase.
Figure 3
Figure 3
Alterations in arginine metabolism and the urea cycle after heat exposure. (A) Change in plasma arginine metabolism biochemicals as a function of time after heat exposure (fold-change, heat/control). (B) Biochemical pathway of inflammation signaling, the urea cycle, and energy metabolism. *p < 0.05 heat exposed versus control rat, two-way ANOVA with contrasts.
Figure 4
Figure 4
Mitochondrial dysfunction suggested by elevated TCA cycle intermediates and 2-hydroxybutyrate (AHB) at T c,Max . (A) Fold-change from control in biochemicals implicated in the TCA cycle. (B) The TCA cycle, with red circles indicating biochemicals with significant deviation from control after heat stress. *p < 0.05 heat exposed versus control rat, two-way ANOVA with contrasts.
Figure 5
Figure 5
Model of heat stress and recovery and potential biomarkers. Hierarchical clustering was used to compare heated and control animals 48 hours after heat exposure. Data clustered by cardioinflammation score in animals with the greatest histopathological evidence of injury.
Figure 6
Figure 6
Random forest analysis accurately discriminates heat exposure from unheated controls at T c,Max . The top 30 metabolites were identified in out-of-bag (OOB) selection to have 100% predictive power by random forest analysis at Tc,Max. The inset represents predictive power of the 30 biochemicals identified. Colored circles indicate biochemicals that were also significantly up (red) or down (green) regulated, p < 0.05, 2-way ANOVA with contrasts. Biochemicals marked by an asterisk indicate likely identifications based on MS/MS fragmentation and other chemical properties.
Figure 7
Figure 7
Random forest analysis accurately discriminates heat exposure from unheated controls at 24 hours. The top 30 metabolites were identified in OOB selection to have 94% predictive power by random forest analysis at Tc,Max. The inset represents predictive power of the 30 biochemicals identified. Colored circles indicate biochemicals that were also significantly up (red) or down (green) regulated, p < 0.05, 2-way ANOVA with contrasts. One biochemical was not significantly altered by ANOVA analysis (yellow). Biochemicals marked by an asterisk indicate likely identifications based on MS/MS fragmentation and other chemical properties.
Figure 8
Figure 8
Random forest analysis accurately discriminates heat exposure from unheated controls at 48 hours. The top 30 metabolites were identified in OOB selection to have 100% predictive power by random forest analysis at 48 hours. The inset represents the top-scoring molecule (5-methyl-2’deoxycytidne). Colored circles indicate biochemicals that were also significantly up (red) or down (green) regulated, p < 0.05, 2-way ANOVA with contrasts. One biochemical was not significantly altered by ANOVA analysis (yellow). Biochemicals marked by an asterisk indicate likely identifications based on MS/MS fragmentation and other chemical properties.
Figure 9
Figure 9
Metabolites discriminate animals with histopathologic evidence of heart injury versus uninjured animals at 48 hours. Hierarchical clustering was used to compare heated and control animals 48 hours after heat exposure. Data clustered by cardioinflammation score in animals with the greatest histopathological evidence of injury.
Figure 10
Figure 10
Down-regulated biomarkers predictive of cardiac injury. KEGG pathways negatively enriched in animals with cardiac injury were compared across transcriptomics, proteomics, and metabolomics studies in cardiac tissue. For metabolomics results, the Metabolon pathway designation is listed along with the complementary KEGG designation (in DAVID). The metabolomics studies are described in this study and the proteomics and transcriptomics results were described in detail in a previous study. *, previous study; **, this study; red text, common to at least two data sets, with the same direction in all data sets (up-regulated or down-regulated); blue text, common to at least two data sets, but direction (up-regulated or down-regulated) differs.
Figure 11
Figure 11
Up-regulated biomarkers predictive of cardiac injury. KEGG pathways positively enriched in animals with cardiac injury were compared across transcriptomics, proteomics, and metabolomics studies in cardiac tissue. For metabolomics results, the Metabolon pathway designation is listed along with the complementary KEGG designation (in DAVID). The metabolomics studies are described in this study and the proteomics and transcriptomics results were described in detail in a previous study. *, previous study; **, this study; red text, common to at least two data sets, with the same direction in all data sets (up-regulated or down-regulated); blue text, common to at least two data sets, but direction (up-regulated or down-regulated) differs.

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