Metabolomic analysis of pressure-overloaded and infarcted mouse hearts

Abstract

Background: Cardiac hypertrophy and heart failure are associated with metabolic dysregulation and a state of chronic energy deficiency. Although several disparate changes in individual metabolic pathways have been described, there has been no global assessment of metabolomic changes in hypertrophic and failing hearts in vivo. Hence, we investigated the impact of pressure overload and infarction on myocardial metabolism.

Methods and results: Male C57BL/6J mice were subjected to transverse aortic constriction or permanent coronary occlusion (myocardial infarction [MI]). A combination of LC/MS/MS and GC/MS techniques was used to measure 288 metabolites in these hearts. Both transverse aortic constriction and MI were associated with profound changes in myocardial metabolism affecting up to 40% of all metabolites measured. Prominent changes in branched-chain amino acids were observed after 1 week of transverse aortic constriction and 5 days after MI. Changes in branched-chain amino acids after MI were associated with myocardial insulin resistance. Longer duration of transverse aortic constriction and MI led to a decrease in purines, acylcarnitines, fatty acids, and several lysolipid and sphingolipid species but a marked increase in pyrimidines as well as ascorbate, heme, and other indices of oxidative stress. Cardiac remodeling and contractile dysfunction in hypertrophied hearts were associated with large increases in myocardial, but not plasma, levels of the polyamines putrescine and spermidine as well as the collagen breakdown product prolylhydroxyproline.

Conclusions: These findings reveal extensive metabolic remodeling common to both hypertrophic and failing hearts that are indicative of extracellular matrix remodeling, insulin resistance and perturbations in amino acid, and lipid and nucleotide metabolism.

Keywords: amino acids; glycolysis; heart failure; hypertrophy; metabolomics; mitochondria; oxidative stress.

Figure 1. Cardiac echocardiography after sham surgery, transverse aortic constriction (TAC), or permanent coronary ligation (myocardial infarction; MI)

(A) Representative M-mode images. (B) Ejection fraction; n = 5–8 per group, *p<0.05. Other measurements are listed in the Table.

Figure 2. Stage-specific grouping of pressure-overloaded hearts based on metabolic profile

(A) PCA analysis of cardiac metabolites after 1 d and 1 and 8 weeks of TAC compared with their respective shams. (B) Hierarchal cluster analysis of the 50 most significant TAC-affected metabolites; n = 5 per group. A complete list of metabolites significantly different in TAC compared with sham hearts is shown in Supplemental Table I.

Figure 3. Metabolic changes in myocardial metabolites after infarction

(A) PCA analysis of MI and sham groups. (B) Volcano plot analysis of metabolic changes in infarcted hearts (compared with sham hearts); n = 5 per group; for volcano analysis, the fold-change threshold was 1.5 (x-axis) and the p value threshold was set at 0.05 (y-axis). Values in pink were found to be significantly different. Metabolites significantly changed after MI are listed in Supplemental Table II.

Figure 4. Different stages of ventricular dysfunction show unique metabolic signatures

Z-score plot analysis of metabolic changes unique to TAC or MI: Metabolic changes unique to (A) 1 d; (B) 1 week; and (C) 8 weeks of TAC; and (D) 5 d of MI. Data are shown as standard deviation from the mean of respective sham. Each dot represents a single metabolite in one sample; n = 5 per group.

Figure 4. Different stages of ventricular dysfunction show unique metabolic signatures

Z-score plot analysis of metabolic changes unique to TAC or MI: Metabolic changes unique to (A) 1 d; (B) 1 week; and (C) 8 weeks of TAC; and (D) 5 d of MI. Data are shown as standard deviation from the mean of respective sham. Each dot represents a single metabolite in one sample; n = 5 per group.

Figure 5. Metabolic changes common to both TAC and MI

Z-score plot analysis of metabolic changes common between: (A) 1 week TAC and 5 d MI; (B) 8 week TAC and 5 d MI; and (C) 1 week TAC, 8 week TAC and 5 d MI. There were no shared changes with 1 d TAC group. In each plot, the data are shown as standard deviation from the mean of their respective sham. Each dot represents a single metabolite in one sample. n = 5 per group.

Figure 5. Metabolic changes common to both TAC and MI

Z-score plot analysis of metabolic changes common between: (A) 1 week TAC and 5 d MI; (B) 8 week TAC and 5 d MI; and (C) 1 week TAC, 8 week TAC and 5 d MI. There were no shared changes with 1 d TAC group. In each plot, the data are shown as standard deviation from the mean of their respective sham. Each dot represents a single metabolite in one sample. n = 5 per group.

Figure 6. Pathway impact analysis of metabolic changes

Metabolites showing significant changes were analyzed using Metaboanalyst MetPA and the Mus musculus pathway library: (A) 1 d; (B) 1 week; and (C) 8 weeks of TAC; and (D) 5 d of MI. Fisher’s exact test was used for overrepresentation analysis, and relative between-ness centrality was used for pathway topology analysis; n = 5 per group.

Figure 7. Heart failure increases BCAAs, polyamines, and proyllhydroxyproline

GC/MS analysis of cardiac metabolites after 5 d sham and 5 d MI: (A) BCAA levels; (B) Regression analysis of BCAA levels vs. ejection fraction (EF): R² = Val, 0.59; Leu, 0.61; Ile, 0.59; all p<0.0001; (C) Putrescine levels; (D) Regression analysis of putrescine levels vs. EF: R² = 0.73, p<0.0001; (E) Spermidine levels; (F) Regression analysis of spermidine levels vs. EF: R² = 0.63, p<0.0001; (G) Pro-hydroxy-pro levels; (H) Regression analysis of pro-hydroxy-pro levels vs. EF: R² = 0.41, p=0.0009; n = 10–13 per group; *p<0.05 vs. sham (unpaired t-test); in regression analyses, dotted lines indicate 95% confidence intervals.