Am J Physiol Heart Circ Physiol 306: H78–H87, 2014. First published November 1, 2013; doi:10.1152/ajpheart.00465.2013.

Mouse strain differences in metabolic fluxes and function of ex vivo working hearts Fanny Vaillant,1 Benjamin Lauzier,1 Isabelle Poirier,1 Roselle Gélinas,1 Marie-Eve Rivard,1 Isabelle Robillard Frayne,1 Eric Thorin,2 and Christine Des Rosiers1 1

Departments of Nutrition, Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada; and 2Department of Surgery, Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada Submitted 6 June 2013; accepted in final form 1 November 2013

Vaillant F, Lauzier B, Poirier I, Gélinas R, Rivard ME, Robillard Frayne I, Thorin E, Des Rosiers C. Mouse strain differences in metabolic fluxes and function of ex vivo working hearts. Am J Physiol Heart Circ Physiol 306: H78 –H87, 2014. First published November 1, 2013; doi:10.1152/ajpheart.00465.2013.—In mice, genetic background is known to influence various parameters, including cardiac function. Its impact on cardiac energy substrate metabolism-a factor known to be closely related to function and contributes to disease development-is, however, unclear. This was examined in this study. In commonly used control mouse substrains SJL/JCrNTac, 129S6/ SvEvTac, C57Bl/6J, and C57Bl/6NCrl, we assessed the functional and metabolic phenotypes of 3-mo-old working mouse hearts perfused ex vivo with physiological concentrations of 13C-labeled carbohydrates (CHO) and a fatty acid (FA). Marked variations in various functional and metabolic flux parameters were observed among all mouse substrains, although the pattern observed differed for these parameters. For example, among all strains, C57Bl/6NCrl hearts had a greater cardiac output (⫹1.7-fold vs. SJL/JCrNTac and C57Bl/6J; P ⬍ 0.05), whereas at the metabolic level, 129S6/SvEvTac hearts stood out by displaying (vs. all 3 strains) a striking shift from exogenous FA (⬃⫺3.5-fold) to CHO oxidation as well as increased glycolysis (⫹1.7-fold) and FA incorporation into triglycerides (⫹2-fold). Correlation analyses revealed, however, specific linkages between 1) glycolysis, FA oxidation, and pyruvate metabolism and 2) cardiac work, oxygen consumption with heart rate, respectively. This implies that any genetically determined factors affecting a given metabolic flux parameter may impact on the associated functional parameters. Our results emphasize the importance of selecting the appropriate control strain for cardiac metabolic studies using transgenic mice, a factor that has often been neglected. Understanding the molecular mechanisms underlying the diversity of strain-specific cardiac metabolic and functional profiles, particularly the 129S6/SvEvTac, may ultimately disclose new specific metabolic targets for interventions in heart disease. genetic background; isolated working heart; metabolism; function THE NOTION THAT ALTERED MYOCARDIAL substrate metabolism contributes to disease pathogenesis is generally accepted, but the difficulties encountered in translating this knowledge into therapeutic strategies underscore the need to better understand how cardiac metabolism is regulated under pathophysiologically relevant conditions (for reviews, see Refs. 2, 31, 37, and 38). Genetically modified mice have been extremely valuable models in research. Several studies have, however, emphasized the influence of mouse genetic background on the resulting cardiac or cardiovascular phenotype as well as the importance of selecting appropriate control strains for evaluating the im-

Address for reprint requests and other correspondence: C. Des Rosiers, Research Center, Montreal Heart Institute, 5000 Belanger St., Montreal, Quebec, Canada H1T 1C8 (e-mail: [email protected]). H78

pact of a given transgene on these phenotypic features. For example, differences in the recovery of cardiac function ex vivo and in vivo after experimental ischemia-reperfusion injury have been reported among several commonly used inbred mouse strains (3), specifically lower recovery in the hearts of 129X1/SvJ, C57Bl/10SnJ, and 129S1/SvImJ versus C57Bl/6J mice. Other studies discerned differences in susceptibility to specific pathological states, such as atherosclerosis, insulin resistance, and myocardial hypertrophy (4, 11, 12, 21). Numerous parameters differ among healthy control mouse strains and may be determinants of their respective susceptibility to cardiac disease (5, 6, 15, 22, 34, 36). They include gut microflora (15) and coronary vascular function (5) but also systemic metabolic parameters, such as blood glucose or insulin (6). Finally, more specifically at the cardiac level, studies have reported functional differences specifically in heart rate (HR) and left ventricular (LV) ejection fraction among various healthy control mouse strains, such as C57Bl/6 and SV129 (from Harlan, Oxfordshire, UK), both in vivo as well as ex vivo, during Langendorff perfusion in the presence of glucose as sole substrate (3, 4, 34). It is noteworthy, however, that these differences are generally of smaller magnitude than those reported following a stress challenge, both ex vivo (e.g., acute ischemia) and in vivo (e.g., acute hypoxia or aortic constriction) (3, 4). To the best of our knowledge, the impact of mouse genetic background on cardiac energy substrate metabolism has not yet been examined systematically. Our previous work with various transgenic mouse models does, however, provide some evidence of differences among commonly used control mouse strains, particularly in the partitioning of exogenous long-chain fatty acids (LCFA) for oxidation and storage between hearts from 129S6/SvEvTac versus various C57Bl/6 or C57Bl/10 substrains (17, 18, 26, 27, 30). These results were all obtained in our validated experimental paradigm, namely, ex vivo perfusion in working mode, with concomitant evaluation of myocardial contractility and metabolic fluxes with 13C-labeled substrates. There were, however, many potential confounding factors, such as an experimenter effect, mice age, perfusion conditions, and buffer substrate composition, which may explain these differences. The objective of this study was to systematically compare the cardiac, metabolic, and functional phenotype of commonly used mouse strains. We investigated three different control strains, namely, 129S6/SvEvTac, SJL/JCrNTac, and C57Bl/6J, using our aforementioned ex vivo working heart model with normoxic perfusion under identical conditions. This model is commonly used to study cardiac metabolism and allows the examination of intrinsic myocardial properties in the absence

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of external neurohormonal influences and without changes in peripheral resistance. We also compared C57Bl/6J with C57Bl/ 6NCrl mice for some selected parameters, given the reported existence of several single nucleotide polymorphisms between C57Bl/6 substrains (32, 46), including functional nicotinamide nucleotide transhydrogenase deletion, specifically in C57Bl/6J mice (32). Our results demonstrate striking variations in both functional and metabolic flux parameters of ex vivo normoxic working hearts among all four control, healthy mouse substrains evaluated. MATERIALS AND METHODS

Chemicals. The chemicals, biological products, 13C-labeled substrates, and albumin dialysis procedure deployed in this study have been described elsewhere (41). Heart perfusions in semi-recirculating working mode. These experiments were approved by the local Animal Research Ethics Committee of the Montreal Heart Institute in compliance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (NIH Publication No. 85-23, revised 1996). Mice were housed in a specific pathogen-free facility, under a 12-h:12-h light/dark cycle, starting at 7:00 AM, and were provided food and water ad libitum. We assessed the functional and metabolic phenotypes of ex vivo working hearts isolated from 3-mo-old male (n ⫽ 15 per group) control mouse substrains, namely, 129S6/SvEvTac (Taconic, Germantown, NY), SJL/JCrNTac (Taconic, Germantown, NY), C57Bl/6J (The Jackson Laboratory, Bar Harbor, ME), and C57Bl/6NCrl (Charles River Laboratories, Saint-Constant, QC, Canada). The animals were anesthetized with sodium thiopental (100 mg/kg ip), heparinized (5,000 units/kg sc) 15 min before surgery and euthanatized by exsanguination during heart isolation for ex vivo perfusion. A previous publication (26) describes, in detail, the procedure for heart isolation and ex vivo perfusion in working mode as well as measurements of various functional parameters, recorded by an EMKA-IOX2 data acquisition system (EMKA Technologies, Falls Church, VA), except for cardiac cycle parameters, namely, duration of 1) isovolumic contraction, 2) systolic ejection, 3) isovolumic relaxation, and 4) diastolic filling. Myocardial performance index (MPI), a time-interval parameter independent of HR and reflecting LV myocardial performance (9, 10), was calculated from: isovolumic relaxation duration ⫻ isovolumic contraction duration/LV systolic ejection duration. Perfusion protocols. Isolated working mouse hearts were perfused for 30 min with semi-recirculating, modified Krebs-Henseleit buffer containing physiological concentrations of substrates and hormones [11 mM glucose, 0.8 nM insulin, 50 ␮M carnitine, 5 nM epinephrine, 1.5 mM lactate, 0.2 mM pyruvate, 0.7 mM oleate (OLE) bound to 3% albumin]. In any given perfusion, one of the unlabeled substrates was replaced by its corresponding labeled substrate (n ⫽ 5 per group): [U-13C6]glucose [initial molar percent enrichment (MPE): 50%], [U-13C3]pyruvate-[U-13C3]lactate [initial MPE: 100%], or [1-13C18] OLE (initial MPE: 100%) to probe both carbohydrate (CHO) and LCFA metabolism, respectively. As described previously (26), atrial influent and coronary effluent perfusate samples were collected during the experiment to assess 1) lactate dehydrogenase release as an index of membrane integrity; 2) PO2, PCO2, pH, Ca2⫹; and 3) lactate and pyruvate release rates. At the end of perfusion, the hearts were freeze clamped with metal tongs, cooled in liquid nitrogen, weighed (corresponding to heart wet weight), and stored at ⫺80°C for subsequent analyses. All mice had similar body weight and heart wet weight (in grams, heart wet weight: 129S6/SvEvTac, 0.28 ⫾ 0.03; SJL/JCrNTac, 0.25 ⫾ 0.03; C57Bl/6J, 0.28 ⫾ 0.03; and C57Bl/6NCrl, 0.28 ⫾ 0.03; body weight: 129S6/SvEvTac, 25.58 ⫾ 0.40; SJL/JCrNTac, 25.61 ⫾ 0.44; C57Bl/6J, 25.54 ⫾ 0.37; and C57Bl/6NCrl, 25.56 ⫾ 0.54). It is noteworthy that the measured heart wet weights are consistent with

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our previously published data (26, 27, 30), but because oedema occurs following ex vivo heart perfusion with a crystalloid perfusion buffer, a factor of 8.9 ⫾ 0.2 (n ⫽ 29) had previously been determined for conversion of measured gram wet weight to gram dry weight (41). Metabolic flux measurements. Our previously published studies (17, 26, 41) have defined 13C-terminology and described measurement by gas chromatography-mass spectrometry (Agilent 6890N GC coupled with a 5973N mass spectrometer) as well as equations for the calculation of 1) flux ratios relative to substrate selection for mitochondrial citrate synthesis (CS) from the 13C-enrichment of acetyl (carbons 4 and 5) and oxaloacetate (carbons 1, 2, 3, and 6) moieties of citrate, 2) absolute flux rates for the citric acid cycle (CAC) from the stoichiometric relationship between oxygen consumption and citrate formation from various substrates assessed from determined flux ratios, and 3) efflux rates of unlabeled lactate and pyruvate reflecting cytosolic glycolysis from exogenous glucose (by [U13 C6]glucose perfusion). Quantification and 13C-enrichment of triglycerides. Fatty acids (FA) from heart tissue triglycerides (TG) were analyzed according to a previously described, modified gas chromatography-mass spectrometry method, which included lipid extraction and TG isolation, the addition of mixed internal and external standards for quantification (in nmol: 60 [2H27]myristic acid, 300 [U-13C16]palmitic acid, 300 [2H35]stearic acid, 300 [U-13C18]oleic acid, 300 [U-13C18]linoleic acid, 300 [2H3]arachidic acid, 300 [2H3]docosanoic acid), and FA transmethylation before sample injection into a Agilent 6890 N gas chromatograph equipped with a Varian CP7420 polar capillary column (100 m; 0.25-mm internal diameter; 0.23-␮m thickness) coupled with a 5973 detector operated in positive chemical ionization mode with ammonia as reagent gas (18, 27). 13C-enrichment of OLE in TG was assessed by monitoring at mass-over-charge (m/z) 314 (for unlabeled OLE) and 315 (for [1-13C]OLE). Protein expression and phosphorylation status. We undertook Western blotting in ex vivo perfused hearts after membrane and cytosol fraction isolation (29) to evaluate the membrane abundance of GLUT4 and CD36 and the abundance and phosphorylation status of AMPK. Primary antibodies against GLUT4 (1/15,000; Millipore, Billerica, MA), CD36 (1/5,000; Genetex, Irvine, CA), total ␣-AMPK (1/1,000), and phospho-␣AMPK (Thr172; 1/1,000) (Cell Signaling Technology, Danvers, MA) were tested. Pan-cadherin (1/10,000; Invitrogen, Burlington, ON, Canada), a transmembrane protein, and ␤-actin (1/10,000; Abcam, Cambridge, MA) served as loading controls for membrane and cytosol fractions, respectively. After wash, the membranes were incubated with secondary antibody conjugated to horseradish peroxidase (1/5,000). Signals were obtained with Western Lightning Blot Plus (Perkin Elmer, Waltham, MA) and quantified by densitometric analysis with Quantity One software (Bio-Rad, Mississauga, ON, Canada). RT-quantitative PCR gene expression analysis. To minimize variations and confounding factors, we have freeze clamped the hearts from all groups at the same time of the day, namely in the morning (between 9 and 11 AM, light phase). We selected 1) genes involved in FA oxidation, namely, acyl-CoA oxidase 1 (Acox1), medium-chain acyl-CoA dehydrogenase (Acadm), carnitine palmitoyltransferase 1b (Cpt1b); 2) genes related to CHO metabolism or its regulation, such as glucose transporter-4 (Slc2a4), phosphofructokinase-1 (Pfkm), phosphofructokinase-2 (Pfk2), pyruvate dehydrogenase kinase 4 (Pdk4); and 3) genes related to mitochondrial biogenesis peroxisome proliferative activated receptor ␥ coactivator 1 ␣ (Ppargc1a). RTquantitative PCR was performed as described previously (17). Gene primer pairs not reported previously (17, 18) are Acox1 (forward: CTTCCAATCATGCGATAGTCC; reverse: TTGTCCATCTTCAGGTAGCC), Cpt1b (forward: ACCAGTCTTAGCCTCTACG; reverse: TGTAGCCCAGGTGAAAGG), Pfk2 (forward: ACCAGTCTTAGCCTCTACG; reverse: GTCTACAGCATCCACATTCAG), and Ppargc1a (forward: TGGATGAAGACGGATTGC; reverse: TGGTTCTGAGTGCTAAGAC). Gene primers were generated with the Beacon Designer

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version 5.0 program using mouse sequences available in GenBank. Only primer pairs giving ⱖ90% efficiency were retained. Cycling was undertaken in an MX3005p cycler (Stratagene, La Jolla, CA). Conditions were 95°C for 10 min and 40 cycles of 95°C for 30 s, 60°C for 1 min, 55°C for 30 s, and 95°C for 30 s. The levels of selected gene transcripts in each sample were averaged and normalized to the housekeeping gene hypoxanthine phosphoribosyltransferase 1 (Hprt1). This gene was selected because, among all tested housekeeping genes [glyceraldehyde-3-phosphate dehydrogenase (Gapdh), Hprt1, and cyclophilin A (Ppia)], it displayed the lowest changes between various experimental conditions. Statistical analysis. The data are expressed as means ⫾ SE. Statistical significance was reached at P ⬍ 0.05. ANOVA (1-way or 2-way for repeated measures) was followed by Tukey’s multiple selected comparisons posttest. Pearson correlations tested relationships between variations in functional and metabolic parameters among all mouse groups. For these analyses, we have used individual metabolic flux data and corresponding functional data assessed in the same mouse heart, without distinction between groups. It is noteworthy that the number of mice per group used for a correlation analysis between a given metabolic flux and functional parameter varies between three and six because we have perfused hearts with different 13 C-labeled substrates to assess the different metabolic flux parameters, but in any given perfusion, one (glucose or OLE) or two (pyruvate and lactate) of the unlabeled substrates was replaced by the corresponding labeled substrate(s). RESULTS

Ex vivo working mouse hearts from commonly used substrains display marked differences in functional parameters. Upon ex vivo perfusion at a physiological afterload of 50 mmHg with a buffer containing a mixture of substrates and hormones mimicking the in vivo milieu (11 mM glucose, 1.5 mM lactate, 0.2 mM pyruvate, 0.7 mM oleate bound to 3% albumin, and 0.8 nM insulin), hearts from all mouse groups maintained stable values for all functional and physiological parameters over the entire 30-min time period and displayed marginal release of lactate dehydrogenase, indicating that cardiac membrane integrity was preserved (Table 1). All these values remained also within what would be considered a normal range for such preparation. They were, however, striking differences in many functional cardiac parameters, which

included those reflecting flows, contractility, and cardiac cycle, in the various mouse strains (Table 1 and Fig. 1). The patterns observed among the various mouse substrains depended on the parameters measured. For example, working hearts from 129S6/SvEvTac and C57Bl/6NCrl mice presented significantly greater values of cardiac output (Fig. 1A), cardiac power (Fig. 1B), and HR (Fig. 1C), but those from C57Bl/6NCrl mice had the highest stroke volume (Fig. 1D). Furthermore, both 129S6/SvEvTac and SJL/JCrNTac hearts stood out, with significantly greater first derivative maximum of LV pressure (LV dP/dtmax) and first derivative minimum of LV pressure (LV dP/dtmin) values (Fig. 1, E and F), but SJL/JCrNTac hearts had the lowest HR (Fig. 1C), concurring with their more prolonged duration of diastolic filling (Table 1). Finally, major differences were also found between the two C57Bl/6 substrains in cardiac flows (Fig. 1, A and B), stroke volume (Fig. 1D), and heart cycle parameters (Table 1). Despite the aforementioned cardiac functional differences between mouse strains, myocardial oxygen consumption (MVO2) (Table 1) was not significantly different among groups and, consequently, the pattern observed for cardiac efficiency (Table 1) was similar to that of cardiac power (Fig. 1B). In summary, significant variations were found for most baseline functional parameters of ex vivo normoxic working hearts among all four control healthy mouse substrains evaluated. Ex vivo working mouse hearts from commonly used substrains display marked differences in metabolic flux parameters. Figure 2 reports on metabolic flux parameters relevant to CHO and LCFA metabolism, namely 1) lactate and pyruvate production from exogenous glucose, i.e., glycolysis (Fig. 2A); 2) the contribution of exogenous CHO, specifically glucose (Glc) and lactate plus pyruvate, as well as LCFA, specifically OLE to acetyl-CoA formation for CS (Fig. 2B); 3) the contribution of CHO to oxaloacetate to CS through anaplerotic pyruvate carboxylation (PC/CS; Fig. 2C); (iv) OLE contribution to TG formation (Fig. 2D); and 5) the TG pool (Fig. 2E). Similar to what was observed with the functional parameters, differences

Table 1. Physiological parameters of ex vivo working hearts from 3-mo-old healthy control substrains: 129S6/SvEvTac, SJL/ JCrNTac, C57Bl/6J, and C57Bl/6NCrl Physiological Parameters

129S6/SvEvTac

SJL/JCrNTac

C57Bl/6J

max-P, mmHg min-P, mmHg End diastolic pressure, mmHg LVDP, mmHg RPP, mmHg·beats⫺1·min⫺1 Cardiac power, mWatts Coronary flow, ml/min Coronary flow per beat, ml·min⫺1·beat⫺1 MVO2, ␮mol/min Cardiac efficiency, mW·mmolO2.1·min1 Lactate dehydrogenase, mU/min Isovolumic contraction duration, ms Systolic ejection duration, ms Isovolumic relaxation duration, ms Diastolic filling duration, ms Myocardial performance index

99 ⫾ 3 ⫺5.20 ⫾ 2.03 14.81 ⫾ 1.37 104 ⫾ 5 40,200 ⫾ 2,149 1.839 ⫾ 0.182 1.72 ⫾ 0.16 0.004 ⫾ 0.0003 1.033 ⫾ 0.093 1.836 ⫾ 0.242 32.02 ⫾ 4.39 23.9 ⫾ 3.07 17.5 ⫾ 2.45 40.4 ⫾ 2.26 78.7 ⫾ 6.54 4.05 ⫾ 0.85

103 ⫾ 3 2.57 ⫾ 1.05** 17.79 ⫾ 1.28 102 ⫾ 4 28,166 ⫾ 1,036*** 1.163 ⫾ 0.123** 1.67 ⫾ 0.17 0.006 ⫾ 0.001 1.013 ⫾ 0.070 1.112 ⫾ 0.081* 24.79 ⫾ 2.15 34.7 ⫾ 4.27 11.5 ⫾ 2.40 49.4 ⫾ 3.18 121.0 ⫾ 11.22** 13.86 ⫾ 4.10*

89 ⫾ 4 1.33 ⫾ 1.21* 13.53 ⫾ 1.04# 88 ⫾ 5* 29,548 ⫾ 1,924*** 1.050 ⫾ 0.098*** 1.69 ⫾ 0.17 0.006 ⫾ 0.001 1.215 ⫾ 0.105 1.052 ⫾ 0.134** 33.69 ⫾ 3.77 29.7 ⫾ 3.21 19.5 ⫾ 34.01 44.1 ⫾ 3.56 94.3 ⫾ 12.22 7.8 ⫾ 1.85

C57Bl/6NCrl

94 ⫾ 4 ⫺4.07 ⫾ 1.67# 8.29 ⫾ 0.96***,###,$ 96 ⫾ 5 37,645 ⫾ 1,633##,$ 1.75 ⫾ 0.131$$,# 1.62 ⫾ 0.10 0.004 ⫾ 0.0004 1.093 ⫾ 0.076 1.545 ⫾ 0.121 30.80 ⫾ 4.62 37.8 ⫾ 2.62* 31.7 ⫾ 2.01 33.4 ⫾ 3.46## 65.3 ⫾ 4.79### 2.47 ⫾ 0.43##

Values are means ⫾ SE of 11–15 hearts for the 25- to 30-min perfusion period. LVDP, left ventricular developed pressure; max-P, maximum pressure; min-P, minimum pressure; RPP, rate pressure product; MVO2, myocardial oxygen consumption. *P ⬍ 0.05, **P ⬍ 0.01, and ***P ⬍ 0.001 vs. 129S6/SvEvTac. $P ⬍ 0.05 and $$P ⬍ 0.01 vs. SJL/JCrNTac. #P ⬍ 0.05 and ##P ⬍ 0.01 and ###P ⬍ 0.001 vs. C57Bl/6J. AJP-Heart Circ Physiol • doi:10.1152/ajpheart.00465.2013 • www.ajpheart.org

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Fig. 1. Functional parameters of ex vivo working hearts from 3-mo-old control substrains (129S6/SvEvTac, SJL/JCrNTac, C57Bl/6J, and C57Bl/6NCrl). Values are reported as means ⫾ SE of 11–15 perfused hearts for the 25- to 30-min perfusion period. A: cardiac output. B: cardiac power. C: heart rate. D: stroke volume. E: LV maximal change in pressure over time (dP/dtmax). F: LV minimum change in pressure over time (dP/dtmin). *P ⬍ 0.05, **P ⬍ 0.01, ***P ⬍ 0.001 vs. 129S6/SvEvTac. #P ⬍ 0.05, ##P ⬍ 0.01, ###P ⬍ 0.001 vs. SJL/JCrNTac. $P ⬍ 0.05, $$P ⬍ 0.01, and $$$P ⬍ 0.001 vs. C57Bl/ 6J. Bpm, beats/min.

were also apparent among the four control substrains for the various flux parameters measured. However, in this case, working hearts from one mouse strain, namely, 129S6/ SvEvTac, stood out among all groups for its metabolic phenotype. These hearts displayed increased glycolysis (⫹70%; Fig. 2A) and a shift from exogenous OLE to CHO (specifically glucose) oxidation (Fig. 2B) associated with heightened OLE incorporation in TG (Fig. 2D) and a decreased total TG pool (Fig. 2E). From the various flux data depicted in Fig. 2B, we have also estimated the contribution of other substrates (OS), most likely FA released from the hydrolysis of endogenous TG stores, to the formation of acetyl-CoA [OS/CS ⫽ 1 ⫺ (Glc/CS ⫹ PL/CS ⫹ OLE/CS)]; this was significantly greater for 129S6/ SvEvTac hearts (39.1. ⫾ 3.2%) than for other mouse strains, namely 23.4 ⫾ 3.2 for SJL/JCrNTac (P ⬍ 0.05) and negligible for the two C57Bl/6 substrains (P ⬍ 0.001). Nevertheless, C57Bl/6NCrl hearts presented a decrease (⫺37%; P ⬍ 0.05) in

the relative contribution of glucose to acetyl-CoA formation for CS compared with C57Bl/6J, whereas that of pyruvate plus lactate or OLE was similar. It is noteworthy that despite the aforementioned differences in flux ratio parameters among the various groups, which reflect variations in energy substrate selection for mitochondrial oxidation, the calculated absolute CAC flux rate, which reflects the total rate of mitochondrial oxidation from all substrates, was similar (in ␮mol/min: 129S6/SvEvTac, 0.37 ⫾ 0.05; SJL/JCrNTac, 0.38 ⫾ 0.04; C57Bl/6J, 0.30 ⫾ 0.03; and C57Bl/6NCrl, 0.37 ⫾ 0.02). The observed differences in metabolic flux parameters among the control mouse strains were not readily explained by changes in transcript levels for selected commonly assessed marker genes coding for enzymes involved in CHO (Fig. 3, A-D) and LCFA (Fig. 3, E–G) metabolism and markers of mitochondrial biogenesis (Fig. 3H). In fact, although we found significant differences in the levels of some measured myocar-

Fig. 2. Metabolic flux parameters of ex vivo working hearts from 3-mo-old control substrains (129S6/SvEvTac, SJL/JCrNTac, C57Bl/6J, and C57Bl/6NCrl). Values are means ⫾ SE of 3–6 perfused hearts. The metabolic parameters assessed in these hearts perfused for 30 min with [U-13C6]glucose, [U-13C3]pyruvate ⫹ [U13 C3]lactate, or [1-13C18]OLE are glycolysis (A), rates of pyruvate and lactate production from exogenous glucose (Glc); substrates ¡ acetylCoA (B), relative contributions of exogenous carbohydrates (CHO) (glucose, pyruvate plus lactate) and fatty acid (FA) [oleate (OLE)] to acetyl-CoA formation for citrate synthesis (CS); pyruvate carboxylation (PC)/CS (C), relative contribution of CHO to oxaloacetate formation (pyruvate anaplerosis) for CS; OLE ¡ triglycerides (TG) (D), incorporation of exogenous OLE into TG; and TG pool (E), total concentration of FA in TG. *P ⬍ 0.05, **P ⬍ 0.01, and ***P ⬍ 0.001 vs. 129S6/SvEvTac. #P ⬍ 0.05 vs. SJL/ JCrNTac. $P ⬍ 0.05 vs. C57Bl/6J. PL, pyruvatelactate.

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Fig. 3. Cardiac mRNA levels of selected genes involved in CHO (A–F) and FA (G and H) metabolism in 3-mo-old control strains (129S6/SvEvTac, SJL/JCrNTac, and C57Bl/6J). Values are expressed as means ⫾ SE of 4 to 5 perfused hearts. mRNA levels of glucose transporter-4 (Slc2a4; A), phosphofructokinase-1 (Pfkm; B), phosphofructokinase-2 (Pfk2; C), pyruvate dehydrogenase kinase-4 (Pdk4; D), carnitine palmitoyltransferase 1b (Cpt1b; E), medium-chain acyl-CoA dehydrogenase (Acadm; F), acyl-CoA oxidase 1 (Acox1; G), and peroxisome proliferative activated receptor, ␥ coactivator 1 ␣ (Pparg1a; H) are normalized to hypoxanthine phosphoribosyltransferase 1 (Hprt1). *P ⬍ 0.05 vs. 129S6/SvEvTac. #P ⬍ 0.05, ##P ⬍ 0.01 vs. SJL/JCrNTac.

dial mRNA transcripts among the control mouse strains tested, 129S6/SvEvTac, SJL/JCrNTac, and C57Bl/6J, especially in C57Bl/6J hearts, none of them could explain the observed changes in metabolic flux ratios reported in Fig. 2, especially the particular metabolic and functional phenotype of 129S6/ SvEvTac hearts. In addition, these differences were not explained by changes in the ratio of phosphorylated to total protein level of AMPK as a surrogate of its total activity. This nutrient signaling pathway regulating cardiac energy metabolism and function is known to be activated under stress conditions associated with a compromised energy status (14). AMPK is also known to phosphorylate and inhibit acetyl-CoA carboxylase, which, in turn, determines the cardiac level of malonyl-CoA, a negative regulator of carnitine palmitoyl transferase I and thereby LCFA ␤-oxidation. Furthermore, AMPK plays a crucial role in translocation of the LCFA transporter CD36 from endogenous stores to the plasma membrane, an essential step in enhancing exogenous LCFA uptake (19). Finally, AMPK is responsible for the regulation of the glucose membrane transporter, GLUT4, and the activation of the Pfk2 inducing a stimulation of the glycolysis (23). Figure 4 shows that there were no significant differences in the ratio of phosphorylated to total protein level of AMPK assessed in ex vivo working hearts from the three control strains, suggesting that the energy status of all these hearts was similar. Accordingly, no differences in the level of membrane transporters GLUT4 and CD36 were evident among the various groups (data not reported).

In summary, significant variations were also found in metabolic flux parameters of ex vivo normoxic working hearts among all four control healthy mouse substrains evaluated, which cannot be readily explained by changes in mRNA levels for selected metabolic genes or AMPK activity. Relationship between specific metabolic fluxes and functional parameters. Considering that cardiac metabolism is closely related to function (24, 33, 35, 40), we aimed at evaluating linkages between strain-dependent variations in metabolic flux and functional parameters. Previous studies have reported positive association 1) between glycolysis and functional parameters relevant to myocardial work (1, 13, 45) and 2) LCFA oxidation and myocardial perfusion (8, 20, 28, 41). Because these associations were not readily apparent from mean values reported for the various cardiac functional (Fig. 1 and Table 1) and metabolic flux parameters of the four mouse substrains (Fig. 1), we further tested for potential linkages between individual metabolic flux and functional parameters by conducting correlation analyses in all mouse strains, without distinction between groups. As depicted in Table 2 and in Fig. 5 for selected representative results, this analysis revealed significant associations between specific metabolic flux ratios and functional parameters, specifically for OLE oxidation (OLE/CS), glycolysis, pyruvate decarboxylation (PDC), and anaplerotic carboxylation (PC/CS). For FA metabolism (column 1), a strong positive correlation (P ⬍ 0.001) was noted between the OLE/CS flux ratio (OLE/CS: exogenous OLE contribution to acetyl-CoA

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Fig. 4. AMPK activity in ex vivo working hearts from 3-mo-old control strains (129S6/SvEvTac, SJL/JCrNTac, and C57Bl/6J). Representative immunoblots (A) and densitometry (B) of total AMPK are normalized to ␤-actin, and phosphorylated-to-total AMPK is normalized to ␤-actin (C; P-AMPK/AMPK). Data are means ⫾ SE of 4 to 5 perfused hearts.

formation) and myocardial oxygen consumption (MVO2) as well as global or per beat coronary flow (Fig. 5A). For CHO metabolism (columns 2– 4), first, glycolysis (column 2) was strongly and positively correlated with indexes of cardiac work, namely, cardiac power (P ⬍ 0.001; Fig. 5B), stroke volume (P ⬍ 0.05), cardiac output (P ⬍ 0.01), and LV dP/dtmax (P ⬍ 0.05), and negatively correlated with LV dP/ dtmin (P ⬍ 0.05). Flux ratios relevant to PDC (PDC/CS; column 3) and anaplerotic carboxylation (PC/CS; column 4) were positively associated with HR and rate pressure product (P ⬍ 0.05; Fig. 5C), but negatively associated with myocardial perfusion (P ⬍ 0.01) and stroke volume (P ⬍ 0.001; Fig. 5D). In contrast, glucose contribution to acetylCoA formation did not correlate with any of the physiological parameters measured (data not included). Finally, correlation analyses did not discern any significant association between cardiac cycle durations and metabolic flux parameters (data not reported). In summary, our correlation data concur and expand previously suggested associations between some specific fluxes and functional parameters beyond mouse strains. They emphasize also the complexity of the relationship between the cardiac functional and metabolic phenotype. DISCUSSION

To the best of our knowledge, this is the first study to provide a systematic and extensive simultaneous comparison

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of the cardiac functional and metabolic phenotype between four commonly used control mouse substrains: the 129S6/ SvEvTac, SJL/JCrNTac, C57Bl/6J, and C57Bl/6NCrl. For our comparison, we have used our well-established model of normoxic working mouse hearts perfused ex vivo with 13C-labeled substrates under physiologically relevant conditions of substrate supply and energy demand (18, 26, 27, 30, 41). The main advantages of this model are that it allows for detailed and simultaneous measurements of 1) the dynamics of cardiac energy substrate metabolism, information that is not accessible from static measurements of mRNA, protein expression or even enzymatic activity and 2) contractile function. With this model, we evaluate the intrinsic myocardial properties of the heart in the absence of external neurohormonal influences and without changes in peripheral resistance. Strain-dependent variations in cardiac functional parameters. We found that the genetic background of four commonly used healthy control mice, including C57Bl/6 substrains, markedly affects ex vivo heart baseline function, the pattern observed in various mouse substrains being dependent on the parameters evaluated. Globally, our findings concur with previously published studies in which cardiac function was assessed both in vivo (assessed by Millar or echocardiography) and in vitro (using the Langendorff perfusion) (3). It is, however, more difficult to make specific comparisons for a given functional parameter or mouse strain between our study and that of others. First, strain-dependent variations in cardiac functional parameters have been found to be affected by the physiological test setting used to assess cardiac function (3, 16, 34), being generally of greater magnitude after a stress challenge both ex vivo (e.g., acute ischemia) and in vivo (e.g., acute hypoxia or aortic constriction) (3, 4, 16). Second, none of these studies have used exactly the same study model, conditions, or control mouse strains as in our study, although some of them included C57Bl/6, 129Sv, and SJL mouse strains. With respect to these specific strains, there are some differences between our results and that of others. For example, in contrast with our results, marginal strain-dependent variations were reported in ex vivo Langendorff perfusion under basal condition (3). One potential explanation for this is that the ex vivo working heart model recapitulates greater, albeit physiological, loading conditions than the Langendorff model, which may be considered a form of “stress challenge.” When compared with the in vivo setting, for contractility parameters, 129Sv showed higher values than C57Bl/6J in our study (e.g., LV dP/dtmax and cardiac power), whereas the opposite was observed in vivo (e.g., fractional shortening and LV ejection fraction) (34). Similarly, for heart rate, SJL/JCrNTac mice showed the lowest values of all mouse strains examined in our study, but aged SJL/J mice (same strain but from another supplier) had slightly higher values than others in vivo (7, 43). These apparently paradoxical results concur, however, with the notion proposed by Barnabei et al. (3) that the multiple intrinsic and extrinsic factors that regulate cardiac function may be modified by specific genetic loci that respond differentially to the test conditions. Strain-dependent variations in metabolic flux parameters. The major new finding of this study, however, concerns metabolic flux parameters. Indeed, to the best of our knowledge, ours is the first investigation to demonstrate that mouse genetic background affects cardiac metabolic fluxes relevant to energy substrate metabolism. Previously published comparisons be-

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Table 2. Correlations between physiological and metabolic flux parameters assessed in ex vivo working hearts from various mouse strains Fatty Acid Metabolism

Heart rate, beats/min max-P, mmHg min-P, mmHg End diastolic pressure, mmHg dP/dtmin, mmHg/s dP/dtmax, mmHg/s LVDP, mmHg RPP, mmHg·beats⫺1·min⫺1 Aortic flow, ml/min Cardiac output, ml/min Coronary flow, ml/min Coronary flow/beat, ml·min⫺1·beat⫺1 Stroke volume, ml/beat Cardiac power, mWatts MVO2, ␮mol/min

Carbohydrate Metabolism

Oleate Oxidation/ Citrate Synthesis

Glycolysis, ␮mol/min

Pyruvate Decarboxylation/ Citrate Synthesis

Pyruvate Carboxylation/ Citrate Synthesis

⫺0.07 0.26 ⫺0.26 0.11 0.11 ⫺0.10 ⫺0.29 ⫺0.26 ⫺0.26 0.02 0.67**

0.26 ⫺0.41 0.27 0.25 ⫺0.48* 0.49* 0.19 0.51* 0.62** 0.63** 0.13

0.49* ⫺0.27 ⫺0.20 ⫺0.39 0.01 ⫺0.25 0.02 0.48* ⫺0.01 0.02 ⫺0.36

0.67** ⫺0.28 ⫺0.26 ⫺0.56** 0.01 ⫺0.27 ⫺0.01 0.50* 0.01 ⫺0.02 ⫺0.24

0.79*** 0.12 ⫺0.13 0.70***

⫺0.23 0.60* 0.75*** 0.13

⫺0.56** ⫺0.72*** ⫺0.05 ⫺0.29

⫺0.47* ⫺0.51* 0.02 ⫺0.07

Pearson correlation coefficients are reported for various physiological and metabolic flux parameters. Correlation analyses were conducted without distinction between groups, using individual metabolic flux data, assessed with a given 13C-labeled substrate, and the corresponding functional parameters evaluated in the same heart (n ⫽3– 6). *P ⬍ 0.05, **P ⬍ 0.01, and ***P ⬍ 0.001. Significant values are shown in bold. dP/dt, change in pressure over time.

tween mouse strains focused on systemic metabolic parameters, such as blood glucose or insulin (6), or lipoprotein size (39). Although our previous studies led us to suspect marked differences in exogenous LCFA ␤-oxidation in ex vivo working C57Bl/6J (27) and the 129S6/SvEvTac (18) mouse hearts, we had not conducted a systematic comparison of all metabolic flux parameters between these two mouse strains. Results from this study reveals that ex vivo working 129S6/SvEvTac hearts have a completely different metabolic phenotype compared with C57Bl/6J hearts, but also to SJL/JCrNTac and C57Bl/ 6NCrl hearts. The most striking differences are seen for FA trafficking. In fact, 129S6/SvEvTac hearts shuttle proportionally more exogenous oleate to TGs (storage) than to ␤-oxidation (energy) than any other strains, while having a heightened contribution of endogenous sources, most likely TGs, to ␤-oxidation (from OS/CS) and a lower TG pool, suggesting an accelerated TG turnover. The potential significance of this finding will be further discussed below. It is noteworthy that the reported strain-dependent variations in metabolic flux parameters were not explained by changes in AMPK activity, indicating that the energy status our ex vivo working hearts was similar and normal for all mouse strains. Although we did not assess this parameter in nonperfused hearts, we previously reported no difference in this parameter between similarly perfused and nonperfused control C57Bl/10 mouse hearts (25). Furthermore, these variations, including the peculiar phenotype of the 129S6/SvEvTac hearts, could not readily be explained by changes in the transcript levels of commonly assessed genes encoding for key metabolic enzymes involved in CHO or FA metabolism. In fact, for this aspect, C57Bl/6J mouse hearts displayed the greatest differences compared with all other strains. Undoubtedly, a more comprehensive analysis of gene or protein expression as well of enzyme activity for all substrains, including the C57Bl/6NCrl, would be needed to further address this issue.

Relationship between cardiac metabolic flux and functional parameters. Our correlation data provide, however, some insight into linkages between cardiac metabolic flux and functional parameters, which need to be considered in data interpretation. Specifically, results from our correlation analyses concur and expand previously suggested associations between some specific fluxes and functional parameters, namely a positive association between glycolysis and functional parameters relevant to cardiac work (1, 13, 45) and the flux ratio relevant to exogenous FA oxidation (OLE/CS), MVO2, and myocardial perfusion (8, 20, 28, 41). In addition, they are revealing some new ones, namely a positive correlation between flux ratios relevant to pyruvate oxidation (PDC/CS) or anaplerosis (PC/CS) and HR-related parameters. The aforementioned associations were not readily apparent from data depicted in Fig. 1 and Table 1, and Fig. 2. These data represent, however, mean values per group for 11–15 hearts perfused with all 13C-labeled substrates versus 3 to 4 heart perfusion experiments with a specific 13C-labeled substrate, respectively. In our correlation analyses, we have used corresponding values for metabolic and functional parameters assessed in the same heart perfusion. Thus this analysis is not only independent of mouse strains but also of intergroup variations in the measured parameters, which for some of them were similar or greater than the intragroup variations [e.g., the percent coefficient variations (%CV ⫽ SD/mean) for coronary flow and MVO2 values averaged 33% and 27%, respectively]. This may have obscured the identification of any linkages between straindependent variations metabolic flux and functional parameters using mean values from the various groups. Taken together results from our correlation analyses emphasize the complexity of the relationship between the global cardiac functional and metabolic phenotype. Although these analyses do not provide any information about cause-effect relationship between strain-specific variations in metabolic

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Fig. 5. Representative correlations between metabolic and functional parameters in ex vivo working hearts from 3-mo-old control strains (129S6/SvEvTac, SJL/JCrNTac, C57Bl/ 6J, and C57Bl/6NCrl). Correlations between OLE/CS and coronary flow (P ⬍ 0.01; A), glycolysis and cardiac power (P ⬍ 0.001; B), PC/CS and heart rate (P ⬍ 0.01; C), and pyruvate decarboxylation (PDC)/CS and stroke volume (P ⬍ 0.001; D) are shown. Data are means ⫾ SE of 3– 6 perfused hearts. Bpm, beats/min.

versus functional parameters, they imply that any factors affecting a cardiac metabolic flux parameter (e.g., glycolysis), which may be modulated by genetic divergence between mouse strains, will impact on the associated functional parameters (e.g., cardiac work). Conversely, any genetically determined factors regulating a given functional parameter will impact on the associated metabolic flux parameters. Although further studies are needed to clarify the (patho)physiological significance of the observed linkages as well as the reported strain-dependent variations in functional and metabolic flux parameters, the following evidence suggests that divergence in gene loci regulating intracellular calcium homeostasis and/or cGMP signaling may participate to the observed differences between the 129/SvJ and other mouse strains. First, when compared with C57Bl/6J, 129/SvJ mouse hearts were reported to have a higher gene expression for Serca2 and ryanodine receptor 2 (42), a finding that would be consistent with differences in cardiac work-related parameters and glycolysis between these two strains observed in our study. Second, 129/SvJ mice were also reported to be more resistant than C57Bl/6J to cardiac hypertrophy development induced by pressure overload (4, 44). Interestingly, these mice recapitulate a cardiac metabolic phenotype similar to transgenic mice that selectively express a constitutively activated guanylate cyclase domain of natriuretic peptide receptor A in cardiomyocytes [characterized by a heightened cardiomyocyte cGMP signaling (27)], which are also resistant to pressure-overload-induced cardiac hypertrophy (4). Study limitations. Strain-dependent variations in cardiac functional and metabolic flux parameters reported in this study represent the intrinsic response of the heart to the specific

condition in which these parameters were assessed, namely ex vivo perfusion in the working mode under normoxia under baseline condition. However, despite what are generally accepted to be physiological levels of workload, nutrients, and calcium supply in the ex vivo perfusion environment are provided, these hearts are uncoupled from systemic homeostatic mechanisms that may also be differentially affected by mouse genetic background. These include neurohormonal factors, such as plasma level of epinephrine or insulin for which strain-dependent variations have been reported (6). In conclusion, our findings highlight major differences among four commonly used control mouse strains, including two C57Bl/6 substrains, for many functional and metabolic flux parameters assessed in ex vivo working hearts perfused under baseline condition, a commonly used study model for investigations in the cardiovascular field. These results emphasize the importance of selecting appropriate control mouse strains for transgenic mouse studies, a factor that has often been underestimated. Finally, our observations of linkages between specific metabolic flux and functional parameters provide new insights into the complex relationship between energy substrate metabolism and function in the mammalian heart, which holds beyond mouse strains. This warrants further investigations to clarify the molecular mechanisms underlying these linkages, which are affected by mouse genetic background; this may ultimately reveal new specific metabolic targets for interventions in heart disease. Specifically, the possibility that the peculiar metabolic phenotype of the 129/ SvJ heart versus that of other mouse strains might contribute to the resistance to hypertrophy development of the heart and

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possibly another form of stress challenges deserves further investigations. 11.

ACKNOWLEDGMENTS We thank Dr. Nathalie Thorin-Trescases for helpful comments, as well as Ovid Da Silva, France Thériault, and Tina Louise Boivin for editorial and secretarial assistance, respectively. The present work was presented in part at meetings of the Society for Heart and Vascular Metabolism in Kananaskis, Alberta, Canada, in 2010, and in Brussels, Belgium, in 2011; Experimental Biology in Washington, DC, in 2011; and the European Society of Cardiology in Paris, France, in 2011. Present address of F. Vaillant: Centre de Recherche Cardio-thoracique, INSERM U1045 and L’Institut de RYthmologie et de Modélisation Cardiaque, Université de Bordeaux, France. Present address of B. Lauzier: L’Institut du Thorax, INSERM, UMR-1087, CNRS, UMR-6291, Université de Nantes, Nantes, France.

12.

13.

14. 15.

GRANTS

16.

This work was supported by the Canadian Institutes of Health Research (Grant No. 9575 to C. Des Rosiers and Grant No. 14496 to E. Thorin). This funding source had no role in our study.

17.

DISCLOSURES No conflicts of interest, financial or otherwise, are declared by the author(s).

18.

AUTHOR CONTRIBUTIONS Author contributions: F.V., B.L., and C.D.R. conception and design of research; F.V., B.L., I.P., R.G., and I.R.F. performed experiments; F.V., B.L., I.P., R.G., M.-E.R., and I.R.F. analyzed data; F.V. and C.D.R. interpreted results of experiments; F.V. and C.D.R. prepared figures; F.V. and C.D.R. drafted manuscript; F.V., E.T., and C.D.R. edited and revised manuscript; F.V., E.T., and C.D.R. approved final version of manuscript.

19.

20.

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