Journal of Molecular and Cellular Cardiology 42 (2007) 526 – 539
www.elsevier.com/locate/yjmcc

Original article

NMR and ...
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

Adipose tissue is now considered as a...
528

J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

or down-regulated in a statistic...
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

529

Table 1
Body and heart weight, b...
530

J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

clustering analysis, identity of...
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

531

Fig. 2. Hierarchical cluster ana...
532

J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

Fig. 2 (continued).
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

533

Table 4
Genes identities for clu...
534

J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

Table 4 (continued)
#Gene ID

Ge...
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

535

Table 5
Quantitation of water-so...
536

J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

Table 6
Quantitation of lipids b...
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

excessive collagen type I and III tha...
538

J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

[9] Guerre-Millo M. Adipose tiss...
J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539

[54]
[55]

[56]

[57]

[58]

molecula...
Upcoming SlideShare
Loading in …5
×

C dna array analysis prior to heart failure reveals an incr

240 views

Published on

scientific article

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

C dna array analysis prior to heart failure reveals an incr

  1. 1. Journal of Molecular and Cellular Cardiology 42 (2007) 526 – 539 www.elsevier.com/locate/yjmcc Original article NMR and cDNA array analysis prior to heart failure reveals an increase of unsaturated lipids, a glutamine/glutamate ratio decrease and a specific transcriptome adaptation in obese rat heart J. Roncalli a , F. Smih a , F. Desmoulin b , N. Dumonteil a , R. Harmancey a , S. Hennig c , L. Perez a , A. Pathak a , M. Galinier a , P. Massabuau a , M. Malet-Martino b , J.M. Senard a , P. Rouet a,⁎ a Unite de recherches sur les obesités, INSERM UPS U586, Institut Louis-Bugnard, Université Paul-Sabatier, CHU Rangueil, BP 84225, 31432 Toulouse cedex 4, France b Groupe de RMN Biomédicale, Laboratoire SPCMIB (UMR CNRS 5068), Université Paul Sabatier, Toulouse, France c Deutsches Ressourcenzentrum für Genomforschung GmbH, Heubnerweg 6, D-14059 Berlin, Germany Received 12 June 2006; received in revised form 13 October 2006; accepted 8 November 2006 Available online 11 January 2007 Abstract Obesity is a risk factor for heart failure through a set of hemodynamic and hormonal adaptations, but its contribution at the molecular level is not clearly known. Therefore, we investigated the kinetic cardiac transcriptome and metabolome in the Spontaneous Hypertensive Heart Failure (SHHF) rat. The SHHF rat is devoid of leptin signaling when homozygous for a mutation of the leptin receptor (ObR) gene. The ObR−/− SHHF rat is obese at 4 months of age and prone to heart failure after 14 months whereas its lean counterpart ObR−/+ is prone to heart failure after 16 months. We used a set of rat pangenomic high-density macroarrays to monitor left ventricle cardiac transcriptome regulation in 4- and 10month-old, lean and obese animals. Comparative analysis of left ventricle of 4- and 10-month-old lean rat revealed 222 differentially expressed genes while 4- and 10-month-old obese rats showed 293 differentially expressed genes. 1H NMR analysis of the metabolome of left ventricular extracts displayed a global decrease of metabolites, except for taurine, and lipid concentration. This may be attributed to gene expression regulation and likely increased extracellular mass. The glutamine to glutamate ratio was significantly lower in the obese group. The relative unsaturation of lipids increased in the obese heart; in particular, omega-3 lipid concentration was higher in the 10-month-old obese heart. Overall, several specific kinetic molecular patterns act as a prelude to heart failure in the leptin signaling deficient SHHF obese rat. © 2006 Elsevier Inc. All rights reserved. Keywords: Obesity; Heart failure; NMR; Transcriptome; Metabolome; Arrays 1. Introduction Obesity is defined by excessive adipose mass, which is characterized by a body mass index (BMI) over 30 kg/m2. Obesity prevalence is reaching around 10% to 30% of the population in industrialized countries and this rate is doubling every 10 years [1]. Obesity is leading to enhanced mortality [2] and cardiovascular morbidity by means of arterial hypertension (HTA) and endocrino-metabolic abnormalities like type II ⁎ Corresponding author. Faculté de Médecine, Laboratoire de Pharmacologie, INSERM U586, 37 allées Jules Guesde, 31073 Toulouse cedex 7, France. Tel.: +33 5 61 14 59 98; fax: +33 5 61 25 51 16. E-mail address: Philippe.Rouet@toulouse.inserm.fr (P. Rouet). 0022-2828/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.yjmcc.2006.11.007 diabetes or dyslipidemia, that are associated in the X metabolic syndrome [3]. Obesity was shown to be an independent factor for cardiovascular risk [4]. A positive correlation was noticed between overweight and cardiovascular mortality, even after adjusting for other risk factors. Additionally, obesity is an independent risk factor for heart failure (HF): a BMI over 30 kg/m2 doubles the risk for HF in both genders [5]. Few works have studied specifically the cardiac abnormalities associated with obesity. Late stage obesity is characterized by left ventricular hypertrophy (LVH), that leads to metabolic change [6], and also by the alteration of the vegetative control of cardiac frequency [7]. Severity and onset of these abnormalities suggest a specific role for obesity, independent from HTA, in the development of cardiomyopathies.
  2. 2. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 Adipose tissue is now considered as an endocrine organ that secretes a number of hormones and cytokines that are regrouped under the name “adipokines” [8,9]. The hypothesis of a specific role for adipose tissue has been supported by the fact that many adipose tissue secretions have been shown to be involved in cardiovascular pathologies [10]. We recently used a dog nutritional model (hypercaloric high fat diet: HFD) of obesity-related hypertension to show early-and late-specific cardiac genes regulated at the transcriptome level [11,12]. These transcriptome regulations are responsible, at least in part, for the cardiac remodeling that leads to LVH. More recently, using human heart samples (right appendage) representative of the right auricle obtained from non-diabetic patients undergoing open heart surgery and heart bypass, we have shown different cardiac gene regulations that are specific to obesity and independent from obesity-associated hypertension [13]. These observations are consistent with cardiac remodeling in response to obesity which could be mediated, at least in part, by adipokines. In this work, our aim was to evaluate the molecular mechanisms involved in the development of a dilated cardiomyopathy (DCM) during the onset of heart failure (HF) and the contribution of obesity in the early steps of the development of this disease. Therefore, we used the SHHF/ Mcc-cp rat model of obesity, non-insulin dependent diabetes and congestive heart failure [14]. As obese rats are prone to heart failure several months before their lean counterpart [14], experiments were performed with 4 and 10 months old, lean and obese animals; before any sign of heart failure. We analyzed in parallel the cardiac transcriptome and metabolome using a cDNA array and an NMR approach respectively. 2. Materials and methods 527 After shaving of the chest, echocardiograms were performed by using the Vivid 7 pro 7 echocardiographic system (GE Medical System), equipped with a i13 L 14-MHz linear-array transducer. Images were obtained from rats lightly anesthetized by 1–2% isoflurane (AErrane, Baxter) lying on their back side with transducer placed on the left hemithorax. Two-dimensional parasternal long- and short-axis images of the left ventricle were obtained, and two-dimensional targeted M-mode tracings were recorded at a sweep speed of 200 mm/s. All measurements were performed according to the recommendations of the American Society for Echocardiography leading-edge method from three consecutive cardiac cycles (n) with the roundness of the left ventricular cavity (2D-image) as a criterion that the image was on axis, great effort was taken to achieve a good image quality to visualize the endocardial and epicardial borders of the heart by gently moving and angulating the transducer. Measurements and calculations used are as follows: percent LV fractional shortening (FS), a measure of LV systolic function, was calculated as follows: FS = (EDD − ESD) / EDD × 100, where EDD and ESD are end-diastolic and end-systolic diameters, respectively. All values were averaged over three consecutive cycles. Twice a month, weight, systolic blood pressure (SBP) and heart rate (HR) were measured in both lean and obese groups. SBP and HR were recorded on vigil animals, after 15 min rest, using a PowerLab System apparatus (ADInstruments, Australia). Attention was given to the possible appearance of CHF symptoms such as subcutaneous oedema, hydrothorax, ascites, dyspnea and cyanosis. At the end of the experiment, animals were anesthetized, and surgery was quickly performed. Left ventricular and right auricle samples were taken from 5 lean and 5 obese rats at the age of 4 months or 10 months. Samples were immediately washed in cold Phosphate Buffer Saline Buffer, snap frozen in liquid nitrogen and maintained at − 80 °C until analysis. 2.1. Animals and general procedures 2.2. RNA extraction and cDNA labeling All animal procedures were performed according to the guidelines of the French Ministry of Agriculture. Rats were housed at the Toulouse IFR31 animal facility in a room lit 12 h per day (6 AM–6 PM) at an ambient temperature of 22 ± 1 °C. Animals were allowed 1 week to adjust after arrival and had access to regular rodent diet and tap water ad libitum during the experiment. Ten lean heterozygous (+/cp) and 10 obese homozygous (cp/cp) Spontaneously Hypertensive and Heart Failure-prone rats (SHHF), were provided by Charles River Laboratories and included in the study. This strain was obtained by backcrossing a Koletsky obese rat to a Spontaneously Hypertensive Rat (SHR/N). Obese males develop congestive heart failure (CHF) at 10–14 months, lean males at 14–18 months [14,15]. From the Koletsky rat, SHHF rats harbor a nonsense mutation of the leptin receptor gene, designated fa, resulting in a premature stop codon in the leptin receptor. This mutation is a recessive autosomic trait. As a consequence, cp/cp homozygous animals for the fa mutation are obese. Lean rats are heterozygous for this fa mutation or wild type for the cp locus. Total RNA was isolated from left ventricular samples; quality check, concentration control and labeling were performed as previously described [13]. 2.3. cDNA array hybridization and expression analysis cDNA array hybridization, washes, and expression analysis were performed as previously described [13] using pangenomic macroarrays membranes (RZPD, Berlin) containing 26, 592 unique rat cDNA sequences spotted in duplicate. This pangenomic array contained both known genes and nonannoted genes that required further computing analysis. Therefore, to achieve the maximum information for the respective probes on the membrane, the corresponding accession numbers were used to identify both the respective NCBI-Unigene clusters [16] and the respective EST contigs provided by the TIGR gene index [17]. To limit the number of false positive results and to increase specificity of our data, we used the following selection criteria to determine whether the expression of a gene is up-regulated
  3. 3. 528 J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 or down-regulated in a statistically significant manner: (1) expression ratio of obese/lean was > 1.5 or <0.67, (2) expression level was required to be over 3 times above membrane background after local background subtraction (membrane background was calculated by averaging the signal intensity on empty spots i.e. generated by buffer deposition from DNA free wells), (3) the difference between 2 experimental settings was statistically significant by Student's t test, (4) attention was given to avoid rejecting eventual highly induced genes with initially low expression level (under 3 times the membrane background). Statistical relevance of biological data and realtime PCR results was assessed with Student's t test or Mann– Whitney rank sum test when normality test failed, using SigmaStat 3.0 software (SPSS). Hierarchical clustering was performed on normalized to the mean intensity of all spots X-dot reader dataset with MultiExperiment Viewer 2.2 software (TIGR) [18]. The clustering was performed on the average linkage clustering method for the genes and the arrays. 2.4. Real-time PCR control of differential expression A set of genes was randomly chosen for real-time PCR validation of the observed differential expression [19]. Oligos were synthesized by Proligo Company and designed with Primer Express 2.0 software (supplemental Table 1 on-line). Real time PCR was performed as previously described [11–13]; gene expressions were normalized to 18S RNA quantification which has been found to be a reliable internal control gene in our hands and others [20,21]. Real-time PCR data were statistically analyzed with SigmaStat software (SPSS Science). 2.5. Heart left ventricular (LV) tissue dual phase metabolites extraction Frozen sections of LV (n = 20, 90 ± 39 mg) were powdered in liquid nitrogen with a mortar and pestle and then immediately subjected to a simultaneous extraction of lipids and watersoluble metabolites [22]. Briefly, frozen tissue fragments were homogenized for 20 s in ice-cold purex-analytical-grade methanol and chloroform in a 2:1 ratio (1.5 ml) by using an Ultra-Turrax homogenizer. After 15 min in contact with the first solvents at 4 °C, 0.5 ml chloroform and 0.5 ml distilled water were added and homogenized to form an emulsion. The samples were then centrifuged at 2000 × g for 30 min, 4 °C. The aqueous phase was then separated from the organic phase. Both fractions were dried in a Speed Vac concentrator. Extracts were maintained at − 80 °C until their preparation for NMR analysis. Prior NMR analysis, extracts were reconstituted in 600 μl of D2O with 10 μl of a 10 mM 3-(trimethylsilyl)-1-propanesulfonate sodium salt (TMPS) solution and 600 μl of CDCl3 / MeOD (2:1, v/v) for aqueous fractions and organic fractions, respectively. 2.6. NMR analysis 1 H NMR spectra were recorded on a Bruker Avance 400 NMR spectrometer, equipped with a z-gradient 5 mm TBO probe. Fully relaxed 1H spectra (without saturation effects) were obtained in 12 min by accumulating 128 FID resulting from 30° excitation pulses. Typical acquisition parameters were a SW of 9.8 kHz, 32 K data points and 2.5 s repetition time. For analysis of aqueous fractions, a presaturation pulse sequence was used to suppress the residual intensity of the 1H water resonance peak. Typical processing parameters were 65 K zerofilling and a LB of 1 Hz applied prior to Fourier transform. Characteristic metabolites [23–25] and lipid signals [26–28] have been assigned by reference to literature data and on the basis of 2D homonuclear correlation spectroscopy (COSY) experiments performed on the extracts (data not shown). The signal of TMPS (δ = 0 ppm) for water-soluble metabolite and residual CHCl3 (δ = 7.36 ppm) for lipids served as references for chemical shift and concentration. A 32 mM concentration of residual CHCl3 was previously determined with trichlorobenzene as standard for our batch of deuterated chloroform [29]. Only resonances giving a signal to noise ratio over 15 were taken into account for statistical analysis. The number of protons giving rise to a signal was considered in the calculations of relative and absolute concentrations. Signals to quantify water-soluble metabolites (chemical shift value in ppm, and relative number of protons) were as fellows: lactate (1.33, 3); alanine (1.48, 3) acetate (1.92, 3); glutamate (2.35,2); glutamine (2.44, 2) creatine + phosphocreatine (3.04, 3), taurine (3.41, 2; this resonance was chosen according to its specificity); TMPS (0, 9); see also Fig. 3. Concentration of fatty acyl chains was determined using the area of the α-methylene resonances at 2.31 ppm as 100% of fatty acyl chains (peak 8 in Fig. 4). Cholesterol was quantified using the area of the C-18 methyl singlet at 0.67 ppm (peak 1 in Fig. 4). n-3 fatty acyl chains were quantified using the characteristic triplet at 0.96 ppm of the terminal-CH3 (peak 3 of Fig. 4). Total choline phospholipids were determined from the trimethyl group at 3.20 ppm (peak 10 in Fig. 4). Mean unsaturation was calculated as the ratio of vinyl from fatty chain (–CHfCH–) determined from the signal at 5.35 ppm (peak 14 in Fig. 4) minus the contribution of cholesterol proton, to the signal of total fatty chains (peak 8 in Fig. 4). Mean poly-unsaturation was calculated as the ratio of the signal of the allylic methylene at 2.80 ppm (peak 9 in Fig. 4) to the signal of total fatty acyl chains. 3. Results 3.1. Rat phenotype analysis CHF symptoms such as subcutaneous oedema, hydrothorax, ascites, dyspnea and cyanosis were not observed in lean and obese 4- and 10-month-old animals. Four-month-old obese rats had an average weight of 508 ± 12 g versus 386 ± 2 g for the lean rats: this 32% increase in weight is statistically significant (Student's t test, p = 0.008) (Table 1). At 10 months these differences in weight were even more evident as obese rats had an average weight of 727 ± 10 g and lean 477 ± 5 g; p ≤ 0.001). Clearly, obesity was due to increased fat mass as reported for this rat model [14].
  4. 4. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 529 Table 1 Body and heart weight, blood pressure, BNP levels and cardiac echographic analysis SHHF 4 months Lean (n = 5) Body weight (g) Heart weight (g) SBP (mm Hg) BNP (ng/ml) Diastolic septal wall thickness (mm) Diastolic Posterior wall thickness (mm) Systolic septal wall thickness (mm) Systolic Posterior wall thickness (mm) LV End diastolic diameter EDD (mm) LV End systolic diameter ESD (mm) LV Fraction shortening FS (%) 386 ± 2 1.27 ± 0.04 212 ± 7 3.8 ± 0.9 1.90 ± 0.16 2.15 ± 0.39 3.21 ± 0.18 3.41 ± 0.52 7.73 ± 0.61 4.30 ± 0.65 44.37 ± 6.70 SHHF 10 months Obese (n = 5) Lean (n = 5) Obese (n = 5) † 508 ± 12* 1.35 ± 0.02 204 ± 4 6.2 ± 2.2 1.78 ± 0.14 2.15 ± 0.34 3.18 ± 0.20 3.16 ± 0.37 7.78 ± 0.55 4.54 ± 0.49 41.65 ± 5.77 727 ± 10*,† 1.88 ± 0.09† 218 ± 2*,† 9.66 ± 2.3 2.18 ± 0.13* 2.32 ± 0.26 3.53 ± 0.26* 3.32 ± 0.27 8.56 ± 0.57* 5.20 ± 0.57* 39.25 ± 4.36 477 ± 5 1.7 ± 0.04† 207 ± 3 6.7 ± 2.8 2.13 ± 0.19* 2.08 ± 0.23 3.35 ± 0.25 3.2 ± 0.31 8.23 ± 0.48 4.87 ± 0.57 39.36 ± 7.26 Values statistically relevant are indicated by * for p < 0.05 (obese versus lean) and by † for 10-month-old versus 4-month-old statistical analysis (Student's t test). Data are mean of five measurements per group ± S.E.M. Left ventricle (LV) percent left ventricular fractional shortening (FS) was calculated as follows: FS = (EDD − ESD) / EDD × 100, where EDD and ESD are end-diastolic and end-systolic diameters, respectively. *p < 0.05 with Student's t test for obese or lean rats at 4-month-old versus 10-month-old. Macroscopic examination of the heart did not reveal any abnormalities or any cardiac infarction lesions. No significant difference in weight were observed between lean and obese average weight of the hearts in 4- or 10-month-old rats (respectively 1.27 ± 0.04 g versus 1.35 ± 0.02 g; and 1.7 ± 0.04 g versus 1.88 ± 0.09 g). Nevertheless, heart mass significantly increased in 10-month-old versus 4-month-old lean or obese rats. Systolic blood pressure (SBP) was not statistically significantly different: 212 ± 7 mm Hg in 4-month-old lean rats versus 204 ± 4 mm Hg in obese rats. In 10-month-old animals, SBP (207 ± 3 mm Hg) was maintained in lean rats and was significantly increased in obese (218 ± 2 mm Hg). Cardiac frequencies were similar in each group of animals (data not shown). Moreover, we did not observe any clinical sign of HF in 10-month-old animals (Table 1). Echocardiographic analysis of obese SHHF and lean SHHF animals demonstrated similar concentric LVH characterised by increased wall thickness with similar LV cavity dimensions (Table 1). LV systolic function assessed by LV fractional shortening was similar in both groups (Table 1) at 4 and 10 months. However, we noted that in obese rats, there was an increase in diastolic and systolic septal wall thickness and not in posterior wall thickness between 4 and 10 months. End diastolic and systolic diameters were also increased in obese rats. Moreover, echocardiographic analysis did not reveal any left ventricular dysfunction. 3.2. Left ventricle transcriptome analysis We first determined the number of detectable genes using our cDNA macroarrays. Once the local background was subtracted, we were able to detect an average of 9529 out of 26,592 genes with a signal greater than 1-fold and up to 37fold over the mean membrane background level (data not shown). 7 out of 8 differentially expressed genes harboring an expression level between 1.95 and 17.9 fold over background were confirmed by Realtime PCR (87% validation) (Table 2). According to this observation and previously published works [30], we defined an expression level that had to be over 3 fold over background to be the minimum expression level for which we could reliably monitor differential gene expression. The following data take this parameter into account. 3.2.1. 10-month-old versus 4-month-old lean and obese rat ventricle transcriptome analysis Comparison of gene expression between 10-month-old animals and 4-month-old animals revealed 222 differentially Table 2 Comparison of DNA array analysis and real-time PCR Gene name or GeneBank # Macroarray Expr. Ratio level/bgd Lean analysis AA 858 801/NFKB1 AA963 792 AA 998 657 Obese analysis AA 875 581/MRLCB AI 028 924 AI 054 986 Gene common to lean and obese analysis AA 924 587 AA 819 584/ATP2A2 12.2 17 10 1.95 8.9 9.2 p Ratio p 10 m/4 m 10 m/4 m 2.59 0.0002 1.51 0.002 2.41 0.002 1.43 0.090 2.57 0.002 2.47 0.001 10 m/4 m 10 m/4 m 2.42 0.0014 2.26 0.017 2.65 0.017 2.46 0.003 2.87 0.01 2.16 0.018 10 m/4 m 4.36 17.9 rt-PCR 3.05 1.99 10 m/4 m 0.0005 1.63 0.005 2.09 0.038 0.038 Differential expression validation by realtime PCR for a set of randomly chosen genes. Induction (if > 1.5) and repression (if < 0.6) ratios are represented. Mean values are indicated for 5 measurements. cDNA macroarrays analysis were performed on left ventricle (LV). m: months. Genes were randomly chosen among those found to be statistically differentially regulated by Bioplot analysis and harboring at least a 2 fold over the background expression intensity value. Expr. level/bgd: ratio of signal intensity for a gene/mean background intensity. Ratio Obese/Lean: signal intensity in Obese group/signal intensity in Lean group. Ratio 10m/4m: signal intensity in 10-month-old group/signal intensity in 4-month-old group. NFKB1: nuclear factor kappa B p105 subunit; MRLCB: myosin regulatory light chain; ATP2A2: ATPase, Ca2+ transporting, cardiac muscle, slow twitch 2.
  5. 5. 530 J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 clustering analysis, identity of 9 genes could be provided in the main cluster (Fig. 2B and Table 4B). 3.3. Left ventricle metabolome analysis Fig. 1. Differential expression analysis schematic diagram. Number of differentially expressed genes is indicated. Arrows indicate the statistical analysis of the data performed using Student's t test. 102 genes were found to be common to the differentially expressed genes list from the obese and the lean animals, 222 and 293 genes were found differentially expressed between 4- and 10-month-old lean and obese animals respectively. expressed genes (17 repressed and 205 over-expressed) and 293 differentially expressed genes (30 repressed and 263 overexpressed) in lean and obese rats, respectively (Fig. 1). Homologies searches generated a list of 95 defined genes from the lean rat analysis and 132 defined genes from the obese rat analysis (Table available as a supplementary data on line). 3.2.2. 10 months versus 4 months differential expression of a set of cardiac-relevant genes We identified a set of well-known function genes common to lean and obese analysis (Table 3). These genes differentially expressed between 4- and 10-month-old animals are indicators of: neurohormonal activation (NPPA, ACE, ECE 1); apoptosis (CASP1); inflammation (Il-6 receptor), fibrosis (TGF-β); energetic metabolism (SLCA1, SLC25A10); hypertrophy (GATA4); structure (MYH 6). 3.2.3. Hierarchical clustering of differentially expressed genes Hierarchical clustering organization provided us with a global view of the changes in cardiac gene expression induced by the duration of hypertension in lean rats and the combination of hypertension and obesity in obese rats. In addition, hierarchical clustering was efficient at grouping gene expression profiles correctly without any intervention. Thus, gene expression profiles were specific to the age of the lean or obese SHHF rat hearts. Interpretation of the data is challenging for some clusters due to a limited number of differentially expressed genes, which limited cluster size. Nevertheless, one could consider the set of 17 and 30 down-regulated genes in 10 months, lean (Fig. 2A) and obese (Fig. 2B) rats, respectively. We first focused our attention on genes co-regulated in lean animals and down regulated in 10-month-old animals (cluster 1, Fig. 2A). We obtained gene identities or homologies for 11 genes out of 17 in this cluster (Table 4A). For obese animal Characteristic signals arising from metabolites such as creatine + phosphocreatine, taurine, glutamine, glutamate, acetate, alanine and lactate were detected in the 1H NMR spectrum of the aqueous fraction of LV heart extract (Fig. 3). Concentration of metabolites and statistical analysis of the data showed a set of differences (Table 5). These differences are related to the age and/or the obesity status. A significant decrease in creatine and glutamate concentration was observed and exclusively correlated to the age. Taurine concentration was significantly decreased in the 10-month-old lean group while its concentration was unchanged in other groups. Glutamine concentration was significantly increased in the lean group compared to that in the obese group. Moreover, glutamine concentration was correlated to the age of rats and significantly decreased in the 10-month-old lean group compared to that in the 4-month-old lean group. The glutamine to glutamate ratio was lower in obese animals, independent of age. A 1H NMR spectrum of total lipid extract of an obese SHHF heart rat is shown in Fig. 4. Characteristic signals of lipid moieties allowed the determination of total fatty acyl chains, cholesterol, choline phospholipids (Table 6). Moreover, mean unsaturation which represents the number of vinyl moieties per fatty acyl chain and the mean poly-unsaturation which represents the number of diallylic methylene per fatty acyl chain were calculated. Choline phospholipids, cholesterol and fatty acyl chains contents were similar at the same ages in the obese group and in the lean group. Surprisingly, we Table 3 Differentially expressed genes selection, according to their well-known function, resulting from lean and obese SHHF 4-month-old/10-month-old comparison Gene name and Gene bank accession # Expr. level/bgd Ratio p (Bioplot) IL-6 R/AA 963 567 CASP 1/AI 071 441 GATA 4/AA 997 121 TGFB/AI 548 079 NPPA/AA 819 343 ACE/AI 556 575 ECE 1/AA 817 947 MYH 6/AA 819 464 FN 1/AA 955 600 SLC1A1/AA 996 752 SLC25A10/AA 859 666 4.26 9.27 3.51 3.26 3.04 5.68 3.21 4.87 14.2 5.31 3.15 1.74 2.14 2.38 1.64 1.67 1.66 2.11 0.58 1.88 2.03 0.56 0.006 0.038 0.027 0.033 0.0061 0.010 0.039 0.042 0.038 0.044 0.040 Expr. level/bgd: ratio of signal intensity for a gene/mean background intensity. Ratio: signal intensity in 10-month-old group/signal intensity in 4-month-old group. IL-6 R: InterLeukine 6 Receptor. CASP 1: Caspase 1. GATA 4: GATA binding protein 4. TGFB: Transforming Growth Factor Beta. NPPA: Natriuretic Peptide Precursor type A. ACE: Angiotensin Converting Enzyme. ECE 1: Endothelin Converting Enzyme 1. MYH 6: Myosin Heavy chain polypeptide 6 (Myosin Heavy Chain alpha). FN 1: FibroNectine 1. SLC1A1: solute carrier family 1, member 1. SLC25A10: solute carrier family 25, member 10.
  6. 6. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 531 Fig. 2. Hierarchical cluster analysis of differential expression. (A) Clustering performed with lean transcriptome analysis data. (B) Clustering performed with obese transcriptome analysis data. 1 to 5: 4-month-old animals; 6 to 10: 10-month-old animals. Genes close to each other harboring correlated expressions are illustrated by the tree on the left side. Genes in cluster 1 from lean and obese analysis are detailed in Table 4.
  7. 7. 532 J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 Fig. 2 (continued).
  8. 8. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 533 Table 4 Genes identities for cluster 1 from Figs. 2A and B #Gene ID Gene name Ratio 4 months 10 months p-value Identities and homologies (A) Gene co-regulated in lean animals and down regulated in 10-month-old animals (cluster 1, Fig. 2A) AA926219 B4GALT1 0.54 1.42 0.77 0.00025917 UDP-Gal:betaGlcNAc beta 1.4-galactosyltransferase. Polypeptide 1 (Rattus norvegicus) AI454214 0.59 1.54 0.91 0.04882843 No sequence homology AA900596 CSN4 0.27 1.43 0.38 0.04986411 Similar to COP9 complex subunit 4 (Homo sapiens) AA957598 0.27 1.68 0.46 0.03521052 No sequence homology AI556930 PKD1 1.52 1.93 2.93 0.01708796 Polycystic kidney disease 1 (Pkd 1) and tuberous sclerosis 2 (Tsc2) genes (Homo sapiens) AI556150 0.66 1.49 0.98 0.04582136 No sequence homology AA900578 ALAD 0.6 1.5 0.91 0.03495742 Aminolevulinate. delta-. dehydratase (Alad) (Rattus norvegicus) AA925164 0.63 1.47 0.93 0.00010271 No sequence homology AI547418 0.64 1.85 1.19 8.90e − 05 Similar to XP_141317.3 RIKEN cDNA A430105I19 gene (Mus musculus) AA925172 FGFR1OP 0.65 1.39 0.9 0.00086072 FGFR1 oncogene partner (Homo sapiens) AA957860 0.66 1.49 0.98 0.00555646 No sequence homology AI575762 CABP1 0.61 1.55 0.95 0.00496229 Calcium binding protein 1 (Cabp1) (Rattus norvegicus) highly similar to NP_839983.1 sorting nexin 26 [Mus musculus]. Human trichohyalin. Potential multiple AI454874 THH 0.63 1.44 0.9 0.00654817 Roles as a functional EF-hand-like calcium-binding protein. A cornified cell envelope precursor and an intermediate filament-associated (cross-linking) protein (Homo sapiens) AI136136 CHMP1.5 0.62 1.55 0.95 0.00646823 CHMP1.5 protein (Homo sapiens) function: putative vesicle trafficking with VPS4 and putative chromatin structure regulation in the nuclear matrix AI555253 0.62 1.54 0.96 6.78e − 05 Similar to KIAA1632 prot. proline–serine–threonine phosphatase interacting protein 2 (Homo sapiens) AA925236 0.64 1.61 1.03 0.00126788 No sequence homology AA956865 SEB4D 0.66 1.6 1.06 0.0010949 Similar to dJ259A10.1 (ssDNA binding protein (SEB4D). RNA-binding region (RNP1. RRM) (Homo sapiens) (B) Genes co-regulated in obese animals and down regulated in 10-month-old animals (cluster 1, Fig. 2B) AA859561 0.08 1.51 0.11 0.04220966 No sequence homology AA900791 CFI 1.54 1.7 2.62 0.04129383 Complement factor I. (Rattus norvegicus) AA925981 0.09 1.48 0.14 0.00609678 Similar to 2410001C21Rik protein (Homo sapiens) chromosome 20 open reading frame 43 AI501277 0.52 1.45 0.75 0.00210187 No sequence homology AI136302 0.55 1.57 0.86 0.00989101 No sequence homology AA926219 B4GALT1 0.56 1.55 0.87 0.00531375 UDP-Gal:betaGlcNAc beta 1.4-galactosyltransferase. polypeptide 1 (Rattus norvegicus) AA925093 0.58 1.45 0.84 0.00311088 No sequence homology AI043693 0.63 1.53 0.96 0.00859156 No sequence homology AA925880 SPOCK2 0.58 2.16 1.25 0.00768181 Similar to testican-2 protein (LOC361840) AA858560 0.49 1.79 0.88 0.00540406 No sequence homology AA859429 0.59 1.63 0.96 0.01117883 Similar to hypothetical protein MGC31967(function: translation initiation factor activity) (Rattus norvegicus) AI145122 Centa2 0.62 1.65 1.03 0.03227733 Centaurin-alpha2 protein (Centa2) (Rattus norvegicus) AI136080 0.63 1.73 1.09 0.037447 Similar to zinc finger protein 198 AI145732 0.6 1.8 1.08 0.01230905 No sequence homology AA858454 0.65 1.71 1.12 0.02969404 Transcribed locus. Moderately similar to XP_346694.1 hypothetical gene supported by NM_022857 AI715210 KS1 0.64 1.5 0.96 0.0019953 KRAB/zinc finger suppressor protein 1 (KS1)(LOC246264)(Rattus norvegicus) AI113019 0.57 2.21 1.25 0.00374868 No sequence homology AI043993 0.62 1.45 0.9 0.00679676 No sequence homology AI112905 0.65 2.22 1.45 0.00955169 No sequence homology AI145673 0.65 1.97 1.28 0.00586599 Transcribed locus. Weakly similar to XP_346694.1 hypothetical gene supported by NM_022857 AI030972 0.48 1.58 0.75 0.00063431 No sequence homology (continued on next page)
  9. 9. 534 J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 Table 4 (continued) #Gene ID Gene name Ratio 4 months 10 months p-value Identities and homologies (B) Genes co-regulated in obese animals and down regulated in 10-month-old animals (cluster 1, Fig. 2B) AA900732 0.61 1.45 0.88 0.00367707 Transcribed locus. Similar to NP_080656.1 RIKEN cDNA 6530413N01 gene (Mus musculus) AI060068 FABP3 0.62 1.44 0.9 0.00212252 Fatty acid binding protein 3 (Fabp3) (Rattus norvegicus) AA926237 TNNI3 0.66 2.06 1.36 0.01630635 Troponin 1. Type 3 (Tnni3) (Rattus norvegicus) AI029660 0.52 1.56 0.82 0.00171603 No sequence homology AA858808 0.63 2.09 1.32 0.0010659 No sequence homology AA858866 NT5 0.6 1.81 1.09 0.00320482 5 Nucleotidase (Nt5) (Rattus norvegicus) AA925124 KIR7.1 0.63 1.98 1.24 0.00392794 Inward rectifier potassium channel Kir7.1 [(Rattus norvegicus) AI137849 0.66 1.57 1.04 0.01128975 No sequence homology noticed a lower fatty acid chain concentration in 10-monthold animals when compared to the 4-month-old animals that was significant for the obese group. Obese animals displayed a higher level of n-3 fatty acid chains when compared to their lean counterparts. In accordance with these observations, 10-month-old obese animals displayed enhanced unsaturation and poly-unsaturation. Unsaturation levels increased with age only in obese animals. Therefore, increased unsaturation is related to the obesity state and its duration. 4. Discussion We combined cDNA macroarrays with NMR metabolic profiling to characterize the cardiac transcriptome and metabolome of two groups of SHHF rats at 4 and 10 months of age which differed in their state of obesity and the associated X metabolic syndrome [14,15]. Our main objective was to define the molecular adaptation in heart at the onset of heart failure development and to evaluate the impact of obesity on the mechanism of heart failure development. Prior to the molecular adaptations analysis, we examined the biological parameters of the subject animals. As previously described, the obese rats, homozygous for the fa mutation, displayed a significant increase of weight (+ 32% at 4 months; + 52% at 10 months; Table 1). However, we noticed two major distinctions when analyzing the biological parameters. First, both groups of animals were hypertensive and 10-month-old obese animals were significantly more hypertensive than their lean counterparts. This may be a consequence of the obesity. Second, 10-month-old obese animals were expected to be at the onset of a DCM [14] but Fig. 3. 1H NMR spectrum of a water-soluble fraction of a heart extract from a lean SHHF rat. Within the aliphatic region (−0.2 to 4.5 ppm) of the NMR spectra, resonances have been assigned to lactate (Lac), alanine (Ala), acetate (Acet), glutamate (Glu), glutamine (Gln), creatine + phosphocreatine (Cr + PCr), taurine (Taur), Sodium 3-(trimethyl-silyl)-1-propanesuffonate (TMPS); pH = 7.4.
  10. 10. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 535 Table 5 Quantitation of water-soluble metabolites by 1H NMR SHHF 4 months Lean Taurine Creatine + Phoshocreatine Glutamate (Glu) Glutamine (Gln) Gln/Glu ratio Acetate Alanine Lactate SHHF 10 months Obese a 33.70 ± 3.55 16.55 ± 1.20 a 5.72 ± 0.56 a 7.82 ± 0.71 a 1.37 ± 0.12 1.70 ± 0.44 2.20 ± 0.12 a 14.49 ± 5.09 Lean Obese 34.71 ± 5.98 15.24 ± 2.72 a 5.75 ± 0.93 a 6.56 ± 1.30 b 1.14 ± 0.10 b 1.62 ± 0.55 2.07 ± 0.05 b 21.45 ± 9.06 a 28.48 ± 3.32 12.19 ± 1.37 4.43 ± 0.76 6.71 ± 0.78 1.53 ± 0.23 1.71 ± 0.65 1.75 ± 0.32 11.03 ± 1.72 33.01 ± 3.52 b 10.98 ± 1.27 4.53 ± 0.83 5.47 ± 0.89 b 1.21 ± 0.09 b 1.14 ± 0.37 2.09 ± 0.44 10.26 ± 1.39 Data are means ± S.E. (in nmol/mg wet wt) from 5 hearts in each group. a Obese 4-month-old is significantly different from obese 10-month-old, or lean 4-month-old is significantly different from lean 10-month-old, Student's t test p < 0.05. b Obese is significantly different from lean of the same age, Student's t test p < 0.05. we did not observe any SBP normalization in this group of animals. This is evidence for the lack of development of a DCM in these 10-month-old animals. Nevertheless, our observations are in accordance with previously published data [15]. Hearts weights at 4 and 10 months of age were not significantly different in the obese and lean groups. Thus, 10-month-old heart weights were in accordance with published data [14] and correspond to LVH hearts when compared to normotensive Wistar–Kyoto or Sprague–Dawley rats of this age [14,31]. At 10 months of age we did not notice a more pronounced macroscopic LVH in obese hearts when compared to lean hearts. This lack of macroscopic differences was also reflected in the BNP levels that were not significantly increased by obesity in 10-month-old rats (Table 1). However, echocardiographic analysis displayed an increased diastolic and systolic septal wall thickness in obese 10-month-old animals (Table 1). This slight structural remodeling is likely a consequence of blood volume increase observed in obese animals. Alternatively, it could be the result of altered kinetic gene regulations which were observed between lean and obese animals, as discussed below. We confirmed differential expression of 2 fold above background (Table 2 and data not shown), which is common in microarray analysis. Therefore, to eliminate false positives and focus on truly differentially expressed genes we set the limit for reliable analysis of gene expression at 3-fold over Fig. 4. 1H NMR spectrum of a total lipid extract from an obese SHHF heart rat. Main peaks or regions are assigned as follows: 1, Cholesterol (Chol, C18); 2, CH3 terminal of fatty acyl chain (FA) and Chol (C26, C27); 3, characteristic triplet of CH3 terminal of (n-3) polyunsaturated FA; 4, Chol (C19); 5, (CH2)n of FA; 6, –CH2– CH2–COO–; 7, –CH2–CHfCH–CH2–CHfCH–CH2–; 8, –CH2–COO–; 9, –CHfCH–CH2–CHfCH–; 10, –N+(CH3)3 of phosphatidylcholine; 11, –CH2N+ of phosphatidylcholine; 12, mainly glycerol (C1 and C3) and –O–CH2–CH2N+ of phosphatidylcholine; 13, esterified glycerol (C2); 14, –CHfCH– of FA and Chol (C6); S, solvents (methanol + H2O).
  11. 11. 536 J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 Table 6 Quantitation of lipids by NMR SHHF 4 months Lean Acyl chains (nmol/mg w.w.) (n-3) Acyl chains % Cholesterol (nmol/mg w.w.) Choline phospholipids (nmol/mg w.w.) Mean unsaturation Mean poly-unsaturation 103.49 ± 11.28 8.80 ± 0.56 5.09 ± 0.33 14.54 ± 1.00 1.70 ± 0.09 1.17 ± 0.06 SHHF 10 months Obese 117.49 ± 16.87 10.35 ± 0.79 b 5.33 ± 0.95 15.72 ± 2.06 a 1.78 ± 0.12 1.23 ± 0.08 a Lean a Obese 90.74 ± 6.11 8.11 ± 1.25 4.61 ± 0.62 13.57 ± 0.89 1.78 ± 0.10 1.23 ± 0.07 84.49 ± 4.73 9.52 ± 0.62 b 5.11 ± 0.78 12.72 ± 082 1.90 ± 0.02 b 1.33 ± 0.02 b Data are means ± S.E. from 5 hearts in each group. a Obese 4 months is significantly different from obese 10 months, or lean 4-month-old is significantly different from lean 10-month-old, Student's t test p < 0.05. b Obese is significantly different from lean of the same age, Student's t test p < 0.05. background. According to previously published work [30], this lowers the false positive rate to 0.7%. According to these criteria, we did not have any evidence of an effect of obesity in 4- or 10-month-old animals. However, macroarrays analysis on a set of known cardiac genes recapitulated the differential expression of genes involved in hypertension and the development of heart failure (Table 3). Hierarchical clustering analysis of gene expression from lean animals revealed in the same main cluster 3 genes out of 11 encoding proteasome proteins COP9 subunit 4 (CSN4), aminolevulinate deltadehydratase (ALAD) and CHMP1.5 (Table 4A) PKD1 is a cation channel regulator [32], a complex involved in many regulatory processes, including control of development and regulation of morphogenesis. Two genes, UDPGal:betaGlcNAc beta 1,4-galactosyltransferase (B4GALT1) and trichohyalin (THH), encode for proteins that have a structural role. UDP N-acetylglucosamine β-1,4 galactosyltransferase is a widely distributed enzyme which catalyzes the transfer of galactose to N-acetylglucosamine residues of glycoproteins and glycolipids [33]. Elevated B4GALT1 expression was already shown to occur in the failing hearts of spontaneously hypertensive rats [34] and was found down regulated in our experiments that monitored rat heart gene expression prior to HF. In this cluster of down regulated genes, we also observed a member of a novel Ca2+-binding protein subfamily (CABP1), that is a component of Ca2+-mediated cellular signal transduction in the heart [35]. We also noticed a down regulation of genes involved in cell proliferation and/or protein synthesis in muscle and heart tissue such as the RNA binding protein (SEB4D) that was shown to be also down regulated in colon cancer [36]. FGFR1OP, which protects cells from apoptosis, was down regulated as well [37]. Cluster 1 in the obese analysis contained a set of genes encoding remodeling and structural proteins and a second group of genes with designated miscellaneous functions (Table 4B). In the group of genes encoding remodeling and structural proteins, we noticed upregulation of complement factor 1 (CF1). It is well known that both free radicals and complement activation can injure tissue. Local complement activation may represent a mechanism by which free radicals mediate tissue injury [38]. It has been shown that complement activation is directly involved in chronically sustained myocardial damage [39]. Up-regulation of CF1 probably contributes to cardiac remodeling. The second gene is B4GALT1, mentioned above and the third gene is SPOCK2. SPOCK2 encodes a protein called testican-2, which is able to abrogate inhibition of membrane type metalloproteinases [40]. KS1 belongs to the largest family of zinc-finger transcription factors containing the KRAB domain. The functions proposed for members of the KRAB-containing protein family are transcriptional repression of RNA polymerase and binding and splicing of RNA. KS1 counteracts neoplastic transformation induced by several oncogenes [41]. Therefore KS1 could be involved in maintenance of the nucleolus, cell differentiation, cell proliferation, apoptosis and neoplastic transformation as found for other KRAB-containing proteins [42]. In addition, we found heart-type fatty acid binding protein (FABP3) down-regulated in this cluster. FABP3 is involved in lipid metabolism and constitutes a biochemical marker of myocyte injury in HF [43] whose expression was found to be regulated in the heart in response to fatty acid levels [44]. Interestingly, troponin (TNNI3), a well known marker of HF [43], was also found down regulated in this cluster. In the group of miscellaneous genes, we found down regulation of 5′-nucleotidase (NT5). NT5 degrades the adenosine moiety of ATP. Ischemic preconditioning was found to activate NT5. Moreover, it was found that plasma adenosine levels are increased in patients with chronic HF. NT5 activity also increased in the blood and the myocardium in patients with chronic HF, which may explain the increases in adenosine levels in the plasma and the myocardium. In addition, it was found that further elevation of plasma adenosine levels due to either dipyridamole or dilazep reduces the severity of chronic HF. Thus, endogenous adenosine was proposed for cardioprotection in chronic HF and against ischemia and reperfusion injury [45]. The last gene in this cluster is KIR7.1, encoding for an inwardly rectifying potassium (Kir) channel. Kir channels are ubiquitously expressed and serve functions as diverse as regulation of resting membrane potential, maintenance of K+ homeostasis, control of heart rate and hormone secretion [46]. NMR analysis allowed the quantitation of a number of metabolites. Metabolite contents determined were in accordance with published data [47–49]. We noticed a general drop in metabolite concentrations linked to the age of the animals. In addition, the glutamine to glutamate ratio was significantly lowered in obese animals. Globally lowered metabolite levels can be explained by an increase of non-cellular mass such as
  12. 12. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 excessive collagen type I and III that accumulate in the myocardium in hypertensive heart tissue [50]. A lowered glutamine to glutamate ratio could be a consequence of increased TGF-β stimulation since the TGF-β upregulates the phosphate dependant glutaminase and the Na+/H+ exchanger 3 gene expression pathway [51,52] favoring intracellular glutamine conversion to glutamate. Moreover, plasmatic TGF-β circulating levels are increased in obesity [53] and favor extracellular matrix expansion [54]. In addition, this lowered glutamine to glutamate ratio in obese may reflect the use of glutamine to generate more ATP to fuel the cardiomyocytes and to respond to an increased energy need [52]. Fatty acyl chain concentrations were lower in 10-monthold animals when compared to 4-month-old obese animals. In accordance with this observation, FABP3, involved in lipid uptake, was down regulated in obese 10-month-old animals versus 4-month-old (Table 4B). Besides, this apparent lowering of fatty acyl chain concentrations might also be related to the increase of myocardial mass by extracellular matrix accumulation as mentioned above [55]. In accordance with this hypothesis, we observed a kinetic up-regulation of fribronectin gene expression in the obese group (supplemental Table 2 on-line). Fibronectin is known to accumulate in the interstitial space [55]. Since extracellular matrix accumulation is a known occurrence in the remodelling heart and global metabolite levels were lower in obese animals, (except for taurine), this suggests that the obese heart is more fibrotic than its lean counterpart at the same age. This would explain earlier heart failure development in the obese compared to the lean SHHF rat [14]. Interestingly, n-3 fatty acyl chains percentage was higher in obese animals. This observation was confirmed by an increased total fatty acyl unsaturation in obese animals. Hypertension and obesity are known to induce myocardium remodelling [56,57]. Such omega-3 accumulation has already been recently observed during muscle regeneration [27] and may reflect mechanisms involved in the increase of septal wall thickness as observed in the 10 months obese rats (Table 1). The increase of unsaturated lipids was accompanied by conservation of taurine levels (Table 5). Taurine was shown to preserve unsaturated membrane lipids from lipid peroxidation [58]. We propose that cardiac remodelling may share some similarities with muscle regeneration. This increase of omega-3 may contribute to the stimulation of membrane expansion from cardiac cells as observed in PC12 cells [59]. In this present work we could identify a transcriptome kinetic adaptation that was different in lean and obese animals (Figs. 1 and 2) but the 10-month-old lean and obese animals had a very similar transcriptome. In our previous work performed on human right appendage biopsies, we had shown that obese hypertensive patients had clearly distinct cardiac gene expression patterns when compared to hypertensive patients. Thus, several hypotheses are plausible. We could surmise that extreme hypertension in the rat masks most obesity's contribution to gene regulation. In addition, the duration of obesity is much longer in patients than in rats and this point may be important to allow for observation of the contribution of obesity to gene 537 regulations at the transcriptome level in hypertensive animals. Although, leptin is one element among a number of adipokines that are present in obese's bloodstream and that can regulate heart gene expression, the lack of leptin signaling in the SHHF obese rats may have some consequences on specific obesityinduced gene expression pattern. Indeed, it has been observed that leptin added to cardiomyocytes in cell culture can induce hypertrophy [60] and hyperplasia [61]. In hypertensive, nonobese humans, plasmatic levels of leptin have been associated with myocardial wall thickness [62]. However, protein synthesis regulation is known to occur not only at the mRNA level and we did not perform a proteome analysis in this work to investigate for post-transcriptional or post-traductional gene regulations but rather a 1H NMR metabolomic analysis which may be considered as a more downstream observation that takes into account not only the protein level but also its enzymatic activities. 1H NMR metabolomic analysis revealed significant differences in left ventricle metabolites concentration between obese and lean animals. Therefore, this work provides some new insights both at the heart metabolome and transcriptome level during the early steps of heart failure development in the SHHF rat. Acknowledgments We thank Dr Sergueï Sokol (Toulouse Genomic Core Facilities) respectively for array data WEB management. We are indebted to Dr Peter J. Romanienko (Memorial SloanKettering Cancer Center, New York, New York, USA) for critical reading of the manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.yjmcc.2006.11.007. References [1] Basdevant A. Obesity: epidemiology and public health. Ann Endocrinol (Paris) 2000;61(Suppl 6):6–11. [2] Contaldo F, Pasanisi F, Finelli C, de Simone G. Obesity, heart failure and sudden death. Nutr Metab Cardiovasc Dis 2002;12:190–7. [3] Mykkanen L, Kuusisto J, Pyorala K, Laakso M. Cardiovascular disease risk factors as predictors of type 2 (non-insulin-dependent) diabetes mellitus in elderly subjects. Diabetologia 1993;36:553–9. [4] Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation 1983;67: 968–77. [5] Kenchaiah S, Evans JC, Levy D, Wilson PW, Benjamin EJ, Larson MG, Kannel WB, Vasan RS. Obesity and the risk of heart failure. N Engl J Med 2002;347:305–13. [6] Kagaya Y, Kanno Y, Takeyama D, Ishide N, Maruyama Y, Takahashi T, Ido T, Takishima T. Effects of long-term pressure overload on regional myocardial glucose and free fatty acid uptake in rats. A quantitative autoradiographic study. Circulation 1990;81:1353–61. [7] Beske SD, Alvarez GE, Ballard TP, Davy KP. Reduced cardiovagal baroreflex gain in visceral obesity: implications for the metabolic syndrome. Am J Physiol Heart Circ Physiol 2002;282:H630–5. [8] Ahima RS, Flier JS. Adipose tissue as an endocrine organ. Trends Endocrinol Metab 2000;11:327–32.
  13. 13. 538 J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 [9] Guerre-Millo M. Adipose tissue and adipokines: for better or worse. Diabetes Metab 2004;30:13–9. [10] Guerre-Millo M. Adipose tissue hormones. J Endocrinol Invest 2002;25:855–61. [11] Philip-Couderc P, Smih F, Pelat M, Vidal C, Verwaerde P, Pathak A, et al. Cardiac transcriptome analysis in obesity-related hypertension. Hypertension 2003;41:414–21. [12] Philip-Couderc P, Smih F, Hall JE, Pathak A, Roncalli J, Harmancey R, et al. Kinetic analysis of cardiac transcriptome regulation during chronic high-fat diet in dogs. Physiol Genomics 2004;19:32–40. [13] Philip-Couderc P, Pathak A, Smih F, Dambrin C, Harmancey R, Buys S, et al. Uncomplicated human obesity is associated with a specific cardiac transcriptome: involvement of the Wnt pathway. FASEB J 2004;18: 1539–40. [14] McCune S, Baker P, Still H. SHHF/Mcc-cp rat: model of obesity, noninsulin-dependent diabetes, and congestive heart failure. ILAR News 1990;32:23–7. [15] Heyen JR, Blasi ER, Nikula K, Rocha R, Daust HA, Frierdich G, et al. Structural, functional, and molecular characterization of the SHHF model of heart failure. Am J Physiol Heart Circ Physiol 2002;283:H1775–84. [16] Wheeler DL, Church DM, Federhen S, Lash AE, Madden TL, Pontius JU, et al. Database resources of the National Center for Biotechnology. Nucleic Acids Res 2003;31:28–33. [17] Liang F, Holt I, Pertea G, Karamycheva S, Salzberg SL, Quackenbush J. Gene index analysis of the human genome estimates approximately 120,000 genes. Nat Genet 2000;25:239–40. [18] Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, et al. TM4: a free, open-source system for microarray data management and analysis. BioTechniques 2003;34:374–8. [19] Rajeevan MS, Ranamukhaarachchi DG, Vernon SD, Unger ER. Use of real-time quantitative PCR to validate the results of cDNA array and differential display PCR technologies. Methods 2001;25:443–51. [20] Schmittgen TD, Zakrajsek BA. Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RTPCR. J Biochem Biophys Methods 2000;46:69–81. [21] Aerts JL, Gonzales MI, Topalian SL. Selection of appropriate control genes to assess expression of tumor antigens using real-time RT-PCR. BioTechniques 36 2004; 84–86, 88, 90–91. [22] Tyagi RK, Azrad A, Degani H, Salomon Y. Simultaneous extraction of cellular lipids and water-soluble metabolites: evaluation by NMR spectroscopy. Magn Reson Med 1996;35:194–200. [23] Desmoulin F, Confort-Gouny S, Masson S, Bernard M, Doddrell DM, Cozzone PJ. Application of reverse-DEPT polarization transfer pulse sequence to study the metabolism of carbon-13-labeled substrates in perfused organs by 1H NMR spectroscopy. Magn Reson Med 1990;15: 456–61. [24] Bollard ME, Murray AJ, Clarke K, Nicholson JK, Griffin JL. A study of metabolic compartmentation in the rat heart and cardiac mitochondria using high-resolution magic angle spinning 1H NMR spectroscopy. FEBS Lett 2003;553:73–8. [25] Mayr M, Metzler B, Chung YL, McGregor E, Mayr U, Troy H, et al. Ischemic preconditioning exaggerates cardiac damage in PKC-delta null mice. Am J Physiol Heart Circ Physiol 2004;287:H946–56. [26] Adosraku RK, Choi GT, Constantinou-Kokotos V, Anderson MM, Gibbons WA. NMR lipid profiles of cells, tissues, and body fluids: proton NMR analysis of human erythrocyte lipids. J Lipid Res 1994;35:1925–31. [27] Gillet B, Sebrie C, Bogaert A, Bleneau S, de la Porte S, Beloeil JC. Study of muscle regeneration using in vitro 2D 1H spectroscopy. Biochim Biophys Acta 2005;1724:333–44. [28] Astrakas LG, Goljer I, Yasuhara S, Padfield KE, Zhang Q, Gopalan S, et al. Proton NMR spectroscopy shows lipids accumulate in skeletal muscle in response to burn trauma-induced apoptosis. FASEB J 2005;19:1431–40. [29] Siddiqui N, Sim J, Silwood CJ, Toms H, Iles RA, Grootveld M. Multicomponent analysis of encapsulated marine oil supplements using high-resolution 1H and 13C NMR techniques. J Lipid Res 2003;44: 2406–27. [30] Liu T, Lai H, Wu W, Chinn S, Wang PH. Developing a strategy to define [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] the effects of insulin-like growth factor-1 on gene expression profile in cardiomyocytes. Circ Res 2001;88:1231–8. Haas GJ, McCune SA, Brown DM, Cody RJ. Echocardiographic characterization of left ventricular adaptation in a genetically determined heart failure rat model. Am Heart J 1995;130:806–11. Vandorpe DH, Chernova MN, Jiang L, Sellin LK, Wilhelm S, Stuart-Tilley AK, et al. The cytoplasmic C-terminal fragment of polycystin-1 regulates a Ca2+-permeable cation channel. J Biol Chem 2001;276:4093–101. Shaper NL, Mann PL, Shaper JH. Cell surface galactosyltransferase: immunochemical localization. J Cell Biochem 1985;28:229–39. Humphries DE, Sirokman G, Bing OH. Enhanced galactosyltransferase expression in the failing hearts of spontaneously hypertensive rats. Biochem Biophys Res Commun 1996;218:320–4. Haeseleer F, Sokal I, Verlinde CL, Erdjument-Bromage H, Tempst P, Pronin AN, et al. Five members of a novel Ca(2+)-binding protein (CABP) subfamily with similarity to calmodulin. J Biol Chem 2000;275:1247–60. Scanlan MJ, Welt S, Gordon CM, Chen YT, Gure AO, Stockert E, et al. Cancer-related serological recognition of human colon cancer: identification of potential diagnostic and immunotherapeutic targets. Cancer Res 2002;62:4041–7. Guasch G, Ollendorff V, Borg JP, Birnbaum D, Pebusque MJ. 8p12 stem cell myeloproliferative disorder: the FOP-fibroblast growth factor receptor 1 fusion protein of the t(6;8) translocation induces cell survival mediated by mitogen-activated protein kinase and phosphatidylinositol 3-kinase/ Akt/mTOR pathways. Mol Cell Biol 2001;21:8129–42. Tanhehco EJ, Yasojima K, McGeer PL, Washington RA, Lucchesi BR. Free radicals upregulate complement expression in rabbit isolated heart. Am J Physiol Heart Circ Physiol 2000;279:H195–201. Yasojima K, Schwab C, McGeer EG, McGeer PL. Human heart generates complement proteins that are upregulated and activated after myocardial infarction. Circ Res 1998;83:860–9. Nakada M, Miyamori H, Yamashita J, Sato H. Testican 2 abrogates inhibition of membrane-type matrix metalloproteinases by other testican family proteins. Cancer Res 2003;63:3364–9. Gebelein B, Fernandez-Zapico M, Imoto M, Urrutia R. KRAB-independent suppression of neoplastic cell growth by the novel zinc finger transcription factor KS1. J Clin Invest 1998;102:1911–9. Urrutia R. KRAB-containing zinc-finger repressor proteins. Genome Biol 2003;4:231. Sato Y, Kita T, Takatsu Y, Kimura T. Biochemical markers of myocyte injury in heart failure. Heart 2004;90:1110–3. Van Bilsen M, de Vries JE, Van der Vusse GJ. Long-term effects of fatty acids on cell viability and gene expression of neonatal cardiac myocytes. Prostaglandins Leukot Essent Fatty Acids 1997;57:39–45. Kitakaze M, Minamino T, Node K, Takashima S, Funaya H, Kuzuya T, et al. Adenosine and cardioprotection in the diseased heart. Jpn Circ J 1999;63:231–43. Abraham MR, Jahangir A, Alekseev AE, Terzic A. Channelopathies of inwardly rectifying potassium channels. FASEB J 1999;13:1901–10. Decker R, von Stuckrad-Barre S, Milakofsky L, Hofford JM, Harris N, Vogel WH. Effect of stress on amino acids and related compounds in various tissues of the rat. Life Sci 1995;57:1781–90. Nielsen LB, Perko M, Arendrup H, Andersen CB. Microsomal triglyceride transfer protein gene expression and triglyceride accumulation in hypoxic human hearts. Arterioscler Thromb Vasc Biol 2002;22:1489–94. Michael O'Donnell J, Narayan P, Bailey MQ, Abduljalil AM, Altschuld RA, McCune SA, et al. 31P-NMR analysis of congestive heart failure in the SHHF/Mcc-facp rat heart. J Mol Cell Cardiol 1998;30:235–41. Diez J, Gonzalez A, Lopez B, Querejeta R. Mechanisms of disease: pathologic structural remodeling is more than adaptive hypertrophy in hypertensive heart disease. Nat Clin Pract Cardiovasc Med 2005;2: 209–16. Nelson D, Rumsey WL, Erecinska M. Glutamine catabolism by heart muscle. Properties of phosphate-activated glutaminase. Biochem J 1992 (Mar 1);282(Pt 2):559–64. Welbourne T, Routh R, Yudkoff M, Nissim I. The glutamine/glutamate couplet and cellular function. News Physiol Sci 2001;16:157–60. Peterson MC. Circulating transforming growth factor beta-1: a partial
  14. 14. J. Roncalli et al. / Journal of Molecular and Cellular Cardiology 42 (2007) 526–539 [54] [55] [56] [57] [58] molecular explanation for associations between hypertension, diabetes, obesity, smoking and human disease involving fibrosis. Med Sci Monit 2005;11:RA229–32. Lijnen PJ, Petrov VV, Fagard RH. Induction of cardiac fibrosis by transforming growth factor-beta(1). Mol Genet Metab 2000;71:418–35. Sharov VG, Kostin S, Todor A, Schaper J, Sabbah HN. Expression of cytoskeletal, linkage and extracellular proteins in failing dog myocardium. Heart Fail Rev 2005;10:297–303. Gosse P, Dallocchio M. Left ventricular hypertrophy: epidemiological prognosis and associated critical factors. Eur Heart J 1993;14(Suppl D):16–21. Wakatsuki T, Schlessinger J, Elson EL. The biochemical response of the heart to hypertension and exercise. Trends Biochem Sci 2004;29: 609–17. Nandhini AT, Thirunavukkarasu V, Ravichandran MK, Anuradha CV. [59] [60] [61] [62] 539 Effect of taurine on biomarkers of oxidative stress in tissues of fructose-fed insulin-resistant rats. Singapore Med J 2005;46:82–7. Darios F, Davletov B. Omega-3 and omega-6 fatty acids stimulate cell membrane expansion by acting on syntaxin 3. Nature 2006;440: 813–7. Rajapurohitam V, Gan XT, Kirshenbaum LA, Karmazyn M. The obesityassociated peptide leptin induces hypertrophy in neonatal rat ventricular myocytes. Circ Res 2003;93:277–9. Tajmir P, Ceddia RB, Li RK, Coe IR, Sweeney G. Leptin increases cardiomyocyte hyperplasia via extracellular signal-regulated kinase- and phosphatidylinositol 3-kinase-dependent signaling pathways. Endocrinology 2004;145:1550–5. Paolisso G, Tagliamonte MR, Galderisi M, Zito GA, Petrocelli A, Carella C, et al. Plasma leptin level is associated with myocardial wall thickness in hypertensive insulin-resistant men. Hypertension 1999;34:1047–52.

×