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Review Article

Insights into RNA transcriptome profiling of cardiac tissue in obesity and hypertension conditions† Alzenira Costa, PhD1 and Octavio Luiz Franco, PhD1,2* 1

Universidade Católica de Brasília. Pós-Graduação em Ciências Genômicas e Biotecnologia Centro de Análises Proteômicas e Bioquímicas, SGAN 916, Av. W5, Módulo C. Brasília – DF CEP 70.790-160. Brazil 2 Universidade Católica Dom Bosco, Pós-graduação em Biotecnologia, Av. Tamandaré 6000, Campo Grande, MS CEP 79090-100, Brazil * Corresponding author: Dr. Octavio Luiz Franco. e-mail: [email protected] Universidade Católica de Brasília Pós-Graduação em Ciências Genômicas e Biotecnologia Centro de Análises Proteômicas e Bioquímicas SGAN 916, Av. W5, Módulo C, sala 213 C. Brasília - DF, Brazil, CEP 70790-160 http://www.capb.com.br Phone number: +55 61 34487265 Fax number: + 55 61 33474797

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This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: [10.1002/jcp.24807]

Received 14 August 2014; Accepted 5 September 2014 Journal of Cellular Physiology © 2014 Wiley Periodicals, Inc. DOI 10.1002/jcp.24807

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ABSTRACT Several epidemiological studies suggest that obesity and hypertension are associated with cardiac transcriptome modifications that could be further associated with inflammatory processes and cardiac hypertrophy. In this field, transcriptome studies have demonstrated their importance to elucidate physiological mechanisms, pathways or genes involved in many biological processes. Over the past decade, RNA microarray and RNA-seq analysis has become an essential component to examine metabolic pathways in terms of mRNA expression in cardiology. In this review, cardiac muscle gene expression in response to effects of obesity and hypertension will be focused, providing a broad view on cardiac transcriptome and physiological and biochemical mechanisms involved in gene expression changes produced by these events, emphasizing the use of new technologies for gene expression analyses. This article is protected by copyright. All rights reserved Keywords: RNA transcriptome, obesity, hypertension, microarray, RNA-seq

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1. Introduction The central dogma of biology describes a direct correlation between mRNA transcript and protein expressed. Nevertheless, complete human genome sequencing has demonstrated that this statement was incomplete (Roy B et al, 2013). Discovering that the human genome is composed of approximately 25,000 genes caused immense surprise, since these data do not fit with the idea that each gene produces a protein (Roy B et al, 2013). Furthermore, proteins are involved in all human biosystems and are the result of all information encoded in the genome. Consequently, modifications in protein expression levels may lead to cell function alterations (Vivanco F et al, 2003). Transcriptome studies have been demonstrated to be important in understanding the physiological mechanisms, pathways or genes involved in many biological processes. Therefore, expression of proteins can be used as a specific marker for many disease phenotypes, such as obesity and hypertension, allowing researchers to predict their importance in mobility and mortality associated with cardiac diseases and, hence, their impact on public health (Zhang J, 2011). Several studies affirm that obesity and hypertension can produce changes in cardiomyocyte gene expression profile (Couderc P et al, 2003), resulting from increased formation of reactive oxygen species (ROS) and/or decreased antioxidant reserve (Figure 1). Oxidative stress is implicated in important processes involved in vascular remodeling in hypertension such as endothelial dysfunction, inflammation, hypertrophy, apoptosis, migration, fibrosis, angiogenesis and rarefaction (Touyz RM et al, 2011). All these process are intimately related to the development of cardiovascular diseases, and particularly to atherosclerosis (White CR, 1994), which has been regarded as a major factor leading to the development of hypertension (Su W et al, 2012). Recently, chronic hypertension has been pinpointed as the cause of mechanical stress, leading to cardiac remodeling and also generating inflammatory response (Touyz RM et al, 2011), cardiomyocyte hypertrophy, apoptosis and changes in extracellular matrix (MEC) (LeGrice IJ et al, 2012). These events are important in the patient’s clinical background, but at moment the mechanisms by which they participate in these processes are not very well understood. On the other hand, human genome sequencing has not answered many questions about proteins. This new knowledge has led to the emergence of new technologies that allow gene expression analysis to be performed, so that This article is protected by copyright. All rights reserved

4 changes can be identified in the gene transcript or mRNAs (Benjamin AM et al, 2014), and these may explain how 25,000-30,000 genes are able to produce millions of different proteins. Alternative splicing seems to be the main process that could explain this paradox of gene expression (Roy B et al, 2013). Currently there are several techniques for profiling RNA and/or RNA transcriptome analysis, such as DNA microarray (Malone JH et al, 2011; Raghavachari N, 2013), cDNA amplified fragment length polymorphism (cDNA-AFLP) (Vuylsteke M et al, 2007; Poland J et al, 2012), expressed sequence tag (EST) sequencing (Lee EJ et al, 2013), serial analysis of gene expression (SAGE) (Kavak E et al, 2010), massive parallel signature sequencing (MPSS) (Chen G et al, 2011; Liu Z et al, 2012) and many others. RNA microarrays and RNA sequencing are considered the two main kinds of high-throughput technologies employed to assess transcriptome profile. Moreover are the more useful methodologies in gene expression studies, becoming an essential component of biology and biomedical research. Lately genome-wide profiling studies have achieved significant progress. Nevertheless current knowledge in quantitative changes in cardiac gene expression is limited and does not offer a clear understanding of the total complexity of cardiac transcriptome. Most of the studies have been conducted in animal models, including insects such as Drosophila (Piazza N et al, 2011) and mammals such as mice and rats (Dixon JA et al, 2010), due to the difficulty in achieving human cardiac tissue. Several epidemiological studies suggest that obesity and hypertension are associated with changes in cardiac transcriptome. These modifications could be associated with the obesity characteristics, inflammatory processes and cardiac hypertrophy present in hypertension, initiated with the volume overload that appears with hypertension. Obesity is a chronic, complex and multifactorial disease associated with genetic and environmental factors (Nguyen DM et al, 2010), characterized by an inflammatory state with an excessive accumulation of fat and increased body mass index (BMI ≥ 30). This inflammatory condition triggers the release of pro-inflammatory cytokines, such as interleukins 1 (IL-1) and 6 (IL-6), intracellular and vascular adhesion molecules (ICAM and VCAM), tumor necrosis factor α (TNF-α), high levels of C-reactive protein (CRP) and others pro-inflammatory cytokines (Colombo PC et al, 2012). This situation produces an imbalance between ROS production and antioxidant defense capacity. These changes in oxidative stress have been associated with several chronic diseases such as heart diseases, diabetes, hypertension, stroke, obstructive sleep apnea (OSA) (Dumitrascu R et al, 2013) and some cancer forms (Chang J et al, 2012).

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5 Obesity is also recognized as major cause of cardiovascular diseases such as heart failure (HF) and hypertension. Currently worldwide there are more than one and a half billion obese people, with lower quality of life and a shorter life expectancy than normal individuals (Mahgerefteh B et al, 2013). Therefore the molecular mechanisms involved in left ventricular hypertrophy and remodeling are directly related to gene expression changes that occur in cardiac muscle. Moreover, an important second cardiac problem is hypertension, which can also be related to obesity. Hypertension is a chronic disease, affecting nearly 40% of the population in developed countries, being one of the most important controllable cardiovascular risk factors (Barnegas JR, 2005). It is estimated that 60-70% of cases of hypertension in adults are due to obesity and genetic, metabolic, endocrine and environmental components (Kotchen TA, 2010), and that imbalances and abnormalities in psychosocial factors, like excess calories, poor diet and inactivity seem to be involved in the pathogenesis of obesity (von Hippel P et al, 2013). Although hypertension is a risk for heart disease, it is unknown why this disease causes cardiac abnormalities regardless of coronary atherosclerosis. In this review, cardiac muscle gene expression and effects of some pathologies like obesity and hypertension will be focused, providing a broad view on cardiac transcriptome and physiological and biochemical mechanisms involved in gene expression changes produced by these events, emphasizing the use of new technologies for gene expression analyses. 2. Whole Heart transcriptome Some studies have suggested that cardiac transcriptome could be involved in the pathogenesis of several diseases and may provide biomarkers for pathological changes occurring before, during or after developing the disease (Gora M, 2013). To date, gene expression studies have principally involved analysis of microarray and RNA-seq of different tissues, besides studies in small gene groups or even studies made in only one gene. The information from these analyses can provide a comprehensive understanding of the molecular mechanisms involved in specific biological processes and diseases. Gene expression could take part of important activities in the pathogenesis or progression of several diseases associated with obesity, such as hypertension and coronary artery disease (CAD)/atherosclerosis, congestive heart failure (CHF), common congenital heart disease (CHD), cardiac hypertrophy, etc. Some studies confirm the participation of molecular mechanisms in obesity-related hypertension and genes related to the processes of inflammation, oxidative stress and cardiovascular risk (Montezan AC et al, 2012). Thus, their participation in obesity and

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6 hypertension is very well defined, but the underlying molecular mechanisms that link these two processes are not yet fully understood. Recently, some studies have suggested an elevation in expression of genes involved in cellular damage, such as those related to stress or inflammatory response, in older peoples. Simultaneously the expression of metabolic genes decreases with aging (Wright KJ et al, 2014). A differential gene expression study comparing 10 month-old versus 4 month-old hypertensive rats analysed a set of cardiac-relevant genes in hypertension and identified several well-known function genes common to lean and obese analysis. These differentially expressed genes were involved in neurohormonal activation, apoptosis, inflammation, fibrosis, energy metabolism, hypertrophy, structure, etc (Table 1, Figure 2b). Results revealed 222 differentially expressed genes in lean (17 repressed and 205 over-expressed) and 293 differentially expressed genes (30 repressed and 263 overexpressed) in obese rats, respectively (Roncalli J et al, 2007). It has also been suggested that disruption of coordinated tissue growth and angiogenesis in the heart could contribute to the progression from adaptive cardiac hypertrophy to heart failure. Over the last decade, transcriptomic studies, particularly microarrays, have been used to better understand mRNA changes in obesity. Analysis of gene expression from lean and obese animals has revealed 3 genes encoding proteasome proteins COP9 subunit 4 (CSN4), aminolevulinate deltadehydratase (ALAD) and charged multivesicular body protein 1b (CHMP1.5) (Shaper NL, 1985) (Table 1). Several proteins were detected, including UDPGal:betaGlcNAc beta 1,4-galactosyltransferase (B4GALT1) and trichohyalin (TH), encode for structural proteins. UDP N-acetylglucosamine β-1,4 galactosyltransferase is a widely distributed enzyme which catalyses the transfer of galactose to N-acetylglucosamine residues of glycoproteins and glycolipids (Shaper NL et al, 1985). B4GALT1 expression was down regulated in the failing hearts of spontaneously hypertensive rats (Humphries DE et al, 1996). Recently Puig and al (2010) identified gene expression changes related to hypertension and identified several novel potential targets for the pharmacological treatment of hypertension. They conducted a study comparing gene expression in heart, kidney and aorta tissues of hypertensive, normal and hypotensive mice. In heart tissue, 972 transcripts were found with opposite regulation. Genes involved in fatty acid oxidation and genes belonging to mitochondrion gene families are increased in high blood pressure (Table 1, Figure 2b). According to the authors’ results, in opposite regulation, all up regulated genes in the heart of low blood pressure (BPL) mice were simultaneously down regulated in the heart of high blood pressure (BPH) mice, suggesting that certain metabolic functions in the heart could

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7 have an impact on blood pressure (Mendizábal Y et al, 2013), especially PPARγ-renin-angiotensin receptor, which has been reported to be involved in the expression regulation of renin (Márquez-Salom G et al, 2013). Cardiac transcriptome undergoes several changes during the development of human life, also being affected by the hypoxia produced during hypertension development. Hypoxia reduces the transcription process and, together with the inflammation process present in obesity, disturbs cellular gene expression, including cardiomyocytes (Essop MF, 2007). Thus cardiac transcriptome can shift under certain stimuli or circumstances to adapt to the new situation (Figure 1). Gene expression studies performed by microarray techniques have proportioned numerous and important results that contributed to a better understanding of cardiac transcriptome and development of new drugs and biological markers. Asakura and Kitakaze (2009) conducted various independent microarray studies and identified 107 genes related to heart failure, identifying genes involved in the regulation of the structure, cell growth and functioning, and mitochondrial oxidative phosphorylation (Table 1, Figure 2b). Yang et al (2000) also used microarray analysis to compare transcriptome from human myocardium, analysing samples from patients with heart failure, dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM). They have found 12 genes with altered expression in failing hearts compared with the normal hearts and 5 genes that were only detectable in the failing hearts (Table 1, Figure 2b). Another microarray study performed by Barrans et al (2002) (Table 1, Figure 2b) analysed gene expression in the hearts of patients who suffered DCM and identified 111 genes that were differentially expressed in control patients, highlighting the fact that the ANP gene was 19-fold up-regulated in failing hearts. These studies provided the basis for development of a chip for microarray technique with 10.848 non-redundant cDNA sequences. It is believed that multiple molecular pathways are responsible for transducing of mechanical stimuli in changes in gene expression. Nowadays microarrays have demonstrated to be a potent methodology to assess these pathways in terms of mRNA expression. Some studies evaluating gene expression in myocardium failure have confirmed the existence of differences in the levels of expression. Another interesting transcriptomic study comparing gene expression of pathologic and physiologic cardiac hypertrophy in mammalian hearts using RNA-seq, performed by Hong Ki Song et al (2012), demonstrated that in both models the expression markers’ levels were significantly different (Table 1). Among such markers are included Nppa, Nppb, Myh7, and Acta1, which were considerably up regulated in pathologic hypertrophy mice, while Pln was down regulated. In physiological hypertrophy mice, Nppa was up regulated; Acta1 was

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8 down regulated, while Nppb, Myh7, and Pln expression levels remained unchanged. These results confirmed the previous results by Chien et al (1991) and Komuro et al (1993). 3. Atrium transcriptomics In addition to whole heart transcriptomic studies, several research groups have focused on specific regions of cardiac tissue separating the atrium from ventricle and also the left region from right. Lately atrium structure and function evaluation has acquired interest and several recent studies have focused changes in a wide range of pathologies (Lupu S et al, 2014). In this sense, the study of atrium transcriptome has provided very interesting results that suggest the existence of a distinct gene expression pattern between right and left atria adults and that these variations could originate a particular molecular pattern with special importance in the atrial pathophysiology. Gene expression analysis in tissues from left and right atria from mice of different genetic backgrounds and age groups confirmed some of the main identified differences in tissue from human atria. In 3 microarray studies a total of 576 transcripts of 534 genes differentially expressed in mouse left and right atrium were found (Kahr PC et al, 2011), but in the end only 77 genes were accounted for, because 118 gene probes overlapped between two datasets and 83 probes across all three datasets. Most of the overexpressed genes were from the right atrial side (59%). Among the 77 genes that were found differentially expressed in right and left atria, Pitx2c gene had the highest overexpression in the left atrium, whereas Bmp10, a member of the TGFb family, displayed highest enrichment in the right atrium. In addition, 2 genes up regulated on the left side participated in the blood coagulation process. Seven genes coding for adhesion proteins and other six genes were involved in blood vessel development (Table 2, Figure 2a). In addition, genes have also been found related to coagulation, platelet activity and thrombogenesis, DNA damage and hypoxia-induced cell death, which were highly differentially expressed. Ppp1r1b is another gene expressed in the left atrium that coded for a regulatory subunit of protein phosphatase 1 (PP1), and therefore its inhibitor is implicated in the development of heart failure at the ventricular level (Wittkopper K et al, 2010).

Phillip Courdech and co-workers (2004) have investigated the cardiac transcriptome of obesity-related hypertension in a dog model by using microarray, reporting that about 200 genes were differently expressed in an obese dog’s atrium. They found 13 new genes that were potentially altered compared with the control group and 11 new genes (Table 1, Figure 2b) of unknown function that were statically significant. Interestingly β-actin, myoferlin, α-

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9 hemoglobin and tensin were found to be not differentially expressed in northern blot experiments. Many of these differently expressed genes could be involved in cellular functions, such as extracellular matrix remodeling processes, cytoskeletal, nuclear and sarcolemma structure, energy metabolism, ion flux, cell proliferation, stress response, cardiac hypertrophy, etc., demonstrating that, at least in part, the transcriptome is responsible for heart remodeling. Atrial fibrillation is the most common arrhythmia diagnosed in clinical practice (Camm AJ et al, 2010). Some atrial pattern injure, such structural and electrical remodeling, are the main cause of atrial fibrillation. Jeffrey Hsu et al (2012) also performed a gene expression study in left and right atrium tissue from patients with atrial fibrillation and valve disease, using transplant donors as the control group, revealing gene expression differences (Table 2, Figure 2a). In the left atrium there were 305 over-expressed genes and 441 in the right atrium, respectively, showing 746 genes differentially expressed in total. In addition, also have been found 11 novels non-coding RNAs with unknown functions, which gene expression displayed in left and right atria were modified. Pitx2 gene expression was 116-fold higher in the left atria compared to the expression in the right atria. 4. Ventricle transcriptomics Changes of gene expression that occur in the hypertrophic heart during cardiac remodeling are produced as a consequence of mechanical overloading and play a critical role in normal cardiac function and pathogenesis of heart failure. To understand these changes and their biological roles, several studies of gene expression have been carried out and many genes have been found differentially expressed in the cardiac ventricle. A microarray study realized in animal model (Stanton LW et al, 2000; Archacki S et al, 2004) found a total of 731 genes differentially expressed from the left ventricle and the interventricular septum (Figure 2a). Atrial natriuretic peptide (ANP), sarcoplasmic/endoplasmic reticulum Ca2þ-ATPase (SERCA), fibulin, laminin, decorin, secreted protein acidic and rich in cysteine (SPARC) fibrillin, osteoblast specific factor-2, fibronectin, collagen and tissue inhibitor of metalloproteinase-3 genes were up regulated (Table 3). The majority of the up regulated genes were classified as extracellular matrix (ECM) and cytoskeletal proteins, whereas down regulated genes were catalogued as contractile proteins or fatty acid metabolism-related genes, suggesting the existence of modification in the energy-generating processes in the ischemic and injured myocardial tissue (Archacki S et al, 2004; Baandrup JD et al, 2011).

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10 GeneChip analysis study carried out in hypertrophy of the right ventricle of rats and in hypoxy conditions revealed alterations in the expression of 172 genes involved in apoptosis, inflammation, heart function and growth. A short group of genes expressed in the hypoxia and polypyrimidine tract-binding protein (PTB) groups indicate pressure load as the main contributor to development of right ventricular hypertrophy (Baandrup JD et al, 2011). Other analyses of the left ventricle transcriptome using cDNA microarrays detected various genes which had an average of 9529 out of 26,592 genes with a signal greater than 1-fold and up to 37-fold over the mean level, with 87% of the differentially expressed genes harboring an expression level between 1.95 and 17.9 fold over the background level confirmed by real time PCR (Essop MF, 2007). Although hypertension is considered the major determinant of left ventricular hypertrophy and remodeling being frequently seen in hypertensive subjects, left ventricular (LV) hypertrophy is the result of a complex interaction of several hemodynamic and non-hemodynamic variables (Nadruz W, 2014). Essop MF (2007) suggests that during development of right ventricular hypertrophy the myocardium changes metabolism to avoid ischemia. Their research group identified genes encoding for enzymes participating in beta-oxidation of fatty acids (Table 3) that were down regulated in right ventricles from hypoxic rats. Some studies of gene expression from chronic hypoxy in rats showed increased expression of genes associated with glucose metabolism and biochemical and morphological changes in the left ventricle, indicating that chronic hypoxia also contributes to altered gene expression. Takahiro Isono et al (2012) compared results of the RNA-seq between human and dog samples using the hg19 and canFam2 databases. An enhanced expression in CHF LV muscles compared with normal LV muscles in p53 pathway related genes and the inflammatory interleukin related genes was detected. Interestingly, a comparison between dog and human genome was performed, annotating only 933 genes shared between the two species. It is believed that multiple molecular pathways are responsible for transducing mechanical stimuli in gene expression changes. Microarrays have demonstrated to be a potent platform for this molecular pathway identification. Over the past decade, RNA microarray analysis has become an essential component to examine these pathways in terms of mRNA expression in biology and biomedical research (Sealfon SC et al, 2011). Tan et al (2002) studied genes with different expression in failing myocardium, using tissue of the left ventricular free wall and found 103 genes differentially expressed in failing and non-failing hearts (Table 3, Figure 2a). Of these 65 (63.1%), were up-regulated and 38 (36.8%) were down regulated, respectively. The majority (77%) of these genes encoded extracellular matrix and This article is protected by copyright. All rights reserved

11 cytoskeletal proteins and were up regulated in failing hearts. In addition, an increased expression of collagen types I and III has been observed. For first time, up regulation of fibromodulin, t-plastin, fibronectin and desmosome-associated protein were reported in heart failure. The majority of genes (80%) encoding proteolysis related stress proteins showed increased expression and seventeen genes encoding proteins involved in metabolism were differentially expressed. Genes encoding proteins involved in fatty acid metabolism, such as apoliprotein D, fatty acid synthase and phospholipid transfer protein were down-regulated, and genes related to glucose metabolism, such as fructose 1-6 biphosphatase and mitochondrial NADP-dependent malic enzyme, were up-regulated. Most studies conducted to assess gene expression in cardiac muscle are done in the left ventricle, probably because this muscle is most affected by cardiac hypertrophy. Lee et al (2011) conducted a RNA-seq study in mouse heart ventricle and observed a wide dynamic range in the expression values. As example, in the control group hearts, Ankrd12 gene expressed about 1 copy per cell meanwhile mt-Co1 presented 8,048 copies. They have also found a different expression of at least 1.5 fold in heart failure for Myh7, Egr1, Nppb, Pln and Actb genes, and this quantification was improved by using RNA-Seq (Table 3). In addition, changes in expression of several genes that never had been associated with heart failure or cardiac hypertrophy before were also identified in spliced variants for Tp2a2, Cacna1c, Slc6a8, Ank2 genes and isoforms of heart-related transcripts such as Actc1, Atp2a2, Myh6, Tnnt2, and Tpm1 genes. Many genes implicated in heart failure and involved in chromatin and histone modifications have shown different scores at different stages of heart failure. Differential expression patterns of individual transcript isoforms were also analysed, identifying 1087 genes with significant isoform-specific expression change. 5. Cardiomyocytes Cardiac myocytes are complex and highly specialized and structured cells that rapidly proliferate during fetal life and in the perinatal period, but are unable to proliferate in the adult phase (Ahuja P et al, 2007). Despite not being a substitute for an intact heart, cardiomyocyte culture has become one of the most important molecular biology techniques used for analysis of cardiac physiology. In addition, it offers the advantage of permitting study under a controlled environment without interference from other cell types such as fibroblasts and endothelial cells. Heart cell culture provides a reduction in animal euthanasia, and in time and money spent on maintenance of animals in vivariums and separating the cells (Sander V et al, 2013). In adult phase increases in cardiac mass are achieved through an increase in cell size or hypertrophy structural changes that occur in the remodeling process during cardiac hypertrophy are This article is protected by copyright. All rights reserved

12 regulated by several factors. Previous studies have suggested that NFATc4 has a role in hypertrophic signaling, whereas NFATc1 plays a key role in cardiac development and maintaining of adult myocytes (Konhilas JP et al, 2006). Some studies of gene expression from chronic hypoxy in rats show increased expression of genes associated with glucose metabolism and biochemical and morphological changes in the left ventricle, indicating that chronic hypoxia also contributes to altered gene expression. Grzeskowiak et al (2003) identified 364 nonredundant, differentially expressed genes among 30,336 cDNA clones, analyzing gene expression in cardiac biopsies. In this report they demonstrated that enzymes involved in glycolysis and in the TCA cycle proteins involved in oxidative phosphorylation and various mitochondrial carriers was found up regulated. It was also observed an up regulation of several genes related to apoptosis and simultaneous down regulation in defense response and several cell-cycle control genes. Likewise, an increase in expression of genes associated with metabolism or degradation of lipids and fatty acid oxidation was observed. Yung et al (2004) identified 165 genes with differential expression between non-failing and failing myocardium, including several apoptotic genes and the plakin family of cytoskeletal linker proteins (Table 4, Figure 2a), which had not been reported before using the Affymetrix HG-U133A GeneChip microarray. Several transcription factors and nuclear receptors were regulated in end-stage human heart failure, such as signal transducer and activator of transcription 3 (STAT3) that is down-regulated by 56% of samples. Furthermore, several apoptotic genes that have not been reported to have previous involvement in dilated cardiomyopathy have shown significant expression changes in end-stage heart failure. The antioxidant protein 2 (AOP2) is highly expressed in murine liver and cardiac tissue, and with a role in atherosclerosis susceptibility, was down-regulated in 38% in human failing hearts. 6. MicroRNA transcriptome profiling in cardiac tissue MicroRNA expression profiling is the newest research area in the transcriptomic field. It is a class of short RNA molecules on average 22 nucleotides long, encoded within the genome, derived from endogenous small hairpin, a precursor that negatively regulates gene expression by targeting the 30-un-translational region (30-UTR) of specific messenger RNA (mRNA) for transcript degradation or translational repression (Bagnall RD, 2012; Duisters RF, 2009). Currently there are approximately 1000 known mature human miRNAs and it is estimated that about 30% are involved in messenger RNA transcription (Rao PK, 2009). Of these, 18 have consistently been shown to dominate cardiac miRNA

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13 gene expression (Rao PK, 2009; Schnabel RB, 2012) and can be detected during physiological cardiac development and in pathologic cardiovascular diseases states. Cardiac damage causes the release of cardiomyocyte-specific miRNAs into the circulation, but is not known whether this degradation is through the degradation of cells (Corsten MF, 2010). Relating to tissue specificity, it was detected an increase in the miRNAs expression profiling in different tissues of murine heart in miR-1, let-7, miR-26a, miR-30c, miR-126-3p and miR-133 (Divakaran V, 2008). Atypical expression of miRNAs has been linked to a wide number of myocardial pathological conditions including hypertrophy, fibrosis, apoptosis, regeneration, arrhythmia and heart failure (Rao PK, 2009; Topkara VK, 2011). The miR-30 family is highly conserved in vertebrates and is composed of six miRNAs (miR-30a, -30b, -30c-1, -30c-2, -30d and -30e). In particular, miR-30b is considered to be a tumor-suppressor miRNA and in recent years, it has been reported to be involved in the pathogenesis of cardiovascular diseases. Actually, Duisters RF et al (2009) demonstrated that there is a robust down regulation of multiple members of the miR-30 family in the LV of failing hearts. miR-133 and miR-30c are two potent molecules able to exert negative control on the gene expression in the heart and are among the most highly expressed miRNAs in cardiac myocytes. If their levels decrease substantially in the course of pathological hypertrophy, this may produce heart failure. According to previous studies, miR-133 levels are reduced in cardiac myocytes, hypertrophic hearts from LVH and heart failure. Liu et al (2008) also reported a very important role for miR-133a in the regulation of gene expression in tissue cardiac and functions related to the modified genes, demonstrating that in mice double deletion carriers in miR-133a-1 and miR-133a-2 produces lethal ventricular–septal defects in half of the embryos. Mice double mutation carriers that attained adulthood have died due to dilated cardiomyopathy and heart failure. miR-29 was also implicated in fibrosis process and recently was reported to be down regulated in hypertrophied myocardium and was also reported as regulator of cardiac fibrosis (van Rooij E et al, 2008). In contrast to miR-133, MiR-1 expression significantly reduced protein synthesis and fetal gene expression (Care A et al, 2007). Experiments in rats suggested that down regulation of miR-133 and cardiac-specific miR-1was involved in exercise induced cardiac hypertrophy (Gielen S et al, 2010). Other studies have suggested that decreased miR-1 may partly contribute to cardioprotection against ischemic injury. Xiaohong Wang et al (2012) demonstrated an aberrant miRNA up-regulation of miR-144, -451, -762, -551b, -763, -142-3p, -294, -706 and 290-5p, and down-regulation of miR1, -805, -133a, -467b, -466a-5p,466b-5p, -675-5p, -200a, -297b-3p, 574-5p, -27b and -466i analyzing microRNA This article is protected by copyright. All rights reserved

14 expression alterations in hypertrophic and ischemic/reperfused hearts. The same group also showed that miR-144/451 protects cardiomyocytes against simulated I/R. However, the involvement of miR-144/451 group expression in the IPCelicited cardioprotection remains unknown. Besides miR-1 and miRNA 133, miRNA 208 has also been reported to be involved in physiological hypertrophy (Care A, 2007; Ikeda S, 2009). In mice cardiac tissue, miRNA-195 over expression could generate hypertrophy and disturbs myocytes normal development resulting in DCM and heart failure (van Rooij et al, 2006; Nishi H et al, 2013). Lately elevated expression has been also found of miRNA-21, miRNA-23b, miRNA-199b, and miRNA-208b in atrium fibrillation (AF). Nishi H et al (2013) have demonstrated a difference in 98 miRNAs expression between preoperative AF and control group, with 94 miRNAs regulated and 4 down regulated in the preoperative AF group. Moreover, miRNAs are differentially expressed in the failing myocardium. Recent studies suggest that they could play an important role in biological processes related to heart failure progression due to these miRNAs are targets to genes that control several functions involved in cardiac tissue modification (Matkovich SJ et al, 2012). The researchers produced transgenic mouse with tissue cardiac that expressed pre-microRNAs from 6 to 16-fold normal levels of microRNA (miR)-143, miR-378, and miR-499 to investigate the genome-wide mRNA and microRNA signatures. Deep sequencing defined the expression profile provoked by each miRNA demonstrating that hundreds of cardiac mRNAs and 15 to 30 cardiac microRNAs were indirectly regulated by miR-378 and miR-499. However MicroRNA overexpression did not alter normal processing of either transgenic or endogenous cardiac miRNAs and microRNA-mediated regulation of microRNAs expressed in parent genes sequence or was expressed in tandem with mRNAs from which it originated. MicroRNA regulation by miR-378 and miR-499 produced a concrete stimulus, contributing furthermore to mRNA down regulation (Matkovich SJ et al, 2013). Currently, significant differences in miRNAs have also been demonstrated between left and right side gene expression in atrium and ventricle (Jeffrey Hsu, 2012). This report described differences in the expression of 32 miRNAs between the left and right atria. miRNA 143 was the most highly expressed, with miRNA in the atria, which represented, on average, 32.7 ± 1.5% and 26.7 ± 1.8% of all mapped reads in the left and right atria, respectively. They found an increased expression of miR-133 in the left atrium and decreased gene expression targets simultaneously. 7. Prospects and concluding remarks

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15 Despite the enormous advances in science generated by complete human genome sequencing, the information is not widely applied in clinical cardiology. We are only beginning to understand the complex interrelationship between obesity, hypertension and the human genome. Next-generation sequencing technologies will provide an advance in laboratory medicine by refinements in genotyping methods, improving assessments of epigenetics and transcriptome, and identifying novel biomarkers in hypertension and obesity that will totally change cardiovascular medicine. This gives us hope that in the near future, gene expression profiling can be used to identify biomarkers for clinically relevant parameters. The use of transcriptome analysis may open new frontiers in personalized medicine and in the near future could offer a precise diagnosis, personalized treatment and predict how a patient will react to a treatment (Kittleson MM et al, 2005). As mentioned above, most studies are conducted in animals or tissue from transplanted patients’ samples or biopsies. Normally researchers find so much difficulty in analyzing the results due to difficulty in obtaining healthy cardiac muscle to set a control group to contrast the results. As a consequence, human studies of gene expression in cardiac muscle are limited. The great majority of studies are done in small groups of a particular gene or genes separately and exposed to a certain stimulus, without standardized protocols. The use of cardiomyocytes proceeding from cell culture seems to the alternative of choice. The study of transcriptome is still just beginning, but it is already possible to see that use of this new technology will be useful for the discovery of new biomarkers and can help us understand the mechanisms underlying pathophysiology of obesity and hypertension disease. Transcriptome is a promising approach for diagnostic evaluation in routine clinical practice and could be useful to predict disease progression. Additionally, this emerging approach will allow us to formulate new hypotheses about the protein networks involved in hypertension and obesity, as well as the mechanisms involved in these processes. Microarray and RNA-seq analysis of the cardiac muscles’ transcriptome can identify differentially expressed genes encoding extracellular secreted molecules, which have the potential to become circulating biomarkers for diagnostic and prognostic purposes. Transcriptional profiling of diseased myocardium can help to develop multigene expression classifiers to identify disease etiology or outcome predictors of heart failure severity. Finally, transcriptomic techniques could be useful for identification of target pathways that may relate to molecular mechanisms, predicting pathways that can be the focus of drug development for future use in therapeutics for personalized medicine. This article is protected by copyright. All rights reserved

16 8. Acknowledgements This work was supported by CAPES, CNPq and FAPDF. 9. Bibliography [1] Roy B, Larisa M. Haupt and Lyn R. Griffiths. Alternative Splicing (AS) of genes as an approach for generating protein complexity. Cur Gen 2013, 14:182-194. [2] Vivanco F, López-Bescós L, Tuñónd J y Egido J. Proteómica y enfermedad cardiovascular. Rev Esp Cardiol 2003, 56(3):289-302. [3] Zhang J, Guy MJ, Norman HS, Chen YC, Xu Q, Dong X et al. Top-down quantitative proteomics identified phosphorylation of cardiac troponin I as a candidate biomarker for chronic heart failure. Prot Res 2011, 10(9):4054–4065. [4] Couderc P, Smih F, Pelat M, Vidal C, Verwaerde P, Pathak A et al. Cardiac transcriptome analysis in obesityrelated hypertension. Hypertension 2003, 41:414-42. [5] Touyz RM and Briones AM. Reactive oxygen species and vascular biology: implications in human hypertension. Hypert Res 2011, 34:5-14. [6] White CR, Brock TA, Chang LY, Crapo J, Briscoe P, Ku D et al. Superoxide and peroxynitrite in atherosclerosis. Proc Natl Acad Sci USA 1994, 91:1044-1048. [7] Su W, Gao F, Lu J, Wu W, Zhou G and Lu S. Levels of matrix metalloproteinase-9 and tissue inhibitor of metalloproteinase-1 mRNAs in patients with primary hypertension or hypertension-induced atherosclerosis. The Jour of Intern Med Res 2012, 40:986-994. [8] LeGrice IJ, Pope AJ, Sands GB, Whalley G, Doughty RN and Smaill, BH. Progression of myocardial remodeling and mechanical dysfunction in the spontaneously hypertensive rat. Am J Physiol Heart Circ Physiol 2012, 303:13531365. [9] Benjamin AM, Nichols M, Burke TW, Ginsburg GS and Lucas JE. Comparing reference-based RNA-Seq mapping methods for non-human primate data. BMC Genomics 2014, 15:570. This article is protected by copyright. All rights reserved

17 [10] Malone JH and Oliver B. Microarrays deep sequencing and the true measure of the transcriptome. BMC Biology 2011, 9:34. [11] Raghavachari N. Microarray technology: Basic methodology and application in clinical research for biomarker discovery in vascular diseases. Methods Mol Biol 2013, 1027:47-84. [12] Vuylsteke M, Peleman, JD and van Eijk MJT. AFLP-based transcript profiling (cDNA-AFLP) for genome-wide expression analysis. Nat Protoc 2007, 2:1399-1413. [13] Poland J, Pareek CS, Pierzchala M, Michno J, Szymanska S and Smoczyn R. Gene expression profiling by cDNAAFLP and identification of differentially expressed transcripts of bovine pituitary gland in growing bulls of dairy breeds. An Sci Pap and Rep 2012, 30(2):103-119. [14] Lee EJ, Kamli MR, Pokharel S, Malik A, Tareq KMA, et al. Expressed sequence tags for bovine muscle satellite cells, myotube formed-cells and adipocyte-like cells. PLoS ONE. 2013, 8(11):e79780. [15] Kavak E, Ünlü M, Nistér M and Koman A. Meta-analysis of cancer gene expression signatures reveals new cancer genes, SAGE tags and tumor associated regions of co-regulation. Nuc Ac Res 2010, 38(20):7008-7021. [16] Chen G, Wang C, Shi TL. Overview of available methods for diverse RNA-Seq data analyses. Sci China Life Sci. 2011, 54:1121-1128. [17] Liu Z, Zhou C, Liu Y, Wang S, Ye P, Miao X et al. The Expression Levels of Plasma micoRNAs in Atrial Fibrillation Patients. PLoS One 2012, 7(9):e44906. [18] Piazza N and Wessells RJ. Drosophila Models of Cardiac Disease. Prog Mol Biol Transl Sci. 2011, 100:155-210. [19] Dixon JA and Spinale FG. Large Animal Models of Heart Failure. A Critical Link in the Translation of Basic Science to Clinical Practice. Circ Heart Fail. 2009, 2:262-271. [20] Nguyen DM and El-Serag HB. The epidemiology of obesity. Gastroenterol Clin North Am 2010, 39(1):1-7. [21] Colombo PC, Ganda A, Lin J, Onat D, Harxhi A and Iyasere JE et al. Inflammatory activation: Cardiac, renal, and cardio-renal interactions in patients with the cardiorenal syndrome. Heart Fail Rev 2012, 17:177-190. [22] Dumitrascu R, Heitmann J, Seeger W, Weissmann N and Schulz R. Obstructive sleep apnea. Oxidative stress and cardiovascular disease: Lessons from animal studies. Oxidative medicine and cellular longevity. 2013, Article ID 234631, pp e1-e7.http://dx.doi.org/10.1155/2013/2346. [23] Chang J, Oikawa S, Ichihara G, Nanpei Y, Hotta Y, Yamada Y et al. Altered gene and protein expression in liver of This article is protected by copyright. All rights reserved

18 the obese spontaneously hypertensive/NDmcr-cp rat. Nut & Met 2012, 9:87. [24] Mahgerefteh B, Vigue M, Freestone Z, Silver S and Nguyen Q. New drugs therapies for the treatment of overweight and obesity patients. Am Health Drugs Benef 2013, 6(7): 423-430. [25] Barnegas JR. Epidemiologia de la iipertensión arterial en España. Hipertensión 2005, 22(9):353-62. [26] Kotchen TA. Obesity-related hypertension: Epidemiology, athophysiology and clinical management. Am J Hypertens 2010, 23(11):1170-1178. [27] von Hippel P and Benson R. Obesity and the natural environment across US counties. Am Jour of Pub Health 2013, 0(0):pp. e1-e7. [28] Gora M, Kiliszek M and Burzynska B. Will global transcriptome analysis allow the detection of novel prognostic markers in coronary artery disease and heart failure? Cur Genom 2013, 14:388-396. [29] Montezan AC and Touyz RM. Molecular mechanisms of hypertension-reactive oxygen species and antioxidants: A basic science update for the clinician. Can Jour of Card 2012, 28:288-295. [30] Wright KJ, Thomas MM, Betik AC, Belke D and Hepple RT. Exercise training initiated in late middle age attenuates cardiac fibrosis and advanced glycation end-product accumulation in senescent rats. Exp Gerontol 2014, 50:9-18. [31] Roncalli J, Smih F, Desmoulin F, Dumonteil N, Harmancey R, Hennig S, Perez L et al. 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. Jour of Mol and Cel Cardiol 2007, 42:526-539. [32] Shaper NL, Mann PL and Shaper JH. Cell surface galactosyltransferase: Immunochemical localization. J Cell Biochem. 1985, 28:229-39. [33] Humphries DE, Sirokman G, Bing OH. Enhanced galactosyltransferase expression in the failing hearts of spontaneously hypertensive rats. Biochem Biophys Res Com 1996, 218:320-324. [34] Puig O, Wang IM, Cheng P, Pris Zhou, Roy S, Cully D et al. Transcriptome profiling and network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension. Physiol. Gen 2010, 42A(1):24-32. [35] Mendizábal Y, Llorens S and Nava E. Hypertension in metabolic syndrome: Vascular pathophysiology. Intern Journ of Hypert 2013, ep http://dx.doi.org/10.1155/2013/230868. [36] Márquez-Salom G and Diez J. PPAR and local renin-angiotensin systems in cardiovascular and metabolic This article is protected by copyright. All rights reserved

19 diseases associated with obesity: A unifying hypothesis. Open Journ of Endoc and Metab Dis 2013, 3:1-10. [37] Essop MF. Cardiac metabolic adaptations in response to chronic hypoxia. J Physiol. 2007, 584(3):715-726. [38] Asakura M and Kitakaze M. Global gene expression profiling in the failing myocardium. Circ Jour 2009, 73(9):1568-7. [39] Yang J, Moravec CS, Sussman MA, DiPaola NR, Fu D, Hawthorn L et al. Decreased SLIM1 expression and increased elsolin expression in failing human hearts measured by high-density oligonucleotide arrays. Circulation 2000, 102:3046-3052. [40] Barrans JD, Allen PD, Stamatiou D, Dzau VJ, Liew CC. Global gene expression profiling of end-stage dilated cardiomyopathy using a human cardiovascular-based cDNA microarray. Am J Pathol 2002, 160:2035-2043. [41] Song HK, Hong SE, Kim T, Kim DH. Deep RNA sequencing reveals novel cardiac transcriptomic signatures for physiological and pathological hypertrophy. PLoS One 2012, 7(4):e35552. [42] Chien KR, Knowlton KU, Zhu H, Chien S. Regulation of cardiac gene expression during myocardial growth and hypertrophy: molecular studies of an adaptive physiologic response. Faseb J 1991, 5:3037-3046. [43] Komuro I and Yazaki Y. Control of cardiac gene expression by mechanical stress. Annu Rev Physiol 1993, 55:5575. [44] Lupu S, Mitre A and Dobreanu D. Left atrium function assessment by echocardiography-physiological and clinical implications. Med Ultrason 2014, 16(2):152-159. [45] Kahr PC, Piccini I, Fabritz L, Greber B, Schöler H et al. Systematic analysis of gene expression differences between left and right atria in different mouse strains and in human atrial tissue. PLoS One 2011, 6(10):e26389. [46] Wittkopper K, Fabritz L, Neef S, Ort KR, Grefe C et al. Constitutively active phosphatase inhibitor-1 improves cardiac contractility in young mice but is deleterious after catecholaminergic stress and with aging. J Clin Invest 2010, 120:617-626. [47] 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 Jour 2004, express article 10.1096/fj.03-1242fje. [48] Camm AJ, Kirchhof P, Lip GYH, Schotten U, Savelieva I, Ernst S et al. Guidelines for the management of atrial fibrillation. Eur Heart Jour 2010, 3:2369-2429. [49] Hsu J, Hanna P, Van Wagner DR, Barnard J, Serre D, Chung MK et al. Whole genome expression differences in This article is protected by copyright. All rights reserved

20 human left and right atria ascertained by RNA-sequencing. Circ Cardiovasc Genet 2012, 5(3):327-335. [50] Stanton LW, Garrard, LJ, Damm, D. et al. Altered patterns of gene expression in response to myocardial infarction. Circ Res 2000, 86:939-945. [51] Archacki S and Wang Q. Expression profiling of cardiovascular disease. Hum Genom 2004, 1:355-370. [52] Baandrup JD, Markvardsen LH, Peters CD, Uffe K. Schou, Jensen JL, Magnusson NE et al. Pressure load: The main factor for altered gene expression in right ventricular hypertrophy in chronic hypoxic rats. PLoS One. 2011, 6(1)e15859. [53] Nadruz W. Myocardial remodeling in hypertension. J Hum Hypertens. 2014, 36. ep doi:10.1038/jhh.2014.36. [54] Isono T, Matsumoto T, Wada A, Suzaki M and Chano T. Global transcriptome analysis of a dog Model of congestive heart failure with the human genome as a reference. Journ of cardiac fail 2012, 18(11):872-878. [55] Sealfon SC, Chu TT. RNA and DNA microarrays. Biological microarrays methods in molecular biology. Editor Ali Kademhousseini 2011, 671:3-34. [56] Tan FL, Moravec CS, Li J, Hansen CA, McCarthy PM, Young JB et al. The gene expression fingerprint of human heart failure. PNAS 2002, 99(17):11387-11392. [[57] Lee JH, Chen Gao, Peng G, Greer C, Ren S, Wang Y et al. Analysis of transcriptome complexity through RNA sequencing in normal and failing murine hearts. Circ Res 2011, 109:1332-1341. [58] Ahuja P, Sdek P and Maclellan WR. Cardiac myocytes cell cycle control in development disease and regeneration. Physiol Rev. 2007, 87(2):521-544. [59] Sander V, Suñe G, Jopling C, Morera C and Belmonte JCI. Isolation and in vitro culture of primary cardiomyocytes from adult zebrafish hearts. Nat protoc 2013, 8(4):800-809. [60] Konhilas JP, Watson PA, Maass A, Boucek DM, Horn T, Brian L et al. Exercise can prevent and reverse the severity of hypertrophic cardiomyopathy. Circ Res 2006, 98:540-548. [61] Grzeskowiak R, Witt H, Drungowski M, Thermann R, Hennig S, Perrot A et al. Expression profiling of human idiopathic dilated cardiomyopathy. Cardiovasc Res 2003, 59:400-411. [62] Yung CK, Halperin VL, Tomaselli GF, Winslow RL. Gene expression profiles in end-stage human idiopathic dilated cardiomyopathy: Altered expression of apoptotic and cytoskeletal genes. Genomics 2004, 83:28-297. [63] Bagnall RD, Tsoutsman T, Shephard RE, Ritchie W, Semsarian C. Global microRNA profiling of the mouse ventricles during development of severe hypertrophic cardiomyopathy and heart failure. PLoS ONE 2012, This article is protected by copyright. All rights reserved

21 7(9):e44744. [64] Duisters RF, Tijsen AJ, Schroen B, Leenders JJ, Lentink V, van der Made I et al. miR-133 and miR-30 regulate connective tissue growth factor implications for a role of microRNAs in myocardial matrix remodeling. Circ Res 2009;104:170-178. [65] Rao PK, Toyama Y, Chiang HR, Gupta S, Bauer M, Medvid R et al. Loss of cardiac microRNA mediated regulation leads to dilated cardiomyopathy and heart failure. Circ Res. 2009, 105:585-94. [66] Schnabel RB, Baccarelli A, Lin H, Ellinor PT and Benjamin EJ. Next steps in cardiovascular disease genomic. Research sequencing, epigenetics, and transcriptomics. Clin Chem 2012, 58(1):113-126. [67] Corsten MF, Dennert R, Jochems S, Kuznetsova T, Devaux Y, Hofstra L et al. Circulating microRNA-208b and microRNA-499 reflect myocardial damage in cardiovascular disease. Circ Cardiovasc Genet 2010, 3:499-506. [68] Divakaran V and Mann DL. The emerging role of microRNAs in cardiac remodeling and heart failure. Circ Res 2008, 103:1072-1083. [69] Topkara VK and Mann DL. Role of microRNAs in cardiac remodeling and heart failure. Cardiovasc Drugs Ther 2011, 25(2):171-82. [70] Liu N, Bezprozvannaya S, Williams AH, Qi X, Richardson JA, Bassel-Duby R et al. microRNA-133a regulates cardiomyocyte proliferation and suppresses smooth muscle gene expression in the heart. Genes Dev 2008, 22:3242–3254. [71] van Rooij E, Sutherland LB, Thatcher JE, Dimaio JM, Naseem RH, Marshall WS et al. Dysregulation of microRNAs after myocardial infarction reveals a role of miR-29 in cardiac fibrosis. Proc Natl Acad Sci USA 2008, 105:1302713032. [72] Care A, Catalucci D, Felicetti F, Bonci D, Addario A, Gallo P et al. MicroRNA-133 controls cardiac hypertrophy. Nat Med 2007, 13:613-618. [73] Gielen S, Schuler G and Adams V. Cardiovascular effects of exercise training: Molecular mechanisms. Circulation 2010, 122:1221-1238. [74] Wang X, Zhu H, Zhang X, Liu Y, Chen J, Medvedovic M et al. Loss of the miR-144/451 cluster impairs ischaemic preconditioning-mediated cardioprotection by targeting Rac-1. Cardiovasc Res 2012, 94:379-390. [75] Ikeda S, He A, Kong SW, Lu J, R Bejar R, Bodyak N et al. MicroRNA-1 negatively regulates expression of the hypertrophy-associated calmodulin and Mef2a genes. Mol Cell Biol. 2009, 29:2193-204. This article is protected by copyright. All rights reserved

22 [76] van Rooij E, Sutherland LB, Liu N, Williams AH, McAnally J, Gerard RD et al. A signature pattern of stressresponsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proc of the Nati Acad of Sci of the USA 2006, 103:18255-18260. [77] Nishi H, Sakaguchi T, Miyagawa S, Yoshikawa Y, Fukushima S et al. Impact of microRNA expression in human atrial tissue in patients with atrial fibrillation undergoing cardiac surgery. PLoS ONE 2013, 8(9):e73397. [78] Matkovich SJ, Hu Y, Eschenbacher WH, Dorn LE, Dorn GW 2nd. Direct and indirect involvement of microRNA-499 in clinical and experimental cardiomyopathy. Circ Res 2012, 111:521-531. [79] Matkovich SJ, Hu Y and Dorn GW 2nd. Regulation of Cardiac MicroRNAs by Cardiac MicroRNAs. Circ Res. 2013, 113:62-71. [80] Kittleson MM and Hare JM. Molecular signature analysis: Using the myocardial transcriptome as a biomarker in cardiovascular disease. Trends Cardiovasc Med 2005, 15:130-138.

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23

Figures Legends Figure 1: Schematic representation of gene expression modifications produced by obesity and hypertension in cardiac muscle. Obesity and hypertension produce an inflammation state and a volume overloads that trigger pro-inflammatory cytokines and ROS particles liberation that will affect the cardiac transcriptome. ROS: Reactive Oxygen Species.

Figure 2 (A) Global Expression: Genes identified in cardiac tissue studies. The graphic includes humans, rats and mice samples results, with normal or altered expression, identified using microarray and RNA-seq methodology according to results of the authors. HF: Heart failing, DCM: Dilated Cardiomyopathy, ICM: Ischemic Cardiomyopathy, MI: Myocardial Infarction. (B) Genes` classification: Most of genes modified by obesity and hypertension in cardiac tissue, identified in the studies and included in this review are classified into the functional group displayed in the graphic above.

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24 Table 1: Whole transcriptome. List of main genes expressed and identified in cardiac tissue by microarray and RNA-seq from hypertensive and obese cardiac tissue. Name

Symbol

Function

Reference

Natriuretic Peptide Precursor type A

NPPA

Neuronal activation

(31,41)

Endothelin Converting Enzyme 1

ECE 1

Neuronal activation

(31)

Angiotensin Converting Enzyme

ACE

Neuronal activation

(31,34)

Caspase 1

CASP1

Apoptosis

(31)

Inter Leukine 6 Receptor

IL6-R

Inflammation

(31)

Transforming Growth Factor beta

TGF-β

Fibrosis

(31)

SLCA1, SLC25A10

Energetic metabolism

(31)

GATA Binding Protein 4

GATA4

Hypertrophy

(31)

Myosin Heavy Chain Polypeptide 6

MYH 6

Structural

(31)

Constitutive Photomorphogenesis 9 Signalosome

COP9 or CSN4

Structural

(31)

Amino Levulinate Delta Dehydratase

ALAD

Metabolism

(31)

Charged Multivesicular Body Protein 1.5

CHMP1.5

Structural

(31)

UDPGal: beta GlcNAc Beta 1,4Galactosyltransferase

B4GALT1

Structural

(31)

TH

Structural

(31)

CABP1

Cell signaling

(31)

harged Multivesicular Body Protein 1.5

CHMP1.5

Structural

(31)

UDPGal: betaGlcNAc beta 1,4galactosyltransferase

B4GALT1

Structural

(31)

TH

Structural

(31)

CABP1

Cell signaling

(31, 38)

SEB4D(RBM38)

Cell proliferation

(31)

Solute Carrier Family 1, 25

Trichohyalin Calcium Binding Protein 1

Trichohyalin Calcium Binding Protein 1 ssDNA-binding Protein (RNA binding motif protein 38)

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25 Fibroblast Growth Factor Receptor 1 Oncogene Partner Polycystic Kidney Disease 1 Sparc/Osteonectin, Cwcv and Kazal-like Domains Proteoglycin 2 Complement Factor Cxcl14 Chemokine (C-X-C motif) Ligand 14, 2

FGFR1OP

Cell proliferation

(31)

PKD1

Development, morphogenesis

(31)

SPOCK2

Cell signaling

(31)

CF1

Structural Cell proliferation, differentiation, apoptosis and neoplastic transformation

(31)

CXCL 14, KS1, Or CXCL2

(31,34)

Fatty Acid Binding Protein

FABP3

Lipid metabolism

(31)

Troponin

TNNI3

Structural

(31, 38, 39)

NT5

Hydrolysis extracellular

(31)

Inward Rectifier Potassium Channel

KIR7.1

Homeostasis, hormonal secretion

(31)

Natriureticpeptide Receptor/Guanylatecyclase A

NPR1

Cell signaling

(34)

Chemokine Receptor

CCR5

Cell signaling

(34)

Cytochrome P450, Family 2

CYP2J2

Metabolism

(34)

Protocadherin 17

PCDH17

Cell adhesion

(34)

KIF1B

Cytoskeleton

(34)

MAPRE1

Cytoskeleton

(34)

Actin Related Protein 2/3 Complex, Subunit 2

ARPC2

Cytoskeleton

(34)

Tropomodulin 4

TMOD4

Cytoskeleton

(34)

Myotilin

MYOT

Cytoskeleton

(34)

Peroxisome ProliferatorActivated Receptor

PPARγ

Uncoupling Protein 1

UCP1

Mitochondrial transporter proteins

(34)

Phosphoenol Pyruvate Carboxykinase 1

PCK1

Gluconeogenesis

(34)

ANGPTL4

Fat metabolism

(34)

5′-nucleotidase (Neurotrophin 5)

Kinesin Family Member 1B Microtubule-Associated Protein, RP/EB Family

Angiopoietin-Like 41

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Lipid metabolism

(34)

26 Adiponectin

ADIP

Apoptosis

(34)

Wiskott-Aldrich Syndrome

WAS

Cytoskeleton

(34)

Insulin Receptor Substrate 2

IRS2

Cell signaling

(34)

RGS14

Cell signaling

(34)

Carboxipeptidase E

CPE

Metabolic and glucose homeostasis

(34)

Lumican

LUM

Structural

(34)

Regulator Of G-Protein Signaling 14

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27 Table 2: Atrium transcriptome. List of main genes expressed and identified in atrium by microarray and RNA-seq from hypertensive and obese cardiac tissue.

Name Coagulation Factor XIII, A1 Polypeptide

Symbol

Function

Reference

F13A1

Blood coaguation

(45)

Ectonucleoside Triphosphate Diphosphohydrolase 2

ENTPD2

Adhesion proteins

(45)

Filamin Binding LIM Protein 1

FBLIM1

Adhesion proteins

(45)

Reelin

RELN

Adhesion proteins

(45)

Vitronectin

VTN

Adhesion proteins

(45)

Microfibrillar-Associated Protein 4

MFAP4

Adhesion proteins

(45)

Elastin Microfibril Interfacer 2

EMILIN2

Adhesion proteins

(45)

Adhesion Molecule with Ig-Like Domain 2

AMIGO2

Adhesion proteins

(45)

Von Willebrand Factor

VWF

Adhesion proteins

(45)

Transcription Factor 21

TCF21

Blood vessel development

(45)

Hairy/Enhancer-of-Split Related with YRPW Motif

HEY1

Blood vessel development

(45)

ID1

Blood vessel development

(45)

Apolipoprotein E

APOE

Blood vessel development

(45)

Dopamine Beta-Hydroxylase

DBH

Blood vessel development

(45)

Paired-Like Homeodomain 2

PITX2C

Blood vessel development

(45, 49,)

Murine Rretrovirus Integration Site 1 Homolog

MRVI1

DNA damage, hypoxiainduced cell death

(45)

Chemokine (C-X-C motif) Ligand 14

CXCL14

DNA damage, hypoxiainduced cell death

(45)

DNA-Damage-Inducible Transcript 4-Like

DDIT4L

DNA damage, hypoxiainduced cell death

(45)

PPP1R1B

Development

(45)

BMP10

Development and morphogenesis

(45)

ADM

Development and morphogenesis

(45)

SMARCD

Development and

(45)

Inhibitor of DNA Binding 1

Protein Phosphatase 1, Regulatory (inhibitor) Subunit 1B Bone Morphogenetic Protein 10

Adrenomedullin

SWI/SNF Related, Matrix Associated, Actin Dependent This article is protected by copyright. All rights reserved

28 Regulator Of Chromatin, Subfamily D Chemokine (C-C Motif) Ligand 21

morphogenesis CCL 11, 13, 14, 21

Immunoregulatory

(45)

V-Set And Immunoglobulin Domain Containing 4

VSIG4

Regulatory function

(45)

Cluster of Differentiation 207

CD207

Regulatory function

(45)

Cluster of Diferentiation 163

CD163

Anti-inflammatory function

(45)

Acetylcholine-Activated Inward-Rectifying Potassium

IK,ACh

Heart rate regulation

(45)

Potassium Voltage-Gated Channel

KCNC4

Voltage gaiting

(45)

Dopamine Beta-Hydroxylase

DBH

Signal transmition

(45)

Alpha-2-HS-Glycoprotein

AHSG

Development

(45)

TIMP2, 3, 4

Cellular matrix degradation

(45)

ALOX5

Fat acid metabolism

(45)

C3

Immune system

(45)

MMP2

Remodeling

(45)

β-Actin

ACTB

Development

(4)

Myoferlin

MYOF

Development

(4)

α-Hemoglobin

HBA

Development

(4)

Tensin

TNS

Development

(4)

SERCA2

Ca2+ signalling

(4)

Amyotrophic Lateral Sclerosis 2

ALS2

GTPase regulator

(4)

Nuclear Factor Erythroid 2-Related Factor

NRF2

Transcription factor

(4)

Nucleophosmin

NPM

Proliferation

(4)

Extracellular Signal-Regulated Kinase

ERK3

Cell signalling

(4)

Myosin Light Chain2

MYL2

Cytoskeleton

(49)

Hyperpolarization Activated Cyclic Nucleotide-Gated Potassium Channel

HCN4

Voltage gaiting

(49)

Hepcidin Antimicrobial Peptide

HAMP

Iron metabolism

(49)

Metallopeptidase Inhibitor Arachidonate 5-Lipoxygenase Complement 3 Matrix Metallopeptidase 2

Sarcoplasmic Reticulum Ca2 ATPase

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29

Table 3: Ventricle transcriptome. List of main genes expressed and identified in right and left ventricle by microarray and RNA-seq from hypertensive and obese cardiac tissue.

Name

Symbol

Function

ANP

Cardiovascular homeostasis

(50,56,52)

SERCA

ATP hydrolysis

(50)

Decorin

DCN

Structural

(50)

Fibulin

FBLN

Structural

(50)

Tissue Inhibitor of Metalloproteinase-3

TIM3

Structural

(50, 52)

Fibrillin

FBN

Structural

(50)

Laminin

LAM

Extracellular matrix

(50)

TIMSB4,B10

Cytoskeleton

(50)

MSN

Cytoskeleton

(50)

Transgelin

TAGLN

Cytoskeleton

(50)

Tropomyosin 4

TPM4

Cytoskeleton

(50)

Troponin 1

TNN1

Cytoskeleton

(50)

Secreted Protein Acidic and Rich in Cysteine

SPARC

Remodeling

(50)

Osteoblastspecific Factor-2

OSF-2

Remodeling

(50)

Acetyl-CoAAcyl Transferase 2

ACAA2

Fatty acid betaoxidation

(37)

Hydroxyacyl-CoA Dehydrogenase/3Ketoacyl-CoA thiolase/Enoyl-CoA Hydratase (trifunctionalprotein), Alpha Subunit

HADHA

Fatty acid betaoxidation

(50, 37)

DNA-Damage-Inducible Transcript 4-Like

DDIT4L

Chemokine (C-X-C motif) ligand 14

CXCL14

Immunoregulatory and inflammatory processes

(37)

Protein Phosphatase 1, Regulatory (inhibitor) Subunit 1B

PPP1R1B

Development

(37)

Alpha and Beta Cytoskeletal Actin B

Α, βACT

Structural

Atrial Natriuretic Peptide

Sarco-Endoplasmic Reticulum Ca2þ-ATPase

Thymosins B4 and B10 Moesin

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Cell growth, proliferation and survival

Reference

(37)

(37, 52, 37)

30 Collagen

COL

Structural

(50,56)

Fibronectin

FN

Structural

(50,56)

Fibromodulin

FMOD

Structural

(56)

t-Plastin

T-PLS

Structural

(56)

Apoliprotein D

APOD

Lipid metabolism

(56)

Fatty acid Synthase

FASN

Lipid metabolism

(56)

Phospholipid Transfer Protein

PLTP

Lipid metabolism

(56)

Fructose 1–6 Biphosphatase

FBP

Glucose metabolism

(56)

Mitochondrial NADP-Dependent Malic Enzyme

ME

Lipid metabolism

(56)

MYOF

Muscle contraction, blood circulation

(47)

HBA

Oxygen transporter

(47)

TN

Adhesion molecule

(47)

Myosin Heavy Chain 6 and 7

MYH7

Motor activity

(56,57)

Early Growth Response 1

EGR1

Growth factor

(57)

Natriuretic Peptide B

NPPB

Cardiac hormone

(57, 56)

PLN

Ca regulation

(57)

Ankyrin Repeat Domain 121

ANKRD12

Cell signalling, cytoskeleton

(57)

Transforming Growth Factor

TGF

Proliferation, differentiation

(52)

Myoferlin

α-Hemoglobin

Tensin

Phospholamban

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31

Table 4: Cardiomyocytes transcriptome. List of main genes expressed and identified in cardiomyocytes by microarray and RNA-seq from hypertensive and obese cardiac tissue. Name

Symbol

Function

Reference

Nuclear Factor of Activated T-cells, Cytoplasmic, Calcineurin-Dependent 1

NFATc1

Cardiac development and maintaining of adult myocytes

(60)

Nuclear Factor of Activated T Cells 4

NAFT4

Cell signalling

(60)

Phosphofructokinase

PFK

Glycolysis

(61)

Trioseisomerase

TPI

Glycolysis

(61)

Protein Kinase 5

PK5

Glycolysis

(61)

P-Glycerate Kinase

PCGK

Glycolysis

(61)

Dehydrogenase Units

DHO

TCA cycle

(61)

Succinate CoA ligase

SUCL

TCA cycle

(61)

Solute Carrier Family

SLC25A4, SLC25A1, 4.1 and SLC24A11

Oxidative phosphorylation and mitochondrial carriers

(61)

LPL

Metabolism, degradation of lipids fatty acid oxidation

(61)

VDAC1,2

Apoptosis

(61)

Kallikrein 11

KLK11

Apoptosis

(61)

Prostaglandin D

PTGD

Apoptosis

(61)

Major Histocompatibility Complex 1 A

HLA-A

Defence response

(61)

Complement 1Q

C1Q

Defence response

(61)

Fatty Acid Binding Protein

FABP

Metabolism, degradation of lipids fatty acid oxidation

(61)

PRDX1

Defence response

(61)

Anexin

ANX

Defence response

(61)

Induced TNF Factor A (PIG7)

LITAF

Transcription regulation

(61)

Cyclin D Binding Myb-Like Transcription Factor 1

DMTF1

Transcription regulation

(61)

Cyclin-Dependent Kinase 5 Regulatory Subunit 1

CDK5R1

Transcription regulation

(61)

Lipoprotein Lipase

Voltage Dependent Anion Channel 1 and 2

Peroxiredoxin 1,3,5

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32 Antioxidant Protein 2

AOP2

Apoptosis

(62)

Microtubule-Associated Protein 1

MAP1

Apoptosis

(62)

PLAGL1

Apoptosis

(62)

PK

Cytoskeletal linker proteins

(62)

STAT3

Cell signaling

(62)

Pleiomorphic Adenoma Gene-Like 1 Plakin Family Signal Transducer and Activator of Transcription 3

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33

Figure 1

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34

Figure 2

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