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• W2550900176 abstract "•Co-expression networks were created from heart transcriptomes of 100 mouse strains•A module was identified that strongly correlated to heart-failure-related traits•The NEO algorithm identified Adamts2 as an important driver of the module•In vitro studies demonstrated that Adamts2 is a regulator of cardiac hypertrophy We previously reported a genetic analysis of heart failure traits in a population of inbred mouse strains treated with isoproterenol to mimic catecholamine-driven cardiac hypertrophy. Here, we apply a co-expression network algorithm, wMICA, to perform a systems-level analysis of left ventricular transcriptomes from these mice. We describe the features of the overall network but focus on a module identified in treated hearts that is strongly related to cardiac hypertrophy and pathological remodeling. Using the causal modeling algorithm NEO, we identified the gene Adamts2 as a putative regulator of this module and validated the predictive value of NEO using small interfering RNA-mediated knockdown in neonatal rat ventricular myocytes. Adamts2 silencing regulated the expression of the genes residing within the module and impaired isoproterenol-induced cellular hypertrophy. Our results provide a view of higher order interactions in heart failure with potential for diagnostic and therapeutic insights. We previously reported a genetic analysis of heart failure traits in a population of inbred mouse strains treated with isoproterenol to mimic catecholamine-driven cardiac hypertrophy. Here, we apply a co-expression network algorithm, wMICA, to perform a systems-level analysis of left ventricular transcriptomes from these mice. We describe the features of the overall network but focus on a module identified in treated hearts that is strongly related to cardiac hypertrophy and pathological remodeling. Using the causal modeling algorithm NEO, we identified the gene Adamts2 as a putative regulator of this module and validated the predictive value of NEO using small interfering RNA-mediated knockdown in neonatal rat ventricular myocytes. Adamts2 silencing regulated the expression of the genes residing within the module and impaired isoproterenol-induced cellular hypertrophy. Our results provide a view of higher order interactions in heart failure with potential for diagnostic and therapeutic insights. Heart failure (HF) is a common disorder characterized by impaired heart function, cardiac hypertrophy, and chamber remodeling (Frangogiannis, 2012Frangogiannis N.G. Regulation of the inflammatory response in cardiac repair.Circ. Res. 2012; 110: 159-173Crossref PubMed Scopus (784) Google Scholar). Despite reports of significant genetic heritability, genome-wide association studies (GWAS) involving tens of thousands of patients have had only modest success, likely due to the complex, heterogeneous nature of the disease (reviewed in Rau et al., 2015aRau C.D. Lusis A.J. Wang Y. Genetics of common forms of heart failure: challenges and potential solutions.Curr. Opin. Cardiol. 2015; 30: 222-227Crossref PubMed Scopus (24) Google Scholar). These complexities can be minimized in genetic studies of model organisms such as mice, and classical quantitative trait locus (QTL) linkage analyses in mice have identified a number of novel HF-related genes (McNally et al., 2015McNally E.M. Barefield D.Y. Puckelwartz M.J. The genetic landscape of cardiomyopathy and its role in heart failure.Cell Metab. 2015; 21: 174-182Abstract Full Text Full Text PDF PubMed Scopus (76) Google Scholar, Wheeler et al., 2009Wheeler F.C. Tang H. Marks O.A. Hadnott T.N. Chu P.L. Mao L. Rockman H.A. Marchuk D.A. Tnni3k modifies disease progression in murine models of cardiomyopathy.PLoS Genet. 2009; 5: e1000647Crossref PubMed Scopus (64) Google Scholar). In previous work, we have shown that a GWAS approach can be applied to populations of common inbred strains of mice if associations are corrected for population structure (Bennett et al., 2010Bennett B. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (234) Google Scholar). We studied a population of over 100 commercially available inbred strains of mice selected for diversity, constituting a resource that we termed the Hybrid Mouse Diversity Panel (HMDP). The mapping resolution of this approach is at least an order of magnitude better than traditional QTL analyses involving genetic crosses and has led to the identification of novel genes for a number of traits (reviewed in Lusis et al., 2016Lusis A.J. Seldin M.M. Allayee H. Bennett B.J. Civelek M. Davis R.C. Eskin E. Farber C.R. Hui S. Mehrabian M. et al.The Hybrid Mouse Diversity Panel: a resource for systems genetics analyses of metabolic and cardiovascular traits.J. Lipid Res. 2016; 57: 925-942Crossref PubMed Scopus (88) Google Scholar). We recently applied this approach to identify loci and genes that contribute to HF traits in an isoproterenol (ISO) model, which mimics the chronic β-adrenergic stimulation that often occurs in human HF. Association analyses identified both known and novel genes contributing to hypertrophy, cardiac fibrosis, and echocardiographic traits (Rau et al., 2015bRau C.D. Wang J. Avetisyan R. Romay M.C. Martin L. Ren S. Wang Y. Lusis A.J. Mapping genetic contributions to cardiac pathology induced by beta-adrenergic stimulation in mice.Circ Cardiovasc Genet. 2015; 8: 40-49Crossref PubMed Scopus (53) Google Scholar, Wang et al., 2016Wang J.J.C. Rau C. Avetisyan R. Ren S. Romay M.C. Stolin G. Gong K.W. Wang Y. Lusis A.J. Genetic dissection of cardiac remodeling in an isoproterenol-induced heart failure mouse model.PLoS Genet. 2016; 12: e1006038Crossref PubMed Scopus (57) Google Scholar). We now report an extension of this study in which we seek to understand genes and pathways that contribute to HF through the modeling of biological networks. We apply an improved version of the Maximal Information Component Analysis (MICA) algorithm (Rau et al., 2013Rau C.D. Wisniewski N. Orozco L.D. Bennett B. Weiss J. Lusis A.J. Maximal information component analysis: a novel non-linear network analysis method.Front. Genet. 2013; 4: 28Crossref PubMed Scopus (20) Google Scholar), with increased versatility and power, to left ventricular transcriptomes of the HMDP population before and after treatment with ISO to form modules of functionally related genes. Several modules that showed significant association to HF-related phenotypes were identified. We focused our analysis on a module based on treated expression data as it exhibited striking correlations with a number of HF traits and contained several genes previously implicated in HF, such as Nppa and Timp1. We then applied the NEO (Near Edge Orientation) algorithm (Aten et al., 2008Aten J.E. Fuller T.F. Lusis A.J. Horvath S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software.BMC Syst. Biol. 2008; 2: 34Crossref PubMed Scopus (112) Google Scholar) to develop a directed network with predicted causal interactions among the module genes. The results suggested that Adamts2, a metalloproteinase not previously associated with HF, plays a key role in modulating the expression of other genes in the module in response to ISO stimulation. Using an in vitro model, we validated several of these causal links and demonstrated that Adamts2 expression affected several proxy measurements of cardiac hypertrophy. Prior research (Farber, 2013Farber C.R. Systems-level analysis of genome-wide association data.G3 (Bethesda). 2013; 3: 119-129Crossref PubMed Scopus (48) Google Scholar) using the HMDP benefited from the use of systems-level transcriptomics to generate mRNA co-expression networks. We previously reported an unbiased gene network construction algorithm, termed MICA, which has several conceptual improvements over traditional co-expression methods in that it captures both linear and nonlinear interactions within the data and allows genes to be spread proportionally across multiple modules (Rau et al., 2013Rau C.D. Wisniewski N. Orozco L.D. Bennett B. Weiss J. Lusis A.J. Maximal information component analysis: a novel non-linear network analysis method.Front. Genet. 2013; 4: 28Crossref PubMed Scopus (20) Google Scholar). Previous research on gene networks (Langfelder and Horvath, 2008Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics. 2008; 9: 559Crossref PubMed Scopus (10410) Google Scholar) has shown that weighted network construction algorithms, in which all edges are included in the analysis, have greater versatility and power than unweighted algorithms, in which edges are included or excluded based on a hard threshold. Therefore, we have improved upon our original algorithm (STAR Methods) and developed a modified, weighted form of MICA, which we term wMICA. We describe here the first application of wMICA to the analysis of HF, using gene expression data across inbred strains of mice from the HMDP HF study. Left ventricular tissue from the HMDP was processed using Illumina Mouse Ref 2.0 gene expression arrays. Probes were filtered for transcripts that were significantly expressed in at least 25% of samples and had a coefficient of variation of at least 5%. This resulted in a final set of 8,126 probes, representing 31.6% of the total probes on the array. Three gene networks, with 20 modules each, were generated from these data: one based only on transcripts from the untreated hearts, one based only on the treated hearts, and a third based on the change in gene expression between these two conditions (Data S1). Two measures were used for the preliminary analyses of these networks. We calculated significant Gene Ontology (GO) enrichments within each of these modules at several module membership cutoffs, using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Significant enrichment for one or more GO terms suggests that the module represents a collection of genes that are biologically related to one another and are less likely to be an artifact of the module identification process. We also used principal-component analysis (PCA) to identify the first principal component (often called the “eigengene”) of each module. This eigengene can be correlated to HF-related phenotypes to identify modules that, as a whole, are most likely related to specific features of cardiac pathogenesis. As wMICA allows genes to reside within multiple modules, we used a weighted PCA algorithm to calculate the first weighted principal component of each module based on each gene’s module membership. The weighted eigengene was then correlated to a number of HF-related phenotypes from the HMDP panel. They include seven organ weights, eight echocardiographic parameters, and five plasma traits that, in conjunction with either an organ weights or functional parameters, suggest a change in metabolism status associated with HF. Three MICA-generated modules were observed to have very strong (DAVID score > 7) enrichments, while five other modules showed significant (score > 3) enrichments. Only a single module (not one of the eight with strong enrichments to a GO category) had even suggestive (p < 0.01) correlations to an HF-related phenotype. Therefore, the co-expression gene modules identified in the pre-treated hearts appear to have limited correlation with susceptibility to HF. Thirteen of the modules of the MICA network constructed from treated hearts had significant DAVID enrichments, including three modules (3, 4, and 5) with enrichment scores greater than 10 (Figure 1A; Table S1). Three modules (1, 5, and 19) contained at least one significant (p < 10−4) correlation between the eigengene and either an organ weight or echocardiographic parameter (Figure 1B; Figure S1). Four additional modules (4, 12, 15, and 18) contained at least two suggestive (p < 0.01) correlations between a module and a HF-related trait. Module 5 has 309 genes that have maximal module membership within the module. It showed strong correlations to 7 of 20 measured HF traits: total heart weight, left and right ventricle weights, left ventricular internal dimension, lung weight, liver weight, and plasma free fatty acids, suggesting a strong relationship between this module and cardiac hypertrophy and HF (Figure 1B). Module 5 also showed highly significant GO enrichments for several biological processes: extracellular matrix (p < 10−18), secreted signaling (p < 10−16), and cell adhesion (p < 10−9). Of the 47 probes (42 genes) that possess greater than 70% module membership (Figure 2A), 20 genes (47.7%) have previously been described as involved in heart hypertrophy or cardiac remodeling based on transgenic studies in animal models or mendelian forms of HF. These genes include Nppa, a well-known marker of cardiac hypertrophy; Timp1, an extracellular matrix regulator (Barton et al., 2003Barton P.J.R. Birks E.J. Felkin L.E. Cullen M.E. Koban M.U. Yacoub M.H. Increased expression of extracellular matrix regulators TIMP1 and MMP1 in deteriorating heart failure.J. Heart Lung Transplant. 2003; 22: 738-744Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar); and the collagen genes Col12a1, Col14a1, and Col6a2. We further observed three genes (Pcolce, Sprx, and Sprx2) that we had previously identified via GWAS as candidate genes for cardiac phenotypes in this panel of mice (Rau et al., 2015bRau C.D. Wang J. Avetisyan R. Romay M.C. Martin L. Ren S. Wang Y. Lusis A.J. Mapping genetic contributions to cardiac pathology induced by beta-adrenergic stimulation in mice.Circ Cardiovasc Genet. 2015; 8: 40-49Crossref PubMed Scopus (53) Google Scholar). These features led us to characterize module 5 in greater detail. We observed the strongest associations between gene modules and GO terms in the MICA network generated from the changes in gene expression induced by ISO and subsequent remodeling, with 11 modules showing significant enrichments. Four of these modules had DAVID scores greater than 10, and two had scores greater than 15. Despite strong enrichments for GO terms, no eigengene/trait correlations for any organ weight or echocardiographic parameter had a p value less than 0.01. Using co-expression networks based on common genetic variation in populations, it is possible to model causal interactions and orient edges. The NEO (Aten et al., 2008Aten J.E. Fuller T.F. Lusis A.J. Horvath S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software.BMC Syst. Biol. 2008; 2: 34Crossref PubMed Scopus (112) Google Scholar) algorithm uses SNPs as anchors to infer directionality between SNP/gene/gene triads based on several possible models (Figure 2B). We applied the NEO algorithm to the largest connected components of the core genes of modules 5. Of the 47 probes in the core network of module 5, 44 had at least one edge with significant directionality (Figure 2C). Genes with directed edges were classified into three categories (reactive, intermediary, and driver) based upon the number of directed edges traveling from the gene of interest to other genes in the module and the number of directed edges traveling from other genes in the module to the gene of interest (Table S2). We observed 12 “reactive” genes in which 75% or more of their directed edges travel to the gene from other genes in the module. The most reactive gene is Timp1, an important marker of left ventricular remodeling and HF (Barton et al., 2003Barton P.J.R. Birks E.J. Felkin L.E. Cullen M.E. Koban M.U. Yacoub M.H. Increased expression of extracellular matrix regulators TIMP1 and MMP1 in deteriorating heart failure.J. Heart Lung Transplant. 2003; 22: 738-744Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar), which is predicted to be affected by 18 other genes in the module. Similarly, we classified 12 genes as drivers based on the observation that, at minimum, 75% of their directed edges originate from the gene and travel to other genes in the module. Of the 12 drivers, 3 are notable. The first, Pcolce, a previously reported GWAS candidate gene (Rau et al., 2015bRau C.D. Wang J. Avetisyan R. Romay M.C. Martin L. Ren S. Wang Y. Lusis A.J. Mapping genetic contributions to cardiac pathology induced by beta-adrenergic stimulation in mice.Circ Cardiovasc Genet. 2015; 8: 40-49Crossref PubMed Scopus (53) Google Scholar), has 11 outputs representing 26% of the genes in the core of module 5. The second, Nox4, which has a single output to Mfap5, has been previously established to be a key contributor to oxidative stress during pressure overload (Kuroda et al., 2010Kuroda J. Ago T. Matsushima S. Zhai P. Schneider M.D. Sadoshima J. NADPH oxidase 4 (Nox4) is a major source of oxidative stress in the failing heart.Proc. Natl. Acad. Sci. USA. 2010; 107: 15565-15570Crossref PubMed Scopus (574) Google Scholar). The third driver, the metalloproteinase Adamts2, has not been previously implicated in HF or muscle development and has the largest number of directed edges within the module, with 23 (56% of module 5 genes) causal and 2 reactive (to the exocytosis regulator Rab15 and the extracellular matrix organizer Ccdc80) edges. Furthermore, Adamts2 showed significant correlations to lung weight (R = 0.46, p = 4.6 ⋅ 10−6), heart weight (R = 0.43, p = 1.5 ⋅ 10−5), and free fatty acids (R = 0.43, p = 2.0 ⋅ 10−5) after ISO stimulation, and it has both the highest degree (30) and third highest betweenness centrality of any gene within the module. Examination of sequence variation in and near the Adamts2 gene revealed a significant cis-eQTL (expression QTL) but no evidence of alternative splicing or deleterious missense mutations (see Table S4 for a sequence-level analysis of each “driver” gene from this module). Taken together, these results suggest that Adamts2 is a previously unidentified regulator of cardiac pathology. To test the NEO-predicted regulation of gene expression by Adamts2, we measured the expression of a set of seven predicted downstream targets residing in module 5, as well as a gene that was not predicted to be a target, the calcium transporter Atp2a2 (Figure 3; Figure S4). These seven genes (Col12a1, Kcnv2, Mfap2, Nppa, Pcolce, Timp1, and Tnc) were selected based on three criteria: strength of predicted relationship to Adamts2, number of directed edges in module 5, and previous associations with cardiac function and/or cardiovascular disease (Table S3). Using small interfering RNA (siRNA) to silence Adamts2, we achieved a 28%–45% decrease in Adamts2 expression when compared to transfection control (Figure 3A). We did not observe any consistent changes in Atp2a2 expression upon siRNA knockdown (Figure S2), consistent with the NEO prediction that it is not a target of Adamts2 (Figure 2). In contrast, we observed significant changes in four genes predicted to be targets (Kcnv2, Mfap2, Nppa, and Tnc) (Figure 3), suggesting that Adamts2 acts to regulate their expression under ISO-treated conditions in cardiomyocytes. The failure to observe the predicted responses for the other genes is likely due either to insufficient knockdown of Adamts2 or incorrect prediction by the NEO algorithm. As module 5 is significantly correlated with changes in numerous HF-related traits, we hypothesized that changes in the expression in Adamts2, a predicted driver of module 5, would alter cardiomyocyte size and/or viability in response to ISO. Indeed, following treatment with ISO, cells transfected with the control siRNA nearly doubled in cellular cross-sectional area (Figures 4A and 4B ). Cells that expressed less Adamts2 due to siRNA transfection were smaller than scramble-transfected cells following treatment with ISO, being about the same size as the control cells without ISO stimulation (Figures 4A and 4B). At the molecular level, treatment with ISO induced the expression of the hypertrophic markers Nppa and Nppb, which rose 2.4-fold and 5.7-fold, respectively, in cells transfected with the control siRNA. Knockdown of Adamts2 strongly impaired these inductions, with Nppa induction reduced by approximately 75% and Nppb expression reduced by approximately 65% (Figures 4C and 4D). The significant response of the hypertrophic markers to relatively modest changes in Adamts2 expression under an adrenergic stimulus suggests a non-linear relationship between these genes. As such, these findings indicate that Adamts2 acts as a regulator of β-adrenergic-induced cardiac hypertrophy in cardiomyocytes. We report network modeling of the molecular pathways contributing to HF in an ISO-treated mouse model. Prior efforts to analyze transcriptome networks underlying HF and related cardiomyopathies in human studies have met with limited successes, likely due in part to the high degree of heterogeneity of HF etiology and progression in humans (Drozdov et al., 2013Drozdov I. Didangelos A. Yin X. Zampetaki A. Abonnenc M. Murdoch C. Zhang M. Ouzounis C.A. Mayr M. Tsoka S. Shah A.M. Gene network and proteomic analyses of cardiac responses to pathological and physiological stress.Circ Cardiovasc Genet. 2013; 6: 588-597Crossref PubMed Scopus (17) Google Scholar, Moreno-Moral et al., 2013Moreno-Moral A. Mancini M. D’Amati G. Camici P. Petretto E. Transcriptional network analysis for the regulation of left ventricular hypertrophy and microvascular remodeling.J. Cardiovasc. Transl. Res. 2013; 6: 931-944Crossref PubMed Scopus (11) Google Scholar). Additionally, human heart samples are generally available only from extremely late-stage HF, masking pathways involved in the initial stages in favor of the reactive pathways. In contrast, by using the HMDP mice, we were able to model networks with minimal environmental heterogeneity. In addition to the conclusions reported here, our weighted module membership tables and network graphs (Data S1) should be useful in exploring how genes or pathways of interest intersect with other genes and pathways involved in HF. Although we constructed MICA networks from three separate sets of gene expression data, we found that the treated network returned the most relevant modules. The control network had generally weaker GO enrichments and only a single module with even suggestive (p < 0.01) correlations with organ weights or echocardiographic parameters. The delta network had GO enrichments on par with the treated modules but no significant correlations with the HF phenotypes. Hierarchical clustering analysis revealed that 11 of the 20 modules from the delta network were highly correlated (r > 0.8) to one another, suggesting that the identified modules in the network are, in fact, largely reflecting a single “mega-module” of genes that have reacted strongly to ISO stimulation but whose underlying genetic variability has been masked to such a degree that they cannot be cleanly differentiated into phenotypically relevant modules. The network based on ISO-treated gene expression, on the other hand, contained well-defined modules strongly enriched for GO categories and very significantly associated with HF traits. We focused on module 5 of the ISO-treated network in detail since it was significantly correlated with the traits of total heart weight, lung weight, liver weight, and plasma free fatty acids, as well as several functional parameters. Additionally, a total of 15 module-5 genes have been implicated in the development of HF and HF-related traits. Of these, six genes are predicted to be cardioprotective, as deficiencies promote cardiac remodeling (Col14a1 [Tao et al., 2012Tao G. Levay A.K. Peacock J.D. Huk D.J. Both S.N. Purcell N.H. Pinto J.R. Galantowicz M.L. Koch M. Lucchesi P.A. et al.Collagen XIV is important for growth and structural integrity of the myocardium.J. Mol. Cell. Cardiol. 2012; 53: 626-638Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar], Dkk3 [Bao et al., 2015Bao M.-W. Cai Z. Zhang X.-J. Li L. Liu X. Wan N. Hu G. Wan F. Zhang R. Zhu X. et al.Dickkopf-3 protects against cardiac dysfunction and ventricular remodelling following myocardial infarction.Basic Res. Cardiol. 2015; 110: 25Crossref PubMed Scopus (49) Google Scholar], and Timp1 [Ikonomidis et al., 2005Ikonomidis J.S. Hendrick J.W. Parkhurst A.M. Herron A.R. Escobar P.G. Dowdy K.B. Stroud R.E. Hapke E. Zile M.R. Spinale F.G. Accelerated LV remodeling after myocardial infarction in TIMP-1-deficient mice: effects of exogenous MMP inhibition.Am. J. Physiol. Heart Circ. Physiol. 2005; 288: H149-H158Crossref PubMed Scopus (106) Google Scholar]) or decrease angiogenesis and neovascularization (Olfml3 [Miljkovic-Licina et al., 2012Miljkovic-Licina M. Hammel P. Garrido-Urbani S. Lee B.P.-L. Meguenani M. Chaabane C. Bochaton-Piallat M.-L. Imhof B.A. Targeting olfactomedin-like 3 inhibits tumor growth by impairing angiogenesis and pericyte coverage.Mol. Cancer Ther. 2012; 11: 2588-2599Crossref PubMed Scopus (38) Google Scholar], Srpx2 [Miljkovic-Licina et al., 2009Miljkovic-Licina M. Hammel P. Garrido-Urbani S. Bradfield P.F. Szepetowski P. Imhof B.A. Sushi repeat protein X-linked 2, a novel mediator of angiogenesis.FASEB J. 2009; 23: 4105-4116Crossref PubMed Scopus (38) Google Scholar], and Ptn [Li et al., 2007Li J. Wei H. Chesley A. Moon C. Krawczyk M. Volkova M. Ziman B. Margulies K.B. Talan M. Crow M.T. Boheler K.R. The pro-angiogenic cytokine pleiotrophin potentiates cardiomyocyte apoptosis through inhibition of endogenous AKT/PKB activity.J. Biol. Chem. 2007; 282: 34984-34993Crossref PubMed Scopus (32) Google Scholar]). In contrast, six genes are probably maladaptive, as their overexpression leads to increases in cardiac remodeling (Col6a2 [Grossman et al., 2011Grossman T.R. Gamliel A. Wessells R.J. Taghli-Lamallem O. Jepsen K. Ocorr K. Korenberg J.R. Peterson K.L. Rosenfeld M.G. Bodmer R. Bier E. Over-expression of DSCAM and COL6A2 cooperatively generates congenital heart defects.PLoS Genet. 2011; 7: e1002344Crossref PubMed Scopus (61) Google Scholar], Cx3cl1 [Xuan et al., 2011Xuan W. Liao Y. Chen B. Huang Q. Xu D. Liu Y. Bin J. Kitakaze M. Detrimental effect of fractalkine on myocardial ischaemia and heart failure.Cardiovasc. Res. 2011; 92: 385-393Crossref PubMed Scopus (66) Google Scholar], Egfr [Messaoudi et al., 2012Messaoudi S. Zhang A.D. Griol-Charhbili V. Escoubet B. Sadoshima J. Farman N. Jaisser F. The epidermal growth factor receptor is involved in angiotensin II but not aldosterone/salt-induced cardiac remodelling.PLoS ONE. 2012; 7: e30156Crossref PubMed Scopus (15) Google Scholar], Pcolce [Kessler-Icekson et al., 2006Kessler-Icekson G. Schlesinger H. Freimann S. Kessler E. Expression of procollagen C-proteinase enhancer-1 in the remodeling rat heart is stimulated by aldosterone.Int. J. Biochem. Cell Biol. 2006; 38: 358-365Crossref PubMed Scopus (33) Google Scholar], and Tnc [Nishioka et al., 2010Nishioka T. Onishi K. Shimojo N. Nagano Y. Matsusaka H. Ikeuchi M. Ide T. Tsutsui H. Hiroe M. Yoshida T. Imanaka-Yoshida K. Tenascin-C may aggravate left ventricular remodeling and function after myocardial infarction in mice.Am. J. Physiol. Heart Circ. Physiol. 2010; 298: H1072-H1078Crossref PubMed Scopus (95) Google Scholar]), oxidative stress (Nox4), or fibrosis (Itga11 [Talior-Volodarsky et al., 2012Talior-Volodarsky I. Connelly K.A. Arora P.D. Gullberg D. McCulloch C.A. α11 integrin stimulates myofibroblast differentiation in diabetic cardiomyopathy.Cardiovasc. Res. 2012; 96: 265-275Crossref PubMed Scopus (79) Google Scholar]) during HF. The remaining four genes have been implicated in the development of vascular disorders such as generalized arterial calcification of infancy (Enpp1 [Hofmann Bowman and McNally, 2012Hofmann Bowman M.A. McNally E.M. Genetic pathways of vascular calcification.Trends Cardiovasc. Med. 2012; 22: 93-98Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar) or thoracic abdominal aneurysm (Mfap5 [Barbier et al., 2014Barbier M. Gross M.-S. Aubart M. Hanna N. Kessler K. Guo D.-C. Tosolini L. Ho-Tin-Noe B. Regalado E. Varret M. et al.MFAP5 loss-of-function mutations underscore the involvement of matrix alteration in the pathogenesis of familial thoracic aortic aneurysms and dissections.Am. J. Hum. Genet. 2014; 95: 736-743Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar). Two genes have been implicated in cardiovascular development through the regulation of ventricular morphogenesis (Fbln1 [Cooley et al., 2012Cooley M.A. Fresco V.M. Dorlon M.E. Twal W.O. Lee N.V. Barth J.L. Kern C.B. Iruela-Arispe M.L. Argraves W.S. Fibulin-1 is required during cardiac ventricular morphogenesis for versican cleavage, suppression of ErbB2 and Erk1/2 activation, and to attenuate trabecular cardiomyocyte proliferation.Dev. Dyn. 2012; 241: 303-314Crossref PubMed Scopus (29) Google Scholar) or cardiac valve formation (Snai1 [Tao et al., 2011Tao G. Levay A.K. Gridley T. Lincoln J. Mmp15 is a direct target of Snai1 during endothelial to mesenchymal transformation and endocardial cushion development.Dev. Biol. 2011; 359: 209-221Crossref PubMed Scopus (41) Google Scholar). Despite the fact that ISO activates the neurohormonal signaling cascade, many of the genes residing within module 5, with the exception of Nppa, are not directly related to canonical β-adrenergic signaling. Rather, approximately one third (29%) have been previously implicated in inflammatory signaling, integrin signaling, or transforming growth factor β (TGF-β) signaling. Four genes (Cx3cl1, Capn5, Cercam, and Ccdc80) are related to the activation of inflammatory signaling. In particular, an inhibitor of Cx3cl1, known as fractalkine, suppresses progression of HF in both MI (myocardial infarction) and TAC (tranverse aortic constriction) models (Xuan et al., 2011Xuan W. Liao Y. Chen B" @default.
• W2550900176 created "2016-11-30" @default.
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• W2550900176 title "Systems Genetics Approach Identifies Gene Pathways and Adamts2 as Drivers of Isoproterenol-Induced Cardiac Hypertrophy and Cardiomyopathy in Mice" @default.
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