Published online before print October 23, 2003, doi: 10.1161/01.RES.0000103171.42654.DD
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
© 2003 American Heart Association, Inc.
Molecular Medicine |
Transcriptional Profiling of the Heart Reveals Chamber-Specific Gene Expression Patterns
From the Donald W. Reynolds Cardiovascular Clinical Research Center (R.T., R.A.W., T.Q.), Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, Calif; and GeneData (USA), Inc (A.L.), South San Francisco, Calif.
Correspondence to Thomas Quertermous, MD, Division of Cardiovascular Medicine Stanford University School of Medicine 300 Pasteur Dr, Falk CVRC Stanford, CA 94305. E-mail tomq1{at}stanford.edu
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Cardiac chamber-specific gene expression is critical for the normal development and function of the heart. To investigate the genetic basis of cardiac anatomical specialization, we have undertaken a nearly genome-wide transcriptional profiling of the four heart chambers and the interventricular septum. Rigorous statistical analysis has allowed the identification of known and novel members of gene families that are felt to be important in cardiac development and function, including LIM proteins, homeobox proteins, wnt and T-box pathway proteins, as well as structural proteins like actins and myosins. In addition, these studies have allowed the identification of thousands of additional differentially expressed genes, for which there is little structural or functional information. Clustering of genes with known and unknown functions provides insights into signaling pathways that are essential for development and maintenance of chamber-specific features. To facilitate future research in this area, a searchable internet database has been constructed that allows study of the chamber-specific expression of any gene represented on this comprehensive microarray. It is anticipated that further study of genes identified through this effort will provide insights into the specialization of heart chamber tissues, and their specific roles in cardiac development, aging, and disease.
Key Words: gene expression • cardiac chambers • microarrays • regulatory networks • development
|
Introduction |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
The four chambers of the mammalian heart are specialized in order to handle differing physiological conditions, such as varying degrees of pressure and volume, throughout embryonic and adult life. Therefore, each chamber is composed of cells with unique functional, structural, metabolic, and electrophysiological characteristics. This specialization is reflected in the expression of different sets of genes from the earliest developmental stages through adulthood (see reviews1,2). Although chamber-specific gene expression is critical for normal development and function of the heart, and is implicated in etiology of various cardiac disease states,3 a comprehensive genome-wide analysis of the patterns of gene expression during cardiac morphogenesis has not been reported.1 Previous studies have described a number of genes coding for regulatory proteins that are critical for the regulation of regional-specific expression patterns during development4–6 and various disease states.7,8 These studies have led to the discovery of basic signaling pathways involved in the establishment and maintenance of chamber-specific phenotypes. Chamber-specific expression patterns of a number of genes coding for cardiovascular proteins have been characterized in the adult heart.1,4–6,9–11 Although these limited studies have provided significant insights, they serve to highlight the need for a comprehensive characterization of the genetic basis of heart chamber specification. Through the association of a broad range of known and unknown genes with chamber-specific expression, specific molecular functions subserved by cells in each chamber could be elucidated, and new signaling pathways linked to these functions.
A number of methods have been developed and used over the past decade to characterize differences in gene expression pattern between various tissues or tissues treated differently. However, the recent utilization of microarray methodology has allowed the development of large amounts of informative data, via a high throughput analysis of gene expression termed transcriptional profiling. Transcriptional profiling with microarrays offers simultaneous expression analysis of thousands of genes, revealing unique biological insights through patterns of expression and suggesting functions of unknown genes. Microarrays have been applied to investigate basic issues in cardiovascular biology, including the transcriptional response to experimental myocardial ischemia12 and gene expression patterns in human heart failure.13 However, the value of such studies are limited due to a lack of understanding regarding basic cell signaling pathways that mediate such fundamental processes as myocardial cell differentiation, and the genetic basis of cellular and tissue-specific gene expression.
Utilizing a comprehensive mouse cDNA microarray containing 42 300 features representing over 25 000 unique genes and ESTs, we have performed microarray analysis of gene expression patterns in the four cardiac chambers and the interventricular septum. Using rigorous statistical tools for analysis of the microarray data, we have identified genes that are expressed in the heart overall, as well as genes with expression limited to a subset of chambers. We have been able to fit expression profiles of signaling molecules into existing models of molecular regulatory networks for the various regions of the heart, assigning new roles for known genes and suggesting roles for unknown genes. A searchable internet database, representing a comprehensive "atlas" of gene expression in the normal heart, has been constructed to allow other investigators to study the chamber-specific patterns of expression of genes of interest. Further study of genes in this database is likely to provide important new insights into cardiovascular genes involved in the development, function, and pathophysiology of the cardiovascular system.
|
Materials and Methods |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Microarray Construction
The Mouse Transcriptome Microarray used in this study was constructed in our laboratory (http://mousedevelopment.org/) in collaboration with the Stanford Functional Genomics core facility (http://microarray.org). Briefly, the microarray is composed of 43 200 probes representing
25 000 unique genes and ESTs. It was constructed by combining the National Institutes of Aging (NIA) clone set (15 000 clones) (http://lgsun.grc.nia.nih.gov/cDNA/ cDNA.html) and the RIKEN clone set (21 000 clones) (http://fantom.gsc.riken.go.jp/doc/introduction.html#fantom), as well as 5000 genes that were donated by various investigators. A continuously updated and annotated list of the cDNAs included on this array is available at the Stanford Microarray Database (http://smd.stanford.edu).
RNA Preparation and Hybridization to Microarray
Fifteen 8-week-old C57Bl/6 female mice (http://labanimals. stanford.edu/index.html) were anesthetized with Avertin and perfused with normal saline after left ventricular puncture until liver blanching was noted. The four cardiac chambers and the interventricular septum of the mice were carefully dissected under a dissecting microscope, flash frozen in liquid nitrogen, and divided into three pools for further RNA isolation (5 mice per pool). The chambers were separated by first removing LA and RA from the base of the heart. The RV was then dissected off the LV/Septum by inserting iris scissors into the tricuspid valve opening and cutting around the interface of the RV and septum, leaving a 1.0- to 1.5-mm rim of RV tissue at the margins. The LV free wall was then dissected away from the septum by inserting iris scissors into the mitral valve opening and cutting around the interface of the LV and septum leaving a 1.0- to 1.5-mm rim of LV tissue at the margins. An oblong portion of septum was then dissected free of remaining ventricular tissues, with the interface regions discarded. Valvular apparatus and outflow tract were excluded from each sample. Total RNA was isolated using a modified two-step purification protocol employing homogenization (PRO250 Homogenizer, 10-mmx105-mm generator, PRO Scientific IN) in Trizol (Invitrogen) followed by purification over a Qiagen RNeasy column (Qiagen). First strand cDNA was synthesized from 15 µg of total RNA from each pool and from whole e17.5-day embryo for reference RNA in the presence of Cy3 or Cy5 dUTP, respectively, and hybridized to the microarray generating three biological replicates for each cardiac chamber (details provided at http://mousedevelopment.org/).
Quantitative Real-Time Reverse Transcriptase–Polymerase Chain Reaction
Primers and probes for 10 representative differentially expressed genes were obtained from Applied Biosystems Assays-on-Demand. cDNA was synthesized from 5 µg of total RNA using MMLV reverse transcriptase (SuperScript II kit, Invitrogen). Amplification was performed in triplicate at 50°C for 2 minutes and 95°C for 10 minutes followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. Reactions without template and/or enzyme were used as negative controls. 18S ribosomal RNA was used as an internal control. A standard curve derived from e17.5 day mouse embryonic RNA was plotted for each target gene by linear regression using SPSS version 11.0 software (Applied Biosystems). RNA quantity was expressed relative to the corresponding 18S control. Fold differences were calculated by dividing the combined RA and LA by the combined LV and RV results, and plotted on a log10 scale. Primers and probes used are listed in the online data supplement available at http://www.circresaha.org.
Data Acquisition, Analysis, and Statistical Analysis
Image acquisition of the mouse cDNA microarrays was performed on an Agilent G2565AA Microarray Scanner System. Feature Extraction was performed with GenePix 4.0 software (Axon, Inc). Numerical raw data were migrated from GenePix, without processing, into an Oracle relational database (CoBi) that has been designed specifically for microarray data analysis (GeneData, Inc, USA). The data were then analyzed using Expressionist software (http://www. genedata.com/products/expressionist/). After background subtraction (by calculating median "local" background of neighboring 121 features) and dye bias normalization,14 poor quality features were excluded from further analysis. Features with low signal intensity in the reference channel were filtered if percentage coverage area was less than 40%, and if signal-to-noise ratio was less than 2.5. To obtain a manageable sized dataset for further analysis, a number of filtering algorithms were also utilized including filtering by valid-value-proportion. Features with valid values in at least 50% of the experiments and present in at least one of the replicates were retained for further analysis. For further statistical analysis of the data, a K-nearest-neighbor (KNN) algorithm was applied to impute for missing values.15 To identify genes with an expression level statistically different in at least one of the experimental groups, we used ANOVA (Figure 1). For two-group comparisons, we used t tests to generate a list of genes that differentiated between the experimental groups with high statistical significance (Figure 1). As an additional stringent statistical tool to validate the gene list generated by the ANOVA and two-groups t tests, we also used the significance analysis of microarrays (SAM) algorithm.16 This method uses replicate experiments to develop a measure of variance that is used to test whether observed differences in gene expression, in two cell-type partitions, are likely to be real (http://www-stat. stanford.edu/tibs/SAM/). For hierarchical clustering of the experiments, we used positive correlation for distance determination and required complete linkage, which uses the greatest distance between genes in two clusters to ascribe similarity. For 2-dimensional hierarchical clustering, we applied similar settings for clustering of the gene groups.
|
|
Heart Chamber Gene Expression Database
A database containing normalized relative gene expression values is available for the hybridizations presented in this article and can be searched interactively from a web browser (http://mousedevelopment. org). Expression patterns for any gene present on the array can be accessed by searching by gene name, accession number, or sequence BLAST.
|
Results |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Large-Scale Analysis of Mouse Cardiac Chamber–Specific Gene Expression
cDNA microarrays containing a large portion of the mouse transcriptome were used to study the repertoire of genes expressed in the murine cardiac chambers. Triplicate microarray experiments were performed using pooled RNA from the four cardiac chambers and the interventricular septum (IVS) of C57Bl/6 female mice. The approach to data handling and analysis is summarized (Figure 1, see also online data supplement flow chart). To identify genes with an expression level statistically different in at least one of the experimental groups, we used rigorous comparison tests, which included ANOVA and multiclass SAM.16 A list of 4262 differentially expressed genes was first generated using ANOVA (P<0.01) alone. To confirm this result, we used multiclass SAM to obtain a similar sized group of genes. The list of genes generated by SAM revealed a false detection rate of 3% and had
90% identity with the list generated by ANOVA, suggesting that for this dataset SAM was not greatly different from high stringency t tests. Hierarchical clustering of the experiments using the above list of genes revealed the expected pattern where each experiment clustered first with its biological replicate before clustering with the other cardiac chambers (Figure 2). Coclustering of particular chambers serves as a quality control for the entire study showing the high degree of correlation among the replicate experiments (Figure 2). Moreover, the pattern of clustering is consistent with underlying cardiac function, where the 2 atria, and the 2 ventricles, have similar patterns of gene expression (Figure 2). The left and right atria are shown to cluster together first, before clustering with the ventricles and the IVS. As would be expected, the degree of correlation of gene expression, demonstrated by the hierarchical clustering, is higher between the IVS and the left ventricle than between the two ventricles.
|
Chamber-Specific Gene Expression
To identify genes primarily expressed in a particular chamber, we used the large dataset consisting of 23 607 features to perform t test comparisons between each chamber (3 replicates) and all other chambers combined (12 experiments). We repeated this procedure for each cardiac chamber and the interventricular septum (IVS) (Figure 1). From each of the 5 t tests performed, we chose those genes with statistically significant (P<0.01) higher expression levels in a particular chamber when compared with the other chambers (Figure 3, online Lists 1a through 1e). Interestingly, the IVS was found to share several genes with the left ventricle (LV) as well as the right ventricle (RV), but a number of genes were found to be uniquely expressed in the IVS alone (Figure 3).
Atrial and Ventricular Gene Expression
Our analyses demonstrated a very different pattern of gene expression between the atria and the ventricles. SAM analysis at a false detection rate of 2% identified 2460 genes with higher expression in the atria and 2970 genes with higher expression in the ventricles (Figure 4, online Lists 2a and 2b). Among the 2460 genes more highly expressed in the atria were those encoding transcription factors such as LIM proteins, cysteine rich proteins, and dickkopf homolog 3 (Dkk3); cytoskeletal genes such as myosin light chain, alkali, cardiac atria (Myla, MLC 1a), myosin light chain, regulatory A (MLC 2A) (see online List 8), actin related protein 2/3 complex; extracellular matrix molecules such as VCAM and EGF containing fibulin; growth related proteins such as cyclin I, growth arrest specific-1 and -6; metabolism-related molecules such as insulin-like growth factor binding protein-6; and several ESTs and uncharacterized genes. Genes with higher expression in the ventricles represented similar general categories of gene families. In comparison with atria, the ventricles had higher expression of the ventricular myosins Myosin light chain-1V (MLC 1v) and myosin light chain, phosphorylatable, cardiac ventricles (MLC 2V) (see online List 8) and several tropomyosins, as well as isoforms of actins and myosins, including cardiac 
-actin, actinin 2, and myosin Ib and X. The higher relative expression of several of these myocyte-specific genes may be due to the higher myocyte contribution to tissue mass in the ventricular samples versus the atrial tissue. Several transcription factors were found to have higher expression in ventricles. These included LIM domain proteins such as Elfin, four and half LIM domain 2, and homeobox genes such as Iroquois-related homeobox-3 and -4. Expression patterns of other cardiac-specific genes such as those coding proteins important in calcium metabolism (sarcoplasmic reticulum calcium ATPase (SERCA) family), sodium and potassium handling (NaK-ATPase family), energy metabolism proteins, and several matrix proteins were also noted to be chamber specific.
|
Left and Right Heart Gene Expression
Direct comparison between the LV and the RV using SAM analysis revealed a differential pattern of expression for several important gene groups (Figure 4, online Lists 2c and 2d). As expected, the number of genes that differentiated between the two ventricles was much smaller than those differentiating between the atria and the ventricles. Direct comparison between the two atria also revealed a differential pattern of expression of several important gene groups (data not shown).
Chamber-Specific Distribution of Biologically Relevant Genes
The above analyses revealed differential expression of gene families that have been linked to critical aspects of myocardial development and function. Using gene ontology annotation, members of these gene families, as well as other families of biological interest such as transcription factors, homeobox genes, cytoskeletal proteins, and developmentally related genes, were compiled for a closer examination of their chamber-specific expression (see online data supplement). Similar analytical algorithms were applied to this smaller subset of genes as to the larger dataset. Many of these genes revealed statistically significant chamber-specific expression (online List 3). Two-dimensional hierarchical clustering with these genes demonstrated that they were able to accurately classify tissue samples regarding their chamber of origin (Figure 5). Direct comparisons between the atria and ventricles and LV versus RV using SAM revealed differential patterns of expression for members of these interesting protein families (online Lists 4 and 5). Some of the most interesting findings were the expression patterns of transcription factors. Genes with higher expression in the ventricle relative to atria included Irx3, Irx4, Nkx2.5, MAD homolog 3, and the paired-related homeobox-1. Important genes with higher relative expression in the atria include T-box 5 and Dkk3. Using the data obtained from this analysis, we have been able to fit specific expression profiles of known signaling molecules into existing models of molecular regulatory networks that maintain chamber-specific features in the adult heart (Figure 7).
|
|
Quantitative Real-Time RT-PCR Confirms the Accuracy of Microarray Hybridization Results
Differential expression of 10 representative genes from a number of pathways was confirmed by qRT-PCR. The genes assayed included Myla (myosin light chain, alkali, cardiac atria), sarcolipin, Dkk3, Sdccag28 (serologically defined-colon-CA-antigen-28), Pdlim3 (PDZ and LIM-domains-3), Ednrb (endothelin receptor b), Lmcd1 (LIM and cysteine-rich domains 1), robo4 (roundabout homolog 4), Nd-1 (Ivns1abp, Unigene Mm.33764), and CD36 antigen. Overall, there was good correlation between the two methods, with the qRT-PCR data showing greater measured differences in most cases (Figure 6).
|
Chamber Classification by ESTs
To identify genes with an expression level statistically different in at least one of the experimental groups, we used ANOVA (P<0.01) and multiclass SAM analysis. The resultant list of 4200 genes included approximately 900 ESTs and uncharacterized features. Hierarchical clustering of the experiments using these ESTs revealed an accurate clustering of the cardiac chambers (data not shown). This finding suggests that uncharacterized genes included on the array have chamber-specific gene expression, which may warrant further detailed analysis.
|
Discussion |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
In this study, we have performed comprehensive transcriptional profiling with murine heart tissues, with the goal of identifying genes that play a role in specialization and maintenance of adult heart chamber phenotypes. Chamber-specific gene expression patterns have been identified for members of known gene families that play functional roles in the heart, including those encoding LIM, homeobox, wnt pathway, and structural proteins such as actins and myosins. In addition, a large number of differentially expressed novel genes and ESTs have been identified. The identification of genes with known differential expression in the heart chambers validates the microarray methodology, and the identification of novel factors provides avenues for future investigation of chamber-specific phenotypic specialization.
Embryonic heart development is characterized by morphogenetic events that are orchestrated by complex patterns of gene expression.10,17–20 Patterned differences in gene expression, regulated by known developmental transcription factor families, are important for cell movement and response to local inductive signals. In general, such developmental pathways are not commonly considered in the context of adult tissue function, although reactivation of embryonic signaling is often linked to pathophysiology. However, a striking finding of these studies has been the documentation of sustained expression of critical developmental factors in the adult cardiac tissues. Certainly, different cardiac chambers are exposed to different hemodynamic forces, and required to adopt their specialized functions early in development. Thus, transcription factors that regulate chamber-specific developmental events may also determine transcription of molecules that define unique aspects of the contractile and cytoskeletal apparatus, energy management, ion-channel function, and electrophysiological properties. By combining information regarding known signaling pathways that are used in cardiac development with the information provided through these studies documenting chamber-specific differential gene expression, it is possible to propose a paradigm of transcriptional regulatory networks that maintain chamber-specific features in the adult heart (Figure 7).
Data presented in this study and in Figure 7 suggests important roles for wnt signaling pathways in cardiac chamber maintenance or function. Wnt pathways are among the most important determinants of embryonic patterning, and the Drosophila prototype gene Wingless (Wg) is critical for heart formation.21–24 Coordinated expression of the mammalian wnt antagonists, Crescent and dickkopf-1 (Dkk1), in the cardiac crescent overcomes repressive wnt signaling and allows commitment of cells to the cardiac fate.25,26 Mice that lack the disheveled-2 gene, a downstream signaling molecule in the wnt pathway, develop with outflow tract abnormalities.27,28 Any ongoing role of wnt signaling in the maintenance of cell identity in adult heart tissues has not been clearly described.
Dkk3, which also acts as a potent inhibitor of wnt signaling, is expressed in cells of the bulbis cordis and sinus venosus in the 9-day-old embryo. However, its expression becomes restricted to the atria and endocardial cushions by embryonic day 12.5 (as is Dkk1), suggesting that it plays a role in the establishment of atrial identity.29 Our findings show that Dkk3 maintains this highly localized expression in the adult atrium, with little expression in the ventricle, suggesting that it may also be important for the maintenance of atrial identity by antagonizing wnt signaling.9 In keeping with this possibility is the observation that dishevelled-1 (Dvl1) is significantly more highly expressed in the ventricles than in the atria. An appealing hypothesis is suggested, such that wnt signaling is opposed in the atria by Dkk3 and continues unopposed in the ventricles as evidenced by high levels of Dvl1 mRNA, resulting in wnt pathway input into the maintenance of phenotype in the adult cardiac chambers. Wnt mediators often operate in combination with other pathways such as LIM proteins. Recent studies have shown the four and a half LIM-only protein 2 (FHL2) is a novel ß-catenin–interacting protein and coactivator.30 Our results demonstrate that FHL2 has higher expression in the ventricles.
A number of LIM domain proteins were identified in these studies and likely serve critical overlapping functions in different chambers (Figure 7). Many known LIM proteins have proven roles in differentiation, function, and the maintenance of phenotype of portions of the cardiovascular system. These include Cipher31 muscle LIM protein (MLP)7,32 and CRP1 and 2, among others.33 In our study, LIM proteins were found to have unique expression patterns among the cardiac chambers (Figure 5, online List 3). Consistent with prior studies, our results reveal that FHL2 and Elfin are highly expressed in the ventricles.9,34,35 Perhaps more interestingly, these studies show that thymus LIM protein is expressed in the heart with a ventricular-restricted pattern. Cardiac expression of this LIM gene family member has not been previously reported. We have identified four other poorly characterized LIM domain proteins with restricted expression patterns. Further study of these members of this important gene family is likely to provide significant insights into developmental and functional features of the ventricle.
We have identified hundreds of other named genes and uncharacterized ESTs, which have restricted expression patterns within the heart. Many of these ESTs are assigned to Unigene clusters with limited sequences, so it is not possible to assign them to structural protein families. Further studies of these genes will likely lead to identification of additional new gene families and pathways involved in the development, function, and pathophysiology of the cardiovascular system.
Previous studies using microarrays and transcriptional profiling have investigated gene expression differences between the different cardiac chambers.9,11,36 We have compared our data with that obtained by these other groups, and found that genes identified in their studies as having large expression differences between the heart chambers were also identified in the analysis presented here (online Lists 8 and 9). However, in looking at the overall data, there are significant differences in gene expression patterns among these different studies, due to differences in experimental design, methodology, and data analysis.
By using microarrays to analyze gene expression patterns in the heart on a genome wide scale, we have identified large numbers of genes, which are differentially regulated and likely play important roles in the development, function, and pathophysiology of the cardiovascular system. Although definitive experiments are required to fully characterize the function of such candidate genes, grouping of genes on the basis of their coordinated expression allows the development of hypotheses regarding signaling functions. This study provides a comprehensive gene expression atlas of the mammalian heart, establishing a strong foundation for further study of cardiac development and disease states.
|
Acknowledgments |
|---|
This work was supported by the Donald W. Reynolds Cardiovascular Clinical Research Center at Stanford University. We sincerely thank Alicia Deng for technical assistance and Daniel Estes for his invaluable help in developing the interactive database.
|
Footnotes |
|---|
*Both authors contributed equally to this study.
Original received July 1, 2003; revision received September 17, 2003; accepted October 15, 2003.
|
References |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
1. Franco D, Lamers WH, Moorman AF. Patterns of expression in the developing myocardium: towards a morphologically integrated transcriptional model. Cardiovasc Res. 1998; 38: 25–53.
2. Lyons GE. Vertebrate heart development. Curr Opin Genet Dev. 1996; 6: 454–460.[CrossRef][Medline] [Order article via Infotrieve]
3. Bruneau BG, Logan M, Davis N, Levi T, Tabin CJ, Seidman JG, Seidman CE. Chamber-specific cardiac expression of Tbx5 and heart defects in Holt-Oram syndrome. Dev Biol. 1999; 211: 100–108.[CrossRef][Medline] [Order article via Infotrieve]
4. Bao ZZ, Bruneau BG, Seidman JG, Seidman CE, Cepko CL. Regulation of chamber-specific gene expression in the developing heart by Irx4. Science. 1999; 283: 1161–1164.
5. Yamagishi H, Yamagishi C, Nakagawa O, Harvey RP, Olson EN, Srivastava D. The combinatorial activities of Nkx2.5 and dHAND are essential for cardiac ventricle formation. Dev Biol. 2001; 239: 190–203.[CrossRef][Medline] [Order article via Infotrieve]
6. Bruneau BG, Bao ZZ, Tanaka M, Schott JJ, Izumo S, Cepko CL, Seidman JG, Seidman CE. Cardiac expression of the ventricle-specific homeobox gene Irx4 is modulated by Nkx2-5 and dHand. Dev Biol. 2000; 217: 266–277.[CrossRef][Medline] [Order article via Infotrieve]
7. Arber S, Hunter JJ, Ross J Jr, Hongo M, Sansig G, Borg J, Perriard JC, Chien KR, Caroni P. MLP-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell. 1997; 88: 393–403.[CrossRef][Medline] [Order article via Infotrieve]
8. Hein S, Kostin S, Heling A, Maeno Y, Schaper J. The role of the cytoskeleton in heart failure. Cardiovasc Res. 2000; 45: 273–278.
9. Zhao XS, Gallardo TD, Lin L, Schageman JJ, Shohet RV. Transcriptional mapping and genomic analysis of the cardiac atria and ventricles. Physiol Genomics. 2002; 12: 53–60.
10. Sucov HM. Molecular insights into cardiac development. Annu Rev Physiol. 1998; 60: 287–308.[CrossRef][Medline] [Order article via Infotrieve]
11. Kaynak B, von Heydebreck A, Mebus S, Seelow D, Hennig S, Vogel J, Sperling HP, Pregla R, Alexi-Meskishvili V, Hetzer R, Lange PE, Vingron M, Lehrach H, Sperling S. Genome-wide array analysis of normal and malformed human hearts. Circulation. 2003; 107: 2467–2474.
12. Amberger A, Schneeberger S, Hernegger G, Brandacher G, Obrist P, Lackner P, Margreiter R, Mark W. Gene expression profiling of prolonged cold ischemia and reperfusion in murine heart transplants. Transplantation. 2002; 74: 1441–1449.[CrossRef][Medline] [Order article via Infotrieve]
13. Yang J, Moravec CS, Sussman MA, DiPaola NR, Fu D, Hawthorn L, Mitchell CA, Young JB, Francis GS, McCarthy PM, Bond M. Decreased SLIM 1 expression and increased gelsolin expression in failing human hearts measured by high-density oligonucleotide arrays. Circulation. 2000; 102: 3046–3052.
14. Goryachev AB, Macgregor PF, Edwards AM. Unfolding of microarray data. J Comput Biol. 2001; 8: 443–461.[CrossRef][Medline] [Order article via Infotrieve]
15. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17: 520–525.
16. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001; 98: 5116–5121.
17. Lamers WH, Moorman AF. Cardiac septation: a late contribution of the embryonic primary myocardium to heart morphogenesis. Circ Res. 2002; 91: 93–103.
18. Zaffran S, Frasch M. Early signals in cardiac development. Circ Res. 2002; 91: 457–469.
19. Chen J, Kubalak SW, Minamisawa S, Price RL, Becker KD, Hickey R, Ross J Jr, Chien KR. Selective requirement of myosin light chain 2v in embryonic heart function. J Biol Chem. 1998; 273: 1252–1256.
20. Pawloski-Dahm CM, Song G, Kirkpatrick DL, Palermo J, Gulick J, Dorn GW II, Robbins J, Walsh RA. Effects of total replacement of atrial myosin light chain-2 with the ventricular isoform in atrial myocytes of transgenic mice. Circulation. 1998; 97: 1508–1513.
21. Wharton KA Jr. Runnin’ with the Dvl: proteins that associate with Dsh/Dvl and their significance to Wnt signal transduction. Dev Biol. 2003; 253: 1–17.[CrossRef][Medline] [Order article via Infotrieve]
22. Huelsken J, Birchmeier W. New aspects of Wnt signaling pathways in higher vertebrates. Curr Opin Genet Dev. 2001; 11: 547–553.[CrossRef][Medline] [Order article via Infotrieve]
23. Moon RT, Bowerman B, Boutros M, Perrimon N. The promise and perils of Wnt signaling through ß-catenin. Science. 2002; 296: 1644–1646.
24. Park M, Wu X, Golden K, Axelrod JD, Bodmer R. The wingless signaling pathway is directly involved in Drosophila heart development. Dev Biol. 1996; 177: 104–116.[CrossRef][Medline] [Order article via Infotrieve]
25. Tzahor E, Lassar AB. Wnt signals from the neural tube block ectopic cardiogenesis. Genes Dev. 2001; 15: 255–260.
26. Schneider VA, Mercola M. Wnt antagonism initiates cardiogenesis in Xenopus laevis. Genes Dev. 2001; 15: 304–315.
27. Hamblet NS, Lijam N, Ruiz-Lozano P, Wang J, Yang Y, Luo Z, Mei L, Chien KR, Sussman DJ, Wynshaw-Boris A. Dishevelled 2 is essential for cardiac outflow tract development, somite segmentation and neural tube closure. Development. 2002; 129: 5827–5838.[CrossRef][Medline] [Order article via Infotrieve]
28. Klingensmith J, Yang Y, Axelrod JD, Beier DR, Perrimon N, Sussman DJ. Conservation of dishevelled structure and function between flies and mice: isolation and characterization of Dvl2. Mech Dev. 1996; 58: 15–26.[CrossRef][Medline] [Order article via Infotrieve]
29. Monaghan AP, Kioschis P, Wu W, Zuniga A, Bock D, Poustka A, Delius H, Niehrs C. Dickkopf genes are co-ordinately expressed in mesodermal lineages. Mech Dev. 1999; 87: 45–56.[CrossRef][Medline] [Order article via Infotrieve]
30. Wei Y, Renard CA, Labalette C, Wu Y, Levy L, Neuveut C, Prieur X, Flajolet M, Prigent S, Buendia MA. Identification of the LIM Protein FHL2 as a coactivator of ß-catenin. J Biol Chem. 2003; 278: 5188–5194.
31. Zhou Q, Chu PH, Huang C, Cheng CF, Martone ME, Knoll G, Shelton GD, Evans S, Chen J. Ablation of Cypher, a PDZ-LIM domain Z-line protein, causes a severe form of congenital myopathy. J Cell Biol. 2001; 155: 605–612.
32. Su Z, Yao A, Zubair I, Sugishita K, Ritter M, Li F, Hunter JJ, Chien KR, Barry WH. Effects of deletion of muscle LIM protein on myocyte function. Am J Physiol Heart Circ Physiol. 2001; 280: H2665–H2673.
33. Chang DF, Belaguli NS, Iyer D, Roberts WB, Wu SP, Dong XR, Marx JG, Moore MS, Beckerle MC, Majesky MW, Schwartz RJ. Cysteine-Rich LIM-Only Proteins CRP1 and CRP2 Are Potent Smooth Muscle Differentiation Cofactors. Dev Cell. 2003; 4: 107–118.[CrossRef][Medline] [Order article via Infotrieve]
34. Chu PH, Ruiz-Lozano P, Zhou Q, Cai C, Chen J. Expression patterns of FHL/SLIM family members suggest important functional roles in skeletal muscle and cardiovascular system. Mech Dev. 2000; 95: 259–265.[CrossRef][Medline] [Order article via Infotrieve]
35. Kotaka M, Lau YM, Cheung KK, Lee SM, Li HY, Chan WY, Fung KP, Lee CY, Waye MM, Tsui SK. Elfin is expressed during early heart development. J Cell Biochem. 2001; 83: 463–472.[CrossRef][Medline] [Order article via Infotrieve]
36. Chugh SS, Whitesel S, Turner M, Roberts CT Jr, Nagalla SR. Genetic basis for chamber-specific ventricular phenotypes in the rat infarct model. Cardiovasc Res. 2003; 57: 477–485.
This article has been cited by other articles:
 |
|
 |
|
  T. Moore-Morris, A. Varrault, M. E. Mangoni, A. Le Digarcher, V. Negre, C. Dantec, L. Journot, J. Nargeot, and B. Couette Identification of Potential Pharmacological Targets by Analysis of the Comprehensive G Protein-Coupled Receptor Repertoire in the Four Cardiac Chambers Mol. Pharmacol., May 1, 2009; 75(5): 1108 - 1116. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Urashima, M. Zhao, R. Wagner, G. Fajardo, S. Farahani, T. Quertermous, and D. Bernstein Molecular and physiological characterization of RV remodeling in a murine model of pulmonary stenosis Am J Physiol Heart Circ Physiol, September 1, 2008; 295(3): H1351 - H1368. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Gambetta, M. K. Al-Ahdab, M. N. Ilbawi, N. Hassaniya, and M. Gupta Transcription repression and blocks in cell cycle progression in hypoplastic left heart syndrome Am J Physiol Heart Circ Physiol, May 1, 2008; 294(5): H2268 - H2275. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Burstein, E. Libby, A. Calderone, and S. Nattel Differential Behaviors of Atrial Versus Ventricular Fibroblasts: A Potential Role for Platelet-Derived Growth Factor in Atrial-Ventricular Remodeling Differences Circulation, April 1, 2008; 117(13): 1630 - 1641. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Philip-Couderc, N. I. Tavares, A. Roatti, R. Lerch, C. Montessuit, and A. J. Baertschi Forkhead Transcription Factors Coordinate Expression of Myocardial KATP Channel Subunits and Energy Metabolism Circ. Res., February 1, 2008; 102(2): e20 - e35. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. S. Barth, R. Kuner, A. Buness, M. Ruschhaupt, S. Merk, L. Zwermann, S. Kaab, E. Kreuzer, G. Steinbeck, U. Mansmann, et al. Identification of a Common Gene Expression Signature in Dilated Cardiomyopathy Across Independent Microarray Studies J. Am. Coll. Cardiol., October 17, 2006; 48(8): 1610 - 1617. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Tabibiazar, R. A. Wagner, A. Deng, P. S. Tsao, and T. Quertermous Proteomic profiles of serum inflammatory markers accurately predict atherosclerosis in mice Physiol Genomics, April 13, 2006; 25(2): 194 - 202. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. C. Wu, J. M. Spin, F. Cao, S. Lin, X. Xie, O. Gheysens, I. Y. Chen, A. Y. Sheikh, R. C. Robbins, A. Tsalenko, et al. Transcriptional profiling of reporter genes used for molecular imaging of embryonic stem cell transplantation Physiol Genomics, March 13, 2006; 25(1): 29 - 38. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. L. Merklinger, R. A. Wagner, E. Spiekerkoetter, A. Hinek, R. H. Knutsen, M. G. Kabir, K. Desai, S. Hacker, L. Wang, G. M. Cann, et al. Increased Fibulin-5 and Elastin in S100A4/Mts1 Mice With Pulmonary Hypertension Circ. Res., September 16, 2005; 97(6): 596 - 604. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Tabibiazar, R. A. Wagner, E. A. Ashley, J. Y. King, R. Ferrara, J. M. Spin, D. A. Sanan, B. Narasimhan, R. Tibshirani, P. S. Tsao, et al. Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease Physiol Genomics, July 14, 2005; 22(2): 213 - 226. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Ellinghaus, R. J. Scheubel, D. Dobrev, U. Ravens, J. Holtz, J. Huetter, U. Nielsch, and H. Morawietz Comparing the global mRNA expression profile of human atrial and ventricular myocardium with high-density oligonucleotide arrays J. Thorac. Cardiovasc. Surg., June 1, 2005; 129(6): 1383 - 1390. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S.-K. Park and T. A. Prolla Gene expression profiling studies of aging in cardiac and skeletal muscles Cardiovasc Res, May 1, 2005; 66(2): 205 - 212. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Tabibiazar, R. A. Wagner, J. M. Spin, E. A. Ashley, B. Narasimhan, E. M. Rubin, B. Efron, P. S. Tsao, R. Tibshirani, and T. Quertermous Mouse Strain-Specific Differences in Vascular Wall Gene Expression and Their Relationship to Vascular Disease Arterioscler. Thromb. Vasc. Biol., February 1, 2005; 25(2): 302 - 308. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. M. Spin, S. Nallamshetty, R. Tabibiazar, E. A. Ashley, J. Y. King, M. Chen, P. S. Tsao, and T. Quertermous Transcriptional profiling of in vitro smooth muscle cell differentiation identifies specific patterns of gene and pathway activation Physiol Genomics, November 17, 2004; 19(3): 292 - 302. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||