© 2000 American Heart Association, Inc.
Molecular Medicine |
Altered Patterns of Gene Expression in Response to Myocardial Infarction
From Scios Inc, Sunnyvale Calif.
Correspondence to R. Tyler White, Scios Inc, 820 W Maude Ave, Sunnyvale CA 94086. E-mail white{at}sciosinc.com
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Abstract |
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Abstract—The use of cDNA microarrays has made it possible to simultaneously analyze gene expression for thousands of genes. Microarray technology was used to evaluate the expression of >4000 genes in a rat model of myocardial infarction. More than 200 genes were identified that showed differential expression in response to myocardial infarction. Gene expression changes were monitored from 2 to 16 weeks after infarction in 2 regions of the heart, the left ventricle free wall and interventricular septum. A novel clustering program was used to identify patterns of expression within this large set of data. Unique patterns were revealed within the transcriptional responses that illuminate changes in biological processes associated with myocardial infarction.
Key Words: myocardial infarction • tissue remodeling • gene expression • DNA microarrays
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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In response to a transmural myocardial infarction (MI), the left ventricle (LV) undergoes significant remodeling.1 Progressive alterations in ventricular architecture occur in infarcted and noninfarcted regions. Typically, after initial inflammatory responses and scar formation, LV chamber volume increases, and there is a thinning of the chamber wall and a loss of contractility due to fibrosis. It is believed that initially remodeling is required for maintenance of cardiac output but ultimately leads to LV dysfunction. Clinical and experimental studies of MI have exposed a number of cellular changes that contribute to morphological and functional adaptations, including cardiac fibroblast proliferation, cardiac myocyte hypertrophy, and extracellular matrix (ECM) deposition.
A rat model of MI has been extensively used to better understand the
functional, structural, and molecular changes associated with clinical
ischemic heart disease.2 Animals that survive with
large transmural infarctions develop heart failure without another
ischemic event, as is typically seen in humans. It is clear
that substantial alterations in gene expression are needed to afford
such profound changes within cells of the remodeling
myocardium. Alterations in expression of several genes have
been described in the rat model of MI. Changes in actin and myosin gene
expression are associated with alterations in cytoskeleton and
contractile apparatus in surviving
myocytes.3 4 Fibrosis of LV myocardium is, in
part, the result of elevated collagen and fibronectin
expression.5 6 Atrial natriuretic peptide mRNA
and protein levels in the cardiomyocyte are elevated in
response to MI as a compensatory response to improve
hemodynamics.6 7 In addition, certain
cytokine genes, interleukins 1ß and 6, and tumor necrosis
factor-
are transcriptionally regulated in the remodeling MI rat
heart.8
Recent technological advances in the production of cDNA microarrays have made it possible to profile gene expression of tens of thousands of genes simultaneously.9 10 High-density arrays of cDNA inserts, >1000 genes per cm2, are produced on glass slides by high-speed robotic printing. Each printed cDNA insert on the microarray is suitable for molecular hybridization, thus allowing rapid assessment of mRNA expression of all arrayed genes in tissues of interest. Microarray analysis of gene expression has been applied to studies in yeast, Arabidopsis, Streptococcus, and mammalian cell lines. Disease-related changes in gene expression have been evaluated by microarray methods for cancer,11 rheumatoid arthritis and inflammatory bowel disease,12 and metabolic disorders.13
We present here application of cDNA microarray technology to identify gene expression changes in the rat heart after infarction. Approximately 7000 cDNAs collected from rat heart cDNA libraries were printed onto microarrays and profiled for expression in the LV free wall and interventricular septum (IVS) at 2, 4, 8, 12, and 16 weeks after surgically induced MI in the rat. Patterns within a set of 731 differentially expressed genes have been identified with newly developed clustering algorithms. Many of the changes in expression are found in genes that encode proteins that have been implicated in cytoskeletal architecture, ECM, contractility, and metabolism.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Myocardial Infarction Model
The procedures for the care and treatment of animals were approved by the institutional animal care and use committee at Scios Inc and follow the guidelines of the American Association for Accreditation of Laboratory Animal Care. MIs were produced in male Wistar rats (Coromed Inc, Troy, NY) by partial ligation of the coronary artery. Animals were sacrificed 2, 4, 8, 12, and 16 weeks after surgery by laparotomy under anesthesia. The heart of each animal was removed for histological evaluation and for the preparation of mRNA.
Gene Expression Profiling
Fluorescently labeled cDNA probes were generated by
reverse transcription of mRNA prepared from control or infarcted rat
hearts in the presence of Cy3 or Cy5 dCTP (Amersham).
Fluorescently labeled probe pairs were applied to microarrays
that contained 
7000 rat heart cDNA clones and allowed to hybridize
to each of the 7000 elements. Degree of hybridization at each element
was quantified by sequential excitation of the 2 fluorophores with a
scanning laser read at an appropriate wavelength for each emission.
Differential expression values were expressed as a ratio of intensities
from the two emissions where positive and negative values indicated an
increase or decrease, respectively, relative to control.
An expanded Materials and Methods section is available online at http://www.circresaha.org.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Preparation of Infarcted Myocardial Tissue
Heart tissue was taken from rats with healed MI to examine changes in gene expression associated with disease. Following established procedures,2 transmural infarctions were produced by coronary artery ligation (for details, see online Materials and Methods; http://www.circresaha.org). At various time points after infarction (2, 4, 8, 12, and 16 weeks), and from sham-operated controls, heart tissue was removed, weighed, and scored for size of the healed infarction (see Table online; http://www.circresaha.org). Heart weight-to-body-weight measurements indicated substantial enlargements of the hearts in MI animals relative to control animals. Histological examination of the hearts revealed significant fibrosis in the region of infarction (Figure 1
). mRNA was extracted separately from LV
free wall and IVS for gene expression analysis.
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cDNA Microarray Analysis of Infarcted Myocardium
Approximately 7000 cDNA clones, isolated from a normalized rat LV
cDNA library, were randomly chosen for analysis on microarrays.
Partial sequencing of the clones indicated that there were 4258
distinct sequences, which is a rough estimate of the number of distinct
genes printed. Sequence alignment14 of the 7000 printed
clones against GenBank entries indicated that 59% matched named genes,
23% matched only expressed sequence tags (ESTs), and 18% had no
significant match. Polymerase chain reaction–amplified inserts of each
cDNA were printed as high-density arrays on treated glass surfaces (for
details, see online Materials and Methods;
http://www.circresaha.org).
The microarrayed target genes were probed with Cy3-labeled cDNA prepared from mRNA of each time point and Cy5-labeled reference cDNA prepared from sham-operated control tissue. LV and IVS samples were prepared and assayed independently. Tissues from 4 animals were pooled to improve yields in preparation of poly A+ RNA and to minimize variations among animals. Duplicate hybridizations were performed on the 7000-element microarray with LV and IVS tissue for all time points. All target genes that showed changes of at least 1.8-fold were reprinted onto a subarray for further analysis. The subarray was hybridized, in duplicate, with probe derived separately from LV and IVS tissue at each time point. Thus, all target genes described here were independently assayed 4 times. Expression data generated for a few target genes, in certain experiments, did not pass acceptance criteria (see online Materials and Methods; http://www.circresaha.org). Therefore, some target genes did not produce 4 independent values, and only those genes that had at least 2 independent values were considered further. Median values were determined, and target genes that showed a decrease or increase of at least 1.8-fold at any time point were considered differentially expressed. The threshold value for differential expression of 1.8-fold proved reliable for genes that produced expression values significantly over background (data not shown). Low-level expression values, 3- to 4-fold over background, were less reproducible. Consistent determinations among genes that had multiple representation on the arrays were a testament to the method (see Figure 6 online; http://www.circresaha.org). For many genes, differential expression was observed in multiple time points, which also indicated reliability in the method.
We identified 731 cDNA clones that were differentially expressed in LV or IVS in at least 1 time point. Among these differentially expressed genes were 198 that matched only to ESTs in GenBank and 69 clones that had no significant match in GenBank. A total of 464 clones corresponded to 230 unique, named genes that were differentially expressed. Summary representations of the results are presented in this article, and a more complete report of expression data and gene identification information is available (see Figure 6 online; http://www.circresaha.org). Many of the genes identified have not been previously reported as differentially expressed in the MI model. Confirming the ability of microarrays to identify differentially expressed genes, a number of genes that showed elevated expression were previously reported as such in MI heart tissue utilizing traditional methodology. These include atrial natriuretic peptide,4 7 sarcoplasmic/endoplasmic reticulum Ca2+ ATPase,15 collagen,5 6 and fibronectin.6 Indeed, many other investigators have found that cDNA microarray methods are reliable when compared with more traditional methods of gene expression analysis.16 17 18
Clustering Gene Expression Patterns
We developed a computer program (GExpA) to cluster and
visualize similar gene expression patterns from very large sets of data
generated by microarray analysis (Q. Zheng and L.J. Garrard,
unpublished program, 1999). The data collected from genes that showed
differential expression were analyzed by GExpA to reveal
temporal and tissue-specific patterns of gene expression. For
clustering, expression values for each target gene were normalized to
emphasize trends in expression changes over the magnitude of response.
Within each time course, differential expression values were divided by
the absolute maximum value to ensure that all values are between –1
and 1. Before normalization, insignificant median values of
differential expression between –1.4 and 1.4 were set to a neutral
value of 0. Expression curves were generated for each gene from the 5
time points in the LV appended to the 5 time points in IVS. An example
of a cluster derived from our data is shown (Figure 2). The characteristic curve of the
cluster is shown together with actual curves for 10 genes that compose
that cluster. As testament to the accuracy of the clustering, replicate
genes within the data set were found in the same (see atrial
natriuretic factor [ANF] and ribosomal protein S27 clones
in Figure 2) or very similar clusters. Expression patterns fell
into a total of 58 different clusters in this analysis. A
graphic representation of the clustering of all 731
differentially expressed genes is shown in Figure 3. Each row represents different
target cDNAs, and the columns reflect the 5 time points in the LV and
the 5 time points in the IVS. All genes are grouped together within
their assigned expression cluster, and clusters are arranged by
similarity to the other clusters. Through this visual display of the
data, a number of temporal patterns are evident. In some clusters,
there are consistent decreases or increases in gene expression
across the time course, whereas other sets indicate transient changes
in gene expression. It is also readily apparent that although certain
genes are similarly altered in the LV and IVS, a majority of the
patterns reveal a distinct response in these 2 regions of the
heart.
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Functional Clusters Within Expression Clusters
Many of the genes that displayed differential expression encode
proteins with known functions, whereas others corresponded to genes of
unknown function, including novel and previously identified ESTs. Genes
were classified on the basis of biological function of the encoded
protein using a modified version of a previously established
classification scheme.19 The classification scheme was
composed of 7 major functional categories and several minor functional
categories within the major categories. Genes were placed into a single
class if a function of the encoded protein has been well established
(for complete classification, see Figure 6 online;
http://www.circresaha.org).
Within the functional groups, clustering analysis
elucidated additional expression patterns. The GExpA clustering program
was applied to each of the 7 major functional groups of genes that
showed differential expression (Figure 4). The results show that there was a
nonrandom distribution of expression patterns within the functional
groups of genes. For example, within the Protein Expression group,
nearly all of the genes showed increased expression, primarily in the
LV. Although most of the elevated expression was for ribosomal
proteins, genes encoding enzymes involved in protein modification and
degradation were likewise enhanced. In the Cell Structure/Motility
group, there are both enhanced and repressed genes. However, enhanced
genes predominantly encode cytoskeletal and ECM proteins, whereas the
repressed genes preferentially encode contractile proteins. Many genes
in the Metabolism category encode proteins involved in
energy metabolism and, within that group, lipid
metabolism genes were primarily repressed. In contrast,
very few genes in the categories of Cell Division (1%) and
Cell/Organism Defense (7%) were found to be differentially expressed.
As no attempt was made to populate the microarray with equal
representation across the various functional classes, one
should not overinterpret the statistical or biological significance of
the number of changes within each functional category.
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Gene Expression Patterns Indicative of Cardiac Remodeling
A large number of genes that encode ECM proteins displayed
enhanced expression in MI relative to a normal myocardium.
Regional differences in expression profiles indicate that involvement
of these proteins is more profound in the post-MI processes taking
place in LV than in IVS. In addition to several types of collagen,
other proteins that are structural components of the ECM were
transcriptionally elevated, including fibronectin, laminin, fibrillin,
fibulin, SC1/ECM2, and decorin. The expression patterns of these ECM
genes in MI showed that they are elevated primarily in the LV free wall
throughout the time course studied here, 2 to 16 weeks after
infarction. In addition, we observed elevated expression, primarily in
LV tissue, of the metalloproteinase inhibitor, TIMP-3,
which appears to play a role in ECM deposition by preventing the
destruction of newly produced structural components of the matrix.
It is of interest that expression profiling revealed no genes
that were differentially expressed only in the IVS. There were many
genes that were altered uniquely in LV, or similarly in LV and IVS, but
none that were unique to IVS. Thus, although the remodeling process is
different in LV and IVS, it appears that the changes in IVS are a
subset of the changes in the LV. In addition, certain genes that are
altered in both LV and IVS are more substantially altered in the LV,
either in magnitude or duration. ANF expression, for example, was
elevated in both LV and IVS, but to different extents. In LV, ANF
expression continued to increase over the time course from 7- to
17-fold, whereas in IVS, ANF was consistently elevated 9-fold
throughout the time course (Figure 5).
ECM proteins such as collagen and fibronectin displayed clearly
different patterns of expression between LV and IVS. In LV, these genes
were elevated substantially in the early time points (2 to 4 weeks),
and less so at later times. In IVS, collagen and fibronectin were
elevated, but to a lesser extent than in LV (Figure 5). This is
consistent with a greater need for matrix deposition in the
damaged region of LV. The patterns of gene expression in LV and IVS
indicate that a similar programmed response to infarction is invoked in
the 2 regions of heart, but there is apparent flexibility within the
program to modulate the degree of response.
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Also showing differential expression was SPARC (secreted protein acidic and rich in cysteine), a protein that has been implicated in control of endothelial cell interactions with ECM.20 SPARC expression was elevated in LV tissue, but not in IVS. Recent studies have shown that SPARC transcription is elevated in regions of myocardial damage in a rat model of hypertrophy.21 It is likely that these changes in expression of genes that encode ECM structural and regulatory proteins are important in remodeling of the surviving myocardium after an ischemic event.
An interesting result was finding elevated expression of
osteoblast-specific factor-2 (OSF-2), a protein thought to be expressed
uniquely in osteoblasts.22 Expression of OSF-2 was
significantly elevated in LV across the time course of the study. On
clustering of the entire data set, we found that OSF-2 coclustered with
several isoforms of collagen, laminin, and fibronectin (Figure 5
). Very little is known about the function of this protein, but
it has been implicated as an extracellularly deposited protein that may
establish intercellular contacts in osteoblasts.23 Our
discovery that OSF-2 is expressed in heart and that its expression
clusters very closely with collagen, laminin, and fibronectin suggests
that this protein may play a key role in ECM deposition, fibrosis, and
tissue remodeling in MI. It remains to be determined whether OSF-2 is a
structural component of ECM or is involved in regulation of matrix
deposition. Of course, clustering by expression pattern alone is not
sufficient to ascribe a role for OSF-2. However, combining clustered
expression data and structural information for a gene will aid in
predicting function.
A number of other differentially expressed genes that were
identified are likely to play a role in the remodeling process.
Although many of these genes have not been identified as differentially
expressed in MI, they clearly play a role in cytoskeletal architecture.
Some of the genes encode proteins that comprise structural components
of the cytoskeleton, including vimentin, spectrin, and actin
microfilaments. In addition, elevated expression was observed for genes
that regulate cytoskeletal assembly, including thymosins B4 and B10,
moesin, transgelin, and proteins of the Arp2/3 actin assembly complex
p41arc, p21arc, p16arc, and Arp3. Coordinate expression patterns of
these genes strongly implicates a role for the encoded proteins,
particularly actin assembly, in the disease process. Although changes
in cytoskeletal gene expression were likely necessary to maintain
contractility, contractile proteins were not similarly
affected. In fact, expression of certain genes that encode proteins
related to contractile apparatus, including titin,
tropomyosin 4, troponin I, and telethonin, were repressed (Figure 4). Altered contractility has been described
within postinfarction myocardium,1 and these
genes may play a part in that shift.
Many of the differentially expressed genes encode proteins involved in energy metabolism. Notably, several lipid metabolism genes were found in expression clusters populated with genes consistently repressed across the time course in LV. Of the repressed genes, there was a prevalence for those involved in catabolism of fatty acids, including enoyl-coenzyme A (CoA) isomerase, dienoyl-CoA reductase, hydroyacyl-CoA dehydrogenase, long-chain acyl-CoA synthase, and ketoacyl-CoA thiolase. All of these enzymes serve in the process of ß-oxidation of fatty acids for energy production. Their coordinate repression in infarcted LV indicates a shift away from use of fatty acids as an energy source. Fatty acid catabolism is also dependent on lipoprotein lipase and CD36, a fatty acid transporter, and these genes are also repressed. It has been well established that fatty acids are primary fuels for energy production in healthy hearts. However, in response to ischemia, glucose becomes the favored energy source in recovering myocardium.24 25 Our data indicate that there is a long-term adaptation in expression of fatty acid metabolism genes as a means to accomplish this metabolic reprogramming.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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We have used cDNA microarrays to assess changes in gene expression that result from MI to gain insight into the underlying molecular basis of the disease. In this study, an experimental rat model was exploited to look at changes in expression as they develop over time, in particular areas of the heart, in response to an ischemic event. More than 400 different genes were found that showed altered expression in the remodeling myocardium. This survey of
4500 genes examined only a fraction of expressed
genes, perhaps only 5% of all rat genes. In addition, as no effort was
made in this study to select cDNAs produced from infarct tissue,
certain disease-specific genes would be missed. Nonetheless, this
survey revealed some clear patterns that highlight certain biological
processes perturbed in MI. Both temporal and spatial patterns of
altered gene expression were observed. The clustering of similar
expression patterns for gene products with related function has
revealed molecular footprints of biological processes that have been
affected. Coordinated changes in expression of genes related to
cellular and extracellular architecture are consistent with the
remodeling process of the postischemic heart. A reflection
of altered bioenergetics in a failing heart was evident as a systematic
decrease in expression of lipid catabolism genes. It is encouraging
that patterns, which are revealing of biological processes, are
recognized from analysis of a limited segment of the genome, as
shown here and elsewhere.16 26 As we rapidly approach a
complete sequence of mammalian genomes, a more comprehensive assessment
of gene expression will be attainable, as has been accomplished by
expression profiling in yeast.18 27
Certain patterns were prominently revealed from expression data and are
indicative of functional responses underlying MI. Expression
information alone is not sufficient to establish firm functional
associations among proteins. However, the type of expression data
presented here is very useful in generating testable
hypotheses. For instance, a large number of genes that encode cell
signaling molecules displayed differential expression (Figure 4). It is reasonable to hypothesize that some of these signaling
molecules play a role in mediating remodeling processes. Similarly, it
is tempting to speculate about transcription factors that showed
differential expression. Coordinate changes in expression of many genes
may be orchestrated by a few transcription factors of which the
expression is regulated. Of particular note are the transcription
factors cardiac ankyrin repeat protein (CARP), which has been
implicated as a regulator of cardiac gene expression,28
and transforming growth factor-ß–stimulated clone (TSC)-22, which
acts as a transforming growth factor-ß–inducible repressor of
transcription.29 CARP and TSC-22 mRNA levels are elevated
throughout the MI time course in LV and IVS (Figure 4),
suggesting that they play a role in controlling the transcriptional
response to infarction. Patterns that appear from large-scale gene
expression analysis should aid in our understanding of
regulatory mechanisms that mediate the
physiological and pathological processes in the
heart.
Unique patterns of expression were revealed for 2 regions of the heart profiled in this study, LV and IVS. This is not surprising, given that disparate biological processes are taking place in these 2 regions in response to MI. In particular, there is significant tissue repair activity in the LV, which is exemplified by altered expression of ECM proteins in LV with little or no change in IVS. Indeed, it can be suggested that many of the genes that are altered only in LV play a primary role in the healing process. In comparison, genes that are altered in both IVS and LV are more likely to be associated with a more restricted response to failure, per se. Good examples of this are natriuretic peptides, ANF and brain natriuretic peptide, that are elevated in IVS and LV and are known to reduce the load on a failing heart.
DNA microarrays are facilitating systematic exploration of gene expression on a genome-wide scale. Working with expanded sets of genes, more complete in number and functional characterization, should yield a wealth of information about the physiology and pathology of the heart. Comparisons of gene expression changes among different models should contribute greater understanding of the relationship between genes and disease. Much could be learned by examining cardiac gene expression changes in rats treated with pharmacological agents, such as ß-blockers and angiotensin-converting enzyme inhibitors, that show beneficial effects. In searches for better treatments of heart failure, large-scale gene expression analyses may prove very useful in disease diagnosis and prognosis and accelerate identification of novel therapeutic targets.
Received January 13, 2000; accepted March 21, 2000.
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L. Elsherif, M. S. Huang, S.-Y. Shai, Y. Yang, R. Y. Li, J. Chun, M. A. Mekany, A. L. Chu, S. J. Kaufman, and R. S. Ross Combined Deficiency of Dystrophin and {beta}1 Integrin in the Cardiac Myocyte Causes Myocardial Dysfunction, Fibrosis and Calcification Circ. Res., May 9, 2008; 102(9): 1109 - 1117. [Abstract] [Full Text] [PDF]
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A. Behfar, C. Perez-Terzic, R. S. Faustino, D. K. Arrell, D. M. Hodgson, S. Yamada, M. Puceat, N. Niederlander, A. E Alekseev, L. V. Zingman, et al. Cardiopoietic programming of embryonic stem cells for tumor-free heart repair J. Exp. Med., February 19, 2007; 204(2): 405 - 420. [Abstract] [Full Text] [PDF]
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R. S. Vasan Biomarkers of Cardiovascular Disease: Molecular Basis and Practical Considerations Circulation, May 16, 2006; 113(19): 2335 - 2362. [Full Text] [PDF]
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H. Rupp, T. P. Rupp, and B. Maisch Fatty acid oxidation inhibition with PPAR{alpha} activation (FOXIB/PPAR{alpha}) for normalizing gene expression in heart failure? Cardiovasc Res, June 1, 2005; 66(3): 423 - 426. [Full Text] [PDF]
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J. Rysa, H. Leskinen, M. Ilves, and H. Ruskoaho Distinct Upregulation of Extracellular Matrix Genes in Transition From Hypertrophy to Hypertensive Heart Failure Hypertension, May 1, 2005; 45(5): 927 - 933. [Abstract] [Full Text] [PDF]
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S. W. Kong, N. Bodyak, P. Yue, Z. Liu, J. Brown, S. Izumo, and P. M. Kang Genetic expression profiles during physiological and pathological cardiac hypertrophy and heart failure in rats Physiol Genomics, March 21, 2005; 21(1): 34 - 42. [Abstract] [Full Text] [PDF]
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R. A. Kloner and B. Z. Simkhovich Benefit of an exercise program before myocardial infarction J. Am. Coll. Cardiol., March 15, 2005; 45(6): 939 - 940. [Full Text] [PDF]
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F. Schwartz, A. Duka, I. Duka, J. Cui, and H. Gavras Novel targets of ANG II regulation in mouse heart identified by serial analysis of gene expression Am J Physiol Heart Circ Physiol, November 1, 2004; 287(5): H1957 - H1966. [Abstract] [Full Text] [PDF]
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M. W.M. Schellings, Y. M. Pinto, and S. Heymans Matricellular proteins in the heart: possible role during stress and remodeling Cardiovasc Res, October 1, 2004; 64(1): 24 - 31. [Abstract] [Full Text] [PDF]
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R. Sandhu, K. Teichert-Kuliszewska, S. Nag, G. Proteau, M. J. Robb, A. I.M. Campbell, M. A. Kuliszewski, M. J.B. Kutryk, and D. J. Stewart Reciprocal regulation of angiopoietin-1 and angiopoietin-2 following myocardial infarction in the rat Cardiovasc Res, October 1, 2004; 64(1): 115 - 124. [Abstract] [Full Text] [PDF]
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N. Katsuragi, R. Morishita, N. Nakamura, T. Ochiai, Y. Taniyama, Y. Hasegawa, K. Kawashima, Y. Kaneda, T. Ogihara, and K. Sugimura Periostin as a Novel Factor Responsible for Ventricular Dilation Circulation, September 28, 2004; 110(13): 1806 - 1813. [Abstract] [Full Text] [PDF]
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M. V. Podgoreanu and D. A. Schwinn Genomics and the circulation Br. J. Anaesth., July 1, 2004; 93(1): 140 - 148. [Abstract] [Full Text] [PDF]
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O. Tarnavski, J. R. McMullen, M. Schinke, Q. Nie, S. Kong, and S. Izumo Mouse cardiac surgery: comprehensive techniques for the generation of mouse models of human diseases and their application for genomic studies Physiol Genomics, February 13, 2004; 16(3): 349 - 360. [Abstract] [Full Text] [PDF]
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M. Mirotsou, C. M.H. Watanabe, P. G. Schultz, R. E. Pratt, and V. J. Dzau Elucidating the molecular mechanism of cardiac remodeling using a comparative genomic approach Physiol Genomics, October 17, 2003; 15(2): 115 - 126. [Abstract] [Full Text] [PDF]
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B. Z. Simkhovich, P. Marjoram, C. Poizat, L. Kedes, and R. A. Kloner Brief episode of ischemia activates protective genetic program in rat heart: a gene chip study Cardiovasc Res, August 1, 2003; 59(2): 450 - 459. [Abstract] [Full Text] [PDF]
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D. Wang, S. Oparil, J. A. Feng, P. Li, G. Perry, L. B. Chen, M. Dai, S. W.M. John, and Y.-F. Chen Effects of Pressure Overload on Extracellular Matrix Expression in the Heart of the Atrial Natriuretic Peptide-Null Mouse Hypertension, July 1, 2003; 42(1): 88 - 95. [Abstract] [Full Text] [PDF]
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R. L. Winslow and M. S. Boguski Genome Informatics: Current Status and Future Prospects Circ. Res., May 16, 2003; 92(9): 953 - 961. [Abstract] [Full Text] [PDF]
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G. M. Diffee, E. A. Seversen, T. D. Stein, and J. A. Johnson Microarray expression analysis of effects of exercise training: increase in atrial MLC-1 in rat ventricles Am J Physiol Heart Circ Physiol, March 1, 2003; 284(3): H830 - H837. [Abstract] [Full Text] [PDF]
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K. J Ashton, K. Holmgren, J. Peart, A. R Lankford, G Paul Matherne, S. Grimmond, and J. P Headrick Effects of A1 adenosine receptor overexpression on normoxic and post-ischemic gene expression Cardiovasc Res, March 1, 2003; 57(3): 715 - 726. [Abstract] [Full Text] [PDF]
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S. S Chugh, S. Whitesel, M. Turner, C. T Roberts Jr., and S. R Nagalla Genetic basis for chamber-specific ventricular phenotypes in the rat infarct model Cardiovasc Res, February 1, 2003; 57(2): 477 - 485. [Abstract] [Full Text] [PDF]
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M. Steenman, Y.-W. Chen, M. Le Cunff, G. Lamirault, A. Varro, E. Hoffman, and J. J. Leger Transcriptomal analysis of failing and nonfailing human hearts Physiol Genomics, January 15, 2003; 12(2): 97 - 112. [Abstract] [Full Text] [PDF]
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E. O. Weinberg, M. Mirotsou, J. Gannon, V. J. Dzau, R. T. Lee, and R. E. Pratt Sex dependence and temporal dependence of the left ventricular genomic response to pressure overload Physiol Genomics, January 15, 2003; 12(2): 113 - 127. [Abstract] [Full Text] [PDF]
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O. Juhasz, Y. Zhu, R. Garg, S. V. Anisimov, and K. R. Boheler Analysis of altered genomic expression profiles in the senescent and diseased myocardium using cDNA microarrays Eur J Heart Fail, December 1, 2002; 4(6): 687 - 697. [Abstract] [Full Text] [PDF]
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C. Depre, M. Hase, V. Gaussin, A. Zajac, L. Wang, L. Hittinger, B. Ghaleh, X. Yu, R. K. Kudej, T. Wagner, et al. H11 Kinase Is a Novel Mediator of Myocardial Hypertrophy In Vivo Circ. Res., November 29, 2002; 91(11): 1007 - 1014. [Abstract] [Full Text] [PDF]
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S. A. Cook and A. Rosenzweig DNA Microarrays: Implications for Cardiovascular Medicine Circ. Res., October 4, 2002; 91(7): 559 - 564. [Abstract] [Full Text] [PDF]
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K. Rouger, M. Le Cunff, M. Steenman, M.-C. Potier, N. Gibelin, C. A. Dechesne, and J. J. Leger Global/temporal gene expression in diaphragm and hindlimb muscles of dystrophin-deficient (mdx) mice Am J Physiol Cell Physiol, September 1, 2002; 283(3): C773 - C784. [Abstract] [Full Text] [PDF]
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C. Napoli, F. de Nigris, J. S. Welch, F. B. Calara, R. O. Stuart, C. K. Glass, and W. Palinski Maternal Hypercholesterolemia During Pregnancy Promotes Early Atherogenesis in LDL Receptor-Deficient Mice and Alters Aortic Gene Expression Determined by Microarray Circulation, March 19, 2002; 105(11): 1360 - 1367. [Abstract] [Full Text] [PDF]
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F. Mehraban and J. E. Tomlinson Application of industrial scale genomics to discovery of therapeutic targets in heart failure Eur J Heart Fail, December 1, 2001; 3(6): 641 - 650. [Abstract] [Full Text] [PDF]
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R. T. Lee Functional Genomics and Cardiovascular Drug Discovery Circulation, September 18, 2001; 104(12): 1441 - 1446. [Full Text] [PDF]
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J. S. Rao and M. Bond Microarrays : Managing the Data Deluge Circ. Res., June 22, 2001; 88(12): 1226 - 1227. [Full Text] [PDF]
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M. Abdellatif Leading the Way Using Microarray : A More Comprehensive Approach for Discovery of Gene Expression Patterns Circ. Res., May 12, 2000; 86(9): 919 - 920. [Full Text] [PDF]
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T.-j. Liu, H.-c. Lai, W. Wu, S. Chinn, and P. H. Wang Developing a Strategy to Define the Effects of Insulin-Like Growth Factor-1 on Gene Expression Profile in Cardiomyocytes Circ. Res., June 22, 2001; 88(12): 1231 - 1238. [Abstract] [Full Text] [PDF]
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