Published online before print June 7, 2001, doi: 10.1161/hh1201.092036
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© 2001 American Heart Association, Inc.
Molecular Medicine |
Developing a Strategy to Define the Effects of Insulin-Like Growth Factor-1 on Gene Expression Profile in Cardiomyocytes
From the Departments of Medicine and Biological Chemistry (T.L., H.L., W.W., S.C., P.H.W.), Division of Endocrinology, Diabetes, and Metabolism, University of California, Irvine, and the Veterans General Hospital-Taichung and National Yang-Ming University (T.L., H.L.), Taipei, Taiwan.
Correspondence to Ping H. Wang, MD, Department of Medicine, Med Sci I, Room C240, University of California, Irvine, CA 92697. E-mail phwang{at}uci.edu
Abstract
Abstract—Insulin-like growth factor (IGF)-1 activates intracellular signaling pathways and regulates myocardial structure and function. This study used DNA microarray to define the effects of IGF-1 on gene expression in cardiomyocytes. Despite DNA microarray becoming a popular tool for profiling gene expression, the specificity of DNA microarray results is rarely addressed. Our data showed that the specificity of a DNA microarray study can be increased by repetitive experiments and by excluding minimally expressed genes. In this study, the false-positive rates were reduced to <0.2%. Future DNA microarray studies should incorporate a proper strategy to minimize false-positive results. IGF-1 modulates the expression of genes in 17 functional categories, but most genes clustered around the regulation of intracellular signaling, cell cycle, transcription/translation, cellular respiration and mitochondrial function, cell survival, ion channels and calcium signaling, and humoral factors. To further explore whether extracellular signal–regulated kinase (ERK) and phosphatidylinositol (PI) 3 kinase specifically regulate different sets of genes, the effects of IGF-1 were inhibited with PD98059 or LY294002. The results showed that the majority of genes regulated by IGF-1 required activation of both ERK and PI 3 kinase. Thus, PI 3 kinase and ERK coordinately mediate the transcriptional regulatory effects of IGF-1 in cardiac muscle cells. These findings provide novel insight into how IGF-1 signaling modulates the programming of cardiac muscle gene expression.
Key Words: insulin-like growth factor • cardiac muscle • gene expression • phosphatidylinositol 3 kinase • mitogen-activated protein kinase
Insulin-like growth factor (IGF)-1 plays important roles in cardiac muscle biology.1 2 The actions of IGF-1 are mediated through activation of intracellular signaling pathways.2 The two major pathways of IGF-1 receptor signaling are the phosphatidylinositol (PI) 3 kinase pathway and the mitogen-activated protein (MAP) kinase pathway.3 Activation of intracellular signaling pathways may ultimately lead to modulation of gene expression and regulation of cell function. Although a number of genes are known to be upregulated or downregulated by IGF-1, such as c-jun and myosin heavy chain,3 4 these genes likely represent a small fraction of the genes that are regulated by IGF-1. Because the biological actions of IGF-1 are mediated in part through modulation of gene expression, it will be important to systemically define the effects of IGF-1 on gene expression.
The DNA microarray is a powerful technology that offers comprehensive profiling of gene expression.5 Like any new technology, the microarray is not free from experimental errors, and it may inherit the unwanted variables introduced by the samples applied to the microarray.6 Existing literature involving DNA microarray study rarely address the specificity of microarray data. A lack of information on the specificity of microarray data may impede the development of adequate strategies for data analysis and, thus, poses a serious problem in the experiments involving DNA microarray.
In the present study, we used primary cardiomyocytes as a model to investigate whether the DNA microarray spotted on nylon membrane can be reliably used to profile the effects of IGF-1 on cardiac muscle gene expression. The results showed that multiple experiments and appropriate analysis strategies are needed to avoid false-positive results. Once an appropriate analysis strategy was established, our results showed that IGF-1 modulated the expression of many genes involved in the regulation of diverse biological functions. Moreover, the regulatory effects of IGF-1 on the majority of these genes require concomitant activation of phosphatidylinositol (PI) 3 kinase and extracellular signal–regulated kinase (ERK) signaling pathways.
Materials and Methods
Materials
Sprague-Dawley rats were obtained from Simonsen
Laboratory (Gilroy, Calif). IGF-1 was a gift from
Genentech (South San Francisco, Calif). PD098059
and LY294002 were purchased from Biomol. DNase I and Superscript II RT
were obtained from GIBCO-BRL. Salmon testis DNA was purchased from
Pharmacia.
[
-32P]ATP was from
Amersham. Other chemicals were purchased from
Sigma Chemical Co or
Fisher. cDNA Rat 1.2 Microarray, Synthesis
Primer Mix, ExpressHyb Hybridization Solution, and AtlasImage 1.01a
analysis software were from
Clontech.
Primary Cardiomyocyte Culture
Primary cultures of rat cardiomyocytes
were prepared from the cardiac ventricles of Sprague-Dawley neonates as
previously described.7 All
surgical procedures were approved by the institutional review board at
the University of California, Irvine. At the time of experiments, these
preparations contained >95% cardiomyocytes. Myocytes were
cultured in 100-mm dishes in DMEM containing 10% FBS. To study the
effects of IGF-1 on gene expression, primary cardiomyocytes
were rinsed twice with serum-free DMEM, serum-deprived overnight, and
then incubated with IGF-1 (10-6
mmol/L) or vehicles for indicated time intervals. A specific PI 3
kinase inhibitor (LY294002) and a specific MAP
kinase kinase (MEK) inhibitor (PD098059) were added to
culture medium 45 minutes before the addition of IGF-1 to inhibit PI 3
kinase or MEK signaling when indicated.
Extraction and Processing of RNA
The attached cells were scraped, and the detached
cells floating in the medium were collected with
centrifugation
(4000g, 4°C, 10 minutes). The
cells were washed once and lysed; total RNA was isolated with the
Rneasy Mini Kit according to the manufacturer’s instructions
(Qiagen). RNA samples were treated with DNase I
(1 U/µL) to remove any residual DNA, and the quality of RNA was
confirmed with denaturing formaldehyde/agarose gel electrophoresis.
Each RNA sample was pooled from the RNAs harvested from three to four
100-mm dishes of primary
cardiomyocytes.
Labeling of cDNA Probes and Hybridization
to Microarray
Gene-specific primers were used to generate labeled
cDNA probes. In brief, total RNA (5 µg) was mixed with gene-specific
primer mixture and heated to 70°C for 10 minutes and
reverse-transcribed into radiolabeled cDNA probes with SuperScript II
in the presence of [-32P]dATP, dNTPs,
and dithiothreitol for 50 minutes at 42°C. Reverse transcription was
terminated by heating to 70°C for 15 minutes. Labeled cDNAs were
purified with QIAquick Nucleotide Removal Kit
(Qiagen) to remove unincorporated
[32P]dATP and small cDNA fragments. This
protocol typically yields >2x106 cpm of
labeled cDNA probes from each sample. The Rat 1.2 cDNA Microarrays were
first soaked in 0.5% SDS at 85°C for 2 minutes and rinsed with
distilled deionized water. The microarrays were then prehybridized in
1x ExpressHyb Hybridization Solution in the presence of 20 µg/mL
heat-denatured salmon testis DNA at 68°C for 4 hours.
32P-labeled cDNA probes were then added to
the solution and hybridized to the array at 68°C for 16 hours. Then,
the microarrays were washed four times with 1x SSC+1% SDS (68°C, 30
minutes each), one time with 0.1xSSC+0.5% SDS at 68°C for 30
minutes, and finally, one time with 2x SSC at room temperature for 5
minutes. The arrays were exposed to the Storage Phosphor Screen from
Molecular Dynamics.
Microarray Data Analysis and
Statistics
Microarray signals on the Storage Phosphor Screen
were read with ImageQuant 4.0 Software and PhosphorImager
(Molecular Dynamics). The scanned images were
aligned and analyzed with the AtlasImage 1.01a software.
Because background signals were generally homogeneous in
these arrays, a globally averaged background from each microarray
membrane was subtracted from the density in each specific array signal
spot. If the signal intensity in specific array spot is equal or less
than the background intensity, the signal intensity in this spot is
defined as zero. All array spots that contain zero readings in the
control and IGF-I–treated samples were excluded from final
analysis, because the expression of these genes was either
silent or undetectable in cardiomyocytes. We have visually
inspected the images in all array spots in every experiment to ensure
that the signal density is not the result of artifact or high
background signal. The relative level of expression for each gene was
calculated as a fraction of the total signal intensity on the entire
membrane. The ratio of expression levels between the control and
IGF-I–treated samples was obtained from each set of the experiment. A
differentially expressed gene is defined by a ratio of IGF-1
treatment/control >1.5 or <0.67. For statistical analysis,
the differences between the control and IGF-1–treated samples were
determined with a 2-tailed t
test via the Internet (Cyber T), and a value of
P<0.05 was considered
statistically significant.
Results
Specificity of Data Obtained From
Microarray
Because of the high costs of microarray study, most
investigators elected to limit the number of repetitive array
experiments. It is possible that a limited number of repetitions may
lead to false-positive results. To determine whether microarray study
is associated with experimental artifacts and whether the artifacts can
be offset by adequate analytical strategies, we first compared the
results of microarray study derived from independent RNA samples
extracted from control cardiomyocytes. Because these
control cardiomyocytes were grown and harvested under the
same protocol, any differential expression of a given gene in two
different sets of control RNA can be interpreted as false-positive
results. We used the following criteria to determine whether the
expression of a gene is upregulated or downregulated: (1) the
difference between two experimental settings was statistically
significant by t test, or (2)
differential expression was consistently upregulated or
downregulated in five of six experiments. The second criterion was
added because the t test
usually underestimates the significance of difference when the sample
size is small. Insufficient repetition of a given experiment leads to
low sensitivity, and with the high cost of microarray, it is unlikely
that a microarray experiment can be repeated many times to reach high
sensitivity. In a typical microarray study with a small sample size,
relying on the t test alone
will lead to an underestimation of the number of genes that are truly
differentially expressed. Using the above criteria, we compared the
gene expression profile in two paired sets of control RNA samples, six
samples in each set. The
Figure
shows that there is a linear relationship of gene expression between
these two sets of control RNAs. Two differentially expressed genes
between these two sets of control samples were found
(Figure),
indicating that with the use of the above approach, the false-positive
rate is very low (<0.2%).
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Further analysis revealed that two factors, the
number of repetitive experiments and the level of gene expression,
might increase the risk of obtaining false-positive data.
Table 1 shows that the number of differentially expressed
genes between two sets of controls (false positive) was in proportion
to the number of RNA samples included in the microarray study. These
data suggest that in each experimental group, at least six independent
RNA samples are needed to minimize the risk of a false-positive result.
The levels of expression also contributed to the specificity of
microarray results. Although the correlation has been linear, as shown
in the
Figure
,
a higher level of variation was seen in those genes that were minimally
expressed. When minimally expressed genes were included in the
analysis, the risk of false-positive results was increased
(Table 2). The background signal intensity on these
microarray membranes is 
1.0x10-4 of
the total signal on membrane; therefore, the above data indicate that
reliable results are more likely to be obtained when the expression
level is >4 times over background.
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Regulation of Gene Expression by IGF-1
To define the effects of IGF-1 on gene expression,
cardiomyocytes were stimulated with IGF-1 at various time
intervals as described in Materials and Methods. No visible
morphological changes were observed in cardiomyocytes after
6 hours of IGF-1 treatment. Radiolabeled cDNA probes were generated
from the RNA extracted from control and IGF-1–stimulated
cardiomyocytes and hybridized to microarray membranes.
Using the analysis strategy described above with six
independent sets of RNA in each experimental group and a background
cutoff at 4x10-4, we have identified 68
genes (5.7%) that were differentially expressed on IGF-1 stimulation
(Table 3). The time course of gene expression showed
that IGF-1 regulated the coordinated expression of cardiac muscle
genes. Most differentially expressed genes were found after 2 hours of
IGF-1 incubation. These genes are involved in diverse cellular and
biochemical functions. IGF-1–regulatable genes can be categorized into
17 major groups: cell cycle regulators, transcription factors,
RNA synthesis/processing, protein processing, proto-oncogenes, tumor
suppressors, signal transduction, cellular respiration and energy
production, stress response and antioxidation, ion channels,
cell surface receptors, cell survival, cytoskeleton structure,
transporter, humoral factors, carbohydrate metabolism, and
cell-cell interaction. However, the majority of these genes are
involved in the regulation of intracellular signaling, cell cycle,
transcription/translation, cellular respiration and mitochondrial
function, cell survival, ion channels and calcium signaling, and
humoral factors.
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Most genes were upregulated; only six genes were downregulated by IGF-1. Some genes were persistently downregulated or upregulated, whereas some other genes were only upregulated or downregulated at specific time points. Many genes that are known to modulate myocardial structure and function were modulated by IGF-1, such as several proteins related to calcium signaling, ion channels, heat shock proteins, G proteins, copper-zinc superoxide dismutase-1, plasminogen activator inhibitor-1, metalloproteinase inhibitor-3, vascular endothelial growth factor (VEGF), natriuretic peptide, c-jun, inhibitor of differentiation-1, and a list of genes related to mitochondrial metabolism and energy production. Several genes related to cell cycle regulation were coordinately regulated. For example, cyclin D1, cyclin D2, cyclin D3, proliferating cell nuclear antigen, v-fos transformation effector (Fte-1), and casein kinase II were upregulated at various time intervals on IGF-1 stimulation. Other signal transduction proteins and proto-oncogenes that are regulated by IGF-1 in cardiomyocytes have also been previously implicated in the regulation of cell cycle regulation, suggesting that regulation of the cell cycle is a major function of IGF-1.
IGF-1 Regulation of Gene Expression Involves
Coordinated Activation of PI 3 Kinase and MAP Kinase Pathways in
Cardiac Muscle
To
determine whether IGF-1 regulation of gene expression involves
activation of PI 3 kinase or ERK signaling pathways, we
have used chemical inhibitors of PI 3 kinase (LY294002) and
MEK (PD98059) to dissect the roles of PI 3 kinase and ERK
pathways in IGF-1 regulation of gene expression in cardiac muscle cells
(Table 4). Because two thirds of the genes modulated by IGF-1 occurred
after 2 hours of stimulation, these experiments were carried out with 2
hours of IGF-1 incubation. To our surprise, the effect of IGF-1 on 31
genes can be inhibited either by LY294002 alone or by PD98059 alone,
indicating that IGF-1 actions on the expression of these genes require
activation of both PI 3 kinase and ERK pathways. Nevertheless, the
effects of IGF-1 on nine genes were inhibited by LY294002 and not by
PD98059, suggesting that the effects of IGF-1 on these genes are
dependent on PI 3 kinase alone. Conversely, IGF-1 regulation of
transducin-ß2 was blocked by PD98059 but not
by LY294002, suggesting that IGF-1 regulation of
transducin-ß2 expression was dependent on
activation of the MEK/ERK signaling pathway alone. The effects of IGF-1
on three genes cannot be blocked by either LY294002 or PD98059,
suggesting that additional IGF-1 signaling pathways independent of PI 3
kinase and ERK were involved in the regulation of these
genes.
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These data suggest that PI 3 kinase and MEK/ERK are major signaling pathways mediating the IGF-1 regulation of gene expression in cardiac muscle cells. However, the IGF-1 regulation of cardiac gene expression may require coordinated activation of more than one signaling pathway because IGF-1 regulation of many genes requires the activation of both pathways.
Discussion
Pitfalls in DNA Microarray Experiment
DNA microarrays provide a powerful tool for
systemic analysis of gene expression in response to growth
factor actions.8 9
However, the specificity of DNA microarray data was seldom explored in
the literature, and the risk of false-positive results was hardly ever
discussed. Our results clearly demonstrate the potential risk of
obtaining false-positive results if adequate analytical strategy is not
implemented. The number of repetitive experiments required and the
cutoff point for minimally expressed genes should be carefully examined
at the beginning of a microarray project.
Manufacturers of DNA microarrays usually recommend 2- or
3-fold changes as "significant changes" of gene
expression.8 9 10 11
However, our data do not support such a recommendation. From our
analysis, a false-positive gene can be differentially expressed
in the range of 2-fold or 3-fold. Conversely, a 1.5-fold change in gene
expression after IGF-1 treatment can be highly consistent and
statistically significant. These data indicate that using 2-fold or
3-fold changes as lone criteria to judge whether gene expression is
altered in a microarray study is not reliable and may underestimate the
number of genes differentially expressed. When the same RNA sample was
hybridized to two different microarrays, 95% of the gene expression
pattern can be reproduced. Thus, the reproducibility appears quite
good. However, we were not able to define the exact sensitivity (the
chance of positive being a true positive) of our microarray data,
because the regulatory effects of IGF-1 on the majority of genes
included in this array were not previously known.
The vast majority of published microarray studies did not report an analysis of specificity; a lack of such information may dampen the validity of microarray data and mislead future research direction. One solution is to perform detailed specificity analysis at the beginning of a microarray project and to report the results of such an analysis. Because more and more investigators will be using this revolutionary tool to study gene expression, there is an urgent need to explore the feasibility of a basic format to report microarray data. An ideal format should include at least the following key elements: the strategies used to exploit data specificity, statistical justification for the chosen strategy, and the risk of false-positive results under the experimental setting in each study.
IGF-1 Modulation of Gene Expression in
Cardiomyocytes
IGF-1 modulates multiple aspects of
cardiovascular function, and the data presented
in the present study clearly reflect diverse biological actions of
IGF-1 in cardiac muscle cells. The majority of the genes modulated by
IGF-1 reported in the present study had not been shown in the past;
thus, novel actions of IGF-1 on gene expression are
represented. We have searched the literature and found that
among the 68 genes that were regulated by IGF-1 in this study, 15 genes
were modulated by IGF-1 in previous studies, mostly in different
experimental systems. However, one of the 68 genes (bone morphogenetic
protein-4) had a different pattern of regulation on IGF-1 stimulation
(upregulation versus downregulation) in human dental pulp fibroblasts,
a cell type vastly different from
cardiomyocytes.
Several signaling proteins were induced by IGF-1 in cardiac muscle; interestingly, the protein products of these genes may function downstream from IGF-1 receptor signaling. A-Raf, an upstream serine/threonine kinase of ERK pathways,12 plays a pivotal role in mediating growth-promoting effects. Casein kinase II is a highly conserved ubiquitous serine/threonine kinase that participates in the regulation of cell cycle, cell homeostasis, and cell survival.13 Signal transducer(s) and activator(s) of transcription (STAT)3 activates gene transcription and plays a role during cell differentiation and survival.14 In addition, 14-3-3 protein has been implicated in the regulation of the Raf/ERK signaling pathway.14 These findings suggest that IGF-1 may have dual effects on these signaling pathways. An acute action that activates these intracellular signaling proteins through phosphorylation cascades, and a delayed effect that modulates their expression via acute activation of signaling.
The G1 cyclins, including D-type cyclins, are positive regulators of cell cycle progression from G1 to S phase.15 In addition to cyclin Ds, our data showed that IGF-1 upregulated proliferating cell nuclear antigen (a protein involved in DNA synthesis), c-jun (a component of activator protein-1), HNRNP-K (a transcription factor for c-Myc), STAT3 (a key element in the Janus kinase/STAT pathway), and ID-1 (a helix-loop-helix transcription factor). The transcriptional regulatory effects of IGF-1 on these molecules may partially explain the well-known actions of IGF-1 on DNA synthesis and mitogenesis. Although whether cardiomyocyte DNA synthesis and proliferation occur in the adult heart remains a controversial issue, there is good evidence suggesting that the development of cardiac hypertrophy involves perturbation of cell cycle controls16 and that chronic infusion of IGF-1 leads to mild cardiac hypertrophy.17 It is possible that IGF-1 regulation of cell cycle control may contribute to the pathophysiological actions of IGF-1 in the heart.
Series of genes involved in the regulation of cellular respiration and energy production are modulated by IGF-1. Oxidative metabolism is the fundamental mechanism through which energy, in the form of ATP, is provided to the cardiac muscle cells.18 Genetic defects of mitochondrial oxidative phosphorylation and fatty acid oxidation may lead to the development of cardiomyopathy,19 and mitochondrial dysfunction had been proposed to contribute to the decline of cardiac function in elderly patients.20 Moreover, abnormal mitochondrial structure and function have been observed in experimental and human cardiomyopathy, and decreased mitochondrial respiration has been observed in an animal model of heart failure.21 It is possible that the effects of IGF-1 on mitochondrial enzyme expression may lead to the modulation of mitochondrial function in the heart.
Detoxifying proteins are responsible for metabolizing toxins and drugs into less adverse metabolites. Similarly, antioxidant enzymes, which catalyze peroxides into nontoxic molecules, prevent cells from excessive oxidative stress. The fact that IGF-1 stimulates the gene expression of detoxifying proteins and antioxidant enzymes reflects the protective role of IGF-1 on cardiomyocyte survival. IGF-1 has been shown to regulate the Bcl-2 family of proteins,22 and microarray results indicate that IGF-1 increased the expression of the antiapoptotic protein Bcl-2. IGF-1 also increased the expression of cyclin D and STAT3, two proteins that have been shown to increase cell resistance to the induction of apoptosis.23 24 These results are consistent with numerous previous studies indicating that IGF-1 increases cardiac muscle survival in experimental models of heart failure.25 26
Previous studies have demonstrated the inotropic effects of IGF-1 in normal animals and experimental heart failure.2 27 Calcium and other ion channels play important roles in the regulation of myocardial contractility. In the present study, we found that IGF-1 regulates the expression of the L-type cardiac calcium channel, sodium channel, and Na+-H+ exchange protein. Moreover, calmodulin was also upregulated by IGF-1. These data raised the possibility that IGF-1 may exert positive inotropic effects through modulation of sodium channels and provide further evidence that that IGF-1 modulation of calcium signaling could have contributed to its inotropic effects.2 27
Several growth factors and humoral factors were modulated by IGF-1, some of which are known to modulate myocardial function. IGF-1 is known to induce VEGF expression in various cells.28 29 In the present study, we also found that the expression of glioma-derived vascular endothelial cell growth factor, a member of the VEGF family, could be stimulated by IGF-1. Some other humoral factors regulated by IGF-1, such as SMAD5 and plasminogen activator inhibitor-1, have also been implicated in angiogenesis.30 31 These findings suggest that IGF-1 may promote angiogenesis in cardiac muscle. PAIs belong to a family of proteins that exert stimulatory effects on cell proliferation during developmental stage. IGF-1 has been previously reported to increase PAI-1 mRNA levels in Hep G2 cells,32 mainly through prolonging its half-life at the post-transcriptional level.
Independent and Coordinated Regulation of Gene
Expression by Intracellular Signaling Pathways
IGF-1 and other peptide growth factors bind to specific
cell-surface receptors and trigger multiple intracellular signaling
pathways. A great deal of research during the last decade had been
focused on the specificity of each signaling pathway in relation to
their unique biological actions. However, recent studies indicate that
multiple signaling pathways can be coordinately activated to
regulate a biological action. For example, insulin activation of
glucose transporter-4 translocation and of glucose transport
requires activation of the PI 3 kinase pathway and a PI 3
kinase–independent
pathway.33 There is also
evidence indicating that activation of the PI 3 kinase and ERK pathways
synergistically regulates insulin receptor trafficking and cell
survival.34 35 It
is not yet clear how different signaling pathways, such as the PI 3
kinase pathway and ERK pathway, coordinately regulate cardiac muscle
function. The present study provides a good argument that a
significant fraction of IGF-1–regulatable genes are coordinately
modulated through the activation of PI 3 kinase and MEK/ERK pathways.
Nevertheless, the specificity of signaling pathway on gene expression
indeed exists in cardiomyocytes because activation of PI 3
kinase or ERK by IGF-1 independently leads to specific modulation of
mRNA levels in some genes. Thus, IGF-1 activation of PI 3 kinase and
ERK signaling independently and coordinately mediates the IGF-1
regulation of gene
expression.
Acknowledgments
This study was supported by grants from the National Heart, Lung, and Blood Institute, American Heart Association, and American Diabetes Association (to Dr Wang). The authors wish to thank Dr Wesley Hatfield for helpful data discussion.
Footnotes
Original received November 1, 2000; revision received April 24, 2001; accepted April 24, 2001.
References
1. Lee W, Chen J, Ting C, Lin S, Wang PH. Changes of IGF I system in acute myocardial infarction: implications on left ventricular remodeling. J Clin Endocrinol Metabol. 1999;84:1575–1581.
2. Ren J, Samson WK, Sowers JR. Insulin-like growth factor I as a cardiac hormone: physiological and pathophysiological implications in heart disease. J Mol Cell Cardiol. 1999;31:2049–2061.
3. Petley T, Graff K, Jiang W, Yang H, Florini J. Variation among cell types in the signaling pathways by which IGF-I stimulates specific cellular responses. Horm Metab Res. 1999;31:70–76.
4. Musaro A, Rosenthal N. Maturation of the myogenic program is induced by postmitotic expression of insulin-like growth factor I. Mol Cell Biol. 1999;19:3115–3124.
5. Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JCF, Trent JM, Staudt LM, Hudson J Jr, Boguski MS, Lashkari D, Shalon D, Botstein D, Brown P. The transcriptional program in the response of human fibroblasts to serum. Science. 1999;283:83–87.
6. Carulli JP, Artinger M, Swain PM, Root CD, Chee L, Tulig C, Guerin J, Osborne M, Stein G, Lian J, Lomedico PT. High throughput analysis of differential gene expression. J Cell Biochem. 1998;30–31(suppl):286–296.
7. Wu W, Lee W, Wu Y, Chen D, Liu T, Sharma PM, Wang PH. Expression of constitutively active phosphatidylinositol 3 kinase inhibits activation of caspase 3 and apoptosis of cardiac muscle cells. J Biol Chem. 2000;275:40113–40119.
8. Khan J, Bittner ML, Saal LH, Teichmann U, Azorsa DO, Gooden GC, Pavan WJ, Trent JM, Meltzer PS. cDNA microarrays detect activation of a myogenic transcription program by the PAX3-FKHR fusion oncogene. Proc Natl Acad Sci U S A. 1999;96:13264–13269.
9. Kudoh K, Ramanna M, Ravatn R, Elkahloun AG, Bittner ML, Meltzer PS, Trent JM, Dalton WS, Chin KV. Monitoring the expression profiles of doxorubicin-induced and doxorubicin-resistant cancer cells by cDNA microarray. Cancer Res. 2000;60:4161–4166.
10. Zimmermann J, Erdmann D, Lalande I, Grossenbacher R, Noorani M, Furst P. Proteasome inhibitor induced gene expression profiles reveal overexpression of transcriptional regulators ATF3, GADD153 and MAD1. Oncogene. 2000;19:2913–2920.
11. Stanton LW, Garrard LJ, Damm D, Garrick BL, Lam A, Kapoun AM, Zheng Q, Protter AA, Schreiner GF, White RT. Altered patterns of gene expression in response to myocardial infarction. Circ Res. 2000;86:939–945.
12. Bogoyevitch MA, Marshall CJ, Sugden PH. Hypertrophic agonists stimulate the activities of the protein kinases c-Raf and A-Raf in cultured ventricular myocytes. J Biol Chem. 1995;270:26303–26310.
13. Blanquet PR. Casein kinase 2 as a potentially important enzyme in the nervous system. Prog Neurobiol. 2000;60:211–246.
14. Li S, Janosch P, Tanji M, Rosenfeld GC, Waymire JC, Mischak H, Kolch W, Sedivy JM. Regulation of Raf-1 kinase activity by the 14-3-3 family of proteins. EMBO J. 1995;14:685–696.
15. Sherr CJ. The Pezcoller lecture: cancer cell cycles revisited. Cancer Res. 2000;60:3689–3695.
16. Wagner M, Mascareno E, Siddiqui MA. Cardiac hypertrophy: signal transduction, transcriptional adaptation, and altered growth control. Ann N Y Acad Sci. 1999;874:1–10.
17. Tanaka N, Ryoke T, Hongo M, Mao L, Rockman HA, Clark RG, Ross J Jr. Effects of growth hormone and IGF-I on cardiac hypertrophy and gene expression in mice. Am J Physiol. 1998;275(pt 2):H393–H399.
18. DiMauro S, Hirano M. Mitochondria and heart disease. Curr Opin Cardiol. 1998;13:190–197.
19. Antozzi C, Zeviani M. Cardiomyopathies in disorders of oxidative metabolism. Cardiovasc Res. 1997;35:184–199.
20. Muller-Hocker J. Cytochrome-c-oxidase deficient cardiomyocytes in the human heart: an age-related phenomenon: a histochemical ultracytochemical study. Am J Pathol. 1989;134:1167–1173.
21. Sharov VG, Goussev A, Lesch M, Goldstein S, Sabbah HN. Abnormal mitochondrial function in myocardium of dogs with chronic heart failure. J Mol Cell Cardiol. 1998;30:1757–1762.
22. Wang L, Ma W, Markovich R, Chen J, Wang PH. Regulation of cardiomyocyte apoptotic signaling by insulin-like growth factor I. Circ Res. 1998;83:516–522.
23. Sheng Z, Knowlton K, Chen J, Hoshijima M, Brown JH, Chien KR. Cardiotrophin 1 (CT-1) inhibition of cardiac myocyte apoptosis via a mitogen-activated protein kinase-dependent pathway: divergence from downstream CT-1 signals for myocardial cell hypertrophy. J Biol Chem. 1997;272:5783–5791.
24. Lahti JM, Li H, Kidd VJ. Elimination of cyclin D1 in vertebrate cells leads to an altered cell cycle phenotype, which is rescued by overexpression of murine cyclins D1, D2, or D3 but not by a mutant cyclin D1. J Biol Chem. 1997;272:10859–10869.
25. Lee W, Chen J, Ting C, Ishiwata T, Lin S, Korc M, Wang PH. Insulin-like growth factor I improves cardiovascular function and suppresses apoptosis of cardiomyocytes in established canine cardiomyopathy. Endocrinology. 1999;140:4831–4840.
26. Li Q, Li B, Wang X, Leri A, Jana K, Liu Y, Kajstura J, Baserga R, Anversa P. Overexpression of insulin-like growth factor I in mice protects from myocyte death after infarction, attenuating ventricular dilation, wall stress, and cardiac hypertrophy. J Clin Invest. 1997;100:1991–1999.
27. Cittadini A, Ishiguro Y, Stromer H, Spindler M, Moses AC, Clark R, Douglas PS, Ingwall JS, Morgan JP. Insulin-like growth factor-1 but not growth hormone augments mammalian myocardial contractility by sensitizing the myofilament to Ca2+ through a wortmannin-sensitive pathway: studies in rat and ferret isolated muscles. Circ Res. 1998;83:50–59.
28. Miele C, Rochford JJ, Filippa N, Giorgetti-Peraldi S, Van Obberghen E. Insulin and insulin-like growth factor-I induce vascular endothelial growth factor mRNA expression via different signaling pathways. J Biol Chem. 2000;275:21695–21702.
29. Smith LE, Shen W, Perruzzi C, Soker S, Kinose F, Xu X, Robinson G, Driver S, Bischoff J, Zhang B, Schaeffer JM, Senger DR. Regulation of vascular endothelial growth factor-dependent retinal neovascularization by insulin-like growth factor-1 receptor. Nat Med. 1999;5:1390–1395.
30. Yang X, Castilla LH, Xu X, Li C, Gotay J, Weinstein M, Liu PP, Deng CX. Angiogenesis defects and mesenchymal apoptosis in mice lacking SMAD5. Development. 1999;126:1571–1580.
31. Bajou K, Noel A, Gerard RD, Masson V, Brunner N, Holst-Hansen C, Skobe M, Fusenig NE, Carmeliet P, Collen D, Foidart M. Absence of host plasminogen activator inhibitor 1 prevents cancer invasion and vascularization. Nat Med. 1998;4:923–928.
32. Anfosso F, Alessi MC, Nalbone G, Chomiki N, Henry M, Juhan-Vague I. Up-regulated expression of plasminogen activator inhibitor-1 in Hep G2 cells: interrelationship between insulin and insulin-like growth factor 1. Thromb Haemost. 1995;73:268–274.
33. Baumann CA, Ribon V, Kanzaki M, Thurmond DC, Mora S, Shigematsu S, Bickel PE, Pessin JE, Saltiel AR. CAP defines a second signalling pathway required for insulin-stimulated glucose transport. Nature. 2000;407:202–207.
34. Sasaoka T, Wada T, Ishihara H, Takata Y, Haruta T, Usui I, Ishiki M, Kobayashi M. Synergistic role of the phosphatidylinositol 3-kinase and mitogen-activated protein kinase cascade in the regulation of insulin receptor trafficking. Endocrinology. 1999;140:3826–3834.
35. Parrizas M, Saltiel AR, LeRoith D. Insulin-like growth factor 1 inhibits apoptosis using the phosphatidylinositol 3'-kinase and mitogen-activated protein kinase pathways. J Biol Chem. 1997;272:154–161.
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