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Physiol. Genomics 27: 156-170, 2006. First published August 1, 2006; doi:10.1152/physiolgenomics.00234.2005
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Received 21 September 2005; accepted in final form 15 July 2006.
Physiological Genomics 27:156-170 (2006)
1094-8341/06 $8.00 © 2006 American Physiological Society

Microarray analysis of Akt1 activation in transgenic mouse hearts reveals transcript expression profiles associated with compensatory hypertrophy and failure

Stephan Schiekofer , Ichiro Shiojima , Kaori Sato , Gennaro Galasso , Yuichi Oshima and Kenneth Walsh

Molecular Cardiology/Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
To investigate molecular mechanisms involved in the development of cardiac hypertrophy and heart failure, we developed a tetracycline-regulated transgenic system to conditionally switch a constitutively active form of the Akt1 protein kinase on or off in the adult heart. Short-term activation (2 wk) of Akt1 resulted in completely reversible hypertrophy with maintained contractility. In contrast, chronic Akt1 activation (6 wk) induced extensive cardiac hypertrophy, severe contractile dysfunction, and massive interstitial fibrosis. The focus of this study was to create a transcript expression profile of the heart as it undergoes reversible Akt1-mediated hypertrophy and during the transition from compensated hypertrophy to heart failure. Heart tissue was analyzed before transgene induction, 2 wk after transgene induction, 2 wk of transgene induction followed by 2 days of repression, 6 wk after transgene induction, and 6 wk of transgene induction followed by 2 wk of repression. Acute overexpression of Akt1 (2 wk) leads to changes in the expression of 826 transcripts relative to noninduced hearts, whereas chronic induction (6 wk) led to changes in the expression of 1,611, of which 65% represented transcripts that were regulated during the pathological phase of heart growth. Another set of genes identified was uniquely regulated during heart regression but not growth, indicating that nonoverlapping transcription programs participate in the processes of cardiac hypertrophy and atrophy. These data define the gene regulatory programs downstream of Akt that control heart size and contribute to the transition from compensatory hypertrophy to heart failure.

transgenic mice; cardiac hypertrophy and contractile dysfunction; DNA microarrays; Akt


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
THE SERINE-THREONINE PROTEIN KINASE Akt [or protein kinase B (PKB)] is activated by growth factors and cytokines in a phosphatidylinositol-3 kinase (PI3K)-dependent manner in a variety of systems (61). Akt signaling promotes cell growth, proliferation, and survival. There are three Akt genes in mammalian genomes (Akt1/PKB{alpha}, Akt2/PKBß, and Akt3/PKB{gamma}), and the Akt1 isoform is predominantly expressed in the heart (35). Some transcriptional targets of Akt have been identified (6, 28), including Glut-1 (1), vascular endothelial growth factor (VEGF) (52), Bcl-2 (38), FLIP (51), and heat shock protein 70 (25). Akt directly phosphorylates and inactivates a subset of Forkhead box transcription factors (FOXOs) in the heart and other tissues (40, 49) and may also regulate transcription through the modulation of activator protein-1, cAMP-responsive element-binding protein, and NF-{kappa}B transcription factors (13, 26, 36, 39).

Expression of the constitutively active form of Akt1 has been shown to protect myocardium from ischemia-reperfusion injury (16, 32, 33) and doxorubicin toxicity (55), indicating its potential utility in promoting myocyte survival and function in the diseased heart. Akt signaling contributes to physiological heart growth in response to insulin and nutritional status (47) and exercise training (12, 27, 58). Akt signaling also appears to participate in pathological heart growth (20, 34). Prolonged Akt1 or Akt3 activation in mouse heart induces extensive cardiac hypertrophy, contractile dysfunction, and interstitial fibrosis (31, 45, 54). In contrast, other mouse systems expressing Akt1 in the heart exhibit either modest hypertrophy and increased contractility (9) or no hypertrophy and protection from ischemia-reperfusion injury (48). The reasons why Akt transgenic mice exhibit such divergent phenotypes are poorly understood.

Because little is known about the mechanisms that distinguish between normal and pathological heart growth in response to Akt signaling, a tetracycline-regulated transgenic system to conditionally switch Akt1 signaling on or off in the adult heart was developed (46). Short-term activation of Akt1 results in completely reversible hypertrophy with maintained contractility, whereas prolonged Akt1 activation induces extensive cardiac hypertrophy, contractile dysfunction, and interstitial fibrosis. In this study, transcript expression profiles were performed in heart following short- and long-term transgene induction and following transgene inactivation at these early and late time points. This analysis revealed the regulation of overlapping and nonoverlapping sets of genes that were associated with cardiac hypertrophy and atrophy. This analysis also identified a set of genes that were specifically regulated during the transition to pathological hypertrophy but not during the earlier time points when heart growth was associated with normal contractile function. Gene regulatory networks were constructed to illustrate some of these relationships.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Double transgenic mice.
Generation and phenotypic characterization of myr-Akt mice have been previously described elsewhere in detail (46). In brief, two lines of transgenic (TG) mice [Tet-myrAkt1 and {alpha}-myosin heavy chain (MHC)-tTA] were used to generate cardiac-specific inducible Akt1 TG mice (Fig. 1A). The Tet-myrAkt1 TG line harbors a constitutively active form of Akt1 (myrAkt1) transgene under the control of multimerized tetracycline operator (tetO) sequences, and the {alpha}MHC-tTA TG line constitutively expresses tetracycline transactivator (tTA: a fusion protein of tetO-binding domain and VP16 transactivation domain) in the heart driven by the {alpha}MHC promoter. Treatment of double TG (DTG) mice harboring both of two transgenes with doxycycline (Dox, 0.5 mg/ml) results in repression of myrAkt1 expression because Dox associates with tTA and inhibits its binding to tetO sequences. On the other hand, withdrawal of Dox results in tTA binding to tetO elements and induction of myrAkt1 expression (Fig. 1B). Mating of Tet-myrAkt1 mice and {alpha}MHC-tTA mice resulted in the generation of mice with four genotypes [wild-type (WT), Tet-myrAkt1 TG mice, {alpha}MHC-tTA TG mice, and DTG mice] in expected frequencies. The Tet-myrAkt1 transgene and the {alpha}MHC-tTA transgene were detected by PCR as previously described (46). DTG mice were treated with Dox (0.5 mg/ml) in their drinking water at 12 wk of age. Akt1 transgene expression was activated by removing Dox from the drinking water. Dox treatment was restarted at different time points following induction to repress the transgene expression.


Figure 1
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  		Fig. 1. Cardiac-specific inducible Akt1 transgenic (TG) mice. A: schematic illustration of the tetracycline-regulated transgenic system to conditionally switch Akt1 signaling on or off in the adult heart. Double transgenic mice (DTG) result from the cross of Tet-myrAkt1 and {alpha}-myosin heavy chain-tetracycline transactivator (MHC-tTA) TG mice. In DTG mice, tTA expressed in the heart drives the expression of constitutively active Akt1 transgene. Administration of doxycycline (Dox) in the drinking water results in the binding of Dox to tTA and repression of the Akt1 transgene. Mice were divided into Dox (+) and Dox (–) groups and treated with Dox (0.5 mg/ml) in their drinking water or not. Akt1 transgene expression was detected only in the heart of DTG mice withdrawn from Dox. B: time course of progression and regression of Akt1-mediated hypertrophy. Schematic illustration of the study time course displaying relative heart weight/body weight (HW/BW) ratio. Hearts from DTG mice were harvested at 5 time points: before Akt1 transgene induction (1), 2 wk after transgene induction (2), 2 wk after transgene induction followed by Akt1 repression of 2 days (3), 6 wk after transgene induction (4), and 6 wk after transgene induction followed by repression of 2 wk (5). Four animals were analyzed for each time point.

 
Serial echocardiography measurements.
Before transgene induction (time point 1), 2 wk after transgene induction (acute) (time point 2), after 2 wk of transgene induction followed by repression (time point 3), 6 wk after transgene induction (chronic) (time point 4), and 6 wk after transgene induction followed by repression (time point 5) serial echocardiography was performed. The anterior chest was shaved, and mice were placed in the supine position. Transthoracic M-mode and two-dimensional echocardiography was performed with an Acuson 256 sector scanner equipped with a 13-Mhz broadband transducer. Images were recorded on videotape. Briefly, the heart was imaged in the two-dimensional parasternal short-axis view, and an M-mode echocardiogram of the midventricle was recorded at the level of papillary muscles. Heart rate and end-diastolic and end-systolic internal dimensions of the left ventricle were measured from the M-mode image. Systolic blood pressure (mmHg) was measured by tail cuff. Left ventricular internal dimensions were determined according to the leading-edge method of the American Society of Echocardiography. All measurements were based on the average of three consecutive cardiac cycles. Fractional shortening (FS) was calculated from the following equations: FS = (LVDD – LVSD)/LVDD x 100, where LVDD represents left ventricular diastolic dimension and LVSD represents left ventricular systolic dimension. Cardiac output was calculated by the cubed method [1.047 x (LVDD3 – LVSD3) x heart rate]. All values are presented as means ± SE. For the echocardiographic data where the variables were examined at various intervals, ANOVA with repeated measure was performed. Statistical significance was accepted at the level of P < 0.05.

Preparation of cRNA for microarray analysis.
At the time of death, hearts were excised and weighed. Frozen hearts from DTG-positive mice were placed in TRIzol (Ultraspec-II RNA Isolation System; Biotecx, Houston, TX) and homogenized to extraction of total RNA. Total RNA was extracted from the DTG mice hearts according to the manufacturer's recommendations. RNA was resuspended in DEPC-treated H2O and further purified using RNATAckTmResin (Ultraspec-II RNA Isolation System, Biotecx) according to the manufacturer's instructions. After extraction an aliquot was electrophoresed in a 2.0% agarose gel and visualized by staining with ethidium bromide to confirm the absence of significant degradation. RNA was quantified, and samples were aliquoted such that aliquoted RNA represented equal amounts (10 µg) of RNA from DTG-positive mice hearts. These samples were used to generate cDNA using poly-dT primer incorporating T7 promoter in the Superscript Choice system (Invitrogen, Grand Island, NY) according to the Affymetrix protocol (Affymetrix, Santa Clara, CA). Resulting cDNA was used to generate biotin-labeled cRNA by incorporating biotinylated CTP and UTP using the ENZO Bioarray High Yield transcript labeling kit (Affymetrix). cRNA (20 µg) was fragmented in fragmentation buffer [40 mM Tris (pH 8.1), 100 mM potassium acetate, 30 mM magnesium acetate] for 35 min at 94°C. Subsequently samples were hybridized to Affymetrix GeneChip Mouse Expression Set 430 microarrays A and B for 16 h at 45°C, and bound sequences were quantified by staining and scanning according to Affymetrix protocols. The microarrays were first washed and stained with streptavidin-phycoerythrin, washed again, incubated with antistreptavidin antibody, stained again, and then washed according to the manufacturer's instructions. The arrays were then scanned at 488 nm using a G25000A gene array scanner (Agilent, Palo Alto, CA). Scanned images were quantified using Microarray Suite 5.0 software (MAS 5.0) (Affymetrix). The transcript expression levels were then scaled to an average intensity of 500 units on each chip. DTG-positive mouse hearts were examined before the induction of the transgene Akt1 (time point 1), 2 wk after the induction of the transgene Akt1(time point 2), 2 wk after the induction of the transgene Akt1 and 2 days after the repression of the transgene Akt1 (time point 3), 6 wk after the induction of the transgene Akt1 (time point 4), and 6 wk after the induction of the transgene Akt1 and 2 wk after the repression of the transgene Akt1 (time point 5). Four sets of independent hybridizations from individual mice were performed for each time point.

Data quantification, normalization, and analysis.
After data acquisition, the scanned images were quantified using MAS 5.0 software (Affymetrix), yielding a signal intensity for each probe on the GeneChip. The signal intensities from the probes for each transcript were then used to determine an overall expression level, a detection confidence score, and a present/absent call according to algorithms implemented in MAS 5.0 software. The arrays were then linearly scaled to an average expression level of 500 units on each chip in MAS 5.0. We also scaled/normalized the B chips to the A chips. For each transcript, fold change, statistical significance of differential expression and false discovery rate (FDR) were calculated. Fold change was calculated using the average signal from each experimental group. Statistical significance was calculated using a standard two-factor (time point) ANOVA implemented in the NIA Array Analysis Tool (http://cgd.rrc.uic.edu/anova/) using a Bayesian estimate of the variance between replicates. ANOVA P values were corrected for multiple hypothesis testing using the method of Benjamini and Hochberg (2) and calculating the FDR from ANOVA P values in the selected transcripts with FDR <0.01%. Each transcript was annotated based on a download of the NetAffx database (Affymetrix). An automated analysis of related transcripts was performed using Ingenuity Pathways Analysis, a web-delivered application that evaluates biological networks (www.ingenuity.com). For this analysis, four sets of gene identifiers, representing time points 2 through 5 revealed as significantly changed with a fold change >/< 1.8, an FDR <0.01 and P < 0.05 were mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base, which is derived from the scientific literature. These Focus Genes were used for generating biological networks based upon reported interactions with genes/proteins. The software computes a score for each network according to the fit of the Focus Genes that were significantly changed using a Fisher's exact test. The score is derived from a P value and indicates the likelihood of the Focus Genes in a network being found together due to random chance. A score of 2 indicates that there is a 1% chance that the Focus Genes are together in a network due to random chance. Therefore, scores of ≥2 have ≥99% confidence (and ≥3 have ≥99.9%) of not being generated by random chance alone.

Gene ontology.
Each gene after acute (2 wk) and chronic (6 wk) transgene activation was annotated based on a complete download of the NetAffx database (Affymetrix). In addition, automated analysis of groups of biologically related genes was performed using GenMAPP and MAPPFinder (http://www.genmapp.org). MAPPFinder is a tool that integrates the annotations of the Gene Ontology (GO) Project (http://www.geneontology.org/) with the free software package GenMAPP. MAPPFinder identifies GO terms with overrepresented numbers of gene-expression charges. GenMAPP generates graphical files within which gene expression data can be viewed in the context of the biological knowledge contained in the GO database.

Quantitative RT-PCR analysis.
Total RNA was isolated and purified from the hearts as described above by RNA-TRIzol extraction (Ultraspec-II RNA Isolation System, Biotecx). RNA (2 µg) was treated (30 min at 37°C) with amplification grade DNase 1 (Invitrogen) following phenol-chloroform extraction (1:1). cDNA was produced using Taqman reverse transcription (Invitrogen) kits. Real-time polymerase chain reaction (QRT-PCR) was performed in triplicate on ABI-Prism 7900 using SYBR Green I as a double-stranded DNA-specific dye according to the manufacturer's instruction (PE-Applied Biosystems, Foster City, CA) using SYBR Green PCR Master Mix (1:1, PE-Applied Biosystems), forward and reverse primers (200 nM each), and sample DNA. The primers for rodent GAPDH were obtained from Integrated DNA Technologies. Primers were designed to be compatible with a single QRT-PCR thermal profile (95°C for 10 min, and 40 cycles of 95°C for 30 s and 60°C for 1 min) such that multiple transcripts could be analyzed simultaneously. Accumulation of PCR product was monitored in real time (PE-Applied Biosystems), and the crossing threshold (Ct) was determined using the PE-Applied Biosystems software. For each set of primers, a no-template control and a no-reverse-amplification control were included. Postamplification dissociation curves were performed to verify the presence of a single amplification product in the absence of DNA contamination. Fold changes in transcript expression were determined using the Ct method. To standardize the quantitation of the selected transcripts, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) from each sample was quantified by QRT-PCR, and the selected transcripts were normalized to GAPDH. Data of QRT-PCR analysis are shown as means ± SE. All data were evaluated with a two-tailed, unpaired Student's t-test or compared by one-way ANOVA (P < 0.05).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Effect of acute and chronic Akt1 overexpression and repression in DTG mice.
Two lines of TG mice (Tet-myrAkt1 and {alpha}MHC-tTA) were used to generate cardiac-specific inducible Akt1 TG mice for these experiments (Fig. 1A). The Tet-myrAkt1 TG line harbors an active form of Akt1 (myrAkt1) transgene under the control of multimerized tetO sequences, and the {alpha}MHC-tTA TG line expresses tTA (a fusion protein of tetO-binding domain and VP16 transactivation domain) in the heart driven by the {alpha}MHC promoter (46). Treatment of DTG mice harboring both transgenes with Dox in the drinking water results in repression of myrAkt1 expression because Dox associates with tTA and inhibits its binding to tetO sequences, whereas withdrawal of Dox results in tTA binding to tetO elements and induction of myrAkt1 expression in the heart.

As shown in Fig. 1B, 12-wk-old DTG mice were killed to examine the heart weight/body weight (HW/BW) ratio before transgene induction (time point 1), 2 wk after transgene induction (time point 2), after 2 wk of transgene induction followed by repression for 2 days (time point 3), 6 wk after transgene induction (time point 4), and 6 wk after transgene induction followed by repression for 2 wk (time point 5). The activation of transgene expression for 2 wk resulted in a 51% increase in the HW/BW ratio (Table 1). Repression of the transgene by Dox readministration for 2 days (time point 3) resulted in a ~15% regression in HW/BW ratio (P < 0.05). As previously reported (46), cardiac growth resulting from this short-term activation of Akt1 transgene was completely reversed at later time points of transgene repression, but the 2-day time point was chosen for this study to identify early changes in gene expression that were associated with the atrophy process. Maintaining the transgene expression for 6 wk (time point 4) resulted in a 97% increase in HW/BW ratio relative to control (time point 1). When the transgene expression was maintained for 6 wk and then repressed for 2 wk, HW/BW ratio regressed by 36% relative to time point 4.


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  		Table 1. HW/BW ratios in DTG mice after transgene induction

 
Serial echocardiographic measurements were made at the five experimental time points to assess heart function at the different time points chosen for microarray analysis. Representative echocardiography measurements from DTG mice are shown for noninduced 12-wk-old mice (time point 1), 2 wk after transgene induction (time point 2), and 6 wk after transgene induction (time point 4) (Fig. 2A). DTG mice showed a significant increase in posterior wall thickness at both 2 and 6 wk after the Akt1 transgene induction (data not shown). At 6 wk after transgene induction (time point 4), TG mice exhibited left ventricular dilatation (Fig. 2B) and contractile dysfunction (Fig. 2C). Repression of transgene for 2 wk after 6-wk induction (time point 5) led to a further increase in chamber dilatation and a more severe decrease in contractility. Additional characteristics of these mice at the different time points are listed in Table 2.


Figure 2
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  		Fig. 2. Transthoracic echocardiographic measurements of DTG mice. A: representative M-mode echocardiograms of DTG before Akt1 induction, 2 wk after Akt1 induction, and 6 wk after Akt1 induction. B: left ventricular chamber dilatation (LVDD) occurs upon prolonged Akt1 transgene expression. Significant increases in LVDD occurred at time point 4 compared with time point 1 (control). Further chamber dilatation occurred when the transgene was repressed following prolonged activation time point 5 vs. 4. C: calculated fractional shortening for the different time points. *P < 0.01, #P < 0.05.

 

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  		Table 2. BW, heart rate, systolic blood pressure, and cardiac output in DTG mice

 
Transcript expression in the hearts of DTG mice.
To identify transcripts differentially regulated by Akt1 in the heart we examined the expression profiles of DTG mice at the five time points. Four mouse hearts were analyzed for each time point. Compared with the noninduced mouse, 3,053 transcripts (~6.7% of total) were differentially regulated at one or more of the subsequent four time points (P < 0.05, fold change > or <1.8 and FDR < 0.01). A hierarchical clustering of these 3,053 transcripts at each time point is shown in Fig. 3. A complete list of transcripts can be found at the National Cancer Center for Biotechnology Information Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/).


Figure 3
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  		Fig. 3. Hierarchal clustering diagram representing the 3,053 transcripts that were differentially expressed at 1 or more time points. The horizontal axis represents the time points included in the study: before Akt1 transgene induction (1), 2 wk after transgene induction (2), after 2 wk of transgene induction followed by Akt1 repression of 2 days (3), 6 wk after transgene induction (4), and 6 wk after transgene induction followed by repression of 2 wk (5). Four samples were analyzed for each time point, and the horizontal axis represents the up- or downregulation of significantly regulated transcripts (fold change >/<1.8, false discovery rate <0.01, P < 0.05). For each transcript and the specific time point there is a thin colored band, which visually describes the expression of that particular transcript in the sample. Red bands represent upregulated transcripts; blue bands represent downregulated transcripts; and white bands represent transcripts showing approximately equal expression.

 
Differentially regulated transcripts were identified by comparing microarray profiles obtained at different time points of transgene induction and repression. The data were analyzed for transcripts that were reversibly regulated following acute transgene induction and repression (Fig. 4A). Figure 4A indicates the numbers of transcripts that were differentially regulated by acute Akt1 induction (2 wk) and 2-wk induction followed by repression for 2 days, representing time points 1, 2, and 3. Rapid growth of the heart with preserved contractile function was associated with the upregulation of 366 genes, of which 145 were downregulated following transgene suppression. Rapid heart growth was also associated with the downregulation of 459 transcripts following acute Akt1 induction, of which 261 showed reversible expression over this time course. A partial list of these transcripts ranked according to greatest fold change in expression is shown in Table 3. Of interest, rapid heart regression resulting from acute transgene inactivation led to the upregulation of 487 and downregulation of 242 transcripts whose expression did not significantly change during transgene-induced heart growth. Presumably, this subset of transcripts represents genes that participate in the atrophy but not growth of the heart. A partial list of these transcripts ranked according to greatest fold change in expression is shown in Table 4.


Figure 4
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  		Fig. 4. Subsets of transcripts regulated by acute and chronic Akt1 overexpression. A: subsets of transcripts regulated by 2 wk of Akt1 overexpression (time point 2) relative to control (time point 1) and overexpression followed by 2 days of repression (time point 3) relative to 2 wk of Akt1 overexpression (time point 2). B: subsets of transcripts regulated by 2 wk of Akt1 overexpression (time point 2) and 6 wk of Akt1 overexpression (time point 4) relative to control. C: Venn diagram representing the sets of transcripts in DTG mice that are induced after acute or chronic Akt activation relative to control mice. Black represents transcripts differentially regulated following acute Akt induction (2 wk), white represents transcripts differentially regulated following chronic Akt induction (6 wk), and gray represents transcripts differentially regulated relative to control at both time points.

 

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  		Table 3. Transcripts up- or downregulated after acute (2 wk) transgene activation and the effect of transgene repression (2 day)

 

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  		Table 4. Transcripts up- or down regulated only after transgene repression (2 d), but not after acute transgene activation (2 wk)

 
The data were also analyzed to identify transcripts that were differentially regulated following acute and chronic transgene induction that is associated with normal and diminished heart function, respectively. Figure 4B indicates the numbers of transcripts comparing acute (2 wk) and chronic (6 wk) transgene induction. This data set represents time points 1, 2, and 4. Of interest, the majority of upregulated (259/366) or downregulated (356/459) transcripts following acute transgene induction did not change further as the hearts transitioned to failure at the 6-wk time point. Rather, another set of transcripts were upregulated (211) and downregulated (328) during the transition to failure that were not differentially expressed as heart grew in the absence of diminished contractile function.

To highlight the differences between the gene expression profiles following acute and chronic Akt1 induction, a Venn diagram was constructed to compare the overlap in sets of transcripts regulated in response to 2-wk transgene activation (time points 1 and 2) and 6-wk transgene activation (time points 1 and 4). As can be seen in Fig. 4C, most transcripts regulated during acute growth associated with preserved contractile function are maintained at the 6-wk time point (569/826). However, the majority of transcript changes at 6 wk (1,042/1,611) represent genes that were only regulated in association with prolonged transgene activation and pathological growth. A partial list of these transcripts ranked according to greatest fold change is shown in Table 5.


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  		Table 5. Transcripts up- or downregulated in response to chronic (6 wk) transgene activation, but not in response to acute transgene activation (2 wk)

 
Quantitative RT-PCR of selected transcripts.
A number of transcripts were randomly selected and tested for Akt-mediated regulation in heart by quantitative RT-PCR. Included in this list are transcripts that showed the greatest magnitude of Akt-mediated regulation [e.g., MCM6, triadin (Trd), ryanodine receptor 1 and myomesin 2] in addition to those that displayed relatively modest levels of regulation. Of 31 selected transcripts, QRT-PCR analysis confirmed that 26 were differentially regulated after acute Akt1 overexpression (Table 6) in accordance to the microarray data. Of these 26 transcripts, Trd and mesenchyme homeobox 1, although significantly induced following Akt1 transgene induction, did not achieve statistical significance (P < 0.05) in the regression of expression following repression of the transgene. In contrast, five transcripts encoding homeodomain interacting kinase 2, secreted frizzled-related sequence protein 5, adenylate cyclase 7 (Adcy 7), angiotensin II receptor type 2, and activity-regulated cytoskeletal-associated protein did not display statistically significant differential expression in the QRT-PCR analysis (data not shown).


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  		Table 6. Comparison of transcripts identified as up-/down regulated after 2 wk of acute Akt1 transgene activation and 2 days of repression by microarray and quantitative RT-PCR analyses

 
QRT-PCR analysis also confirmed the regulation of 15 of 21 transcripts identified as differentially expressed following chronic Akt1 overexpression (Table 7). However, of these 15 transcripts, glypican 4 and G protein-coupled receptor 56 did not achieve statistical significance (P < 0.05). In contrast, six transcripts encoding human DEAD-box RNA helicase (Ddx26), artilage intermediate layer protein, nucleotide pyrophosphohydrolase, Adcy 5, growth arrest and DNA-damage-inducible 45, coagulation factor II (thrombin) receptor-like 1, and tissue inhibitor of metalloproteinase 1 did not display differential expression in the QRT-PCR analysis (data not shown).


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  		Table 7. Comparison of transcripts identified as up-/downregulated after 6 wk of Akt1 transgene activation and 2 wk repression by microarray and quantitative RT-PCR analyses

 
Biological network analyses.
Analyses of functionally related transcripts was performed using Ingenuity Pathways Analysis software (42). These analyses focused on two subsets of transcripts. First, gene relationships were constructed for the 1,146 transcripts indicated in Fig. 4A that were differentially regulated in the heart when the Akt1 transgene was inactivated following 2 wk of induction (time point 3 vs. 2). The network of 35 genes shown in Fig. 5A had a computed probability score of 42. Many of these transcripts encode signaling molecules and 31 were upregulated, whereas four were downregulated. In Fig. 5B, the 825 transcripts that were differentially regulated in hearts between control and 2 wk of gene induction (time point 2 vs. 1) were superimposed upon this network. Of this subset only 12/35 transcripts displayed statistically significant regulation, but in every case the direction of the regulation was opposite of that observed when the transgene was inactivated. Thus this network analysis of the microarray data illustrates that heart regression (after growth) is associated with the induction of a genetic program that substantially differs from the program that is activated during the growth of the heart.


Figure 5
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  		Fig. 5. A: network of functionally related genes constructed from differentially regulated transcripts between time point 3 (2 wk Akt1 induction followed by 2 days of repression) and time point 2 (2 wk Akt1 induction). The network represents transcripts that control "cellular development, cancer, cellular growth and proliferation" according to assignment by Ingenuity software. The extent of shading is indicative of the magnitude of regulation. Red shading indicates upregulation, whereas green shading indicates downregulation. Node shapes indicate function. Diamond designates an enzyme; square, growth factor; triangle, kinase; circle, other; ___ indicates physical interactions (e.g., formation of complexes); -> indicates functional interaction (activation); ___| indicates inhibition. B: transcripts differentially regulated at time point 2 (2 wk Akt1 induction) compared with time point 1 (before transgene induction) superimposed upon the network constructed in A. Symbol, transcript name, and fold change (time point 3 vs. 2/2 vs. 1) are as follows: AKAP13, A kinase (PRKA) anchor protein 13, 1.9/–2.2; APC, adenomatosis polyposis coli, 2.6/–; CD28, CD28 antigen (Tp44), 2.3/–; CD44, CD44 antigen (homing function and Indian blood group system), 7.4/–1.9; CDH5, cadherin 5, type 2, VE-cadherin (vascular epithelium), 2.6/–; CTNNAL1, catenin (cadherin-associated protein), alpha-like 1, 2.4/–2.0; CTNNB1, catenin (cadherin-associated protein), beta 1, 88kDa, 2.2/–; CTNND1, catenin (cadherin-associated protein), delta 1, 1.9/–; D1S155E, upstream of NRAS, 2.0/–; DLG1, discs, large homolog 1 (Drosophila), 2.1/–; DYRK1A, dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A, 2.9/–2.0; EPB41, erythrocyte membrane protein band 4.1 (elliptocytosis 1, RH-linked), 2.2/–2.6; ERN1, ER to nucleus signaling 1, 2.3/–; FER, fer (fps/fes related) tyrosine kinase (phosphoprotein NCP94), 2.4/–; GAB1, GRB2-associated binding protein 1, 2.6/–; GHR, growth hormone receptor, 2.8/–; GJA1, gap junction protein, alpha 1, 43 kDa (connexin 43), 8.4/–3.3; GNA12, guanine nucleotide binding protein (G protein) alpha 12, 4.5/–2.3; GNA13, guanine nucleotide binding protein (G protein), alpha 13, 4.2/–2.6; IL6ST, interleukin 6 signal transducer (gp130, oncostatin M receptor), 3.3/–; IRS1, insulin receptor substrate 1, 2.2/–2.6; JAK1, Janus kinase 1 (a protein tyrosine kinase), 3.3/–; MAPT, microtubule-associated protein tau, 2.8/–2.6; PIK3R1, phosphoinositide-3-kinase, regulatory subunit, polypeptide 1 (p85 alpha), 2.3/–2.2; PIK3R3, phosphoinositide-3-kinase, regulatory subunit, polypeptide 3 (p55, gamma), –2.1/2.5; PIP5K1B, phosphatidylinositol-4-phosphate 5-kinase, type I, beta, 2.2/–; PSEN1, presenilin 1 (Alzheimer disease 3), 3.0/–; PTPN11, protein tyrosine phosphatase, nonreceptor type 11 (Noonan syndrome 1), 4.0/–; PTPRE, protein tyrosine phosphatase, receptor type, E, 1.8/–; RDX, radixin, 2.0/–; STAT5A, signal transducer and activator of transcription 5A, 2.2/–; STATIP1, signal transducer and activator of transcription 3 interacting protein 1, –2.5/–; STRAP, serine/threonine kinase receptor associated protein, –2.3/–; TGFBR2, transforming growth factor, beta receptor II (70/80kDa), 5.4/–; USP9X, ubiquitin specific protease 9, X-linked (fat facets-like, Drosophila), 2.0/–.

 
The second pathway analysis constructed a network from the 1,611 transcripts in Fig. 4C that were differentially regulated in hearts following 6 wk of transgene induction compared with control hearts (time point 4 vs. 1). This network is represented in Fig. 6A, and it has a probability score of 38. In Fig. 6B, the subset of 826 transcripts that were differentially regulated in hearts following 2 wk of transgene induction (acute) compared with control hearts (time point 2 vs. 1) was superimposed on this network. Only 14/35 transcripts from this subset displayed significant regulation, but in every case the direction of the regulation was the same. Collectively, these data illustrate a functional network in which many of the gene regulatory changes occurring during heart growth are associated with contractile dysfunction rather than heart growth with preserved contractility.


Figure 6
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  		Fig. 6. A: network of functionally related genes constructed from differentially regulated transcripts between time point 4 (6 wk after transgene induction) and time point 1 (before transgene induction). The network represents transcripts that control "cancer, cellular movement and neurological disease" according to assignment by Ingenuity software. Shades, shapes, and lines are described in Fig. 5. B: transcripts differentially regulated at time point 2 (2 wk after Akt induction) compared with time point 1 (before transgene induction) superimposed upon the network constructed as described in A. Symbol, transcript name, and fold change (time point 4 vs. 1/2 vs. 1) are as follows: ADRBK2, adrenergic, beta, receptor kinase 2, 2.5/–; AHR, aryl hydrocarbon receptor, –1.9/–; ARNTL, aryl hydrocarbon receptor nuclear translocator-like, 2.4/–; CDH5, cadherin 5, type 2, VE-cadherin (vascular epithelium), –1.9/–; CSPG2, chondroitin sulfate proteoglycan 2 (versican), 2.1/2.4; CXCL12, chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1), –2.7/–; CXCR4, chemokine (C-X-C motif) receptor 4, –2.8/–; DSP, desmoplakin, –3.1/–3.2; EDNRA, endothelin receptor type A, –2.0/–; F2R, coagulation factor II (thrombin) receptor, 3.1/1.9; FBN1, fibrillin 1 (Marfan syndrome), 1.9/2.6; FER, fer (fps/fes related) tyrosine kinase (phosphoprotein NCP94), –5.4/–; GHR, growth hormone receptor, –2.5/–; GRB10, growth factor receptor-bound protein 10, –2.2/–2.3; GRK6, G protein-coupled receptor kinase 6, –2.2/–; IDE, insulin-degrading enzyme, –2.9/–2.0; IRS1, insulin receptor substrate 1, –3.2/–2.6; NCL, nucleolin, –2.0/–; NEDD4, neural precursor cell expressed, developmentally downregulated 4, –2.6/–1.8; NFYA, nuclear transcription factor Y, alpha, –2.6/–; NR2C2, nuclear receptor subfamily 2, group C, member 2, –2.4/–2.2; NR3C1, nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor), –2.8/–; NRIP1, nuclear receptor interacting protein 1, –3.2/–2.0; PCAF, p300/CBP-associated factor, –2.7/–; PECAM1, platelet/endothelial cell adhesion molecule (CD31 antigen), –2.0/–; POU2F1, POU domain, class 2, transcription factor 1, –4.4/–3.1; PPARGC1A, peroxisome proliferative activated receptor, gamma, coactivator 1, alpha, –3.1/–2.0; PTPN11, protein tyrosine phosphatase, nonreceptor type 11 (Noonan syndrome 1), 2.1/–; SERPINE1, serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1, 4.8/–; SLC2A4, solute carrier family 2 (facilitated glucose transporter), member 4, –2.8/–2.8; SLC25A20, solute carrier family 25 (carnitine/acylcarnitine translocase), member 20, 2.5/2.4; STAT5A, signal transducer and activator of transcription 5A, –3.3/–; VLDLR, very low density lipoprotein receptor, –2.4/–; ZHX1, zinc fingers and homeoboxes 1, –2.3/–.

 
Differential regulation of functional gene clusters.
To better understand the molecular pathways contributing to progression to heart failure as the result of chronic Akt1 expression, transcripts that were differentially regulated following acute (2 wk) and chronic (6 wk) Akt1 expression were grouped into subcategories using GO software (Fig. 7). The categories investigated included apoptosis regulator activity, induction of apoptosis, antiapoptosis, inflammation, response to oxidative stress, and stress. This analysis included transcripts that were differentially regulated between time points 1 and 2 (acute) and time points 1 and 4 (chronic). Similar increases were noted in transcripts classified as "induction of apoptosis" and "antiapoptosis" in the chronic versus acute transgenic hearts. In contrast, a larger number of transcripts associated with stress were modulated in the transgenic hearts, and this effect was most pronounced in the chronic condition.


Figure 7
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  		Fig. 7. Categories of biologically related transcripts revealed by Ingenuity. Each transcript of the acute (time point 2 vs. 1) and the chronic (time point 4 vs. 1) overexpression subset was annotated based on a complete download of the NetAffx database, and the automated analysis of groups/categories of biologically related transcripts was performed using Gene Ontology Analysis.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The transition from adaptive cardiac hypertrophy to heart failure is poorly understood. A number of studies have employed microarray technology to characterize gene expression profiles during cardiac hypertrophy and heart failure (3, 22, 50, 60, 63). These studies have reported that heart failure is associated with a proapoptotic shift in gene expression and changes in the expression of genes associated with myofibrillar organization, protein turnover, and energy metabolism. Of interest, gene expression profiles of failing hearts following left ventricle assist device support have revealed changes in the phosphoinositide-3-kinase-forkhead pathway and changes in endothelial nitric oxide synthase (eNOS) expression (8). The Akt protein kinase regulates both eNOS-dependent nitric oxide production (17, 30) and forkhead-dependent changes in skeletal myofiber (40) and cardiac myocyte size (49). Because Akt signaling controls both cardiac myocyte size and survival, a tetracycline-regulated transgenic system to conditionally switch an activated form of Akt1 on or off in the adult heart was developed (46). Short-term activation of Akt1 (2 wk) results in completely reversible hypertrophy with maintained contractility. In contrast, prolonged activation of Akt1 (6 wk) induces extensive cardiac hypertrophy, interstitial fibrosis, and contractile dysfunction. In this unique transgenic model, the focus of the current study was to create a cDNA expression profile of the heart as it undergoes reversible Akt1-mediated hypertrophy and during the transition from compensated hypertrophy to heart failure. To this end, RNA from hearts were analyzed at different time points following transgene activation and inactivation via hybridization to microarrays representing ~45,000 cDNAs and expressed sequence tags.

A number of findings can be derived from these analyses. First, the reversible growth of the heart following acute Akt1 transgene expression is associated with reversible changes in the expression of some but not all transcripts. As shown in Fig. 4A, 366 transcripts are upregulated and 459 transcripts are downregulated as the heart undergoes rapid growth following 2 wk of transgene induction. Changes in the expression of 49% of these transgenes are reversed by transgene inactivation at the 2-day time point. Of note, 487 transcripts were found to be upregulated and 242 were downregulated when the heart undergoes atrophy due to transgene inactivation, but no change in the expression of these transcripts was detected during transgene-induced heart growth. Thus 729 transcripts are differentially regulated only when hearts undergo atrophy but not growth, suggesting that heart growth and atrophy are controlled, at least in part, by nonoverlapping transcriptional programs. This relationship is illustrated by the network of functionally related transcripts in Fig. 5. This network comprises numerous signaling molecules of which 31/35 are upregulated following transgene inactivation and heart regression (Fig. 5A). In contrast, relatively few of these transcripts are regulated during heart growth (Fig. 5B). A similar conclusion was also reached using a pharmacological model of hypertrophy that led to the identification of 32 genes that were altered only during the induction of hypertrophy and 8 genes that were altered during regression (15).

Another finding of this study is the identification of a unique set of transcripts that are differentially regulated during pathological heart growth that results from chronic Akt1 induction compared with the transcript set that is differentially regulated upon acute transgene induction. For example, of the 366 genes upregulated during acute Akt1 induction, 259 (71%) are maintained at this induced level at the 6-wk time point when the heart exhibits contractile dysfunction. Likewise, of the 459 genes downregulated during acute Akt1 induction, 356 (78%) are maintained at this repressed level at the 6-wk time point. However, a subset of 539 transcripts is differentially regulated in hearts following chronic transgene expression but not acute transgene expression (211 are upregulated and 328 are downregulated, Fig. 4B). This relationship can also be seen in the Venn diagram in Fig. 4C, which compares differentially expressed transcripts following acute (time point 2 vs. 1) and chronic (time point 4 vs. 1) transgene expression. Collectively, these data indicate that the transition to pathological heart growth following chronic transgene induction is probably not due to further Akt1-mediated amplification of the genetic program that exists at the 2-wk time point but, rather, is associated with the induction of a distinct gene regulatory program that confers a pathological phenotype to the heart. An example of a network of functionally related transcripts that are predominantly expressed during pathological Akt-mediated heart growth is shown in Fig. 6.

Much attention has been focused on Akt signaling in the heart because it functions as a regulator of both physiological and pathological cardiac growth (56). In this regard, five different lines of transgenic mice have been described where Akt transgene is chronically expressed in the heart. These lines exhibit divergent phenotypes that can be characterized as physiological growth (9), pathological growth (31, 48, 54), or cardioprotection and no growth (48). Results from the inducible, cardiac-specific Akt1 transgene mouse used in this study provide an explanation, at least in part, for these divergent phenotypes. Based upon an analysis of these mice, it is proposed that a low level or short duration of Akt overexpression will lead to a modest degree of heart growth with preserved contractile function (i.e., physiological growth) (46). However, a high level or long duration of Akt expression will lead to excessive hypertrophy that is associated with pathological remodeling or a loss of contractile function. Importantly, this study showed that the pathological features associated with excessive cardiac growth were not caused by the extent of Akt-mediated myocyte hypertrophy per se, but by the uncoupling of coordinated myocyte growth and angiogenesis as the heart grows excessively large (23, 46).

The goal of our study was to employ the inducible Akt transgenic system to dissect the contribution of signaling system components in the processes of both physiological and pathological heart growth. However, a limitation of the current study is that acute and chronic expression of constitutively active Akt1 may not accurately reflect changes in the transcriptional profiles of hearts that undergo physiological and pathological growth, respectively, as the result of more conventional stimuli. Keeping this limitation in mind, a number of lines of evidence suggest that Akt signaling participates in the physiological growth of the heart. For example, adenovirus-mediated overexpression of myristoylated Akt1 in cultured, neonatal rat ventricular myocytes promotes hypertrophy in the absence of atrial natriuretic peptide gene expression or reorganization of the actin cytoskeleton (47), both of which are markers of pathological hypertrophy (41). Consistent with these observations, short-term expression of myristoylated Akt1 in the mouse heart leads to completely reversible hypertrophy with preserved contractile function in the absence of detectable interstitial fibrosis of fetal gene induction (46). These observations are also consistent with the observation that Akt1-deficient mice are resistant to the swim training-induced cardiac hypertrophy (11). Of note, Akt1-deficient mice also display an exaggerated cardiac hypertrophic response to pressure overload, suggesting that physiological levels of Akt are essential to suppress pathways that promote pathological growth.

Whereas short-term induction of the Akt1 transgene promotes heart growth that appears "physiological" by a number of criteria, chronic expression of constitutively activated Akt1 in the heart replicates biochemical and histological aspects of heart failure, including induction in the expression of atrial natriuretic peptide and ß-MHC and the presence of extensive interstitial fibrosis (46). Of particular note, the uncoupling of angiogenesis and myocyte growth that contributes to heart failure in this transgenic model is also reflected by histological sections of resected hearts from patients with dilated cardiomyopathy. Similar to the capillary pattern in the dilated failing hearts from the transgenic model, patient hearts with dilated cardiomyopathy display decreased capillary density dispersed in an irregular spatial pattern (24). Finally, it was recently shown that inactivation of endogenous VEGF will lead to a reduction in myocardial capillary density and impairs the adaptive response to pressure overload (23). Thus multiple lines of evidence support the notion that microvascular rarefaction is a common underlying mechanism that contributes to the maladaptive growth of the heart (29).

Although numerous microarray studies have been performed on the heart to characterize transcript changes associated with cardiac failure, it is difficult to identify common patterns of differential gene expression because investigators have employed different array systems and examined different experimental models (37, 44). However, a few common patterns in gene expression are apparent. For example, heart failure is associated with matrix remodeling, and a number of extracellular matrix genes are induced (4, 5, 21). In our model, changes in extracellular matrix gene expression was observed during both physiological (2 wk) and pathological growth (6 wk). Examples of changes included procollagen type V, {alpha}1 (5.4- and 2.7-fold induction at 2 and 6 wk after transgene activation, respectively, relative to control), procollagen type V, {alpha}2 (2.8- and 2.7-fold induction at 2 and 6 wk after transgene activation, respectively), and procollagen type VIII, {alpha}1 (3.3- and 2.7-fold induction at 2 and 6 wk after transgene activation, respectively). Furthermore, heart failure is associated with cellular turnover and alterations in cell cycle gene expression (62). In our model, changes in cell cycle gene expression included the p21 cyclin-dependent kinase inhibitor 1A (2.8- and 20.9-fold induction after 2 and 6 wk transgene activation, respectively, relative to control), cell division cycle-associated 1 protein (7.3-fold induction after 2 wk transgene induction), and minichromosome maintenance-deficient 5 protein (20.5-fold induction after 2 wk transgene activation). Thus, with regard to matrix production and cell turnover, chronic expression of constitutively activated Akt1 replicates aspects of the genetic transcriptional profile associated with heart failure that are produced by conventional surgical approaches in animal models or chronic disease states in patients.

In a previous report, Cook et al. (10) analyzed gene expression profiles in 6-wk-old TG mice that chronically express myristoylated Akt1, the same transgene examined in this study. At this time point, hearts exhibited hypertrophy but not decompensation. An analysis of ~11,000 transcripts revealed 21 genes were upregulated and 19 downregulated in TG vs. control mice (≥1.6-fold change, P < 0.05). In contrast, we analyzed inducible myrAkt1 TG mice at five time points using a microarray that represents ~45,000 transcripts and identified 3,053 transcripts that were differentially regulated at one or more of these time points (≥1.8-fold change, P < 0.05). The relative paucity of transcript changes observed in the chronic transgenic model described by Cook et al. (10) may result from widespread adaptations to continuous Akt expression in this transgenic model that could serve to normalize the heart to this perturbation. There was overlap of only seven transcripts between the study of Cook et al. and the current study. These transcripts included procollagen, type VIII, {alpha}1 (upregulated relative to control at both acute and chronic time points in our study), cardiac ankyrin repeat protein (upregulated in both acute and chronic relative to control), heterogeneous nuclear ribonucleoprotein L (upregulated in only acute), procollagen C-proteinase enhancer protein (upregulated only in chronic), and methylmalonyl-coenzyme A mutase (downregulated only in chronic). Based upon this limited data set it appears that the chronic Akt TG mice at 6 wk of age represent a midpoint between the 2-wk and 6-wk induction time points in the inducible model (where the transgene is induced in 12-wk-old mice).

In-depth analyses of the inducible Akt1 TG mouse model indicates that acute (2 wk) transgene expression results in hypertrophy that is reminiscent of physiological cardiac growth based upon preserved contractile function, lack of fibrosis, and a lack of fetal cardiac gene expression (46). In contrast, chronic (6 wk) induction leads to contractile dysfunction, fibrosis, and the induction of fetal genes. Thus features of "physiological" and "pathological" hypertrophy can be sequentially induced in this model by varying the extent of Akt1 transgene activation. In this highly defined system, we used GO classifications to analyze changes in functionally related gene clusters. This analysis did not reveal dramatic quantitative changes in gene clusters associated with apoptosis, which has been causally linked to heart failure (14, 57). These results are consistent with the well-established antiapoptotic function of Akt (16) and the finding that apoptosis is not detected in the TG mouse hearts regardless of the time point examined (I. Shiojima, unpublished results). Furthermore, heart failure following prolonged transgene expression did not appear to be associated with dramatic changes in the expression of genes that control the energy status of the cell (data not shown). However, hearts expressing Akt1 displayed pronounced changes in genes associated with stress, and these changes were most notable in the chronically induced hearts. These observations are consistent with the widely held hypothesis that Akt signaling is a key determinant in the ability of cardiac myocytes to adapt to stress (7). However, the inappropriate regulation of genes involved in stress adaptation may contribute to cardiac dysfunction. For example, microarray analysis revealed that chronic Akt1 transgene expression led to substantial reductions in the expression of hypoxia inducible factor 1{alpha} (HIF1{alpha}) and endothelial PAS domain protein 1 (EPAS1) transcript expression. HIF1{alpha} is an important component of the transcriptional response to low oxygen tension, and it functions to activate the transcription of angiogenic growth factors including VEGF-A and angiopoietin-2 (Ang-2) (43, 59). EPAS1 shares homology with HIF1{alpha} and modulates the coordinated expression of the angiogenesis-regulatory factors VEGF, Flt-1, Flk-1, and Tie2 (53). In this regard, pathological cardiac growth resulting from chronic Akt activation was associated with a reduction in capillary density and a loss in VEGF-A and Ang-2 expression (46). Thus the misappropriate regulation of HIF1{alpha} and other "master-regulators" of angiogenesis may contribute to the progression to heart failure in this model.

Finally, microarray data from the inducible Akt TG mouse system provide an information framework that could aid in the identification of factors that are therapeutically useful in the heart. It is well established that Akt functions as a cardioprotective signaling molecule (16, 48, 55), and it is conceivable that some of these effects are mediated by Akt-dependent changes in protein secretion (18). In this regard, it is increasingly recognized that stem cells may primarily exert their cardioprotective actions through paracrine mechanisms. Recently it was shown that Akt-transduced mesenchymal stem cells (Akt-MSC) do not differentiate into myogenic cells when injected at the infarct border zone of rat hearts and that the conditioned medium from Akt-MSC is sufficient to limit infarct size and improve left ventricular function at an early time point (19). These improvements correlated with the upregulation of several secreted factors, including VEGF, bFGF, HGF, IGF-1, and TMB4, in the Akt-MSC. Using the data described in the current study, we have identified a number of additional secreted factors that are upregulated following acute (2 wk) Akt transgene induction, and these candidates are currently being evaluated for their effects on cardiac growth and function.

In summary, we have utilized an inducible TG mouse system to characterize gene expression profiles associated with "physiological" and "pathological" Akt-mediated hypertrophy. Gene expression profiles were also analyzed in hearts undergoing atrophy in response to a cessation of transgene expression. Sets of genes were identified that were uniquely associated with either pathological or physiological growth. Furthermore, a set of genes was identified that were uniquely associated with either growth or atrophy of the heart. These time-dependent changes in gene expression profiles may provide a molecular framework to explain the complex differences in phenotypes of cardiac-specific Akt TG mice that have been developed in different laboratories. Further analyses of these data sets could also offer insights about the downstream effector molecules that mediate protective and pathological actions of Akt signaling on the heart.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was support by National Institutes of Health Grants N01-HV-28178, HL-77774, HL-66957, AR-40197, and AG-15052 and by a research grant from Centelion to K. Walsh.


   ACKNOWLEDGMENTS
 
Current address for Stephan Schiekofer: Ulm University, Dept. of Medicine I, Robert-Koch Str. 8, 89081 Ulm, Germany.


   FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: K. Walsh, Molecular Cardiology/Whitaker Cardiovascular Inst., Boston Univ. School of Medicine, 715 Albany St., W611, Boston, MA 02118 (e-mail address: kxwalsh{at}bu.edu).


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

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