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J Thorac Cardiovasc Surg 2009;137:232-238
© 2009 The American Association for Thoracic Surgery

Cardiopulmonary Support and Physiology

Regression of pressure-induced left ventricular hypertrophy is characterized by a distinct gene expression profile

^a Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC
^b Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
^c Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
^d Department of Surgery, University of Utah, Salt Lake City, Utah

Received for publication January 17, 2008; revisions received July 14, 2008; accepted for publication August 7, 2008.

^_* Address for reprints: Craig H. Selzman, MD, Division of Cardiothoracic Surgery, University of Utah, Rm 3C 127 SOM, 50 North, 1900 East, Salt Lake City, UT 84132. (Email: craig.selzman{at}hsc.utah.edu).

	Abstract

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 
Objective: Left ventricular hypertrophy is a highly prevalent and robust predictor of cardiovascular morbidity and mortality. Existing studies have finely detailed mechanisms involved with its development, yet clinical translation of these findings remains unsatisfactory. We propose an alternative strategy focusing on mechanisms of left ventricular hypertrophy regression rather than its progression and hypothesize that left ventricular hypertrophy regression is associated with a distinct genomic profile.

Methods: Minimally invasive transverse arch banding and debanding (or their respective sham procedures) were performed in C57Bl6 male mice. Left ventricular hypertrophy was assessed physiologically by means of transthoracic echocardiographic analysis, structurally by means of histology, and molecularly by means of real-time polymerase chain reaction. Mouse hearts were genomically analyzed with Agilent (Santa Clara, Calif) mouse 44k developmental gene chips.

Results: Compared with control animals, animals banded for 28 days had a robust hypertrophic response, as determined by means of heart weight/body weight ratio, histologic analysis, echocardiographic analysis, and fetal gene expression. These parameters were reversed within 1 week of debanding. Whole-genome arrays on left ventricular tissue revealed 288 genes differentially expressed during progression, 265 genes differentially expressed with regression, and only 23 genes shared by both processes. Signaling-related expression patterns were more prevalent with regression rather than the structure-related patterns associated with left ventricular hypertrophy progression. In addition, regressed hearts showed comparatively more changes in energy metabolism and protein production.

Conclusions: This study demonstrates an effective model for characterizing left ventricular hypertrophy and reveals that regression is genomically distinct from its development. Further examination of these expression profiles will broaden our understanding of left ventricular hypertrophy and provide a novel therapeutic paradigm focused on promoting regression of left ventricular hypertrophy and not just halting its progression.

	Introduction

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 
Left ventricular hypertrophy (LVH) affects approximately 25% of adult Americans and is most often associated with pressure overload, as seen with essential hypertension, aortic stenosis, and ischemic heart disease.¹ LVH is highly morbid and is a robust risk factor for cardiovascular mortality.² Yet despite optimal blood pressure control, treated hypertensive patients rarely achieve more than a 15% to 20% reduction in left ventricular (LV) mass. Furthermore, nearly half of these patients will continue to have LVH progression and increased cardiovascular events, including death.³ Thus there remains a great need to expand our understanding and treatment of LVH.

To date, most experimental and therapeutic approaches addressing pressure-induced LVH have been directed at its progression. Numerous studies have profiled the genomic response to aortic banding at multiple time points,⁴ in different chambers,⁵ and in different sexes.⁶ Collectively, these types of studies have exquisitely detailed and identified mechanistic targets that can be blocked to delay or restrict LVH progression^7,8 but have not been effective in advancing therapy for existing disease. As opposed to the plethora of studies focusing on LVH development, few studies have attempted to characterize independent features of LVH regression. Friddle and colleagues⁹ first detailed genomic differences with LVH regression using nascent gene array technology after administration and withdrawal from β-adrenergic agonists. Although no significant physiologic differences between groups were discerned, 8 genes unique to the drug removal were identified. Despite only 4000 genes on the array platform, this study revealed that gene expression differences between groups might exist in the absence of detectable physiologic changes and that array technology might be sensitive enough to highlight these differences.

We and others have demonstrated that removal of a previously placed constrictive aortic band can allow reversal of LVH.^10-12 Similarly, studies of human patients with LVH who undergo aortic valve replacement for aortic stenosis are highly successful in causing LVH regression.¹³ It remains unknown, however, whether LVH regression is truly a unique process and thus a target for focused study and intervention. Accordingly, we sought to determine whether gene expression associated with physiologic LVH regression was not simply the reverse of those genes associated with LVH progression but rather a distinct genomic pattern.

	Materials and Methods

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 
Surgical Model and Experimental Design
In accordance with both an institutionally approved Institutional Animal Care and Use Committee protocol and National Institutes of Health guidelines, 10-week-old C57Bl6 male mice were randomly assigned to one of 4 groups: sham, band, sham deband, and deband. The 60 mice enrolled were partitioned as follows: 10 in the sham group, 15 in the band group, 13 in the sham deband group, and 21 in the deband group. Mice in the sham and band groups underwent minimally invasive transverse aortic banding, as previously described.¹⁰ Briefly, animals were anesthetized with inhaled isoflurane administered through a facemask. A midline neck incision was used to approach the anterior mediastinum. The transverse arch was identified, and a constrictive band was placed and tightened to the approximate diameter of a 27-gauge needle. The only difference between the sham and band groups was that in the sham group, the constrictive band was not tightened. Adequate placement of the band was verified by means of evaluation of carotid Doppler scans both before and after placement of the aortic band. Adequate banding was accepted when the Doppler velocity ratio doubled from the right to left carotid arteries. Animals in the deband and sham deband groups had initial procedures identical to those for the band and sham groups, respectively. At 4 weeks, the aortic band was removed, heretofore referred to as being debanded.¹⁰ Efficiency of debanding was verified by means of carotid Doppler scanning with normalization of carotid velocities. Animals from the deband and sham deband groups were killed 1 week after the deband procedure for all evaluations except histology, for which animals were killed both at 1 and 4 weeks after debanding.

Transthoracic Echocardiography
Transthoracic echocardiography was performed with the Vevo 660 High Resolution Biomicroscopy System equipped with a 30-Mhz transducer (Visual Sonics, Toronto, Calif). During examination, mice were anesthetized with 1% to 1.5% inhaled isoflurane. The depth of anesthesia was standardized by recording images at heart rates of 480 to 520 beats/min. Images were recorded in all animals before surgical intervention, at 2 and 4 weeks after banding, and at 1, 2, 3, and 4 weeks after removal of aortic constriction (debanding). Two technicians blinded to the animals' experimental status performed examinations and measurements.

Tissue Procurement
At the time of death, hearts were rapidly excised, and the LV apices were sectioned and flash-frozen in liquid nitrogen for gene array analysis. Additional animals allocated to histologic analysis were perfused with phosphate-buffered saline followed by 10% formalin, fixed overnight in formalin, and then processed for histologic analysis with periodic acid–Schiff staining.¹⁴ Cardiomyocyte cross-sectional area was measured with ImageJ software (version 1.38j; National Institutes of Health, Bethesda, Md).¹⁵

RNA Preparation, MicroArray Process, and Real-time Polymerase Chain Reaction
Semipooled groups of mice were created to minimize variation between mice within a tested group and to enhance detection of variation between experimental groups. For each experimental group, 9 mice were selected from a group of up to 22 based on echocardiographic criteria of both progression with banding (increase in LV mass of >20%) and regression with debanding (decrease in LV mass of >10%). Within these groups, subgroups of 3 animals were randomly pooled so that 3 samples would be created for each experimental group.

LV apices were homogenized in 0.5 mL of ice-cold Trizol solution (Sigma, St Louis, Mo) by using a bead mill homogenizer (Retsch, Newtown, Pa). RNA was then isolated with the standard Trizol procedure, with additional steps for removal of DNA and fibrous tissue. Purity of RNA was verified by using a 260/280 ratio of 1.95 or greater. An Agilent (Santa Clara, Calif) BioAnalyzer 2100 instrument was used to verify RNA integrity for all samples. Five hundred nanograms of total RNA was labeled with Cyanine-5-CTP in a T-7 transcription reaction by using the Agilent Low Input Linear RNA Amplification/Labeling System. Labeled cRNA from samples was then hybridized to Agilent mouse 44k developmental microarray slides in the presence of equimolar concentrations of Cyanine-3-CTP–labeled mouse reference RNA prepared from pools of 1-day-old mouse pups. Real-time polymerase chain reaction (PCR) was performed with Taqman primers and probes (Applied Biosystems International, Foster City, Calif).

Western Blotting
Protein fractions were isolated in ice-cold lysis buffer during Dounce homogenization. Concentrations were determined by using the Bradford assay. Protein fractions were denatured in loading buffer. Thirty micrograms of each sample was then loaded into alternating lanes for gel electrophoresis. Membrane transfer was performed overnight, and rabbit anti-mouse antibody was used to probe for polo-like kinase 1 (Plk1) and hippocalin-like 1 (Hpcal1). Glyceraldehyde-3-phosphate dehydrogenase was used as the loading control.

Statistical Methods
All physiologic data are presented as the mean ± standard error, except where noted. Real-time PCR data were log-transformed before comparison. All comparisons of physiologic data were performed by using 2-tailed type 3 or type 1 t tests. Microarray data (n = 12 arrays) were less normalized, and probes were filtered for features having a normalized intensity of less than 30 aFU in either red or green channels. Probes were removed if data were not present in at least 9 of 12 samples. Missing data points were imputed (to facilitate further statistical comparisons) by using the k nearest-neighbors algorithm (k = 2). Samples were then standardized (µ = 0, = 1) using a custom Perl script (ActiveState Perl 5.8.1, build 807; ActiveState Software, Inc, Vancouver, British Columbia, Canada). Differences in expressed genes were validated by using significance analysis of microarrays with a false discovery rate of 5%.

Linear models for microarray (Limma)¹⁶ was used to model the variation in all data sets and perform the following contrasts: band versus sham, deband versus sham deband, and (deband – sham deband) versus (band – sham). These contrasts were executed by using custom scripts written in the R statistical language and environment (R Version 2.2.1, build r36812). Genes were identified as significant in each contrast with the criteria of a P value of .01 or less and a mean fold change of 1.2 or greater.

Unsupervised gene and array clustering was performed for each comparison: band versus sham, deband versus band, and deband versus sham deband. Data for each gene being compared were filtered for 3 instances of a mean fold change of 2 or greater. Data were further adjusted by median centering the genes. Average linkage hierarchic clustering with a centered correlation similarity metric for both genes and arrays was performed with Gene Cluster 3.0 (Michiel de Hoon, University of Tokyo, Human Genome Center, Tokyo, Japan). Data were then visualized with Java TreeView (version 1.0.13).

Gene ontologies of significant genes in each contrast were identified by using the National Institutes of Health–curated Database for Annotation, Visualization, and Integrated Discovery (DAVID) 2007.¹⁷ To facilitate interpretation of gene ontology, functional clustering was performed, and results were rank ordered by using DAVID with enrichment analysis and were further organized according to the number of genes in that cluster relative to the total number of genes identified within a particular contrast. The primary advantage of the DAVID technique is its ability to take large lists of genes and identify the biologic processes and functions that are most important (within the set of significant genes) to the biologic phenomenon under study. Orthogonal filtering of data sets was performed by using the Significance Analysis of Functional Expression (SAFE)¹⁸ with custom R scripts. In this method, also called orthogonal filtering, all genes are first organized based on their gene ontologies or gene ontology categories. Each ontologic category is then evaluated based on the degree of variation of individual genes within that category, regardless of the significance of individual genes. The more randomly the genes within each ontology are distributed, the less likely that that ontology is contributing to the observed differences in the experimental groups. Ontologies are then ranked based on their relative significance to the process under study. Essentially, the entire gene array is used to generate meaningful information about the process under study, rather than just a few dozen genes that are identified as significantly different. Gene ontologies with a P value of .1 or less were identified in all contrasts.

	Results

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 
Regression of Pressure-induced LVH
Sixty mice were used in this study: 2 died perioperatively, and 2 were excluded with wound infections. Our technique of minimally invasive banding and debanding effectively produced LVH with subsequent regression. Doppler velocity ratios between the right and left carotid arteries verified creation and relief of arch obstruction, as previously demonstrated.¹⁰ Grossly, heart weight/body weight ratios similarly increased with banding and normalized after debanding (Figure 1, A). Echocardiographically, aortic constriction resulted in increased wall thickness, greater chamber dimensions, and significantly greater LV mass (Figure 1, A, and Table E1). One week after band removal, most parameters changed in the direction of baseline, with a favorable trend noted in fractional shortening. Histologically, cardiomyocyte cross-sectional areas increased approximately 20% after banding and decreased significantly after debanding (Figure 1, B and C). Three common genetic expression markers of ventricular hypertrophy were evaluated by using real-time PCR, including β myosin heavy chain, natriuretic peptide type B, and skeletal muscle 1-actin (n = 6–8), to determine the corresponding molecular changes. All were significantly upregulated in the banded animals and normalized in debanded mice. For example, natriuretic peptide levels increased 65.4 ± 25.4–fold with banding (sham, 1.0 ± 0.2–fold; P < .05) and decreased with debanding (2.9 ± 0.8, P < .05 vs banding). Taken together, these data comprehensibly demonstrate a reproducible model to study mechanisms independently associated with LVH regression.

View larger version (84K): [in this window] [in a new window]   Figure 1. Physiologic and structural evidence of left ventricular hypertrophy reversal. A, Heart weight (HW)/body weight (BW) ratios (n = 11–22 per group) and serial echocardiographic measurements showing changes over the depicted time period (n = 11–22 per group). B, Left ventricular (LV) cross-sections stained with periodic acid–Schiff. C, Calculated cardiomyocyte cross-sectional areas (n = 4 per group). * P < .05 versus the sham group, P < .05 versus the band group.

View larger version (35K): [in this window] [in a new window]   Figure 2. Gene expression associated with left ventricular hypertrophy regression. A, Venn diagram depicts different and shared genes in each experimental contrast (P < .01 and absolute fold change >1.2). B, Most differentially expressed genes in the regression contrast (deband vs band). C, Western Blot for selected proteins identified in the deband versus band comparison. D, Unsupervised gene clustering between groups. B, Band; S, sham; DB, deband; SDB, sham deband.

Although we identified changes in expression of individual genes, we sought to determine whether LVH regression was associated with a distinct genomic pattern. Indeed, unsupervised clustering of the samples in each comparison showed clear separation of the band and sham groups and of the deband and band groups (Figure 2, D). The separation was less distinct in the deband versus sham deband comparison, with 1 sample from the deband group and 1 sample from the sham deband group not clustering appropriately. This observation indicates that the expression patterns of the deband and sham deband groups were less significant than might otherwise have been predicted based on individual gene differences originally identified by means of Limma. Taken together, the global expression pattern depicted in the unsupervised clustering suggests that differences between groups were not limited to a few hundred genes but rather to several thousand, and those differences were consistent from one group of animals to another.

Gene Expression Profiles Associated With LVH Regression
In the LVH progression analysis gene ontology clustering of our most significant genes showed expected characteristics of hypertrophic stress, including a large focus on actin, microtubules, cytoskeletal integrity, and contractile function, with the remainder including organelle production, transport, and organization (Table 1 and see Figure E1). Similarly designed gene array studies of LVH progression (in mice) have identified these same genomic themes.⁵ In the regression analysis none of these cytoskeletal or contractile clusters were differentially expressed. Instead, there was a predominance of intracellular and extracellular signaling themes. Further breakdown of the functional clusters involved in the regression process indicated that cell growth, morphogenesis, and cell cycle were all highly upregulated, whereas metabolism and protein and nucleotide production were downregulated. Lastly, the regressed state (deband vs sham deband) had 126 overlapping genes that demonstrated mostly activity in metabolism and in mitochondria and other organelles. This is markedly different from the patterns associated with either LVH progression or regression. This latter observation suggests that although the debanded hearts appear structurally similar to the sham-debanded hearts, they remain genomically distinct.

	Discussion

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 
Our results compare favorably with those of a recently published study that detailed gene expression by means of microarray in rats after transverse arch debanding.¹² Although they identified 52 regression-related genes, animals that underwent an operation 2 weeks before heart procurement were compared with animals 24 hours after reoperative chest surgery to remove an inflammatory silk band. Thus many transcripts might actually reflect differences attributed to the perioperative systemic inflammatory response. We purposefully and rigorously designed (cage strategy and circadian cycling of procedures) our study in a minimally invasive murine model to avoid confounding perioperative variables. Furthermore, no quantifiable physiologic data are reported to indicate that LVH regression actually occurred. Additionally, statistical issues evaluating fold changes between groups (differences are expressed in relation to a single sham control group and not between the contrasting groups) are magnified when the essential comparison only used 3 animals, as opposed to 9 animals in each of our groups. Finally, their study used a limited array platform with less than 10,000 transcripts, possibly limiting their scope of observation, especially when compared with the whole-genome platform used in our present study.

Hypertrophy and atrophy have similarly been studied in skeletal muscle, and the processes have much in common with the progression and regression of LVH. For example, atrophy has been previously demonstrated to be an active process with a distinct pattern of gene expression rather than simply the genomic reverse of muscle hypertrophy.^20,21 Functional genes associated with atrophy and regression involve increased protein degradation, downregulation of adenosine triphosphate production, decreased glycolysis, a decrease in growth protein synthesis, and altered extracellular matrix production, all categories that we identified in regression compared with LVH. Fundamentally, however, muscle atrophy typically describes progression to a disease state, whereas regression of LVH is a return to a normal physiologic state. In this regard, and because of the intrinsic physiologic differences between skeletal and cardiac muscle, we believe that mechanistic inferences must be limited in all but the final pathways that directly result in decreased muscle mass.

Our results must be viewed with several caveats. In this study we have examined in detail the effects of banding at only 1 time point (4 weeks) and for only a discreet period of pressure unloading (1 week). We suspect that the influence of pressure relief when the ventricle is either less (2–3 weeks of banding) or more (6 weeks of banding) stressed would offer different structural and genomic patterns. We thus are likely witnessing a continuum of change as the heart both negatively and positively remodels. We chose to organize the array around the 1-week time point because our preliminary data indicated that the effective physiologic and structural reversal of LVH was nearly complete at this time (and far enough away from the surgical procedure), despite differences in gene expression. As demonstrated simply in the Venn diagram, structural reversal did not create a "normal heart," as assessed genomically. These observations will propel more detailed studies focusing on timing and better understanding of the reversed state. Finally, we acknowledge that this model is really a model that relieves acute pressure overload (banding). Thus parallels to the insidious pressure overload that occurs with typical aortic stenosis in human subjects and its subsequent reversal with aortic valve replacement^13,22 can only be inferred.

In conclusion, we demonstrate that relief of pressure overload–induced LVH is not simply the reverse of LVH progression but rather a unique process with a specific gene expression profile. Therapeutic approaches to LVH based on enhancing regression, rather than impeding progression, are rarely pursued. Elaborating mechanisms associated with both regression and the regressed state have great potential for identifying novel and clinically relevant strategies for treating patients with LVH.

Clinical Implications
Much attention is given to the study of mechanisms involved with the development of LVH and heart failure. Unfortunately, current strategies have not markedly altered the natural history of this disease. Aortic valve replacement for severe aortic stenosis (ie, removing the band) has clearly demonstrated a favorable effect in cardiac remodeling. Yet there is a large subset of patients with LVH from pressure overload that do not regress and have increased risk of cardiovascular morbidity and mortality. Although an allure exists to jump to elaborate therapies for heart failure (eg, cellular replacement and stem cell injections), a great need exists to identify the underlying mechanisms associated with myocardial recovery that can subsequently be targeted for sole therapy or perhaps in combination with mechanical unloading (aortic valve replacement or ventricular assist). Although LVH experimentation in mice is temporally quite different from that in human subjects, it is nonetheless an important first step in characterizing the genomic signature associated with cardiac remodeling and will enhance future work in myocardial recovery therapy.

	Materials and Methods

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 
Surgical Model and Experimental Design
In accordance with an institutionally approved Institutional Animal Care and Use Committee protocol, 10-week-old C57Bl6 male mice were randomly assigned to one of 4 groups: sham, band, sham deband, and deband. Mice in the sham and band groups underwent minimally invasive transverse aortic banding. The only difference between the 2 groups was that in the sham group the constrictive band was placed but not tightened. Adequate placement of the band was verified by means of evaluation of carotid Doppler scans both before and after placement of the aortic band. Adequate banding was accepted when the Doppler velocity ratio doubled from the right to left carotid arteries. Animals in the deband and sham deband groups had initial procedures identical to those for the band and sham groups, respectively. At 4 weeks, the aortic band was removed, heretofore referred to as debanded.^E1 Debanding efficiency was verified by means of carotid Doppler scanning with normalization of carotid velocities. Deband and sham deband animals were killed 1 week after the deband procedure for all evaluations except histology, in which animals were killed both at 1 and 4 weeks after debanding.

Semipooled groups of mice were created to minimize variation between mice within a tested group and to enhance detection of variation between experimental groups. For each experimental group, 9 mice were selected from a group of up to 22 based on echocardiographic criteria of LVH reversal. Within these groups, subgroups of 3 animals were pooled so that 3 samples would be created for each experimental group.

Tissue Procurement
At the time of death, hearts were rapidly excised, and the left ventricular apices were sectioned and flash-frozen in liquid nitrogen for gene array analysis. Additional animals allocated to histologic analysis were perfused with PBS followed by 10% formalin, fixed overnight in formalin, and then processed for histologic analysis by using periodic acid–Schiff staining. Cardiomyocyte cross-sectional area was measured with ImageJ software (version 1.38j).^E2

Transthoracic Echocardiography
Transthoracic echocardiography was performed with the Vevo 660 High Resolution Biomicroscopy System equipped with a 30-Mhz transducer (Visual Sonics). During examination, mice were anesthetized with 1% to 1.5% inhaled isoflurane. Depth of anesthesia was standardized by recording images at heart rates of 480 to 520 beats/min. Images were recorded in all animals before surgical intervention, at 2 and 4 weeks after banding, and at 1, 2, 3, and 4 weeks after removal of aortic constriction (debanding). Two technicians, blinded to the animals' experimental status, performed examinations and measurements.

RNA Preparation, MicroArray Process, and Real-time PCR
LV apices were homogenized in 0.5 mL of ice-cold Trizol solution (Sigma) with a bead mill homogenizer (Retsch). RNA was then isolated with the standard Trizol procedure, with additional steps for removal of DNA and fibrous tissue. Purity of RNA was verified by using a 260/280 ratio of 1.95 or greater. An Agilent BioAnalyzer 2100 instrument was used to verify RNA integrity for all samples. Five hundred nanograms of total RNA was labeled with Cyanine-5-CTP in a T-7 transcription reaction by using the Agilent Low Input Linear RNA Amplification/Labeling System. Labeled cRNA from samples was then hybridized to Agilent mouse 44k developmental microarray slides in the presence of equimolar concentrations of Cyanine-3-CTP–labeled mouse reference RNA prepared from pools of 1-day-old mouse pups. Real-time PCR was performed by using Taqman primers and probes (Applied Biosystems International).

Statistical Methods
All physiologic data are presented as the mean ± standard error, except where noted. Real-time PCR data were log transformed before comparison. All comparisons of physiologic data were performed by using 2-tailed type 3 or type 1 t tests. Microarray data (n = 12 arrays) were LOESS normalized, and probes were filtered for features having a normalized intensity of less than 30 aFU in either red or green channels. Probes were removed if data were not present in at least 9 of 12 samples. Missing data points were imputed (to facilitate further statistical comparisons) by using the k nearest neighbors algorithm (k = 2). Samples were then standardized (µ = 0, = 1) by using a custom Perl script (ActiveState Perl 5.8.1, build 807). Differences in expressed genes were validated by using significance analysis of microarrays, with a false discovery rate of 5%.

Limma^E3 was used to model the variation in all data sets and perform the following contrasts: band versus sham, deband versus sham deband, and (deband – sham deband) versus (band – sham). These contrasts were executed by using custom scripts written in the R statistical language and environment (Version 2.2.1, build r36812, release date 2005-12-20.). Genes were identified as significant in each contrast with the criteria of a P value of .01 or less and a mean fold change of 1.2 or greater.

Unsupervised gene and array clustering was performed for each comparison: band versus sham, deband versus band, and deband versus sham deband. Data for each gene being compared were filtered for 3 instances of a mean fold change of 2 or greater. Data were further adjusted by median centering the genes. Average linkage hierarchic clustering with a centered correlation similarity metric for both genes and arrays was performed by using Gene Cluster 3.0. (Michiel de Hoon, University of Tokyo, Human Genome Center, 2006). Data were then visualized with Java TreeView software (version 1.0.13).

Ontologies of significant genes in each contrast were identified by using DAVID 2007.^E4 Functional clustering was performed to facilitate interpretation of gene ontology results, and functional clusters were rank ordered by using DAVID with enrichment analysis and were further organized according to the number of genes in that cluster relative to the total number of genes identified within a particular contrast.

Orthogonal filtering of data sets was performed with SAFE^E5 by using custom R scripts. Briefly, this test evaluates the significance of a given gene ontology relative to the comparison being studied by using the normality of distribution of all the genes (within that ontology) that are identified within the data set, regardless of the significance of individual genes. All gene ontologies with a P value of .1 or less were identified in all contrasts.

	Figure E1

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Gene ontology clustering of significant genes between respective groups, as assessed by using DAVID 2007. Numbers represent the percentage of our significantly expressed genes in a particular cluster compared with the total number of significant genes. Individual genes might be representative of more than 1 gene ontologic category (thus total percentages will not add to 100%).

	Table E1

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Echocardiographic measurements at each time point for each of the experimental groups

Sham Band Sham deband Deband Baseline  IVSd (mm) 0.63 ± 0.02 0.57 ± 0.02 0.66 ± 0.02 0.56 ± 0.02  LVIDd (mm) 4.2 ± 0.06 4.23 ± 0.08 4.12 ± 0.08 4.14 ± 0.06  LVPWd (mm) 0.56 ± 0.02 0.57 ± 0.02 0.6 ± 0.02 0.56 ± 0.02  IVSs (mm) 0.69 ± 0.02 0.67 ± 0.02 0.73 ± 0.02 0.64 ± 0.02  LVIDs (mm) 3.22 ± 0.09 3.16 ± 0.08 3.15 ± 0.13 3.13 ± 0.1  LVPWs (mm) 0.71 ± 0.02 0.73 ± 0.03 0.72 ± 0.03 0.69 ± 0.01  LVFS (%) 23.1 ± 1.2 25 ± 1.8 23.5 ± 1.9 23.9 ± 1.9  LVEF (%) 46.6 ± 2.1 49.5 ± 2.7 47 ± 3.2 47.7 ± 3.2  LVMass (mg), uncorrected 90.9 ± 4 87.8 ± 2.7 92.9 ± 2.1 83.4 ± 2.8  LVMass (mg), corrected 72.7 ± 3.2 70.2 ± 2.2 74.3 ± 1.7 66.7 ± 2.2 4 wk Banded  IVSd (mm) 0.62 ± 0.02 0.72 ± 0.02 * 0.66 ± 0.02 0.66 ± 0.02 *  LVIDd (mm) 4.23 ± 0.14 4.47 ± 0.09 * 4.19 ± 0.07 4.44 ± 0.07 *  LVPWd (mm) 0.58 ± 0.02 0.67 ± 0.02 * 0.59 ± 0.01 0.62 ± 0.01 *  IVSs (mm) 0.68 ± 0.02 0.78 ± 0.02 * 0.74 ± 0.02 0.73 ± 0.02 *  LVIDs (mm) 3.23 ± 0.16 3.58 ± 0.15 * 3.23 ± 0.11 3.54 ± 0.11 *  LVPWs (mm) 0.71 ± 0.03 0.84 ± 0.04 * 0.74 ± 0.03 0.77 ± 0.02 *  LVFS (%) 23.6 ± 1.7 20.7 ± 2.3 * 23.4 ± 1.5 19.8 ± 1.8 *  LVEF (%) 47.1 ± 3 41.7 ± 3.7 * 46.9 ± 2.4 40.4 ± 3.1 *  LVMass (mg), uncorrected 92.9 ± 5.5 125 ± 4.9 * 97 ± 1.8 109.9 ± 3 *  LVMass (mg), corrected 74.3 ± 4.4 100 ± 3.9 * 77.6 ± 1.4 87.9 ± 2.4 * 1 wk Debanded  IVSd (mm) 0.69 ± 0.02 0.58 ± 0.02  LVIDd (mm) 4.23 ± 0.06 4.37 ± 0.08  LVPWd (mm) 0.58 ± 0.01 0.58 ± 0.01  IVSs (mm) 0.73 ± 0.02 0.66 ± 0.02  LVIDs (mm) 3.28 ± 0.07 3.47 ± 0.13  LVPWs (mm) 0.72 ± 0.02 0.69 ± 0.02  LVFS (%) 21.3 ± 1.2 20.3 ± 1.6  LVEF (%) 43.5 ± 2.1 41.4 ± 2.8  LVMass (mg), uncorrected 100.8 ± 2.2 94 ± 3.7  LVMass (mg), corrected 80.6 ± 1.8 75.2 ± 3 * P < .05 versus baseline. P < .05 versus 4 weeks banded.

IVSd, Interventricular septum in diastole; LVIDd, left ventricular internal diameter in diastole; LVPWd, left ventricular posterior wall in diastole and systole; IVSs, interventricular septum in diastole and systole; LVIDs, left ventricular internal diameter in diastole and systole; LVPWs, left ventricular posterior wall in systole; LVFS, left ventricular fractional shortening; LVEF, left ventricular ejection fraction; LVMass, Left ventricular mass.

	Table E2.A

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Genes most differentially expressed in each comparison: Deband versus band groups

Direction (deband) Accession no. Symbol Name Fold + NM_030679 Myh1 Myosin, heavy polypeptide 1, skeletal muscle, adult 2.742 + U01063 Plk1 Polo-like kinase 1 (Drosophila) 2.475 + NM_016677 Hpcal1 Hippocalcin-like 1 2.074 + NM_001012336 Mdk Midkine 2.044 + NM_029948 Pramef12 PRAME family member 12 1.979 + NM_147089 Olfr572 Olfactory receptor 572 1.978 + AK036567 Mgat5 Mannoside acetylglucosaminyltransferase 5 1.962 + AK083490 Arfrp1 Adenosine diphosphate–ribosylation factor–related protein 1 1.916 + NM_009303 Syngr1 Synaptogyrin 1 1.845 + NM_182991 5330410G16R RIKEN cDNA 5330410G16 gene 1.730 + NM_172817 Zfp647 Zinc finger protein 647 1.713 + NM_007472 Aqp1 Aquaporin 1 1.673 + AC124532 Calcoco1 Calcium binding and coiled coil domain 1 1.638 + NM_146201 Zfp553 Zinc finger protein 553 1.628 + NM_001002272 Tro Trophinin 1.626 + NM_011601 Tlm T lymphoma oncogene 1.596 + NM_007444 Amd2 S-adenosylmethionine decarboxylase 2 1.550 + AC155922 Rps6ka5 Ribosomal protein S6 kinase, polypeptide 5 1.538 + XM_925008 Slc44a5 Solute carrier family 44, member 5 1.538 + AC161037 Hnrpul1 Heterogeneous nuclear ribonucleoprotein U-like 1 1.530 – NM_020581 Angptl4 Angiopoietin-like 4 2.550 – NM_008725 Nppa Natriuretic peptide precursor type A 2.052 – XM_621314 Dsp Desmoplakin 1.928 – XM_283556 Fer1l3 Fer-1-like 3, myoferlin (C elegans) 1.758 – NM_001013390 Scn4b Sodium channel, type IV, β 1.701 – NM_021453 Pga5 Pepsinogen 5, group I 1.651 – NM_024478 Grpel1 GrpE-like 1, mitochondrial 1.645 – NM_053176 Hrg Histidine-rich glycoprotein 1.625 – NM_019662 Rrad Ras-related associated with diabetes 1.623 – NM_171826 Cldnd1 Claudin domain containing 1 1.614 – NM_133786 Smc4 Structural maintenance of chromosomes 4 1.607 – NM_023733 Crot Carnitine O-octanoyltransferase 1.605 – AC1017091 Lama2 Laminin, 2 1.601 – NM_029977 Polq Polymerase (DNA directed), theta 1.592 – NM_134072 Akr1c14 Aldo-keto reductase family 1, member C14 1.585 – NM_012037 Vat1 Vesicle amine transport protein 1 homolog 1.584 – NM_145406 Slc10a3 Solute carrier family 10, member 3 1.576 – NM_175277 Bola3 BolA-like 3 (E coli) 1.555 – NM_008961 Pter Phosphotriesterase related 1.554 – NM_008937 Prox1 Prospero-related homeobox 1 1.551

	Table E2.B

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Genes most differentially expressed in each comparison: Band versus sham groups

Direction Accession no. Symbol Name Fold + XM_001002752 Zc3h7b Zinc finger CCCH type containing 7B 1.884 + NM_008725 Nppa Natriuretic peptide precursor type A 1.754 + NM_007489 Arntl Aryl hydrocarbon receptor nuclear translocator-like 1.645 + NM_013468 Ankrd1 Ankyrin repeat domain 1 (cardiac muscle) 1.557 + NM_015784 Postn Periostin, osteoblast-specific factor 1.534 + AC034265 Ankrd23 Ankyrin repeat domain 23 1.530 + NM_134129 Prpf19 PRP19/PSO4 pre-mRNA processing factor 19 homolog 1.497 + NM_133357 Krt75 Keratin 75 1.480 + NM_010480 Hsp90aa1 Heat shock protein 90 kd , class A member 1 1.454 + NM_031260 Mov10l1 Moloney leukemia virus 10-like 1 1.445 + NM_021453 Pga5 Pepsinogen 5, group I 1.445 + NM_009221 Snca Synuclein, 1.439 + NM_007564 Zfp36l1 Zinc finger protein 36, C3H type-like 1 1.424 + NM_172621 Clic5 Chloride intracellular channel 5 1.414 + NM_133744 Ccdc71 Coiled-coil domain containing 71 1.413 + NM_178701 Lrrc8d Leucine-rich repeat containing 8D 1.410 + NM_009007 Rac1 RAS-related C3 botulinum substrate 1 1.394 + NM_173442 Gcnt1 Glucosaminyl (N-acetyl) transferase 1, core 2 1.388 + NM_029614 Prss23 Protease, serine, 23 1.378 + NM_008524 Lum Lumican 1.365 – NM_007606 Car3 Carbonic anhydrase 3 2.672 – NM_030679 Myh1 Myosin, heavy polypeptide 1, skeletal muscle, adult 2.131 – NM_010005 Cyp2d10 Cytochrome P450, family 2, subfamily d, polypeptide 10 2.051 – NM_009416 Tpm2 Tropomyosin 2, β 2.016 – U01063 Plk1 Polo-like kinase 1 (Drosophila) 1.982 – NM_017370 Hp Haptoglobin 1.971 – NM_147089 Olfr572 Olfactory receptor 572 1.925 – NM_001012336 Mdk Midkine 1.911 – NM_011066 Per2 Period homolog 2 (Drosophila) 1.867 – BC004722 Malat1 Metastasis-associated lung adenocarcinoma transcript 1 (noncoding RNA) 1.859 – NM_013900 Mfi2 Antigen p97 (melanoma associated) 1.727 – NM_010404 Hap1 Huntingtin-associated protein 1 1.627 – NM_177709 Tusc5 Tumor suppressor candidate 5 1.597 – XM_981394 Rhobtb1 Rho-related BTB domain containing 1 1.586 – AK083490 Arfrp1 Adenosine diphosphate-ribosylation factor related protein 1 1.567 – NM_016693 Map3k6 Mitogen-activated protein kinase kinase kinase 6 1.562 – NM_009920 Cnih2 Cornichon homolog 2 (Drosophila) 1.549 – NM_025866 Cdca7 Cell division cycle associated 7 1.534 – NM_001025156 Ccdc93 Coiled-coil domain containing 93 1.524 – NM_009119 Sap18 Sin3-associated polypeptide 18 1.520

	Table E2.C

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Genes most differentially expressed in each comparison: Deband versus sham deband groups

Direction Accession no. Symbol Name Fold + NM_133777 Ube2s Ubiquitin-conjugating enzyme E2S 1.967 + AK036567 Mgat5 Mannoside acetylglucosaminyltransferase 5 1.731 + NM_029948 Pramef12 PRAME family member 12 1.704 + NM_011202 Ptpn11 Protein tyrosine phosphatase, nonreceptor type 11 1.620 + NM_144791 Tor1aip1 Torsin A interacting protein 1 1.507 + AB009392 Hnrpl Heterogeneous nuclear ribonucleoprotein L 1.505 + NM_010890 Nedd4 Neural precursor cell, developmentally downregulation gene 4 1.501 + NM_028004 Ttn Titin 1.497 + NM_001024955 Pik3r1 Phosphatidylinositol 3-kinase, reg. sub., polypeptide 1 1.496 + NM_008609 Mmp15 Matrix metallopeptidase 15 1.492 + NM_146087 Csnk1a1 Casein kinase 1, 1 1.483 + NM_173364 Zfp445 Zinc finger protein 445 1.480 + NM_025403 Nola3 Nucleolar protein family A, member 3 1.472 + NM_152134 Homer1 Homer homolog 1 (Drosophila) 1.469 + NM_011601 Tlm T lymphoma oncogene 1.466 + NM_009652 Akt1 Thymoma viral proto-oncogene 1 1.464 + NM_146201 Zfp553 Zinc finger protein 553 1.464 + NM_173028 Vps13a Vacuolar protein sorting 13A (yeast) 1.464 + NM_008714 Notch1 Notch gene homolog 1 (Drosophila) 1.447 + NM_029657 Mgrn1 Mahogunin, ring finger 1 1.445 – NM_020581 Angptl4 Angiopoietin-like 4 1.875 – NM_001013390 Scn4b Sodium channel, type IV, β 1.744 – XM_001001760 Gan Giant axonal neuropathy 1.671 – NM_145741 Gdf10 Growth differentiation factor 10 1.651 – NM_178882 D2hgdh D-2-hydroxyglutarate dehydrogenase 1.578 – NM_012037 Vat1 Vesicle amine transport protein 1 homolog 1.572 – XM_001006025 Rpl19 Ribosomal protein L19 1.558 – NM_010937 Nras Neuroblastoma ras oncogene 1.553 – NM_008937 Prox1 Prospero-related homeobox 1 1.540 – XM_915717 Ube2e1 Ubiquitin-conjugating enzyme E2E 1, UBC4/5 homolog 1.539 – XR_001538 Tfb2m Transcription factor B2, mitochondrial 1.527 – NM_053176 Hrg Histidine-rich glycoprotein 1.524 – NM_025276 Evpl Envoplakin 1.520 – AK041640 Zfpn1a4 Zinc finger protein, subfamily 1A, 4 (Eos) 1.517 – NM_019794 Dnaja2 DnaJ (Hsp40) homolog, subfamily A, member 2 1.516 – XM_621314 Dsp Desmoplakin 1.514 – NM_027777 Pex1 Peroxisome biogenesis factor 1 1.505 – NM_007666 Cdh6 Cadherin 6 1.502 – NM_133786 Smc4 Structural maintenance of chromosomes 4 1.501 – NM_012010 Eif2s3x Eukaryotic translation initiation factor 2, subunit 3 1.491

	Table E3

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Significant gene ontologies and GSEA categories identified by means of SAFE analysis in LVH regression (excluding those identified in LVH progression)

GO categories GSEA categories GO:0015629 Actin cytoskeleton Actin pathway GO:0007411 Axon guidance Adrenergic GO:0003824 Catalytic activity Akt pathway GO:0006968 Cellular defense response Alk pathway GO:0004197 Cysteine-type endopeptidase activity Bcl2 family and reg network GO:0004519 Endonuclease activity Carm-er pathway GO:0005789 Endoplasmic reticulum membrane Cell cycle GO:0016251 RNA polymerase II transcription factor Creb pathway GO:0004364 Glutathione transferase activity Death pathway GO:0006811 Ion transport Differentiation pathway in PC12 cells GO:0007254 JNK cascade Erk5 pathway GO:0000287 Magnesium ion binding G I pathway GO:0006120 Mitochondrial electron transport, NADH to ubiquinone Glutamine down GO:0005554 Molecular function unknown il2rb pathway GO:0008137 NADH dehydrogenase (ubiquinone) IL-4 receptor in B lymphocytes GO:0003954 NADH dehydrogenase activity INS GO:0045786 Negative regulation of cell cycle IL-4 pathway GO:0009968 Negative regulation signal transduction MAPK pathway GO:0030182 Neuron cell differentiation Monocyte pathway GO:0005643 Nuclear pore mRNA processing GO:0000786 Nucleosome mRNA splicing GO:0005634 Nucleus mtor pathway GO:0016491 Oxidoreductase activity p53 hypoxia pathway GO:0003755 Peptidyl-prolyl cis-transisomerase p53 signaling GO:0007204 Positive regulation of cytosolic [Ca++] Phosphoinositide-3-kinase pathway GO:0006470 Protein amino acid dephosphorylation PIP3 signaling in B lymphocytes GO:0006412 Protein biosynthesis Protein modification GO:0006457 Protein folding Ptdins pathway GO:0004722 Protein serine/threonine phosphatase Pyruvate metabolism GO:0004725 Protein tyrosine phosphatase activity Rarrxr pathway GO:0016567 Protein ubiquitination RNA polymerase GO:0015992 Proton transport Stress pathway GO:0008217 Regulation of blood pressure Tid pathway GO:0042127 Regulation of cell proliferation Wnt pathway GO:0006446 Regulation of translational initiation GO:0006950 Response to stress GO:0005840 Ribosome GO:0003723 RNA binding GO:0008380 RNA splicing GO:0006814 Sodium ion transport GO:0030528 Transcription regulator activity GO:0003743 Translation initiation factor activity GO:0006512 Ubiquitin cycle GO:0000151 Ubiquitin ligase complex GO:0006511 Ubiquitin-dependent protein catabolism GO:0004842 Ubiquitin-protein ligase activity GO:0030018 Z disc GO:0008270 Zinc ion binding

GSEA, Gene set enrichment analysis; SAFE, Significance Analysis of Functional Expression; LVH, left ventricular hypertrophy; GO, gene ontology.

	Table E4

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

Significant gene ontologies and GSEA categories identified by means of SAFE analysis in both LVH progression and regression

GO:0005516 Calmodulin binding GO:0003677 DNA binding GO:0005874 Microtubule GO:0005762 Mitochondrial large ribosomal subunit GO:0003704 Specific RNA polymerase II transcription factor activity GO:0004221 Ubiquitin thiolesterase activity

GSEA, Gene set enrichment analysis; SAFE, Significance Analysis of Functional Expression; LVH, left ventricular hypertrophy; GO, gene ontology.

	Acknowledgments

 
We thank Monte Willis, MD, PhD, for his critical comments; David Threadgill, PhD, for his assistance with experimental design and interpretation; and Margaret Alford Cloud for her editorial assistance.

	Footnotes

 
Supported by grants from the Thoracic Surgery Foundation for Research and Education (WES, CHS), the American Heart Association (PCC), the National Institutes of Health (CP), and the American College of Surgeons (CHS).

	References

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

 

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	References

Top Abstract Introduction Materials and Methods Results Discussion Materials and Methods Figure E1 Table E1 Table E2.A Table E2.B Table E2.C Table E3 Table E4 References References

1. Stansfield WE, Rojas M, Corn D, Willis M, Patterson C, Smyth SS, et al. Characterization of a model to independently study regression of ventricular hypertrophy. J Surg Res 2007;142:387-393.[Medline]
2. Abramoff MD, Magelhaes PJ, Ram SJ. Image processing with Image. J. Biophotonics Int 2004;11:36-42.
3. Smyth GK. Bioinformatics and computational biology solutions using R and Bioconductor: Limma: linear models for microarray data. In: Gentleman RCV, Huber W, Irizarry R, Dudoit S, editors. Statistics for biology and health. Vol 19. New York: Springer; 2005. pp. 397-420.
4. Dennis Jr. G, Sherman BT, Hosack DA, Yang J, Gao W, Lane H, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003;4:P3.[Medline]
5. Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005;21:1943-1949.[Abstract/Free Full Text]

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