Surgical Model and Experimental Design
In accordance with both an institutionally approved IACUC protocol and NIH guidelines, 10-week-old C57Bl6 male mice were randomly assigned to one of 4 groups: Sham (S), Band (B), Sham Deband (SDB), and Deband (DB). The 60 mice enrolled were partitioned as follows: 10 S, 15 B, SDB 13, and DB 21. Mice in the Sham and Band groups underwent minimally invasive transverse aortic banding as previously described.10
Briefly, animals were anesthetized using inhaled isoflurane by 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 sham and band groups was that in the sham animals, the constrictive band was not tightened. Adequate placement of the band was verified by evaluation of carotid Dopplers both before and after placement of the aortic band. Adequate banding was accepted when the Doppler velocity ratio doubled from right to left carotid arteries. Animals in the Deband and Sham Deband had initial procedures identical to those for the Band and Sham animals, respectively. At 4 weeks, the aortic band was removed, heretofore referred to as debanded.10
Efficiency of debanding was verified by carotid Doppler with normalization of carotid velocities. Deband and Sham Deband animals were sacrificed one week following the deband procedure for all evaluations except histology, where animals were sacrificed both at 1 and 4 weeks after debanding.
Transthoracic echocardiography was performed using the Vevo 660 High Resolution Biomicroscopy System equipped with a 30 Mhz transducer (Visual Sonics, Toronto, CA). During examination, mice were anesthetized with 1–1.5% inhaled isoflurane. Depth of anesthesia was standardized by recording images at heart rates of 480–520 bpm. Images were recorded in all animals before surgery, 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 exams and measurements.
At time of sacrifice, 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 histological analysis were perfused with PBS followed by 10% Formalin, fixed overnight in Formalin, and then processed for histological analysis using periodic acid Schiff staining.14
Cardiomyocyte cross-sectional area was measured using ImageJ (v. 1.38j).15
RNA Preparation, MicroArray Process, and Real-Time PCR
To minimize variation between mice within a tested group and to enhance detection of variation between experimental groups, semi-pooled groups of mice were created. 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 LVmass of >20%) and regression with debanding (decrease in LVmass 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 0.5ml of ice cold Trizol solution (Sigma) using a bead mill homogenizer (Retsch, Newtown, PA). RNA was then isolated using 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 ≥1.95. An Agilent BioAnalyzer 2100 instrument was used to verify RNA integrity for all samples. 500 ng of total RNA was labeled with Cyanine-5 CTP in a T-7 transcription reaction 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 using Taqman primers and probes (Applied Biosystems International (ABI), Foster City, CA).
Protein fractions were isolated in ice cold lysis buffer during dounce homogenization. Concentrations were determined using the Bradford assay. Protein fractions were denatured in loading buffer. 30 μg 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 Plk1 and Hpcal1. GAPDH was used as the loading control.
All physiologic data are presented as mean ± standard error (SE) except where noted. Real-time PCR data were log transformed prior to comparison. All comparisons of physiologic data were performed 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 <30 aFU in either red or green channels. Probes were removed if data was not present in at least 9 of 12 samples. Missing data points were imputed (to facilitate further statistical comparisons) using the k nearest neighbors algorithm (k=2). Samples were then standardized (μ=0, σ=1) using a custom Perl script (ActiveState Perl 5.8.1, build 807, release date 2003-11-6). Differences in expressed genes were validated using significance analysis of microarrays (SAM) 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 vs.
Sham, Deband vs.
Sham Deband, and (Deband –Sham Deband) vs.
(Band – Sham). These contrasts were executed using custom scripts written in the R statistical language and environment (“R”; Version 2.2.1, build r36812, release date 2005-12-20). Genes were identified as significant in each contrast with the criteria P ≤0.01 and mean fold change ≥ 1.2.
Unsupervised gene and array clustering was performed for each comparison: Band vs. Sham, Deband vs. Band, and Deband vs. Sham Deband. Data for each gene being compared were filtered for 3 instances of mean fold change ≥2. Data were further adjusted by median centering the genes. Average linkage hierarchical clustering using centered correlation similarity metric for both genes and arrays was performed using Gene Cluster 3.0 (Michiel de Hoon, University of Tokyo, Human Genome Center, 2006). Data were then visualized using Java TreeView (version 1.0.13).
Gene ontologies of significant genes in each contrast were identified using the NIH curated DAVID (Database for Annotation, Visualization, and Integrated Discovery) 2007.17
To facilitate interpretation of gene ontology, functional clustering was performed and results were rank ordered by DAVID using 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 biological processes and functions that are most important (within the set of significant genes) to the biological phenomenon under study. Orthogonal filtering of data sets was performed using the Significance Analysis of Functional Expression (SAFE)18
using custom R scripts. In this method, also called orthogonal filtering, all genes are first organized based on their gene ontologies, or GO 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 0.1 or less were identified in all contrasts.