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Title: Gene Expression Profiling In Rat Smooth Muscle Cells Modulated by Rapamycin and Paclitaxel.      
Transcriptome or Gene expression
Background: Drug-eluting stents (DES) have rapidly become a standard therapy for treatment of obstructive coronary artery disease. Differences exist in therapeutic responses to different DES formulations, and mechanisms to predict drug responses in the preclinical setting have not been standardized. Results: We have used gene expression profiling to characterize the patterns of gene regulation within cultures of rat aortic smooth muscle cells (RASMC) treated with rapamycin or paclitaxel, the two drugs widely used in commercially available DES. These studies further validate the use of the in vitro RASMC culture system as a model of insulin resistance. Gene expression profiles easily distinguish RASMC grown under normal or high glucose conditions, and we therefore followed this approach in order to understand the activity of these drugs under conditions that approximate those seen in type 2 diabetics. Remarkably, although both drugs are used to arrest smooth muscle proliferation when delivered by DES, there were major differences in gene regulatory responses. Paclitaxel caused marked changes in expression of tubulin-related genes, and also caused striking changes in the transcription of VEGF, PDGF, JAG-1 and their respective receptors, suggesting an important effect on paracrine and autocrine response to mitogens. However, the gene expression signatures elicited by paclitaxel showed little variation under different cell culture conditions. In contrast, these gene expression responses to rapamycin varied considerably depending on the glycemic conditions of culture, and rapamycin had a dramatic dose- and metabolic status- dependent effect on the transcription of key members of the AKT signaling axis, providing a transcriptional explanation for the paradoxical proliferative effect of rapamycin at low dose in the setting of high glucose concentrations and insulin resistance. Conclusions: Gene expression signatures for drugs eluted from coronary stents vary dramatically in ways that correlate with known differences in biological activities of these drugs. Gene expression profiling may provide a useful preclinical method to characterize the activities of candidate drugs for stent impregnation and to understand their biological activities. Keywords: Cardiovascular disease, drug eluting stent, vascular smooth muscle, rapamycin, paclitaxel Overall design: Reagents: Paclitaxel, rapamycin, insulin, dimethyl sulfoxide and glucose were obtained from Sigma-Aldrich (St. Loius, MO, USA). Dulbecco’s Minimum Essential Medium (DMEM), Trypsin/ EDTA, antibiotics/antimycotics, tissue culture grade phosphate-buffered saline (PBS) and trypan blue were obtained from GibCO (Grand Island, NY, USA). Filtered, mycoplasma-/endotoxin-free fetal bovine serum (FBS) was purchased from Gemini Bioproducts (Woodland, CA, USA). RNeasy Mini Kits and Rnase-free DNase I were obtained from Qiagen Sciences (Maryland, USA). Microarray slides, hybridization chambers, hybridization buffer, Low Input Linear RNA Amplification/Labeling kits and free-radical scavenging wash buffer were obtained from Agilent Technologies (Palo Alto, CA, USA). Cyanine-5 and cyanine-3 CTP was purchased from Perkin Elmer/NEN (Wellesley, MA, USA). 20X standard saline citrate (SSC) buffer was obtained from the Promega Corporation (Madison, WI, USA). All other salts, buffers and detergents were obtained from Sigma-Aldrich (St. Louis, MO, USA). Cell culture: RASMC were explanted from aortas of 1 day old rat pups, purified and cultured essentially as previously described (10) Patterson (9). RASMC were maintained in DMEM containing 5 mM glucose, 10% FBS and an antibiotic/antimycotic cocktail (penicillin 100 IU/ml; streptomycin, 100 μg/ml; and amphotericin-B, 2 μg/ml) at 37°C in a 5% CO2 /100% humidity atmosphere. RASMC were used between passage level 8 and 11. Cells were plated into 60mm polystyrene dishes (Falcon cat num 35100) and grown to 85% confluence. Dishes of RASMC were quiessed for 72 hrs prior to stimulation or drug treatment by reduction of the FBS concentration to 0.1%. Normo- or hyper-glycemic conditions (5 or 25 mM glucose, respectively) were established in the final 24 hrs of quiessesence by re-feeding with medium containing 5mM or 25mM glucose (respectively) supplemented with 0.1% FBS and antibiotic/antimycotic cocktail. RASMC were stimulated with 100nM insulin (or PBS vehicle) for 30 min at 37°C prior to drug treatment. Un-stimulated and insulin stimulated cells grown under normal and high glucose conditions were treated with paclitaxel (0.01ng/mL, 1ng/mL, or 1000ng/mL final concentration), rapamycin (0.01ng/mL, 1ng/mL or 100ng/mL final concentration), or vehicle alone (dimethylsulfoxide, DMSO) for either 6 or 24 hours at 37°C prior to harvesting RNA. RNA extraction and microarray analysis: Total RNA was extracted from RASMC using the Qiagen RNeasy Minikit in accordance with the manufacturer’s instructions. RNA integrity was verified by assay on an Agilent BioAnalyzer 2100. Five hundred nanograms of RASMC 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 test samples was hybridized to Agilent G4130A Rat 22K microarray slides in the presence of equimolar concentrations of Cyanine-3 CTP labeled rat reference RNA prepared from pools of 1 day old rat pups(11). Statistical Methods: Microarray data (N=224 arrays) were loess normalized and genes were filtered for features having a normalized intensity of < 30 aFU in either channel and for <70% good data across the entire dataset. Missing data points were imputed using the k nearest-neighbors algorithm (three nearest neighbors). Principal component analysis (PCA) was used to identify eigenvectors in the data matrices that contributed to differences between the four replicate runs that were unrelated to the biological conditions under study (13), and were removed from the dataset using scripts written in the R Statistical Language and Environment (“R”; Version 2.2.1, build r36812, release date 2005-12-20. See supplemental materials for R scripts). The dataset was standardized with a custom Perl script (ActiveState Perl 5.8.1, build 807, release date 2003-11-6. See supplemental materials for Perl scripts). Lists of differentially expressed genes were identified using the statistical analysis of microarray algorithm (14-16) (“SAM”, Version 2.21, release date 2005-8-24. Typical false discovery rate of < 5%), two-tailed heteroscedastic T-Test (significance threshold of p<0.05), and custom R scripts written in our laboratory (see supplemental materials). Unsupervised, semi-supervised and supervised clustering analysis was performed on genelists essentially as described (17, 18) using Cluster (Version 2.11, Heatmaps of cluster analyses were visualized with JavaTreeView (Version 1.0.12, release date 2005-3-14; High-level pathway analysis and mapping to gene ontology (“GO”, categories were performed on genelists using the Expression Analysis Systematic Explorer (“EASE” Version 1.21, released date 2003-6-9; and PathArtTM (Jubilant Biosys, Version 2005 R3) analysis environments. The significance analysis of function and expression (“SAFE”(4)) algorithm was used for ab initio statistical analysis of the GO categories present within our dataset (Wilcoxon statistic significance threshold of p<0.05). The SAFE technique was extended to allow exporting of genelists and ontologies for heatmap visualizations of the expression patterns of genes present within the GO categories using customizations to the SAFE R scripting and Perl scripts (see supplemental materials).
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