Gene Expression Profiling In Rat Smooth Muscle Cells Reveals Novel Regulatory Pathways Modulated by Rapamycin and Paclitaxel
P Charles, S Mapara, J Parker, R Herrmann, C Patterson
P Charles, S Mapara, J Parker, R Herrmann, C Patterson. Gene Expression Profiling In Rat Smooth Muscle Cells Reveals Novel Regulatory Pathways Modulated by Rapamycin and Paclitaxel. The Internet Journal of Genomics and Proteomics. 2006 Volume 3 Number 1.
Drug-eluting stents 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. We have used gene expression profiling to characterize the patterns of gene regulation within cultures of rat aortic smooth muscle cells treated with rapamycin or paclitaxel. Although both drugs are used to arrest smooth muscle proliferation when delivered by drug eluting stents, there were major differences in gene regulatory responses. Paclitaxel caused marked changes in expression of tubulin and microtubule-related genes. In contrast, the gene expression responses to rapamycin demonstrated dramatic effects on the transcription of genes involved in cell signaling and macromolecular biosynthesis. 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.
The advent of drug-eluting stent (DES) technology has dramatically altered the care and management of patients with cardiovascular disease [1,2,3]. Percutaneous interventions with DES can delay (and in some cases, prevent) the need for more invasive coronary artery bypass grafting by removing obstruction and reducing the incidence of in-stent restenosis. Nevertheless, questions remain about the optimal anti-proliferative drugs for delivery, the appropriate choice of drug/stent design based on clinical presentation of the patient, and in which settings DES is superior to bypass grafting . Although drug-eluting vascular stents containing rapamycin (RAPA) or paclitaxel (PTXL) have revolutionized the treatment of coronary artery disease, a complete understanding of the mechanisms of action of these drugs is necessary to fully realize the benefits of this new technology.
Medial smooth muscle proliferation and migration in response to stent-induced injury after DES deployment are generally considered the pathophysiological underpinnings of restenosis . PTXL is a semisynthetic diterpenoid that binds microtubules and prevents their depolymerization and mitotic spindle formation, blocking the cell cycle at the G1 and G2/M junctions . RAPA is a macrocyclic lactone that engages FK506-binding protein 12, inhibiting the mTORC1 complex, effectively arresting the cell at the G1 to S phase transition [6, 7]. Both are inhibitors of smooth muscle cell proliferation over a wide range of doses, and now have a proven track-record in human patients when delivered
In this report we describe the use of gene transcription profiling to explore the patterns of gene expression within an
Previous work from this laboratory has shown common effects of both drugs in an
Gene transcription profiling of drug effects on cells, tissues, organs or whole organisms has the potential to provide tremendous amounts of data about the molecular changes induced by the pharmacotherapy. One can obtain a gestalt of the response to a drug when tens of thousands of genes are interrogated at once. This allows an unbiased view of the totality of the impact of the agent on all of the systems of a cell. While pharmacogenomics is in its infancy, it is poised to dramatically alter the way in which new therapeutics are developed, studied and approved .
Gene expression profiling has allowed us to define patterns of expression that may have important implications for the use of cytostatic drugs delivered
The gene lists identified by EDGE for each comparison group were subsequently subjected to analysis by GATHER  and Chilibot . Gather provides a statistical functional analysis of genes, giving a variety of outputs. These analyses include Gene Ontology, MEDLINE Words, MeSH, KEGG Pathway, Protein Binding, Literature Net, miRNA, TRANSFAC, and ChromosomeGenes. We focused on the output from the Gene Ontology (GO) assignments, looking for functional relationships among the genes modulated by drug treatment.
PTXL modulates genes in RASMC that are, for the most part, involved in the production and maintenance of the cytoskeleton, consistent with its primary mode of action. A list of genes that were differentially expressed in response to PTXL treatment was identified by EDGE. Notably, the vast majority of genes were down-regulated. As expected, various isoforms of tubulin were the most significantly up-regulated genes, and displayed a dose-dependent expression profile (
Analysis of the patterns of gene expression following RAPA treatment in the
Our results show striking similarities in the patterns of transcriptional activation induced by both RAPA and PTXL. This observation, while not entirely unexpected in light of the utility of these drugs in preventing restenosis, points to the inter-relatedness of a variety of cellular pathways that are key to the proliferation and migration of vascular smooth muscle cells. Of particular interest are the patterns of transcriptional regulation that are common to both drugs, and those that are different. The implications of this data are that the therapeutic value of each drug may vary based upon the biological status of the patient. Further analysis of the contrasting mechanisms of action of these two drugs, guided by our initial genomic screen, may point out specific indications for each drug, improving the therapeutic outcome.
Lists of differentially expressed genes were identified using the statistical analysis of microarray algorithm [22,23,24,25,26] (“SAM”, Version 2.21, release date 2005-8-24. Typical false discovery rate of < 5%), EDGE  (significance threshold of p<0.01), and custom R scripts written in our laboratory. Unsupervised, semi-supervised and supervised clustering analysis was performed on genelists essentially as described  using Cluster (Version 2.11, http://rana.lbl.gov/EisenSoftware.htm). Heatmaps of cluster analyses were visualized with JavaTreeView (Version 1.0.12, release date 2005-3-14; http://sourceforge.net/projects/jtreeview/).
High-level pathway analysis and mapping to gene ontology (“GO”, http://www.geneontology.org/) categories were performed on genelists using the Expression Analysis Systematic Explorer (“EASE” Version 1.21, released date 2003-6-9; http://david.niaid.nih.gov/david/ease.htm) and GATHER  (http://gather.genome.duke.edu) analysis environments. The Chilibot  (http://www.chilibot.net/) contextual datamining algorithm was used for text mining of the PubMED database with selected genes and keywords.
Micorarray experiments were performed in compliance with the minimum information about a microarray experiment (MIAME) guidelines . The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE 5337.
PCC and SM carried out the cell culture, performed the gene transcription profiling, and drafted the manuscript. JSP and PCC participated in the design of the study, performed the statistical analysis and authored the Perl and R scripts. CP and RAH conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
This work was funded in part by NIH grants HL 61656, HL 03658 and HL 072347 (to CP) and by funds from Boston Scientific, Natick, MA. PCC is a recipient of an American Heart Association Scientist Development Grant (0635100N). CP is an Established Investigator of the American Heart Association and a Burroughs Wellcome Fund Clinician Scientist in Translational Research.
Peter C. Charles; Carolina Cardiovascular Biology Center, CB# 7126, School Of Medicine, University of North Carolina, Chapel Hill, North Carolina, 27599-7126. 011.919.843.1610 firstname.lastname@example.org