Abstract 1572: Identification of Candidate Genes Within the Dyscalc1 Region Using a Chromosomal Segment Expression Profile
Introduction Cardiovascular calcification is a hallmark of necrosis at sites of atherosclerosis and myocardial infarction. In mice this phenotype was previously reported as a spontaneously occuring complex trait controlled by a major locus named Dyscalc1. The Dyscalc1 locus on mouse chromosome 7 is a gene-rich region containing about 160 candidate genes.
Aim: Lacking a standardized method to determine candidate genes for QTL loci, we developed a novel RT-PCR-based strategy that combines tissue-specific gene expression and differential gene expression profiles to identify genes within the Dyscalc1 region that might contribute to dystrophic cardiac calcification (DCC) as response to myocardial injury.
Methods: We designed primer pairs for each of the 160 genes containing within the Dyscalc1 locus using sequences from (www.ensembl.org/Musmusculus/) and used freeze-thaw injury to enhance myocardial calcification in DCC-susceptible C3H/He and DCC-resistant C57BL/6 mice (control strain). A relative real time RT-PCR method was employed to test the expression level of these genes on injured and healthy myocardium of both strains.
Results: Using tissue-specific RT-PCR, we tested all genes for expression in myocardial tissue and found that 35% (56/160) of the genes were not expressed in myocardium among which 29% (47/160) showed expression on other adult mouse tissues but 6% failed completely to amplify specific PCR products at least on these tested tissues. By analyzing differential gene expression between injured and non-injured myocardial tissue, about 50% (79/160) of the genes remained unregulated as response to injury. Interestingly by combining results from both methods, only 15% (24/160) of the genes were both expressed in the heart and differentially regulated for further investigation. The genes expressed differentially encode for factors present in cell homeostasis (n=7), extracellular matrix (n=5), intracellular signal transduction (n=3), cell proliferation (n=2), inflammation (n=2), DNA repair (n=2), apoptosis (n=1), as well as 2 genes with unknown function.
Conclusion Our data demonstrate that combining expression and regulation analysis is a powerful tool for identifying candidate genes encoding QTL for complex traits.