Dataset Information

Comparative Genome-Wide Transcriptional Analysis of Bilateral Internal Mammary Arteries

ABSTRACT: In Coronary Artery Bypass Grafting (CABG), the combined use of Left or Right Internal Mammary Artery (LIMA or RIMA) -collectively known as Bilateral IMAs (BIMAs)- provides a survival advantage over the LIMA alone. Several studies analyzed the gene expression in LIMAs and other conduits, however they either used a candidate gene approach or analyzed a small number of samples. Additionally, RIMA has never been analyzed compared to LIMA. Here we report a genome-wide transcriptional analysis of BIMA to investigate the expression profile of these conduits in patients undergoing CABG. Marginal differences were reported between LIMA and RIMA (p <0.05) using a linear model for microarray data. Ingenuity Pathway Assist (IPA) analysis found no consistent set of over-represented pathways and no trends in patterns of gene expression. As expected, in comparing the BIMAs to the aorta, we found differences in pathways and processes associated with atherosclerosis, inflammation, and cell signaling. Although evidence in favor of the use of BIMA in CABG has been available for over a decade, their routine use in clinical practice remains very low accounting for only 4% of CABG procedures in the US. Despite differences in embryologic development, our genome-wide transcriptional analysis, show marginal differences between LIMA and RIMA. Taken together, clinical and genomic analyses provide evidences that could impact the independent or combined use of the BIMAs as a conduit in CABG.

ORGANISM(S): Homo sapiens

PROVIDER: GSE41036 | GEO | 2012/10/31

SECONDARY ACCESSION(S): PRJNA175597

REPOSITORIES: GEO

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Project description:In Coronary Artery Bypass Grafting (CABG), the combined use of Left or Right Internal Mammary Artery (LIMA or RIMA) -collectively known as Bilateral IMAs (BIMAs)- provides a survival advantage over the LIMA alone. Several studies analyzed the gene expression in LIMAs and other conduits, however they either used a candidate gene approach or analyzed a small number of samples. Additionally, RIMA has never been analyzed compared to LIMA. Here we report a genome-wide transcriptional analysis of BIMA to investigate the expression profile of these conduits in patients undergoing CABG. Marginal differences were reported between LIMA and RIMA (p <0.05) using a linear model for microarray data. Ingenuity Pathway Assist (IPA) analysis found no consistent set of over-represented pathways and no trends in patterns of gene expression. As expected, in comparing the BIMAs to the aorta, we found differences in pathways and processes associated with atherosclerosis, inflammation, and cell signaling. Although evidence in favor of the use of BIMA in CABG has been available for over a decade, their routine use in clinical practice remains very low accounting for only 4% of CABG procedures in the US. Despite differences in embryologic development, our genome-wide transcriptional analysis, show marginal differences between LIMA and RIMA. Taken together, clinical and genomic analyses provide evidences that could impact the independent or combined use of the BIMAs as a conduit in CABG. We selected 32 patients from whom we had frozen archival tissue from aorta, LIMA, and RIMA in the Cardiovascular Blood and Tissue Bank at the Valley Columbia Heart Center . The mammary and aortic tissues were harvested at time of CABG. The surgeon dissected the two most distal aspects of LIMA and RIMA segments and obtained a M-bM-^IM-%1cm sample for the tissue bank. The tissue sample was placed in a cryomold and processed as a M-bM-^@M-^\frozen sectionM-bM-^@M-^] specimen using standard OCT gel. Total RNA was extracted using Qiagen RNeasy Mini Kit, RNA integrity was assessed using the Agilent 2100 BioAnalyzer and RNA quantities and purity were determined using a NanoDrop Spectrophotometer. RNA samples were amplified using the NuGen Ovation RNA Amplification SystemM-BM- V2 The resulting cDNA was labeled and hybridized to the Affymetrix U133A 2.0 GeneChip. 73 arrays at the end.