Microarray analysis of gene expression after transverse aortic constriction in mice.


Cardiac hypertrophy is a compensatory response initially beneficial to heart function but can ultimately lead to cardiac decompensation. It is an integrated process involving multiple cellular signaling pathways and their cross talk. Microarray GeneChip technology is a powerful new tool to identify gene expression profiles of cardiac hypertrophy. To identify well-characterized as well as novel adaptive mechanisms, we utilized a murine model of compensated pressure overload hypertrophy (transverse aortic constriction, TAC). At 48 h, 10 days, and 3 wk, hearts were harvested and total RNA hybridized to Affymetrix U74Av2 GeneChips, which contain a 12,488-gene/EST probe set. Verification of gene expression was performed by SYBR quantitative real-time RT-PCR (QRT-PCR) for selected genes. A rigorous evaluation of the adequacy of the control condition was also performed. For statistical analysis we generated a four-step filtering criteria. Our results show an upregulation of 38 genes (48 h), 269 genes (10 days), and 203 genes (3 wk) and downregulation of 15 genes (48 h), 160 genes (10 days), and 124 genes (3 wk). Transcripts differentially expressed after TAC were categorized into 12 functional groups and revealed the presence of several intriguing transcripts, e.g., cell proliferation-related Ki-67 and several apoptosis-related genes. Overall changes in QRT-PCR were in accordance with GeneChip data, with the highest correlation for genes with the largest up- or downregulation with TAC. Thus TAC results in altered expression of genes in several pathways regulating both cardiac structure and function. However, for in vivo gene microarray experiments, it is critical to define adequate controls, perform rigorous statistical analysis, and provide validation by alternative methods.

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@article{Zhao2004MicroarrayAO, title={Microarray analysis of gene expression after transverse aortic constriction in mice.}, author={Mingming Zhao and Amy Chow and Jennifer Powers and Giovanni Fajardo and Daniel M Bernstein}, journal={Physiological genomics}, year={2004}, volume={19 1}, pages={93-105} }