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Physiol. Genomics 15: 105-114, 2003. First published July 15, 2003; doi:10.1152/physiolgenomics.00087.2003
1094-8341/03 $5.00
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Received 21 May 2003; accepted in final form 11 July 2003.
Physiological Genomics 15:105-114 (2003)
1094-8341/03 $5.00 © 2003 American Physiological Society

Differential myocardial gene expression in the development and rescue of murine heart failure

Burns C. Blaxall 1, Rainer Spang 2, Howard A. Rockman 3 and Walter J. Koch 1

1 Departments of Surgery
2 Statistics
3 Medicine, Duke University Medical Center, Durham, North Carolina 27710

Numerous murine models of heart failure (HF) have been described, many of which develop progressive deterioration of cardiac function. We have recently demonstrated that several of these can be "rescued" or prevented by transgenic cardiac expression of a peptide inhibitor of the ß-adrenergic receptor kinase (ßARKct). To uncover genomic changes associated with cardiomyopathy and/or its phenotypic rescue by the ßARKct, oligonucleotide microarray analysis of left ventricular (LV) gene expression was performed in a total of 53 samples, including 12 each of Normal, HF, and Rescue. Multiple statistical analyses demonstrated significant differences between all groups and further demonstrated that ßARKct Rescue returned gene expression toward that of Normal. In our statistical analyses, we found that the HF phenotype is blindly predictable based solely on gene expression profile. To investigate the progression of HF, LV gene expression was determined in young mice with mildly diminished cardiac function and in older mice with severely impaired cardiac function. Interestingly, mild and advanced HF mice shared similar gene expression profiles, and importantly, the mild HF mice were predicted as having a HF phenotype when blindly subjected to our predictive model described above. These data not only validate our predictive model but further demonstrate that, in these mice, the HF gene expression profile appears to already be set in the early stages of HF progression. Thus we have identified methodologies that have the potential to be used for predictive genomic profiling of cardiac phenotype, including cardiovascular disease.

genomics; microarrays




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