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Molecular networks in Dahl salt-sensitive hypertension based on transcriptome analysis of a panel of consomic rats

¹ Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
² Department of Functional Genomics, The Institute for Genomic Research, Rockville, Maryland
³ Biotechnology and Biomedical Engineering Center, Medical College of Wisconsin, Milwaukee, Wisconsin
⁴ Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, Wisconsin

The Dahl salt-sensitive (SS) rat is a widely used model of human salt-sensitive hypertension and renal injury. We studied the molecular networks that underlie the complex disease phenotypes in the SS model, using a design that involved two consomic rat strains that were protected from salt-induced hypertension and one that was not protected. Substitution of Brown Norway (BN) chromosome 13 or 18, but not 20, into the SS genome was found to significantly attenuate salt-induced hypertension and albuminuria. Gene expression profiles were examined in the kidneys of SS and consomic SS-13^BN, SS-18^BN, and SS-20^BN rats with a total of 240 cDNA microarrays. The substituted chromosome was overrepresented in genes differentially expressed between a consomic strain and SS rats on a 0.4% salt diet. F5, Serpinc1, Slc19a2, and genes represented by three other expressed sequence tags (ESTs), which are located on chromosome 13, were found to be differentially expressed between SS-13^BN and all other strains examined. Likewise, Acaa2, B4galt6, Colec12, Hsd17b4, and five other ESTs located on chromosome 18 exhibited expression patterns unique to SS-18^BN. On exposure to a 4% salt diet, there were 184 ESTs in the renal cortex and 346 in the renal medulla for which SS-13^BN and SS-18^BN shared one expression pattern, while SS and SS-20^BN shared another, mirroring the phenotypic segregation among the four strains. Molecular networks that might contribute to the development of Dahl salt-sensitive hypertension and albuminuria were constructed with an approach that merged biological knowledge-driven analysis and data-driven Bayesian probabilistic analysis.

genomics; systems biology; kidney; blood pressure; diet

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