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

摘要

Liang M, Lee NH, Wang H, Greene AS, Kwitek AE, Kaldunski ML, Luu TV, Frank BC, Bugenhagen S, Jacob HJ, Cowley AW, Jr. Molecular networks in Dahl salt-sensitive hypertension based on transcriptome analysis of a panel of consomic rats. Physiol Genomics 34: 54 -64, 2008. First published April 22, 2008; doi:10.1152/physiolgenomics.00031.2008.-The Dahl salt-sensi-tive (SS) rat is a widely used model of human salt-sensitive hyper-tension 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, Serpinc 1 , 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, Colecl2, 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 net-works 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 Bayes-ian probabilistic analysis.