Transcriptome profiling and network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension

Physiol Genomics. 2010 Sep;42A(1):24-32. doi: 10.1152/physiolgenomics.00010.2010. Epub 2010 Jun 29.


Hypertension is a condition with major cardiovascular and renal complications, affecting nearly a billion patients worldwide. Few validated gene targets are available for pharmacological intervention, so there is a need to identify new biological pathways regulating blood pressure and containing novel targets for treatment. The genetically hypertensive "blood pressure high" (BPH), normotensive "blood pressure normal" (BPN), and hypotensive "blood pressure low" (BPL) inbred mouse strains are an ideal system to study differences in gene expression patterns that may represent such biological pathways. We profiled gene expression in liver, heart, kidney, and aorta from BPH, BPN, and BPL mice and determined which biological processes are enriched in observed organ-specific signatures. As a result, we identified multiple biological pathways linked to blood pressure phenotype that could serve as a source of candidate genes causal for hypertension. To distinguish in the kidney signature genes whose differential expression pattern may cause changes in blood pressure from those genes whose differential expression pattern results from changes in blood pressure, we integrated phenotype-associated genes into Genetic Bayesian networks. The integration of data from gene expression profiling and genetics networks is a valuable approach to identify novel potential targets for the pharmacological treatment of hypertension.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Aorta / metabolism
  • Blood Pressure / genetics
  • Disease Models, Animal
  • Gene Expression Profiling*
  • Hypertension / genetics*
  • Kidney / metabolism
  • Liver / metabolism
  • Male
  • Mice
  • Mice, Inbred Strains
  • Models, Biological
  • Myocardium / metabolism*
  • Oligonucleotide Array Sequence Analysis