Proteomics Strategies in Cardiovascular Research

Hans Reinhard Zerkowski, Thomas Grussenmeyer, Peter Matt, Martin Grapow, Stefan Engelhardt, Ivan Lefkovits

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations

Abstract

Cardiovascular research of the past decades dealt with classical pathophysiological descriptions, then shifted toward the identification of relevant receptors, and then proceeded to the analysis of signal transduction pathways. Most recently, hand in hand with the achievements of the human genome project, the research has gone down the road toward molecular biological "disease gene(s) mapping". The application of proteome research will attempt to close the gap between genomic (and genetic) analysis and the physiological research. The rich source of heart surgery specimens represents an excellent starting point in data acquisition of proteomic context. Furthermore, animal models of cardiovascular diseases and deficiencies are considered, and will be explored. Examples of results from feasibility studies are given, with the emphasis on quantitative evaluation of proteomic components, hoping to discover co-regulated sets of proteins that are involved in any particular disease state. Identification of new, not yet discovered proteins will be pursued, though the emphasis of this work will be on the definition of characteristic sets of expressed proteins, which in turn might be able to delimit the state of disease and prognosis of therapy outcome. Besides the systematic issues, this paper refers to a number of methodological questions, like the comparison of the proteins detected by staining procedures and proteins detected in models in which biosynthetic labeling is applicable.

Original languageEnglish
Pages (from-to)200-208
Number of pages9
JournalJournal of Proteome Research
Volume3
Issue number2
DOIs
StatePublished - Mar 2004
Externally publishedYes

Keywords

  • 2D gel electrophoresis
  • Aortic disease
  • Blood sample
  • Cardiac tissue
  • Charge separation
  • Heart disease
  • Image analysis
  • Reverse remodeling
  • Size separation
  • Systems biology

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