Exp Clin Endocrinol Diabetes 2008; 116 - T3
DOI: 10.1055/s-0028-1096324

Proteomic strategies for biomarker discovery – from differential expression to isoforms to pathways

C Turck 1
  • 1Proteomics and Biomarkers, Max Planck Institute of Psychiatry, Munich, Germany

Currently diagnosis of affective disorders is mainly based on categorizing the signs and symptoms of the syndrome. The research of the Proteomics and Biomarkers group is aimed at the identification of markers that can categorize subsets of subjects in a more consistent manner than is presently achievable. This will allow a more precise definition and categorization of affective disorders and in turn facilitate investigations of the pathogenesis of the diseases and enhance our ability for treatment. Proteomic technologies promise to be of great value in molecular medicine, particularly in the detection and discovery of disease markers. The proteome is thought to be directly related to the phenotype of an organism and hence protein profiling will result in the most precise understanding of disease mechanisms as well as the molecular effects of drugs. Our biomarker detection efforts range from classical proteomics approaches such as quantitative mass spectrometry of brain tissue and body fluid proteins to phage display screens with cerebrospinal fluid antibodies. A particular focus of our research efforts is the use of animal models that represent selected endophenotypes characteristic for the respective clinical phenotype in humans. Classical proteomics approaches have resulted in a limited number of biomarkers in a mouse model for trait anxiety. A more comprehensive and sensitive proteomics platform that is based on metabolic labeling of mouse models with stable isotopes allows a precise relative protein quantitation by mass spectrometry. At the same time the method can monitor the metabolic activity of individual brain proteins. Proteomic biomarker discovery reveals pathways that are pertinent to the pathobiology of affective disorders. Supported by the Max Planck Society, BMBF National Genome Research Network, BMBF QuantPro.