Molecular correlates of trait anxiety: expanding biomarker discovery from protein expression to turnover

Zhang, Yaoyang (2010): Molecular correlates of trait anxiety: expanding biomarker discovery from protein expression to turnover. Dissertation, LMU München: Fakultät für Biologie

Abstract

Depression and anxiety disorders affect a great number of people in the world. Although remarkable efforts have been devoted to understanding the clinical and biological basis of these disorders, progress has been relatively slow. Furthermore, no laboratory test currently is available for diagnosis of anxiety and depression. These disorders are mainly diagnosed empirically on the basis of a doctor’s personal observations and experiences. Hence, discovery of biomarkers for these psychiatric disorders deserves much scientific attention. The animal models investigated in the present study represent high, low, and normal anxiety-like phenotypes (HAB, LAB, NAB) and were established by selective inbreeding. To compare the protein expression levels between different animal lines, living animals were metabolically labeled with the 15N stable isotope and then investigated by quantitative mass spectrometry. In addition, metabolomic studies were performed to shed light on pathways affected in the trait anxiety mouse model. A number of proteins and metabolites were found to be significantly altered in their expression levels between the three mouse lines. Both protein and metabolite information was used for in silico network analysis to find pathways pertinent to the pathobiology of anxiety disorders. Another focus of this thesis was the development of new methodologies for the metabolic labeling approach. This includes improved identification of labeled proteins and the analysis of protein turnover. The latter represents another important aspect in the field of proteomics and adds a dynamic dimension to the field. The method allows the detection of protein expression alterations at a much earlier stage. The newly developed ProTurnyer (Protein Turnover Analyzer) algorithm is able to calculate in a high throughput manner turnover for individual proteins.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Themengebiete: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
• Fakultäten: Fakultät für Biologie
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 2. Dezember 2010
• 1. Bericht­erstatter:in: Landgraf, Rainer