Publication Details

Frank, E. T., Kessler, M. S., Filiou, M., Zhang, Y., Maccarrone, G., Reckow, S., Bunck, M., Heumann, H., Turck, C. W., Landgraf, R. & Hambsch, B. (2009). Stable isotope metabolic labeling with a novel 15N-enriched bacteria diet for improved proteomic analyses of mouse models for psychopathologies. Public Library of Science ONE, 4 (11), 1-9.


The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed 15N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from 15N-labeled vs. 14N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals’ psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope 15N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed 15N bacteria diet provides researchers a novel tool to discover diseaserelevant protein expression differences in mouse models using quantitative mass spectrometry.



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