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Protein sets define disease states and predict in vivo effects of drug treatment

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Meierhofer,  David
Mass Spectrometry (Head: David Meierhofer), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Weidner,  Christopher
Nutrigenomics and Gene Regulation (Sascha Sauer), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Hartmann,  Ludger
Animal Unit (Head: Ludger Hartmann), Scientific Service (Head: Manuela B. Urban), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Han,  Chung-Ting
Nutrigenomics and Gene Regulation (Sascha Sauer), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Sauer,  Sascha
Nutrigenomics and Gene Regulation (Sascha Sauer), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Meierhofer, D., Weidner, C., Hartmann, L., Mayr, J. A., Han, C.-T., Schroeder, F. C., et al. (2013). Protein sets define disease states and predict in vivo effects of drug treatment. Molecular and Cellular Proteomics, 12(7), 1965-1979. doi:10.1074/mcp.M112.025031.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0019-10F8-C
Abstract
Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousand proteins and observed significant changes in protein pathway (dys-) regulated in diet-induced obesity mice. Analysis of the expression and posttranslational modifications of proteins in various peripheral metabolic target tissues including adipose, heart and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high fat diet and medication with two anti-diabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated for example early on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates.