Integrative Analysis of the Mitochondrial Proteome in Yeast

Holger Prokisch1,2*, Curt Scharfe3*, David G. Camp II4*, Wenzhong Xiao3*, Lior David3, Christophe Andreoli1, Matthew E. Monroe4, Ronald J. Moore4, Marina A. Gritsenko4, Christian Kozany1, Kim K. Hixson4, Heather M. Mottaz4, Hans Zischka1, Marius Ueffing1, Zelek S. Herman3, Ronald W. Davis3, Thomas Meitinger1,2, Peter J. Oefner3, Richard D. Smith4, and Lars M. Steinmetz

1Institute of Human Genetics, GSF National Research Center for Environment and Health, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
2Institute of Human Genetics, Technical University of Munich, D-81675 Munich, Germany, 3Stanford Genome Technology Center, Department of Biochemistry, Stanford University School of Medicine, 855 California Avenue, Palo Alto, California 94304, USA
4Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
*These authors contributed equally to this work

§Present address: European Molecular Biology Laboratory, Meyerhofstraße 1, D-69117 Heidelberg, Germany

PLoS Biology2(6)795-804 (June 2004)

In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.

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