4.7 Article

A high-throughput approach for subcellular proteome - Identification of rat liver proteins using subcellular fractionation coupled with two-dimensional liquid chromatography tandem mass spectrometry and bioinformatic analysis

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MOLECULAR & CELLULAR PROTEOMICS
卷 3, 期 5, 页码 441-455

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AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.M300117-MCP200

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Four fractions from rat liver ( a crude mitochondria ( CM) and cytosol ( C) fraction obtained with differential centrifugation, a purified mitochondrial ( PM) fraction obtained with nycodenz density gradient centrifugation, and a total liver (TL) fraction) were analyzed with two-dimensional liquid chromatography tandem mass spectrometry analysis. A total of 564 rat proteins were identified and were bioinformatically annotated according to their physicochemical characteristics and functions. While most extreme alkaline ribosomal proteins were identified in the TL fraction, the C fraction mainly included neutral enzymes and the PM fraction enriched alkaline proteins and proteins with electron transfer activity or oxygen binding activity. Such characteristics were more apparent in proteins identified only in the TL, C, or PM fraction. The Swiss-Prot annotation and the bioinformatic prediction results proved that the C and PM fractions had enriched cytoplasmic or mitochondrial proteins, respectively. Combination usage of subcellular fractionation with two-dimensional liquid chromatography tandem mass spectrometry was proved to be a high-throughput, sensitive, and effective analytical approach for subcellular proteomics research. Using such a strategy, we have constructed the largest proteome database to date for rat liver ( 564 rat proteins) and its cytosol ( 222 rat proteins) and mitochondrial fractions ( 227 rat proteins). Moreover, the 352 proteins with Swiss-Prot subcellular location annotation in the 564 identified proteins were used as an actual subcellular proteome dataset to evaluate the widely used bioinformatics tools such as PSORT, TargetP, TMHMM, and GRAVY.

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