Toward a comprehensive quantitative proteome database: protein expression map of lymphoid neoplasms by 2-D DIGE and MS

Authors

  • Kazuyasu Fujii,

    1. Proteome Bioinformatics Project, National Cancer Center Research Institute, Tokyo, Japan
    2. Department of Dermatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
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  • Tadashi Kondo Professor,

    Corresponding author
    1. Proteome Bioinformatics Project, National Cancer Center Research Institute, Tokyo, Japan
    • Proteome Bioinformatics Project, National Cancer Center Research Institute, 5–1-1 Tsukiji, Chuo-ku, Tokyo 104–0045, Japan Fax: +81-3-3097-5298
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  • Masayo Yamada,

    1. Proteome Bioinformatics Project, National Cancer Center Research Institute, Tokyo, Japan
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  • Keiji Iwatsuki,

    1. Department of Dermatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
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  • Setsuo Hirohashi

    1. Proteome Bioinformatics Project, National Cancer Center Research Institute, Tokyo, Japan
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Abstract

Using 2-D DIGE, we constructed a quantitative 2-D database including 309 proteins corresponding to 389 protein spots across 42 lymphoid neoplasm cell lines. The proteins separated by 2-D PAGE were identified by MS and assigned to the expression data obtained by 2-D DIGE. The cell lines were categorized into four groups: those from Hodgkin's lymphoma (HL) (4 cell lines), B cell malignancies (19 cell lines), T cell malignancies (16 cell lines), and natural killer (NK) cell malignancies (3 cell lines). We characterized the proteins in the database by classifying them according to their expression level. We found 28 proteins with more than a 2-fold difference between the cell line groups. We also noted the proteins that allowed multidimensional separation to be achieved (1) between HL cells and other cells, (2) between the cells derived from B cells, T cells and NK cells, and (3) between HL cells and anaplastic large cell lymphoma cells. Decision tree classification identified five proteins that could be used to classify the 42 cell lines according to differentiation. These results suggest that the quantitative 2-D database using 2-D DIGE will be a useful resource for studying the mechanisms underlying the differentiation phenotypes of lymphoid neoplasms.

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