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      Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry

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          Abstract

          • The authors conduct a proteomic and protein interaction network analysis of human T lymphocytes during entry into the first cell cycle.

          • Inhibiting the induction of eIF6 (60S ribosome biogenesis) causes T cells to enter the cell cycle without growing in size.

          • Inhibiting the induction of SF3B2/SF3B4 (U2/U12-dependent RNA splicing) allows an increase in cell size without entering the cell cycle.

          • These results provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells.

          Abstract

          Regulating the transition of cells such as T lymphocytes from quiescence (G 0) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G 0. We also provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells. We employed a proteomic profiling method to identify large-scale changes in chromatin/nuclear matrix-bound and unbound proteins in human T lymphocytes during the transition from G 0 into the first cell cycle and mapped them to form functionally annotated, dynamic protein interaction networks. Inhibiting the induction of two proteins involved in two of the most significantly upregulated cellular processes, ribosome biogenesis (eIF6) and hnRNA splicing (SF3B2/SF3B4), showed, respectively, that human T cells can enter the cell cycle without growing in size, or increase in size without entering the cell cycle.

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          Most cited references60

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          Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

          We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins.

              A panel of antigen-specific mouse helper T cell clones was characterized according to patterns of lymphokine activity production, and two types of T cell were distinguished. Type 1 T helper cells (TH1) produced IL 2, interferon-gamma, GM-CSF, and IL 3 in response to antigen + presenting cells or to Con A, whereas type 2 helper T cells (TH2) produced IL 3, BSF1, and two other activities unique to the TH2 subset, a mast cell growth factor distinct from IL 3 and a T cell growth factor distinct from IL 2. Clones representing each type of T cell were characterized, and the pattern of lymphokine activities was consistent within each set. The secreted proteins induced by Con A were analyzed by biosynthetic labeling and SDS gel electrophoresis, and significant differences were seen between the two groups of T cell line. Both types of T cell grew in response to alternating cycles of antigen stimulation, followed by growth in IL 2-containing medium. Examples of both types of T cell were also specific for or restricted by the I region of the MHC, and the surface marker phenotype of the majority of both types was Ly-1+, Lyt-2-, L3T4+, Both types of helper T cell could provide help for B cells, but the nature of the help differed. TH1 cells were found among examples of T cell clones specific for chicken RBC and mouse alloantigens. TH2 cells were found among clones specific for mouse alloantigens, fowl gamma-globulin, and KLH. The relationship between these two types of T cells and previously described subsets of T helper cells is discussed.
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                Author and article information

                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2012
                13 March 2012
                : 8
                : 573
                Affiliations
                [1 ]Department of Haematological Medicine, Leukaemia Sciences Laboratories, Rayne Institute, King's College London , London, UK
                [2 ]Department of Chemistry and Biochemistry, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin , Austin, TX, USA
                [3 ]Department of Biological Chemistry, David Geffen School of Medicine, Jonsson Comprehensive Cancer Center, Molecular Biology Institute, University of California Los Angeles , Los Angeles, CA, USA
                [4 ]Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research , Los Angeles, CA, USA
                [5 ]MRC Centre for Developmental Neurobiology, King's College London , London, UK
                Author notes
                [a ]King's College London, Department of Haematological Medicine, Leukaemia Sciences Laboratories, Rayne Institute, 123 Coldharbour Lane, London SE5 9NU, UK. Tel.: +44 (0) 20 7848 5818; Fax: +44 (0) 20 7733 3877; nicholas.s.thomas@ 123456kcl.ac.uk
                [*]

                These two authors contributed equally to this work

                [†]

                Present address: National Institutes of Health, Laboratory of Kidney and Electrolyte Metabolism, NHLBI, 10 Center Drive, Building 10 6N260, MSC-1603 Bethesda, MD 20892, USA.

                Article
                msb20125
                10.1038/msb.2012.5
                3321526
                22415777
                141cfe1f-92c7-4e10-a289-52f216962193
                Copyright © 2012, EMBO and Macmillan Publishers Limited

                This is an open-access article distributed under the terms of the Creative Commons Attribution Noncommercial No Derivative Works 3.0 Unported License, which permits distribution and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission.

                History
                : 25 July 2011
                : 30 January 2012
                Categories
                Article

                Quantitative & Systems biology
                cell size,cell cycle,mass spectrometry,proteomics,t cells
                Quantitative & Systems biology
                cell size, cell cycle, mass spectrometry, proteomics, t cells

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