Expression Clustering Reveals Detailed Co-expression Patterns of Functionally Related Proteins during B Cell Differentiation

B cells play an essential role in the immune response. Upon activation they may differentiate into plasma cells that secrete specific antibodies against potentially pathogenic non-self antigens. To identify the cellular proteins that are important for efficient production of these antibodies we set out to study the B cell differentiation process at the proteome level. We performed an in-depth proteomic study to quantify dynamic relative protein expression patterns of several hundreds of proteins at five consecutive time points after lipopolysaccharide-induced activation of B lymphocytes. The proteome analysis was performed using a combination of stable isotope labeling using [13C6]leucine added to the murine B cell cultures, one-dimensional gel electrophoresis, and LC-MS/MS. In this study we identified 1,001 B cell proteins. We were able to quantify the expression levels of a quarter of all identified proteins (i.e. 234) at each of the five different time points. Nearly all proteins revealed changes in expression patterns. The quantitative dataset was further analyzed using an unbiased clustering method. Based on their expression profiles, we grouped the entire set of 234 quantified proteins into a limited number of 12 distinct clusters. Functionally related proteins showed a strong correlation in their temporal expression profiles. The quality of the quantitative data allowed us to even identify subclusters within functionally related classes of proteins such as in the endoplasmic reticulum proteins that are involved in antibody production.

[1]  Blagoy Blagoev,et al.  A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling , 2003, Nature Biotechnology.

[2]  S. Nigam,et al.  Multiple Molecular Chaperones Complex with Misfolded Large Oligomeric Glycoproteins in the Endoplasmic Reticulum* , 1997, The Journal of Biological Chemistry.

[3]  A. Helenius,et al.  Interactions between Newly Synthesized Glycoproteins, Calnexin and a Network of Resident Chaperones in the Endoplasmic Reticulum , 1997, The Journal of cell biology.

[4]  Marek Michalak,et al.  Contrasting functions of calreticulin and calnexin in glycoprotein folding and ER quality control. , 2004, Molecular cell.

[5]  H. Herscovitz,et al.  Nascent Lipidated Apolipoprotein B Is Transported to the Golgi as an Incompletely Folded Intermediate as Probed by Its Association with Network of Endoplasmic Reticulum Molecular Chaperones, GRP94, ERp72, BiP, Calreticulin, and Cyclophilin B* , 2003, The Journal of Biological Chemistry.

[6]  Albert J R Heck,et al.  Sequential waves of functionally related proteins are expressed when B cells prepare for antibody secretion. , 2003, Immunity.

[7]  S. Gygi,et al.  Quantitative Cancer Proteomics: Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) as a Tool for Prostate Cancer Research*S , 2004, Molecular & Cellular Proteomics.

[8]  A I Saeed,et al.  TM4: a free, open-source system for microarray data management and analysis. , 2003, BioTechniques.

[9]  J. Yates,et al.  Large-scale analysis of the yeast proteome by multidimensional protein identification technology , 2001, Nature Biotechnology.

[10]  F. Regnier,et al.  Fractionation of isotopically labeled peptides in quantitative proteomics. , 2001, Analytical chemistry.

[11]  F. Regnier,et al.  Quantification in proteomics through stable isotope coding: a review. , 2004, Journal of proteome research.

[12]  William Stafford Noble,et al.  Exploring Gene Expression Data with Class Scores , 2001, Pacific Symposium on Biocomputing.

[13]  Anthony K. L. Leung,et al.  Nucleolar proteome dynamics , 2005, Nature.

[14]  P. Bork,et al.  Dynamic Complex Formation During the Yeast Cell Cycle , 2005, Science.

[15]  Alfonso Valencia,et al.  A hierarchical unsupervised growing neural network for clustering gene expression patterns , 2001, Bioinform..

[16]  Richard D. Smith,et al.  Stable isotope-coded proteomic mass spectrometry. , 2003, Current opinion in biotechnology.

[17]  D. Cyr,et al.  From the cradle to the grave: molecular chaperones that may choose between folding and degradation , 2001, EMBO reports.

[18]  P. Connell,et al.  The co-chaperone CHIP regulates protein triage decisions mediated by heat-shock proteins , 2000, Nature Cell Biology.

[19]  Ruedi Aebersold,et al.  The study of macromolecular complexes by quantitative proteomics , 2003, Nature Genetics.

[20]  A. Heck,et al.  Recent liquid chromatographic-(tandem) mass spectrometric applications in proteomics. , 2003, Journal of chromatography. A.

[21]  Rolf Apweiler,et al.  The Proteome Analysis database: a tool for the in silico analysis of whole proteomes , 2003, Nucleic Acids Res..

[22]  S. Gygi,et al.  Quantitative analysis of complex protein mixtures using isotope-coded affinity tags , 1999, Nature Biotechnology.

[23]  K. Parker,et al.  Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents*S , 2004, Molecular & Cellular Proteomics.

[24]  Joaquín Dopazo,et al.  FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes , 2004, Bioinform..

[25]  D. Wiest,et al.  Membrane biogenesis during B cell differentiation: most endoplasmic reticulum proteins are expressed coordinately , 1990, The Journal of cell biology.

[26]  C. Alberini,et al.  Differentiation in the murine B cell lymphoma I.29: individual μ+ clones may be induced by lipopolysaccharide to both IgM secretion and isotype switching , 1987, European journal of immunology.

[27]  L. Hendershot,et al.  A subset of chaperones and folding enzymes form multiprotein complexes in endoplasmic reticulum to bind nascent proteins. , 2002, Molecular biology of the cell.

[28]  M. Mann,et al.  Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). , 2003, Journal of proteome research.

[29]  Lance F. Barton,et al.  Regulation of Immunoproteasome Subunit Expression In Vivo Following Pathogenic Fungal Infection1 , 2002, The Journal of Immunology.

[30]  Jeroen Krijgsveld,et al.  Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics , 2003, Nature Biotechnology.

[31]  Andrew Hayes,et al.  Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting , 2002, Proteomics.

[32]  L. Staudt,et al.  XBP1, downstream of Blimp-1, expands the secretory apparatus and other organelles, and increases protein synthesis in plasma cell differentiation. , 2004, Immunity.

[33]  T. Colgan,et al.  Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry. , 2005, Journal of proteome research.

[34]  Joshua E. Elias,et al.  Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. , 2003, Journal of proteome research.

[35]  M. Mann,et al.  Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* , 2002, Molecular & Cellular Proteomics.

[36]  Matthias Mann,et al.  Mass spectrometric-based approaches in quantitative proteomics. , 2003, Methods.