Quantitative Proteomics of Gut-Derived Th1 and Th1/Th17 Clones Reveal the Presence of CD28+ NKG2D- Th1 Cytotoxic CD4+ T cells
2016; Elsevier BV; Volume: 15; Issue: 3 Linguagem: Inglês
10.1074/mcp.m115.050138
ISSN1535-9484
AutoresTahira Riaz, Ludvig M. Sollid, Ingrid Olsen, Gustavo A. de Souza,
Tópico(s)IL-33, ST2, and ILC Pathways
ResumoT-helper cells are differentiated from CD4+ T cells and are traditionally characterized by inflammatory or immunosuppressive responses in contrast to cytotoxic CD8+ T cells. Mass-spectrometry studies on T-helper cells are rare. In this study, we aimed to identify the proteomes of human Th1 and Th1/Th17 clones derived from intestinal biopsies of Crohn's disease patients and to identify differentially expressed proteins between the two phenotypes. Crohn's disease is an inflammatory bowel disease, with predominantly Th1- and Th17-mediated response where cells of the "mixed" phenotype Th1/Th17 have also been commonly found. High-resolution mass spectrometry was used for protein identification and quantitation. In total, we identified 7401 proteins from Th1 and Th1/Th17 clones, where 334 proteins were differentially expressed. Major differences were observed in cytotoxic proteins that were overrepresented in the Th1 clones. The findings were validated by flow cytometry analyses using staining with anti-granzyme B and anti-perforin and by a degranulation assay, confirming higher cytotoxic features of Th1 compared with Th1/Th17 clones. By testing a larger panel of T-helper cell clones from seven different Crohn's disease patients, we concluded that only a subgroup of the Th1 cell clones had cytotoxic features, and these expressed the surface markers T-cell-specific surface glycoprotein CD28 and were negative for expression of natural killer group 2 member D. T-helper cells are differentiated from CD4+ T cells and are traditionally characterized by inflammatory or immunosuppressive responses in contrast to cytotoxic CD8+ T cells. Mass-spectrometry studies on T-helper cells are rare. In this study, we aimed to identify the proteomes of human Th1 and Th1/Th17 clones derived from intestinal biopsies of Crohn's disease patients and to identify differentially expressed proteins between the two phenotypes. Crohn's disease is an inflammatory bowel disease, with predominantly Th1- and Th17-mediated response where cells of the "mixed" phenotype Th1/Th17 have also been commonly found. High-resolution mass spectrometry was used for protein identification and quantitation. In total, we identified 7401 proteins from Th1 and Th1/Th17 clones, where 334 proteins were differentially expressed. Major differences were observed in cytotoxic proteins that were overrepresented in the Th1 clones. The findings were validated by flow cytometry analyses using staining with anti-granzyme B and anti-perforin and by a degranulation assay, confirming higher cytotoxic features of Th1 compared with Th1/Th17 clones. By testing a larger panel of T-helper cell clones from seven different Crohn's disease patients, we concluded that only a subgroup of the Th1 cell clones had cytotoxic features, and these expressed the surface markers T-cell-specific surface glycoprotein CD28 and were negative for expression of natural killer group 2 member D. The CD4+ T cells also called T-helper (Th) 1The abbreviations used are:ThT-helperCD28T-cell-specific surface glycoprotein CD28NKG2Dnatural killer group 2 member DLFQlabel-free quantitativeGZMgranzymePRFperforinPEPposterior error probabilityIFNinterferonGATAtrans-acting T-cell-specific transcription factorILinterleukinRORRAR-related orphan receptorFOXPforkhead boxMXinterferon-induced GTP-binding protein MxGIMAPGTPase of immunity associated proteinACNacetonitrileFAformic acidAGCautomatic gain controlNCEnormalized collision energyITinjection timeFDRfalse discovery rateFITCfluorescein isothiocyanateAPCallophycocyaninPerCPperidinin chlorophyll protein complexPBMCperipheral blood mononuclear cellRUNXrunt-related transcription factorThPOKT-helper-inducing POZ/Krueppel-like factorEtsProtein C-EtsNKnatural killerNKTnatural killer T. cells are important constituents of the immune system. As their name indicates, Th cells provide help to other cells of the immune system and thereby aid in combatting of both intracellular and extracellular pathogens. However, Th cells can also have an exaggerated or inappropriate response and trigger allergic disorders or other immune-mediated diseases. Th cells are classically grouped into Th1, Th2, Th17, and Treg cells where the distinct Th phenotypes are attributed unique functions. Th1 cells are characterized by the production of IFN-γ (1.Mosmann T.R. et al.Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins.J. Immunol. 1986; 136: 2348-2357Crossref PubMed Google Scholar) and expression of the key transcription factor T-bet (2.Szabo S.J. Kim S.T. Costa G.L. Zhang X. Fathman C.G. Glimcher L.H. A novel transcription factor, T-bet, directs Th1 lineage commitment.Cell. 2000; 100: 655-669Abstract Full Text Full Text PDF PubMed Scopus (2728) Google Scholar), Th2 cells produce IL-4 (1.Mosmann T.R. et al.Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins.J. Immunol. 1986; 136: 2348-2357Crossref PubMed Google Scholar) and the transcription factor GATA-3 (3.Zheng W. Flavell R.A. The transcription factor GATA-3 is necessary and sufficient for Th2 cytokine gene expression in CD4 T cells.Cell. 1997; 89: 587-596Abstract Full Text Full Text PDF PubMed Scopus (1891) Google Scholar), and Th17 cells produce IL-17 (4.Park H. Li Z. Yang X.O. Chang S.H. Nurieva R. Wang Y.H. Wang Y. Hood L. Zhu Z. Tian Q. Dong C. A distinct lineage of CD4 T cells regulates tissue inflammation by producing interleukin 17.Nat. Immunol. 2005; 6: 1133-1141Crossref PubMed Scopus (3445) Google Scholar, 5.Harrington L.E. Hatton R.D. Mangan P.R. 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Control of regulatory T cell development by the transcription factor Foxp3.Science. 2003; 299: 1057-1061Crossref PubMed Scopus (51) Google Scholar). T-helper T-cell-specific surface glycoprotein CD28 natural killer group 2 member D label-free quantitative granzyme perforin posterior error probability interferon trans-acting T-cell-specific transcription factor interleukin RAR-related orphan receptor forkhead box interferon-induced GTP-binding protein Mx GTPase of immunity associated protein acetonitrile formic acid automatic gain control normalized collision energy injection time false discovery rate fluorescein isothiocyanate allophycocyanin peridinin chlorophyll protein complex peripheral blood mononuclear cell runt-related transcription factor T-helper-inducing POZ/Krueppel-like factor Protein C-Ets natural killer natural killer T. The classical view of specific and unique cytokines and transcriptions factors as master regulators working in a synergy to result in a set of distinguished Th cell subtypes has proved to be a simplification (9.Oestreich K.J. Weinmann A.S. Master regulators or lineage-specifying? Changing views on CD4+ T cell transcription factors.Nat. Rev. Immunol. 2012; 12: 799-804Crossref PubMed Scopus (105) Google Scholar). The Th cells have shown to have plasticity features, and their end-fate is not necessarily final as previously believed (10.Zhou L. Chong M.M. Littman D.R. Plasticity of CD4+ T cell lineage differentiation.Immunity. 2009; 30: 646-655Abstract Full Text Full Text PDF PubMed Scopus (1150) Google Scholar, 11.Wan Y.Y. Multi-tasking of helper T cells.Immunology. 2010; 130: 166-171Crossref PubMed Scopus (133) Google Scholar). The different cytokines and transcription factors have been proposed to impact plasticity from one Th cell type to another (12.O'Shea J.J. Paul W.E. Mechanisms underlying lineage commitment and plasticity of helper CD4+ T cells.Science. 2010; 327: 1098-1102Crossref PubMed Scopus (990) Google Scholar, 13.Murphy K.M. Stockinger B. Effector T cell plasticity: Flexibility in the face of changing circumstances.Nat. Immunol. 2010; 11: 674-680Crossref PubMed Scopus (355) Google Scholar). Th cells of "mixed" phenotypes also contribute greatly to the complexity of Th cell subtypes (9.Oestreich K.J. Weinmann A.S. Master regulators or lineage-specifying? Changing views on CD4+ T cell transcription factors.Nat. Rev. Immunol. 2012; 12: 799-804Crossref PubMed Scopus (105) Google Scholar), and mixed phenotypes like e.g. Th1/Th2 (14.Peine M. Rausch S. Helmstetter C. Fröhlich A. Hegazy A.N. Kühl A.A. Grevelding C.G. Höfer T. Hartmann S. Löhning M. Stable T-bet(+)GATA-3(+) Th1/Th2 hybrid cells arise in vivo, can develop directly from naive precursors, and limit immunopathologic inflammation.PLos Biol. 2013; 11: e1001633Crossref PubMed Scopus (109) Google Scholar) and Th1/Th17 (15.Duhen T. Campbell D.J. IL-1beta promotes the differentiation of polyfunctional human CCR6+CXCR3+ Th1/17 cells that are specific for pathogenic and commensal microbes.J. Immunol. 2014; 193: 120-129Crossref PubMed Scopus (83) Google Scholar) are reported in the literature. The important role of Th cells in protective immunity to pathogens and in immune-mediated diseases makes it of great interest to characterize and better understand the molecular basis contributing to such roles of these cells. Mass-spectrometry-based proteomics is a powerful tool to study immune cells more comprehensively, and it is a growing field. Previous proteomic studies on Th cells focused mainly on comparison of the Th1 and Th2 proteome. The CD4+ T cell response to IL-12 and IL-4 signaling has also been studied (16.Rosengren A.T. Nyman T.A. Lahesmaa R. Proteome profiling of interleukin-12 treated human T helper cells.Proteomics. 2005; 5: 3137-3141Crossref PubMed Scopus (29) Google Scholar, 17.Moulder R. Lönnberg T. Elo L.L. Filén J.J. Rainio E. Corthals G. Oresic M. Nyman T.A. Aittokallio T. Lahesmaa R. Quantitative proteomics analysis of the nuclear fraction of human CD4+ cells in the early phases of IL-4-induced Th2 differentiation.Mol. Cell. Proteomics. 2010; 9: 1937-1953Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar). These studies all used in vitro differentiation of naive CD4+ T cells from human blood by cytokine supplementation of the growth medium (16.Rosengren A.T. Nyman T.A. Lahesmaa R. Proteome profiling of interleukin-12 treated human T helper cells.Proteomics. 2005; 5: 3137-3141Crossref PubMed Scopus (29) Google Scholar, 17.Moulder R. Lönnberg T. Elo L.L. Filén J.J. Rainio E. Corthals G. Oresic M. Nyman T.A. Aittokallio T. Lahesmaa R. Quantitative proteomics analysis of the nuclear fraction of human CD4+ cells in the early phases of IL-4-induced Th2 differentiation.Mol. Cell. Proteomics. 2010; 9: 1937-1953Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar, 18.Filén J.J. Filén S. Moulder R. Tuomela S. Ahlfors H. West A. Kouvonen P. Kantola S. Björkman M. Katajamaa M. Rasool O. Nyman T.A. Lahesmaa R. Quantitative proteomics reveals GIMAP family proteins 1 and 4 to be differentially regulated during human T helper cell differentiation.Mol. Cell. Proteomics. 2009; 8: 32-44Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar, 19.Loyet K.M. Ouyang W. Eaton D.L. Stults J.T. Proteomic profiling of surface proteins on Th1 and Th2 cells.J Proteome Res. 2005; 4: 400-409Crossref PubMed Scopus (48) Google Scholar, 20.Rautajoki K. T.A. Nyman T.A. Lahesmaa R. Proteome characterization of human T helper 1 and 2 cells.Proteomics. 2004; 4: 84-92Crossref PubMed Scopus (42) Google Scholar), while we here investigate in vivo differentiated Th cell clones isolated from gut-derived biopsies. In a study on protein differences between Th1 and Th2 cells, 70 differentially expressed proteins were identified. Out of these, only 14 were reproduced for data collected from cells polarized for different time points (7 and 14 days), indicating the dynamic nature of proteomes of differentiating cells (20.Rautajoki K. T.A. Nyman T.A. Lahesmaa R. Proteome characterization of human T helper 1 and 2 cells.Proteomics. 2004; 4: 84-92Crossref PubMed Scopus (42) Google Scholar). In the present study, we wanted to focus on tissue-derived Th cell clones with the up-to-date mass spectrometry technology. We have previously isolated intestinal Th1 and Th1/Th17 clones from Crohn's disease patients and investigated their reactivity to various intestinal commensals and opportunistic pathogens (21.Olsen I. Tollefsen S. Aagaard C. Reitan L.J. Bannantine J.P. Andersen P. Sollid L.M. Lundin K.E. Isolation of Mycobacterium avium subspecies paratuberculosis reactive CD4 T cells from intestinal biopsies of Crohn's disease patients.PLoS ONE. 2009; 4: e5641Crossref PubMed Scopus (42) Google Scholar, 22.Olsen I. Lundin K.E. Sollid L.M. Increased frequency of intestinal CD4+ T cells reactive with mycobacteria in patients with Crohn's disease.Scand. J. Gastroenterol. 2013; 48: 1278-1285Crossref PubMed Scopus (7) Google Scholar). Crohn's disease is an intestinal disease where exaggerated Th1 and Th17 responses appear to be important (23.Annunziato F. Cosmi L. Santarlasci V. Maggi L. Liotta F. Mazzinghi B. Parente E. Filì L. Ferri S. Frosali F. Giudici F. Romagnani P. Parronchi P. Tonelli F. Maggi E. Romagnani S. Phenotypic and functional features of human Th17 cells.J. Exp. Med. 2007; 204: 1849-1861Crossref PubMed Scopus (1505) Google Scholar), and the purpose of this study was to gain increased understanding of the complexity of these CD4+ T cell subsets. In addition to quantifying protein differences between Th1 and Th1/Th17, we also wanted to examine the reproducibility of Th cell clones by common variations in cell culture expansion. All the T cell clones were derived from intestinal biopsies of Crohn's disease patients. Their phenotype based on cytokine production and expression of surface proteins commonly used as markers have been described previously (21.Olsen I. Tollefsen S. Aagaard C. Reitan L.J. Bannantine J.P. Andersen P. Sollid L.M. Lundin K.E. Isolation of Mycobacterium avium subspecies paratuberculosis reactive CD4 T cells from intestinal biopsies of Crohn's disease patients.PLoS ONE. 2009; 4: e5641Crossref PubMed Scopus (42) Google Scholar, 22.Olsen I. Lundin K.E. Sollid L.M. Increased frequency of intestinal CD4+ T cells reactive with mycobacteria in patients with Crohn's disease.Scand. J. Gastroenterol. 2013; 48: 1278-1285Crossref PubMed Scopus (7) Google Scholar). Briefly, the production of the cytokines IFN-γ, IL-4, and IL-17 together with expression of CD161 and C-C chemokine receptor 6 were used to classify the clones into the Th1, Th2, or Th17 subgroups. Clones expressing large amounts of both IFN-γ and IL-17 were classified to have a mixed Th1/Th17 phenotype, while clones expressing both IFN-γ and IL-4 were classified to have a mixed Th1/Th2 phenotype. Th1 and Th1/Th17 cell clones were selected for proteomic studies. The Th cell clones were isolated from three different donors. The Th cell clones TCC958.A.N.1 (958.1) and TCC958.A.M.2 (958.17) were from the same patient, while TCC955.A.N.6 (955.1) and TCC946.A.8.2B.17 (946.17) were each from different patients. The cells were stimulated with anti-CD3 and anti-CD28 either by beads or plate coated, grown at time apart, and harvested after different passage number. For a more detailed overview, see Table S1. Five expansions each (independent growths) were used for 958.1 and 958.17 as replicates from same clone, and one expansion each for 955.1 and 946.17 as biological replicates from different patients. In total, six replicates were used for each phenotype for proteomics studies; see an overview for the workflow in Fig. S1. All cells were harvested in their resting stage. The study was approved by the Regional Committee for Medical Research Ethics, South Norway. The Th cell clones were expanded using irradiated feeder cells, anti-CD3/anti-CD28-coated beads (one bead/cell) or plate-bound anti-CD3 (0.5 μg/ml) together with soluble anti-CD28 (0.2 μg/ml) as previously described (21.Olsen I. Tollefsen S. Aagaard C. Reitan L.J. Bannantine J.P. Andersen P. Sollid L.M. Lundin K.E. Isolation of Mycobacterium avium subspecies paratuberculosis reactive CD4 T cells from intestinal biopsies of Crohn's disease patients.PLoS ONE. 2009; 4: e5641Crossref PubMed Scopus (42) Google Scholar). In short, cells were dissolved in complete cell culture medium (RPMI 1640 (Gibco) containing 10% human serum, β mercaptoethanol, penicillin and streptomycin, 10 U/ml human IL-2 (R&D Systems, Abingdon, UK), and 1 ng/ml human IL-15 (R&D Systems). The cells were then stimulated using either plate-bound anti-CD3 (clone UCHT1, Biolegend) with soluble anti-CD28, (clone CD28.2, Biolegend) or Dynabeads® Human T-Activator CD3/CD28 (Life Technologies). The cells were expanded for 8 to 9 days with splitting and addition of fresh medium when necessary. Resting cells as examined by visual examination were used for proteomic analyses. Prior to protein extraction, the cells were washed four times in phosphate-buffered saline, pH 7.4 (GIBCO, Life technologies), with centrifugation at 400 × g for 6 min at 4 °C. The cell pellet was frozen down at −80 °C until further use. To lyse the cells, they were first thoroughly crushed inside the Eppendorf tube with a pestle and incubated at 95 °C for 1 min. After three freeze and thaw cycles, 2% SDS/50 mm DTT/25 mm Tris-HCl, pH = 7.6 (SDS: UltraPure, 10%, GIBCO, Invitrogen; DTT: Sigma), was added and incubated again at 95 °C for 1 min. The samples were vortexed briefly (10 s) prior to incubation at 4 °C overnight. The following day, the cells were sonicated for 30 min to fully lyse them. Protein concentration was determined by direct detect instrument from Millipore. To the cell lysate containing 115 μg of proteins, five volumes of ice cold acetone were added and precipitated at −20 °C o/n. After centrifugation at 4 °C with 14.000 × g for 10 min, the supernatant was discarded, and the pellet was dried. In-solution digest was performed as described by G. L Christensen et al. in 24.Christensen G.L. Kelstrup C.D. Lyngsø C. Sarwar U. Bøgebo R. Sheikh S.P. Gammeltoft S. Olsen J.V. Hansen J.L. Quantitative phosphoproteomics dissection of seven-transmembrane receptor signaling using full and biased agonists.Mol. Cell. Proteomics. 2010; 9: 1540-1553Abstract Full Text Full Text PDF PubMed Scopus (118) Google Scholar with slight modifications. Briefly, the proteins were dissolved in 6 m urea in 10 mm HEPES, pH = 8. Proteins were reduced in 1 mm DTT at room temperature for 30 min, followed by alkylation in 5.5 mm iodoacetamide (Sigma-Aldrich) at room temperature for 15 min. The proteins were then digested with 1:100 endoLys-C (Wako, Germany) for 4 h at room temperature. A fourfold dilution was done with deionized water before o/n digestion at room temperature with 1:100 of the enzyme trypsin (modified grade, Promega). In order to reduce the complexity of the samples, peptides were fractionated with strong anion exchange columns as described by J. R Wisniewski et al. (25.Wiśniewski J.R. Zougman A. Mann M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome.J. Proteome Res. 2009; 8: 5674-5678Crossref PubMed Scopus (437) Google Scholar). Peptides were subsequently eluted with pH 11, pH 8, pH 6, pH 5, pH 4, and pH 3. Before desalting, the pH 8 and pH 6 fractions were combined and so were the pH 5, pH 4, and pH 3 fractions. The peptides were desalted using an in-house made C18 StageTip. Each StageTip was made with three layers of C18 Empore Extraction disks (Varian, St. Paul, MN, USA). The samples were acidified to 0.1% TFA (Fluka, Sigma). Prior to loading on the sample, the C18 material was activated with 100% methanol and equilibrated twice with 0.1% TFA. The sample was passed through the StageTip twice, followed by wash with 0.1% TFA. The peptides were kept on the C18 StageTip, and stored at 4 °C until peptide elution (not more than 1 week). The peptides were eluted with 95% ACN/0.1% FA (ACN: Fluka Analytical, Sigma-Aldrich, FA: Fluka Analytical), dried in a SpeedVac (Eppendorf) until approximately 3 μl, and reconstituted to a total volume of 20 μl in 0.1% FA. nLC-MS/MS analysis was performed on a Dionex Ultimate 3000 nLC (Sunnyvale, CA, USA) system coupled to an Q-Exactive (Thermo Electron, Bremen, Germany) mass spectrometer, equipped with a nanospray flex ion source with direct junction (Thermo Scientific). The direct junction was mounted with a stainless steel emitter at the end. For peptide separation, a two-column setup with C18 Acclaim PepMap Nano-Trap Column (5 μm particle size) and 50 cm C18 Acclaim PepMap RSLC (100 Å, 2 μm particle size, 75 μm inner diameter) analytical column (Thermo Scientific) was used. Solvent A was 0.1% FA and solvent B 90% ACN in 0.1% FA. The gradient had a flow rate of 300 nl/min and was 5% B in 5 min, 5–32% until 225 min, then to 45% until 240 min, followed by wash at 90% B for 10 min, and then back to 5% B until 252 min and kept at that until 260 min. The mass spectrometer was operated in a data-dependent mode with top 10 MS/MS scans, and the survey of full-scan MS spectra was from 300–1750 m/z. The following parameters for MS scan were applied: lock mass: off, resolution: 70,000, AGC target: 3e6, and maximum IT: 50 ms. The MS/MS scans were performed at: resolution: 17,500, AGC target: 2e5, maximum IT: 100 ms, isolation window: 3.0 m/z, NCE: 25, underfill ratio: 10.0%, intensity threshold: 2.0e5, and dynamic exclusion: 45.0 s. Protein identification and label-free quantitation was performed in MaxQuant (version 1.3.0.5) using the Andromeda search engine (26.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar). Database search was carried out in Andromeda against the human International Protein Index (IPI) database (v3.84) supplemented with contaminants. The IPI database consisted of 90,160 entries. The following parameters were applied: enzyme: trypsin/P; variable modifications: oxidation (M), acetyl (protein N-term), Gln->pyro-Glu and Glu->pyro-glu; fixed modifications: carbamidomethyl (C); max. peptide PEP: 0.1; min. peptide length: 7; min. unique peptides: 1; advanced: re-quantity, keep low-scoring versions of identified peptides, match between runs (3 min time window), label-free quantitation, and second peptide MS2 identification. Otherwise, the default parameters of MaxQuant were considered. For single peptide identifications, only those that annotated spectra could be retrieved from MaxQuant Viewer were included (File S1). t test calculations were performed in Perseus (version 1.4.0.20). Label-free quantitative (LFQ) intensities were chosen as expression, and log2 was calculated. Proteins marked as possible contaminants, hits from the reverse sequences false discovery rate (FDRs), and proteins marked as "only identified by site" by MaxQuant were removed from the list of identifications prior to statistical analysis. The zero intensity values were imputed by normal distribution. This gave LFQ intensity values that simulate the distribution of low abundant proteins (27.Deeb S.J. D'Souza R.C. Cox J. Schmidt-Supprian M. Mann M. Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles.Mol. Cell. Proteomics. 2012; 11: 77-89Abstract Full Text Full Text PDF PubMed Scopus (138) Google Scholar). No filtering based on valid values was done prior to imputation. No skewing of the data distribution was inserted regardless if missing values were filtered or imputation was performed as the chosen approach (see Figs. S2 and S3 for more information on impact on the data with and without imputation and with or without filtering for valid values). The original LFQ values, including zero intensity values from MaxQuant data output for all samples (biological and technical replicates), can be found in Table S2. Prior to t test calculations, replicates from the same donor were grouped together, and p value<.01 was used as t test threshold. Pearson correlation was calculated for replicates of the clones 958.1 and 958.17 based on the normalized LFQ values to show clone reproducibility. Publicly available databases (such as the Database for Annotation, Visualization and Integration Discovery, DAVID, and Search Tool for the Retrieval of Interacting Genes, STRING) were also searched for functional enrichment analysis. These results were not included since they did not provide any further insight into the biology of the cell clones. Transcription factors were identified from the total protein identification list by cross-search with human transcription factors listed in the Swissregulon Database (http://swissregulon.unibas.ch/) in addition to manual search with use of the word "transcription" followed by recheck in literature and with gene names of transcription factors familiar to the authors. The following antibodies were used for flow-cytometric analyzes: anti-CD4 (FITC, clone OKT4, 5 μl, Biolegend), anti-CD4 (APC, clone RPA-T4, eBioscience), anti-GZMB (Alexa Fluor® 647, clone GB11, Biolegend), anti-PRF (PE, clone BD48, Biolegend), anti-CD28 (Alexa Fluor® 488, clone CD28.2, Biolegend), anti-CX3CR1 (PE, clone 2A9–1, Biolegend), anti-NKG2D (PE, clone 1D11, Abcam), and PerCP anti-CD8 (PerCP, clone SK1, Biolegend). Briefly, the T- cell clones (500,000 cells) were stained with anti-CD4 for 20 min, washed, and mixed with unstained PBMC. The mix was subsequently stained with surface markers (proteins commonly used as phenotypic markers) followed by fixation in 1% paraformaldehyde for 1 h and permeabilization in PBS with 2% FCS and 0.2% saponin for 30 min. The cells were stained with antibodies against GZMB and PRF and analyzed on a FACS Calibur flow cytometer (Becton Dickinson) equipped with Cell-Quest software. Mixing unstained PBMC with the CD4 stained T-cell clones provided an internal control for subsequent staining. The PBMC contains a population of cells that are positive and negative for the various markers. Occasional well-to-well variations in fluorescence intensities were corrected by comparing the expression of the markers in the CD4-labeled T-cell clones to the negative and positive populations in the PBMC. Plates were coated with anti-CD3 (1 μl/ml, clone UCHT1, Biolegend) in PBS at 4 °C for 24 h. Th cell clones were added together with anti-CD28, (0.2 μl/ml, clone CD28.2, Biolegend), anti-CD49d (1 μg/ml), and anti-human CD107a (5 μl/ml, Alexa Fluor® 647 (LAMP-1). Wells without anti-CD3 were used as negative controls. The samples were incubated for 1 h at 37 °C, GolgiStop™ was added followed by incubation for 5 additional hours. The samples were analyzed on FACS Calibur. The expression of CD107a in anti-CD3-stimulated wells was compared with the expression in control wells containing only anti-CD28 and anti-CD49d. Label-free quantitative mass-spectrometry analysis of Th1 and Th1/Th17 clones resulted in the identification of a total number of 7401 unique protein groups with protein false discovery rate at 0.01. The obtained protein identifications and quantitative values (protein ID, LFQ values, sequence coverage, unique peptides, PEP score, t test significant, t test p value, and t test difference,) are reported in Table S3. The effect of expansion number and method on protein expression was assessed by using two different expansion protocols and three different expansion numbers for a Th1 and a Th1/Th17 clone derived from the same patient (Table S1). Pearson correlation was calculated between the replicates of the Th1 cell clone 958.1 and the Th1/Th17 cell clone 958.17 based on label-free quantitative (LFQ) values. The five replicates gave a reproducibility of 94.8%-97.9% for 958.1 and 93.2%-97.2% for 958.17 (Figs. S4 and S5). There were no systematic differences in protein expression between replicates from the same clone that could be related to variations in the different growth parameters applied. These results indicated that difference in in vitro conditions had low impact on cell expression profiles. Two additional clones from two different patients were included in an attempt to identify differentially expressed proteins that were specific for the two phenotypes and patient independent. These two clones, 955.1 and 946.17, had a Th1 and a Th1/Th17 phenotype, respectively (Table S1). Hierarchical clustering was made based on the LFQ values for all the samples (Fig. S6), and the overall results showed that the Th cell clone proteomes in general were very similar. The replicates from 958.1 and 958.17 (derived from the same patient) clustered together, demonstrating that patient origin had a stronger impact on protein expression than Th phenotype. The number of differential expressed proteins identified by mass-spectrometry analysis was 1267 between 958.1 and 958.17 (including all five replicates) and 1405 proteins between 955.1 and 946.17. The total number of differentially expressed proteins present in both datasets was 430 proteins, and of these, 334 were similarly over- or underrepresented in both datasets for the same phenotype. Most of these 334 proteins, although signifi
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