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Quantitative interactomics in primary T cells unveils TCR signal diversification extent and dynamics

Nature Immunology volume 20, pages 1530–1541 (2019)Cite this article

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Abstract

The activation of T cells by the T cell antigen receptor (TCR) results in the formation of signaling protein complexes (signalosomes), the composition of which has not been analyzed at a systems level. Here, we isolated primary CD4+ T cells from 15 gene-targeted mice, each expressing one tagged form of a canonical protein of the TCR-signaling pathway. Using affinity purification coupled with mass spectrometry, we analyzed the composition and dynamics of the signalosomes assembling around each of the tagged proteins over 600 s of TCR engagement. We showed that the TCR signal-transduction network comprises at least 277 unique proteins involved in 366 high-confidence interactions, and that TCR signals diversify extensively at the level of the plasma membrane. Integrating the cellular abundance of the interacting proteins and their interaction stoichiometry provided a quantitative and contextual view of each documented interaction, permitting anticipation of whether ablation of a single interacting protein can impinge on the whole TCR signal-transduction network.

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Fig. 1: Composition of the protein–protein network assembling around 15 canonical proteins of the TCR-signaling pathway over 600 s of stimulation.
Fig. 2: Evolution over 600 s of TCR stimulation of bait–prey interaction stoichiometry among the 15 interactomes.
Fig. 3: Organizing the 15 interactomes using cellular protein abundance and interaction stoichiometry.
Fig. 4: Stoichiometry plots of the 15 baits.
Fig. 5: Ab initio prediction of the composition and stoichiometry of novel interactomes.
Fig. 6: TCR signals branch at the level of the plasma membrane leading to the assembly of multiple signalosomes.
Fig. 7: A model summarizing the protein–protein interactions occurring during the first 10 min of TCR activation.
Fig. 8: ARGHAP45 is crucial for proper T and B cell migration.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon request. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (http://www.ebi.ac.uk/pride) with the dataset identifiers PXD012826, PXD007660 and PXD003972.

References

  1. Chakraborty, A. K. & Weiss, A. Insights into the initiation of TCR signaling. Nat. Immunol. 15, 798–807 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Brownlie, R. J. & Zamoyska, R. T cell receptor signalling networks: branched, diversified and bounded. Nat. Rev. Immunol. 13, 257–269 (2013).

    CAS  PubMed  Google Scholar 

  3. Shah, N. H. et al. An electrostatic selection mechanism controls sequential kinase signaling downstream of the T cell receptor. eLife 5, e20105 (2016).

    PubMed  PubMed Central  Google Scholar 

  4. Roncagalli, R. et al. Quantitative proteomics analysis of signalosome dynamics in primary T cells identifies the surface receptor CD6 as a Lat adaptor-independent TCR signaling hub. Nat. Immunol. 15, 384–392 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Astoul, E., Edmunds, C., Cantrell, D. A. & Ward, S. G. PI 3-K and T-cell activation: limitations of T-leukemic cell lines as signaling models. Trends Immunol. 22, 490–496 (2001).

    CAS  PubMed  Google Scholar 

  6. Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712–723 (2015).

    CAS  PubMed  Google Scholar 

  7. Caron, E. et al. Precise temporal profiling of signaling complexes in primary cells using SWATH mass spectrometry. Cell Rep. 18, 3219–3226 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Gaud, G. et al. The costimulatory molecule CD226 signals through VAV1 to amplify TCR signals and promote IL-17 production by CD4+ T cells. Sci. Signal. 11, eaar3083 (2018).

    PubMed  Google Scholar 

  9. Voisinne, G. et al. Co-recruitment analysis of the CBL and CBLB signalosomes in primary T cells identifies CD5 as a key regulator of TCR-induced ubiquitylation. Mol. Syst. Biol. 12, 876 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    PubMed  Google Scholar 

  11. Wisniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 3497–3506 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res. 38, D497–D501 (2010).

    CAS  PubMed  Google Scholar 

  13. Huang, Y. et al. Deficiency of ADAP/Fyb/SLAP-130 destabilizes SKAP55 in Jurkat T cells. J. Biol. Chem. 280, 23576–23583 (2005).

    CAS  PubMed  Google Scholar 

  14. Seet, B. T. et al. Efficient T-cell receptor signaling requires a high-affinity interaction between the Gads C-SH3 domain and the SLP-76 RxxK motif. EMBO J. 26, 678–689 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Giuriato, S. et al. SH2-containing inositol 5-phosphatases 1 and 2 in blood platelets: their interactions and roles in the control of phosphatidylinositol 3,4,5-trisphosphate levels. Biochem. J. 376, 199–207 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu, S. L. et al. Quantitative lipid imaging reveals a new signaling function of phosphatidylinositol-3,4-bisphophate: isoform- and site-specific activation of Akt. Mol. Cell 71, 1092–1104.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Goulden, B. D. et al. A high-avidity biosensor reveals plasma membrane PI(3,4)P2 is predominantly a class I PI3K signaling product. J. Cell. Biol. 218, 1066–1079 (2018).

    PubMed  Google Scholar 

  18. Yang, M. et al. K33-linked polyubiquitination of Zap70 by Nrdp1 controls CD8 T cell activation. Nat. Immunol. 16, 1253–1262 (2015).

    CAS  PubMed  Google Scholar 

  19. Paster, W. et al. GRB2-mediated recruitment of THEMIS to LAT is essential for thymocyte development. J. Immunol. 190, 3749–3756 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Balagopalan, L., Kortum, R. L., Coussens, N. P., Barr, V. A. & Samelson, L. E. The linker for activation of T cells (LAT) signaling hub: from signaling complexes to microclusters. J. Biol. Chem. 290, 26422–26429 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Kumari, S. et al. Actin foci facilitate activation of the phospholipase C-gamma in primary T lymphocytes via the WASP pathway. eLife 4, e04953 (2015).

    PubMed Central  Google Scholar 

  22. Ditliev, J. A. et al. A composition-dependent molecular clutch between T cell signaling condensates and actin. eLife 8, e42695 (2019).

    Google Scholar 

  23. Choi, S. et al. THEMIS enhances TCR signaling and enables positive selection by selective inhibition of the phosphatase SHP-1. Nat. Immunol. 18, 433–441 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Mehta, M. et al. Themis-associated phosphatase activity controls signaling in T cell development. Proc. Natl Acad. Sci. USA 115, E11331–E11340 (2018).

    CAS  PubMed  Google Scholar 

  25. Zvezdova, E. et al. Themis1 enhances T cell receptor signaling during thymocyte development by promoting Vav1 activity and Grb2 stability. Sci. Signal. 9, ra51 (2016).

    PubMed  Google Scholar 

  26. Murphy, T. L. & Murphy, K. M. Slow down and survive: enigmatic immunoregulation by BTLA and HVEM. Annu. Rev. Immunol. 28, 389–411 (2010).

    CAS  PubMed  Google Scholar 

  27. Celis-Gutierrez, J. et al. Quantitative interactomics in primary T cells provides a rationale for concomitant PD-1 and BTLA coinhibitor blockade in cancer immunotherapy. Cell Rep. 27, 3315–3330.e7 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Seki, A. & Rutz, S. Optimized RNP transfection for highly efficient CRISPR/Cas9-mediated gene knockout in primary T cells. J. Exp. Med. 215, 985–997 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. de Kreuk, B. J. et al. The human minor histocompatibility antigen 1 is a RhoGAP. PLoS ONE 8, e73962 (2013).

    PubMed  PubMed Central  Google Scholar 

  30. Huse, M. et al. Spatial and temporal dynamics of T cell receptor signaling with a photoactivatable agonist. Immunity 27, 76–88 (2007).

    CAS  PubMed  Google Scholar 

  31. Lasserre, R. et al. Release of serine/threonine-phosphorylated adaptors from signaling microclusters down-regulates T cell activation. J. Cell. Biol. 195, 839–853 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Hashimoto-Tane, A. & Saito, T. Dynamic regulation of TCR-microclusters and the microsynapse for T cell activation. Front. Immunol. 7, 255 (2016).

    PubMed  PubMed Central  Google Scholar 

  33. Yi, J., Balagopalan, L., Nguyen, T., McIntire, K. M. & Samelson, L. E. TCR microclusters form spatially segregated domains and sequentially assemble in calcium-dependent kinetic steps. Nat. Commun. 10, 277 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. Andreotti, A. H., Joseph, R. E., Conley, J. M., Iwasa, J. & Berg, L. J. Multidomain control over TEC kinase activation state tunes the T cell response. Annu. Rev. Immunol. 36, 549–578 (2018).

    CAS  PubMed  Google Scholar 

  35. Bouchet, J. et al. Rab11-FIP3 regulation of lck endosomal traffic controls TCR signal transduction. J. Immunol. 198, 2967–2978 (2017).

    CAS  PubMed  Google Scholar 

  36. Geng, L., Raab, M. & Rudd, C. E. Cutting edge: SLP-76 cooperativity with FYB/FYN-T in the up-regulation of TCR-driven IL-2 transcription requires SLP-76 binding to FYB at Tyr595 and Tyr651. J. Immunol. 163, 5753–5757 (1999).

    CAS  PubMed  Google Scholar 

  37. Veale, M. et al. Novel isoform of lymphoid adaptor FYN-T-binding protein (FYB-130) interacts with SLP-76 and up-regulates interleukin 2 production. J. Biol. Chem. 274, 28427–28435 (1999).

    CAS  PubMed  Google Scholar 

  38. Marcos, T. et al. Proline-serine-threonine phosphatase interacting protein 1 inhibition of T-cell receptor signaling depends on its SH3 domain. FEBS J. 281, 3844–3854 (2014).

    CAS  PubMed  Google Scholar 

  39. Stanford, S. M., Mustelin, T. M. & Bottini, N. Lymphoid tyrosine phosphatase and autoimmunity: human genetics rediscovers tyrosine phosphatases. Semin. Immunopathol. 32, 127–136 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Xu, Q. et al. Phosphatase PP2A is essential for TH17 differentiation. Proc. Natl Acad. Sci. USA 116, 982–987 (2019).

    CAS  PubMed  Google Scholar 

  41. Clauset, A., Newman, M. E. & Moore, C. Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004).

    Google Scholar 

Download references

Acknowledgements

We thank D. Mori, C. Wülfing (University of Bristol) and A. Zarubica for discussions and E. Bergot, S. Li, T. Chao, S. Durand and the late F. Danjan for technical help. This work was supported by CNRS, INSERM, the European Research Council (ERC) under FP7 program (grant agreement no. 322465 (INTEGRATE), to B.M.) and the European Union’s Horizon 2020 research and innovation program (grant agreement no. 787300 (BASILIC), to B.M.), Agence Nationale de la Recherche (BASILIC project, to M.M.), the MSDAVENIR Fund (to B.M.), the Investissement d’Avenir program of the French Ministry of Research ProFI (Proteomics French Infrastructure, ANR-10-INBS-08, to O.B.-S.), and PHENOMIN (French National Infrastructure for mouse Phenogenomics; ANR-10-INBS-07, to B.M.), the National Natural Science Foundation of China (grant nos 81471595 and 31400759, to Y.L.) and the Education Department of Henan Province, China (16HASTIT030, to Y.L.) and by fellowships from the INTEGRATE (to M.G.M, G.V., K.K. and K.C.), MSDAVENIR (to Y.O.) and PHENOMIN (to L.G.) projects.

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Authors and Affiliations

  1. Centre d’Immunologie de Marseille-Luminy, Aix Marseille Université, INSERM, CNRS, Marseille, France

    Guillaume Voisinne, Kristof Kersse, Julie Chaix, Marisa Goncalves Menoita, Laura Girard, Youcef Ounoughene, Marie Malissen, Romain Roncagalli & Bernard Malissen

  2. Institut de Pharmacologie et de Biologie Structurale, Département Biologie Structurale Biophysique, Protéomique Génopole Toulouse Midi Pyrénées CNRS UMR 5089, Toulouse, France

    Karima Chaoui, Odile Burlet-Schiltz & Anne Gonzalez de Peredo

  3. School of Laboratory Medicine, Xinxiang Medical University, Xinxiang, China

    Liaoxun Lu, Lichen Zhang, Hui Wang & Yinming Liang

  4. Laboratory of Mouse Genetics, Institute of Psychiatry and Neuroscience, Xinxiang Medical University, Xinxiang, China

    Liaoxun Lu

  5. Centre d’Immunophénomique, Aix Marseille Université, INSERM, CNRS UMR, Marseille, France

    Laura Girard, Hervé Luche, Frédéric Fiore, Marie Malissen & Bernard Malissen

  6. Laboratory of Immunophenomics, School of Laboratory Medicine, Xinxiang Medical University, Xinxiang, China

    Hervé Luche, Marie Malissen, Yinming Liang & Bernard Malissen

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  1. Guillaume Voisinne
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Contributions

B.M., R.R. and G.V. conceived the project. B.M. and F.F supervised the construction of OST-tagged mice. R.R. and M.M. performed the experiments shown in Supplementary Figs. 1–3 with the help of L.G., Y.O. and M.G.M. K.K. characterized the SHIP1OST, PLC-γ1OST and PTPN6OST mice with the help of J.C. A.G.de P., K.C. and O.B.-S. performed the MS experiments. G.V. designed the computational and bioinformatics analysis. Y.L., M.M., H.L. and B.M designed the experiments shown in Fig. 8 and L.L., L.Z. and H.W. performed them. B.M., G.V. and R.R. wrote the manuscript.

Corresponding authors

Correspondence to Yinming Liang, Romain Roncagalli or Bernard Malissen.

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The authors declare no competing interests.

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Peer review information Laurie A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Tables 1 and 2 and Notes 1–4.

Reporting Summary

Supplementary Dataset 1

List of the bait–prey interactions identified in the present study. In the tab ‘bait–prey interactions’, each line shows an interaction between a bait and a prey and includes the corresponding FDRs, enrichments and stoichiometries before stimulation and at each stimulation time point. Cellular abundances (number of copies per cell) of the listed proteins are also specified. Where applicable, protein–protein interactions identified in public databases are indicated with the corresponding references and detection methods. The lists of the high-confidence bait–prey interactions identified for each bait are also shown in tabs CBL to VAV1.

Supplementary Dataset 2

Proteome of antigen-experienced conventional CD4+ T cells. The proteins identified in CD4+ T cells from wild-type and OST-tagged mice and their cellular abundance (number of copies per cell) are shown (see Methods).

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Voisinne, G., Kersse, K., Chaoui, K. et al. Quantitative interactomics in primary T cells unveils TCR signal diversification extent and dynamics. Nat Immunol 20, 1530–1541 (2019). https://doi.org/10.1038/s41590-019-0489-8

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