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Dynamics in protein translation sustaining T cell preparedness

Nature Immunology volume 21, pages 927–937 (2020)Cite this article

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Abstract

In response to pathogenic threats, naive T cells rapidly transition from a quiescent to an activated state, yet the underlying mechanisms are incompletely understood. Using a pulsed SILAC approach, we investigated the dynamics of mRNA translation kinetics and protein turnover in human naive and activated T cells. Our datasets uncovered that transcription factors maintaining T cell quiescence had constitutively high turnover, which facilitated their depletion following activation. Furthermore, naive T cells maintained a surprisingly large number of idling ribosomes as well as 242 repressed mRNA species and a reservoir of glycolytic enzymes. These components were rapidly engaged following stimulation, promoting an immediate translational and glycolytic switch to ramp up the T cell activation program. Our data elucidate new insights into how T cells maintain a prepared state to mount a rapid immune response, and provide a resource of protein turnover, absolute translation kinetics and protein synthesis rates in T cells (https://www.immunomics.ch).

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Fig. 1: A pulsed SILAC approach shows that a small set of important proteins is rapidly renewed in naive T cells.
Fig. 2: Constitutive protein degradation in naive T cells.
Fig. 3: Rapid turnover and tunability of transcription factors.
Fig. 4: Metabolic preparedness of T cells.
Fig. 5: Translational preparedness of naive T cells.
Fig. 6: Post-transcriptional regulations.
Fig. 7: Preparedness of memory T cells.

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Data availability

The MS proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD017159. RNA-seq and ChIP-seq data have been deposited to the NCBI Gene Expression Omnibus (GEO) with the accession numbers GSE147229 and GSE146787. The data that support the findings of this study are available at https://www.immunomics.ch, attached as Supplementary Tables 1–6 or are available from the corresponding author upon request.

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Acknowledgements

We thank D. Jarrossay for cell sorting. This work was supported by a grant from the Swiss SystemsX.ch initiative, evaluated by the Swiss National Science Foundation (grant no. 142032 to R.G.), the European Research Council (grant no. 803150 to R.G.), the Swiss National Science Foundation (grant no 170213 to F.S.), the Federal Ministry of Education and Research (GAIN_ 01GM1910A to B.G.) and the German Research Foundation (SFB1160/2_B5, RESIST–EXC 2155–Project ID 39087428 to B.G). The Institute for Research in Biomedicine is supported by the Helmut Horten Foundation.

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Author notes

  1. These authors contributed equally: Tobias Wolf, Wenjie Jin.

Authors and Affiliations

  1. Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland

    Tobias Wolf, Wenjie Jin, Giada Zoppi, Ian A. Vogel, Murodzhon Akhmedov, Tim Beltraminelli, Marco Benevento, Samuele Notarbartolo, Antonio Lanzavecchia, Federica Sallusto, Ivo Kwee & Roger Geiger

  2. Institute of Microbiology, ETH Zürich, Zurich, Switzerland

    Tobias Wolf & Federica Sallusto

  3. Biozentrum, University of Basel, Basel, Switzerland

    Christopher K. E. Bleck & Dirk Bumann

  4. Experimental Systems Immunology, Max Planck Institute of Biochemistry, Munich, Germany

    Jan C. Rieckmann & Felix Meissner

  5. Institute for Immunodeficiency, Center for Chronic Immunodeficiency, Medical Center, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, Germany

    Neftali J. Ramirez & Bodo Grimbacher

  6. Integrated Research Training Group (IRTG) Medical Epigenetics, Collaborative Research Centre 992, Freiburg, Germany

    Neftali J. Ramirez

  7. Institute of Innate Immunity, Department of Systems Immunology and Proteomics, Medical Faculty, University of Bonn, Bonn, Germany

    Felix Meissner

  8. DZIF – German Center for Infection Research, Satellite Center Freiburg, Freiburg, Germany

    Bodo Grimbacher

  9. CIBSS – Centre for Integrative Biological Signalling Studies, Albert-Ludwigs University, Freiburg, Germany

    Bodo Grimbacher

  10. RESIST – Cluster of Excellence 2155 to Hanover Medical School, Satellite Center Freiburg, Freiburg, Germany

    Bodo Grimbacher

  11. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Munich, Germany

    Matthias Mann

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Contributions

R.G. conceived the study, designed experiments, analyzed data and wrote the manuscript. T.W., W.J., G.Z., I.A.V. and J.C.R. designed and performed experiments with human T cells. M.A. and I.K. wrote the R shiny code for the online platform. M.B. helped with MS. C.K.E.B. and T.B. performed the electron microscopy and analysis. N.J.R. carried out ATAC-seq, and S.N. carried out ChIP-seq analysis. D.B., F.M., B.G., M.M., A.L., F.S., I.K. and R.G. supervised the work.

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Correspondence to Roger Geiger.

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Extended data

Extended Data Fig. 1 Viability of resting T cells in culture.

(a) Protein mass of naïve T cells does not change after 72 h of culturing. Proteomes of T cells that were either analyzed immediately after isolation or after 72 h of culturing were analyzed by LC-MS. Protein content was estimated using the proteome ruler approach59. n = 5 for freshly isolated and n = 2 for 72h-cultured T cells from different donors. Bars represent the S.E.M (b) FACS-purified naïve and memory T cells were cultured in complete medium without the addition of growth factors. To measure cell viability, T cells were stained with Annexin-V-FITC directly after sorting or after 24 h and 96 h of culturing and then analyzed by flow cytometry. n = 4, Four independent experiments with T cells from two different donors. Bars represent the S.E.M. (c) FACS-purified naïve T cells were cultured for 24 h in complete medium containing either DMSO, 50 µg/ml CHX or 50 µg/ml CHX together with 10 µM bortezomib (CHX + PS). n = 8 from three different donors. Bars represent the S.E.M.

Extended Data Fig. 2 Analysis of hallmark transcription factors.

(a) Frequency of ATAC-Seq peaks annotated to different genomic regions. (b) Frequency of ETS1 ChIP-Seq peaks annotated to different genomic regions. (c) Examples of activation markers that were up-regulated in TILs (CD69 and PD1) and proteins that were downregulated (KLF2, CD62L, and FAM65B). n = 1627 for T cells from blood, n = 2170 for T cells from tumors. Violin plot width is based on a Gaussian kernel density estimate of the data (estimated by the density function with standard parameters), scaled to have maximum width = 1. Data are from Zheng et al. 2016.

Extended Data Fig. 3 Increased turnover of glycolytic enzymes following activation.

(a) Naïve or 6h-activated T cells were analyzed on a Seahorse analyzer. Shown is the Extra Cellular Acidification Rate (ECAR), which is a measure of the glycolytic rate. n = 6 from three donors. Bars represent the S.E.M (b) Gene ontology analysis of the proteome of naïve T cells. Glycolytic proteins (blue) contributed 11% to the cytosolic protein mass. (c) Fraction of newly synthetized (heavily labeled) glycolytic proteins after a 12-hours pulse in naïve or 12h-activated T cells. n = 3 from three different donors. Box plot elements are defined as in Fig. 2b.

Extended Data Fig. 4 3D reconstructions of seven naïve and eight 72 h-activated T cells.

(a) Reconstructions of naïve CD4+ T cells. For the first two cells every layer of the plasma membrane (purple) was drawn, while for the other cells only every third layer was drawn. Scale bar = 2 μm (b) Reconstructions of 72 h-activated CD4+ T cells. For the first four cells every layer of the plasma membrane (purple) was drawn, while for the other cells only every third layer was drawn. Scale bar = 2 μm.

Extended Data Fig. 5 Estimation of the number of ribosomes.

(a) Copy numbers of 82 ribosomal proteins in naïve T cells. n = 7 from seven different donors. Box plot elements are defined as in Fig. 2b (b) Distribution of the copy numbers of ribosomal proteins in naïve T cells. Average values from n = 7 are shown. Dashed line shows the median, which was used as an approximation of total ribosomes. (c) Total RNA in naïve and activated T cells. To estimate the number of ribosomes, it was assumed that 83% of total RNA was ribosomal RNA. n = 7 for naïve, 6 h and 12h-activated T cells. n = 4 for 24 h, 48 h and 72 h activated T cells from different donors. Bars represent the S.E.M (d) mRNA to protein ratio. n = 7 for naïve, n = 3 for 6 h and n = 4 for 24 h activated T cells from different donors. Bars represent the S.E.M (e) Total amount of mRNA per T cell. n = 9 for naïve and 24 h activated T cells, n = 7 for 6 h and 12 h activated T cells from different donors. Bars represent the S.E.M (f) Average mRNA copy numbers per T cell. (g) mRNA processing rate in naïve and 6h-activated T cells. Average values from n = 3 from different donors are shown.

Extended Data Fig. 6 Posttranscriptional regulations.

(a) Absolute copy numbers of CD40LG and JUNB proteins in resting and activated T cells. n = 7 for naïve T cells, n = 3 for 6 h, 12 h, 48 h, 120h-activated T cells, n = 4 for 24 h, 72 h, 96 h activated T cells. Box plot elements are defined as in Fig. 2b (b) Absolute protein synthesis rates in resting and 6h-activated T cells that were untreated or treated with Torin-1. n = 3 from three donors. Bars represent the S.E.M.

Extended Data Fig. 7 Rapidly turned over proteins in naïve and memory T cells.

(a) Comparison of protein turnover kinetics in resting naïve and memory CD4+ T cells of selected proteins. n = 3 for naïve 6 h and naïve 12 h; n = 4 for naïve 24 h, memory 6 h, memory 12 h and memory 24 h from different donors.

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Wolf, T., Jin, W., Zoppi, G. et al. Dynamics in protein translation sustaining T cell preparedness. Nat Immunol 21, 927–937 (2020). https://doi.org/10.1038/s41590-020-0714-5

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