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Quantitative analysis of T cell proteomes and environmental sensors during T cell differentiation

Abstract

Quantitative mass spectrometry reveals how CD4+ and CD8+ T cells restructure proteomes in response to antigen and mammalian target of rapamycin complex 1 (mTORC1). Analysis of copy numbers per cell of >9,000 proteins provides new understanding of T cell phenotypes, exposing the metabolic and protein synthesis machinery and environmental sensors that shape T cell fate. We reveal that lymphocyte environment sensing is controlled by immune activation, and that CD4+ and CD8+ T cells differ in their intrinsic nutrient transport and biosynthetic capacity. Our data also reveal shared and divergent outcomes of mTORC1 inhibition in naïve versus effector T cells: mTORC1 inhibition impaired cell cycle progression in activated naïve cells, but not effector cells, whereas metabolism was consistently impacted in both populations. This study provides a comprehensive map of naïve and effector T cell proteomes, and a resource for exploring and understanding T cell phenotypes and cell context effects of mTORC1.

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Fig. 1: Proteome remodeling during T cell differentiation.
Fig. 2: Expression profile of transcription factors during T cell differentiation.
Fig. 3: Scaling versus selective enrichment of proteins and processes during T cell differentiation.
Fig. 4: Nutrient and amino acid transport in T cells.
Fig. 5: Regulation of mRNA translation in T cells.
Fig. 6: Environmental sensing in T cells.
Fig. 7: Impact of mTORC1 inhibition on CD4+ and CD8+ T cell proteomes.
Fig. 8: Impact of mTORC1 inhibition on cell cycle proteins.

Data availability

All proteomics data are available for interrogation using the EPD (https://peptracker.com/epd). Analysed proteomics data used to generate the figures are available in Supplementary Data 15. Raw mass spectrometry data files and MaxQuant analysis files are available from the ProteomeXchange data repository (http://proteomecentral.proteomexchange.org/cgi/GetDataset) and can be accessed with the identifier PXD012058. Flow cytometry data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors thank members of the Cantrell laboratory for comments on the manuscript, T. Ly for discussions on the data, A. Whigham and R. Clarke from the Flow Cytometry Facility for cell sorting and advice on flow cytometry, and the biological sciences research unit at the University of Dundee. This research was supported by a Wellcome Trust Principal Research Fellowship to D.A.C. (205023/Z/16/Z), a Wellcome Trust Strategic Award to D.A.C. and A.I.L. (105024/Z/14/Z) and a Wellcome Trust Equipment Award to D.A.C. (202950/Z/16/Z). This work is dedicated to Olivia Mason.

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Authors and Affiliations

Authors

Contributions

A.J.M.H., L.S., J.L.H. and L.V.S. performed the experiments. J.L.H. performed the liquid chromatography–mass spectrometry analysis. A.J.M.H., J.L.H. and A.B. analyzed the proteomics data. A.B. designed and implemented the EPD. A.I.L. and D.A.C. conceived the project and discussed the data. A.J.M.H. and D.A.C. wrote the manuscript.

Corresponding authors

Correspondence to Angus I. Lamond or Doreen A. Cantrell.

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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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Integrated supplementary information

Supplementary Figure 1 The cumulative abundance of proteins within T cell populations.

(a) Proteins ranked according to their abundance and plotted against their cumulative abundance. The number of proteins that comprise 25%, 50%, 75% and 100% of the total cellular protein mass is provided adjacent to graphs. (b) Direct comparisons of CD4+ and CD8+ naïve populations and CD4+ and CD8+ effector populations. Proteins that make up the top 75% of naïve and effector proteomes (identified in a), are highlighted with red circles. For a and b, n = 6 biologically independent samples for CD8+ naïve cells and 3 biologically independent samples for each of the other T cell populations. For b, proteins were deemed to change significantly if they had a P value <0.05 (two-tailed t-test with unequal variance) and a fold change > 2 standard deviations from the mean fold change between populations (fold change cut-off indicated with dashed lines).

Supplementary Figure 2 Scaling versus enrichment during T cell differentiation.

(a) The protein content of cellular compartments and processes during T cell differentiation. The protein content of ribosomes (KEGG 03010), mitochondria (GO:0005739), nuclear envelope (GO:0005635) and the glycolytic pathway was calculated using estimates of protein copy numbers per cell as described in the methods section. Data is also presented showing the proportion of the cell that constitutes ribosomes, mitochondria, nuclear envelope and the glycolytic pathway (presented as a % of the total cellular protein content). (b) Copy numbers and concentration of hexokinase 1 and 2 (HK1 and HK2) in CD4+ and CD8+ cells. (c) Expression profile for tRNA synthetase enzymes in CD8+ T cells. The volcano plot compares the expression profile of enzymes in naïve versus effector CD8+ cells (CTL/naïve copy numbers). The horizontal dashed line indicates a P value = 0.05 (two-tailed t-test with unequal variance), vertical dashed line indicates the mean fold change between populations. The protein mass of these enzymes is also presented. (d) Copy numbers for components of the EIF2 complex – subunits alpha (EIF2S1), beta (EIF2S2) and gamma (EIF2S3). For a-d, n = 6 biologically independent samples for CD8+ naïve cells and 3 biologically independent samples for each of the other T cell populations. Histogram bars represent the mean +/- SD.

Supplementary Figure 3 Environmental sensing in T cells.

(a) The impact of immune activation on the lysosomal arginine sensor SLC38A9, the cytosolic arginine sensor CASTOR1, the leucine sensor SESTRIN2 and the mTORC1 activating GTPase RHEB. Histogram bars represent the mean +/- SD. (b) Copy numbers for GATOR complex members in naïve (N), TCR activated (T) and effector (E) CD8+ populations. Copy numbers are the average of replicates. For a and b, n = 6 biologically independent samples for CD8+ naïve cells and 3 biologically independent samples for each of the other T cell populations.

Supplementary Figure 4 The impact of rapamycin on effector molecules, transcription factors, transporters and fatty acid metabolism.

(a) Volcano plots showing the expression profile of effector molecules in T cells in response to mTORC1 inhibition: Granzyme B, C, D, E and N (GZMB, C, D, E and N); perforin (PRF1); interferon-γ (IFN-γ), lymphotoxin alpha (LTA); lymphotoxin beta (LTB); interleukin 2 (IL2); TNF Superfamily Member 11 (TNFSF11); TNF Superfamily Member 8 (TNFSF8); CD40 ligand (CD40LG). (b) The impact of inhibiting mTORC1 on key transcription factors in T cells – T-Box 21 (TBX21/T-bet), Proto-Oncogene C-Myc (MYC), Basic Leucine Zipper ATF-Like Transcription Factor (BATF), Interferon Regulatory Factor 4 (IRF4) and PR Domain Containing 1 (PRDM1/BLIMP1). (c) Abundance of Hypoxia Inducible Factor 1 Subunit Alpha (HIF-1α) in response to rapamycin. (d) The expression profile of glucose transporters SLC2A1 and SLC2A3 and the lactate transporter SLC16A3 in response to mTORC1 inhibition. (e) The impact of mTORC1 inhibition on proteins involved in fatty acid/sterol metabolism: Hydroxy-3-Methylglutaryl-CoA Synthase 1 (HMGCS1); Fatty Acid Desaturase 1 and 2 (FADS1 and FADS2); Stearoyl-CoA Desaturase 2/3 (SCD2/3). For a, b and e fold change calculated as +rapamycin/control using protein copy numbers. The horizontal dashed line on volcano plots indicates a P value = 0.05 (two-tailed t-test with unequal variance) while vertical dashed lines indicate a fold change of 0.67, 1 and 1.5. For a-e, n = 6 biologically independent samples for CD8+ naïve cells and 3 biologically independent samples for each of the other T cell populations. Histogram bars represent the mean +/- SD.

Supplementary Figure 5 The impact of mTORC1 inhibition on mitochondrial processes and the EIF4A1:PDCD4 complex.

(a) The expression profile for all mitochondrial proteins, (b) mitochondrial ribosome proteins and (c) proteins implicated in oxidative phosphorylation. For a, b and c fold change calculated as +rapamycin/control using protein copy numbers. The horizontal dashed line on volcano plots indicates a P value = 0.05 (two-tailed t-test with unequal variance) while vertical dashed lines indicate a fold change of 0.67, 1 and 1.5. (d) The abundance of Programmed Cell Death 4 (PDCD4) and Eukaryotic Translation Initiation Factor 4A1 (EIF4A1) in CD8+ and CD4+ T cells. Histogram bars represent the mean +/- SD. For a-d, n = 6 biologically independent samples for CD8+ naïve cells and 3 biologically independent samples for each of the other T cell populations.

Supplementary Figure 6 The impact of mTORC1 inhibition on DNA replication proteins and cell cycle protein complexes.

(a) The impact of mTORC1 inhibition on proteins implicated in DNA replication (KEGG annotation 03030 plus the addition of thymidine kinase 1 and thymidine kinase 2). Fold change calculated as +rapamycin/control using protein copy numbers. The horizontal dashed line indicates a P value = 0.05 (two-tailed t-test with unequal variance) while vertical dashed lines indicate a fold change of 0.67, 1 and 1.5. (b) Stochiometric model for cell cycle entry and progression in CD4+ T cells. Protein copy numbers are presented for cyclin D2 (CCND2), cyclin D3 (CCND3), cyclin dependent kinase 4 (CDK4), cyclin dependent kinase 6 (CDK6) and the cyclin dependent kinase inhibitor CDKN1B (P27). (c) The impact of rapamycin on the cyclin D/P27 model in CD4+ cells TCR triggered for 24 h in the presence of rapamycin, and effector TH1 cells incubated with rapamycin for 24 h on day 5 of in vitro culture. For a, b and c, n = 3 biologically independent samples for each T cell populations. For b and c, copy numbers are rounded to the nearest thousand and are the average of biological replicates.

Supplementary Figure 7 Representative gating strategy for DNA synthesis data presented in Fig. 8a and system L amino acid transport assay presented in Fig. 4c.

(a) Gating strategy for DNA synthesis assay for TCR activated CD8+ cells treated with rapamycin (Fig. 8a). (b) Gating strategy for DNA synthesis assay for CTLs treated with rapamycin (Fig. 8a). (c) Gating strategy for system L amino acid transport assay described in Fig. 4c,d.

Supplementary Figure 8 Representative flow cytometry data and gating strategy for sorted CD8+ and CD4+ naïve cells.

(a) Pure populations of naïve CD8+ cells (a) and CD4+ cells (b) were generated by cell sorting before processing for proteomics. Representative flow cytometry plots are shown.

Supplementary Figure 9 Representative flow cytometry data and gating strategy for sorted TCR activated CD8+ cells treated with rapamycin.

(a) Pure populations of 24 h TCR activated CD8+ cells without rapamycin (a) and with rapamycin treatment (b) were generated by cell sorting before processing for proteomics. Representative flow cytometry plots are shown.

Supplementary Figure 10 Representative flow cytometry data and gating strategy for sorted TCR activated CD4+ cells treated with rapamycin.

(a) Pure populations of 24 h TCR activated CD4+ cells without rapamycin (a) and with rapamycin treatment (b) were generated by cell sorting before processing for proteomics. Representative flow cytometry plots are shown.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10

Reporting Summary

Supplementary Data 1

Data used for generating heat maps.

Supplementary Data 2

Proteomics data for examining T cell differentiation.

Supplementary Data 3

Proteomics data for comparing CD8 and CD4 cells.

Supplementary Data 4

Proteomics data examining the impact of mTORC1 inhibition.

Supplementary Data 5

Protein categories used in this study.

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Howden, A.J.M., Hukelmann, J.L., Brenes, A. et al. Quantitative analysis of T cell proteomes and environmental sensors during T cell differentiation. Nat Immunol 20, 1542–1554 (2019). https://doi.org/10.1038/s41590-019-0495-x

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