Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Maintenance of CD4 T cell fitness through regulation of Foxo1

Abstract

Foxo transcription factors play an essential role in regulating specialized lymphocyte functions and in maintaining T cell quiescence. Here, we used a system in which Foxo1 transcription-factor activity, which is normally terminated upon cell activation, cannot be silenced, and we show that enforcing Foxo1 activity disrupts homeostasis of CD4 conventional and regulatory T cells. Despite limiting cell metabolism, continued Foxo1 activity is associated with increased activation of the kinase Akt and a cell-intrinsic proliferative advantage; however, survival and cell division are decreased in a competitive setting or growth-factor-limiting conditions. Via control of expression of the transcription factor Myc and the IL-2 receptor β-chain, termination of Foxo1 signaling couples the increase in cellular cholesterol to biomass accumulation after activation, thereby facilitating immunological synapse formation and mTORC1 activity. These data reveal that Foxo1 regulates the integration of metabolic and mitogenic signals essential for T cell competitive fitness and the coordination of cell growth with cell division.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Autoimmunity in mice with T cell–specific dysregulation of Foxo1 activity.
Fig. 2: Foxo1 dysregulation uncouples cell growth and proliferation.
Fig. 3: Maintained Foxo1 activity limits CD4 T cell metabolism and results in a failure to sustain Myc expression.
Fig. 4: Foxo1 mediates downregulation of IL-2Rβ and decreases STAT5 activation.
Fig. 5: Maintained Foxo1 activity suppresses activation-induced increases in cell size and cholesterol content.
Fig. 6: Cholesterol-dependent immunological synapse formation is disrupted by maintained Foxo1 activity.
Fig. 7: Foxo1 activity simultaneously increases Akt and suppresses mTORC1 activation.
Fig. 8: Impaired lysosomal biogenesis and endocytosis of IL-2Rβ upon maintaining Foxo1 activity.

Similar content being viewed by others

References

  1. Manning, B. D. & Toker, A. AKT/PKB signaling: navigating the network. Cell 169, 381–405 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Liao, W., Lin, J. X. & Leonard, W. J. Interleukin-2 at the crossroads of effector responses, tolerance, and immunotherapy. Immunity 38, 13–25 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Smith-Garvin, J. E., Koretzky, G. A. & Jordan, M. S. T cell activation. Annu. Rev. Immunol. 27, 591–619 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kerdiles, Y. M. et al. Foxo1 links homing and survival of naive T cells by regulating L-selectin, CCR7 and interleukin 7 receptor. Nat. Immunol. 10, 176–184 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ouyang, W., Beckett, O., Flavell, R. A. & Li, M. O. An essential role of the Forkhead-box transcription factor Foxo1 in control of T cell homeostasis and tolerance. Immunity 30, 358–371 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kerdiles, Y. M. et al. Foxo transcription factors control regulatory T cell development and function. Immunity 33, 890–904 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ouyang, W. et al. Foxo proteins cooperatively control the differentiation of Foxp3+ regulatory T cells. Nat. Immunol. 11, 618–627 (2010).

    Article  CAS  PubMed  Google Scholar 

  8. Ouyang, W. et al. Novel Foxo1-dependent transcriptional programs control Treg cell function. Nature 491, 554–559 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zeng, H. et al. mTORC1 couples immune signals and metabolic programming to establish Treg-cell function. Nature 499, 485–490 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Yang, K. et al. T cell exit from quiescence and differentiation into Th2 cells depend on Raptor-mTORC1-mediated metabolic reprogramming. Immunity 39, 1043–1056 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kuczma, M. et al. Foxp3-deficient regulatory T cells do not revert into conventional effector CD4+ T cells but constitute a unique cell subset. J. Immunol. 183, 3731–3741 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Kousteni, S. FoxO1, the transcriptional chief of staff of energy metabolism. Bone 50, 437–443 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Webb, A. E., Kundaje, A. & Brunet, A. Characterization of the direct targets of FOXO transcription factors throughout evolution. Aging Cell 15, 673–685 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wilhelm, K. et al. FOXO1 couples metabolic activity and growth state in the vascular endothelium. Nature 529, 216–220 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sinclair, L. V. et al. Control of amino-acid transport by antigen receptors coordinates the metabolic reprogramming essential for T cell differentiation. Nat. Immunol. 14, 500–508 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lord, J. D., McIntosh, B. C., Greenberg, P. D. & Nelson, B. H. The IL-2 receptor promotes proliferation, bcl-2 and bcl-x induction, but not cell viability through the adapter molecule Shc. J. Immunol. 161, 4627–4633 (1998).

    CAS  PubMed  Google Scholar 

  18. Preston, G. C. et al. Single cell tuning of Myc expression by antigen receptor signal strength and interleukin-2 in T lymphocytes. EMBO J. 34, 2008–2024 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Buck, M. D., O’Sullivan, D. & Pearce, E. L. T cell metabolism drives immunity. J. Exp. Med. 212, 1345–1360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. MacIver, N. J., Michalek, R. D. & Rathmell, J. C. Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259–283 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dustin, M. L. The immunological synapse. Cancer Immunol. Res. 2, 1023–1033 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yang, W. et al. Potentiating the antitumour response of CD8+ T cells by modulating cholesterol metabolism. Nature 531, 651–655 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Choudhuri, K. et al. Polarized release of T-cell-receptor-enriched microvesicles at the immunological synapse. Nature 507, 118–123 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Brunet, A. et al. Protein kinase SGK mediates survival signals by phosphorylating the forkhead transcription factor FKHRL1 (FOXO3a). Mol. Cell. Biol. 21, 952–965 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Feng, J., Park, J., Cron, P., Hess, D. & Hemmings, B. A. Identification of a PKB/Akt hydrophobic motif Ser-473 kinase as DNA-dependent protein kinase. J. Biol. Chem. 279, 41189–41196 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Xu, C. et al. Regulation of T cell receptor activation by dynamic membrane binding of the CD3epsilon cytoplasmic tyrosine-based motif. Cell 135, 702–713 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hui, E. et al. T cell costimulatory receptor CD28 is a primary target for PD-1-mediated inhibition. Science 355, 1428–1433 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sancak, Y. et al. Ragulator-Rag complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids. Cell 141, 290–303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Castellano, B. M. et al. Lysosomal cholesterol activates mTORC1 via an SLC38A9-Niemann-Pick C1 signaling complex. Science 355, 1306–1311 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Marschner, K., Kollmann, K., Schweizer, M., Braulke, T. & Pohl, S. A key enzyme in the biogenesis of lysosomes is a protease that regulates cholesterol metabolism. Science 333, 87–90 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Hecht, V. C. et al. Biophysical changes reduce energetic demand in growth factor-deprived lymphocytes. J. Cell Biol. 212, 439–447 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sancak, Y. et al. The Rag GTPases bind raptor and mediate amino acid signaling to mTORC1. Science 320, 1496–1501 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Efeyan, A. et al. Regulation of mTORC1 by the Rag GTPases is necessary for neonatal autophagy and survival. Nature 493, 679–683 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Verbist, K. C. et al. Metabolic maintenance of cell asymmetry following division in activated T lymphocytes. Nature 532, 389–393 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Laplante, M. & Sabatini, D. M. mTOR signaling in growth control and disease. Cell 149, 274–293 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Brennan, P. et al. Phosphatidylinositol 3-kinase couples the interleukin-2 receptor to the cell cycle regulator E2F. Immunity 7, 679–689 (1997).

    Article  CAS  PubMed  Google Scholar 

  37. Chen, C. C. et al. FoxOs inhibit mTORC1 and activate Akt by inducing the expression of Sestrin3 and Rictor. Dev. Cell 18, 592–604 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Delgoffe, G. M. et al. The kinase mTOR regulates the differentiation of helper T cells through the selective activation of signaling by mTORC1 and mTORC2. Nat. Immunol. 12, 295–303 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zoncu, R., Efeyan, A. & Sabatini, D. M. mTOR: from growth signal integration to cancer, diabetes and ageing. Nat. Rev. Mol. Cell Biol. 12, 21–35 (2011).

    Article  CAS  PubMed  Google Scholar 

  40. Luo, C. T., Liao, W., Dadi, S., Toure, A. & Li, M. O. Graded Foxo1 activity in Treg cells differentiates tumour immunity from spontaneous autoimmunity. Nature 529, 532–536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Aghajani, K., Keerthivasan, S., Yu, Y. & Gounari, F. Generation of CD4CreER(T2) transgenic mice to study development of peripheral CD4-T-cells. Genesis 50, 908–913 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yang, K., Neale, G., Green, D. R., He, W. & Chi, H. The tumor suppressor Tsc1 enforces quiescence of naive T cells to promote immune homeostasis and function. Nat. Immunol. 12, 888–897 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Google Scholar 

  46. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank M. Li (Memorial Sloan Kettering Cancer Center) for Foxo1AAA mice; F. Gounari (University of Chicago) for CD4Cre–ERT2 mice; M. Farrar (University of Minnesota) for a constitutively active STAT5b retroviral construct; Z. Herbert and K. Bi for assistance with transcriptome analysis; the MGH-CNY FACS core facility for cell sorting; and members of the laboratory of L.A.T. for discussions. This work was supported by the US National Institutes of Health (P01HL018646 to L.A.T.; R21AI126143 to L.A.T. and R.H.N.; R01AI124693 to D.J.C.; R01HL011879 and P01AI056299 to B.R.B.; AI101407 and CA176624 to H.C.), the Wellcome Trust (PRF 100262 to M.L.D.) and the European Research Council (ERC-2014-AdG_670930 to M.L.D. and E.B.C.).

Author information

Authors and Affiliations

Authors

Contributions

R.H.N. and L.A.T. designed the study, interpreted data and wrote the manuscript. R.H.N. performed experiments. S.S. performed experiments with Rictor flox/flox animals. J.M.S. performed experiments to help characterize autoimmunity in CD4Cre Foxo1AAA/+ animals. K.B.Y. assisted with RNA-seq data analysis. E.B.C. performed immunological synapse imaging experiments. N.R.-H., B.R.B., S.J.B., W.N.H., M.L.D., D.J.C. and H.C. assisted with data analysis and interpretation.

Corresponding author

Correspondence to Laurence A. Turka.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 T cell and Treg phenotype in CD4Cre Foxo1AAA/+ mice.

(a,b) Total numbers of lymph node cells (a) and splenocytes (b) from 4-8 week old WT (n = 12) and AAA (n = 16) mice. (c-e) Flow cytometric analysis of CD4+Foxp3+ Tregs from CD4Cre (WT; n = 6) and CD4Cre Foxo1AAA/+ (AAA; n = 9) littermates (top), and quantification of MFI of antibody staining (bottom) in 4-12 week old mice. (f) Flow cytometric analysis of CD4 + (upper) and CD8 + (lower) T cells from lymph nodes of 8 week old WT and AAA littermates. Representative of 12 mice, with similar results. (g,h) Flow cytometric analysis of Ifn-γ production in CD4+ T cells (h; n = 6) and Gzm-B production in CD8+ T cells (i; WT, n = 5; AAA, n = 3) from 4-8 week old WT and AAA littermates stimulated directly ex vivo for 4 hrs with a leucocyte activation cocktail containing Brefeldin A. For all graphs, quantification used a two-tailed Student’s t-test, with no adjustments made for multiple comparisons; centre value is the mean, with error bars indicating standard deviation. * P < 0.05; ** P < 0.005; *** P < 0.0005; **** P < 0.0001; ns = not significant.

Supplementary Figure 2 Characterization of B cell autoimmunity in CD4Cre Foxo1AAA/+ mice.

(a) Percentage of CD3+ and CD19+ cells in lymph nodes from 4-8 week old WT (n = 7) and AAA (n = 9) littermates. (b) Flow cytometric analysis of CD3-CD19+ lymph node cells from 4 week old WT and AAA littermates. The GL-7+Fas+ population (upper panels) represents germinal center B cells, and the IgM-IgD- population (lower panels) represents class-switched B cells. Representative of 4 independent experiments, with similar results. (c) Flow cytometric analysis of CD3-CD19+ B cells from 4 week old WT and AAA littermates, representative of three independent experiments, with similar results. (d) Flow cytometric analysis and quantification of CD4+CD19-Foxp3-Bcl6+CXCR5+ Tfh cells in 4-8 week old WT (n = 5) and AAA (n = 7) littermates. (e) Intracellular cytokine staining from splenocytes stimulated with PMA/ionomycin/monensin for 4 hours from 4-8 week old WT and AAA littermates (n = 4). (f-h) Serum antibody levels measured by ELISA in 6-12 week old mice (n = 12). (i,j) Representative microscopy staining of HEp-2 ANA slides stained with serum diluted 1:50 and fluorophore conjugated secondary antibody as indicated. (i): IgM (n = 2); (j): Total Ig (anti-mouse H + L; WT, n = 2; AAA, n = 3). 40× oil magnification. Serum from NZB x NZW mice was used as a positive control. (k) Representative microscopy staining of frozen kidney sections. Sections were stained for total Ig (anti-mouse IgG H + L). (l) Blood glucose levels measured by saphenous vein bleeding in 6-12 week old WT (n = 5) and AAA littermates (n = 6). (m) Percent red blood cells in blood collected from 6-12 week old WT and AAA littermates (n = 5). For all graphs, quantification used a two-tailed Student’s t-test, with no adjustments made for multiple comparisons; centre value is the mean, with error bars indicating standard deviation. * P < 0.05; ** P < 0.005; **** P < 0.0001; ns = not significant.

Supplementary Figure 3 Bone marrow chimera strategy and gene expression profiling from mixed and nonmixed bone marrow chimeras.

(a) Strategy for generating bone marrow chimeras. (b) Schematic of populations used for transcriptome analysis by RNAseq. Population 1: CD4+CD45.1+CD62LhiCD44lo from mixed bone marrow chimeras 10-12 weeks post-transfer; Population 2: CD4+CD45.2+CD62LhiCD44lo from mixed bone marrow chimeras 10-12 weeks post-transfer (taken from same recipients as Population 1); Population 3: CD4+CD45.2+CD62LloCD44hi from bone marrow chimeras receiving only AAA bone marrow, 10-12 weeks post-transfer. 2-3 mice for each population were used for RNAseq. (c-e) Heat maps showing DESeq2-generated normalized global expression values for transcripts isolated from all 3 populations, as described in Supplementary Figure 3; Gene sets shown are representative of genes involved in T cell activation, including cytokines and cytokine receptors (c), chemokine signaling (d), and activation induced cell death (e).

Supplementary Figure 4 Pathway enrichment analysis of CD4+ T cells from Scurfy mice versus CD4+ T cells from Foxo1AAA chimeric mice.

Differentially expressed genes in CD4+ T cells from Scurfy mice vs CD4+ T cells from wild-type mice were obtained from PubMed GEO Dataset GSE11775 analyzed using GEO2R, and were compared to differentially expressed genes from AAA CD4+ T cells from Foxo1AAA single-chimeric animals vs AAA CD4+ T cells from mixed bone marrow chimeras (populations 3 vs 2, as described in Supplementary Figure 3; n = 2). Three lists of genes were generated from this comparison: Genes that were unique to CD4 T cells in Scurfy mice versus WT mice (and not upregulated in activated versus quiescent AAA CD4 T cells), genes that were upregulated in both Scurfy and activated AAA CD4 T cells, and genes that were unique to activated AAA CD4 T cells. (a,b) Comparison of upregulated gene sets, based on log2 fold change > 1, adjusted P value < 0.05. Each gene set was analyzed using the DAVID database, and the top two enriched clusters were presented with P values (-log10) for each annotation term within the clusters, which themselves were generated by selecting the following annotation categories: GOTERM_BP_DIRECT, GOTERM_CC_DIRECT, GOTERM_MF_DIRECT and KEGG_PATHWAY (b). (c,d) Comparison of downregulated gene sets (log2 fold change < -1, adj P < 0.05), analyzed as in a and b. Arrows point to notable clusters, as mentioned in the text, within each group. For b and d, P value is a function of DAVID based on a selected gene set of differentially expressed genes, and not determined based on sample size.

Supplementary Figure 5 Dysregulation of metabolic pathways in activated Foxo1AAA CD4 T cells.

Heat maps indicating fold change in transcripts involved in metabolic pathways in “Activated” vs “Quiescent” AAA CD4 + T cells (left), and expression levels of each gene across each replicate (n = 2; scaled based on values for the entire row) within these populations. Asterisks indicate genes with log2 fold change of < -1 or > 1, and adjusted P value < 0.05. The log10 Benjamin-Hochberg correction adjusted p-values were used, corrected for the direction of fold change, to rank genes. For any adjusted p value cutoff, the Benjamin-Hochberg correction was used to calculate.

Supplementary Figure 6 Kinetics and regulation of Myc and CD122 expression.

(a) Flow cytometric analysis of CellTrace Violet dilutions of GFP- (WT) and GFP+ (AAA) CD4+ T cells from CD4Cre-ERT2 Foxo1AAA/+ (iAAA) mice following 1 and 2 days in culture with plate-bound anti-CD3 and soluble anti-CD28. Representative of 4 independent experiments, with similar results (b) Flow cytometric analysis on days 1-3 of isolated cultures stimulated as in a. Open histograms are GFP- and GFP+ populations as indicated, lightly shaded histograms show isotype control staining for each population. Representative of 4 independent experiments, with similar results (c) Real-time quantitative PCR from iso-cultures of Myc at 0 and 24 hrs post-activation with plate-bound anti-CD3 and soluble anti-CD28 (normalized to beta-Actin; n = 5). (d) Real-time quantitative PCR from iso- and co-cultures of Myc at 0 (n = 2) and 72 hrs (n = 3) post-activation with plate-bound anti-CD3 and soluble anti-CD28 (normalized to beta-Actin). Following 3 days in culture, RNA was extracted directly from iso-cultures, while co-cultures were sorted once again for GFP- and GFP+ populations, and RNA was subsequently extracted. (e) Real-time quantitative PCR from iso-cultures of IL-2Rb at 0 and 48 hrs post-activation stimulated as in a (normalized to beta-Actin; n = 4). (f) Western blot analysis of iso-cultures stimulated for 1 and 2 days as in a, representative of three independent experiments. After sorting, a proportion of GFP- and GFP+ cells were immediately lysed (Day 0). (g) Flow cytometric analysis of surface (non-permeablized cells) and total levels (intracellular and surface levels of fixed and permeablized cells; ICS) of CD122 at 0 and 48 hrs post-activation of iso-cultures stimulated as in a, representative of two independent experiments. For all graphs, quantification used a two-tailed Student’s t-test, with no adjustments made for multiple comparisons; centre value is the mean, with error bars indicating standard deviation. * P < 0.05; ** P < 0.005; ns = not significant.

Supplementary Figure 7 Multiple potential mechanisms to account for hyperactivation of Akt.

(a) Western blot analysis and quantitation (normalized to beta-Actin) of iso-cultures stimulated 2 days with plate-bound anti- CD3 and soluble anti-CD28 (n = 8). (b,c) Western blot analysis of iso-cultures stimulated for 1 to 2 days with platebound anti-CD3 and soluble anti-CD28, representative of three independent experiments, with similar results. After sorting, a proportion of GFP- and GFP+ cells were immediately lysed (Day 0). (d) Western blot analysis and quantitation of iso-cultures stimulated for 1 and 2 days, as in b and c, with either vehicle or an inhibitor (NU7117; 10 mm) of DNA-PK. Representative of two independent experiments, with similar results. (e,f) Flow cytometric analysis and quantitation of GFP- and GFP+ iso-cultures stimulated for 1 and 2 days (representative flow plots shown on day 2) with anti-CD3/28 (n = 8). (g) Western blot analysis and quantitation of iso-cultures stimulated for 1 and 2 days, as in b and c. Quantitation is for PTEN expression normalized to beta-Actin on day 2 (n = 7). For all graphs, quantification used a two-tailed Student’s t-test, with no adjustments made for multiple comparisons; centre value is the mean, with error bars indicating standard deviation. **P < 0.005; *** P < 0.0005; **** P < 0.0001.

Supplementary Figure 8 Altered lysosome and mTORC1 colocalization in nutrient-deplete and nutrient-replete conditions.

Representative immunofluorescence images of mTOR, Lamp-1 and DAPI staining on day 2 of isocultures stimulated with plate-bound anti-CD3 and soluble anti-CD28. 2 hrs prior to fixation and subsequent staining and imaging, cells were adhered to CellTak coated coverslips and incubated in RPMI media with (“Complete media”) or without amino acids, glucose and 10% fetal calf serum (“Base media”). Representative of three independent experiments, with similar results. Scale bars, 10 μm.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Newton, R.H., Shrestha, S., Sullivan, J.M. et al. Maintenance of CD4 T cell fitness through regulation of Foxo1. Nat Immunol 19, 838–848 (2018). https://doi.org/10.1038/s41590-018-0157-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-018-0157-4

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing