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Distinct metabolic programs established in the thymus control effector functions of γδ T cell subsets in tumor microenvironments

Nature Immunology volume 22, pages 179–192 (2021)Cite this article

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Abstract

Metabolic programming controls immune cell lineages and functions, but little is known about γδ T cell metabolism. Here, we found that γδ T cell subsets making either interferon-γ (IFN-γ) or interleukin (IL)-17 have intrinsically distinct metabolic requirements. Whereas IFN-γ+ γδ T cells were almost exclusively dependent on glycolysis, IL-17+ γδ T cells strongly engaged oxidative metabolism, with increased mitochondrial mass and activity. These distinct metabolic signatures were surprisingly imprinted early during thymic development and were stably maintained in the periphery and within tumors. Moreover, pro-tumoral IL-17+ γδ T cells selectively showed high lipid uptake and intracellular lipid storage and were expanded in obesity and in tumors of obese mice. Conversely, glucose supplementation enhanced the antitumor functions of IFN-γ+ γδ T cells and reduced tumor growth upon adoptive transfer. These findings have important implications for the differentiation of effector γδ T cells and their manipulation in cancer immunotherapy.

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Fig. 1: Intratumoral γδ T cell subsets display distinct metabolic profiles.
Fig. 2: Peripheral γδ T cell subsets show different mitochondrial and metabolic phenotypes.
Fig. 3: γδ T cell subsets are metabolically programmed in the thymus.
Fig. 4: Distinct mitochondrial activities underlie effector fate of thymic γδ T cell progenitors.
Fig. 5: γδ17 cells show higher lipid uptake and lipid droplet content than γδIFN cells.
Fig. 6: HFD promotes the expansion of pro-tumoral γδ17 cells in lymph nodes and within tumors.
Fig. 7: Glucose supplementation enhances the antitumor effector functions of γδIFN cells.

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Data availability

The GEO public repository accession codes are GSE150585 for single-cell RNA sequencing; and GSE156782 for bulk RNA sequencing. The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We are grateful for the valuable assistance of the staff of the flow cytometry, bioimaging and animal facilities at our institutions. We thank J.-W. Taanman, A. Magalhães, J. Ribot, K. Serre and N. Sousa for technical suggestions and administrative help. This work was supported by the Wellcome Trust (092973/Z/10/Z to D.J.P.), Biotechnology and Biological Sciences Research Council (BBSRC) UK (BB/R017808/1 to D.J.P.), European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 646701 to B.S.-S.; StG_679173 to L.L.), Science Foundation Ireland (SFI) (16/FRL/3865 to L.L.), NIH (NS115064, HG008155, AG062377 to M.K.), NIH (R01 AI134861 and metabolic core grant S10 OD020100 to L.L.), Fundação Astrazeneca (Prémio FAZ Ciência 2019 to B.S.-S. and N.L.) and PAC-PRECISE LISBOA-01-0145-FEDER-016394, co-funded by FEDER (POR Lisboa 2020 (Programa Operacional Regional de Lisboa, do Portugal 2020)) and Fundação para a Ciência e a Tecnologia (Portugal). N.L. is supported by a postdoctoral fellowship from EMBO (ALTF 752-2018); S.M. was supported by a studentship from the Medical Research Council (MRC) UK; G.F. is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 752932; and A.D., S.C., L.D. and H.P. are supported by Irish Research Council fellowships.

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Author notes

  1. These authors contributed equally: Noella Lopes, Claire McIntyre, Stefania Martin, Mathilde Raverdeau, Bruno Silva-Santos, Daniel J. Pennington, Lydia Lynch.

Authors and Affiliations

  1. Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

    Noella Lopes, Gina J. Fiala & Bruno Silva-Santos

  2. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Claire McIntyre, Ayano C. Kohlgruber, Harry Kane, Colleen Carmody, Michael Brenner & Lydia Lynch

  3. Blizard Institute, Barts and The London School of Medicine, Queen Mary University of London, London, UK

    Stefania Martin, Nital Sumaria & Daniel J. Pennington

  4. Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland

    Mathilde Raverdeau, Lydia Dyck, Harry Kane, Aaron Douglas, Stephen Cunningham, Hannah Prendeville, Roisin Loftus & Lydia Lynch

  5. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA

    Leandro Z. Agudelo & Manolis Kellis

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

    Philippe Pierre & Rafael J. Argüello

  7. Institute for Research in Biomedicine (iBiMED) and Ilidio Pinho Foundation, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal

    Philippe Pierre

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  1. Noella Lopes
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Contributions

N.L., C.M., S.M. and M.R. performed most of the experiments and analyzed the data. G.J.F. designed and performed some experiments. N.S., A.C.K., L.D., H.K., A.D., S.C., H.P., R.L. and C.C. provided technical assistance in some experiments. M.K. and L.Z.A. performed bioinformatic analysis, and M.B. provided reagents, materials and support. P.P. and R.J.A. provided key assistance with the SCENITHTM methodology. B.S.-S., D.J.P. and L.L. conceived and supervised the study. N.L., C.M., B.S.-S., D.J.P. and L.L. wrote the manuscript.

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Correspondence to Daniel J. Pennington.

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Competing interests

B.S.-S. is an inventor of the patented ‘Delta One T cell’ technology, which has been acquired by GammaDelta Therapeutics (London, UK).

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Peer review information Nature Immunology thanks Juan Carlos Zúñiga-Pflücker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 SCENITHTM methodology for analysis of cell metabolism.

a, Experimental design: E0771 breast or MC38 colon cancer cell lines were injected in WT mice; 6 and 15 days later, tumors were extracted for metabolic analysis of gd T cells using SCENITHTM. b, SCENITHTM assesses the impact of metabolic inhibitors on protein synthesis. Mean fluorescence intensity (MFI) of puromycin is analysed in each condition (Co: control-no inhibition; DG: 2-deoxyglucose inhibiting glycolysis; O: oligomycin inhibiting OXPHOS; and DGO: DG+O inhibitors). Glucose dependence, fatty acid and amino acid oxidation capacity, mitochondrial dependence and glycolytic capacity are calculated as detailed in the Methods and reference #23. Error bars show mean+SEM. Data are representative of 3 independent experiments (n=3 mice in triplicates per group and per experiment).

Extended Data Fig. 2 In vitro expanded γδ17 and γδIFN γδ T cells retain their mitochondrial and lipid phenotypes.

a, Representative flow plots of CD3 and TCRγδ expression on γδ17 and γδIFN T cells expanded in vitro from total spleen/LN cells. b, CD27 expression on in vitro expanded γδ17 and γδIFN γδ T cells. c, IL-17 and IFNγ production by in vitro expanded γδ17 and γδIFN T cells respectively, following activation with PMA/ionomycin. d, Vγ1 and Vγ4 expression on in vitro expanded γδ17 and γδIFN T cells. e, Representative staining of in vitro expanded γδ17 and γδIFN T cells for mitotracker, TMRM, lipidTOX and Bodipy-FL-C16. f, MFI of mitotracker, TMRM, lipidTOX and Bodipy-FL-C16 staining in vitro expanded γδ17 and γδIFN T cells. n=3, data representative of 3 independent experiments. Mitotracker p=0.0026; TMRM p=0.0003; LipidTOX p<0.0001; Bodipy FL-C16 p=0.036. Error bars show mean+SD, **p < 0.01, ***p<0.001, ****p < 0.0001, using two-tailed unpaired Student’s t-test.

Extended Data Fig. 3 γδTN cells can generate γδ17 and γδIFN T cells.

Flow cytometry profiles of thymic γδ T cells from E15 thymic lobes that had been cultured for 7-days in fetal thymic organ culture (E15 + 7dFTOC). CD24+ (γδ24+) precursors downregulate CD24 to become a CD24-CD44-CD45RB- (γδTN) population. γδTN cells are able to become either IL-17-secreting CD44+CD45RB- γδ17 cells, or IFN-γ-producing CD44+CD45RB+ γδIFN cells.

Extended Data Fig. 4 Thymic γδ17 cells are increased upon inhibition of glucose uptake.

Flow cytometry profiles of thymic γδTN (CD44-CD45RB-), γδ17 (CD44+CD45RB-) and γδIFN (CD44+CD45RB+) cells in γδ24- cells from E15 thymic lobes in 7-day FTOC with media containing or not Fasentin. Histograms show the number of γδ17 T cells (p<0.0001) and γδ17/γδIFN ratio (p=0.0028). Data are representative of 2 independent experiments (at least 4 lobes pooled per group per experiment). Error bars show mean±SEM, **p < 0.01, ****p < 0.0001, using two-tailed unpaired Student’s t-test.

Extended Data Fig. 5 Mitochondrial activity identifies Vγ4+ progenitors with distinct effector fates at very early stages.

a, Flow cytometry plots pre-sort, and after sorted TMRElo and TMREhi Vγ4+γδ24+ cells were cultured for 5-days on OP9DL1 cells. Percentage of thymic γδ17 and γδIFN cells generated are displayed in the graph on right. Data are representative of 3 independent experiments (cells sorted from n = 4 independent mice pooled per group per experiment). b, Flow cytometry plots for pre- and post-sort TMREhi and TMRElo Vγ4+γδTN cells that were cultured on OP9-DL1 cells for a further 5-days (plots on right). Histogram shows the percentage of each γδ T cell subset generated from cultured TMRElo and TMREhi Vγ4+γδTN cells. Error bars show mean + SD. Data are representative of 2 independent experiments (at least 4 lobes pooled per group per experiment). Error bars show mean±SD, *p < 0.05, ***p < 0.01, using two-tailed unpaired Student’s t-test.

Extended Data Fig. 6 Distinct mitochondrial activities underlie effector fate of thymic γδ T cell progenitors.

a, Experimental design for single-cell RNAseq (10x Genomics) on TMRElo and TMREhi gd24+ cells from E15 + 2d FTOC. b, Heatmap of differentially upregulated genes from comparison of TMRElo and TMREhi gd24+ cells. Genes are grouped in relation to their function in either OxPhos or glucose metabolism.

Extended Data Fig. 7 Enriched lipid metabolism and higher lipid uptake in γδ17 cells.

a, Experimental set up for bulk RNA-sequencing of PLZF+ (gd17) and PLZF− (gdIFN) cells isolated from PLZF-GFP (Zbtb16GFP) mice. b, LipidTOX MFI in γδ17 (CD27-) and γδIFN (CD27+) T cells from LN cells activated in vitro with IL-1β+IL-23 and IL-12+IL-18 respectively. n=9, data pooled from 3 independent experiments. c, Representative plots of LipidTOX staining and IL-17A, IL-17F or RORγt expression in γδ27- T cells from LNs activated in vitro with IL-1β+IL-23 for 6h. Data representative of 3 independent experiments. d, Bodipy-FL-C16 MFI in γδ17 (CD27-) and γδIFN (CD27+) T cells T cells unstimulated or stimulated in vitro with IL-12+IL-18 or IL-1β+IL-23.(n=3, data from 1 experiment; γδ17 p= 0.0044; γδIFN p=0.8035). Error bars show mean+SD, **p < 0.01, ***p < 0.001, ****p < 0.0001 using one-way ANOVA.

Extended Data Fig. 8 Inhibition of dietary fat uptake reduces tumour growth and γδ17 cells in the tumour.

B16F10-tumour bearing mice were given daily injections of either vehicle or orlistat on days 6-9, and tumours were analysed on day 10. a, Percentage body weight following tumor cell injection; arrows indicate when orlistat or vehicle were administered. b, Tumor volume on days 8-10 following B16F10 inoculation. Absolute numbers c, and LipidTOX staining d, of tumor-infiltrating γδ17 cells on day 10. n=8 biologically independent animals, data from 1 independent experiment. Data represents mean+SD, *p<0.06, **p < 0.01 using unpaired Student’s t-test or one-way ANOVA.

Extended Data Fig. 9 Glucose supplementation diminishes γδ17 cell numbers and proliferation.

a, Flow cytometry profiles of peripheral γδ17 T cells cultured with media containing low (5mM) or high (50mM) doses of glucose. Graph depicts total numbers of γδ17 T cells (p=0.0028). b, Number of proliferating Ki-67+ γδ17 T cells cultured with low or high glucose (p=0.0034). n=6 biologically independent animals, data from 2 independent experiments. Error bars show mean±SEM, **p < 0.01, using unpaired two-tailed Student’s t-test.

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Lopes, N., McIntyre, C., Martin, S. et al. Distinct metabolic programs established in the thymus control effector functions of γδ T cell subsets in tumor microenvironments. Nat Immunol 22, 179–192 (2021). https://doi.org/10.1038/s41590-020-00848-3

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