Abstract
CD8+ T cells are essential components of the immune response against viral infections and tumours, and are capable of eliminating infected and cancerous cells. However, when the antigen cannot be cleared, T cells enter a state known as exhaustion1. Although it is clear that chronic antigen contributes to CD8+ T cell exhaustion, less is known about how stress responses in tissues regulate T cell function. Here we show a new link between the stress-associated catecholamines and the progression of T cell exhaustion through the β1-adrenergic receptor ADRB1. We identify that exhausted CD8+ T cells increase ADRB1 expression and that exposure of ADRB1+ T cells to catecholamines suppresses their cytokine production and proliferation. Exhausted CD8+ T cells cluster around sympathetic nerves in an ADRB1-dependent manner. Ablation of β1-adrenergic signalling limits the progression of T cells towards the exhausted state in chronic infection and improves effector functions when combined with immune checkpoint blockade (ICB) in melanoma. In a pancreatic cancer model resistant to ICB, β-blockers and ICB synergize to boost CD8+ T cell responses and induce the development of tissue-resident memory-like T cells. Malignant disease is associated with increased catecholamine levels in patients2,3, and our results establish a connection between the sympathetic stress response, tissue innervation and T cell exhaustion. Here, we uncover a new mechanism by which blocking β-adrenergic signalling in CD8+ T cells rejuvenates anti-tumour functions.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The dataset generated and analysed during the current study has been deposited into the GEO repository under accession number GSE213607. The following published datasets were also used: PRJNA497086, GSE122713, GSE157829, GSE155698 and GSE200997. The mouse reference genome mm10 and the human reference genome GRCh38 were used for RNA-seq and scRNA-seq analysis. Source data are provided with this paper.
Code availability
The R scripts that support the findings of this study are available from the author’s GitHub webpage under the link https://github.com/aglobig.
References
Amezquita, R. A. & Kaech, S. M. Immunology: the chronicles of T-cell exhaustion. Nature 543, 190–191 (2017).
Xie, H. et al. Association of increased circulating catecholamine and glucocorticoid levels with risk of psychological problems in oral neoplasm patients. PLoS ONE 9, e99179 (2014).
Bastos, D. B. et al. Circulating catecholamines are associated with biobehavioral factors and anxiety symptoms in head and neck cancer patients. PLoS ONE 13, e0202515 (2018).
Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).
Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).
Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).
Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).
Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).
Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).
Hudson, W. H. et al. Proliferating transitory T cells with an effector-like transcriptional signature emerge from PD-1+ stem-like CD8+ T cells during chronic infection. Immunity 51, 1043–1058.e4 (2019).
Raju, S. et al. Identification of a T-bethi quiescent exhausted CD8 T cell subpopulation that can differentiate into TIM3+CX3CR1+ effectors and memory-like cells. J. Immunol. 206, 2924–2936 (2021).
Zehn, D., Thimme, R., Lugli, E., de Almeida, G. P. & Oxenius, A. ‘Stem-like’ precursors are the fount to sustain persistent CD8+ T cell responses. Nat. Immunol. 23, 836–847 (2022).
Chung, H. K., McDonald, B. & Kaech, S. M. The architectural design of CD8+ T cell responses in acute and chronic infection: parallel structures with divergent fates. J. Exp. Med. https://doi.org/10.1084/jem.20201730 (2021).
Chen, Y. et al. BATF regulates progenitor to cytolytic effector CD8+ T cell transition during chronic viral infection. Nat. Immunol. 22, 996–1007 (2021).
Kasmani, M. Y. et al. Clonal lineage tracing reveals mechanisms skewing CD8+ T cell fate decisions in chronic infection. J. Exp. Med. https://doi.org/10.1084/jem.20220679 (2023).
Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).
Schmiechen, Z. C. & Stromnes, I. M. Mechanisms governing immunotherapy resistance in pancreatic ductal adenocarcinoma. Front. Immunol. 11, 613815 (2020).
Mueller, S. N. Neural control of immune cell trafficking. J. Exp. Med. https://doi.org/10.1084/jem.20211604 (2022).
Devi, S. et al. Adrenergic regulation of the vasculature impairs leukocyte interstitial migration and suppresses immune responses. Immunity 54, 1219–1230.e7 (2021).
Grebe, K. M. et al. Sympathetic nervous system control of anti-influenza CD8+ T cell responses. Proc. Natl Acad. Sci. USA 106, 5300–5305 (2009).
Sanders, V. M. et al. Differential expression of the β2-adrenergic receptor by Th1 and Th2 clones: implications for cytokine production and B cell help. J. Immunol. 158, 4200–4210 (1997).
Daher, C. et al. Blockade of β-adrenergic receptors improves CD8+ T-cell priming and cancer vaccine efficacy. Cancer Immunol. Res. 7, 1849–1863 (2019).
Felten, S. Y. & Olschowka, J. Noradrenergic sympathetic innervation of the spleen: II. Tyrosine hydroxylase (TH)-positive nerve terminals form synapticlike contacts on lymphocytes in the splenic white pulp. J. Neurosci. Res. 18, 37–48 (1987).
Giles, J. R. et al. Human epigenetic and transcriptional T cell differentiation atlas for identifying functional T cell-specific enhancers. Immunity 55, 557–574.e7 (2022).
Sassone-Corsi, P. The cyclic AMP pathway. Cold Spring Harb. Perspect. Biol. https://doi.org/10.1101/cshperspect.a011148 (2012).
Maine, C. J., Teijaro, J. R., Marquardt, K. & Sherman, L. A. PTPN22 contributes to exhaustion of T lymphocytes during chronic viral infection. Proc. Natl Acad. Sci. USA 113, e7231–e7239 (2016).
Sandu, I., Cerletti, D., Claassen, M. & Oxenius, A. Exhausted CD8+ T cells exhibit low and strongly inhibited TCR signaling during chronic LCMV infection. Nat. Commun. 11, 4454 (2020).
Staron, M. M. et al. The transcription factor FoxO1 sustains expression of the inhibitory receptor PD-1 and survival of antiviral CD8+ T cells during chronic infection. Immunity 41, 802–814 (2014).
Riley, J. L. PD-1 signaling in primary T cells. Immunol. Rev. 229, 114–125 (2009).
Aandahl, E. M. et al. Protein kinase A type I antagonist restores immune responses of T cells from HIV-infected patients. FASEB J. 12, 855–862 (1998).
Joshi, N. S. et al. Inflammation directs memory precursor and short-lived effector CD8+ T cell fates via the graded expression of T-bet transcription factor. Immunity 27, 281–295 (2007).
Sarkar, S. et al. Functional and genomic profiling of effector CD8 T cell subsets with distinct memory fates. J. Exp. Med. 205, 625–640 (2008).
Gerlach, C. et al. The chemokine receptor CX3CR1 defines three antigen-experienced CD8 T cell subsets with distinct roles in immune surveillance and homeostasis. Immunity 45, 1270–1284 (2016).
Gorre, F. & Vandekerckhove, H. Beta-blockers: focus on mechanism of action. Which beta-blocker, when and why? Acta Cardiol. 65, 565–570 (2010).
Baker, J. G. The selectivity of β-adrenoceptor antagonists at the human β1, β2 and β3 adrenoceptors. Br. J. Pharmacol. 144, 317–322 (2005).
Rawla, P., Sunkara, T. & Gaduputi, V. Epidemiology of pancreatic cancer: global trends, etiology and risk factors. World J. Oncol. 10, 10–27 (2019).
Woods, S. C. & Porte, D. Jr Neural control of the endocrine pancreas. Physiol. Rev. 54, 596–619 (1974).
Bayne, L. J. et al. Tumor-derived granulocyte–macrophage colony-stimulating factor regulates myeloid inflammation and T cell immunity in pancreatic cancer. Cancer Cell 21, 822–835 (2012).
Stromnes, I. M. et al. Targeted depletion of an MDSC subset unmasks pancreatic ductal adenocarcinoma to adaptive immunity. Gut 63, 1769–1781 (2014).
Mohammadpour, H. et al. β2 adrenergic receptor-mediated signaling regulates the immunosuppressive potential of myeloid-derived suppressor cells. J. Clin. Invest. 129, 5537–5552 (2019).
Kokolus, K. M. et al. Beta blocker use correlates with better overall survival in metastatic melanoma patients and improves the efficacy of immunotherapies in mice. Oncoimmunology 7, e1405205 (2018).
Gandhi, S. et al. Phase I clinical trial of combination propranolol and pembrolizumab in locally advanced and metastatic melanoma: safety, tolerability, and preliminary evidence of antitumor activity. Clin. Cancer Res. 27, 87–95 (2021).
Fjæstad, K. Y. et al. Blockade of β-adrenergic receptors reduces cancer growth and enhances the response to anti-CTLA4 therapy by modulating the tumor microenvironment. Oncogene 41, 1364–1375 (2022).
Ganesan, A. P. et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 18, 940–950 (2017).
Milner, J. J. et al. Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours. Nature 552, 253–257 (2017).
Banchereau, R. et al. Intratumoral CD103+ CD8+ T cells predict response to PD-L1 blockade. J. Immunother. Cancer https://doi.org/10.1136/jitc-2020-002231 (2021).
Kennedy, O. J. et al. Prognostic and predictive value of β-blockers in the EORTC 1325/KEYNOTE-054 phase III trial of pembrolizumab versus placebo in resected high-risk stage III melanoma. Eur. J. Cancer 165, 97–112 (2022).
Udumyan, R. et al. Beta-blocker drug use and survival among patients with pancreatic adenocarcinoma. Cancer Res. 77, 3700–3707 (2017).
Beg, M. S. et al. Impact of concurrent medication use on pancreatic cancer survival—SEER–Medicare analysis. Am. J. Clin. Oncol. 41, 766–771 (2018).
Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).
Pircher, H., Bürki, K., Lang, R., Hengartner, H. & Zinkernagel, R. M. Tolerance induction in double specific T-cell receptor transgenic mice varies with antigen. Nature 342, 559–561 (1989).
Wang, J. et al. UV-induced somatic mutations elicit a functional T cell response in the YUMMER1.7 mouse melanoma model. Pigment Cell Melanoma Res. 30, 428–435 (2017).
Li, J. et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity 49, 178–193.e7 (2018).
Nüssing, S. et al. Efficient CRISPR/Cas9 gene editing in uncultured naive mouse T cells for in vivo studies. J. Immunol. 204, 2308–2315 (2020).
Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Research 5, 2122 (2016).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 19, 3796–3798 (2021).
Radtke, A. J. et al. IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nat. Protoc. 17, 378–401 (2022).
Radtke, A. J. et al. IBEX: a versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc. Natl Acad. Sci. USA 117, 33455–33465 (2020).
Schalper, K. A. et al. Objective measurement and clinical significance of TILs in non-small cell lung cancer. J. Natl Cancer Inst. 107, dju435 (2015).
Acknowledgements
We thank all members of the Kaech Laboratory for helpful discussions; K. Chung for support with ribonucleoprotein electroporation experiments; D. Engle and K. Peck for support with ultrasound experiments; and E. Carpenter and M. Pasca di Magliano for clinical annotation of transcriptomic human PDAC data. Adrb1fl/fl mice were a gift from S. Thomas, University of Pennsylvania, PA, USA. A.-M.G. was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, GL 991/1-1). S.Z. was supported by K00CA222741. This work was supported by grants from the NIH 5 R01 CA240909 (S.M.K.), 5 R01 CA216101 (S.M.K.), R37CA245154 (K.A.S.), R01CA262377 (K.A.S.) and P50CA196530 (R. Herbst). This work was supported by the Flow Cytometry Core Facility of the Salk Institute with funding from NIH-NCI CCSG: P30 014195 and Shared Instrumentation Grant S10-OD023689 (Aria Fusion cell sorter). This work was further supported by the Waitt Advanced Biophotonics Core Facility of the Salk Institute with funding from NIH-NCI CCSG: P30 014195 and the Waitt Foundation. The NGS Core Facility of the Salk Institute is supported by NIH-NCI CCSG: P30 014195, the Chapman Foundation and the Helmsley Charitable Trust. This research was supported by the Intramural Research Program of NIAID, NIH and NIGMS MOSAIC K99/R00 GM147841. Tissue Technology Shared Resource is supported by a National Cancer Institute Cancer Center Support Grant (CCSG Grant P30CA23100).
Author information
Authors and Affiliations
Contributions
A.-M.G. and S.M.K. conceptualized, designed and supervised the research. A.-M.G. and S.Z. performed experiments with assistance from J.R., N.A.-O., M.H., O.C., F.A.H., D.C., G.S. and J.W. A.-M.G. analysed the generated data. J.R., V.I.M. and J.G. performed and analysed imaging experiments. A.-M.G. performed gene expression analyses. C.O., S.P., R.N.G., K.A.S. and B.E. provided scientific input. A.-M.G. and S.M.K. prepared the manuscript.
Corresponding author
Ethics declarations
Competing interests
S.M.K. is a scientific advisory board member for Pfizer, EvolveImmune Therapeutics, Arvinas and Affini-T, and an Academic Editor at the Journal of Experimental Medicine. K.A.S. has received honoraria for consulting/advisory board or speaker from Clinica Alemana Santiago, Shattuck Labs, AstraZeneca, EMD Serono, Takeda, Torque/Repertoire Therapeutics, Agenus, Genmab, OnCusp, Parthenon Therapeutics, CDRlife, Bristol-Myers Squibb, Roche, Molecular Templates, PeerView, Forefront Collaborative, Janssen and Merck. The K.A.S. Laboratory at Yale University receives funding from Tesaro/GSK, Takeda, Merck, Bristol-Myers Squibb, AstraZeneca, Ribon Therapeutics, Boehringer-Ingelheim and Roche. The remaining authors declare no conflicts of interest.
Peer review
Peer review information
Nature thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Exhausted CD8+ T cells express ADRB1 and are associated with sympathetic nerves in the spleen and in tumors.
a, Volcano plot depicting genes upregulated in terminal exhausted cells vs in progenitor exhausted cells respectively. Data from PRJNA497086, TEXprog = CD101− TIM3−, TEXeff = CD101− TIM3+, TEXterm = CD101+ TIM3+. b, Frequency of ADRB1 expressing cells within the different exhausted subsets depicted at d30 p.i. with LCMV clone 13. PD-1− = PD-1− P14+, TEXprog = TCF1+ PD-1+ P14+, TEXeff = CX3CR1+ TIM3+ PD-1+ P14+, TEXterm = CD101+ TIM3+ PD-1+ P14+. N = 7 per subset pooled from 2 independent experiments, Friedman test with Dunn’s multiple comparisons test. c, Representative flow plots depicting ADRB1 expression on naïve CD8+ T cells (CD44−) and gp33+ CD8+ T cells from mice infected with LCMV Armstrong (d8) and LCMV clone 13 (d30), representative of 3 independent experiments. d, Representative overview image of WT P14+ cells (cyan), CD101 expression (magenta), and tyrosine hydroxylase (yellow) at d14 p.i. in the spleen of a recipient mouse infected with LCMV clone 13. Four different regions such as this were imaged across the spleens of two different mice from 2 independent experiments and the shortest distance to TH signal for CD101+ P14+ cells vs CD101− P14+ cells was calculated. Image collected using ×20 objective tiled across a 3×3 region. e + f, Representative images of nerves within PDAC tumors with T cell clusters (representative of 5 individual tumors from 2 independent experiments). Scale bar: 50 µm. Image was collected using a ×40 objective tiled across the entire PDAC tumor within the pancreas, with 1.5 µm z step sizes to acquire a 30 µm imaging depth. g, Density of TH+ stromal cells across 164 primary human NSCLCs. h, Density of CD8+ TILs in NSCLCs with low (bottom 80%, n = 130) and high TH (top 20%, n = 34) expression. Mann-Whitney test. Unless otherwise specified, mean ± SEM is indicated in scatter plots. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 2 Catecholamine signaling through ADRB1 impairs CD8+ T cell cytokine production and proliferation.
a, Verification of Adrb1 overexpression. qPCRs were performed in duplicates. b, Flow plots of cytokine production by Adrb1 OE P14+ cells and control empty vector (EV) P14+ cells. Cells were stimulated with gp33 in the presence or absence of 10 µM adrenaline (A) or noradrenaline (NA). Plots are gated on GFP+ CD8+ T cells and indicative of one of 3 independent experiments. c, Cytokine production by control empty vector (EV) retroviral (RV)-transduced P14+ cells (n = 3 individual experiments). Cells were stimulated with gp33 in the presence or absence of 10 µM A or NA. d, Representative flow plots depicting frequency of Adrb1 overexpressing (OE) P14+ cells and control (EV) P14+ cells at d0 and d6 of culture. Cells were mixed at a 1:1 ratio and cultured for 6 days total with and without the addition of 10 µM A or NA. Representative of 4 independent experiments. e, Ratio of Adrb1 OE P14+ cells to control (EV) P14+ cells over 6 days of culture. Cells were mixed at a 1:1 ratio and cultured for 6 days total with and without the addition of 10 µM A or NA. Quantification from 4 independent experiments. Unless otherwise specified, mean ± SEM is indicated in scatter plots.
Extended Data Fig. 3 Adrb1 knockout prevents terminal differentiation of antigen-specific CD8+ T cells.
a, Verification of knockout in Adrb1fl/fl Granzyme BCre+ mice (Adrb1 cKO). Splenocytes were isolated and stimulated with anti-CD3/CD28. Cells were cultured in vitro for 4 days with IL-2 and subsequently CD8+ T cells were sorted. Adrb1 expression was assessed with qPCR performed in triplicates. b, Expression of PD-1 on Adrb1 cKO P14+ cells (red) and WT P14+ cells (black) at d7 p.i. (n = 9) and d40 p.i. (n = 16). Paired t-test. Flow data for both d7 and d40 are each pooled from 3 independent experiments. c, Cytokine production of Adrb1 cKO P14+ cells (red) and wild type P14+ cells (black) at d40 p.i. after 6 h stimulation with gp33. N = 16, paired t-test, pooled from 3 independent experiments. d, Viral titers in spleen from Adrb1 cKO and WT P14+ recipients at d36 p.i. with LCMV clone13 and treated with anti-PD-L1 or IgG2B from d23-d36 p.i. Ordinary one-way ANOVA with Holm–Šídák’s multiple comparisons test with a single pooled variance. WT IgG2B n = 7, WT anti-PD-L1 n = 8, cKO IgG2B n = 8, cKO anti-PD-L1 n = 8, pooled from 2 independent experiments. e, Verification of Crem knockdown using shCrem and qPCR performed in triplicates to determine Crem expression. Splenocytes were transduced with shCrem or shCd19 as control and cultured for 3 days before sorting on Ametrine+ CD8+ T cells. Unpaired t-test. f, Frequency and phenotype of Crem knockdown P14+ and control knockdown P14+ at d7 p.i. with LCMV clone 13. 15,000 Crem knockdown P14+ cells and control knockdown P14+ cells each were mixed at a 1:1 ratio and transferred into recipient mice that were infected with LCMV clone 13 on the same day (n = 15, pooled from 3 independent experiments). Wilcoxon test. g, Representative image of Adrb1 cKO P14+ (red) and WT P14+ cells (cyan) at d31 p.i. in the spleen of a recipient mouse infected with LCMV clone 13. Image is representative of 3 independent regions and 2 independent experiments. B220 stain in blue and F4/80 stain in grey. Image was collected using a ×20 objective tiled across a 3×3 region. Unless otherwise specified, mean ± SEM is indicated in scatter plots. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 4 Pharmacological blockade of ADRB1 prevents advanced exhaustion differentiation of antigen-specific CD8+ T cells.
a + b, Absolute cell counts of gp33+ CD8+ T cells (a), and of different exhausted subsets of gp33+ CD8+ T cells (b) isolated from the spleens of mice treated with atenolol or control water during chronic infection with LCMV clone 13, assessed at d37 p.i. (n = 7 per group, pooled from 3 independent experiments). Mann-Whitney test. c, Absolute cell counts of cytokine producing cells after antigen-specific stimulation with gp33. Cells were isolated from the spleens of mice treated with atenolol or control water during chronic infection with LCMV clone 13, assessed at d37 p.i. (n = 8 per group, pooled from 3 independent experiments). Mann-Whitney test. Unless otherwise specified, mean ± SEM is indicated in scatter plots. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 5 Pharmacological blockade of ADRB1 synergizes with immune checkpoint blockade to increase T cell function.
a, Flow plots depicting ADRB1 expression by different subsets of exhausted T cells isolated from MC38 tumors, plots are gated on the indicated cell populations (left panel). Quantification of ADRB1 expression on different subsets of exhausted CD8+ T cells isolated from MC38 tumors implanted into wild type B6 mice (right panel, n = 16 each, pooled from 3 independent experiments). Friedman test with Dunn’s multiple comparisons test. b, cAMP levels in ADRB1− vs ADRB1+ CD8+ T cells isolated from MC38 tumors (left panel, n = 8), Wilcoxon test was used to determine statistical significance. cAMP levels in different subsets of exhausted CD8+ T cells isolated from MC38 tumors (right panel, n = 8 each). Friedman test with Dunn’s multiple comparisons test. Data pooled from 2 independent experiments. PD-1− = PD-1−CD8+, TEXprog = TIM3− PD-1+ CD8+, TEXeff = CX3CR1+ TIM3+ PD-1+ CD8+, TEXterm = CD101+ TIM3+ PD-1+ CD8+. c, Expression analysis of the indicated exhaustion markers in ADRB1high and ADRB1low CD8+ T cells in RNA Seq data generated from 16 human colorectal cancer samples. Data from GSE200997. Statistics were calculated using a linear mixed model. Boxplots show median. The lower and upper hinges correspond to the first and third quartiles. The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge. d, cAMP levels in ADRB1− vs ADRB1+ CD8+ T cells isolated from YUMMER tumors (n = 4), Wilcoxon test was used to determine statistical significance. cAMP levels in different subsets of exhausted CD8+ T cells isolated from YUMMER tumors (n = 4 each). Friedman test with Dunn’s multiple comparisons test. Data representative of one of 2 independent experiments. PD-1− = PD-1− CD8+, TEXprog = TIM3− PD-1+ CD8+, TEXeff = CX3CR1+ TIM3+ PD-1+ CD8+, TEXterm = CD101+ TIM3+ PD-1+ CD8+. e, Normalized tumor mass of YUMMER tumors from mice under the indicated treatment conditions relative to IgG control (IgG n = 9, atenolol + ICB n = 8, atenolol + ICB + CD8+ depletion n = 9), pooled from 2 independent experiments. Kruskal-Wallis Test with Dunn’s multiple comparisons test. f, ADRB1/ADRB2 selectivity of atenolol and CGP 20712A according to ref. 35. g, Schematic of the experimental setup used in the subsequent figure panels showing YUMMER tumor experiments (left panel). Normalized tumor mass of YUMMER tumors relative to IgG control (IgG n = 5, CGP 20712A n = 7, ICB n = 8, CGP 20712A + ICB n = 8, pooled from 2 independent experiments). Kruskal-Wallis Test with Dunn’s multiple comparisons test (right panel). h, Flow cytometric assessment of production of cytokines by CD8+ T cells after stimulation with PMA/ionomycin (IgG n = 5, CGP 20712A n = 7, ICB n = 8, CGP 20712A + ICB n = 7, pooled from 2 independent experiments). Kruskal-Wallis Test with Dunn’s multiple comparisons test. Unless otherwise specified, mean ± SEM is indicated in scatter plots. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 6 Exhausted CD8+ T cells in PDAC tumors express ADRB1.
a, Representative flow cytometry plots depicting ADRB1 expression of different CD8+ T cell subsets in PDAC tumors implanted into wild type B6 mice. Plots are gated on the indicated cell populations and representative of 2 independent experiments. b, Expression analysis of the indicated exhaustion markers in ADRB1high and ADRB1low CD8+ T cells in RNA Seq data generated from 16 human pancreatic cancer samples. Data from GSE155698. Statistics were calculated using a linear mixed model. Boxplots show median. The lower and upper hinges correspond to the first and third quartiles. The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 7 Pharmacological blockade of ADRB1 or ADRB2 alone is ineffective in PDAC.
a, cAMP levels in ADRB1− vs ADRB1+ CD8+ T cells isolated from PDAC tumors (left panel, n = 6 pooled from 2 independent experiments), Wilcoxon test. cAMP levels in different subsets of exhausted CD8+ T cells isolated from PDAC tumors (second to left panel, n = 6 each pooled from 2 independent experiments). Friedman test with Dunn’s multiple comparisons test. PD-1− = PD-1− CD8+, TEXprog = TIM3− PD-1+ CD8+, TEXeff = CX3CR1+ TIM3+ PD-1+ CD8+, TEXterm = CD101+ TIM3+ PD-1+ CD8+. CREM expression in ADRB1+ vs ADRB1− CD8+ T cells isolated from PDAC tumors (second to right panel, n = 6 pooled from 2 independent experiments), Wilcoxon test. CREM expression in different subsets of exhausted CD8+ T cells isolated from PDAC tumors (right panel, n = 10 each, pooled from 3 independent experiments). Friedman test with Dunn’s multiple comparisons test. PD-1− = PD-1− CD8+, TEXprog = SLAMF6+ PD-1+ CD8+, TEXeff = CX3CR1+ TIM3+ PD-1+ CD8+, TEXterm = CD101+ TIM3+ PD-1+ CD8+. b, Normalized tumor mass of PDAC tumors relative to IgG control (IgG n = 5, atenolol n = 5, ICB n = 5, atenolol + ICB n = 5), representative of 2 independent experiments. Kruskal-Wallis Test with Dunn’s multiple comparisons test. c, ADRB1/ADRB2 selectivity of atenolol and ICI 118551 according to ref. 35 (left panel). Normalized tumor mass of PDAC tumors relative to IgG control (IgG n = 9, ICI 118551 n = 9, ICB n = 8, ICI 118551 + ICB n = 10), pooled from 2 independent experiments. ICI 118551 hydrochloride was administered at 0.2 µg/g i.p. daily. Kruskal-Wallis Test with Dunn’s multiple comparisons test. Unless otherwise specified, mean ± SEM is indicated in scatter plots. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 8 Single cell RNA Seq analysis of tumor infiltrating T cells from PDAC tumors.
UMAP and violin plots depicting the MAGIC imputed expression of the indicated marker genes for the T cell clusters within the PDAC scRNA Seq dataset.
Extended Data Fig. 9 Pharmacological blockade of adrenergic receptors reprograms tumor infiltrating T cells in PDAC.
a, Heatmap showing the top 10 marker genes for each T cell cluster. Expression is normalized across cells. b, Heatmap showing differentially regulated pathways in CD8+ T cells extracted from the PDAC scRNA Seq dataset. T cells were grouped by condition and z score of median pathway activity is shown. c, UMAP visualization of CD8+ T cells extracted from the PDAC scRNA Seq dataset. Cells were annotated using SingleR with the published exhaustion subsets from PRJNA497086. Stacked bar graphs show frequency of exhaustion subsets per treatment condition as annotated per PRJNA497086. d, Expression of the beta-blocker signature identified in Fig. 5l in human T cells isolated from PDAC patients under beta-blocker therapy (n = 2) vs PDAC patients without beta-blocker therapy (n = 15). Data from GSE155698. Tukey’s test. Boxplots show median. The lower and upper hinges correspond to the first and third quartiles. The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge. Two-sided statistical tests were used. **** indicates a p value < 0.0001, ***<0.001, **<0.01, *<0.05.
Extended Data Fig. 10 Pharmacological blockade of adrenergic receptors does not directly affect tumor cell proliferation.
a, Tumor expression of Ki67 relative to IgG determined by IHC in YUMMER tumors from mice under the indicated treatment conditions (IgG n = 14, atenolol n = 6, pooled from 4 independent experiments). Mann Whitney test. b, Tumor expression of Ki67 relative to IgG determined by IHC in YUMMER tumors from mice under the indicated treatment conditions (IgG n = 7, CGP 20712A n = 7, pooled from 2 independent experiments). Mann Whitney test. c, Tumor expression of Ki67 relative to IgG determined by IHC in PDAC tumors from mice under the indicated treatment conditions (IgG n = 8, propranolol n = 8, pooled from 2 independent experiments). Mann Whitney test. Unless otherwise specified, mean ± SEM is indicated in scatter plots. Two-sided statistical tests were used.
Supplementary information
Supplementary Table 1
Marker genes for T cell clusters 1–8 in PDAC scRNA-seq data. Differentially expressed genes between the current group and all other groups were calculated using a binomial test with LFC > 0.1 and FDR < 0.05.
Supplementary Table 2
Differentially expressed genes in the respective treatment conditions compared with IgG in PDAC scRNA-seq data.
Source data
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Globig, AM., Zhao, S., Roginsky, J. et al. The β1-adrenergic receptor links sympathetic nerves to T cell exhaustion. Nature 622, 383–392 (2023). https://doi.org/10.1038/s41586-023-06568-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-06568-6
This article is cited by
-
Stress can be exhausting for T cells
Nature Reviews Immunology (2023)
-
Stress can be exhausting for T cells
Nature Reviews Drug Discovery (2023)
-
An adrenaline kick to exhaust T cells
Nature Immunology (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
