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CD4+ T cell calibration of antigen-presenting cells optimizes antiviral CD8+ T cell immunity

Nature Immunology volume 24, pages 979–990 (2023)Cite this article

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Abstract

Antiviral CD8+ T cell immunity depends on the integration of various contextual cues, but how antigen-presenting cells (APCs) consolidate these signals for decoding by T cells remains unclear. Here, we describe gradual interferon-α/interferon-β (IFNα/β)-induced transcriptional adaptations that endow APCs with the capacity to rapidly activate the transcriptional regulators p65, IRF1 and FOS after CD4+ T cell-mediated CD40 stimulation. While these responses operate through broadly used signaling components, they induce a unique set of co-stimulatory molecules and soluble mediators that cannot be elicited by IFNα/β or CD40 alone. These responses are critical for the acquisition of antiviral CD8+ T cell effector function, and their activity in APCs from individuals infected with severe acute respiratory syndrome coronavirus 2 correlates with milder disease. These observations uncover a sequential integration process whereby APCs rely on CD4+ T cells to select the innate circuits that guide antiviral CD8+ T cell responses.

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Fig. 1: IFNα/β and CD40 induce distinct and combinatorial responses in cDC1s.
Fig. 2: IFNα/β conditions cDC1s to enhance transcription in response to CD40 stimulation.
Fig. 3: IFNαA conditioning enables CD40 to activate a network of transcription factors that includes p65, IRF1 and FOS.
Fig. 4: Combinatorial responses to IFNα/β and CD40 antibody by DCs and monocytes correlate with milder outcomes of COVID-19.
Fig. 5: Enrichment of NF-κB- and FOS-dependent transcriptional responses in APCs from individuals with mild, but not severe, COVID-19.
Fig. 6: Severe outcomes of COVID-19 are associated with ‘unhelped’ CD8+ T cells.

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Data availability

The RNA-seq data set generated in this study can be accessed via the GEO accession number GSE171690.

Code availability

Code used for the analysis of scRNA-seq and scATAC-seq data is available at https://github.com/schultzelab/Gressier_2022. We also provide the scRNA-seq data sets used in this study and the code to analyze the respective data sets via FASTGenomics (https://beta.fastgenomics.org/p/gressier_2022).

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Acknowledgements

We thank L. Loyal, A. Thiel, C. Iwert, C. Meisel, R. Rudraraju and K. Subbarao for discussions, F. Koay and D. Godfrey for Cxcr6–/– mice and M. Cragg for the human CD40 antibody. The technical expertise in breeding, maintaining and manipulating specific pathogen-free mice by the Doherty Bioresources facility is gratefully acknowledged. We also thank D. Kunkel and J. Keye from the BIH Flow and Mass Cytometry Core Facility for sample acquisition. We are grateful to the Genomics platform at the Walter & Eliza Hall Institute for Medical Research in Melbourne. Our research is supported by the National Health and Medical Research Council of Australia (APP1124815, APP1071916, APP1103895 and APP1154540), the Sylvia & Charles Viertel Charitable Foundation, a 350th Anniversary Research Grant from Merck KgGA, The Advanced Genomic Collaboration and the International Research Training Group (IRTG2168) funded by the German Research Council and The University of Melbourne. B.S. received support from the European Union’s Horizon 2020 research and innovation program (INsTRuCT, 860003) and the German Federal Ministry of Education and Research (BMBF) project RECAST (01KI20337). A.H. is supported by the Jürgen Manchot Foundation. E.L. and S.V.S were supported by the German Federal Ministry of Education and Research through the COVIMMUN project (grant 01KI20343). Furthermore, E.L. received support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant 397484323, TRR259. We thank the NGS Core Facility of the University Hospital Bonn for library preparation and the generation of the sequencing data. We also would like to thank the German COVID-19 OMICS Initiative (DeCOI) for providing access to scRNA-seq data. J.L.S. was supported by the DFG (IRTG2168, INST 217/1011-1 and INST 217/1017-1, Excellence Cluster ImmunoSensation2 (EXC2151/1) under project number 390873048) and SYSCID, receiving funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 733100. We are indebted to the participants, their families and the hospital staff for support, without whom this study would not have been possible.

Author information

Author notes

  1. These authors contributed equally: Elise Gressier, Jonas Schulte-Schrepping.

  2. These authors jointly supervised this work: Susanne V. Schmidt, Sammy Bedoui.

Authors and Affiliations

  1. Department of Microbiology and Immunology at the Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia

    Elise Gressier, Paul G. Whitney, Annabell Bachem, Alexandru Odainic, Carolien van de Sandt, Thi H. O. Nguyen, Kayla Wilson, Clare V. L. Oates, Tina Meischel, Katharina Hochheiser, Marie Greyer, Michele Clarke, Sarah S. Gabriel, Sarah L. Londrigan, Axel Kallies, Katherine Kedzierska, Jan Schroeder, Thomas Gebhardt & Sammy Bedoui

  2. Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany

    Jonas Schulte-Schrepping & Joachim L. Schultze

  3. Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany

    Jonas Schulte-Schrepping & Joachim L. Schultze

  4. Translational Immunology, Berlin Institute of Health (BIH) & Charité University Medicine, Berlin, Germany

    Lev Petrov, Tomislav Kostevc & Birgit Sawitzki

  5. Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany

    Sophia Brumhard, Paula Stubbemann, Anna Hiller, Florian Kurth & Leif E. Sander

  6. Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Core Unit Bioinformatics, Berlin, Germany

    Benedikt Obermayer

  7. Institute of Innate Immunity, University of Bonn, Bonn, Germany

    Jasper Spitzer, Alexandru Odainic, Maike Kreutzenbeck, Eicke Latz & Susanne V. Schmidt

  8. Sydney Cytometry Core Research Facility, Charles Perkins Centre, Centenary Institute and University of Sydney, Sydney, New South Wales, Australia

    Thomas Ashhurst

  9. Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, Victoria, Australia

    Linden. J. Gearing & Paul J. Hertzog

  10. Department of Molecular and Translational Science, Monash University, Clayton, Victoria, Australia

    Linden. J. Gearing & Paul J. Hertzog

  11. Würzburg Institute of Systems Immunology, Max Planck Research Group, Julius-Maximilians-Universität Würzburg, Würzburg, Germany

    Wolfgang Kastenmüller

  12. Institute of Experimental Immunology, University of Bonn, Bonn, Germany

    Christian Kurts & Sammy Bedoui

  13. Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia

    Yu-Chen E. Chen & Kristen J. Radford

  14. Department of Biochemistry, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia

    Michael Chopin

  15. PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE and University of Bonn, Bonn, Germany

    Joachim L. Schultze

Authors
  1. Elise Gressier
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  2. Jonas Schulte-Schrepping
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Contributions

Conceptualization: S. Bedoui, E.G., J.S.-S. and S.V.S. Methodology: P.G.W., A.B., K.H., M.K., M. Clarke, T.H.O.N., P.S., K.W., C.V.L.O., B.O., C.v.d.S., Y.-C.E.C., K.J.R., T.M., M. Chopin, S. Brumhard, S.S.G., K.K. and S.L.L. Formal analysis: E.G., J.S.-S., A.O., J. Spitzer, L.J.G., P.J.H., L.P., T.K., T.A., F.K., J. Schroeder and B.S. Investigation: E.G., J.S.-S., P.G.W., A.B., M.G. and F.K. Writing, original draft: S. Bedoui and E.G. Writing, review and editing: S. Bedoui, E.G., J.S.-S., S.V.S., J.L.S., W.K., A.K., T.G., E.L., C.K. and L.E.S. Funding acquisition: S. Bedoui, T.G., E.L., J.L.S. and S.V.S.

Corresponding authors

Correspondence to Elise Gressier or Sammy Bedoui.

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The authors declare no competing interests.

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Nature Immunology thanks Teunis Geijtenbeek, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ioana Visan, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 CD40 synergizes with varying inflammatory stimuli BMDC1.

a, ‘BMDC1-IFN-αA+CD40’ increase secretion of CCL4, TNF-α and CCL5 (from left to right) over time compared to ‘BMDC1-IFN-αA’, ‘BMDC1-CD40’ and ‘BMDC1-unstimulated’. Data are presented as mean ± s.e.m pooled from 3 independent experiments. Adjusted p-value of statistically significant differences between conditions as assessed by one-way ANOVA indicated. b, Changes in Il15 and Cxcl16 expression in ‘BMDC1-IFN-αA+CD40’ and ‘BMDC1-IFN-β+CD40’ compared to ‘BMDC1-IFN-αA’ or ‘BMDC1-IFN-β’ respectively and to ‘BMDC1-CD40’ and ‘BMDC1-unstimulated’. c. Tnf and Ccl4 in expression in BMDC1s stimulated with LPS, CpG or poly(I:C) for 6 h with or without CD40 Ab for the last 30 min. b-c, Data are presented as mean ± s.e.m pooled from 3 independent experiments. Adjusted p-value of statistically significant differences between conditions as assessed by one-way ANOVA indicated; ns = non-significant. d, Percent of MHC-IIhi CD8+ DCs from IFNαR-deficient (Ifnar2−/−) and WT mice naïve or 2 days after epicutaneous HSV-1 infection. Data are presented as mean ± s.e.m pooled from 7 independent experiments (n≥5 per experiment). Statistically significant differences between conditions as assessed by Mann-Whitney test; two-tailed p-value indicated; ns = non-significant. e. ‘BMDC1-IFN-αA’ and ‘BMDC1-unstimulated’ increase CD40 expression to comparable levels over time. Data are presented as mean ± s.e.m pooled from 3 independent experiments. Two-way ANOVA performed between the corresponding conditions ns = non-significant.

Extended Data Fig. 2 CD40 stimulation induces successive waves of transcriptional regulation in IFN-αA-conditioned BMDC1.

a, Genes included in modules 1, 2 and 3 from the co-expression analysis (Fig. 2e) displayed as heatmap. b, Top GO-terms associated with the genes included in modules 1, 2 and 3 (Fig. 2e). c, Representative immunoblotting of IκBα degradation and P65 phosphorylation in 'BMDC1-IFNαA+CD40-15min', 'BMDC1-IFNαA-30min' and ‘BMDC1-IFN-αA+CD40-4h’ compared to ‘BMDC1-IFN-αA’, ‘BMDC1-CD40’ and ‘BMDC1-unstimulated’. Full gels of the two independent experiments are displayed below. Probing of β-actin and/or total P65 served as loading control.

Extended Data Fig. 3 Enrichment of APC with ‘help’-dependent transcriptional profiles in patients with moderate COVID-19.

a, Differentially expressed genes in DCs comparing disease severity and disease stage that correspond to the ‘CD40 unresponsive’, ‘amplified’ and ‘combinatorial’. Data from published DC-enriched scRNAseq data26. b, Average gene expression in CD14+ monocytes per sample across selected key genes in a cohort of control (n=5), mild (n=5) and severe (n=5) COVID-19 patients and 7 samples derived from patients with IFN-AAB. c. Combined data set across 263 samples including controls (n=39), mild COVID-19 (WHO 1-3, n=79), moderate COVID-19 (WHO 4-5, n=82), severe COVID-19 (WHO 6-8, n=52), severe COVID-19 with IFN-AAB (WHO 7-8, n=11). Samples are stratified by disease severity according to the WHO ordinal scale as indicated and segregated by time point of sample collection relative to the onset of symptoms where available. c, Single-sample GSVA of the ‘CD40 unresponsive’, ‘amplified’ and ‘combinatorial’ gene signatures in monocytes from COVID-19 and control samples of the combined data set in b. stratified by disease severity and plotted as box plots of the enrichment scores. Wilcoxon rank-sum test p-value is shown.

Extended Data Fig. 4 Enrichment of CD8+ T cells with ‘help’-dependent transcriptional profiles in patients with moderate COVID-19.

a, Differential expression of selected key genes in CD8+ T cells derived from PBMCs scRNA-seq data of moderate and severe cases of COVID-19 and healthy HC originally as published36. b, AUCell enrichment of CD8+ T cells for ‘helped’ and ‘unhelped’ T cell gene signatures derived from RNA-seq analysis of CD8+ T cells primed in the presence or absence of CD4+ T cell help. Data are stratified by disease severity and plotted as violin plots of the ‘Area Under the Curve’ (AUC) scores. c. AUCell enrichment of CD8+ T cells for ‘helped’ and ‘unhelped’ T cell gene signatures derived from RNA-seq analysis of CD8+ T cells primed in the presence or absence of CD4+ T cell help. Data are derived from scRNA-seq of nasopharyngeal and bronchial samples stratified by disease severity and plotted as violin plots of the ‘Area Under the Curve’ (AUC) scores37. d, Heatmap showing z-scaled expression values of indicated proteins across the clusters identified in the CyTOF data of individuals with COVID-19 and HCs. e. Box plots showing relative cluster abundances of selected clusters across COVID-19 and control samples stratified according to disease severity and presence of IFN-AAB. Benjamini-Hochberg corrected pairwise Wilcoxon p-values are shown.

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Gressier, E., Schulte-Schrepping, J., Petrov, L. et al. CD4+ T cell calibration of antigen-presenting cells optimizes antiviral CD8+ T cell immunity. Nat Immunol 24, 979–990 (2023). https://doi.org/10.1038/s41590-023-01517-x

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