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Targeting PGLYRP1 promotes antitumor immunity while inhibiting autoimmune neuroinflammation

Nature Immunology volume 24, pages 1908–1920 (2023)Cite this article

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Abstract

Co-inhibitory and checkpoint molecules suppress T cell function in the tumor microenvironment, thereby rendering T cells dysfunctional. Although immune checkpoint blockade is a successful treatment option for multiple human cancers, severe autoimmune-like adverse effects can limit its application. Here, we show that the gene encoding peptidoglycan recognition protein 1 (PGLYRP1) is highly coexpressed with genes encoding co-inhibitory molecules, indicating that it might be a promising target for cancer immunotherapy. Genetic deletion of Pglyrp1 in mice led to decreased tumor growth and an increased activation/effector phenotype in CD8+ T cells, suggesting an inhibitory function of PGLYRP1 in CD8+ T cells. Surprisingly, genetic deletion of Pglyrp1 protected against the development of experimental autoimmune encephalomyelitis, a model of autoimmune disease in the central nervous system. PGLYRP1-deficient myeloid cells had a defect in antigen presentation and T cell activation, indicating that PGLYRP1 might function as a proinflammatory molecule in myeloid cells during autoimmunity. These results highlight PGLYRP1 as a promising target for immunotherapy that, when targeted, elicits a potent antitumor immune response while protecting against some forms of tissue inflammation and autoimmunity.

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Fig. 1: Coexpression of Pglyrp1 with co-inhibitory receptors in T cells.
Fig. 2: PGLYRP1-deficient mice have enhanced antitumor immunity.
Fig. 3: scRNA-seq of tumor-infiltrating T cells in PGLYRP1-deficient mice.
Fig. 4: PGLYRP1 deficiency results in protection from EAE.
Fig. 5: Pglyrp1 expression in myeloid cells contributes to EAE pathology.
Fig. 6: scRNA-seq changes in monocytes and neutrophils in PGLYRP1-deficient mice during EAE.

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Data availability

Bulk and single-cell RNA-seq data (related to Fig. 2h–k) have been deposited in Gene Expression Omnibus (GEO) under the accession code GSE223896. Preexisting data accessed in Fig. 1a–c are available in GEO under accession number GSE113968 (subseries numbers are GSE113262, GSE113280, GSE113689, GSE113807 and GSE113811), and the data accessed in Extended Data Fig. 1 are available in GEO under accession number GSE120575. All other data are available in the main article and Supplementary Information or from the corresponding author upon reasonable request.

Code availability

All code is available on GitHub at https://github.com/lhuang1/Pglyrp1_Tumor_EAE.

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Acknowledgements

We would like to thank all members of the Kuchroo laboratory for helpful discussions and feedback. We thank J. Xia, H. Stroh, E. A. Greenfield, R. K. Krishnan and D. Kozoriz for their assistance and technical support and L. Gaffney for help with figures. Additionally, we would like to thank M. Collins for critical feedback on the manuscript.

This work was supported by National Institute of Health grants (P01AI073748, P01AI039671, P01AI056299 and R01AI144166) to V.K.K. and R01CA187975 (A.C.A.). A.S. was supported by a German Academic Scholarship Foundation (Studienstiftung des Deutschen Volkes) PhD fellowship. A.R. was supported by the Klarman Cell Observatory and Howard Hughes Medical Institute.

Author information

Author notes

  1. Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

Authors and Affiliations

  1. Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Mass General Hospital and Harvard Medical School, Boston, MA, USA

    Alexandra Schnell, Linglin Huang, Brianna M. L. Regan, Dominik Vonficht, Alina Bollhagen, Mona Wang, Yu Hou, Lloyd Bod, Norio Chihara, Asaf Madi, Ana C. Anderson & Vijay K. Kuchroo

  2. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Alexandra Schnell, Vasundhara Singh, Yu Hou, Lloyd Bod, Ana C. Anderson, Aviv Regev & Vijay K. Kuchroo

  3. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Linglin Huang

  4. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Linglin Huang

  5. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Dominik Vonficht & Alina Bollhagen

  6. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

    Dominik Vonficht

  7. Liangzhu Laboratory, Zhejiang University, Hangzhou, China

    Yu Hou

  8. Palo Alto Veteran’s Administration Health Care System and Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Raymond A. Sobel

  9. Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Japan

    Norio Chihara

  10. Department of Pathology, Faculty of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel

    Asaf Madi

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Contributions

A.S. and V.K.K. conceived the study, designed the experiments and interpreted the results. A.S., with assistance from B.M.L.R., D.V., A.B., M.W., Y.H. and L.B., performed and analyzed the functional biological experiments. A.S., with assistance from B.M.L.R. and V.S., performed the sequencing experiments. R.A.S. helped with the histological analysis. N.C., A.M. and A.C.A. helped with the original discovery of Pglyrp1 as part of the co-inhibitory module. L.H., with assistance from A.S., A.R. and V.K.K., designed and performed the computational analysis. The manuscript was written by A.S. and L.H. and was edited by A.R., A.C.A. and V.K.K. with input from all authors.

Corresponding author

Correspondence to Vijay K. Kuchroo.

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Competing interests

A.C.A. is a member of the Scientific Advisory Board for Tizona Therapeutics, Trishula Therapeutics, Compass Therapeutics, Zumutor Biologics, ImmuneOncia and Excepgen, which have interests in cancer immunotherapy. A.C.A. is also a paid consultant for iTeos Therapeutics and Larkspur Biosciences. V.K.K. is cofounder of Celsius Therapeutics, Tizona Therapeutics, Larkspur Biosciences and Bicara Therapeutics. A.C.A.’s and V.K.K.’s interests are reviewed and managed by the BWH and Partners Healthcare in accordance with their conflict of interest policies. A.R. is a cofounder of and equity holder in Celsius Therapeutics and an equity holder in Immunitas and was a Scientific Advisory Board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until 31 July 2020. A.R. is an employee of Genentech (member of the Roche Group) since August 2020 and has equity in Roche. The other authors declare no competing interests.

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Nature Immunology thanks Scott Zamvil and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Pglyrp1 is expressed on exhausted T cells in human cancer.

Expression of Pglyrp1 and co-inhibitory receptors in single immune cells of human melanoma samples23. Plotted are the percent of cells with UMI count \(\ge\)1. Annotation as defined in publication.

Extended Data Fig. 2 Association between the expression levels of Pglyrp1 and survival rate in different human cancers.

Overall survival of patients with breast cancer (BRCA) (n = 1,100), HER2-positive BRCA (BRCA-Her2) (n = 82), colon adenocarcinoma (COAD) (n = 458), and lung squamous cell carcinoma (LUSC) (n = 501) grouped by PGLRYP1 expression through TIMER2 database70. Split expression percentage of patients: 20%. Analyses were performed with log-rank Mantel-Cox test. Hazard ratio (HR) and p-value are provided.

Extended Data Fig. 3 Immune-profiling of tumors in Pglyrp1-deficient mice.

(a) Pglyrp1−/− mouse validation. Relative expression (RE) of Pglyrp1 transcript in WT, Pglyrp1−/+, and Pglyrp1−/− CD8+ T cells from the spleen by qPCR (n = 2-3). The expression is depicted as relative to WT cells. The bar indicates the mean. (b–d) Analysis of the immune system in the colon of 7-week-old Pglyrp1−/− mice and WT littermates by flow cytometry (n = 3). General immune system composition (b), and intra-cellular cytokine staining in CD4+ T cells (c) and CD8+ T cells (d) are displayed. (e, f) B16-OVA tumors were implanted into WT and Pglyrp1−/− mice (n = 7). (e) Mean tumor growth and (f) tumor sizes on day 16 are shown. (g) MC38-OVA tumors were implanted into WT and Pglyrp1−/− mice and mice were treated with anti-PD-1 antibody on days 6, 8, and 10 post tumor-implantation. The control group included WT mice injected with control immunoglobulin (Rat IgG2a). (h) Relative expression of Pglyrp1 transcript in different immune populations isolated from MC38-OVA tumors grown in WT mice by qPCR (n = 4). (i) Frequency of different immune populations in MC38-OVA tumors grown in WT and Pglyrp1−/− mice (n = 9) by flow cytometry. In (b-i) data are presented as the mean with ±SEM. Unpaired two-tailed t-tests were performed. NS, not significant.

Extended Data Fig. 4 Immune populations in the dLN and spleen of tumor-bearing Pglyrp1-deficient mice.

(a) Frequency of Treg cells (CD45+ TCRβ+ CD4+ FOXP3+) in the spleen of naïve mice (n = 3). (b, d–g) MC38-OVA tumors were implanted into WT and Pglyrp1−/− mice and TILs were harvested for flow cytometry. (b) Summary plots of the frequency of Treg cells (CD45+ TCRβ+ CD4+ FOXP3+) in the dLN (WT n = 9, Pglyrp1−/− n = 8) (left) and spleen (WT n = 10, Pglyrp1−/− n = 8) (right). (c) Gating strategy for CD8+ T cells in the tumor. (d) Summary plots of the frequency of indicated cytokines in CD8+ T cells in the dLN (WT n = 9, Pglyrp1−/− n = 8) (top) and spleen (WT n = 10, Pglyrp1−/− n = 8) (bottom). (e) Summary plots of the frequency of indicated cytokines in CD4+ T cells in the tumor (WT n = 12, Pglyrp1−/− n = 8) (top), dLN (WT n = 10, Pglyrp1−/− n = 8) (middle) and spleen (WT n = 12, Pglyrp1−/− n = 8) (bottom). (f) Summary plots of the frequency of PD-1- and TIM-3-expressing CD8+ T cells in the dLN (n = 8) (top) and spleen (WT n = 10, Pglyrp1−/− n = 8) (bottom). (g) Summary plots of the frequency of PD-1- and TIM-3-expressing CD4+ T cells in the tumor (WT n = 10, Pglyrp1−/− n = 8). In all panels, data are presented as the mean with ±SEM. Unpaired two-tailed t-tests were performed. NS, not significant.

Extended Data Fig. 5 Characterization of tumor-infiltrating T cells in Pglyrp1-deficient mice.

(a) Heatmap representing cluster-specific upregulated genes (FDR <0.05, log2 fold change > log2(1.5)). If a gene was upregulated in multiple clusters, it is only shown once in the cluster block where it has the biggest fold change. (b) Gene set enrichment analysis of selected Gene Ontology (GO) terms and KEGG and Reactome pathways (top) and published CD8+ T cell signatures (bottom) enriched in CD8+ T cell-1 (stem-like) cluster vs. CD8+ T cell-2 (effector/exhausted) cluster. Only WT cells were included in the analysis. Naïve CD8-1 (Supplementary Table 7); naïve CD8-2 (Supplementary Table 7); terminally exhausted-156; transitory vs. stem-like54; terminally exhausted-253; transitory vs. exhausted54; exhausted T cells75; effector-like56. P-values were computed with the empirical phenotype-based permutation tests (GSEA) and the values shown in the figures were not adjusted for multiple comparisons. (c) RNA velocity analysis was performed on the CD8+ T cell clusters (Fig. 3a) using scVelo31. The velocity vector field is displayed as streamlines (top) and at single-cell level with each arrow showing the direction and speed (thickness) of movement of an individual cell (bottom). (d) Volcano plot of differentially expressed genes comparing WT vs. Pglyrp1−/− cells in the Treg cluster (Fig. 3a). Differential genes were computed as FDR < 0.05 and |log2 fold change| > 0.25. Positive log2 fold change corresponds to upregulation in Pglyrp1−/− cells and vice versa. Log2 fold changes and -log10 p-values were capped within [−1.5, 1.5] and [0, 20] respectively for visualization purposes. P-values were computed with the empirical Bayes quasi-likelihood F-tests in edgeR, then adjusted for multiple comparisons using the Benjamini & Hochberg method (FDR).

Extended Data Fig. 6 Analysis of the tumor phenotype in Pglyrp1fl/fl mice.

(a) Relative expression (RE) of Pglyrp1 in CD8+ T cells, CD4+ T cells, CD11b+ cells, B cells and neutrophils by qPCR (n = 5). MC38-OVA tumors were implanted into WT and E8iCrePglyrp1fl/fl mice. (b) Mean tumor growth of MC38-OVA tumors implanted into WT and LysMCrePglyrp1fl/fl mice (n = 8-9). In (a,b) data are presented as the mean with ± SEM. Unpaired two-tailed t-tests were performed. NS: not significant.

Extended Data Fig. 7 Analysis of CD8+ T cell phenotype and antigen-presentation of LysMCre Pglyrp1fl/fl splenocytes.

(a) Gating strategy of CD4+ T cells in the CNS. (b) On day 18 after immunization, CNS-infiltrating lymphocytes were extracted and analyzed for the expression of intracellular cytokines in CD8+ T cells by flow cytometry (n = 5). (c) Antigen-presentation assay with splenocytes from LysMCre Pglyrp1fl/fl or WT littermate controls with 2D2 naïve CD4+ T cells with or without MOG peptide (n = 4). Cell proliferation was measured by CellTrace Violet (CTV) staining. Quantification (left) and representative plots (right). (d) Cytokine concentration in the culture medium during antigen-presentation assay as in a, measured by legendplex (n = 4). In (b-d) data are presented as the mean with + SEM. Unpaired two-tailed t-tests were performed. NS: not significant.

Extended Data Fig. 8 scRNA-seq of CNS-infiltrated myeloid cells in Pglyrp1−/− mice during EAE.

(a) UMAP of CNS-infiltrating myeloid cells (3,698 cells). EAE was induced in Pglyrp1−/− and WT littermate controls and CNS-infiltrating myeloid cells were sorted (CD45+ CD3− CD19−) for scRNA-seq at disease onset (day 10). (b) Venn diagram depicting the overlap in upregulated genes (FDR < 0.05, |log2 fold change| > 0.25) in WT cells (top) and Pglyrp1−/− cells (bottom) both in mono/MAC and neutrophil clusters as defined in (Fig. 6b). (c) Monocyte treatment with PGN, PGLYRP1, or PGN + PGLYRP1 (n = 3). Bar plot depicting the TNF concentration in the culture medium. Data are presented as the mean with ±SEM. Dotted line indicates the detection limit. Unpaired two-tailed t-tests were performed.

Supplementary information

Reporting Summary

Supplementary Tables 1–7

Supplementary Table 1. Spearman correlation of genes with co-inhibitory and stem-like genes. P values were computed with two-sided Spearman’s asymptotic t-tests and were adjusted for multiple comparisons using the Benjamini–Hochberg method (FDR). Supplementary Table 2. Differential expression comparing Pglyrp1–/– and WT tumor-infiltrating CD8+ T cells based on bulk RNA-seq data. P values were computed with likelihood ratio tests in edgeR and were adjusted for multiple comparisons using the Benjamini–Hochberg method (FDR). Supplementary Table 3. Differential expression results comparing cells in each tumor-infiltrating T cell cluster versus the rest. P values were computed with empirical Bayes quasilikelihood F-tests in edgeR and were adjusted for multiple comparisons using the Benjamini–Hochberg method (FDR). Supplementary Table 4. Differential expression comparing Pglyrp1–/– and WT tumor-infiltrating T cells based on scRNA-seq data. P values were computed with empirical Bayes quasilikelihood F-tests in edgeR and were adjusted for multiple comparisons using the Benjamini–Hochberg method (FDR). Supplementary Table 5. Differential expression results comparing cells in each CNS-infiltrating myeloid cell cluster versus the rest after EAE induction. P values were computed with empirical Bayes quasilikelihood F-tests in edgeR and were adjusted for multiple comparisons using the Benjamini–Hochberg method (FDR). Supplementary Table 6. Differential expression results comparing Pglyrp1–/– and WT CNS-infiltrating monocytes/macrophages and neutrophils. P values were computed with the empirical Bayes quasilikelihood F-tests in edgeR and were adjusted for multiple comparisons using the Benjamini–Hochberg method (FDR). Supplementary Table 7. Gene signatures for GSEA.

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Schnell, A., Huang, L., Regan, B.M.L. et al. Targeting PGLYRP1 promotes antitumor immunity while inhibiting autoimmune neuroinflammation. Nat Immunol 24, 1908–1920 (2023). https://doi.org/10.1038/s41590-023-01645-4

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