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TREML4 receptor regulates inflammation and innate immune cell death during polymicrobial sepsis

Abstract

Sepsis is a biphasic disease characterized by an acute inflammatory response, followed by a prolonged immunosuppressive phase. Therapies aimed at controlling inflammation help to reduce the time patients with sepsis spend in intensive care units, but they do not lead to a reduction in overall mortality. Recently, the focus has been on addressing the immunosuppressive phase, often caused by apoptosis of immune cells. However, molecular triggers of these events are not yet known. Using whole-genome CRISPR screening in mice, we identified a triggering receptor expressed on myeloid cells (TREM) family receptor, TREML4, as a key regulator of inflammation and immune cell death in sepsis. Genetic ablation of Treml4 in mice demonstrated that TREML4 regulates calcium homeostasis, the inflammatory cytokine response, myeloperoxidase activation, the endoplasmic reticulum stress response and apoptotic cell death in innate immune cells, leading to an overall increase in survival rate, both during the acute and chronic phases of polymicrobial sepsis.

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Fig. 1: Identification and genetic ablation of Treml4.
Fig. 2: Treml4 gene ablation protects thymocytes during acute polymicrobial sepsis.
Fig. 3: TREML4 is a regulator of granulocyte homeostasis in the blood and bone marrow during acute polymicrobial sepsis.
Fig. 4: TREML4 is a regulator of neutrophil homeostasis in the blood and bone marrow during acute polymicrobial sepsis.
Fig. 5: TREML4 is a regulator of neutrophil homeostasis in the blood and bone marrow during the chronic stage of polymicrobial sepsis.
Fig. 6: Treml4 deletion improves phagocytotic clearance of P. aeruginosa infection from the lungs and improves survival in a ‘two-hit’ sepsis model.
Fig. 7: TREML4 is the regulator of serum cytokine levels during polymicrobial sepsis.
Fig. 8: TREML4 regulates apoptosis and cytokine secretion by modulating calcium flux.

Data availability

The authors declare that all relevant data supporting the findings of this study are available within the paper and the Extended Data. The CRISPR screening data can be accessed from BioStudies under accession number S-BSST399. The complete RNA-seq data is available from the Sequence Read Archive (SRA) under submission number SUB7079928. The mass spectrometric data is available from the PRoteomics IDEntifications (PRIDE) database under submission number PXD019055. Source data are provided with this paper.

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Acknowledgements

We thank R. Anders and J. Goding for critically reading the manuscript. We thank A. Baxter for reagents, La Trobe University Animal Ethics Committee and the LARTF staff for facilitating mouse experiments and the La Trobe Bioimaging Platform for the maintenance of the flow cytometry facility. We also thank S. Wilcox and P. Hickey (Walter and Eliza Hall Institute) for RNA-seq and bioinformatics. H.P. and C.N. were supported by La Trobe University funding (RFA-UD) and the Strategic Innovation Fund. J.M. is supported by La Trobe University’s postgraduate scholarship. The generation of Treml4−/− mice used in this study was supported by the Australian Phenomics Network and the Australian Government through the National Collaborative Research Infrastructure Strategy program.

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Authors

Contributions

H.P., C.N., J.M., M. Doerflinger and P. Fao designed the experiments. C.N., J.M., M. Doerflinger, C.L., M. Duan, P. Fon and T.K.P. conducted the experiments. A.J.K. and M.J.H. generated the Treml4−/− mouse strain. H.R. performed the bioinformatics. H.P., M. Duan, M.D.H. and W.C. analyzed the data. H.P. and C.N. prepared the manuscript.

Corresponding author

Correspondence to Hamsa Puthalakath.

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The authors declare no competing interests.

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Peer review information Peer reviewer reports are available. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 HSC reconstitution.

a, Reconstitution of myelo-ablated C57BL/6 mice (CD45.1) with the donor (CD45.2) hematopoietic stem cells containing the lentiviral library before sub-lethal CS injection (these mice were used in the screen as shown in Fig. 1a,b). The level of engraftment was analysed in each of the compartments as shown in the figure. CD45.1 vs CD45.2 bone marrow populations were gated to validate recipient and donor populations pre and post- implantation into mice for initial screening experiment. b, An overlay of BFP positive bone marrow cells that were used for NGS, with control cells.

Source data

Extended Data Fig. 2 T cell phenotype during acute phase sepsis.

a, b, Thymic atrophy observed by dissection 24 h post-CS injection. c, The gating strategy for phenotyping splenic T cells during acute sepsis. d, The gating strategy for measuring apoptosis in splenic CD4 T cells during acute sepsis. e, The gating strategy for measuring apoptosis in splenic CD8 T cells during acute sepsis. f, The gating strategy for phenotyping lymph node T cells during acute sepsis. g, The gating strategy for measuring apoptosis in lymph node CD4 T cells during acute sepsis. h, The gating strategy for measuring apoptosis in lymph node CD8 T cells during acute sepsis. Gating strategy in detail: The entire splenic and LN population was gated to exclude debris using FSC-A vs SSC-A. The remaining population was further gated to exclude doublets using FSC-A vs FSC-H. T-cell exclusive population was derived using CD45.2 vs CD3. To analyse CD4 and CD8 populations specifically, further gating was performed using CD4 vs CD8. Individual populations were analysed for cell death using DAPI (necrotic cell marker) vs AnnexinV (apoptosis marker).

Extended Data Fig. 3 The bone marrow T cell phenotype during the chronic phase of sepsis.

The gating strategy and cellularity of the bone marrow CD4+ T and CD8+ T cells during the ‘two-hit’ model of pneumonia. Error bars ± SD, n = 3 mice in each group, **p < 0.005, ***p < 0.001 and #p > 0.05. Statistical significance was determined by unpaired two- tailed t-test. Gating strategy in detail: The entire bone marrow population was gated to exclude debris using FSC-A vs SSC-A. The remaining population was further gated to exclude doublets using FSC-A vs FSC-H. T-cell exclusive population was derived using CD45.2 vs CD3. To analyse CD4 and CD8 populations specifically, further gating was performed using CD4 vs CD8. Individual populations were analysed for cell death using DAPI (necrotic cell marker) vs AnnexinV (apoptosis marker).

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Extended Data Fig. 4 The gating strategy for granulocytes, macrophages and dendritic cells during acute phase sepsis shown in Fig. 3.

a. Granulocyte gating strategy in the bone marrow. b, Total granulocyte cellularity in the bone marrow. Error bars ± SD, *p < 0.05, #p > 0.05 and n = 3-4 mice per group. c, Granulocyte gating strategy in the blood. d, Total granulocyte cellularity in the blood. Error bars ± SD, *p < 0.05, #p > 0.05 and n = 3-4 mice per group. e, f, The gating strategy and phenotyping of macrophages in the bone marrow. g, Cellularity of bone marrow macrophages based on the profile shown in f. Error bars ± SD, *p < 0.05, #p > 0.05 and n = 3-4 mice per group. h, Apoptosis measurement in macrophages. Error bars ± SD, n = 6-7 per group, *p < 0.05, **p < 0.005, and #p > 0.05. i, The gating strategy for splenic dendritic cells that was used in measuring splenic DC count (j), apoptosis measurement (k, m) and percentage dendritic cells (l). Error bars ± SD, n = 6–8 mice per group for j, l and m, *p < 0.05 and #p > 0.05. Statistical significance was determined by unpaired two-tailed t-test (b, d, g, h, j, l and m). Gating strategy in detail: The entire cell population within each of the tissues was gated to remove doublets using FSC-A vs FSC-H. For visualisation of the granulocyte populations in the BM and blood; CD45.2 vs FSC-A was used to isolate all leukocytes, proceeded to use CD11b vs Ly6C for phenotype analysis. For macrophage phenotype analysis in the BM; CD45.2 vs CD115 positive populations were further gated for Ly6C vs CD11b (maturation a status). For splenic dendritic cell death analysis; CD45.2 positive population was gated for MHC II (high) vs CD11c (Pan DC population), this population was further analysed for cell death using DAPI vs AnnexinV.

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Extended Data Fig. 5 The gating strategy for neutrophils cells during acute phase sepsis.

a, b, The gating strategy for bone marrow neutrophils apoptosis. c, d, The gating strategy for blood neutrophils apoptosis. This gating was used for the data shown in Fig. 4. Gating strategy in detail: The entire cell population within each of the tissues was gated to remove doublets using FSC-A vs FSC-H. Neutrophils were isolated further using CD45.2 vs Ly6G. This population was further analysed for cell death using DAPI vs AnnexinV. Additional CD11b and CXCR2 markers were used to gate neutrophils for their presence and their maturation status.

Extended Data Fig. 6 Neutrophils are the regulators of cell survival during polymicrobial sepsis.

a, Treml4 deletion leads to an improved body weight upon sub-lethal injection of CS (n = 4 mice in each group). b, Treml4 deletion leads to an improved survival during lethal CS-induced polymicrobial sepsis (n = 4 mice in each group). Error bars ± SD, **p < 0.005 and ***p < 0.001. Statistical significance was determined by unpaired two-tailed t-test.

Source data

Extended Data Fig. 7 The gating strategy for analyzing the BAL neutrophils shown in Figs. 5 and 6.

Gating strategy in detail: The entire cell population within the BALwas gated to remove doublets using FSC-A vs FSC-H. Neutrophils were isolated further using CD45.2 vs Ly6G. Gating strategy in detail: The entire cell population within each of the tissues was gated to remove doublets using FSC-A vs FSC-H. Neutrophils were isolated further using CD45.2 vs Ly6G. This population was further analysed for cell death using DAPI vs AnnexinV. Additionally, CXCR2 marker was used to gate neutrophils for their presence and their maturation status.

Extended Data Fig. 8 Treml4–/– neutrophils regulate cell survival and bacterial load during polymicrobial sepsis but do not protect against viral infection.

a, Typical neutrophil depletion profile in Treml4–/– mice up on anti-Ly6G antibody injection supporting the data in Fig. 6h. b, Recovery of P. aeruginosa in the lungs of mice 24 h post-administration in a two-hit model (n = 4 in each group). c, The bacterial load in the kidney and spleen of mice 6 h post CS injection (n = 4 mice in each group). d, Survival analysis of mice challenged with influenza virus (PR8) after sublethal CS injection (n = 4 mice in each group). Error bars ± SD, **p < 0.005 and ***p < 0.001. Statistical significance was determined by unpaired two-tailed t-test (b, c) and by log rank test (d).

Source data

Extended Data Fig. 9 TREML4 receptor regulates the inflammatory pathway during polymicrobial sepsis and is independent of TLR4.

a, Time series analysis of the gene expression of wild type and Treml4–/– BMDMs, projected onto their principal components. Colours on the dots indicates time. The PCA was drawn using normalized counts from data shown in Fig. 7a. b, Protein abundance between wild type and Treml4–/– BAL neutrophils, before and after treating with CS, projected to their principal components. Colors on the dots indicate the treatment condition. The PCA was drawn using the maxLFQ values from the data presented in Fig. 8g. c, d, Raw RNASeq data for Ifn-γ and Ifn-β respectively, curated from the data presented in Fig. 7a. Statistical significance was determined by two-tailed, moderated t-test with adjustments made for multiple comparisons using Benjamini-Hochberg method.

Extended Data Fig. 10 Treml4 gene ablation does not impact TLR4 signaling.

a, b, Cytokine measurements in BMDMs and bone marrow neutrophils respectively, after challenging them with various concentrations of LPS in vitro. Error bars ± SD, n = 3 independent biological replicates and # indicates lack of any statistical significance. Statistical significance was determined by unpaired two-tailed t-test. c, Densitometric quantitation of signals for each protein from the western blots presented in Fig. 8e. Band intensity measured on ImageJ for each protein in each sample was divided by the corresponding intensity of Akt signal (used as the loading control).

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Nedeva, C., Menassa, J., Duan, M. et al. TREML4 receptor regulates inflammation and innate immune cell death during polymicrobial sepsis. Nat Immunol 21, 1585–1596 (2020). https://doi.org/10.1038/s41590-020-0789-z

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