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Metabolites released from apoptotic cells act as tissue messengers

Abstract

Caspase-dependent apoptosis accounts for approximately 90% of homeostatic cell turnover in the body1, and regulates inflammation, cell proliferation, and tissue regeneration2,3,4. How apoptotic cells mediate such diverse effects is not fully understood. Here we profiled the apoptotic metabolite secretome and determined its effects on the tissue neighbourhood. We show that apoptotic lymphocytes and macrophages release specific metabolites, while retaining their membrane integrity. A subset of these metabolites is also shared across different primary cells and cell lines after the induction of apoptosis by different stimuli. Mechanistically, the apoptotic metabolite secretome is not simply due to passive emptying of cellular contents and instead is a regulated process. Caspase-mediated opening of pannexin 1 channels at the plasma membrane facilitated the release of a select subset of metabolites. In addition, certain metabolic pathways continued to remain active during apoptosis, with the release of only select metabolites from a given pathway. Functionally, the apoptotic metabolite secretome induced specific gene programs in healthy neighbouring cells, including suppression of inflammation, cell proliferation, and wound healing. Furthermore, a cocktail of apoptotic metabolites reduced disease severity in mouse models of inflammatory arthritis and lung-graft rejection. These data advance the concept that apoptotic cells are not inert cells waiting for removal, but instead release metabolites as ‘good-bye’ signals to actively modulate outcomes in tissues.

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Fig. 1: Conserved metabolite secretome from apoptotic cells.
Fig. 2: Activation of PANX1 and continued metabolic activity of dying cells orchestrates metabolite release.
Fig. 3: Metabolites from apoptotic cells influence gene programs in live cells.
Fig. 4: PANX1-dependent metabolite release during apoptosis modulates phagocyte gene expression in vivo and can alleviate inflammation.

Data availability

RNA-seq data have been submitted to the Gene Expression Omnibus (GEO) under accession number GSE131906. Source Data for Figs. 14 and Extended Data Figs. 110 are provided with the paper. Other data that support the findings of this study are available from the corresponding author upon request.

Code availability

R code used for heat map generation, volcano plots and bioinformatic analysis is available from the corresponding author upon request.

References

  1. 1.

    Fuchs, Y. & Steller, H. Programmed cell death in animal development and disease. Cell 147, 742–758 (2011).

    Article  CAS  Google Scholar 

  2. 2.

    Rothlin, C. V., Carrera-Silva, E. A., Bosurgi, L. & Ghosh, S. TAM receptor signaling in immune homeostasis. Annu. Rev. Immunol. 33, 355–391 (2015).

    Article  CAS  Google Scholar 

  3. 3.

    Lindsten, T. et al. The combined functions of proapoptotic Bcl-2 family members bak and bax are essential for normal development of multiple tissues. Mol. Cell 6, 1389–1399 (2000).

    Article  CAS  Google Scholar 

  4. 4.

    Gregory, C. D. & Pound, J. D. Cell death in the neighbourhood: direct microenvironmental effects of apoptosis in normal and neoplastic tissues. J. Pathol. 223, 178–195 (2011).

    Article  CAS  Google Scholar 

  5. 5.

    Ryoo, H. D., Gorenc, T. & Steller, H. Apoptotic cells can induce compensatory cell proliferation through the JNK and the Wingless signaling pathways. Dev. Cell 7, 491–501 (2004).

    Article  CAS  Google Scholar 

  6. 6.

    Ke, F. F. S. et al. Embryogenesis and adult life in the absence of intrinsic apoptosis effectors BAX, BAK, and BOK. Cell 173, 1217–1230.e17 (2018).

    Article  CAS  Google Scholar 

  7. 7.

    Medina, C. B. & Ravichandran, K. S. Do not let death do us part: ‘find-me’ signals in communication between dying cells and the phagocytes. Cell Death Differ. 23, 979–989 (2016).

    Article  CAS  Google Scholar 

  8. 8.

    Elliott, M. R. et al. Nucleotides released by apoptotic cells act as a find-me signal to promote phagocytic clearance. Nature 461, 282–286 (2009).

    ADS  Article  CAS  Google Scholar 

  9. 9.

    Van Opdenbosch, N. et al. Caspase-1 engagement and TLR-induced c-FLIP expression suppress ASC/caspase-8-dependent apoptosis by inflammasome sensors NLRP1b and NLRC4. Cell Rep. 21, 3427–3444 (2017).

    Article  CAS  Google Scholar 

  10. 10.

    Chekeni, F. B. et al. Pannexin 1 channels mediate ‘find-me’ signal release and membrane permeability during apoptosis. Nature 467, 863–867 (2010).

    ADS  Article  CAS  Google Scholar 

  11. 11.

    Poon, I. K. H. et al. Unexpected link between an antibiotic, pannexin channels and apoptosis. Nature 507, 329–334 (2014).

    ADS  Article  CAS  Google Scholar 

  12. 12.

    Good, M. E. et al. Pannexin 1 channels as an unexpected new target of the anti-hypertensive drug spironolactone. Circ. Res. 122, 606–615 (2018).

    Article  CAS  Google Scholar 

  13. 13.

    Madeo, F., Eisenberg, T., Pietrocola, F. & Kroemer, G. Spermidine in health and disease. Science 359, eaan2788 (2018).

    Article  CAS  Google Scholar 

  14. 14.

    Liu, X. et al. PNPT1 release from mitochondria during apoptosis triggers decay of poly(A) RNAs. Cell 174, 187–201.e12 (2018).

    Article  CAS  Google Scholar 

  15. 15.

    Morioka, S. et al. Efferocytosis induces a novel SLC program to promote glucose uptake and lactate release. Nature 563, 714–718 (2018).

    ADS  Article  CAS  Google Scholar 

  16. 16.

    Wang, Y. et al. Mitochondrial fission promotes the continued clearance of apoptotic cells by macrophages. Cell 171, 331–345.e22 (2017).

    Article  CAS  Google Scholar 

  17. 17.

    Perry, J. S. A. et al. Interpreting an apoptotic corpse as anti-inflammatory involves a chloride sensing pathway. Nat. Cell Biol. 21, 1532–1543 (2019).

    Article  CAS  Google Scholar 

  18. 18.

    Ipseiz, N. et al. The nuclear receptor Nr4a1 mediates anti-inflammatory effects of apoptotic cells. J. Immunol. 192, 4852–4858 (2014).

    Article  CAS  Google Scholar 

  19. 19.

    Chung, E. Y. et al. Interleukin-10 expression in macrophages during phagocytosis of apoptotic cells is mediated by homeodomain proteins Pbx1 and Prep-1. Immunity 27, 952–964 (2007).

    Article  CAS  Google Scholar 

  20. 20.

    Zaiss, D. M. W., Gause, W. C., Osborne, L. C. & Artis, D. Emerging functions of amphiregulin in orchestrating immunity, inflammation, and tissue repair. Immunity 42, 216–226 (2015).

    Article  CAS  Google Scholar 

  21. 21.

    Goessling, W. et al. Genetic interaction of PGE2 and Wnt signaling regulates developmental specification of stem cells and regeneration. Cell 136, 1136–1147 (2009).

    Article  CAS  Google Scholar 

  22. 22.

    Shayakul, C., Clémençon, B. & Hediger, M. A. The urea transporter family (SLC14): physiological, pathological and structural aspects. Mol. Aspects Med. 34, 313–322 (2013).

    Article  CAS  Google Scholar 

  23. 23.

    Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

    Article  CAS  Google Scholar 

  24. 24.

    Scher, J. U. & Pillinger, M. H. The anti-inflammatory effects of prostaglandins. J. Investig. Med. 57, 703–708 (2009).

    Article  CAS  Google Scholar 

  25. 25.

    Korganow, A. S. et al. From systemic T cell self-reactivity to organ-specific autoimmune disease via immunoglobulins. Immunity 10, 451–461 (1999).

    Article  CAS  Google Scholar 

  26. 26.

    Veras, F. P. et al. Fructose 1,6-bisphosphate, a high-energy intermediate of glycolysis, attenuates experimental arthritis by activating anti-inflammatory adenosinergic pathway. Sci. Rep. 5, 15171 (2015).

    ADS  Article  CAS  Google Scholar 

  27. 27.

    Krupnick, A. S. et al. Orthotopic mouse lung transplantation as experimental methodology to study transplant and tumor biology. Nat. Protocols 4, 86–93 (2009).

    Article  CAS  Google Scholar 

  28. 28.

    Stewart, S. et al. Revision of the 1996 working formulation for the standardization of nomenclature in the diagnosis of lung rejection. J. Heart Lung Transplant 26, 1229–1242 (2007).

    Article  Google Scholar 

  29. 29.

    Kouskoff, V. et al. Organ-specific disease provoked by systemic autoimmunity. Cell 87, 811–822 (1996).

    Article  CAS  Google Scholar 

  30. 30.

    Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81, 6656–6667 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank members of the Ravichandran laboratory, and members of the Pannexin Interest Group at UVA for numerous discussions, and critical reading of the manuscript, and M. Lamkanfi for the Nlrp1b transgenic mice. This work is supported by grants to K.S.R. from NHLBI (P01HL120840), NIGMS R35GM122542, and the Center for Cell Clearance/University of Virginia School of Medicine, and the Odysseus Award from the FWO, Belgium, EOS Grant from the FWO (3083753-DECODE), and the NHLBI (P01HL120840) and NIAID (R21 AI139967 and R21 AI135455) to U.L. Additional support was received through the NIH T32 Pharmacology Training Grant (T32GM007055) (C.B.M, and B.B.), Mark Foundation Fellowship from the Cancer Research Institute and a K99 from the NCI (to J.S.A.P.), and the Kanye Foundation of Japan (to S.M.). Current address for Justin Perry: Immunology Program, Memorial Sloan-Kettering Cancer Center, New York, NY. P.M. is supported by a FWO-Senior postdoctoral fellowship.

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Affiliations

Authors

Contributions

C.B.M. and K.S.R designed the experiments. C.B.M. performed most experiments. P.M.M. performed the macrophage apoptosis and polyamine tracing experiments. S.A. and C.B.M. performed the arthritis experiments. J.S.A.P. assisted with the bioinformatic analyses. Y.G. and A.S.K. assisted with the lung transplant experiments. S.M., B.B. and S.W. provided experimental expertise on a few specific experiments. B.G. assisted with the polyamine mass spectrometry and U.L. provided mice and conceptual advice. C.B.M. and K.S.R wrote the manuscript with input from co-authors.

Corresponding author

Correspondence to Kodi S. Ravichandran.

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The authors declare no competing interests.

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Peer review information Nature thanks Seamus Martin, Gary Siuzdak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Metabolite release from apoptotic Jurkat cells.

a, Jurkat cells were induced to undergo apoptosis by UV irradiation. Staining with 7AAD and annexin V (AV) was used to determine the percentage of live (AV7AAD), apoptotic (AV+7AAD) or necrotic (AV+7AAD+) cells after 4 h. b, Quantitative analysis of apoptosis (top) and secondary necrosis (bottom) (n = 4). Data are mean ± s.d. ****P < 0.0001, unpaired two-tailed Student’s t-test. c, d, Volcano plot (c) and heat map (d) from untargeted metabolomics of supernatants from Jurkat T cells, representing statistically enriched or reduced (P < 0.05, two-sided Welch’s two-sample t-test) metabolites in the apoptotic supernatants relative to live supernatants. Data are representative of four biological replicates.

Source data

Extended Data Fig. 2 Reciprocal metabolite changes between apoptotic supernatant and pellet.

a, Heat map produced from untargeted metabolomics of Jurkat T cell pellets, representing statistically enriched or reduced (P < 0.05, two-sided Welch’s two-sample t-test) metabolites in the apoptotic pellet relative to the live cell pellet (n = 4 biologically independent samples). b, Bi-directional plot representing the 85 metabolites that were statistically enriched in the apoptotic supernatant and simultaneously reduced in the apoptotic cell pellet relative to live cell conditions (P < 0.05, two-sided Welch’s two-sample t-test). Metabolites were grouped by metabolic pathways (n = 4 biologically independent samples). cf, Mass spectrometry was used to determine the relative amount of spermidine (c), inosine (d), UDP-glucose (e) and AMP (f) in supernatants and cell pellets from Jurkat T cells in live and apoptotic conditions (n = 4 biologically independent samples). *P = 0.014, ****P < 0.0001, unpaired two-tailed Student’s t-test. Data are mean ± s.d.

Source data

Extended Data Fig. 3 Conserved metabolite release during apoptosis.

a, Mass spectrometry was used to measure the concentration of the five metabolites that were released across all conditions and platforms tested, in live or apoptotic supernatants per million Jurkat T cells or isolated primary thymocytes (back-calculated from total cells used in experimental set-up) (n = 3). Metabolites are grouped by metabolic pathways. Data are mean ± s.d. *P = 0.014, **P = 0.0014, ***P = 0.0002, ****P < 0.0001, unpaired two-tailed Student’s t-test. b, The concentration of ATP released in the supernatant across the different apoptotic Jurkat cells was determined by luciferase assay (n = 4). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. c, Table outlining the different cell types, apoptotic stimuli, techniques and metabolites screened for untargeted (more than 3,000 features or compounds) and targeted (116 metabolites) metabolomics, including ATP, spermidine, glycerol-3-phosphate and creatine.

Source data

Extended Data Fig. 4 PANX1 activation and inhibition during cell death.

a, Top, representative histograms of TO-PRO-3 dye uptake in thymocytes across the different conditions. Bottom, PANX1 activation in live and apoptotic thymocytes from wild-type (Panx1+/+) and PANX1-knockout (Panx1−/−) mice as assessed via flow cytometry by measuring the mean fluorescent intensity of TO-PRO-3 dye uptake (n = 3 biological replicates). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. b, Top, representative histograms of TO-PRO-3 dye uptake in Jurkat cells, across the different conditions described. Bottom, PANX1 activation as assessed by flow cytometry of the uptake of TO-PRO-3 dye in apoptotic wild-type Jurkat cells, Jurkat cells expressing mutant PANX1-DN, and Jurkat cells treated with PANX1 inhibitor spironolactone (50 μM) or trovafloxacin (25 μM) (n = 4 biological replicates). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test.

Source data

Extended Data Fig. 5 PANX1 inhibition does not influence apoptotic cell death.

a, Control (Panx1+/+) or Panx1−/− thymocytes were treated with anti-Fas antibody (5 μg ml−1) for 1.5 h. Cells were stained with 7AAD and annexin V to determine the percentage of live, apoptotic or necrotic cells, as in Extended Data Fig. 1a. b, Quantification of apoptosis (top) and secondary necrosis (bottom) of control and PANX1-knockout thymocytes (n = 3). Data are mean ± s.e.m. ***P = 0.0004, ordinary one-way ANOVA with Turkey’s multiple comparison test. c, d, Quantification of apoptosis (c) and secondary necrosis (d) from Jurkat cells before metabolomics analysis (n = 4). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. e, Cells were stained with 7AAD and annexin V to determine the percentage of live, apoptotic or necrotic cells.

Source data

Extended Data Fig. 6 PANX1-dependent metabolite release during apoptosis.

a, Mass spectrometry was used to determine the relative amounts of AMP, GMP, UDP-glucose and FBP in supernatants from Jurkat T cells across different conditions (n = 4). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. b, Jurkat cells were induced to undergo apoptosis by treatment with anti-Fas antibody (250 ng ml−1). Mass spectrometry was used to measure the absolute concentration per million cells of AMP (top), UDP-glucose (middle) and FBP (F-1,6-BP) (bottom) in the supernatants of Jurkat T cells across different conditions (back-calculated from total cells used in experimental set-up) (n = 3). Data are mean ± s.e.m. *P = 0.031, **P = 0.0013, ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. c, Mass spectrometry was used to determine the concentrations of AMP, GMP, UDP-glucose and FBP per million cells (back-calculated from total cells used in experimental set-up) in the supernatant from isolated primary thymocytes across different conditions (n = 3). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. d, e, Relative concentrations of inosine (d) and choline (e) in live, apoptotic or apoptotic supernatants in which PANX1 was inhibited were determined by mass spectrometry (n = 4). Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test.

Source data

Extended Data Fig. 7 Conserved PANX1 secretome.

a, Top, three-way Venn diagram comparing PANX1-dependent metabolites released from apoptotic cells across different conditions tested. Bottom, table showing the relative peak intensity (untargeted metabolomics) or absolute concentrations (targeted metabolomics) in the supernatant of the indicated cell treatments and knockout mice. N.D., not determined.

Extended Data Fig. 8 Transcriptional and metabolic changes during apoptosis.

a, Re-analyses of RNA-seq data from apoptotic cells14 demonstrating that the SRM mRNA levels are increased or retained during apoptosis. b, After induction of apoptosis (n = 4), SRM mRNA expression was assessed over time relative to live controls (n = 5). Data are mean ± s.e.m. **P = 0.007, two-way ANOVA. c, Incorporation of 13C-labelled arginine into the polyamine pathway intermediate spermidine and release from Jurkat cells after apoptosis, and its partial reduction by the pan-caspase inhibitor zVAD (n = 3). Data are mean ± s.d. **P = 0.0088, unpaired two-tailed Student’s t-test.

Source data

Extended Data Fig. 9 Transcriptional changes on surrounding phagocytes induced by PANX1-dependent metabolite release during apoptosis.

a, Principal component (PC) analysis on the RNA-seq data as a quality control statistic (n = 4 biological replicates). b, Experimental procedure is described in Fig. 3d. qPCR was used to assess gene expression changes in Ptgs2 (top), Sgk1 (middle) and Slc14a1 (bottom) in phagocytes after treatment with supernatants from Jurkat cells or Jurkat cells expressing DN-PANX1 (n = 7). Data are mean ± s.e.m. Live-AC **P = 0.0074 (live–AC Sgk1), **P = 0.0031 (AC–AC Sgk1), ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. c, Experimental procedure is as described in Fig. 3d, except before treatment of LR73 cells with supernatant, the supernatant was filtered through a 3-kDa filter to remove large molecules. qPCR was used to assess gene expression changes in Sgk1 (top) and Slc14a1 (bottom) in phagocytes after treatment with supernatants under specified conditions (n = 3). Data are mean ± s.e.m. ***P = 0.0001, ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test.

Source data

Extended Data Fig. 10 Analysis of thymic cell death in vivo and effects of supernatants during arthritis.

a, Analysis of thymic populations used for experimental data in Fig. 4a. After thymus isolation, the CD11bCD11c population that contained thymocytes was used for mRNA isolation to test the efficiency of deletion of the Panx1 allele. qPCR analysis of Panx1 mRNA in control mice (Panx1fl/flCd4-cre−/−) (n = 6) or mice in which PANX1 has been knocked out in thymocytes (Panx1fl/flCd4-cre+/−) (n = 7). CD11b+CD11c+ myeloid cells obtained from the thymus of Panx1fl/flCd4-cre+/− mice were analysed for Panx1 expression to demonstrate that PANX1 was not deleted. PANX1 deletion was deleted only from thymocytes and not the myeloid cells that do not express CD4. Data are mean ± s.d. **P = 0.0015, unpaired two-tailed Student’s t-test. b, Representative flow cytometric plots showing the extent of apoptosis induced by dexamethasone in control and Panx1fl/fl CD4-Cre+ mice. After thymus isolation, cells were stained with 7AAD and annexin V to determine the percentage of live, apoptotic or necrotic cells, as in Extended Data Fig. 1a. c, Quantitative analysis of apoptosis (left) and secondary necrosis (right) of CD11bCD11c thymic populations from Panx1fl/fl CD4-Cre (PBS n = 4, Dex n = 10) or Panx1fl/fl CD4-Cre+ (PBS n = 3, Dex n = 9) mice treated with PBS or dexamethasone. Data are mean ± s.e.m. ****P < 0.0001, ordinary one-way ANOVA with Turkey’s multiple comparison test. d, Representative flow cytometry plots demonstrating the purity of CD11b+CD11c+ population after magnetic separation from the different mice and treatment conditions. e, Comparison of the CD11b+CD11c+ cells isolated under different conditions (cre−/−: PBS n = 4, Dex n = 7; cre+/−: PBS n = 3, Dex n = 6). Data are mean ± s.e.m. P > 0.05 (n.s.), ordinary one-way ANOVA with Turkey’s multiple comparison test. f, Apoptotic supernatants alleviate arthritic disease induced by serum from KBx/N mice. C57BL/6J mice were injected with serum from K/BxN mice to induce arthritis. Live (n = 4) or apoptotic (n = 5) supernatant was given for five days after arthritis induction. Paw swelling was measured using a calliper and reported as the percentage change compared with day 0. Data are mean ± s.e.m. *P = 0.0131, two-way ANOVA.

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Supplementary information

Supplementary Information

This file contains the Supplementary Files Guide.

Reporting Summary

Supplementary Table 1: Jurkat UV metabolite release

Supplementary Table . List of metabolites released after UV treatment of Jurkat cells relative to live cell controls (n=4). Two-sided Welch’s two-sample t-test.

Supplementary Table 2: Metabolite supernatant enrichment and pellet decrease

Supplementary Table . List of metabolites released after UV treatment of Jurkat cells relative to live cell controls (n=4) and reciprocally decreased in the cell pellet (n=4).

Supplementary Table 3: HMT metabolites

Supplementary Table . List of metabolites screened in the targeted metabolomics analysis.

Supplementary Table 4:

Supplementary Table Table of metabolites released in a Panx1-dependent manner from untargeted metabolomics of Jurkat T cells undergoing apoptosis. Along with metabolite name, metabolite size (in Daltons), charge, and reference to previous studies, where particular extracellular treatment of the specific metabolites have been attempted.

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Medina, C.B., Mehrotra, P., Arandjelovic, S. et al. Metabolites released from apoptotic cells act as tissue messengers. Nature 580, 130–135 (2020). https://doi.org/10.1038/s41586-020-2121-3

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