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Inflammasome protein scaffolds the DNA damage complex during tumor development

Nature Immunology volume 25, pages 2085–2096 (2024)Cite this article

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Abstract

Inflammasome sensors activate cellular signaling machineries to drive inflammation and cell death processes. Inflammasomes also control the development of certain diseases independently of canonical functions. Here, we show that the inflammasome protein NLR family CARD ___domain-containing protein 4 (NLRC4) attenuated the development of tumors in the Apcmin/+ mouse model. This response was independent of inflammasome signaling by NLRP3, NLRP6, NLR family apoptosis inhibitory proteins, absent in melanoma 2, apoptosis-associated speck-like protein containing a caspase recruitment ___domain, caspase-1 and caspase-11. NLRC4 interacted with the DNA-damage-sensing ataxia telangiectasia and Rad3-related (ATR)–ATR-interacting protein (ATRIP)–Ewing tumor-associated antigen 1 (ETAA1) complex to promote the recruitment of the checkpoint adapter protein claspin, licensing the activation of the kinase checkpoint kinase-1 (CHK1). Genotoxicity-induced activation of the NLRC4–ATR–ATRIP–ETAA1 complex drove the tumor-suppressing DNA damage response and CHK1 activation, and further attenuated the accumulation of DNA damage. These findings demonstrate a noninflammatory function of an inflammasome protein in promoting the DNA damage response and mediating protection against cancer.

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Fig. 1: NLRC4 attenuates tumor development in Apcmin/+ mice.
Fig. 2: NLRC4 attenuates the development of tumors independently of the inflammasome pathway.
Fig. 3: The small intestine of Apcmin/+ and Apcmin/+ Nlrc4−/− mice have similar levels of apoptosis and inflammatory responses during tumor development.
Fig. 4: NLRC4 reduces DNA damage accumulation during tumor development.
Fig. 5: NLRC4 interacts with ATR to promote the tumor-suppressive DNA damage response.
Fig. 6: NLRC4 promotes the recruitment of claspin to the ATR–ATRIP complex during tumor development.

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Data availability

All data are available in the main figures, Supplementary Information, extended data figures or source data. The raw microbiota (available from https://doi.org/10.5281/zenodo.10278738)124 and phosphoproteomics (available from https://doi.org/10.5281/zenodo.10278713)125 data have been deposited to Zenodo. Source data are provided with this paper.

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Acknowledgements

We thank the facilities and scientific and technical assistance of Microscopy Australia at the Centre for Advanced Microscopy (ANU), which is funded by the University and the Federal Government. We thank the National Collaborative Research Infrastructure Strategy (NCRIS) via Phenomics Australia. We thank the South Australian Genomics Centre (SAGC), which is supported by NCRIS via BioPlatforms Australia and by the SAGC partner institutes. We thank the facilities and scientific and technical assistance of the Cytometry, Histology and Spatial Multiomics facility of the John Curtin School of Medical Research, ANU. We thank G. Burgio (ANU) for generating the Nlrp6−/− mice, R. E. Vance and E. Turcotte (University of California) for providing the WT and NLRC4−/− THP-1 cell lines, and J.-W. Yu (Yonsei University College of Medicine) for providing the anti-NLRC4 antibody. We thank members of the Man laboratory for their comments and suggestions; the National Health and Medical Research Council of Australia under project grant no. APP1146864, an ideas grant no. APP2002686 and an investigator grant no. 2026910 (S.M.M.); a CSL Centenary Fellowship (S.M.M.); a John Curtin School of Medical Research PhD Scholarship (C.S.); the Gretel and Gordon Bootes Medical Research Foundation (C.S.); a Cancer Council ACT Research Grant (A.M.); a Gastroenterological Society of Australia GESA Mostyn Family Grant (D.E.T.); and an Australian Research Council Centre of Excellence for the Mathematical Analysis of Cellular Systems grant no. CE230100001 (L.L., H.Y., J.W.).

Author information

Authors and Affiliations

  1. Division of Immunology and Infectious Diseases, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia

    Cheng Shen, Abhimanu Pandey, Daniel Enosi Tuipulotu, Anukriti Mathur, Nilanthi K. Adikari, Chinh Ngo, Weidong Jing, Shouya Feng, Yuwei Hao, Anyang Zhao, Max Kirkby, Melan Kurera, Jing Zhang, Shweta Venkataraman & Si Ming Man

  2. Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia

    Lixinyu Liu, Haoyu Yang & Jiayu Wen

  3. ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems, Canberra, Australian Capital Territory, Australia

    Lixinyu Liu, Haoyu Yang & Jiayu Wen

  4. Conjoint Gastroenterology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia

    Cheng Liu

  5. School of Medicine, University of Queensland, Herston, Queensland, Australia

    Cheng Liu

  6. Mater Pathology, Mater Hospital, South Brisbane, Queensland, Australia

    Cheng Liu

  7. Epigenetics and RNA Biology Laboratory, The School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia

    Renhua Song & Justin J.-L. Wong

  8. The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia

    Ulrike Schumann & Riccardo Natoli

  9. The Shine Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia

    Ulrike Schumann & Riccardo Natoli

  10. The Save Sight Institute, The University of Sydney, Sydney, New South Wales, Australia

    Ulrike Schumann

  11. School of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia

    Riccardo Natoli

  12. Department of Chemical Physiology and Biochemistry, Oregon Health and Science University, Portland, OR, USA

    Liman Zhang

  13. School of Biomedical Sciences, University of New South Wales Sydney, Sydney, New South Wales, Australia

    Nadeem O. Kaakoush

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  1. Cheng Shen
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  2. Abhimanu Pandey
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Contributions

C.S. and S.M.M. conceptualized the study. C.S., A.P., D.E.T., L.L., H.Y., C.N., Y.H., R.S. and N.O.K. devised the methodology. C.S., A.P., D.E.T., A.M., L.L., H.Y., N.K.A., C.N., W.J., S.F., Y.H., A.Z., Max Kirkby, Melan Kurera, J.Z., S.V., C.L., R.S., U.S. and N.O.K. carried out the investigation. C.S., A.P., D.E.T., A.M., L.L., H.Y., C.L., R.S., J.J.-L.W., R.N., J.W., L.Z., N.O.K. and S.M.M. carried out the formal analysis. S.M.M. acquired the funding. C.S. and S.M.M. managed the project. S.M.M. supervised the study. C.S. and S.M.M. wrote the original manuscript draft. All authors reviewed and edited the manuscript draft.

Corresponding author

Correspondence to Si Ming Man.

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Nature Immunology thanks Tyler Curiel, Khashayarsha Khazaie and Igor Brodsky for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey in collaboration with the Nature Immunology editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 The small intestine of Apcmin/+ and Apcmin/+ Nlrc4–/– mice have a similar profile of microbiome.

a, Body weight of 20-week-old Apcmin/+ (n = 12) and Apcmin/+ Nlrc4–/– mice (n = 12), and small intestine and colon length of 20-week-old Apcmin/+ (n = 13) and Apcmin/+ Nlrc4–/– mice (n = 14). b, Species richness, species evenness and Shannon’s diversity of the microbiome in the small intestine of 20-week-old Apcmin/+ (n = 10) and Apcmin/+ Nlrc4–/– mice (n = 14). c, Principal coordinate analysis and differential abundance analysis for the microbiome in the small intestine of 20-week-old Apcmin/+ (n = 10) and Apcmin/+ Nlrc4–/– mice (n = 14). ANOSIM (analysis of similarity, R = −0.011, P = 0.52). q indicates an adjusted p-value. d, Number and size of tumors in the colon of 20-week-old Apcmin/+ (n = 13) and Apcmin/+ Nlrc4–/– mice (n = 14). Each symbol represents one individual mouse (a–d). Data represent mean ± SEM; two-tailed t-test (a, b and d).

Source data

Extended Data Fig. 2 AIM2, caspase-11, NLRP3 or NLRP6 does not substantially affect tumor development in the small intestine and colon of Apcmin/+ mice.

a–d, Number and size of tumors in the small intestine and colon of 20-week-old Apcmin/+ (n = 13) and Apcmin/+ Aim2–/– mice (n = 15) (a); Apcmin/+ (n = 14) and Apcmin/+ Casp11–/– mice (n = 17) (b); Apcmin/+ (n = 11) and Apcmin/+ Nlrp3–/– mice (n = 13) (c); Apcmin/+ (n = 14) and Apcmin/+ Nlrp6–/– mice (n = 12) (d). Each symbol represents one individual mouse (a–d). Data represent mean ± SEM; two-tailed t-test (a–d).

Source data

Extended Data Fig. 3 The small intestine of Apcmin/+ and Apcmin/+ Nlrc4–/– mice have a similar tumor burden at an earlier age.

a, Densitometric quantification of Casp-1 p20 and GSDMD p30 in the small intestine of Apcmin/+ (n = 6) and Apcmin/+ Nlrc4–/– (n = 6) mice in Fig. 2c. b, Number and size of tumors in the small intestine and colon of 10-week-old Apcmin/+ (n = 15) and Apcmin/+ Nlrc4–/– mice (n = 13). c,d, Number and size of tumors in the small intestine of 20-week-old Apcmin/+ (n = 14) and Apcmin/+ Asc–/– mice (n = 12) (c); Apcmin/+ (n = 12) and Apcmin/+ Casp1–/– mice (n = 12) (d). e,f, Number and size of tumors in the colon of 20-week-old Apcmin/+ (n = 14) and Apcmin/+ Asc–/– mice (n = 12) (e); Apcmin/+ (n = 12) and Apcmin/+ Casp1–/– mice (n = 12) (f). Each symbol represents one individual mouse (a–f). Data represent mean ± SEM; two-tailed t-test (a–f).

Source data

Extended Data Fig. 4 The small intestine of Apcmin/+ and Apcmin/+ Nlrc4–/– mice have similar activation levels of the β-catenin pathway during tumor development.

a, Densitometric quantification of β-catenin in Fig. 4a (cytosolic β-catenin was normalized to GAPDH; nuclear β-catenin was normalized to Lamin B1). b,c, Quantification (b) and representative images (c) of nuclear β-catenin in the small intestine of 10-week-old Apcmin/+ (n = 5) and Apcmin/+ Nlrc4–/– mice (n = 5) (each symbol represents one microscopy image). Scale bar for all images, 50 μm. d, Relative gene expression of Axin2, Lgr5 and Tcf7 in healthy tissues from the small intestine of 10-week-old Apc+/+ mice, and in tumor tissues from the small intestine of 10-week-old Apcmin/+ mice. (normalized to Gapdh). e, Relative gene expression of Axin2, Lgr5 and Tcf7 in the small intestine of 10-week-old Apcmin/+ and Apcmin/+ Nlrc4–/– mice (normalized to Gapdh). f, Levels of differential phosphorylated proteins (Supplementary Table 1, P < 0.05) in the small intestine of 20-week-old Apcmin/+ (n = 5) and Apcmin/+ Nlrc4–/– mice (n = 5). g,h, Representative images (g) and quantification (h) of γH2AX in Apc+/+ and Apc+/+ Nlrc4–/– BMDMs treated with a vehicle control or 50 µM etoposide for 20 h (each symbol represents one microscopy image). Scale bar, 40 μm. Image capture and quantification were performed in a blinded manner (b and h). Each symbol or lane represents one individual mouse (a, d, e and f). Data are pooled from multiple Apcmin/+ (n = 5) and Apcmin/+ Nlrc4–/– (n = 5) mice (b); Apc+/+ (n = 5) and Apcmin/+ (n = 4) mice (d); Apcmin/+ (n = 8) and Apcmin/+ Nlrc4–/– (n = 8) mice (e), or from two independent experiments (h). Data represent mean ± SEM; two-tailed t-test (a, b, d, e and h).

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Extended Data Fig. 5 NLRC4 promotes the DNA damage response triggered by various activators.

a,b, Immunoblot analysis (a) and densitometric quantification (b) of p-BRCA1 S1524, p-p53 S15 and p-CHK2 T68 in the small intestine of 10-week-old Apcmin/+ (n = 6) and Apcmin/+ Nlrc4–/– (n = 6) mice (normalized to β-actin in Fig. 5a). c, Immunoblot analysis of NLRC4, p-CHK1 S345, CHK1, and β-actin in Apc+/+ and Apc+/+ Nlrc4–/– BMDMs treated with 50 µM etoposide for the indicated time. d, Immunoblot analysis of NLRC4, p-CHK1 S345, CHK1, and β-actin in Apc+/+ and Apc+/+ Nlrc4–/– BMDMs either left untreated or treated with 6 Gy ionizing radiation, followed by incubation for the indicated time. e, Immunoblot analysis of p-CHK1 S345, CHK1, and β-actin in WT and NLRC4–/– THP-1 cells treated with 10 mM hydrogen peroxide (H2O2) for the indicated time. f–h, Densitometric quantification of p-CHK1 S345 against CHK1 in the blots (f is related to d); (g is related to Fig. 5d); (h is related to e). i,j, Immunoblot analysis (i) and densitometric quantification (j) of p-CHK1 S345, CHK1, p-CDC25A S124, CDC25A, and β-actin in the small intestine of 10-week-old Apc+/+ (n = 6) and Apc+/+ Nlrc4–/– (n = 6) mice. Data represent one of three independent experiments (c–h). Each symbol or lane represents one individual mouse (a, b, i and j). Data represent mean ± SEM; two-tailed t-test (b and j).

Source data

Extended Data Fig. 6 NLRC4 does not affect the production of reactive oxygen species during the DNA damage response.

a, Immunoblot analysis of GFP-NLRC4, ATR, and β-actin following immunoprecipitation of control IgG or anti-ATR antibody in cell lysates of GFP-NLRC4-expressing HEK239T cells. b, Immunoblot analysis of NLRC4, p-CHK1 S345, CHK1 and β-actin in Apcmin/+ and Apcmin/+ Nlrc4–/– small intestinal organoids pre-treated with a vehicle control or 1 µM AZD6738 for 1 h, followed by 50 µM etoposide treatment for the indicated time. AZD6738-treated organoids were maintained under the condition of AZD6738 inhibition during etoposide treatment. c, Analysis of ROS levels in live Apc+/+ and Apc+/+ Nlrc4–/– BMDMs treated with phosphate-buffered saline (PBS), 50 µM etoposide, or 10 mM hydrogen peroxide (H2O2) for the indicated time. d,e, Immunoblot analysis (d) and densitometric quantification (e) of NLRC4, p-CHK1 S345, CHK1, and β-actin in BMDMs pre-treated with a vehicle control or 500 µM N-acetyl cysteine (NAC) for 20 h, followed by 50 µM etoposide treatment for the indicated time. f,g, Immunoblot analysis (f) and densitometric quantification (g) of p-ERK1/2 T202/Y204 and ERK1/2 in BMDMs pre-treated with a vehicle control or 500 µM N-acetyl cysteine (NAC) for 20 h, followed by 1 µg/ml lipopolysaccharide (LPS) treatment for the indicated time. NAC-treated cells were maintained under the condition of NAC inhibition during etoposide treatment (d–g). Data represent one of three independent experiments (a–g). Data represent mean ± SEM; Two-way ANOVA with Holm-Sidak’s multiple comparisons test (c).

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Extended Data Fig. 7 The HEAT and FATC domains of ATR are required for NLRC4-ATR interaction.

a, Schematic representation of the full-length NLRC4 protein and truncated mutants. b, Densitometric quantification of p-CHK1 S345 against CHK1 in the blots of Fig. 6e. c, Schematic representation of the full-length ATR protein and truncated mutants. d, Immunoblot analysis of GFP-NLRC4, β-actin, and the full-length V5-ATR protein or V5-ATR lacking the HEAT ___domain (ΔHEAT), FAT ___domain (ΔFAT), PIKK ___domain (ΔPIKK), PRD ___domain (ΔPRD) or FATC ___domain (ΔFATC) following immunoprecipitation using a control IgG or anti-GFP antibody from the cell lysates of V5-ATR (WT, ΔHEAT, ΔFAT, ΔPIKK, ΔPRD or ΔFATC)- and GFP-NLRC4-expressing HEK239T cells. e, Densitometric quantification of Claspin in the small intestine lysates of 10-week-old Apcmin/+ (n = 6) and Apcmin/+ Nlrc4–/– (n = 6) mice, related to Fig. 6h, i. Data represent one of three independent experiments (d). Data represent mean ± SEM; two-tailed t-test (e).

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Extended Data Fig. 8 The gene expression of human NLRC4 is reduced across multiple cell types during the malignant transformation of colorectal cancer.

a, Dot plot representation of human NLRC4 expression across different cell types during malignant transformation of colorectal cancer (CRC). Data were obtained from GSE201349.

Extended Data Fig. 9 The expression of the human NLRC4 gene is associated with the expression of the genes encoding the DNA damage response in the human small intestine.

a, Pearson correlation analysis of the gene expression between NLRC4 and ATR, ATRIP, CLSPN, CHEK1, and CDC25A in the human small intestine. b, KEGG pathway enrichment analysis of genes showing the highest Pearson correlation coefficients with NLRC4 in the human small intestine. Data were obtained from GTEx (Small Intestine – Terminal Ileum) (a, and b). Two-tailed Pearson correlation coefficient test (a).

Extended Data Fig. 10 Model for the NLRC4-ATR pathway in reducing DNA damage and attenuating tumor development.

Left, in response to genomic stress, such as DNA damage assault, NLRC4 interacts with the ATR-ATRIP-ETAA1 complex to promote the recruitment of Claspin, leading to CHK1 activation. Activated CHK1 phosphorylates CDC25A, resulting in the degradation of CDC25A, and triggers the DNA damage response. Sufficient DNA damage response provides a protective response against tumor development. Right, the lack of NLRC4 hinders the recruitment of Claspin to the ATR-ATRIP-ETAA1 complex, leading to impaired DNA damage response. This defect causes DNA damage accumulation, leading to genomic instability and increased tumor development. Figures are drawn using PowerPoint.

Supplementary information

Supplementary Information

Supplementary Tables 1–4.

Reporting Summary

Peer Review File

Supplementary Tables 1–4

Supplementary Table 1: Phospho-MS data_(NLRC4 WT VS KO);Supplementary Table 2: Top NLRC4-related genes in the human small intestine; Supplementary Table 3: NLRP6 knockout mouse strain information; Supplementary Table 4: Antibodies and primers.

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Shen, C., Pandey, A., Enosi Tuipulotu, D. et al. Inflammasome protein scaffolds the DNA damage complex during tumor development. Nat Immunol 25, 2085–2096 (2024). https://doi.org/10.1038/s41590-024-01988-6

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