Inflammation-induced IgA+ cells dismantle anti-liver cancer immunity

An Author Correction to this article was published on 04 July 2018

An Erratum to this article was published on 22 November 2017

This article has been updated

Abstract

The role of adaptive immunity in early cancer development is controversial. Here we show that chronic inflammation and fibrosis in humans and mice with non-alcoholic fatty liver disease is accompanied by accumulation of liver-resident immunoglobulin-A-producing (IgA+) cells. These cells also express programmed death ligand 1 (PD-L1) and interleukin-10, and directly suppress liver cytotoxic CD8+ T lymphocytes, which prevent emergence of hepatocellular carcinoma and express a limited repertoire of T-cell receptors against tumour-associated antigens. Whereas CD8+ T-cell ablation accelerates hepatocellular carcinoma, genetic or pharmacological interference with IgA+ cell generation attenuates liver carcinogenesis and induces cytotoxic T-lymphocyte-mediated regression of established hepatocellular carcinoma. These findings establish the importance of inflammation-induced suppression of cytotoxic CD8+ T-lymphocyte activation as a tumour-promoting mechanism.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: NASH-induced IgA+ plasmocyte accumulation in human and mouse liver.
Figure 2: IgA ablation inhibits while CD8a ablation accelerates HCC appearance.
Figure 3: Liver IgA+ cells induce CD8+ T-cell exhaustion.
Figure 4: PD-L1 blockade reactivates and expands antigen-specific CD8+ T cells to induce HCC regression.

Change history

  • 22 November 2017

    Please see accompanying Erratum (http://doi.org/10.1038/nature25028). In the main text of this Article, '(Iga is also known as Cd79a)' was changed to '(Iga is also known as Igha)'. This error has been corrected online.

  • 04 July 2018

    In this Article, 'non-recurrent coding mutations' should have been 'non-recurrent mutations' in the sentence starting: 'After 7 months of HFD, MUP-uPA mice developed HCC...'. This has been corrected online. The Supplementary Information to the accompanying Amendment provides comparisons of all variants and coding variants to human mutations.

References

  1. 1

    Sharma, P., Wagner, K., Wolchok, J. D. & Allison, J. P. Novel cancer immunotherapy agents with survival benefit: recent successes and next steps. Nat. Rev. Cancer 11, 805–812 (2011)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2

    Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015)

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3

    Mittal, D., Gubin, M. M., Schreiber, R. D. & Smyth, M. J. New insights into cancer immunoediting and its three component phases — elimination, equilibrium and escape. Curr. Opin. Immunol. 27, 16–25 (2014)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4

    Corthay, A. Does the immune system naturally protect against cancer? Front. Immunol. 5, 197 (2014)

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  5. 5

    Singal, A. G. & El-Serag, H. B. Hepatocellular carcinoma from epidemiology to prevention: translating knowledge into practice. Clin. Gastroenterol. Hepatol. 13, 2140–2151 (2015)

    PubMed  PubMed Central  Article  Google Scholar 

  6. 6

    Luedde, T. & Schwabe, R. F. NF-κB in the liver—linking injury, fibrosis and hepatocellular carcinoma. Nat. Rev. Gastroenterol. Hepatol. 8, 108–118 (2011)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7

    Wolf, M. J. et al. Metabolic activation of intrahepatic CD8+ T cells and NKT cells causes nonalcoholic steatohepatitis and liver cancer via cross-talk with hepatocytes. Cancer Cell 26, 549–564 (2014)

    CAS  PubMed  Article  Google Scholar 

  8. 8

    Nakamoto, Y., Guidotti, L. G., Kuhlen, C. V., Fowler, P. & Chisari, F. V. Immune pathogenesis of hepatocellular carcinoma. J. Exp. Med. 188, 341–350 (1998)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9

    Dhanda, A. D. & Collins, P. L. Immune dysfunction in acute alcoholic hepatitis. World J. Gastroenterol. 21, 11904–11913 (2015)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10

    Baroni, G. S. et al. Interferon gamma decreases hepatic stellate cell activation and extracellular matrix deposition in rat liver fibrosis. Hepatology 23, 1189–1199 (1996)

    CAS  PubMed  Article  Google Scholar 

  11. 11

    Flavell, R. A., Sanjabi, S., Wrzesinski, S. H. & Licona-Limón, P. The polarization of immune cells in the tumour environment by TGFβ. Nat. Rev. Immunol. 10, 554–567 (2010)

    CAS  PubMed  Article  Google Scholar 

  12. 12

    Mauri, C. & Menon, M. The expanding family of regulatory B cells. Int. Immunol. 27, 479–486 (2015)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13

    McPherson, S., Henderson, E., Burt, A. D., Day, C. P. & Anstee, Q. M. Serum immunoglobulin levels predict fibrosis in patients with non-alcoholic fatty liver disease. J. Hepatol. 60, 1055–1062 (2014)

    CAS  PubMed  Article  Google Scholar 

  14. 14

    Shalapour, S. et al. Immunosuppressive plasma cells impede T-cell-dependent immunogenic chemotherapy. Nature 521, 94–98 (2015)

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15

    Nakagawa, H. et al. ER stress cooperates with hypernutrition to trigger TNF-dependent spontaneous HCC development. Cancer Cell 26, 331–343 (2014)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16

    Font-Burgada, J. et al. Hybrid periportal hepatocytes regenerate the injured liver without giving rise to cancer. Cell 162, 766–779 (2015)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17

    Park, E. J. et al. Dietary and genetic obesity promote liver inflammation and tumorigenesis by enhancing IL-6 and TNF expression. Cell 140, 197–208 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18

    Malik, A. et al. IL-33 regulates the IgA-microbiota axis to restrain IL-1α-dependent colitis and tumorigenesis. J. Clin. Invest. 126, 4469–4481 (2016)

    PubMed  PubMed Central  Article  Google Scholar 

  19. 19

    Cerutti, A. The regulation of IgA class switching. Nat. Rev. Immunol. 8, 421–434 (2008)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20

    Schulze, K. et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat. Genet. 47, 505–511 (2015)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21

    He, G. et al. Identification of liver cancer progenitors whose malignant progression depends on autocrine IL-6 signaling. Cell 155, 384–396 (2013)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22

    Brandtzaeg, P. Secretory IgA: designed for anti-microbial defense. Front. Immunol. 4, 222 (2013)

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. 23

    Dapito, D. H. et al. Promotion of hepatocellular carcinoma by the intestinal microbiota and TLR4. Cancer Cell 21, 504–516 (2012)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24

    Seki, E. et al. TLR4 enhances TGF-β signaling and hepatic fibrosis. Nat. Med. 13, 1324–1332 (2007)

    CAS  PubMed  Article  Google Scholar 

  25. 25

    Phan, T. G. et al. B cell receptor-independent stimuli trigger immunoglobulin (Ig) class switch recombination and production of IgG autoantibodies by anergic self-reactive B cells. J. Exp. Med. 197, 845–860 (2003)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26

    Ma, C. et al. NAFLD causes selective CD4+ T lymphocyte loss and promotes hepatocarcinogenesis. Nature 531, 253–257 (2016)

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27

    Good-Jacobson, K. L. et al. PD-1 regulates germinal center B cell survival and the formation and affinity of long-lived plasma cells. Nat. Immunol. 11, 535–542 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28

    Kim, P. S. & Ahmed, R. Features of responding T cells in cancer and chronic infection. Curr. Opin. Immunol. 22, 223–230 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29

    Shalapour, S. & Karin, M. Immunity, inflammation, and cancer: an eternal fight between good and evil. J. Clin. Invest. 125, 3347–3355 (2015)

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30

    Mellman, I. et al. De-risking immunotherapy: report of a consensus workshop of the Cancer Immunotherapy Consortium of the Cancer Research Institute. Cancer Immunol. Res. 4, 279–288 (2016)

    CAS  PubMed  Article  Google Scholar 

  31. 31

    Kang, T. W. et al. Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature 479, 547–551 (2011)

    ADS  CAS  Article  Google Scholar 

  32. 32

    El-Khoueiry, A. B. et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 389, 2492–2502 (2017)

    CAS  PubMed  Article  Google Scholar 

  33. 33

    Madan, R. et al. Nonredundant roles for B cell-derived IL-10 in immune counter-regulation. J. Immunol. 183, 2312–2320 (2009)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34

    Kitamura, D., Roes, J., Kühn, R. & Rajewsky, K. A B cell-deficient mouse by targeted disruption of the membrane exon of the immunoglobulin μ chain gene. Nature 350, 423–426 (1991)

    ADS  CAS  Article  Google Scholar 

  35. 35

    Harriman, G. R. et al. Targeted deletion of the IgA constant region in mice leads to IgA deficiency with alterations in expression of other Ig isotypes. J. Immunol. 162, 2521–2529 (1999)

    CAS  PubMed  Google Scholar 

  36. 36

    Koh, D. R. et al. Less mortality but more relapses in experimental allergic encephalomyelitis in CD8-/- mice. Science 256, 1210–1213 (1992)

    ADS  CAS  PubMed  Article  Google Scholar 

  37. 37

    Mombaerts, P. et al. RAG-1-deficient mice have no mature B and T lymphocytes. Cell 68, 869–877 (1992)

    CAS  PubMed  Article  Google Scholar 

  38. 38

    Chen, J. et al. Immunoglobulin gene rearrangement in B cell deficient mice generated by targeted deletion of the JH locus. Int. Immunol. 5, 647–656 (1993)

    CAS  PubMed  Article  Google Scholar 

  39. 39

    Silveira, P. A. et al. The preferential ability of B lymphocytes to act as diabetogenic APC in NOD mice depends on expression of self-antigen-specific immunoglobulin receptors. Eur. J. Immunol. 32, 3657–3666 (2002)

    CAS  PubMed  Article  Google Scholar 

  40. 40

    Shimada, S. et al. Generation of polymeric immunoglobulin receptor-deficient mouse with marked reduction of secretory IgA. J. Immunol. 163, 5367–5373 (1999)

    CAS  PubMed  Google Scholar 

  41. 41

    Forrester, E. et al. Effect of conditional knockout of the type II TGF-beta receptor gene in mammary epithelia on mammary gland development and polyomavirus middle T antigen induced tumor formation and metastasis. Cancer Res. 65, 2296–2302 (2005)

    CAS  PubMed  Article  Google Scholar 

  42. 42

    Phan, T. G., Gardam, S., Basten, A. & Brink, R. Altered migration, recruitment, and somatic hypermutation in the early response of marginal zone B cells to T cell-dependent antigen. J. Immunol. 174, 4567–4578 (2005)

    CAS  PubMed  Article  Google Scholar 

  43. 43

    Hogquist, K. A. et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994)

    CAS  PubMed  Article  Google Scholar 

  44. 44

    Fujii, M. et al. A murine model for non-alcoholic steatohepatitis showing evidence of association between diabetes and hepatocellular carcinoma. Med. Mol. Morphol. 46, 141–152 (2013)

    CAS  PubMed  Article  Google Scholar 

  45. 45

    Kleiner, D. E. & Makhlouf, H. R. Histology of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis in adults and children. Clin. Liver Dis. 20, 293–312 (2016)

    PubMed  Article  Google Scholar 

  46. 46

    He, G. et al. Hepatocyte IKKβ/NFκB inhibits tumor promotion and progression by preventing oxidative stress-driven STAT3 activation. Cancer Cell 17, 286–297 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47

    Meng, F., et al. Interleukin-17 signaling in inflammatory, Kupffer cells, and hepatic stellate cells exacerbates liver fibrosis in mice. Gastroenterology 143, 765–776 e761-763 (2012)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48

    Yoshida, R. et al. A new method for quantitative analysis of the mouse T-cell receptor V region repertoires: comparison of repertoires among strains. Immunogenetics 52, 35–45 (2000)

    CAS  PubMed  Article  Google Scholar 

  49. 49

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013)

    Google Scholar 

  50. 50

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52

    de Hoon, M. J., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53

    Saldanha, A. J. Java Treeview–extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004)

    CAS  Article  PubMed  Google Scholar 

  54. 54

    Tripathi, S . et al. Meta- and orthogonal integration of influenza “OMICs” data defines a role for ubr4 in virus budding. Cell Host Microbe 18, 723–735 (2015)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55

    Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003)

    CAS  PubMed  Article  Google Scholar 

  56. 56

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57

    Totoki, Y. et al. Trans-ancestry mutational landscape of hepatocellular carcinoma genomes. Nat. Genet. 46, 1267–1273 (2014)

    CAS  PubMed  Article  Google Scholar 

  58. 58

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59

    Van der Auwera, G.A., et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 11.10.1–33 (2013)

    Google Scholar 

  60. 60

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61

    Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011)

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63

    Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013)

    Article  CAS  Google Scholar 

  64. 64

    Teufel, A., et al. Comparison of gene expression patterns between mouse models of nonalcoholic fatty liver disease and liver tissues from patients. Gastroenterology 151, 513–525 (2016)

    CAS  PubMed  Article  Google Scholar 

  65. 65

    Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016)

    PubMed  Article  CAS  Google Scholar 

  66. 66

    Xia, J. & Wishart, D.S . Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr. Protoc. Bioinform. 55, 14.10.11–14.10.91 (2016)

    Article  Google Scholar 

  67. 67

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69

    Ammon, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191–16 (2017)

    Google Scholar 

  70. 70

    Mirarab, S., Nguyen, N. & Warnow, T. SEPP: SATé-enabled phylogenetic placement. Pac. Symp. Biocomput. 17, 247–258 (2012)

    Google Scholar 

  71. 71

    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012)

    CAS  Article  Google Scholar 

  72. 72

    McDonald, D. et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience 1, 7 (2012)

    PubMed  PubMed Central  Article  Google Scholar 

  73. 73

    Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992)

    Article  Google Scholar 

  74. 74

    Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75

    Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013)

    PubMed  PubMed Central  Article  Google Scholar 

  76. 76

    Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015)

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank S. Choi, B. H. Dang, K. Nguyen, I. Mellman, G. Ackermann, H. Reeves, R. Quinn, G. Di Caro, G. Karin, and J. Haybaeck for help and advice. The research was supported by the National Institutes of Health (NIH), National Institute of Environmental Health Sciences Superfund Research Program, Horizon-2020-Framework Program of the European Union (Q.M.A.), Newcastle NIHR Biomedical Research Centre, CMI seed grant, Irvington Cancer Research Institute (S.S., Z.Z.), Prostate Cancer Foundation Young Investigator Award (S.S.), Southern California Research Center for ALPD and Cirrhosis grant (S.S., I.N.B.), Prevent Cancer Foundation Board of Directors Research Award (Z.Z.), and American Liver Foundation liver scholar award (D.D.). M.K. holds the Ben and Wanda Hildyard Chair. NIH funding was as follows: CA127923, CA118165, AI043477, and U01AA022614 to M.K.; P41GM103484-07 and GMS10RR029121 to P.C.D.; P42ES010337 to M.K., R.M.E., and R.L.; DK106419 to R.L.; and EPoS-634413 to Q.M.A.

Author information

Affiliations

Authors

Contributions

M.K. and S.S. conceived and designed the project. S.S. designed and performed experiments and analysed data with M.K. X.L., I.N.B., W.L., A.P., Z.Z., D.D., and J.X. assisted with experiments and data analysis. T.M. analysed TCR/BCR repertoires. J.B., A.D.B., R.L., and Q.M.A. collected and analysed human specimens. C.B., M.D., R.T.Y., R.M.E., X.L., and S.S. conducted and analysed sequencing data. A.F.V., A.A.A., J.A.N., P.C.D., and R.K. performed microbiome and metabolomic analyses. M.K. and S.S. wrote the manuscript, with all authors contributing and providing feedback and advice.

Corresponding authors

Correspondence to Shabnam Shalapour or Michael Karin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks A. Biragyn, M. Bodogai, G. Gores and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 NASH-induced accumulation of IgA+ plasmocytes in human liver.

a, Serum IgA levels of patients with NAFLD/NASH (Newcastle cohort; n = 502) were analysed and compared with clinical data. b, Serum was collected from a second cohort of patients with NAFLD/NASH (San Diego cohort; n = 96), whose degree of liver fibrosis was measured by MRI and classified as grades 0 (n = 51), 1–2 (n = 33), and 3–4 (n = 12). Serum concentrations of IgA, IgG, and IgM were determined by ELISA and plotted against the fibrosis grade. c, Single-cell suspensions were prepared from liver biopsies taken from patients with NASH (n = 4) with fibrosis, stained with antibodies to IgA, CD19, and PD-L1, and analysed by flow cytometry. Blood collected from three healthy donors (used as a surrogate for liver biopsies that could not be obtained from such individuals; HD), stained with antibodies, as indicated. Shown are representative scatter plots from two patients and two healthy donors, and bar graphs compiling data from all samples and depicting PD-L1 expression by IgA+ cells. d, Single-cell suspensions were prepared from liver biopsies as above (n = 3 per group) and stained with antibodies to CD4, CD8, Tim-3, and PD-1. Shown are representative data from one patient and one healthy donor with bar graphs compiling results obtained from three patients and depicting frequencies of PD-1+CD8+ T cells. χ2 test (a) and two-sided t-test (means ± s.e.m.; ad) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001; NS, not significant.

Extended Data Figure 2 Experimental scheme and comparison of different NASH, HCC, and liver damage mouse models.

a, In the HFD-fed MUP-uPA model, MUP-uPA, MUP-uPA/Iga−/−, MUP-uPA/μMT−/−, MUP-uPA/Rag1−/−, and MUP-uPA/Cd8a−/− male mice were kept on normal chow (NC) or placed on high-fat diet (HFD) starting at 8 weeks of age. Mice were analysed for NASH-related immune parameters and pathology at 6 months of age and for HCC-related parameters at 11 months of age (n = 543 mice analysed in 50 experiments). Mice were treated with a cocktail of broad spectrum antibiotics (Abx) from 3 to 6 months or from 6 to 11 months to determine microbe impact on NASH and HCC, respectively. Mice were also subjected to weekly anti-PD-L1 or anti-CD8 antibody injections starting at 9 months of age for a total of 8 or 6 weeks before being evaluated for HCC. In the STAM model, male mice of the indicated genotype were subcutaneously injected with 200 μg streptozotocin (STZ) 2 days after birth and fed with HFD after 4 weeks of age. Tumour multiplicity, immune parameters, and pathology were determined when the mice were 25 or 20 weeks of age in either the BL6 or FVB backgrounds, respectively (n = 123 mice analysed in 15 experiments). STAM-Iga−/− mice were also subjected to weekly anti-CD8 antibody injections for a total of 5 weeks before being evaluated for HCC. In the MCD-HFD-fed model, mice were kept on a methionine and choline deficient (MCD) diet with 60 kcal% fat, for 4 weeks (n = 65 analysed in three experiments). In the CCl4 model, mice were injected intraperitoneally with CCl4 twice a week for 8 weeks (n = 82 analysed in six experiments). b, Paraffin-embedded and frozen mouse liver sections (n = 3-9 per group as indicated) were stained with haematoxylin and eosin (H&E) to determine liver histology (scale bar, 100 μm), Sirius Red (SR) to determine collagen fibre deposition (scale bar, 100 μm), or Oil Red O (ORO) to determine lipid droplet accumulation (scale bar, 50 μm). c, Paraffin-embedded human liver sections from patients with NASH and ASH were stained with Sirius Red to determine collagen fibre deposition (n = 5 per group with one representative shown; scale bar, 250 μm). The data were validated in at least two or three experiments. d, Spleen content of IgA+ cells (absolute cell number per spleen) in different mouse strains and treatment groups (n = 11, 6, 8, 13, 11, 9). e, f, Indicated mice were placed on the different models described in a. At the endpoints, mouse body weights were measured (e) (n = 6, 5, 8, 10, 3, 10, 11, 31, 15, 6, 9) and sera were analysed for ALT (f) (n = 6, 5, 6, 5, 3, 13, 3, 10, 8, 3, 6), an indicator of liver damage. g, h, Collagen deposition and lipid droplets were quantified using image analysis software, and data points for individual mice are shown (g: n = 11, 6, 3, 9, 3, 8, 4, 6, 5; h: n = 6, 5, 3, 3, 3, 5, 4, 8, 4, 3, 6). i, Total RNA was extracted from livers of indicated mice and analysed by RT-PCR using primers for Tgfb1 (n = 6, 5, 3, 7, 7, 6, 6). j, Hepatic stellate cells (HSC), CD11b+ Kupffer cells (KC), and CD11b non-parenchymal cells (NPC) from the indicated mice were isolated from livers. Total RNA was extracted from these cells and analysed for Tgfb1 mRNA expression (n = 3–5). k, l, CD4+ T cells were sorted from livers of normal chow- and HFD-fed WT or HFD-fed MUP-uPA mice using CD4, CD8, CD45, and CD3 antibodies. RNA was extracted and analysed by RT-PCR using primers for Il21, Bcl6, and Icos (n = 4–7 as indicated). m, Total RNA was extracted from livers of indicated mice and analysed by RT-PCR for Il33 (n = 4, 5, 3, 7, 8, 6, 6). n, o, Total RNA was extracted from livers of indicated mice at 6 months (n = 29 mice in total, with 3–8 mice per group as indicated) (n) or tumours of indicated mice at 11 months of age (n = 7, 9) (o), and analysed by RT-PCR for chemokine and cytokine mRNA. The heat map in n depicts the differential expression of immune-regulatory genes. Two-sided t-test (means ± s.e.m.; gm) and Mann–Whitney test (median; df, o) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group are shown either in individual panels or in legends for each group from left to right accordingly.

Extended Data Figure 3 IgA characterization in the indicated tissue of STAM and MUP-uPA mice.

ae, Single splenocyte or liver cell suspensions from tumour (HCC)-bearing mice (STAM-B6) were stained with CD45, CD19, IgA, CD138, B220, and PD-L1, and FVD-eF780 was used to exclude dead cells. a, The gating strategies for splenocytes and liver lymphocytes: lymphocyte gate, dead cell exclusion, doublets exclusion, and CD45+ population gate. b, Flow cytometry analysis of IgA and CD19 expression of indicated strains, gated on the CD45+ population. Spleen or liver from Iga−/− mice was used to set up the gating for the populations. Tgfbr2ΔB and Pdl1/2−/− mice clearly showed less IgA+ cells than WT mice. ce, IgA subpopulations were gated as indicated and analysed for CD138, B220, and PD-L1 expression. IgA subpopulations were gated on the basis of CD19 expression, in the indicated strains, and the IgA+CD19+, IgA+CD19−/low/int, and IgACD19+ subpopulations were further analysed for their B220 and CD138 expression. The decisions for CD19 and CD138 levels were also based on their mean fluorescence intensity (MFI; red). The analyses showed two main populations: (1) IgA+CD19+B220+CD138low/− and (2) IgA+CD19−/intB220CD138+/hi. These two populations and the IgACD19+ populations were further analysed for their ability to express PD-L1 (percentage and mean fluorescence intensity). fj, Single-cell suspensions were prepared from the spleen, liver, or intestine of MUP-uPA mice kept on normal chow or HFD, and were stained with CD45, CD19, IgA, B220, CD138, CD11b, MHCII, PD-L1, and CD5, and analysed by flow cytometry. f, The B220 and CD138 expression in total IgA+ cells (CD19+ and CD19 populations) confirmed that most of the IgA+ cells in spleen and liver of HFD-fed MUP-uPA mice are CD138+ cells. g, h, IgA subpopulations in liver (g) and spleen (h) were gated as indicated and analysed for CD138, B220, and MHCII expression. i, Representative dot-plots gated on IgA+ cells, showing that most of the IgA+ cells are not CD11b+. j, PD-L1 and CD5 expression of IgA+ cells in spleen, liver, and intestine.

Extended Data Figure 4 IgA+ cells in NASH livers express PD-L1.

a, Single-cell suspensions were prepared from the livers of tumour-bearing mice (STAM-B6) as indicated. Shown are the PD-L1 and IgA staining in the indicated strains. Spleen or liver from Iga−/− and Pdl1/2−/− mice were used as control for IgA and PD-L1 staining. b, Liver single-cell suspensions of MUP-uPA mice and indicated strains were stained for CD45, IgA, and PD-L1. Percentages of liver PD-L1+ cells in CD45+IgA (labelled: CD45+) or IgA+ cell populations from the indicated strains are shown (n = 8, 6, 6, 7, 6, 6, 18, 16, 11, 12). c, Percentages of liver PD-L1+CD45+ cells are shown in the indicated strains (n = 5, 6, 6, 16, 10). The data were validated in at least three different experiments. d, e, CD138+ cells were divided into IgA+ and IgA populations and analysed for PD-L1 expression. d, Shown is the mean fluorescence intensity of PD-L1 expression for CD138+ cells from spleen (gated on either CD138+IgA or CD138+IgA+ cells). e, Mean fluorescence intensity of PD-L1 expression is shown for CD138+ plasmocytes from livers of 19 mice (gated on either CD138+IgA or CD138+IgA+ plasma cells). f, Percentages of intestinal IgA+ cells in MUP-uPA mice kept on normal chow or HFD (n = 6, 13; left). Percentages of PD-L1+IgA+ cells in intestine and liver are included for comparison (n = 7, 8; right). g, Percentages of liver IgA+ cells are shown in the indicated strains kept on HFD at the age of 3 months (n = 9, 7). h, Percentages of PD-L1+ cells are shown gated on CD45+CD19IgA cells in the indicated strains (n = 3, 4, 5). The data were validated at least in two or three experiments. Two-sided Mann–Whitney tests (median; b, c, e–h) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group in each panel are provided from left to right accordingly.

Extended Data Figure 5 CD8+ T cells and IgA+ plasmocytes are only marginal players in the MUP-uPA NASH model.

a, Comparison of weight gain by different strains of mice kept either on normal chow or HFD. Mice were continuously monitored from 8 to 46 weeks, and 20 data points were used to analyse weight gain, with at least 11–52 mice per time point (see Supplementary Source Data), and 5 BL6 mice on normal chow were used as controls. be, Sera, blood, and liver samples were collected from mice of the indicated strains and ages. Serum and blood samples were analysed for ALT (n = 13, 13, 16) (b), glucose tolerance test (n = 3–6 as indicated) (c), cholesterol (n = 5, 8, 9, 7) (d), or triglycerides (n = 5, 8, 9, 7) (e, left). Liver extracts were analysed for triglycerides (e, right) (n = 5, 9, 11, 7). f, Total RNA was extracted from livers of indicated mice and analysed by RT-PCR for Col1a1 mRNA (n = 4, 4, 3, 6, 4, 8). gi, Paraffin-embedded and frozen liver sections were stained with haematoxylin and eosin to determine liver histology (scale bar, 100 μm except 50 μm in the second panel of g), Sirius Red to determine collagen fibre deposition (scale bar, 100 μm), or Oil Red O to determine lipid droplet accumulation (scale bar, 50 μm) in the indicated strains and models (Extended Data Fig. 2a). Representative images are shown and were validated at least two or three times. Collagen deposition and lipid droplets were quantified using image analysis software and data points for individual mice are shown on the right for the MUP-uPA (g) (Sirius Red: n = 9, 14, 10; Oil Red O: 5, 6, 3) and MCD (h) (n = 3 per group) models. i, Representative Sirius Red staining for the CCl4 model (n = 4 or 5 mice per group). j, Total RNA from livers of 6-month-old mice of the indicated genotypes was subjected to RNA-seq analysis. Shown is the hierarchical clustering of gene expression profiles comparing HFD- and normal-chow-fed mouse liver samples. The top three enriched pathways/functional categories from Metascape were reported for major clusters of genes. k, Total RNA from livers of indicated mice was analysed by RT-PCR for alpha-fetoprotein (Afp) mRNA (n = 5, 5, 6, 5, 6, 6, 5, 8, 6, 6). The data were validated at least in two or three experiments (see Supplementary Tables 1 and 2). Two-way ANOVA (means ± s.e.m.; a, c), two-sided t-test (means ± s.e.m.; fh, k), and Mann–Whitney test (median; b, d, e, g, h) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group are shown either in individual panels or in legends for each group from left to right accordingly.

Extended Data Figure 6 IgA ablation inhibits, while CD8 deficiency accelerates HCC development.

a, b, Total DNA was extracted from HCC nodules of 11-month-old mice (n = 16) of the indicated genotypes with or without anti-PD-L1 treatment, and subjected to exome sequencing. Shown are the number of point mutations identified per sample (a) and mutational signatures (b). Horizontal axis shows the 96-substitution patterns with substitution subtypes on top, and vertical axis indicates the probability of each pattern in b. c, Top 20 hallmark gene sets sorted by normalized enrichment score (NES) are shown to depict HCC progression in MUP-uPA and MUP-uPA/Iga−/− mice by gene set enrichment analysis. Immune-related gene sets are coloured blue. The gene sets previously described20,57 for human HCC are marked with a red asterisk. d, Total RNA was extracted from livers of 6-month-old mice and from HCC nodules of 11-month-old mice of the indicated genotypes and subjected to RNA-seq analysis. Hierarchical clustering of gene expression profiles comparing HCC and non-HCC mouse samples according to the RNA-seq. The top three enriched pathways/functional categories from Metascape are reported for major clusters of genes. e, Representative images of liver histology at different time points and indicated strains are shown with detailed n values. Scale bars, 100 μm (3 months), 250 μm (6, 11 months). f, Total tumour numbers in 3-month-old MUP-uPA mice (n = 11, 7, 8). g, Heat map depicting differential expression of 17 liver-specific genes and 33 HCC-related genes in the indicated strains, illustrating the upregulation of some HCC-related genes in MUP-uPA/Cd8a−/− livers at 6 months of age (total mice number 29; n = 3 or 4 per group). hj, BL6 mice of the indicated phenotype were subjected to the STAM protocol and their tumour volumes (n = 14, 6, 6, 4, 3, 9, 3) (h) and histopathology (i, j) were evaluated at 25 weeks of age. The data were validated at least in two or three experiments. Paraffin-embedded and frozen liver sections from these mice were stained with haematoxylin and eosin, Sirius Red, or Oil Red O, as indicated. Shown are typical images of tumour-containing and tumour-free areas, the borders between which are marked by the black lines. Scale bars: haematoxylin and eosin, 100 μm; Sirius Red, 100, 250, or 500 μm; Oil Red O, 50 μm. Oil Red O-positive areas were quantitated and are shown on the right. The Sirius Red-stained areas for each mouse were calculated by image analysis of the whole-tissue scan and normalized to the haematoxylin and eosin stain (n = 3 or 4 per group). k, Tumour volumes are shown for STAM-WT (n = 10), STAM-Iga−/− (n = 13), and STAM-Iga−/− after CD8 depletion (n = 3). l, MUP-uPA/Iga−/−-HFD mice were injected weekly with anti-CD8 for 6 weeks and tumour multiplicity was determined (n = 3). k, l, CD8 depletion experiments were repeated using two different HCC models (MUP and STAM). m, Heat map depicting the differential expression of 59 genes involved in allograft rejection, IFNγ response, and inflammation (total mouse number 41; 6 months: n = 3 and 11 months: 3 or 4 per group). n, o, Paraffin-embedded and frozen liver sections from 11-month-old mice (n) and adoptively transferred mice (o) were stained with haematoxylin and eosin, Sirius Red, or Oil Red O as indicated and analysed (Sirius Red: n = 9, 16, 14, 6; Oil Red O: n = 8, 5, 5, 8 for n). Shown are typical images of tumour-containing and tumour-free areas, the borders between which are marked by the black lines. p, Liver cells from MUP-uPA/Rag1−/− mice 1 week after being adoptively transferred with CFSE-labelled T cells with or without B cells as indicated (n = 3 in each group) were stained and analysed by flow cytometry. Shown are the percentage of CD8+ T cells among CD45+ cells (left), and histogram of proliferating CFSE-labelled T cells with the corresponding mean fluorescence intensity (right). q, Liver sections for MUP-uPA/Rag1−/− and the corresponding adoptive lymphocyte transfer mice (4 weeks after adoptive transfer (AT)) were stained with alpha-SMA, IgA, CD3, and CD8 antibodies, counterstained with DAPI and examined by fluorescent microscopy (scale bars, 50 μm). For CD3/CD8 staining, images with higher magnification are shown (scale bars, 20 μm). Single-cell suspensions were prepared from the corresponding liver, stained with antibodies to CD45, IgA, CD19, B220, CD8, and CD4, and analysed by flow cytometry. Shown are representative scatter plots. MUP;Rag1−/− mouse livers have been used for validation of CD4, CD8, IgA, and CD19, both for flow cytometry and for immunofluorescence analyses. The data were validated in at least two experiments. r, STAM-BL6 mice of the indicated phenotypes were analysed for IgA serum amounts by ELISA (n = 11, 4, 4, 4, 4). s, Absolute IgA+ cell number in livers of indicated STAM-BL6 mice (n = 13, 4, 4, 9, 8). Each dot represents a mouse. Two-sided t-test (means ± s.e.m.; f, j, l, n, r) and Mann–Whitney test (median; h, k, n, s) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group are shown either in individual panels or in legends for each group from left to right accordingly.

Extended Data Figure 7 Gut microbes promote HCC development and microbial translocation does not account for the anti-tumorigenic effect of IgA ablation.

aj, MUP-uPA and MUP-uPA/Iga−/− mice were placed on HFD and treated with broad spectrum antibiotics (Abx) as described in Extended Data Fig. 2a, from 3 to 6 months (all panels except right part of b, and d, h, i) or 6 to 11 months (right part of b, and d, h, i) of age. At the end of the treatments, the stool contents of the corresponding mice were subjected to eubacterial 16S rRNA encoding DNA sequencing. a, Principal coordinate analysis plot of microbiome data using unweighted UniFrac distances; antibiotic treatment was significant by PERMANOVA (pseudo-F statistic = 105.5, P = 0.001) (left: n = 181, 27 as indicated; right: n = 6, 3, 6, 3). Mouse weight (6 months: n = 5, 6, 13, 7; 11 months: n = 8, 6, 5, 7) (b) and circulating ALT (n = 12, 5, 12, 6) (e) were measured. c, d, Paraffin-embedded and frozen liver sections from the above mice were stained with haematoxylin and eosin, Sirius Red, or Oil Red O, and were analysed for collagen deposition and lipid droplets as indicated. Scale bars, 50 μm for Oil Red O; 100 μm for haematoxylin and eosin, and Sirius Red (Sirius Red: n = 9, 6, 14, 7 for c and 4, 4, 5 for d; Oil Red O: n = 5, 3, 6, 3 mice). The data were validated at least in two or three experiments. fh, j, Liver cell suspensions were stained with antibodies as indicated, and analysed by flow cytometry. Each dot represents one mouse. Shown are percentages of CD8+ cells in total cells (n = 11, 5, 9, 7), CD8+CD44+ (n = 10, 4, 11, 6) or CD8+IFNγ+CD107a+TNF+ (n = 8, 6, 9, 7) cells in CD8+ T cells (f), CD19+B220+ cells in CD45+ cells (n = 9, 6, 11, 7) (g), IgA+ cells in CD45+ cells (n = 6, 6, 6, 3, 6, 6) (h), and CD4+ cells in CD45+ cells (n = 9, 6, 11, 7) or IL-17+ cells in CD4+ T cells (n = 9, 6, 10, 6) (j). i, MUP-uPA mice placed on HFD and treated with antibiotics were analysed for serum IgA by ELISA (n = 4, 8, 5, 6, 5, 5). Note that flow cytometry data of MUP-uPA and MUP-uPA/Iga−/− mice, which were not treated with antibiotics (control mice), are also shown in Fig. 3 and Extended Data Fig. 8q–y. The data were validated at least in two or three experiments. kr, Effects of HFD and immunological background on mouse intestinal microbiomes and metabolomes (total mouse number n = 288). Each dot represents one mouse. k, The most pronounced differences are engendered by HFD compared with normal chow. Left to right: principal coordinate analysis (PCoA) plot of microbiome data using unweighted UniFrac distances (PERMANOVA, pseudo-F statistic = 46, P = 0.001 comparing diet); barchart of relative abundances of bacterial phyla; principal component analysis plot of metabolome. k, l, Subsequent effect of immune status for the MUP-uPA HFD-fed mice: WT, Iga−/−, Cd8a−/−, μMT−/−, and Rag1−/− groups. l, Left to right: PCoA plot of microbiome data using unweighted UniFrac distances (PERMANOVA, pseudo-F statistic = 4.37, P = 0.001 comparing immune status); principal component analysis plot of metabolome; partial least squares discriminant analysis (PLS-DA) plot (the tenfold cross validation Q2 value was 0.817) of metabolome. Large differences between categories are evident. Subsequent juxtaposition of the (m) MUP and MUP;Iga−/−: PCoA of plot of microbiome using unweighted UniFrac distances (PERMANOVA, pseudo-F statistic = 7.31, P = 0.001 comparing immune status) (left); PLS-DA (the tenfold cross validation Q2 value of 0.926) plot of metabolome (right) and (n) MUP and MUP;Cd8a−/−: PCoA plot of microbiome data using unweighted UniFrac distances (PERMANOVA, pseudo-F statistic = 4.61, P = 0.001 comparing immune status) (left); PLS-DA plot (the tenfold cross validation Q2 value of 0.934) plot of metabolome (right) illustrates the discordance stemming from these specific immune status differences. o, Bacterial Faith’s phylogenetic diversity metric (alpha diversity, box plot with minimum to maximum) calculated with rarefaction at 4,500 sequences per sample using Faith’s phylogenetic diversity metric (n = 14, 8, 14, 25, 16, 6, 44, 34). p, Heat map of abundant bacterial taxa by immune status, genetic background, and diet. Trends in significantly differing taxa (ANCOM) by immune status include increased Gammaproteobacteria in Iga−/− with HFD and increased Ruminococcaceae in WT versus Iga−/− with HFD. Mucispirillum schaedleri was elevated in Iga−/− for all groups except normal-chow-fed MUP-uPA. q, Discordance according to the immune status for the STAM model mice. Left to right: PCoA plot of microbiome data using unweighted UniFrac distances; principal component analysis plot of metabolome (the tenfold cross validation Q2 value of the corresponding partial least squares discriminant analysis is 0.814). r, Box plot (minimum to maximum) of unweighted UniFrac distances comparing distances within Iga−/−, Pigr−/−, and IgHEL/MD4 strains with distances between these strains (n = 44, 40, 56) and WT or Cd8a−/− STAM mice (n = 16, 12, 12) for microbiome data. Two-sided t-test (means ± s.e.m.; a, b, i, r) and Mann–Whitney test (median; c–h, j, o) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group are shown either in individual panels or in legends for each group from left to right accordingly.

Extended Data Figure 8 IgA+ plasmocytes regulate tumour killing by CD8+ T cells.

a, Dih10 and dihXY HCC cells were transfected with an inducible ovalbumin (Ova) expression vector, and Ova expression and presentation were confirmed by flow cytometry, using an antibody that recognized the SIINFEKL peptide on the MHCI molecule H-2Kb. bh, Ova-expressing dih cells or controls (dih–RFP) were starved overnight (5% cell death), after which their medium was changed and B cells from WT, Iga−/−, Pdl1/2−/−, or SW-HEL mice were added in the presence of TGFβ (5 ng ml−1) and CTGF (3 ng ml−1), for an additional 24 h. Thereafter, the medium was replaced and CFSE-labelled OT-I T cells were added to the cultures that either contained or did not contain the B cells described above. After 4–6 days, the co-cultured cells were analysed by flow cytometry, while the secretory IgA was analysed by ELISA (n = 2–4 wells per group per day). aj, Experiments were repeated with two different Ova-expressing HCC and one prostate cancer cell lines. Shown are the representative flow cytometry histograms or plots depicting (b) OT-I CD8+ T-cell proliferation, (c) PD-L1 and SIINFEKL/H-2Kb expression on cancer cells, (d) PD-L1 expression on B cells, (e) cancer cell death. f, Relative dih-Ova–RFP killing by OT-I CD8+ T cells in the presence or absence of the indicated B cells. g, Total secretory IgA and anti-OVA-IgA antibody amounts in culture supernatants. h, Percentages of OT-I CD8+ cells in each culture, as indicated. i, j, TRC2-Ova–RFP cells or its control cell line (TRC2–RFP) were co-cultured with OT-I cells and splenic B cells (WT and Il10−/−), as described for ah. i, Proliferation of OT-I cells was analysed using CFSE (n = 3, 7, 5, 4). j, The amounts of secretory IgA were analysed using ELISA as indicated (n = 3 per group). ko, Liver cells from indicated 3-month-old mice were stained and analysed by flow cytometry. Experiments were repeated at least two or three times. Each dot represents one mouse. Shown are the percentage of CD8+ T cells among CD45+ cells (n = 6, 5, 3, 7, 4) (k), absolute CD8+ T-cell number per gram of liver (n = 3, 3, 7, 4) (l), the percentage of CD8+CD44+Ki-67+ T cells with representative scatter plots (n = 3 or 4 per group) (m, n), and the representative scatter plots of perforin and GrzB among CD8+CD44+Ki-67+ T cells (o). p, q, Liver cell suspensions from the indicated mice were stained as shown and analysed by flow cytometry to determine the absolute CD8+ T-cell number in both STAM-BL6 and STAM-FVB mice (n = 8, 4, 5, 3, 5, 5, 6, 7) (p), and the percentage of TEM cells using CD8, CD44, and CD62L (n = 4, 4, 6, 9) (q). r, Liver cells from indicated 3-, 6-, and 11-month-old mice kept on HFD (n = 4-10) were stained and analysed by flow cytometry. Shown are the percentage of CD8+IFNγ+CD107a+ T cells. Detailed n values are shown in Fig. 3e. sy, Liver cell suspensions from the indicated mice were stained as shown and analysed by flow cytometry to determine the percentage of Th17 cells using CD4 and IL-17a (n = 4, 7, 9, 10, 4, 9, 11) (s), the percentage of regulatory T cells using CD4 and Foxp3 (n = 4, 3, 8, 11, 7, 13) (t), the percentage of Tfh-like cells using CXCR5, PD1 and CD4 (3, 5, 6, 5) (u), the percentage of B220+CD19+ B cells (n = 4, 7, 7, 4, 5, 3, 9, 3, 11) (v), absolute B220+CD19+ B-cell number per gram of liver (n = 5, 7, 12, 27, 15, 7) (w), the percentage of IgG+ cells (n = 4, 3, 14, 11) (x), and CD138+ plasma cells (n = 4, 7, 4, 7, 7, 8, 5, 8, 6, 6, 13, 27, 17) (y). Two-sided t-test (means ± s.e.m.; fm) and Mann–Whitney test (median; py) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. Mouse ages are indicated in the graphs. N values for each group in each panel are provided from left to right accordingly.

Extended Data Figure 9 The response to PD-L1 blockade is dependent on CD8+ T cells and clonal expansion of HCC-directed CD8+ T cells.

MUP-uPA and MUP-uPA/Iga−/− mice were placed on HFD and treated with anti-PD-L1, as described in Extended Data Fig. 2a. a, At the end of the treatments, mouse weights were measured (n = 3, 2, 4, 3, 3, 7, 3, 7). b, MUP-uPA mice treated with anti-PD-L1 were analysed for serum IgA by ELISA (n = 10, 6). c, Liver/body weight ratio of indicated strains kept on HFD that received the indicated treatments and were of the indicated ages (6 months, 11 months) (n = 4, 6, 9, 19, 6, 2, 4, 15, 11, 17, 3, 14, 7, 20). d, e, Paraffin-embedded and frozen liver sections from HCC-bearing MUP-uPA mice were stained with Oil Red O, haematoxylin and eosin, or Sirius Red and analysed (n = 4 or 5). The experiments were repeated at least two times. Low and high magnifications are shown in e to demonstrate the absence of tumour-invading immune cells in a mouse that failed to respond to anti-PD-L1 and their presence within a tumour of a treatment responsive mouse. Non-responsiveness to anti-PD-L1 treatment correlates with a fibrotic tumour stroma. Scale bars: haematoxylin and eosin, 250 μm; Sirius Red, 250 μm; Oil Red O, 50 μm. f, Response to PD-L1 blockade is dependent on CD8+ T cells. HCC-bearing MUP-uPA/Cd8a−/− mice (n = 3) were treated with anti-PD-L1 for 8 weeks and tumour multiplicity was determined. g, Two-dimensional plot showing the frequency of the top ten TCRα CDR3 sequences expressed by CD8+ T cells from spleens and livers of HCC-bearing MUP-uPA mice. h, CD8+ T cells were sorted from spleens and livers of normal-chow- and HFD-fed WT mice (n = 5 mice), their RNA was extracted, and TCR α-chain (top) and β-chain (bottom) CDR3 sequences were amplified and analysed by deep sequencing (ten samples). The panels show the clonality, the frequency of the top 50 TCR α- and β-chain sequences, and the percentage of productive unique TCR α- and β-chain sequences. ik, CD8+ T cells were sorted from spleens and livers of HCC-bearing mice of the indicated strains and treatments (n = 13 mice, 26 samples as indicated) and their TCR α- and β-chain CDR3 sequence diversity was analysed. Shown are the clonality (i), the diversity (inverse Simpson’s index) (j), and percentage of productive unique TCR β-chain sequences in the indicated strains (k). Note that TCR sequencing data of WT, MUP-uPA, and MUP-uPA/Iga−/− mice are also shown in Fig. 4g, h and Extended Data Fig. 9h. ln, Splenocytes from the indicated mice treated with or without anti-PD-L1 were stimulated overnight with alpha-fetoprotein, stained as indicated, and analysed by flow cytometry. Shown are the percentage of IFNγ+CD107a+ cells (n = 3, 3, 2, 5, 3, 2, 3, 4, 4) (l) and IFNγ+TNF+ cells (n = 4, 3, 5, 4) (m) gated on CD8+ T cells. n, The percentage of IFNγ+TNF+ cells gated on CD4+ T cells (n = 3, 3, 2, 4, 3, 2, 3, 6, 4). Two-sided t-test (ad, f, hn) and Mann–Whitney test (l) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group in each panel are provided from left to right accordingly.

Extended Data Figure 10 Liver-IgA+ cells are oligoclonal, and effects of HFD on control WT, Iga−/−, and Cd8a−/− mice.

a, b, B cells and plasmocytes (CD19+, CD138+, IgA+ cells) were sorted from spleens (n = 3) and livers (n = 3) of HCC-bearing MUP-uPA mice and their μ (IgM) and α (IgA) locus genetic diversities were determined by BCR sequencing. c, Circulating ALT in HFD-fed WT, Iga−/−, and Cd8a−/− mice at 6 months of age (n = 5, 4, 8). df, Paraffin-embedded and frozen liver sections from the above mice were stained with haematoxylin and eosin, Oil Red O, or Sirius Red, as indicated. The data were validated at least twice. Scale bars: haematoxylin and eosin, 100 μm; Sirius Red, 100 μm; Oil Red O, 50 μm. The Sirius Red (n = 3, 4, 8) (d) and Oil Red O (n = 3, 3, 6) (e) stained areas were quantitated. gi, Serum cholesterol (g), serum triglycerides (h), and liver triglycerides (i) were measured (n = 5, 4, 3). Two-sided t-test (a, di) and Mann–Whitney test (c) were used to determine significance. *P < 0.05; **P < 0.01; ***P < 0.001. N values for each group in each panel are provided from left to right accordingly.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-3. (PDF 112 kb)

Life Sciences Reporting Summary (PDF 181 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shalapour, S., Lin, X., Bastian, I. et al. Inflammation-induced IgA+ cells dismantle anti-liver cancer immunity. Nature 551, 340–345 (2017). https://doi.org/10.1038/nature24302

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing