Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Acute liver failure is regulated by MYC- and microbiome-dependent programs

Abstract

Acute liver failure (ALF) is a fulminant complication of multiple etiologies, characterized by rapid hepatic destruction, multi-organ failure and mortality. ALF treatment is mainly limited to supportive care and liver transplantation. Here we utilize the acetaminophen (APAP) and thioacetamide (TAA) ALF models in characterizing 56,527 single-cell transcriptomes to define the mouse ALF cellular atlas. We demonstrate that unique, previously uncharacterized stellate cell, endothelial cell, Kupffer cell, monocyte and neutrophil subsets, and their intricate intercellular crosstalk, drive ALF. We unravel a common MYC-dependent transcriptional program orchestrating stellate, endothelial and Kupffer cell activation during ALF, which is regulated by the gut microbiome through Toll-like receptor (TLR) signaling. Pharmacological inhibition of MYC, upstream TLR signaling checkpoints or microbiome depletion suppress this cell-specific, MYC-dependent program, thereby attenuating ALF. In humans, we demonstrate upregulated hepatic MYC expression in ALF transplant recipients compared to healthy donors. Collectively we demonstrate that detailed cellular/genetic decoding may enable pathway-specific ALF therapeutic intervention.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mouse liver cell census in acute liver failure mouse models.
Fig. 2: Activation of resident cell population in ALF.
Fig. 3: Heterogeneity of infiltrating cells in ALF.
Fig. 4: Common activation signature of resident cells is regulated by MYC.
Fig. 5: The microbiome modulates response to acute insult via MYC and TLR.
Fig. 6: The MAPK pathway relays signaling from TLR to MYC.

Similar content being viewed by others

Data availability

The small cytoplasmic RNA-seq data have been deposited with ArrayExpress under accession no. E-MTAB-8263, and 16S sequencing data with the European Nucleotide Archive under accession no. ERP116956. Source data are provided with this paper.

References

  1. Bernal, W. & Wendon, J. Acute liver failure. N. Engl. J. Med. 369, 2525–2534 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Hinson, J. A., Roberts, D. W. & James, L. P. Mechanisms of acetaminophen-induced liver necrosis. Handb. Exp. Pharmacol. 196, 369–405 (2010).

  3. Jaeschke, H. Reactive oxygen and mechanisms of inflammatory liver injury: present concepts. J. Gastroenterol. Hepatol. 26, 173–179 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Andrew Clayton, T. et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440, 1073–1077 (2006).

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rühlemann, M. C. et al. Faecal microbiota profiles as diagnostic biomarkers in primary sclerosing cholangitis. Gut 66, 753–754 (2017).

    Article  PubMed  Google Scholar 

  7. Henao-Mejia, J. et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179–185 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Llopis, M. et al. Intestinal microbiota contributes to individual susceptibility to alcoholic liver disease. Gut 65, 830–839 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Hajovsky, H. et al. Metabolism and toxicity of thioacetamide and thioacetamide S-oxide in rat hepatocytes. Chem. Res. Toxicol. 25, 1955–1963 (2012).

  10. Woolbright, B. et al. The impact of sterile inflammation in acute liver injury. J. Clin. Transl. Res. 3, 170–188 (2017).

  11. Mederacke, I., Dapito, D. H., Affò, S., Uchinami, H. & Schwabe, R. F. High-yield and high-purity isolation of hepatic stellate cells from normal and fibrotic mouse livers. Nat. Protoc. 10, 305–315 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Heng, T. S. P. & Painter, M. W., Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Strauss, O., Phillips, A., Ruggiero, K., Bartlett, A. & Dunbar, P. R. Immunofluorescence identifies distinct subsets of endothelial cells in the human liver. Sci. Rep. 7, 44356 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kalucka, J. et al. Single-cell transcriptome atlas of murine endothelial cells. Cell 180, 764–779 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Yang, C.-Y. et al. CLEC4F is an inducible C-type lectin in F4/80-positive cells and is involved in alpha-galactosylceramide presentation in liver. PLoS ONE 8, e65070 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Krenkel, O. & Tacke, F. Liver macrophages in tissue homeostasis and disease. Nat. Rev. Immunol. 17, 306–321 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Cavalli, M. et al. A multi-omics approach to liver diseases: integration of single nuclei transcriptomics with proteomics and HiCap bulk data in human liver. OMICS 24, 180–194 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Krenkel, O., Hundertmark, J., Ritz, T. P., Weiskirchen, R. & Tacke, F. Single cell RNA sequencing identifies subsets of hepatic stellate cells and myofibroblasts in liver fibrosis. Cells 8, 503 (2019).

  21. Reynaert, H., Thompson, M. G., Thomas, T. & Geerts, A. Hepatic stellate cells: role in microcirculation and pathophysiology of portal hypertension. Gut 50, 571–581 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schmidt-Arras, D. & Rose-John, S. IL-6 pathway in the liver: from physiopathology to therapy. J. Hepatol. 64, 1403–1415 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Maeshima, K. et al. A protective role of interleukin 11 on hepatic injury in acute endotoxemia. Shock 21, 134–138 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Campisi, J. & d’Adda di Fagagna, F. Cellular senescence: when bad things happen to good cells. Nat. Rev. Mol. Cell Biol. 8, 729–740 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Coppé, J.-P., Desprez, P.-Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu. Rev. Pathol. 5, 99–118 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Poisson, J. et al. Liver sinusoidal endothelial cells: physiology and role in liver diseases. J. Hepatol. 66, 212–227 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Shaulian, E. & Karin, M. AP-1 as a regulator of cell life and death. Nat. Cell Biol. 4, E131–E136 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Karlmark, K. R., Wasmuth, H. E., Trautwein, C. & Tacke, F. Chemokine-directed immune cell infiltration in acute and chronic liver disease. Expert Rev. Gastroenterol. Hepatol. 2, 233–242 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Fabregat, I. et al. TGF-β signalling and liver disease. FEBS J. 283, 2219–2232 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Ramilowski, J. A. et al. A draft network of ligand–receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Dambach, D. M., Watson, L. M., Gray, K. R., Durham, S. K. & Laskin, D. L. Role of CCR2 in macrophage migration into the liver during acetaminophen-induced hepatotoxicity in the mouse. Hepatology 35, 1093–1103 (2002).

    Article  CAS  PubMed  Google Scholar 

  32. Huebener, P. et al. The HMGB1/RAGE axis triggers neutrophil-mediated injury amplification following necrosis. J. Clin. Invest. 125, 539–550 (2019).

    Article  Google Scholar 

  33. McDonald, B. et al. Intravascular danger signals guide neutrophils to sites of sterile inflammation. Science 330, 362–366 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Silvestre-Roig, C., Hidalgo, A. & Soehnlein, O. Neutrophil heterogeneity: implications for homeostasis and pathogenesis. Blood 127, 2173–2181 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Mayadas, T. N., Cullere, X. & Lowell, C. A. The multifaceted functions of neutrophils. Annu. Rev. Pathol. 9, 181–218 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Nguyen, T., Nioi, P. & Pickett, C. B. The Nrf2-antioxidant response element signaling pathway and its activation by oxidative stress. J. Biol. Chem. 284, 13291–13295 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Brempelis, K. J. & Crispe, I. N. Infiltrating monocytes in liver injury and repair. Clin. Transl. Immunol. 5, e113 (2016).

    Article  CAS  Google Scholar 

  38. Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Hart, J. R. et al. Inhibitor of MYC identified in a Kröhnke pyridine library. Proc. Natl Acad. Sci. USA 111, 12556–12561 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Thomas, M. P. et al. Apoptosis triggers specific, rapid, and global mRNA decay with 3′ uridylated intermediates degraded by DIS3L2. Cell Rep. 11, 1079–1089 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Elinav, E. et al. NLRP6 inflammasome regulates colonic microbial ecology and risk for colitis. Cell 145, 745–757 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510.e12 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Maerki, C. et al. Potent and broad-spectrum antimicrobial activity of CXCL14 suggests an immediate role in skin infections. J. Immunol. 182, 507–514 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Hari, P. et al. The innate immune sensor Toll-like receptor 2 controls the senescence-associated secretory phenotype. Sci. Adv. 5, eaaw0254 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wu, C. et al. Proteomic analysis reveals IRAK4 as a therapeutic target in chronic lymphocytic leukemia. Blood 130, 3838 (2017).

    Article  Google Scholar 

  46. Zhou, J. et al. TAK1 mediates excessive autophagy via p38 and ERK in cisplatin-induced acute kidney injury. J. Cell. Mol. Med. 22, 2908–2921 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Vyrla, D. et al. TPL2 kinase is a crucial signaling factor and mediator of NKT effector cytokine expression in immune-mediated liver injury. J. Immunol. 196, 4298–4310 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Wu, X. et al. MEK-ERK pathway modulation ameliorates disease phenotypes in a mouse model of Noonan syndrome associated with the Raf1L613V mutation. J. Clin. Invest. 121, 1009–1025 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mannangatti, P., NarasimhaNaidu, K., Damaj, M. I., Ramamoorthy, S. & Jayanthi, L. D. A role for p38 mitogen-activated protein kinase-mediated threonine 30-dependent norepinephrine transporter regulation in cocaine sensitization and conditioned place preference. J. Biol. Chem. 290, 10814–10827 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Berger, S. et al. Characterization of GSK′963: a structurally distinct, potent and selective inhibitor of RIP1 kinase. Cell Death Discov. 1, 15009 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Scaffidi, P., Misteli, T. & Bianchi, M. E. Release of chromatin protein HMGB1 by necrotic cells triggers inflammation. Nature 418, 191–195 (2002).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  53. Yang, L. & Seki, E. Toll-like receptors in liver fibrosis: cellular crosstalk and mechanisms. Front. Physiol. 3, 138 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Yamamoto, M. Role of adaptor TRIF in the MyD88-independent Toll-like receptor signaling pathway. Science 301, 640–643 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Reimand J. et al. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, 193–200 (2007).

  57. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bolyen, E. et al. QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 37, 852–857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Elinav laboratory and the DKFZ cancer-microbiome division for discussions. We thank C. Bar-Nathan for dedicated assistance with animal work. A.A.K. is a recipient of EMBO Long Term Fellowship no. 2016‐1088 and the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska‐Curie grant agreement no. 747114. I.A. is supported by the Chan Zuckerberg Initiative, an HHMI international scholar award, a European Research Council consolidator grant (ERC-COG, no. 724471-Hem-Tree2.0), the Thompson Family Foundation, an MRA established investigator award (no. 509044), the Israel Science Foundation (no. 703/15), the Ernest and Bonnie Beutler Research Program for Excellence in Genomic Medicine, a Helen and Martin Kimmel award for innovative investigation, a NeuroMac DFG/Transregional Collaborative Research Center grant, International Progressive MS Alliance/NMSS (no. PA-1604-08459), an Adelis Foundation grant and the SCA award of the Wolfson Foundation. E.E. is supported by Yael and Rami Ungar; the Leona M. and Harry B. Helmsley Charitable Trust; the Adelis Foundation; the Pearl Welinsky Merlo Scientific Progress Research Fund; the Lawrence and Sandra Post Family Foundation; the Daniel Morris Trust; the Park Avenue Charitable Fund; the Hanna and Dr Ludwik Wallach Cancer Research Fund; the Howard and Nancy Marks Charitable Fund; Aliza Moussaieff; the estates of Malka Moskowitz, Myron H. Ackerman and Bernard Bishin (for the WIS-Clalit Program); Donald and Susan Schwarz; the V. R. Schwartz Research Fellow Chair; grants funded by the European Research Council; Israel Science Foundation; Israel Ministry of Science and Technology; Israel Ministry of Health; the Helmholtz Foundation; the Else Kroener Fresenius Foundation; the Garvan Institute; the European Crohn’s and Colitis Organization; Deutsch-Israelische Projektkooperation; and Welcome Trust. E.E. is the incumbent of the Sir Marc and Lady Tania Feldmann Professorial Chair; a senior fellow, Canadian Institute of Advanced Research and an international scholar; and The Bill & Melinda Gates Foundation and Howard Hughes Medical Institute.

Author information

Authors and Affiliations

Authors

Contributions

A.A.K. and E.E. designed, analyzed and interpreted all experiments, and wrote the manuscript. A.A.K performed all experiments with the help of S.F., N.Z., G.M., S.H., A.L., M.D.-B. and H.S. D.R. and E.Z. provided experimental support. T.M.S. assisted with cell sorting. A.H. assessed tissue histology. A.T. and A.S. provided human clinical data, insights and material. I.A. and E.E. supervised the study.

Corresponding authors

Correspondence to Ido Amit or Eran Elinav.

Ethics declarations

Competing interests

E.E. is a paid consultant at DayTwo and BiomX. None of this work is related to, funded or endorsed by, shared or discussed with or licensed to any commercial entity.

Additional information

Editor recognition statement Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Acute liver failure model and characterisation of cell type.

a, Activity of hepatic enzymes aspartate transaminase (AST) and alanine transaminase (ALT) in serum of mice injected with APAP and TAA, significance was determined using one-sided Wilcoxon test; n = 5 for each group. Boxplot shows 25th to 75th percentiles with 50th denoted with a line, whiskers show 1.5 times interquartile range or maximum or minimum if they are smaller than that. b, FACS of retinoid fluorescence positive cells. c, Boxplot showing sum of normalised and scaled expression of MHCII in each cell type stellate quiescent n = 7282, stellate fibrotic n = 140, stellate activated (AAs) n = 6470, stellate cycling n = 180, stellate activated MYCi n = 2013, endothelial sinusoidal n = 5736, endothelial arterial n = 932, endothelial venous n = 167, endothelial activated (AAe) n = 4928, endothelial cycling n = 54, endothelial activated MYCi n = 2287, mesothelial n = 128, Kupffer n = 2696, Kupffer activated (AAk) n = 697, Kupffer activated MYCi n = 384, Kupffer MyD88 Trif KO n = 265, Kupffer activated MyD88 Trif KO n = 406, Kupffer cycling n = 155, macrophage Cx3cr1+ n = 280, monocyte Ly6C+ n = 5002, monocyte IFN n = 93, monocyte Ly6C- n = 293, plasmacytoid dendritic cell n = 295, dendritic cell Cd209a+ n = 542, dendritic cell Xcr1+ n = 302, dendritic cell Ccr7+ n = 119, dendritic cell cycling n = 83, dendritic cell activated MYCi n = 297, neutrophil n = 3771, neutrophil proinflammatory n = 1804, neutrophil activated MYCi n = 582, neutrophil MyD88 Trif KO n = 152, mast cell n = 41, erythrocyte n = 114, thrombocyte n = 97, B cell n = 2063, B cell memory n = 142, B1a cell n = 21, NK cell n = 381, NKT n = 344, naïve Cd8+ T cell n = 897, cytotoxic Cd8+ T cell n = 174, regulatory T cell n = 408, ɣδT cell n = 1373, ɣδT cell IFN n = 37, T cell cycling n = 128, ɣδT cell MYCi n = 477, hepatocyte n = 963, cholangiocyte n = 332. Boxplot defined as in Extended Data Fig. 1a. d, Boxplot showing number of transcripts expressed in each cell type, number of cells as in c. Boxplot defined as in Extended Data Fig. 1a. e, Boxplot showing number of genes detected in each cell type, which corresponds to the number of detected unique transcripts, number of cells as in c. Boxplot defined as in Extended Data Fig. 1a.

Extended Data Fig. 2 Cell abundance changes in acute liver failure and differences between APAP and TAA models.

a, Percentage of cell populations in control mice (n = 6), APAP (n = 8) and TAA (n = 6) treated mice, significance was determined using two-sided Wilcoxon test. Boxplot defined as in Extended Data Fig. 1a. Data points from SPF samples denoted as , GF - ■ and ABX - ▲ b, Heatmap showing differentially expressed genes in AAs between APAP and TAA treated mice. c, Heatmap showing differentially expressed genes in AAe between APAP and TAA treated mice. e, Violin plots showing normalised and scaled expression of example chemokines upregulated in activated Kupffer cells. d, Violin plots showing normalised and scaled expression of example chemokines, cytokines and extracellular matrix modifiers upregulated in activated endothelial cells. e, Violin plots showing normalised and scaled expression of example chemokines upregulated in activated Kupffer cells.

Extended Data Fig. 3 Receptors and ligands in ALF.

a, Boxplot showing percent of reads mapping to the mitochondrial genome in each cell type, number of cells as in Extended Data Fig. 1c.b, Boxplot showing normalised and scaled expression of Ccl2 receptor, Ccr2 in all cell types, number of cells as in Extended Data Fig. 1c.c, d, Baloon plots showing expression of (c) chemokines and cytokines and (d) their receptors.

Extended Data Fig. 4 Cellular states upon MYC inhibition.

a, Density plot of permutation analysis of number of MYC binding sites in randomly chosen 77 genes (black) in comparison to 77-gene signature (red) b Violin plots showing mild upregulation of expression of Myc in activated cells. c, Western blots of MYC and phospho-MYC and d quantification of phospho-MYC Western blot of control mice (n = 5), APAP (n = 5) and TAA (n = 5) treated mice. Boxplot defined as in Extended Data Fig. 1a. Significance was determined using two-sided Wilcoxon test. e, FACS gating strategy to identify Ly6C-positive monocytes. f, Barplot showing relative frequencies of cells in healthy and mice with ALF in the presence and absence of MYCi.

Source data

Extended Data Fig. 5 Cellular states upon MYC inhibition.

UMAP showing distribution of cell clusters in healthy, APAP and TAA treated mice in the presence and absence of MYCi.

Extended Data Fig. 6 Effect of MYC inhibition on gene expression.

a, b, Gene ontology term enrichment analysis of genes differentially expressed in healthy mice and healthy mice treated with MYCi in stellate cells and in Kupffer cells. GO analysis was performed with GProfiler using standard settings, p-values shown are corrected for multiple hypothesis testing using g:SCS algorithm. c, Volcanoplots showing differentially abundant genes healthy mice and healthy mice treated with MYCi. Y-axis value depicts multiple hypothesis testing corrected p-value calculated using DESeq2 package. d, e, Barplot showing infiltration of (d) Ly6C-positive monocytes and (e) neutrophils in the presence and absence of MYCi. Different colors of bars denote subpopulations of neutrophils; legend as in Extended Data Fig. 5. fh, Boxplots showing expression of 77-gene signature in healthy mice, mice treated with APAP or TAA and mice treated with APAP and MYCi SPF (n = 3, cS=1999, cE=1463, cK=659), SPF + APAP (n = 4, cS=4339, cE=1517, cK=265), SPF + TAA (n = 4, cS=910, cE=1456, cK=285), in presence of MYCi: SPF + APAP + MYCi (n = 2, cS=251, cE=303, cK=125) and SPF + TAA + MYCi (n = 2, cS=198, cE=512, cK=233), *** denotes p-value < 0.001, n = number of mice, cS = number of stellate cells, cE = number of endothelial cells, cK = number of Kupffer cells. Boxplot defined as in Extended Data Fig. 1a. Significance was determined using one-sided Wilcoxon test. p-values in stellate cells: SPF + APAP vs SPF + APAP + MYCi 1.13510−15, SPF + TAA vs SPF + TAA + MYCi 2.66210−14; in endothelial cells: SPF + APAP vs SPF + APAP + MYCi 5.19610−6, SPF + TAA vs SPF + TAA + MYCi 4.93510−6; in Kupffer cells: SPF + APAP vs SPF + APAP + MYCi 1.28710−8, SPF + TAA vs SPF + TAA + MYCi 1.51710−7 (i) Violin plot showing normalised and scaled expression of Cdkn1a in three activated cells types. j, Gene ontology term enrichment analysis of genes differentially expressed in APAP and TAA treated mice with and without MYC inhibitor in stellate, endothelial and Kupffer cells. GO analysis was done as in Extended Data Fig. 6a-b.

Extended Data Fig. 7 Microbiome in acute liver failure.

a, PCA of 16 S microbiome ASV abundance data in the small intestine and in the colon of mice treated with APAP (intraperitoneal injection) and PBS control. b, Volcanoplots showing differential abundance analysis fold change and Benjamini-Hochberg adjusted p-values obtained with two-sided Wilcoxon test c, Alpha diversity metrics in control mice (colon n = 10, small intestine n = 10) and APAP treated mice (colon n = 9, small intestine n = 9), significance was determined using two-sided t-test. P-value for comparison between control and APAP treated mice for OTUs in the small intestine 0.3706, colon 0.0103, for evenness in the small intestine 0.04177, colon 0.04094, for Shannon diversity index in the small intestine 0.05235, colon 0.01271, for Faith’s phylogenetic diversity index in the small intestine 0.08351, colon 0.00511. Boxplot defined as in Extended Data Fig. 1a, ** denotes p-value < 0.01, * denotes p-value < 0.05. d, Box plots showing percent of cells, quiescent stellate cells and AAs within stellate cell populations, endothelial sinusoidal cells and AAe within endothelial cells and the Kupffer cells, AAk cells, monocytes and neutrophils within immune cells. GF n = 2, SPF n = 3, GF + APAP n = 2, ABX + APAP n = 2, SPF + APAP n = 4, GF + TAA n = 2, SPF + TAA n = 4. Boxplot defined as in Extended Data Fig. 1a.

Extended Data Fig. 8 Microbiome effect on cellular responses in acute liver failure.

a, Activity of hepatic enzyme aspartate aminotransferase (AST) and alanine transaminase (ALT) in serum in healthy germ free, antibiotics treated and specific pathogen free mice, significance was determined using one-sided Wilcoxon test; n = 9 for ABX and SPF, n = 10 for GF. Boxplot defined as in Extended Data Fig. 1a. be, Heatmaps showing differentially expressed genes between activated cells in GF APAP-induced and SPF APAP-induced mice. Heatmaps show expression of these genes in quiescent cells in healthy mice and in activated cells in mice treated with APAP, differentially expressed genes shown DESeq2 adjusted p-value <0.05.

Extended Data Fig. 9 TLR ligands in ALF.

a, b, Heatmap and boxplots showing mean levels of 655 nm absorbance in HEK-Blue TLR and NLR reporter cell lines subtracted with absorbance of corresponding Null cell line after application of portal serum from germ free (GF), antibiotics treated (ABX) and specific pathogen free (SPF) mice treated with APAP and TAA. Boxplot defined as in Extended Data Fig. 1a.

Extended Data Fig. 10 Cellular response to APAP in MyD88 Trif KO mice.

a, Relative frequencies of cells in healthy and mice with ALF in the presence and absence of MYCi as in Extended Data Fig. 4f and additionally in GF, ABX and MyD88 Trif-dKO mice. b, Gene ontology term enrichment analysis of genes in Kupffer cells significantly more abundant in wild type (top) and significantly more abundant in Myd88-Trif dKO (bottom). GO analysis was performed with GProfiler using standard settings, p-values shown are corrected for multiple hypothesis testing using g:SCS algorithm c-e, Violin plots showing normalised and scaled expression of key genes in activated stellate cells (top), activated endothelial cells (middle) and activated Kupffer cells (bottom) in wild type mice, in presence of MYCi and in MyD88-Trif dKO mice. f, Violin plots showing normalised and scaled expression of TLP2 coding gene Map3k8 in activated resident populations in presence and absence of MYC inhibition. g, Details of human liver samples including age in years, gender (F = female, M = male) and disease status.

Supplementary information

Source data

Source Data Extended Data Fig. 4

Unprocessed immunolots for panel c.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolodziejczyk, A.A., Federici, S., Zmora, N. et al. Acute liver failure is regulated by MYC- and microbiome-dependent programs. Nat Med 26, 1899–1911 (2020). https://doi.org/10.1038/s41591-020-1102-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-020-1102-2

This article is cited by

Search

Quick links

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