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Platelet GPIbα is a mediator and potential interventional target for NASH and subsequent liver cancer

Nature Medicine volume 25, pages 641–655 (2019)Cite this article

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An Author Correction to this article was published on 18 February 2022

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Abstract

Non-alcoholic fatty liver disease ranges from steatosis to non-alcoholic steatohepatitis (NASH), potentially progressing to cirrhosis and hepatocellular carcinoma (HCC). Here, we show that platelet number, platelet activation and platelet aggregation are increased in NASH but not in steatosis or insulin resistance. Antiplatelet therapy (APT; aspirin/clopidogrel, ticagrelor) but not nonsteroidal anti-inflammatory drug (NSAID) treatment with sulindac prevented NASH and subsequent HCC development. Intravital microscopy showed that liver colonization by platelets depended primarily on Kupffer cells at early and late stages of NASH, involving hyaluronan-CD44 binding. APT reduced intrahepatic platelet accumulation and the frequency of platelet–immune cell interaction, thereby limiting hepatic immune cell trafficking. Consequently, intrahepatic cytokine and chemokine release, macrovesicular steatosis and liver damage were attenuated. Platelet cargo, platelet adhesion and platelet activation but not platelet aggregation were identified as pivotal for NASH and subsequent hepatocarcinogenesis. In particular, platelet-derived GPIbα proved critical for development of NASH and subsequent HCC, independent of its reported cognate ligands vWF, P-selectin or Mac-1, offering a potential target against NASH.

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Fig. 1: Increased platelet numbers and aggregates in liver sinusoids of murine and human NASH.
Fig. 2: Asp-Clo treatment results in attenuation of steatosis, liver damage, NASH and NASH-associated conditions.
Fig. 3: Antiplatelet treatment with Asp-Clo abrogates immune cell infiltration into the liver and prevents NASH-induced HCC development.
Fig. 4: Platelets efficiently populate the liver early during fatty liver pathogenesis.
Fig. 5: Intrahepatic platelet accumulation depends on Kupffer cells, hyaluronan and cargo function.
Fig. 6: Anti-GPIbα antibody treatment as well as genetic dysfunction of GPIbα reduces NASH, fibrosis and HCC development.

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Data availability

Data that support the findings of this study have been uploaded to ArrayExpress (www.ebi.ac.uk/arrayexpress/) and the data set is available under the accession number E-MTAB-6073, entitled ‘Transcriptomic differences in livers of mice fed with normal diet and choline-deficient high-fat diet’.

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Acknowledgements

We thank D. Heide, J. Hetzer, R. Hillermann, C. Gropp, F. Müller, S. Prokosch, D. Kull, R. Dunkl, O. Seelbach, M. Bawohl, R. Maire, M. Bieri, C. Mittmann, H. Honcharova-Biletska, A. Fitsche, A. Adili, P. Münzer, T. Nussbaumer, F. Prutek, G. Dharmalingam and I. Singh for excellent technical assistance. We thank K. Nikolaou for the help with the human cohort recruitment and analysis. M. Malehmir was partially supported by grants from the University Zurich (Zurich Integrative Human Physiology (ZHIP) Sprint Fellowship) and from the Hartmann Müller Stiftung, Zurich. A.W. was supported by a grant from the Swiss National Science Foundation (320030_182764/1). M. Heikenwaelder was supported by an ERC Consolidator grant (HepatoMetaboPath), an EOS grant, SFBTR 209, SFBTR179, Research Foundation Flanders (FWO) under grant 30826052 (EOS Convention MODEL-IDI), Deutsche Krebshilfe projects 70113166 and 70113167, and the Helmholtz-Gemeinschaft, Zukunftsthema ‘Immunology and Inflammation’ (ZT-0027). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 667273 and the DFG (SFB/TR 240 (project 374031971) to B.N. and D.S.), ERC Consolidator grant ‘CholangioConcept’ (to L.Z.), and the German Research Foundation (DFG): grants FOR2314, SFB685 and the Gottfried Wilhelm Leibniz Program (to L.Z.). Further funding was provided by the German Ministry for Education and Research (BMBF) (eMed/Multiscale HCC), the German Universities Excellence Initiative (third funding line: ‘future concept’), the German Center for Translational Cancer Research (DKTK) and the German-Israeli Cooperation in Cancer Research (DKFZ-MOST) (to L.Z. and M. Heikenwaelder). D.I. was supported by an EMBO Long-term Fellowship. J.M.L. is supported by Asociación Española Contra el Cáncer (Accelerator award: HUNTER), Spanish National Health Institute (SAF2013–41027), Generalitat de Catalunya (SGR 1162 and AGAUR, SGR-1358), the Samuel Waxman Cancer Research Foundation, the US Department of Defense (CA150272P3), the European Commission Horizon 2020 Program (HEPCAR, proposal number 667273-2), and the National Cancer Institute (P30 CA196521). D.A.M. is supported by CRUK grant C18342/A23390 and MRC grant MR/K001949/1. M.P. is supported by the German Research Foundation (DFG). M.G., T.G. and D.R. was supported by grants from the German Research Foundation (KFO274 and SFB/TR240 (project 374031971)). D.J.W. received a Wellcome Trust Strategic Award (098565/Z/12/Z) and funding from the Medical Research Council (MC-A654-5QB40). C.L.W. was funded by CRUK project Cancer Research UK Programme Grant C18342/A23390. H.G.A. has been supported by the Deutsche Forschungsgemeinschaft (SFB-TR209 ‘Liver Cancer’).

Author information

Author notes

  1. These authors contributed equally: Mohsen Malehmir, Dominik Pfister, Suchira Gallage, Marta Szydlowska, Donato Inverso.

  2. These authors jointly supervised this work: Achim Weber, Mathias Heikenwalder.

Authors and Affiliations

  1. Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland

    Mohsen Malehmir, Monika Julia Wolf, Marc E. Healy & Achim Weber

  2. Division of Chronic Inflammation and Cancer, German Cancer Research Center Heidelberg (DKFZ), Heidelberg, Germany

    Dominik Pfister, Suchira Gallage, Marta Szydlowska, Elena Kotsiliti, Valentina Leone, Adnan Ali, Alexander Olkus, Tjeerd Sijmonsma & Mathias Heikenwalder

  3. Division of Vascular Oncology and Metastasis, German Cancer Research Center Heidelberg (DKFZ-ZMBH Alliance), Heidelberg, Germany

    Donato Inverso & Hellmut G. Augustin

  4. European Center of Angioscience (ECAS), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Donato Inverso & Hellmut G. Augustin

  5. Institute for Virology, Technische Universität München/Helmholtz Zentrum München, Munich, Germany

    Elena Kotsiliti, Tracy O’Connor & Mathias Heikenwalder

  6. Research Unit of Radiation Cytogenetics, Helmholtz Zentrum München, Neuherberg, Germany

    Valentina Leone & Kristian Unger

  7. Calvin Phoebe & Joan Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Moritz Peiseler & Paul Kubes

  8. Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Moritz Peiseler, Bas G. J. Surewaard & Paul Kubes

  9. Department of Microbiology, Immunology & Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Bas G. J. Surewaard & Paul Kubes

  10. Department of Medical Microbiology, University Medical Center, Utrmeecht, the Netherlands

    Bas G. J. Surewaard

  11. Department of Cardiology and Circulatory Diseases, Internal Medicine Clinic III, Eberhard Karls University Tübingen, Tübingen, Germany

    Dominik Rath, Marcel Rall, Florian Baku, Oliver Borst & Meinrad Gawaz

  12. Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

    Hannah Drescher, Daniela Kroy, Oliver Krenkel, Marlene Kohlhepp & Frank Tacke

  13. Department of Internal Medicine VIII, University Hospital Tübingen, Tübingen, Germany

    Daniel Dauch, Martina Hinterleitner & Lars Zender

  14. Department of Physiology I, Institute of Physiology, Eberhard Karls University Tübingen, Tübingen, Germany

    Daniel Dauch, Martina Hinterleitner & Lars Zender

  15. Center for Translational Cancer Research (TranslaTUM), Technische Universität München, Munich, Germany

    Thomas Engleitner & Roland Rad

  16. Department of Medicine II, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany

    Thomas Engleitner & Roland Rad

  17. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Thomas Engleitner & Roland Rad

  18. Medical Faculty, University of Heidelberg, Heidelberg, Germany

    Alexander Olkus

  19. Institute of Experimental Biomedicine, University Hospital and Rudolf Virchow Center, University of Würzburg, Würzburg, Germany

    Julia Volz, Carsten Deppermann, David Stegner & Bernhard Nieswandt

  20. Hematology, University Hospital and University of Zurich, Zurich, Switzerland

    Patrick Helbling, César Nombela-Arrieta & Anahita Rafiei

  21. Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, UK

    Caroline L. Wilson, Jack Leslie & Derek A. Mann

  22. Institute of Molecular Immunology and Experimental Oncology, Technical University of Munich, Munich, Germany

    Tracy O’Connor & Percy A. Knolle

  23. Centre for Liver Research and National Institute for Health Research (NIHR) Birmingham Liver Biomedical Research Unit, Birmingham, UK

    Christopher J. Weston, Abhishek Chauhan, David H. Adams & Patricia F. Lalor

  24. Liver Unit, University Hospitals Birmingham NHS Trust, Birmingham, UK

    David H. Adams

  25. Institute for Cardiovascular Sciences, University of Birmingham, Birmingham, UK

    Lozan Sheriff

  26. Cancer Cell Biology Programme, Growth Factors, Nutrients and Cancer Group, Spanish National Cancer Research Centre, CNIO, Madrid, Spain

    Ana Teijeiro & Nabil Djouder

  27. Institute of Neuropathology, Medical Faculty, University of Freiburg, Freiburg, Germany

    Marco Prinz

  28. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

  29. Center for NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Marco Prinz

  30. Division of Experimental Hematology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Ruzhica Bogeska, Natasha Anstee & Michael D. Milsom

  31. DKFZ-ZMBH Alliance, Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH) Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Ruzhica Bogeska, Natasha Anstee & Michael D. Milsom

  32. Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany

    Malte N. Bongers & Mike Notohamiprodjo

  33. Department of Cardiovascular Medicine, University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany

    Tobias Geisler

  34. Metabolic Signalling Group, MRC London Institute of Medical Sciences, London, UK

    Dominic J. Withers

  35. Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

    Dominic J. Withers

  36. Department of Physiology and Biophysics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

    Jerry Ware

  37. DKFZ Junior Group Metabolism and Stem Cell Plasticity, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Alexandros Vegiopoulos

  38. Nutrient Metabolism and Signalling Lab, Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, and Metabolism, Diabetes and Obesity Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia

    Adam J. Rose

  39. Mount Sinai Liver Cancer Program (Divisions of Liver Diseases, Department of Medicine, Department of Pathology, Recanati Miller Transplantation Institute), Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Josep M. Llovet

  40. Liver Cancer Translational Research Laboratory, IDIBAPS, Liver Unit, Hospital Clinic, University of Barcelona, Barcelona, Catalonia, Spain

    Josep M. Llovet & Roser Pinyol

  41. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain

    Josep M. Llovet

  42. Institute of Pathology, University Hospital of Basel, Basel, Switzerland

    Matthias Matter

  43. Translational Gastrointestinal Oncology Group, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Lars Zender

Authors
  1. Mohsen Malehmir
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  38. Lozan Sheriff
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  39. Ana Teijeiro
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Contributions

M. Malehmir, D.P., S.G., M.S. and D.I. contributed equally as first authors. E.K., V.L., M.P. and B.G.J.S. contributed equally as second authors. Design of the study: M. Malehmir, M.J.W., D.R., A.W., B.N., M.G. and M. Heikenwaelder. M. Malehmir, E.K., D.P., V.L., M.J.W. and C.D. performed breeding and housing of mice. M. Malehmir, S.G., M.S., E.K., D.P., V.L., D.I., A.A., M.P., B.G.J.S., A.O., C.D., J.V., D.S., D.D., C.L.W., P.H., A.R., A.T., H.D., O.K., M.K., C.J.W., A.C., R.B., N.A., M.E.H., L.S. and M. Hinterleitner performed experiments. D.R., M.R., F.B., T.G., M.N.B., O.B., M.N. and M.G. designed and performed the clinical case study. J.W., R.P., N.D., L.Z., D.J.W, H.G.A, H.D., D.K., F.T., P.F.L., T.O., D.J.W., A.V., M.D.M., A.J.R., R.R., P.K., P.A.K., B.N., A.W., J.M.L, M. Matter, D.A.M., T.S., M.P., L.S., D.H.A., C.N.-A. and J.L. provided tissue samples or mouse strains and/or scientific input. K.U. and T.E. performed biostatistical analyses. All authors analyzed data. M. Malehmir, M.E.H., D.P., S.G., M.S., P.K., B.N., M.G., O.K., T.O., A.W., and M. Heikenwaelder wrote the manuscript, and all authors contributed to writing and provided feedback.

Corresponding authors

Correspondence to Achim Weber or Mathias Heikenwalder.

Ethics declarations

Competing interests

J.M.L. receives consulting fees from Bayer HealthCare Pharmaceuticals, Eli Lilly, Bristol-Myers Squibb, Merck, Eisai Inc, Celsion Corporation, Exelixis, Merck, Ipsen, Glycotest, Navigant, Leerink Swann LLC, Midatech Ltd, Fortress Biotech, Sprink Pharmaceuticals and Nucleix and research support from Bayer HealthCare Pharmaceuticals, Eisai Inc, Bristol-Myers Squibb and Ipsen. This article presents independent research supported in part by the National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, or the Department of Health.

Additional information

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

Extended data

Extended Data Fig. 1 Transcriptional alterations in the course of NASH and physical interaction of immune cells, liver endothelium and platelets.

a. GSEA and expression analysis of genes related clusters of platelet activation, aggregation, degranulation and genes associated to TNFα family regulation, cytokine interaction and lymphocyte migration from 6 months ND-fed versus CD-HFD-fed mice. b–g, Gene expression alterations found in annotated genes of indicated clusters and mice shown in a. h, Gene expression alterations found in annotated genes of indicated cluster of cell chemotaxis (left) and GSEA and expression analysis of genes associated to the cell chemotaxis cluster from mice shown in a (right). i, Transcriptional alterations for genes associated with platelet activation/aggregation of the 7.5 month ND versus WD-HTF fed mice.

Extended Data Fig. 2 Sulindac treatment does not prevent NASH pathology.

a, Left, mouse weight development in 12 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (n = 5 mice/group). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/sulindac (red asterisks). Middle, liver/body weight (6 months: n = 5 mice/group; 12 months: ND n = 3 mice; CD-HFD n = 8 mice; CD-HFD/sulindac n = 7 mice) and (right) ALT levels of 6 and 12 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (6 months: ND n = 3 mice; CD-HFD n = 4 mice; CD-HFD/sulindac n = 5 mice; 12 months: ND n = 7 mice; CD-HFD n = 12 mice; CD-HFD/sulindac = 5 mice). b, (left) Liver triglycerides and (middle) serum cholesterol levels in 6 and 12 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (liver TGs 6 months: ND = 7 mice; CD-HFD n = 11 mice; CD-HFD/sulindac n = 4 mice; Liver TGs 12 months: ND n= 4 mice; CD-HFD n = 9 mice; CD-HFD/sulindac n = 10 mice; cholesterol 6 months: ND n = 4 mice; CD-HFD n = 4 mice; CD-HFD/sulindac n = 5 mice; cholesterol 12 months: ND n = 6 mice; CD-HFD n = 10 mice; CD-HFD/sulindac n = 10 mice). (right) IPGTT performed with 6 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (n = 5 mice/group). Statistics: ND vs CD-HFD (black asterisks). (c) MRI analyses of livers of 6 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (n = 3 mice/group). d, (left) Analysis by H&E indicate damaged hepatocytes (asterisk) and (right) evaluation by NAS in livers of 6 months CD-HFD- or CD-HFD/sulindac-fed mice (ND n = 9 mice; CD-HFD n = 9 mice; CD-HFD/sulindac n = 10 mice), scale bars: 100 µm in 10×, 50 µm in 20×. e, Real-time qPCR analysis for mRNA of genes involved in lipid metabolism in liver of 6 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (ND n = 4 mice; CD-HFD n = 5 mice; CD-HFD/sulindac n = 3 mice). Comparison between CD-HFD vs CD-HFD/sulindac. f, Sudan red staining and quantification for fat accumulation of 6 months ND-, CD-HFD- or CD-HFD/sulindac-fed mice (ND n = 5; CD-HFD n = 4; CD-HFD/sulindac n = 5). Scale bar: 100 µm. All data are shown as mean ± s.e.m. Data in a (left) and b (right) were analyzed by two-way analysis of variance with post hoc Tukey’s multiple comparison test. Data in a (middle and right), b (left and middle), d and f were analyzed by one-way analysis of variance with post hoc Tukey’s multiple comparison test. Data in e were analyzed by two-tailed Mann-Whitney t test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 3 Ticagrelor treatment attenuates CD-HFD-induced NASH and NASH-associated conditions, and prevents HCC.

a, CD42b staining and quantification in 6 months CD-HFD or CD-HFD/Ticagrelor fed mice (CD-HFD n = 4 mice; CD-HFD/Ticagrelor n = 8 mice), scale bar: 50 µm. b, 3D confocal images of platelet (green)/liver endothelium (grey) interaction in livers of 6 months ND, CD-HFD and CD-HFD/Ticagrelor fed mice (n = 4 mice/group), scale bar: 20 μm. Quantification of platelet (PLT) aggregate size, overall PLT surface and quantification of platelet/liver endothelium coverage in focus of view (n = 4 mice/group). For visualization of intravascular events, the transparency of the sinusoidal rendering was set to 50%. c, Body weight development in 12 months ND, CD-HFD or CD-HFD/Ticagrelor fed mice (ND n = 4 mice; CD-HFD n = 6 mice; CD-HFD/Ticagrelor n = 4 mice). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/Ticagrelor (green asterisks). d–h ALT (d), Liver triglyceride (e), serum cholesterol levels (f), VLDL secretion in serum (g) and IPGTT (h) of 6 months ND, CD-HFD or CD-HFD/Ticagrelor fed mice (ALT: ND n = 8 mice; CD-HFD n = 5 mice; CD-HFD/Ticagrelor n = 10 mice; liver triglycerides: ND n = 7 mice; CD-HFD n = 11 mice; CD-HFD/Ticagrelor n = 6 mice; serum cholesterol: n = 8 mice/group; liver triglycerides: ND n = 8 mice; CD-HFD n = 11 mice; CD-HFD/Ticagrelor n = 8 mice; IPGTT: ND n = 5 mice; CD-HFD n = 3 mice; CD-HFD/Ticagrelor n = 3 mice). i,j, Real-time qPCR analysis of hepatic genes associated to catabolic (i) and anabolic processes (j) of lipid metabolism of 6 months ND, CD-HFD or CD-HFD/Ticagrelor fed mice (RT-qPCR for catabolic genes: ND n = 2 mice; CD-HFD n = 6 mice; CD-HFD/Ticagrelor n = 6 mice; RT-qPCR for anabolic genes: ND n = 2 mice; CD-HFD n = 3 mice; CD-HFD/Ticagrelor n = 3 mice). All data are shown as mean ± s.e.m. Data in a were analyzed by two-tailed Student's t test. Data in b, d, e, f, g and j were analyzed by one way ANOVA with the post hoc Tukey’s multiple comparison test. Data in c and h were analyzed by two way ANOVA with the post hoc Tukey’s multiple comparison test. Data in i were analyzed by two-tailed Mann–Whitney test, N.s.: not significant *P < 0.05. **P < 0.01. ***P < 0.001. ****P < 0.0001.

Extended Data Fig. 4 Mice with non-functional platelet aggregation are not protected from NASH development.

a, CD42b staining and quantification of 6 months CD-HFD- or CD-HFD/Itg2b−/−-fed mice (CD-HFD n = 5 mice, CD-HFD/Itg2b–/–n = 4 mice). b, Body weight development of 6 months CD-HFD or CD-HFD/Itg2b−/−-fed mice (n = 5/group). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/Itg2b–/– (blue asterisks). c, ALT (ND n = 4 mice, CD-HFD n = 3 mice, CD-HFD/Itg2b–/–n = 3 mice), AST (ND n = 5 mice, CD-HFD n = 3 mice, CD-HFD/Itg2b–/–n = 3 mice), (d) liver triglycerides (ND n = 7 mice, CD-HFD n = 8 mice, CD-HFD/Itg2b–/–n = 3 mice), and serum cholesterol levels (ND n = 7 mice, CD-HFD n= 3 mice, CD-HFD/Itg2b–/–n = 3 mice), (e) IPGTT from mice shown in a (n = 3–4 field/mouse, ND n = 4 mice, CD-HFD n= 4 mice, CD-HFD/Itg2b–/–n = 10 mice) Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/Itg2b–/– (blue asterisks). (f) Real-time qPCR analysis for genes involved in lipid metabolism/β-oxidation (ND n = 2 mice, CD-HFD n = 4 mice, CD-HFD/Itg2b–/–n = 3 mice). Statistics: CD-HFD vs CD-HFD/tg2b–/– (blue asterisks). (g) NAS evaluation (CD-HFD n = 9 mice, CD-HFD/tg2b–/–n = 7 mice) and (h) quantification of fat by Sudan red staining of mice shown in a (n = 5 mice/group), scale bar: 100 µm in 10×, 50 µm in 20×. All data are shown as mean ± s.e.m. Data shown in a and g were analyzed by two-tailed Student’s t test. Data in b,e were analyzed by two-way analysis of variance with the post hoc Bonferroni multiple comparison test. Data in c,d,h were analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test. Data in f were analyzed by two-tailed Mann-Whitney test; n.s., not significant, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 5 Gp6 mice display severe steatosis, NASH and NASH-associated conditions.

a, CD42b staining and quantification of 6 months CD-HFD or CD-HFD/Gp6–/–-fed mice (CD-HFD n = 5 mice, CD-HFD/Gp6-–/–n = 4 mice), scale bar: 50 µm. b, Body weight (n = 6 mice/group), (c) ALT, liver triglycerides and cholesterol levels of 6 months mice (ND n = 4 mice, CD-HFD n = 4 mice, CD-HFD/Gp6–/–n = 3 mice). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/Gp6 (orange asterisks). d, IPGTT (ND n = 5 mice, CD-HFD n = 5 mice, CD-HFD/Gp6–/–n = 3 mice). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/Gp6–/– (orange asterisks). e, Real-time qPCR analysis for genes involved in lipid metabolism/β-oxidation (ND n = 2 mice, CD-HFD n = 4 mice, CD-HFD/Gp6–/–n = 3 mice). f, NAS evaluation of mice shown in a, damaged hepatocytes are indicated by asterisks (CD-HFD n = 9 mice, CD-HFD/Gp6–/– n = 8 mice), scale bar: 100 µm in 10× and 50 µm in 20×. g, Quantification of fat by Sudan red staining in mice shown in b (n = 3–4 fields/mouse, ND n = 4 mice, CD-HFD n = 4 mice, CD-HFD/Gp6–/–n = 9 mice). All data are shown as mean ± s.e.m. Data in b,d were analyzed by two-way analysis of variance with the post hoc Bonferroni multiple comparison test. Data in c,g were analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test. Data in e were analyzed by two-tailed Mann Whitney’s test, data in f were analyzed by two-tailed Student’s t test. n.s., not significant, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 6 Genetic inactivation of Clec-2 or podoplanin does not protect from CD-HFD-induced NASH.

a,b, Body weight (a) and ALT levels (b) (ND n = 5 mice, CD-HFD n = 10, CD-HFD/Clec-2 n = 9 or CD-HFD/Pdpn–/–n= 3). (c) H&E staining and (d) NAS evaluation (ND n = 5 mice, CD-HFD n = 4, CD-HFD/Clec-2–/–n= 4 or CD-HFD/Pdpn–/–n= 3), scale bar: 100 µm. All data are shown as mean ± s.e.m. and analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test.

Extended Data Fig. 7 MAdCAM-1 has an important role in the platelets recruitment to the liver during NASH development.

a, H&E, CD42b (ND n = 12 mice, WD-HTF n = 17 mice, WD-HTF/MAdCAM-1–/– n = 6 mice, WD-HTF/L-sel–/– n = 11 mice, WD-HTF/Beta7–/– n = 8 mice, WD-HTF/L-sel–/–/Beta7–/– n = 11 mice), CD3 (ND n = 5 mice, WD-HTF n = 8 mice, WD-HTF/MAdCAM-1–/– n = 6 mice, WD-HTF/L-sel–/– n = 5 mice, WD-HTF/Beta7–/– n = 4 mice, WD-HTF/L-sel–/–Beta7–/– n = 4 mice) and F4/80 (ND n = 4 mice, WD-HTF n = 5 mice, WD-HTF/MAdCAM-1–/– n = 5 mice, WD-HTF/-sel–/– n = 4 mice, WD-HTF/Beta7–/– n = 6 mice, WD-HTF/L-sel–/–/Beta7–/– n = 4MAdCAM-1–/– mice) stains and (b) quantification of IHC of livers of mice mentioned in a, scale bar: 50 µm. All data are shown as mean ± s.e.m. Data in b were analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test.

Extended Data Fig. 8 Genetic inactivation of P-selectin does not prevent NASH development.

a,b, Body weight development (ND n = 4 mice, CD-HFD n = 3 mice, CD-HFD/P-sel–/–n = 7 mice) (a) and ALT and AST levels of 6 months ND, CD-HFD or CD-HFD/P-sel–/– mice (ND n = 4 mice, CD-HFD n = 5 mice, CD-HFD/P-sel –/–n = 9 mice). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/P-sel–/– (violet asterisks). c, IPGTT (ND n = 4 mice, CD-HFD n = 5 mice, CD-HFD/ n = 5 mice). Statistics: ND vs CD-HFD (black asterisks), CD-HFD vs CD-HFD/P-sel–/– (violet asterisks). d, NAS evaluation (CD-HFD n = 8 mice, CD-HFD/ P-sel–/–n = 4 mice). scale bar: 100 µm in 10×, 50 µm in 20× . e, Representative CD3, F4/80, MHCII and Ly6G stainings (CD-HFD n = 8 mice, CD-HFD/P-sel–/–n = 4 mice), scale bar: 50 µm. All data are shown as mean ± s.e.m. Data in a and c were analyzed by two-way analysis of variance with the post hoc Bonferroni multiple comparison test. Data in b were analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test. Data in d were analyzed by two-tailed Student’s t test. n.s., not significant *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 9 vWF–/– mice show steatosis, liver damage and conditions associated with NASH.

a, Body weight development of 6 months ND-, CD-HFD- or CD-HFD/vWF-fed mice. Statistics: (n = 6 mice/group). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/vWF–/– (red asterisks). b, ALT (ND n = 4 mice, CD-HFD n = 3 mice, CD-HFD/vWF–/–n = 3 mice), liver triglycerides (ND n = 5 mice, CD-HFD n = 4 mice, CD-HFD/vWF–/–n = 5 mice) and serum cholesterol levels (ND n = 4 mice, CD-HFD n = 4 mice, CD-HFD/vWF–/–n= 3 mice). c, IPGTT (ND n = 5 mice, CD-HFD n = 5 mice, CD-HFD/vWF–/– = 3 mice). Statistics: ND vs CD-HFD (black asterisks), ND vs CD-HFD/vWF–/– (red asterisks). d, Real-time qPCR analysis for genes involved in lipid metabolism/β-oxidation (ND n = 2 mice, CD-HFD n = 4 mice, CD-HFD/ vWF–/–n= 4 mice). e, H&E with enlarged hepatocytes (asterisks) and (f) evaluation of NAS (CD-HFD n = 9 mice, CD-HFD/ vWF–/–n= 4 mice) scale bar: 100 µm in 10X and 50 µm in 20×. g, Sudan red staining and quantification of mice shown in a (n = 2–3 fields/mouse: n = 3 mice/group), scale bar: 100 µm. All data are shown as mean ± s.e.m. Data in a,c were analyzed by two-way analysis of variance with the post hoc Bonferroni multiple comparison test. Data in b,g were analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test. Data in d were analyzed by two-tailed Mann-Whitney test. Data in f were analyzed by two-tailed Student’s t test. n.s., not significant, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 10 Mice lacking Mac-1 show signs of liver injury and develop NASH upon feeding CD-HFD.

a, CD42b staining and quantification of 6 months CD-HFD or CD-HFD/Mac-1–/–-fed mice (CD-HFD n = 5 mice, Mac-1/CD-HFD n = 4 mice), scale bar: 50 µm (b) Body weight (ND n = 5 mice, CD-HFD n = 8 mice, CD-HFD/Mac-1–/–n = 11 mice), (c) ALT, and AST of 6 months ND, CD-HFD or CD-HFD/Mac-1–/–-fed mice (CD-HFD n = 5 mice, CD-HFD/Mac-1–/–n = 12 mice) (d) NAS evaluation of mice shown in a (CD-HFD n = 9 mice, CD-HFD/-Mac-1–/–n = 11 mice) scale bar: 100 µm in 10× and 50 µm in 20×. e, Representative CD3+, F4/80+, MHCII+ and Ly6G+ staining (ND n = 5 mice, CD-HFD n = 8 mice, CD-HFD/Mac-1–/–n = 11 mice) and arrows indicate cell/cell aggregates, scale bar: 50 µm. All data are shown as mean ± s.e.m. Data in a,d were analyzed by two-tailed Student’s t test. Data in b,c were analyzed by one-way analysis of variance with the post hoc Tukey’s multiple comparison test.

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Supplementary Figures 1-25, Supplementary Tables 1-4.

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Western blot gels.

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Malehmir, M., Pfister, D., Gallage, S. et al. Platelet GPIbα is a mediator and potential interventional target for NASH and subsequent liver cancer. Nat Med 25, 641–655 (2019). https://doi.org/10.1038/s41591-019-0379-5

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