Fibrosis is a common pathology in cardiovascular disease1. In the heart, fibrosis causes mechanical and electrical dysfunction1,2 and in the kidney, it predicts the onset of renal failure3. Transforming growth factor β1 (TGFβ1) is the principal pro-fibrotic factor4,5, but its inhibition is associated with side effects due to its pleiotropic roles6,7. We hypothesized that downstream effectors of TGFβ1 in fibroblasts could be attractive therapeutic targets and lack upstream toxicity. Here we show, using integrated imaging–genomics analyses of primary human fibroblasts, that upregulation of interleukin-11 (IL-11) is the dominant transcriptional response to TGFβ1 exposure and required for its pro-fibrotic effect. IL-11 and its receptor (IL11RA) are expressed specifically in fibroblasts, in which they drive non-canonical, ERK-dependent autocrine signalling that is required for fibrogenic protein synthesis. In mice, fibroblast-specific Il11 transgene expression or Il-11 injection causes heart and kidney fibrosis and organ failure, whereas genetic deletion of Il11ra1 protects against disease. Therefore, inhibition of IL-11 prevents fibroblast activation across organs and species in response to a range of important pro-fibrotic stimuli. These results reveal a central role of IL-11 in fibrosis and we propose that inhibition of IL-11 is a potential therapeutic strategy to treat fibrotic diseases.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions

Gene Expression Omnibus


  1. 1.

    , & Fibrosis—a common pathway to organ injury and failure. N. Engl. J. Med. 372, 1138–1149 (2015)

  2. 2.

    & Atrial fibrosis: mechanisms and clinical relevance in atrial fibrillation. J. Am. Coll. Cardiol. 51, 802–809 (2008)

  3. 3.

    & What can target kidney fibrosis? Nephrol. Dial. Transplant. 32 (suppl. 1), i89–i97 (2017)

  4. 4.

    & Myofibroblasts: trust your heart and let fate decide. J. Mol. Cell. Cardiol. 70, 9–18 (2014)

  5. 5.

    & Targeting the TGFβ signalling pathway in disease. Nat. Rev. Drug Discov. 11, 790–811 (2012)

  6. 6.

    et al. Abrogation of TGF-β signaling enhances chemokine production and correlates with prognosis in human breast cancer. J. Clin. Invest. 119, 1571–1582 (2009)

  7. 7.

    et al. Targeted disruption of the mouse transforming growth factor-β1 gene results in multifocal inflammatory disease. Nature 359, 693–699 (1992)

  8. 8.

    Cellular and molecular mechanisms of fibrosis. J. Pathol. 214, 199–210 (2008)

  9. 9.

    et al. NAD(P)H oxidase 4 mediates transforming growth factor-β1-induced differentiation of cardiac fibroblasts into myofibroblasts. Circ. Res. 97, 900–907 (2005)

  10. 10.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

  11. 11.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013)

  12. 12.

    FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014)

  13. 13.

    et al. Dilated cardiomyopathy and heart failure caused by a mutation in phospholamban. Science 299, 1410–1413 (2003)

  14. 14.

    , , & Effects of recombinant human interleukin-11 on hematopoietic reconstitution in transplant mice: acceleration of recovery of peripheral blood neutrophils and platelets. Blood 81, 27–34 (1993)

  15. 15.

    et al. Interleukin-11 is the dominant IL-6 family cytokine during gastrointestinal tumorigenesis and can be targeted therapeutically. Cancer Cell 24, 257–271 (2013)

  16. 16.

    et al. Therapeutic activation of signal transducer and activator of transcription 3 by interleukin-11 ameliorates cardiac fibrosis after myocardial infarction. Circulation 121, 684–691 (2010)

  17. 17.

    & Molecular pathways: IL11 as a tumor-promoting cytokine—translational implications for cancers. Clin. Cancer Res. 20, 5579–5588 (2014)

  18. 18.

    et al. Proteolytic cleavage governs interleukin-11 trans-signaling. Cell Reports 14, 1761–1773 (2016)

  19. 19.

    et al. A designer hyper interleukin 11 (H11) is a biologically active cytokine. BMC Biotechnol. 12, 8 (2012)

  20. 20.

    et al. Adult mice with targeted mutation of the interleukin-11 receptor (IL11Ra) display normal hematopoiesis. Blood 90, 2148–2159 (1997)

  21. 21.

    et al. Wnt1/βcatenin injury response activates the epicardium and cardiac fibroblasts to promote cardiac repair. EMBO J. 31, 429–442 (2012)

  22. 22.

    FDA licensure of NEUMEGA to prevent severe chemotherapy-induced thrombocytopenia. Stem Cells 16 (Suppl 2), 207–223 (1998)

  23. 23.

    et al. Resident fibroblast lineages mediate pressure overload-induced cardiac fibrosis. J. Clin. Invest. 124, 2921–2934 (2014)

  24. 24.

    et al. Molecular cloning of a cDNA encoding interleukin 11, a stromal cell-derived lymphopoietic and hematopoietic cytokine. Proc. Natl Acad. Sci. USA 87, 7512–7516 (1990)

  25. 25.

    et al. Four cases of investigational therapy with interleukin-11 against acute myocardial infarction. Heart Vessels 31, 1574–1578 (2016)

  26. 26.

    et al. Microarray profiling reveals suppressed interferon stimulated gene program in fibroblasts from scleroderma-associated interstitial lung disease. Respir. Res. 14, 80 (2013)

  27. 27.

    et al. Inactivation of IL11 signaling causes craniosynostosis, delayed tooth eruption, and supernumerary teeth. Am. J. Hum. Genet. 89, 67–81 (2011)

  28. 28.

    , , , & Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015)

  29. 29.

    , & TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009)

  30. 30.

    , & HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)

  31. 31.

    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)

  32. 32.

    & WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)

  33. 33.

    , & Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)

  34. 34.

    Babraham Bioinformatics. FastQC, a quality control tool for high throughput sequence data.

  35. 35.

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

  36. 36.

    , & featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014)

  37. 37.

    et al. Molecular profiling of dilated cardiomyopathy that progresses to heart failure. JCI Insight 1, e86898 (2016)

  38. 38.

    et al. Mouse cardiac surgery: comprehensive techniques for the generation of mouse models of human diseases and their application for genomic studies. Physiol. Genomics 16, 349–360 (2004)

  39. 39.

    , , , & Ligand-dependent genetic recombination in fibroblasts : a potentially powerful technique for investigating gene function in fibrosis. Am. J. Pathol. 160, 1609–1617 (2002)

  40. 40.

    et al. Thymosin β4 increases the potency of transplanted mesenchymal stem cells for myocardial repair. Circulation 128 (Suppl 1), S32–S41 (2013)

  41. 41.

    , , & Echocardiography in mice. Curr. Protoc. Mouse Biol. 1, 71–83 (2011)

  42. 42.

    , , , & Quantification of left ventricular volumes by two-dimensional echocardiography: a simplified and accurate approach. Circulation 67, 579–584 (1983)

  43. 43.

    & Proliferation assays (BrdU and EdU) on skeletal tissue sections. Methods Mol. Biol. 1130, 233–243 (2014)

Download references


We thank all patients for taking part in this research, which was performed with approval from the SingHealth Centralised IRB Review Board (CIRB; 2013/103/C). The research was supported by the National Medical Research Council (NMRC) Singapore STaR award (S.A.C.) (NMRC/STaR/0011/2012), the NMRC Centre Grant to the National Heart Centre Singapore (NHCS), Goh Foundation, Tanoto Foundation, NHLBI 5R01HL080494 (J.G.S., C.E.S.), HHMI (C.E.S.) and a grant from the Fondation Leducq (N.H., J.G.S., C.E.S., S.C.). We thank I. Kamer and R. Plehm, Max-Delbrück-Center for Molecular Medicine (MDC), for expert technical help with telemetry blood pressure measurements.

Author information

Author notes

    • Sebastian Schafer
    • , Sivakumar Viswanathan
    •  & Anissa A. Widjaja

    These authors contributed equally to this work.


  1. National Heart Centre Singapore, Singapore

    • Sebastian Schafer
    • , Wei-Wen Lim
    • , Benjamin Ng
    • , Kingsley Chow
    • , Jessie Tan
    • , Lei Ye
    • , Chee Jian Pua
    • , Nicole T. G. Zhen
    • , Chen Xie
    • , Shiqi Lim
    • , See L. Lim
    • , Jia L. Soon
    • , Victor T. T. Chao
    • , Yeow L. Chua
    • , Teing E. Tan
    • , Yee J. Loh
    • , Muhammad H. Jamal
    • , Kim K. Ong
    • , Kim C. Chua
    • , Boon-Hean Ong
    • , Mathew J. Chakaramakkil
    • , Kenny Y. K. Sin
    •  & Stuart A. Cook
  2. Duke–National University of Singapore Medical School, Singapore

    • Sebastian Schafer
    • , Sivakumar Viswanathan
    • , Anissa A. Widjaja
    • , Aida Moreno-Moral
    • , Ester Khin
    • , Sonia P. Chothani
    • , Owen J. L. Rackham
    • , Nicole S. J. Ko
    • , Norliza E. Sahib
    • , Mao Wang
    • , Enrico Petretto
    • , Kristmundur Sigmundsson
    • , Jia L. Soon
    • , Victor T. T. Chao
    • , Kenny Y. K. Sin
    •  & Stuart A. Cook
  3. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Daniel M. DeLaughter
    • , Hiroko Wakimoto
    • , Jonathan G. Seidman
    •  & Christine E. Seidman
  4. Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rossle Strasse 10, 13125 Berlin, Germany

    • Giannino Patone
    • , Henrike Maatz
    • , Kathrin Saar
    • , Susanne Blachut
    • , Sabine Schmidt
    • , Sebastiaan van Heesch
    •  & Norbert Hubner
  5. Inflammation Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia

    • Tracy Putoczki
  6. Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3050, Australia

    • Tracy Putoczki
  7. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA

    • Nuno Guimarães-Camboa
    •  & Sylvia M. Evans
  8. Department of Medicine, University of California at San Diego, La Jolla, California 92093, USA

    • Sylvia M. Evans
  9. Department of Pharmacology, University of California at San Diego, La Jolla, California 92093, USA

    • Sylvia M. Evans
  10. Kandang Kerbau Women’s and Children’s Hospital, Singapore

    • Yee J. Loh
    •  & Kim K. Ong
  11. Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusettes 02115, USA

    • Christine E. Seidman
  12. Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA

    • Christine E. Seidman
  13. DZHK (German Centre for Cardiovascular Research), partner site, Berlin, Germany

    • Norbert Hubner
  14. Charité-Universitätsmedizin, Berlin, Germany

    • Norbert Hubner
  15. Berlin Institute of Health (BIH), Berlin, Germany

    • Norbert Hubner
  16. National Heart and Lung Institute, Imperial College London, London, UK

    • Stuart A. Cook
  17. MRC-London Institute of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK

    • Stuart A. Cook


  1. Search for Sebastian Schafer in:

  2. Search for Sivakumar Viswanathan in:

  3. Search for Anissa A. Widjaja in:

  4. Search for Wei-Wen Lim in:

  5. Search for Aida Moreno-Moral in:

  6. Search for Daniel M. DeLaughter in:

  7. Search for Benjamin Ng in:

  8. Search for Giannino Patone in:

  9. Search for Kingsley Chow in:

  10. Search for Ester Khin in:

  11. Search for Jessie Tan in:

  12. Search for Sonia P. Chothani in:

  13. Search for Lei Ye in:

  14. Search for Owen J. L. Rackham in:

  15. Search for Nicole S. J. Ko in:

  16. Search for Norliza E. Sahib in:

  17. Search for Chee Jian Pua in:

  18. Search for Nicole T. G. Zhen in:

  19. Search for Chen Xie in:

  20. Search for Mao Wang in:

  21. Search for Henrike Maatz in:

  22. Search for Shiqi Lim in:

  23. Search for Kathrin Saar in:

  24. Search for Susanne Blachut in:

  25. Search for Enrico Petretto in:

  26. Search for Sabine Schmidt in:

  27. Search for Tracy Putoczki in:

  28. Search for Nuno Guimarães-Camboa in:

  29. Search for Hiroko Wakimoto in:

  30. Search for Sebastiaan van Heesch in:

  31. Search for Kristmundur Sigmundsson in:

  32. Search for See L. Lim in:

  33. Search for Jia L. Soon in:

  34. Search for Victor T. T. Chao in:

  35. Search for Yeow L. Chua in:

  36. Search for Teing E. Tan in:

  37. Search for Sylvia M. Evans in:

  38. Search for Yee J. Loh in:

  39. Search for Muhammad H. Jamal in:

  40. Search for Kim K. Ong in:

  41. Search for Kim C. Chua in:

  42. Search for Boon-Hean Ong in:

  43. Search for Mathew J. Chakaramakkil in:

  44. Search for Jonathan G. Seidman in:

  45. Search for Christine E. Seidman in:

  46. Search for Norbert Hubner in:

  47. Search for Kenny Y. K. Sin in:

  48. Search for Stuart A. Cook in:


S.A.C. conceived, managed and arranged funding for the project. Wet lab experiments (cell culture, cell biology, molecular biology, RNA-seq) were carried out by S.V., A.A.W., W.-W.L., B.N., G.P., J.T., L.Y., N.E.S., C.J.P., C.X., M.W., S.L., K.Sa., S.B., S.Schm., T.P., N.G.-C., H.W., S.v.H. and K.Si. Single-cell studies were carried out by D.M.D., J.G.S. and C.E.S. In vivo gain-of-function and loss-of-function mouse experiments were performed by A.A.W., W.-W.L., B.N., J.T., E.K., L.Y., N.S.J.K., N.T.G.Z., D.M.D., G.P. and H.M. Data were analysed by S.Scha., S.V., A.A.W., A.M.-M., K.C., S.P.C., O.J.L.R., K.Sa., E.P., S.M.E., J.G.S., C.E.S. and N.H. Patient-based studies were carried out by S.L.L., J.L.S., V.T.T.C., Y.L.C., T.E.T., Y.J.L., M.H.J., K.K.O., K.C.C., B.-H.O., M.J.C. and K.Y.K.S. S.Scha., S.V., A.A.W. and S.A.C. designed experiments and prepared the manuscript with input from co-authors.

Competing interests

S.A.C. and S.Scha. are co-inventors of the patent application (WO2017103108) ‘Treatment of fibrosis’. S.A.C. and S.Scha. are co-founders and shareholders of Enleofen Bio PTE LTD, a company that develops therapeutics based on findings described in this manuscript.

Corresponding author

Correspondence to Stuart A. Cook.

Reviewer Information Nature thanks S. Friedman 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

Supplementary information

PDF files

  1. 1.

    Supplementary Figure

    This file contains source data for all western blot experiments.

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Detailed information about the quality of each RNA sample, RNA-seq library and sample information about each individual that has contributed primary cells for the therapeutic target discovery high-content imaging screening and transcriptome profiling.

  2. 2.

    Supplementary Table 2

    Therapeutic Target Screen results: 1) Differentially expressed genes between TGFB stimulated fibroblasts and non-stimulated fibroblasts, 2) Spearman correlation (SPcor) between delta of fibroblasts expression (stimulated/non-stimulated) and delta of SMA, 3) Jensen–Shannon divergence (JSD) between of each gene across all GTEx tissues and FANTOM primary cell types (see more details in methods), 4) Average expression levels (transcripts per million, TPM) in TGFB1 stimulated and non-stimulated (baseline only) fibroblasts. Log2 fold change, shrunken Log2-fold changes computed by DESeq2 package. BH adj.P, Benjamini-Hochberg (BH) adjusted p-value.

  3. 3.

    Supplementary Table 3

    Gene Ontology database gene set enrichment analysis (GSEA) results for the stimulated versus baseline fibroblasts (GSEA computed by ranking all the genes by DESeq output statistic). Only terms enriched with FDR < 0.05 are presented. NES denotes normalized enrichment score.

About this article

Publication history






Further reading


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.