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Mapping signalling perturbations in myocardial fibrosis via the integrative phosphoproteomic profiling of tissue from diverse sources


Study of the molecular basis of myocardial fibrosis is hampered by limited access to tissues from human patients and by confounding variables associated with sample accessibility, collection, processing and storage. Here, we report an integrative strategy based on mass spectrometry for the phosphoproteomic profiling of normal and fibrotic cardiac tissue obtained from surgical explants from patients with hypertrophic cardiomyopathy, from a transaortic-constriction mouse model of cardiac hypertrophy and fibrosis, and from a heart-on-a-chip model of cardiac fibrosis. We used the integrative approach to map the relative abundance of thousands of proteins, phosphoproteins and phosphorylation sites specific to each tissue source, to identify key signalling pathways driving fibrosis and to screen for anti-fibrotic compounds targeting glycogen synthase kinase 3, which has a consistent role as a key mediator of fibrosis in all three types of tissue specimen. The integrative disease-modelling strategy may reveal new insights into mechanisms of cardiac disease and serve as a test bed for drug screening.

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Fig. 1: Overview of phosphoproteomic workflows.
Fig. 2: Collagen deposition in normal and fibrotic samples.
Fig. 3: Coverage and data distribution across datasets.
Fig. 4: Comparison of merged proteomic and phosphoproteomic datasets between experimental and clinical conditions.
Fig. 5: Pathway-level comparison between datasets.
Fig. 6: Drug treatment with the selected GSK3 inhibitor GW806290X in parallel with a standard GSK3 inhibitor (CHIR 99021) after tissue maturation.
Fig. 7: Drug screening with early treatment upon tissue seeding.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. All data generated during the study, including source data and the data used to make the figures, are available via ProteomeXchange with identifier PXD016492.


  1. 1.

    Mozaffarian, D. et al. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation 131, e29–e322 (2015).

    PubMed  Google Scholar 

  2. 2.

    Niimura, H. et al. Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly. Circulation 105, 446–451 (2002).

    CAS  PubMed  Google Scholar 

  3. 3.

    Braunwald, E. Cardiomyopathies: an overview. Circ. Res. 121, 711–721 (2017).

    CAS  PubMed  Google Scholar 

  4. 4.

    Ho, C. Y. et al. Myocardial fibrosis as an early manifestation of hypertrophic cardiomyopathy. N. Engl. J. Med. 363, 552–563 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    van Berlo, J. H., Maillet, M. & Molkentin, J. D. Signaling effectors underlying pathologic growth and remodeling of the heart. J. Clin. Invest. 123, 37–45 (2013).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Travers, J. G., Kamal, F. A., Robbins, J., Yutzey, K. E. & Blaxall, B. C. Cardiac fibrosis: the fibroblast awakens. Circ. Res. 118, 1021–1040 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Shirani, J., Pick, R., Roberts, W. C. & Maron, B. J. Morphology and significance of the left ventricular collagen network in young patients with hypertrophic cardiomyopathy and sudden cardiac death. J. Am. Coll. Cardiol. 35, 36–44 (2000).

    CAS  PubMed  Google Scholar 

  8. 8.

    Kim, J. B. et al. Polony multiplex analysis of gene expression (PMAGE) in mouse hypertrophic cardiomyopathy. Science 316, 1481–1484 (2007).

    CAS  PubMed  Google Scholar 

  9. 9.

    Ahadian, S. et al. Organ-on-a-chip platforms: a convergence of advanced materials, cells, and microscale technologies. Adv. Healthc. Mater. 7, 1700506 (2018).

    Google Scholar 

  10. 10.

    Sun, X. & Nunes, S. S. Biowire platform for maturation of human pluripotent stem cell-derived cardiomyocytes. Sci. Rep. 101, 21–26 (2016).

    CAS  Google Scholar 

  11. 11.

    Nunes, S. S. et al. Biowire: a platform for maturation of human pluripotent stem cell-derived cardiomyocytes. Nat. Methods 10, 781–787 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhao, Y. et al. A platform for generation of chamber-specific cardiac tissues and disease modeling. Cell 176, 913–927 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Conant, G., Ahadian, S., Zhao, Y. & Radisic, M. Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires. Sci. Rep. 7, 11807 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wang, E. Y. et al. Biowire model of interstitial and focal cardiac fibrosis. ACS Cent. Sci. 5, 1146–1158 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Kuzmanov, U. et al. Global phosphoproteomic profiling reveals perturbed signaling in a mouse model of dilated cardiomyopathy. Proc. Natl Acad. Sci. USA 113, 12592–12597 (2016).

    CAS  PubMed  Google Scholar 

  16. 16.

    Chang, Y. W. et al. Quantitative phosphoproteomic study of pressure-overloaded mouse heart reveals dynamin-related protein 1 as a modulator of cardiac hypertrophy. Mol. Cell. Proteom. 12, 3094–3107 (2013).

    CAS  Google Scholar 

  17. 17.

    Lundby, A. et al. In vivo phosphoproteomics analysis reveals the cardiac targets of β-adrenergic receptor signaling. Sci. Signal. 6, rs11 (2013).

    PubMed  Google Scholar 

  18. 18.

    Gedik, N. et al. Proteomics/phosphoproteomics of left ventricular biopsies from patients with surgical coronary revascularization and pigs with coronary occlusion/reperfusion: remote ischemic preconditioning. Sci. Rep. 7, 7629 (2017).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Mercier, T. et al. Interplay between phosphorylation and O-GlcNAcylation of sarcomeric proteins in ischemic heart failure. Front. Endocrinol. 9, 598 (2018).

    Google Scholar 

  20. 20.

    Schechter, M. A. et al. Phosphoproteomic profiling of human myocardial tissues distinguishes ischemic from non-ischemic end stage heart failure. PLoS ONE 9, e104157 (2014).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Cai, W. et al. An unbiased proteomics method to assess the maturation of human pluripotent stem cell-derived cardiomyocytes. Circ. Res. 125, 936–953 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Cyganek, L. et al. Deep phenotyping of human induced pluripotent stem cell-derived atrial and ventricular cardiomyocytes. JCI Insight 3, e99941 (2018).

    PubMed Central  Google Scholar 

  23. 23.

    Elkins, J. M. et al. Comprehensive characterization of the published kinase inhibitor set. Nat. Biotechnol. 34, 95–103 (2016).

    CAS  PubMed  Google Scholar 

  24. 24.

    Engholm-Keller, K. & Larsen, M. R. Technologies and challenges in large-scale phosphoproteomics. Proteomics 13, 910–931 (2013).

    CAS  PubMed  Google Scholar 

  25. 25.

    Xiao, Y. et al. Microfabricated perfusable cardiac biowire: a platform that mimics native cardiac bundle. Lab Chip 14, 869–882 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Conant, G. et al. High-content assessment of cardiac function using heart-on-a-chip devices as drug screening model. Stem Cell Rev. 13, 335–346 (2017).

    Google Scholar 

  27. 27.

    Mi, H. et al. PANTHER version 11: expanded annotation data from Gene Ontology and reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45, D183–D189 (2017).

    CAS  PubMed  Google Scholar 

  28. 28.

    Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).

  30. 30.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell. Proteom. 11, M111.014647 (2012).

    Google Scholar 

  33. 33.

    Tanigaki, K. et al. Fcγ receptors and ligands and cardiovascular disease. Circ. Res. 116, 368–384 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Li, P., Ge, J. & Li, H. Lysine acetyltransferases and lysine deacetylases as targets for cardiovascular disease. Nat. Rev. Cardiol. 17, 96–115 (2020).

    CAS  PubMed  Google Scholar 

  35. 35.

    Chen, C., Li, R., Ross, R. S. & Manso, A. M. Integrins and integrin-related proteins in cardiac fibrosis. J. Mol. Cell Cardiol. 93, 162–174 (2016).

    CAS  PubMed  Google Scholar 

  36. 36.

    Franchini, K. G. Focal adhesion kinase—the basis of local hypertrophic signaling domains. J. Mol. Cell Cardiol. 52, 485–492 (2012).

    CAS  PubMed  Google Scholar 

  37. 37.

    Döring, Y., Pawig, L., Weber, C. & Noels, H. The CXCL12/CXCR4 chemokine ligand/receptor axis in cardiovascular disease. Front. Physiol. 5, 212 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    He, W. & Dai, C. Key fibrogenic signaling. Curr. Pathobiol. Rep. 3, 183–192 (2015).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Juhaszova, M. et al. Role of glycogen synthase kinase-3β in cardioprotection. Circ. Res. 104, 1240–1252 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Takeishi, Y. et al. Src and multiple MAP kinase activation in cardiac hypertrophy and congestive heart failure under chronic pressure–overload: comparison with acute mechanical stretch. J. Mol. Cell Cardiol. 33, 1637–1648 (2001).

    CAS  PubMed  Google Scholar 

  41. 41.

    Wang, Y. Mitogen-activated protein kinases in heart development and diseases. Circulation 116, 1413–1423 (2007).

    CAS  PubMed  Google Scholar 

  42. 42.

    Gan, B. et al. Role of FIP200 in cardiac and liver development and its regulation of TNFα and TSC–mTOR signaling pathways. J. Cell Biol. 175, 121–133 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Kuwahara, K. Role of NRSF/REST in the regulation of cardiac gene expression and function. Circulation J. 77, 2682–2686 (2013).

    CAS  Google Scholar 

  44. 44.

    Li, Y. et al. Targeted disruption of TCF12 reveals HEB as essential in human mesodermal specification and hematopoiesis. Stem Cell Rep. 9, 779–795 (2017).

    CAS  Google Scholar 

  45. 45.

    Schunke, K. J. et al. Protein kinase C binding protein 1 inhibits hypoxia-inducible factor-1 in the heart. Cardiovasc. Res. 115, 1332–1342 (2019).

    CAS  PubMed  Google Scholar 

  46. 46.

    Lam, M. P. et al. An MRM-based workflow for quantifying cardiac mitochondrial protein phosphorylation in murine and human tissue. J. Proteom. 75, 4602–4609 (2012).

    CAS  Google Scholar 

  47. 47.

    Mosadegh, B. et al. Three-dimensional paper-based model for cardiac ischemia. Adv. Healthc. Mater. 3, 1036–1043 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Caulfield, J. B. & Borg, T. K. The collagen network of the heart. Lab. Investig. 40, 364–372 (1979).

    CAS  PubMed  Google Scholar 

  49. 49.

    Gucek, M. Proteomics approaches to fibrotic disorders. Fibrogenesis Tissue Repair 5, S10 (2012).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Tyanova, S., Temu, T. & Sinitcyn, P. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    CAS  PubMed  Google Scholar 

  51. 51.

    Isserlin, R., Merico, D., Voisin, V. & Bader, G. D. Enrichment Map—a Cytoscape app to visualize and explore OMICs pathway enrichment results. F1000Res. 3, 141 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Tavares, F. X. et al. N-Phenyl-4-pyrazolo[1,5-b]pyridazin-3-ylpyrimidin-2-amines as potent and selective inhibitors of glycogen synthase kinase 3 with good cellular efficacy. J. Med. Chem. 47, 4716–4730 (2004).

    CAS  PubMed  Google Scholar 

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We thank H. Rakowski for initial discussions on study design and execution; GlaxoSmithKline and W. Zuercher for the gift of the published kinase inhibitor set; and J. Greenblatt and E. Macron for allowing access to instrumentation at the Donnelly Centre. This research is part of the University of Toronto’s Medicine by Design initiative which receives funding from the Canada First Research Excellence Fund. This project was funded by the Ted Rogers Centre for Heart Research Translational Biology and Engineering Program to A.G., the Heart and Stroke Richard Lewar Centres of Excellence in Cardiovascular Research and CIHR grants to A.G. (PJT-155921 and PJT-166118; MOP-106538; MOP-123320). S.-H.L. was supported by a NSERC Postgraduate Scholarship, an Ontario Graduate Scholarship and a Ted Rogers Centre for Heart Research Doctoral Fellowship. S.H.-L. was supported by a Canada Graduate Scholarship–Master’s Award from Canadian Institutes of Health Research and an Ontario Graduate Scholarship. U.K. was supported by a Ted Rogers Centre for Heart Research Postdoctoral Fellowship. This work was funded by the Canadian Institutes of Health Research Foundation grant (FDN-167274), Natural Sciences and Engineering Research Council−Canadian Institutes of Health Research Collaborative Health Research grant (CHRP 493737-16) and Killam Fellowship (7025-19-0016) awarded to M.R. E.Y.W. was supported by Alexander Graham Bell Canada Graduate Scholarship–Doctoral Award (CGS−D). A.E. acknowledges a Foundation grant (FDN-148399) from the Canadian Institutes of Health Research.

Author information




A.E., A.G., E.Y.W., M.R. and U.K. designed the study and wrote the manuscript. U.K. performed experimental preparation and bioinformatic data analysis of all (phospho)proteomics samples. E.Y.W. generated the Biowire constructs and performed all associated experimental work, including fluorescence imaging of Biowire, mouse, and patient explant samples. R.V. and F.B. assisted in the design of the study and provided patient surgical explant samples and associated clinical information. D.H.K., S.H.-L., S.-H.L. and P.S. helped in the design and performing of mouse experiments and preparation of mass spectrometry samples. H.G. and M.M. maintained and operated mass spectrometry instrumentation. Y.Z. assisted in the generation of Biowire constructs and design of the study. U.K. wrote the initial manuscript, which was edited by all authors.

Corresponding authors

Correspondence to Milica Radisic or Anthony Gramolini or Andrew Emili.

Ethics declarations

Competing interests

M.R. and Y.Z. are co-founders and hold shares in TARA Biosystems, which uses the Biowire II platform for commercial drug testing.

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

Supplementary information

Supplementary Information

Supplementary figures, tables and descriptions for the supplementary datasets.

Reporting Summary

Supplementary Dataset 1

Normalized quantitative data for unfiltered protein identifications by proteomic analysis of all three types of samples processed.

Supplementary Dataset 2

Normalized quantitative data for phosphorylation sites.

Supplementary Dataset 3

Panther DB output of annotation coverage for all (phospho)protein-level identifications in all analysed datasets for gene ontology and reactome pathways.

Supplementary Dataset 4

GSEA-enriched gene set annotations in each dataset.

Supplementary Dataset 5

Significant phosphorylation sites.

Supplementary Dataset 6

Results of echocardiography analysis of TAC and sham mouse hearts.

Supplementary Fig. 6

High-resolution version of the results of the gene set enrichment analysis visualized in Cytoscape by using the Enrichment Map plugin.

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Kuzmanov, U., Wang, E.Y., Vanderlaan, R. et al. Mapping signalling perturbations in myocardial fibrosis via the integrative phosphoproteomic profiling of tissue from diverse sources. Nat Biomed Eng 4, 889–900 (2020).

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