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Titin-truncating variants affect heart function in disease cohorts and the general population

Nature Genetics volume 49, pages 46–53 (2017)Cite this article

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Abstract

Titin-truncating variants (TTNtv) commonly cause dilated cardiomyopathy (DCM). TTNtv are also encountered in ∼1% of the general population, where they may be silent, perhaps reflecting allelic factors. To better understand TTNtv, we integrated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with disparate TTNtv. In patients with DCM, TTNtv throughout titin were significantly associated with DCM. Ribosomal profiling in rat showed the translational footprint of premature stop codons in Ttn, TTNtv-position-independent nonsense-mediated degradation of the mutant allele and a signature of perturbed cardiac metabolism. Heart physiology in rats with TTNtv was unremarkable at baseline but became impaired during cardiac stress. In healthy humans, machine-learning-based analysis of high-resolution cardiac imaging showed TTNtv to be associated with eccentric cardiac remodeling. These data show that TTNtv have molecular and physiological effects on the heart across species, with a continuum of expressivity in health and disease.

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Figure 1: Ribosome profiling identifies the translational footprint of truncating variants in titin.
Figure 2: Proximal and distal TTNtv in Ttn alter isoform processing and trigger NMD.
Figure 3: Hearts with proximal and distal truncations of titin undergo metabolic reprogramming.
Figure 4: TTNtv in rats and humans adversely affect cardiac geometry and function.

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References

  1. Hershberger, R.E., Hedges, D.J. & Morales, A. Dilated cardiomyopathy: the complexity of a diverse genetic architecture. Nat. Rev. Cardiol. 10, 531–547 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Herman, D.S. et al. Truncations of titin causing dilated cardiomyopathy. N. Engl. J. Med. 366, 619–628 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Roberts, A.M. et al. Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease. Sci. Transl. Med. 7, 270ra6 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Norton, N. et al. Exome sequencing and genome-wide linkage analysis in 17 families illustrate the complex contribution of TTN truncating variants to dilated cardiomyopathy. Circ Cardiovasc Genet 6, 144–153 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Chauveau, C., Rowell, J. & Ferreiro, A. A rising titan: TTN review and mutation update. Hum. Mutat. 35, 1046–1059 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Akinrinade, O., Koskenvuo, J.W. & Alastalo, T.-P. Prevalence of titin truncating variants in general population. PLoS One 10, e0145284 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Akinrinade, O., Alastalo, T.P. & Koskenvuo, J.W. Relevance of truncating titin mutations in dilated cardiomyopathy. Clinic. Genet. 90, 49–54 (2016).

    Article  CAS  Google Scholar 

  8. Robinson, E.B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hinson, J.T. et al. Titin mutations in iPS cells define sarcomere insufficiency as a cause of dilated cardiomyopathy. Science 349, 982–986 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gramlich, M. et al. Stress-induced dilated cardiomyopathy in a knock-in mouse model mimicking human titin-based disease. J. Mol. Cell. Cardiol. 47, 352–358 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zou, J. et al. An internal promoter underlies the difference in disease severity between N- and C-terminal truncation mutations of Titin in zebrafish. eLife 4, e09406 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Forrest, A.R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R. & Weissman, J.S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schafer, S. et al. Translational regulation shapes the molecular landscape of complex disease phenotypes. Nat. Commun. 6, 7200 (2015).

    Article  PubMed  Google Scholar 

  16. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schafer, S. et al. Alternative splicing signatures in RNA–seq data: percent spliced in (PSI). Curr. Protoc. Hum. Genet. 87, 11.16.1–11.16.14 (2015).

    Article  Google Scholar 

  18. Guo, W. et al. RBM20, a gene for hereditary cardiomyopathy, regulates titin splicing. Nat. Med. 18, 766–773 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Maatz, H. et al. RNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing. J. Clin. Invest. 124, 3419–3430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Le Hir, H., Izaurralde, E., Maquat, L.E. & Moore, M.J. The spliceosome deposits multiple proteins 20–24 nucleotides upstream of mRNA exon–exon junctions. EMBO J. 19, 6860–6869 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Le Hir, H., Gatfield, D., Izaurralde, E. & Moore, M.J. The exon–exon junction complex provides a binding platform for factors involved in mRNA export and nonsense-mediated mRNA decay. EMBO J. 20, 4987–4997 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Luo, W., Friedman, M.S., Shedden, K., Hankenson, K.D. & Woolf, P.J. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics 10, 161 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lai, L. et al. Energy metabolic reprogramming in the hypertrophied and early stage failing heart: a multisystems approach. Circ Heart Fail 7, 1022–1031 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shibayama, J. et al. Metabolic remodeling in moderate synchronous versus dyssynchronous pacing-induced heart failure: integrated metabolomics and proteomics study. PLoS One 10, e0118974 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Doenst, T., Nguyen, T.D. & Abel, E.D. Cardiac metabolism in heart failure: implications beyond ATP production. Circ. Res. 113, 709–724 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Stanley, W.C., Recchia, F.A. & Lopaschuk, G.D. Myocardial substrate metabolism in the normal and failing heart. Physiol. Rev. 85, 1093–1129 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Schisler, J.C. et al. Cardiac energy dependence on glucose increases metabolites related to glutathione and activates metabolic genes controlled by mechanistic target of rapamycin. J. Am. Heart Assoc. 4, e001136 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Neubauer, S. The failing heart—an engine out of fuel. N. Engl. J. Med. 356, 1140–1151 (2007).

    Article  PubMed  Google Scholar 

  30. Yano, T. et al. Clinical impact of myocardial mTORC1 activation in nonischemic dilated cardiomyopathy. J. Mol. Cell. Cardiol. 91, 6–9 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Ramos, F.J. et al. Rapamycin reverses elevated mTORC1 signaling in lamin A/C-deficient mice, rescues cardiac and skeletal muscle function, and extends survival. Sci. Transl. Med. 4, 144ra103 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Neishabouri, S.H., Hutson, S.M. & Davoodi, J. Chronic activation of mTOR complex 1 by branched chain amino acids and organ hypertrophy. Amino Acids 47, 1167–1182 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Ait-Mou, Y. et al. Titin strain contributes to the Frank–Starling law of the heart by structural rearrangements of both thin- and thick-filament proteins. Proc. Natl. Acad. Sci. USA 113, 2306–2311 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sen, S. et al. Glucose regulation of load-induced mTOR signaling and ER stress in mammalian heart. J. Am. Heart Assoc. 2, e004796 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shende, P. et al. Cardiac raptor ablation impairs adaptive hypertrophy, alters metabolic gene expression, and causes heart failure in mice. Circulation 123, 1073–1082 (2011).

    Article  PubMed  Google Scholar 

  36. Zhang, D. et al. MTORC1 regulates cardiac function and myocyte survival through 4E-BP1 inhibition in mice. J. Clin. Invest. 120, 2805–2816 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mestroni, L. et al. Guidelines for the study of familial dilated cardiomyopathies. Eur. Heart J. 20, 93–102 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Vasan, R.S., Larson, M.G., Benjamin, E.J., Evans, J.C. & Levy, D. Left ventricular dilatation and the risk of congestive heart failure in people without myocardial infarction. N. Engl. J. Med. 336, 1350–1355 (1997).

    Article  CAS  PubMed  Google Scholar 

  39. Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Newton-Cheh, C. et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666–676 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. de Marvao, A. et al. Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power. J. Cardiovasc. Magn. Reson. 16, 16 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Bai, W. et al. A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26, 133–145 (2015).

    Article  PubMed  Google Scholar 

  43. Ware, J.S. et al. Shared genetic predisposition in peripartum and dilated cardiomyopathies. N. Engl. J. Med. 374, 233–241 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Watkins, H. Tackling the Achilles' heel of genetic testing. Sci. Transl. Med. 7, 270fs1 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Pugh, T.J. et al. The landscape of genetic variation in dilated cardiomyopathy as surveyed by clinical DNA sequencing. Genet. Med. 16, 601–608 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Walsh, R. et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet. Med. http://dx.doi.org/10.1038/gim.2016.90 (2016).

  47. Pua, C.J. et al. Development of a comprehensive sequencing assay for inherited cardiac condition genes. J. Cardiovasc. Transl. Res. 9, 3–11 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res. 43, D662–D669 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Goecks, J., Nekrutenko, A., Taylor, J. & Team, T. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451–1455 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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  CAS  PubMed  PubMed Central  Google Scholar 

  55. Shimoyama, M. et al. The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic Acids Res. 43, D743–D750 (2015).

    Article  CAS  PubMed  Google Scholar 

  56. Haberle, V., Forrest, A.R., Hayashizaki, Y., Carninci, P. & Lenhard, B. CAGEr: precise TSS data retrieval and high-resolution promoterome mining for integrative analyses. Nucleic Acids Res. 43, e51 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA–seq data. Genome Res. 22, 2008–2017 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Price, A.N. et al. Cardiovascular magnetic resonance imaging in experimental models. Open Cardiovasc. Med. J. 4, 278–292 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Heiberg, E. et al. Design and validation of Segment—freely available software for cardiovascular image analysis. BMC Med. Imaging 10, 1 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ross, A.J. et al. Serial MRI evaluation of cardiac structure and function in mice after reperfused myocardial infarction. Magn. Reson. Med. 47, 1158–1168 (2002).

    Article  PubMed  Google Scholar 

  62. Tortoledo, F.A., Quinones, M.A., Fernandez, G.C., Waggoner, A.D. & Winters, W.L. Jr. Quantification of left ventricular volumes by two-dimensional echocardiography: a simplified and accurate approach. Circulation 67, 579–584 (1983).

    Article  CAS  PubMed  Google Scholar 

  63. Sahn, D.J., DeMaria, A., Kisslo, J. & Weyman, A. Recommendations regarding quantitation in M-mode echocardiography: results of a survey of echocardiographic measurements. Circulation 58, 1072–1083 (1978).

    Article  CAS  PubMed  Google Scholar 

  64. Sutherland, F.J. & Hearse, D.J. The isolated blood and perfusion fluid perfused heart. Pharmacol. Res. 41, 613–627 (2000).

    Article  CAS  PubMed  Google Scholar 

  65. Muoio, D.M. et al. Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility. Cell Metab. 15, 764–777 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Soga, T. & Heiger, D.N. Amino acid analysis by capillary electrophoresis electrospray ionization mass spectrometry. Anal. Chem. 72, 1236–1241 (2000).

    Article  CAS  PubMed  Google Scholar 

  67. Soga, T. et al. Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry. Anal. Chem. 74, 2233–2239 (2002).

    Article  CAS  PubMed  Google Scholar 

  68. Sugimoto, M. et al. Differential metabolomics software for capillary electrophoresis–mass spectrometry data analysis. Metabolomics 6, 27–41 (2010).

    Article  CAS  Google Scholar 

  69. de Marvao, A. et al. Precursors of hypertensive heart phenotype develop in healthy adults: a high-resolution 3D MRI study. JACC Cardiovasc. Imaging 8, 1260–1269 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Bellenger, N.G., Grothues, F., Smith, G.C. & Pennell, D.J. Quantification of right and left ventricular function by cardiovascular magnetic resonance. Herz 25, 392–399 (2000).

    Article  CAS  PubMed  Google Scholar 

  71. Grothues, F. et al. Comparison of interstudy reproducibility of cardiovascular magnetic resonance with two-dimensional echocardiography in normal subjects and in patients with heart failure or left ventricular hypertrophy. Am. J. Cardiol. 90, 29–34 (2002).

    Article  PubMed  Google Scholar 

  72. Smith, S.M. & Nichols, T.E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83–98 (2009).

    Article  PubMed  Google Scholar 

  73. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

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Acknowledgements

We thank all the patients and healthy volunteers for taking part in this research and our team of research nurses across the hospital sites. We also thank M. von Frieling-Salewsky for technical support. The research was supported by the MRC Clinical Sciences Centre, UK, to J.S.W., S.A.C., A.d.M. and D.P.O'R., the NIHR Biomedical Research Unit in Cardiovascular Disease at Royal Brompton, the Harefield NHS Foundation Trust and Imperial College London to J.S.W. and S.A.C., the NIHR Imperial Biomedical Research Centre, British Heart Foundation, UK (SP/10/10/28431, PG/12/27/29489) to S.A.C., D.P.O'R. and C.B., the Wellcome Trust, UK (107469/Z/15/Z to J.S.W., 087183/Z/08/Z, 092854/Z/10/Z and WT095908), a Wellcome Trust Fellowship (100211/Z/12/Z and P43579_WMET to T.J.W.D.), Fondation Leducq to J.S.W., the Tanoto Foundation to S.A.C., CORDA, the National Institutes of Health (NHLBI 2R01HL080494 to J.G.S. and C.E.S.), the National Medical Research Council (NMRC) Singapore (CIRG13nov024 and STaR13nov002 to D.P.V.d.K.), the SingHealth Duke–NUS Institute of Precision Medicine, the Rosetrees Trust, the Health Innovation Challenge Fund (HICF-R6-373 to J.S.W.) funding from the Wellcome Trust and the Department of Health, UK, the Howard Hughes Medical Institute, the European Union EURATRANS award (HEALTH-F4-2010-241504 to N.H.), the Helmholtz Alliance ICEMED to N.H., European Union FP7 (CardioNeT-ITN-289600 to F.M.), Deutsche Forschungsgemeinschaft (SFB1002, TPA08 to W.A.L., Forschergruppe 1054 HU 1522/1-1 to N.H. and TP1 to V.R.-Z.), and an EMBO Long-Term Fellowship (ALTF 186-2015 to S.v.H.) and Marie Curie Actions (LTFCOFUND2013, GA-2013-609409 to S.v.H.). This publication includes independent research commissioned by the Health Innovation Challenge Fund (HICF), a parallel funding partnership between the UK Department of Health and the Wellcome Trust. The views expressed in this work are those of the authors and not necessarily those of the UK Department of Health or the Wellcome Trust.

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Author notes

  1. Sebastian Schafer and Antonio de Marvao: These authors contributed equally to this work.

  2. James S Ware, Norbert Hubner and Stuart A Cook: These authors jointly supervised this work.

Authors and Affiliations

  1. National Heart Centre Singapore, Singapore

    Sebastian Schafer, Benjamin Ng, Chee J Pua, David Sim, Laura L H Chan, Calvin W L Chin, Nicole Tee & Stuart A Cook

  2. Duke–National University of Singapore, Singapore

    Sebastian Schafer, Lorna R Fiedler, Ester Khin, Owen J L Rackham, Miao Kui, Nicole S J Ko, Calvin W L Chin, Jean-Paul Kovalik & Stuart A Cook

  3. Cardiovascular and Metabolic Disorders Program, MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK

    Antonio de Marvao, Timothy J W Dawes, Carlo Biffi, Declan O'Regan & James S Ware

  4. Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Eleonora Adami, Sebastiaan van Heesch, Franziska Kreuchwig, Valentin Schneider, Allison Faber & Norbert Hubner

  5. National Heart and Lung Institute and NIHR Royal Brompton Cardiovascular BRU, Imperial College London, London, UK

    Roddy Walsh, Upasana Tayal, Sanjay K Prasad, Francesco Mazzarotto, Paul J Barton, James S Ware & Stuart A Cook

  6. Department of Surgery, National University of Singapore, Singapore

    Dominique P V de Kleijn & Teresa Totman

  7. Departments of Cardiology and Vascular Surgery, University Medical Center, Utrecht, the Netherlands

    Dominique P V de Kleijn

  8. Department of Computing, Imperial College London, London, UK

    Daniel Rueckert

  9. Institute of Gender in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany

    Vera Regitz-Zagrosek

  10. DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany

    Vera Regitz-Zagrosek & Norbert Hubner

  11. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Jonathan G Seidman & Christine E Seidman

  12. Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Christine E Seidman

  13. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA

    Christine E Seidman

  14. Department of Cardiovascular Physiology, Ruhr University Bochum, Bochum, Germany

    Wolfgang A Linke

  15. DZHK (German Centre for Cardiovascular Research), partner site Goettingen, Goettingen, Germany

    Wolfgang A Linke

  16. Charité Universitätsmedizin, Berlin, Germany

    Norbert Hubner

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  1. Sebastian Schafer
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Contributions

S.A.C. conceived, managed and arranged funding for the project. A.d.M., E.A., L.R.F., B.N., E.K., S.v.H., C.J.P., U.T., S.K.P., T.J.W.D., N.S.J.K., D.S., L.L.H.C., C.W.L.C., P.J.B., D.P.V.d.K., T.T., C.B., N.T., V.R.-Z., J.G.S., C.E.S. and W.A.L. performed experiments and contributed clinical data. S.S., A.d.M., O.J.L.R., M.K., R.W., F.M., F.K., D.R., V.S., A.F., J.-P.K., D.P.O'R., J.S.W., N.H. and S.A.C. performed data analysis and interpretation. S.S., B.N. and S.A.C. prepared the manuscript with input from co-authors.

Corresponding author

Correspondence to Stuart A Cook.

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Competing interests

S.A.C. consults for Illumina.

Integrated supplementary information

Supplementary Figure 1 Identification of an alternative, distal transcription start site in Titin.

From outside to inside, track 1 shows the location of the subunits of titin; the gene is on the antisense strand and so is transcribed counterclockwise in this view. Track 2 shows the gene structure of titin with the exons shown as orange rectangles and the introns shown as black lines. Track 3 shows the location of transcription start sites identified by the analysis of CAGE data taken from human heart samples in the FANTOM5 consortium as identified using CAGEr. Track 4 shows the location of H3K4me3 ChIP–seq narrow peaks (a mark of active promoters) from fetal heart samples in the Epigenomics Roadmap data set. Track 5 shows H3K4me3 ChIP–seq peaks from adult heart (left ventricle) taken from the Epigenomics Roadmap data set. Track 6 shows H3K9ac (also a mark of active promoters) taken from the fetal heart samples in the Epigenomics Roadmap data set. Together these data show that in the heart there are likely two transcription start sites, the canonical transcription start site at the beginning of the gene and another transcription start site found close to the start A-band, which appears to be most strongly used in fetal heart but is still present in adult human heart.

Supplementary Figure 2 Etiological fraction of TTNtv in 40 bins across the titin locus.

The constitutive (PSI > 90%) regions of titin are split into 40 bins ranging from the N terminus to the C terminus, and the etiological fraction of TTNtv for each individual region is plotted. The dashed line marks the position of the internal Cronos promoter. Purple, Z-disc; green, I-band; pink, A-band; blue, M-line.

Supplementary Figure 3 Truncating mutations introduced to F344 rats.

(a) The proximal truncating variant in titin is a large deletion located near the Z-disc at the N terminus of the meta transcript (TTNtvZ). It spans from exon 2 (b) to exon 6 (c). Exons 3–5 are not present in TTNtvZ rats and can thus be used to assess transcription and translation from the wild-type allele in heterozygous animals. (d) The deletion causes a frameshift that results in a premature stop codon located in exon 7. (e) The truncating variant in the A-band is located in the large exon 312 and an indel that also causes a frameshift and introduces a stop codon shortly after.

Supplementary Figure 4 RNA sequencing and ribosome profiling data for TTNtvA, TTNtvZ and wild-type rats.

(a,b) RNA–seq reads (a) and Ribo–seq reads (b) mapping to mitochondrial and ribosomal sequences were filtered out, and the remaining sequences were mapped to the genome. (c,d) Uniquely mapping RNA–seq (c) and Ribo–seq (d) reads were counted and used in later analyses to assess gene expression on the transcriptional and translational levels. (e) After adaptor trimming, Ribo–seq libraries displayed a size distribution typical for ribosome profiling experiments: ribosomes mostly protected RNA fragments of 28 and 29 bp in size.

Supplementary Figure 5 Absence of truncated Ttn protein in TTNtvA and TTNtvZ rat hearts.

Representative titin gels and immunoblots from 3-month-old wild-type, TTNtvA and TTNtvZ rat hearts. (a) SDS–PAGE was performed on 2.5% polyacrylamide/1% agarose gels, and total protein was visualized by Coomassie blue staining. Each sample was analyzed in duplicate at lower (left) and higher (right) protein concentration. Mhc was used as a loading control. The relative Ttn/Mhc ratio was determined (below). Data are shown as means ± s.e.m. (n = 4/group). (b) Immunoblotting analysis on 1.8% polyacrylamide/1% agarose gel transferred to PVDF and blotted with T12 antibody against titin and Novex3 (top). The corresponding PVDF blot was used as loading control (bottom).

Supplementary Figure 6 Transcriptional and translational gene expression differences between wild-type and TTNtv rats.

(a,b) Differential gene transcription (a) and translation (b) is compared between TTNtv and wild-type rats. Both TTNtvA and TTNtvZ show highly correlated fold changes in differentially expressed genes (DEseq2 FDR ≤ 0.05) when compared to control rats (Pearson correlation). Genes that were not differentially expressed in any comparison were not considered.

Supplementary Figure 7 Differences in cardiac metabolism between wild-type, TTNtvA and TTNtvZ rats.

(a–c) Metabolite profiles showing branched-chain amino acids, including valine (a), leucine (b) and isoleucine (c). (d) Sum of measured glycolytic intermediates (metabolites are detailed in Supplementary Table 3). (e) Glucose-6-phosphate (G6P) levels in cardiac tissue from wild-type (n = 6) and TTNtv (TTNtvA, n = 6; TTNtvZ, n = 6) rats. Data are shown as mean ± s.d. (Dunnett).

Supplementary Figure 8 TTNtv rat hearts have normal energy substrate abundance.

Metabolite levels of ATP, ADP, AMP and ratio of phosphocreatine (PCr) to creatine (Cr) in 4-month-old wild-type (n = 6) and TTNtv (TTNtvA, n = 6; TTNtvZ, n = 6) rat hearts.

Supplementary Figure 9 Relative expression of titin-associated proteins in wild-type and TTNtv rats.

(a,b) Relative difference (TTNtv/WT) in transcription (a) and translation (b) of titin-associated proteins showing a significant decrease in FHL1 and FHL2 expression in 8-week-old TTNtv (TTNtvA, n = 3; TTNtvZ, n = 3) as compared to wild-type (n = 4) rats. Data are shown as mean fold change ± s.e.m. *P < 0.05 (DEseq2 P value, not corrected for genome-wide testing).

Supplementary Figure 10 mTORC1 signaling is altered in TTNtv rat hearts.

(a) Immunoblot analysis showing increased phosphorylation of mTOR (Ser2448), S6 kinase (Thr389) and 4EBP1 (Thr37/46) in TTNtv as compared to wild-type hearts in extracts from rats immediately after sacrifice. (b) Semiquantitative densitometry of band intensities from several immunoblots across separate experiments and shown as means ± s.e.m. (Student’s t test, Welsh correction). (c) Immunoblot analysis of phosphorylated mTOR (Ser2448) and S6 kinase (Thr389) in myocardial tissue following sham treatment or volume overload in wild-type and TTNtv rat hearts on the Langendorff apparatus perfused for the same duration. (d) Semiquantitative densitometry representation of band intensities from the blot in c and other experiments showing mTOR and S6K response to acute stress relative to wild-type unstressed hearts of the respective genotypes. Data are shown as means ± s.d. (versus unstressed wild-type hearts, Student’s t test, Welsh correction).

Supplementary Figure 11 Young TTNtv rats display concentric remodeling.

Echocardiographic measurements of 4- to 8-month-old male wild-type (n = 4) and TTNtv (TTNtvA, n = 4; TTNtvZ, n = 4) rats. PLVWed (mm), posterior left ventricular wall thickness end diastole; PLVWes (mm), posterior left ventricular wall thickness end diastole; LVIDed (mm), left ventricular internal diameter end diastole; LVIDes (mm), left ventricular internal diameter end systole; EDV (μl), end-diastolic volume; ESV (μl), end-systolic volume; SV (μl), stroke volume. EF (%), ejection fraction. P values indicate statistical analysis by two-way analysis of variance (ANOVA).

Supplementary Figure 12 CMR in TTNtvA and TTNtvZ rats.

(a–c) SV (stroke volume) (a), LVEF (left ventricular ejection fraction) (b) and FWT (fractional wall thickening) (c) measured with CMR in 13- to 16-month-old male wild-type (n = 5) and TTNtv (TTNtvA, n = 8; TTNtvZ, n = 6) rats. Data are shown as means ± s.d. (Dunett).

Supplementary Figure 13 Distribution of TTNtv detected in the general population for MRI.

The top track shows the distribution of TTNtv in healthy volunteers (HVOL) who were phenotyped using cardiac magnetic resonance imaging. The track below depicts the distribution of TTNtv from ExAC data. Only truncations located in exons with PSI >15% are shown.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 1–5. (PDF 3548 kb)

3D cardiac imaging shows the effect of TTNtv on human left ventricular geometry.

Mass univariate regression models show the relationship between TTNtv genotype (cardiac exons with PSI > 15%) and increasing endocardial volume (positive coefficients) in end systole (left) and end diastole (right). Standardized β coefficients are plotted on the endocardial surface with outlines of left (red) and right (blue) ventricles. The area enclosed by the yellow contour has a corrected P <0.05 (multiple linear regression). (MP4 6250 kb)

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Schafer, S., de Marvao, A., Adami, E. et al. Titin-truncating variants affect heart function in disease cohorts and the general population. Nat Genet 49, 46–53 (2017). https://doi.org/10.1038/ng.3719

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