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

Nature Genetics

Volumn 49, Issue 1, 2017, Pages 46-53

  Schafer, Sebastian ^a,b   De Marvao, Antonio ^c   Adami, Eleonora ^d   Fiedler, Lorna R ^b   Ng, Benjamin ^c   Khin, Ester ^b   Rackham, Owen J L ^b   Van Heesch, Sebastiaan ^d   Pua, Chee J ^a   Kui, Miao ^b   Walsh, Roddy ^e   Tayal, Upasana ^e   Prasad, Sanjay K ^e   Dawes, Timothy J W ^c   Ko, Nicole S J ^b   Sim, David ^a   Chan, Laura L H ^a   Chin, Calvin W L ^a,b   Mazzarotto, Francesco ^e   Barton, Paul J ^e   Kreuchwig, Franziska ^d   De Kleijn, Dominique P V ^b,f   Totman, Teresa ^b   Biffi, Carlo ^c   Tee, Nicole ^a   Rueckert, Daniel ^g   Schneider, Valentin ^d   Faber, Allison ^d   Regitz Zagrosek, Vera ^h,i   Seidman, Jonathan G ^j   Seidman, Christine E ^j,k,l   Linke, Wolfgang A ^i,m   Kovalik, Jean Paul ^b   O'Regan, Declan ^c   Ware, James S ^c,e   Hubner, Norbert ^d,h,i   Cook, Stuart A ^a,b,e   more..

^a DEPARTMENT OF CARDIOLOGY   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c HAMMERSMITH HOSPITAL   (United Kingdom)

^d MAX DELBRÜCK CENTER FOR MOLECULAR MEDICINE   (Germany)

^e NATIONAL HEART AND LUNG INSTITUTE   (United Kingdom)

^f UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^g IMPERIAL COLLEGE LONDON   (United Kingdom)

^h CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

ⁱ DZHK GERMAN CENTRE FOR CARDIOVASCULAR RESEARCH   (Germany)

^j HARVARD MEDICAL SCHOOL   (United States)

^k BRIGHAM AND WOMEN S HOSPITAL   (United States)

^l HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^m RUHR UNIVERSITY BOCHUM   (Germany)

more..

Author keywords

[No Author keywords available]

Indexed keywords

CONNECTIN; PROTEIN VARIANT; TITIN TRUNCATING VARIANT; UNCLASSIFIED DRUG; TTN PROTEIN, HUMAN; TTN PROTEIN, RAT;

ADULT; ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; CARDIAC PATIENT; CARDIOVASCULAR MAGNETIC RESONANCE; CONGESTIVE CARDIOMYOPATHY; CONTROLLED STUDY; GENE EXPRESSION PROFILING; GENE FREQUENCY; GENETIC BACKGROUND; GENETIC TRANSCRIPTION; GENOME ANALYSIS; HEART FUNCTION; HEART MUSCLE METABOLISM; HEART STRESS; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MOLECULAR PATHOLOGY; NONHUMAN; NONSENSE MEDIATED MRNA DECAY; PRIORITY JOURNAL; RAT; RIBOSOME; RNA SEQUENCE; RNA TRANSLATION; SIGNAL TRANSDUCTION; STOP CODON; TRANSCRIPTION INITIATION SITE; ANIMAL; CASE CONTROL STUDY; COHORT ANALYSIS; COMPARATIVE STUDY; GENETIC VARIATION; GENETICS; HEART; HIGH THROUGHPUT SEQUENCING; MALE; PATHOLOGY; PHYSIOLOGY;

ANIMALS; CARDIOMYOPATHY, DILATED; CASE-CONTROL STUDIES; COHORT STUDIES; CONNECTIN; GENETIC VARIATION; HEART; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MALE; RATS;

EID: 84997611193     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/ng.3719     Document Type: Article

References (73)

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).
2. Herman, D.S. et al. Truncations of titin causing dilated cardiomyopathy. N. Engl. J. Med. 366, 619-628 (2012).
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).
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).
5. Chauveau, C., Rowell, J. & Ferreiro, A. A rising titan: TTN review and mutation update. Hum. Mutat. 35, 1046-1059 (2014).
6. Akinrinade, O., Koskenvuo, J.W. & Alastalo, T.-P. Prevalence of titin truncating variants in general population. PLoS One 10, e0145284 (2015).
7. Akinrinade, O., Alastalo, T.P. & Koskenvuo, J.W. Relevance of truncating titin mutations in dilated cardiomyopathy. Clinic. Genet. 90, 49-54 (2016).
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).
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).
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).
11. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285-291 (2016).
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).
13. Forrest, A.R. et al. A promoter-level mammalian expression atlas. Nature 507, 462-470 (2014).
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).
15. Schafer, S. et al. Translational regulation shapes the molecular landscape of complex disease phenotypes. Nat. Commun. 6, 7200 (2015).
16. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330 (2015).
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).
18. Guo, W. et al. RBM20, a gene for hereditary cardiomyopathy, regulates titin splicing. Nat. Med. 18, 766-773 (2012).
19. Maatz, H. et al. RNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing. J. Clin. Invest. 124, 3419-3430 (2014).
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).
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).
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).
23. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000).
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).
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).
26. Doenst, T., Nguyen, T.D. & Abel, E.D. Cardiac metabolism in heart failure: implications beyond ATP production. Circ. Res. 113, 709-724 (2013).
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).
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).
29. Neubauer, S. The failing heart - an engine out of fuel. N. Engl. J. Med. 356, 1140-1151 (2007).
30. Yano, T. et al. Clinical impact of myocardial mTORC1 activation in nonischemic dilated cardiomyopathy. J. Mol. Cell. Cardiol. 91, 6-9 (2016).
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).
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).
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).
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).
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).
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).
37. Mestroni, L. et al. Guidelines for the study of familial dilated cardiomyopathies. Eur. Heart J. 20, 93-102 (1999).
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).
39. Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677-687 (2009).
40. Newton-Cheh, C. et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666-676 (2009).
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).
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).
43. Ware, J.S. et al. Shared genetic predisposition in peripartum and dilated cardiomyopathies. N. Engl. J. Med. 374, 233-241 (2016).
44. Watkins, H. Tackling the Achilles' heel of genetic testing. Sci. Transl. Med. 7, 270fs1 (2015).
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).
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).
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).
49. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).
50. Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res. 43, D662-D669 (2015).
51. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
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).
53. Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451-1455 (2005).
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).
55. Shimoyama, M. et al. The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic Acids Res. 43, D743-D750 (2015).
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).
57. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811-2812 (2014).
58. Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008-2017 (2012).
59. Price, A.N. et al. Cardiovascular magnetic resonance imaging in experimental models. Open Cardiovasc. Med. J. 4, 278-292 (2010).
60. Heiberg, E. et al. Design and validation of Segment - freely available software for cardiovascular image analysis. BMC Med. Imaging 10, 1 (2010).
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).
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).
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).
64. Sutherland, F.J. & Hearse, D.J. The isolated blood and perfusion fluid perfused heart. Pharmacol. Res. 41, 613-627 (2000).
65. Muoio, D.M. et al. Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility. Cell Metab. 15, 764-777 (2012).
66. Soga, T. & Heiger, D.N. Amino acid analysis by capillary electrophoresis electrospray ionization mass spectrometry. Anal. Chem. 72, 1236-1241 (2000).
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).
68. Sugimoto, M. et al. Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis. Metabolomics 6, 27-41 (2010).
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).
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).
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).
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).
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).