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Cited by 12Heat shock proteins researchCardiac electrophysiology and arrhythmiasCardiomyopathy and Myosin Studies

Functional analysis of a common BAG3 allele associated with protection from heart failure

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Perez-Bermejo, Juan A., Judge, Luke M., Jensen, Christina L., Wu, Kenneth, Watry, Hannah L., Truong, Annie, Ho, Jaclyn J., Carter, Matthew +8 more·2023·Nature Cardiovascular Research
DOI 10.1038/s44161-023-00288-wPMC PMC12360617

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Abstract

Multiple genetic association studies have correlated a common allelic block linked to the BAG3 gene with a decreased incidence of heart failure, but the molecular mechanism remains elusive. In this study, we used induced pluripotent stem cells to test if the only coding variant in this allele block, BAG3 C151R, alters protein and cellular function in human cardiomyocytes. Quantitative protein interaction analysis identified changes in BAG3 C151R protein partners specific to cardiomyocytes. Knockdown of genes encoding for BAG3-interacting factors in cardiomyocytes followed by myofibrillar analysis revealed that BAG3 C151R associates more strongly with proteins involved in the maintenance of myofibrillar integrity. Finally, we demonstrate that cardiomyocytes expressing the BAG3 C151R variant have improved response to proteotoxic stress in a dose-dependent manner. This study suggests that BAG3 C151R could be responsible for the cardioprotective effect of the haplotype block, by increasing cardiomyocyte protection from stress. Preferential binding partners of BAG3 C151R may reveal potential targets for cardioprotective therapies.

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  • The rs2234962 missense variant sits between two conserved IPV motifs, which are essential for HSPB6/8 binding and may modulate interactions with HSP/HSBPs in vitro.

    “The rs2234962 missense variant sits in between two conserved IPV motifs, which are essential for HSPB6/8 binding 37 and may modulate interactions with HSP/ HSBPs in vitro 38”

    From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [38]

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  1. [1]Virani SS Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation 143, e254– e743 (2021). 33501848 PMC13036842
  2. [2]Burke MA, Cook SA, Seidman JG & Seidman CE Clinical and Mechanistic Insights Into the Genetics of Cardiomyopathy. J. Am. Coll. Cardiol 68, 2871– 2886 (2016). 28007147 PMC5843375
  3. [3]Weintraub RG, Semsarian C & Macdonald P. Dilated cardiomyopathy. Lancet 390, 400– 414 (2017). 28190577
  4. [4]Harper AR, Nayee S & Topol EJ Protective alleles and modifier variants in human health and disease. Nat. Rev. Genet 16, 689– 701 (2015). 26503796
  5. [5]Villard E. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur. Heart J 32, 1065– 1076 (2011). 21459883 PMC3086901
  6. [6]Esslinger U. Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy. PLoS One 12, e0172995 (2017). 28296976 PMC5351854
  7. [7]Aragam KG Phenotypic refinement of heart failure in a national biobank facilitates genetic discovery. Circulation (2018). doi: 10.1161/CIRCULATIONAHA.118.035774 PMC6511334 30586722 · DOI
  8. [8]Shah S. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat. Commun 11, 163 (2020). 31919418 PMC6952380
  9. [9]Choquet H. Meta-Analysis of 26 638 Individuals Identifies Two Genetic Loci Associated With Left Ventricular Ejection Fraction. Circ. Genom. Precis. Med 13, e002804 (2020). 32605384 PMC7446727
  10. [10]Verweij N. The genetic makeup of the electrocardiogram. Cell Syst. 11, 229– 238.e5 (2020). 32916098 PMC7530085
  11. [11]de Denus S. A genetic association study of heart failure: more evidence for the role of BAG3 in idiopathic dilated cardiomyopathy. ESC Heart Fail. (2020). doi: 10.1002/ehf2.12934 PMC7754954 32869539 · DOI
  12. [12]Garnier S. Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23. Eur. Heart J 42, 2000– 2011 (2021). 33677556 PMC8139853
  13. [13]Aung N. Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development. Circulation 140, 1318– 1330 (2019). 31554410 PMC6791514
  14. [14]Norton N. Genome-wide studies of copy number variation and exome sequencing identify rare variants in BAG3 as a cause of dilated cardiomyopathy. Am. J. Hum. Genet 88, 273– 282 (2011). 21353195 PMC3059419
  15. [15]Feldman AM Decreased levels of BAG3 in a family with a rare variant and in idiopathic dilated cardiomyopathy. J. Cell Physiol 229, 1697– 1702 (2014). 24623017 PMC4296028
  16. [17]Behl C. Breaking BAG: The Co-Chaperone BAG3 in Health and Disease. Trends Pharmacol. Sci 37, 672– 688 (2016). 27162137
  17. [18]Knezevic T. BAG3: a new player in the heart failure paradigm. Heart Fail. Rev 20, 423– 434 (2015). 25925243 PMC4463985
  18. [19]Gamerdinger M, Carra S & Behl C. Emerging roles of molecular chaperones and co-chaperones in selective autophagy: focus on BAG proteins. J. Mol. Med 89, 1175– 1182 (2011). 21818581
  19. [20]Martin TG & Kirk JA Under construction: The dynamic assembly, maintenance, and degradation of the cardiac sarcomere. J. Mol. Cell Cardiol 148, 89– 102 (2020). 32920010 PMC7736463
  20. [21]Judge LM A BAG3 chaperone complex maintains cardiomyocyte function during proteotoxic stress. JCI Insight (2017). PMC5518554 28724793
  21. [22]Ulbricht A, Arndt V & Höhfeld J. Chaperone-assisted proteostasis is essential for mechanotransduction in mammalian cells. Commun Integr Biol 6, e24925 (2013). 23986815 PMC3737759
  22. [23]Fang X. Loss-of-function mutations in co-chaperone BAG3 destabilize small HSPs and cause cardiomyopathy. J. Clin. Invest (2017). PMC5531406 28737513
  23. [24]Martin TG Cardiomyocyte contractile impairment in heart failure results from reduced BAG3-mediated sarcomeric protein turnover. Nat. Commun 12, 2942 (2021). 34011988 PMC8134551
  24. [25]Franaszczyk M. The BAG3 gene variants in Polish patients with dilated cardiomyopathy: four novel mutations and a genotype-phenotype correlation. J. Transl. Med 12, 192 (2014). 25008357 PMC4105391
  25. [26]Chami N. Nonsense mutations in BAG3 are associated with early-onset dilated cardiomyopathy in French Canadians. Can. J. Cardiol 30, 1655– 1661 (2014). 25448463
  26. [27]Citro R. Polymorphisms of the antiapoptotic protein bag3 may play a role in the pathogenesis of tako-tsubo cardiomyopathy. Int. J. Cardiol 168, 1663– 1665 (2013). 23582692
  27. [28]Meister-Broekema M. Myopathy associated BAG3 mutations lead to protein aggregation by stalling Hsp70 networks. Nat. Commun 9, 5342 (2018). 30559338 PMC6297355
  28. [29]Lo Sardo V. Unveiling the Role of the Most Impactful Cardiovascular Risk Locus through Haplotype Editing. Cell 175, 1796– 1810.e20 (2018). 30528432 PMC6346426
  29. [30]Kishore S. A Non-Coding Disease Modifier of Pancreatic Agenesis Identified by Genetic Correction in a Patient-Derived iPSC Line. Cell Stem Cell 27, 137– 146.e6 (2020). 32442395 PMC7335348
  30. [31]Wang G. Modeling the mitochondrial cardiomyopathy of Barth syndrome with induced pluripotent stem cell and heart-on-chip technologies. Nat. Med 20, 616– 623 (2014). 24813252 PMC4172922
  31. [32]Warren CR Induced pluripotent stem cell differentiation enables functional validation of GWAS variants in metabolic disease. Cell Stem Cell 20, 547– 557.e7 (2017). 28388431
  32. [33]Domínguez F. Dilated Cardiomyopathy Due to BLC2-Associated Athanogene 3 (BAG3) Mutations. J. Am. Coll. Cardiol 72, 2471– 2481 (2018). 30442290 PMC6688826
  33. [34]McDermott-Roe C. Investigation of a dilated cardiomyopathy–associated variant in BAG3 using genome-edited iPSC-derived cardiomyocytes. JCI Insight (2019). PMC6948852 31723063
  34. [35]Orciuolo E. Unexpected cardiotoxicity in haematological bortezomib treated patients. Br. J. Haematol 138, 396– 397 (2007). 17561972
  35. [36]Grandin EW, Ky B, Cornell RF, Carver J & Lenihan DJ Patterns of cardiac toxicity associated with irreversible proteasome inhibition in the treatment of multiple myeloma. J Card Fail 21, 138– 144 (2015). 25433360
  36. [37]Kieserman JM, Myers VD, Dubey P, Cheung JY & Feldman AM Current landscape of heart failure gene therapy. J. Am. Heart Assoc 8, e012239 (2019). 31070089 PMC6585330
  37. [38]Knezevic T. Adeno-associated Virus Serotype 9 - Driven Expression of BAG3 Improves Left Ventricular Function in Murine Hearts with Left Ventricular Dysfunction Secondary to a Myocardial Infarction. JACC Basic Transl. Sci 1, 647– 656 (2016). 28164169 PMC5289821
  38. [39]Grafton F. Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes. Elife 10, (2021). PMC8367386 34338636
  39. [40]Maddah M. Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method. J. Pharmacol. Toxicol. Methods 105, 106895 (2020). 32629158
  40. [41]Rosales W & Lizcano F. The Histone Demethylase JMJD2A Modulates the Induction of Hypertrophy Markers in iPSC-Derived Cardiomyocytes. Front. Genet 9, 14 (2018). 29479368 PMC5811633
  41. [42]Fine M. Human-induced pluripotent stem cell-derived cardiomyocytes for studies of cardiac ion transporters. Am. J. Physiol. Cell Physiol 305, C481– 91 (2013). 23804202 PMC3761149
  42. [43]Yang J. Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy. Sci. Transl. Med 14, eabl5654 (2022). 35857625
  43. [44]Schroer A, Pardon G, Castillo E, Blair C & Pruitt B. Engineering hiPSC cardiomyocyte in vitro model systems for functional and structural assessment. Prog. Biophys. Mol. Biol 144, 3– 15 (2019). 30579630 PMC6919215
  44. [45]van Mil A. Modelling inherited cardiac disease using human induced pluripotent stem cell-derived cardiomyocytes: progress, pitfalls, and potential. Cardiovasc. Res 114, 1828– 1842 (2018). 30169602 PMC6887927
  45. [46]Musunuru K. Induced pluripotent stem cells for cardiovascular disease modeling and precision medicine: A scientific statement from the american heart association. Circ. Genom. Precis. Med 11, e000043 (2018). 29874173 PMC6708586
  46. [47]Kane RC, Farrell AT, Sridhara R & Pazdur R. United States Food and Drug Administration approval summary: bortezomib for the treatment of progressive multiple myeloma after one prior therapy. Clin. Cancer Res 12, 2955– 2960 (2006). 16707588
  47. [48]Métais A. Asb2α-Filamin A Axis Is Essential for Actin Cytoskeleton Remodeling During Heart Development. Circ. Res 122, e34– e48 (2018). 29374072
  48. [49]Mao Z & Nakamura F. Structure and Function of Filamin C in the Muscle Z-Disc. Int. J. Mol. Sci 21, (2020). PMC7216277 32295012
  49. [50]Collier MP HspB1 phosphorylation regulates its intramolecular dynamics and mechanosensitive molecular chaperone interaction with filamin C. Sci. Adv 5, eaav8421 (2019). 31131323 PMC6530996
  50. [51]Waldt N. Filamin A Phosphorylation at Serine 2152 by the Serine/Threonine Kinase Ndr2 Controls TCR-Induced LFA-1 Activation in T Cells. Front. Immunol 9, 2852 (2018). 30568657 PMC6290345
  51. [52]Klimek C. The Hippo network kinase STK38 contributes to protein homeostasis by inhibiting BAG3-mediated autophagy. Biochim. Biophys. Acta Mol. Cell Res 1866, 1556– 1566 (2019). 31326538 PMC6692498
  52. [53]Fang X, Bogomolovas J, Trexler C & Chen J. The BAG3-dependent and -independent roles of cardiac small heat shock proteins. JCI Insight (2019). PMC6478417 30830872
  53. [54]Muranova LK, Shatov VM, Slushchev AV & Gusev NB Quaternary Structure and Hetero-Oligomerization of Recombinant Human Small Heat Shock Protein HspB7 (cvHsp). Int. J. Mol. Sci 22, (2021). PMC8345930 34360542
  54. [55]Rauch JN & Gestwicki JE Binding of human nucleotide exchange factors to heat shock protein 70 (Hsp70) generates functionally distinct complexes in vitro. J. Biol. Chem 289, 1402– 1414 (2014). 24318877 PMC3894324
  55. [56]Clarke L. The international Genome sample resource (IGSR): A worldwide collection of genome variation incorporating the 1000 Genomes Project data. Nucleic Acids Res. 45, D854– D859 (2017). 27638885 PMC5210610
  56. [57]Marcus JH & Novembre J. Visualizing the geography of genetic variants. Bioinformatics 33, 594– 595 (2017). 27742697 PMC5408806
  57. [58]Chang KT, Guo J, di Ronza A & Sardiello M. Aminode: identification of evolutionary constraints in the human proteome. Sci. Rep 8, 1357 (2018). 29358731 PMC5778061
  58. [60]Miyaoka Y. Isolation of single-base genome-edited human iPS cells without antibiotic selection. Nat. Methods 11, 291– 293 (2014). 24509632 PMC4063274
  59. [61]Hockemeyer D. Genetic engineering of human pluripotent cells using TALE nucleases. Nat. Biotechnol 29, 731– 734 (2011). 21738127 PMC3152587
  60. [62]Regan JF A rapid molecular approach for chromosomal phasing. PLoS One 10, e0118270 (2015). 25739099 PMC4349636
  61. [63]Guan S, Price JC, Prusiner SB, Ghaemmaghami S & Burlingame AL A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling. Mol. Cell Proteomics 10, M111.010728 (2011). PMC3237081 21937731
  62. [64]Verschueren E. Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST. Curr Protoc Bioinformatics 49, 8.19.1– 8.19.16 (2015). PMC4378866 25754993
  63. [65]Teo G. SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. J. Proteomics 100, 37– 43 (2014). 24513533 PMC4102138
  64. [66]Knight JDR ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nat. Methods 14, 645– 646 (2017). 28661499 PMC5831326
  65. [67]Shannon P. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498– 2504 (2003). 14597658 PMC403769
  66. [68]Giurgiu M. CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res. 47, D559– D563 (2019). 30357367 PMC6323970
  67. [69]Cox J & Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol 26, 1367– 1372 (2008). 19029910
  68. [70]Teo G. SAINTq: Scoring protein-protein interactions in affinity purification - mass spectrometry experiments with fragment or peptide intensity data. Proteomics 16, 2238– 2245 (2016). 27119218
  69. [71]Hulsen T, de Vlieg J & Alkema W. BioVenn – a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 9, 488 (2008). 18925949 PMC2584113
  70. [73]Kusebauch U. Human srmatlas: A resource of targeted assays to quantify the complete human proteome. Cell 166, 766– 778 (2016). 27453469 PMC5245710
  71. [74]Pino LK The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev 39, 229– 244 (2020). 28691345 PMC5799042
  72. [76]Choi M. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524– 2526 (2014). 24794931
  73. [77]Ritchie ME limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). 25605792 PMC4402510
  74. [78]Phipson B, Lee S, Majewski IJ, Alexander WS & Smyth GK Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann Appl Stat 10, 946– 963 (2016). 28367255 PMC5373812
  75. [79]Sharma V. Panorama public: A public repository for quantitative data sets processed in skyline. Mol. Cell Proteomics 17, 1239– 1244 (2018). 29487113 PMC5986241
  76. [80]Pau G, Fuchs F, Sklyar O, Boutros M & Huber W. EBImage--an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979– 981 (2010). 20338898 PMC2844988
  77. [81]Schindelin J. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676– 682 (2012). 22743772 PMC3855844
  78. [82]Maddah M. A non-invasive platform for functional characterization of stem-cell-derived cardiomyocytes with applications in cardiotoxicity testing. Stem Cell Rep. 4, 621– 631 (2015). PMC4400609 25801505
  79. [83]Grafton F. Deep Learning Predicts Patterns of Cardiotoxicity in a High-Content Screen Using Induced Pluripotent Stem Cell–Derived Cardiomyocytes. BioRxiv (2021). doi: 10.1101/2021.03.23.436666 PMC8367386 34338636 · DOI
  80. [84]Ritz C, Baty F, Streibig JC & Gerhard D. Dose-Response Analysis Using R. PLoS One 10, e0146021 (2015). 26717316 PMC4696819
  81. [85]Hothorn T, Bretz F & Westfall P. Simultaneous inference in general parametric models. Biom J 50, 346– 363 (2008). 18481363

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Authors
Perez-Bermejo, J. A., Judge, L. M., Jensen, C. L., Wu, K., Watry, H. L., Truong, A., Ho, J. J., Carter, M., Runyon, W. V., Kaake, R. M., Pulido, E. H., Mandegar, M. A., Swaney, D. L., So, P.-L., Krogan, N. J., & Conklin, B. R.
Affiliations
Gladstone Institutes (US)
Corresponding author
Conklin, Bruce R.
ORCID
Venue
Nature Cardiovascular Research
Publisher
Springer Science and Business Media LLC
Published
2023-06-26
Volume
2
Issue
7
Pages
615-628
ISSN
2731-0590 (ISSN-L)
DOI
10.1038/s44161-023-00288-w
PMC
PMC12360617
OpenAlex
W4382055849
Cited by
12
References
3
Citations by year
2026: 2 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 1
FWCI
0.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Cardiovascular Research (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Foundation for the National Institutes of Health (R01-HL130533, R01-HL13535801, F32AI127291, P01 HL146366)
  • Roddenberry Foundation
  • Bristol-Myers Squibb Foundation (63376)
  • California Institute for Regenerative Medicine (TG2-01160)
  • U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (1K12HD072222)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Heat shock proteins research
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiomyopathy and Myosin Studies
Keywords
BAG3, heart failure, cardioprotection, haplotype blocks, cardiomyocytes, protein-protein interaction network, proteotoxic stress, cardiotoxic drugs, iPSC-derived cardiomyocytes, genetic association
MeSH terms
Alleles, Haplotypes, Heart Failure, Humans, Phenotype, Genetic Predisposition to Disease, Myocytes, Cardiac, Adaptor Proteins, Signal Transducing, Apoptosis Regulatory Proteins, Induced Pluripotent Stem Cells

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