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, BAG3C151R, alters protein and cellular function in human cardiomyocytes. Quantitative protein interaction analysis identified changes in BAG3C151R protein partners specific to cardiomyocytes. Knockdown of genes encoding for BAG3-interacting factors in cardiomyocytes followed by myofibrillar analysis revealed that BAG3C151R associates more strongly with proteins involved in the maintenance of myofibrillar integrity. Finally, we demonstrate that cardiomyocytes expressing the BAG3C151R variant have improved response to proteotoxic stress in a dose-dependent manner. This study suggests that BAG3C151R could be responsible for the cardioprotective effect of the haplotype block, by increasing cardiomyocyte protection from stress. Preferential binding partners of BAG3C151R may reveal potential targets for cardioprotective therapies.
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Data availability
Mass spectrometry raw data and Skyline files for the targeted proteomics quantification have been deposited to the ProteomeXchange Consortium using the Panorama Public partner repository (https://panoramaweb.org/28ZWVY.url). All other source data used for analyses are included in the manuscript files, except for the source image files, which are available from the authors upon reasonable request.
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Acknowledgements
We thank the Gladstone Stem Cell Core and the Gladstone Assay Development and Drug Discovery Core for providing their technical support and experimental expertise. We also would like to thank R. Thomas from the Gladstone Institutes Bioinformatics Core for advice on data analysis. We are also very grateful to J. Johnson, R. Huttenhain and G. Jang from the University of California, San Francisco (UCSF) and the Gladstone Institutes for their advice on affinity purification and mass spectrometry experiments. We thank J. Gestwicki and team (UCSF) for their scientific and technical advice. We thank A. Chan, A. Birk, E. Shin, C. Marley and S. Kang (Gladstone Institutes) for their technical support. We also thank F. Chanut from the Gladstone Institutes Editorial Services for feedback on manuscript preparation. We would also like to thank the scientific reviewers at Nature Cardiovascular Research for their invaluable feedback, which contributed greatly to improving this manuscript. J.A.P.-B. was supported by a Graduate Fellowship from Fundación ‘La Caixa’ (ID 100010434, LCF/BQ/US10/10230024), a Bristol Myers Squibb PCO Graduate Fellowship for Assessing Early Drug Liabilities (ID 63376) and a Predoctoral Fellowship from the American Heart Association (15PRE2570008507 and 13PRE1612001307). B.R.C. was supported by the National Institutes of Health (NIH) (R01-HL130533, R01-HL13535801 and P01-HL146366) and by funding from Tenaya Therapeutics. B.R.C. acknowledges support through a gift from the Roddenberry Foundation and Pauline and Thomas Tusher. N.J.K. was supported by P01 HL146366. R.M.K. was supported by NIH fellowship F32AI127291. L.M.J. was supported by a postdoctoral fellowship from the California Institute of Regenerative Medicine (TG2-01160) and a Career Development Award from the National Institute of Child Health and Development (1K12HD072222). The remaining authors received no specific funding for this work.
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J.A.P.-B., L.M.J., P.-L.S., N.J.K. and B.R.C. designed and supervised the study. J.A.P.-B., C.L.J., A.T., J.J.H., W.V.R. and K.W. performed cell line generation, cell culture and differentiation. J.A.P.-B., R.M.K., E.H.P. and D.L.S. performed affinity purification mass spectrometry experiments and analyses. J.A.P.-B., K.W. and M.A.M. performed siRNA knockdown panel and image analysis. J.A.P.-B. and L.M.J. performed bortezomib toxicity assay and analyses. All authors contributed to writing the manuscript and preparing the figures.
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B.R.C. is a founder of Tenaya Therapeutics (www.tenayatherapeutics.com), a company focused on finding treatments for heart failure, including genetic cardiomyopathies. B.R.C. and J.J.H. hold equity in Tenaya Therapeutics. The Krogan laboratory has received research support from Vir Biotechnology and F. Hoffmann-La Roche. N.J.K. has a consulting agreements with the Icahn School of Medicine at Mount Sinai. He is a shareholder of Tenaya Therapeutics, Maze Therapeutics and Interline Therapeutics. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Expanded genetic data graphs for rs2234962, BAG3C151R, along with conservation of residues affected by BAG3C151R and BAG3E455K variants.
(a) Zoomed out version of Fig. 1a, showing a window of 200KB. Dot color indicates type of nucleotide change. (b) Allele frequency map for rs2234962 depicting all 1000 Genomes populations. (c) Top: Diagram of BAG3 domain structure. Bottom: Amino acid conservation plot for matching BAG3 regions. Decreasing Relative Substitution Score regions (valleys) indicate sequences with high conservation across species and are annotated as Evolutionary Constrained Regions (ECRs). (D-E) Zoomed in regions for the ECRs around BAG3C151(d) and BAG3E455(e).
Extended Data Fig. 2 Co-precipitation profiles of different BAG3 variants overexpressed in a HEK293 cell background.
Dot size represents the amount of co-precipitated protein normalized across variants. Dot color represents absolute protein abundance (spectral counts). Dot rim represents statistical significance. Yellow colored variants are known pathogenic variants associated with DCM. Green variant is putative cardioprotective variant BAG3C151R. Red variant (BAG3P209L) is associated with skeletal myofibrillar myopathy. Black variants are not associated to DCM or any other pathology. Rightmost two columns depict data for BAG3 truncated variant without the BAG domain (BAG3_ ΔBAG) and for the BAG3 protein BAG domain only (BAG3_ΔBAG). For the truncated variants, no normalization by bait levels was performed. N = 5 separate pulldown experiments. Bayesian False Discovery Rate (FDR) obtained using SAINTexpress analysis software (see Methods section).
Extended Data Fig. 3 Generation of the isogenic cell lines carrying BAG3 variants and a 3xFLAG epitope tag fusion in the endogenous copy of the BAG3 gene.
(a) Workflow for the cell line generation. (b) Strategy for the insertion of a 3xFLAG epitope fusion at the C-terminal of the BAG3 gene. The BAG3C151R-FLAG variant was generated using the same process on a preexisting cell line bearing the C151R mutation. To generate the BAG3E455K-FLAG cell line, the homology arms were engineered to contain the SNP and insert it during recombination. (C-D) Genotypes of the single-cell clones picked for 3xFLAG insertion (c) and the co-segregation of the BAG3E455K variant (d). (e) Genotyping the products of the 3xFLAG insertion by PCR. These genotyping reactions was performed at least twice with identical results. (f) Cells with a heterozygous insertion of the 3xFLAG epitope tag also had a SNP in the other allele that extended the BAG3 protein product by 4 amino acids. (G-H) A droplet digital PCR phasing test was used to select clones that contained the desired SNP variants and the 3xFLAG C-terminal sequence in the same allele. The test used different probes (g) to generate an estimate of linked molecules for each cell line and probe combination (H; See Methods for more details) (i) Insertion of the 3xFLAG fusion in the BAG3 gene did not alter the protein levels. N = 4 and N = 3 cell extracts from separate differentiation batches; analysis performed using a one-way ANOVA. Data is presented as mean values +/− SEM.
Extended Data Fig. 4 Generation of a cell line with inducible expression of the BAG3WT protein.
(a) Diagram of the editing strategy. On a BAG3−/− cell background, a doxycycline-activated BAG3-3xFLAG expression cassette was inserted in the PPP1R12C (AAVS1) safe-harbor locus. (b) Western blot of the BAG3 expression on BAG3−/−:TetOn-BAG3WT-3xFLAG iPSCs with and without Doxycycline addition. This immunoblotting reaction was performed twice with similar results.
Extended Data Fig. 5 Affinity purification - mass spectrometry characterization of BAG3 binding partners in a cardiomyocyte background.
(a) Venn diagram of the high confidence BAG3WT protein-protein interactions identified in three cellular backgrounds. HEK293T cells had overexpressed baits, while iPSC have much lower levels of endogenous BAG3 expression than iPS-CM, which could have influenced the results. Each cell type specific dataset was scored separately against its own matched control(s). (b) Volcano plots depicting co-precipitation intensity in BAG3WT cardiomyocytes treated with Bortezomib (100 nM) relative to DMSO (1:10.000) for 24 hours. Horizontal dashed line indicates statistical significance threshold (adjusted p-value < 0.01) and vertical dashed lines indicate a fold change of 2. N = 4 biological replicates from separate differentiation batches. Analysis performed using a hypervariate linear model analysis, and p-values were adjusted for multiple comparisons (see Methods section). (c) Network diagram of the iPS-CM co-precipitation partners identified for BAG3 in this study. Nodes in orange indicate partners that significantly changed when pulling down BAG3E455K. Nodes in Green indicate partners that significantly changed when pulling down BAG3C151R. Dashed lines: known interactions in the iRefIndex database. (d) Venn diagram comparing the BAG3 binding partners identified in an iPS-CM background when using endogenous basal levels of expression, an overexpression system, or endogenous expression under proteotoxic stress. The graph highlights the importance of using endogenous expression for accurate characterization of binding partners, and the information gained from using a stress state.
Extended Data Fig. 6 Additional sample micrographs from BAG3 knockdown iPS-CM.
(a) BAG3 silencing by siRNA was effective at reducing protein levels (~85% reduction). N = 3 separate knockdown samples. Data is presented as mean values +/− SEM. (b) Additional sample micrographs from BAG3- and Scr-siRNA-treated iPS-CM, plus a no-siRNA condition. Orange arrowheads: BAG3 accumulation on myofibrillar breaks; white arrowheads: BAG3 accumulation on polar ends of cells; yellow arrowheads: iPS-CM displaying myofibrillar aggregation and collapse. These are sample micrographs from a set of randomly acquired images, 9 images per well across 3 separate wells. (c) Sample images in the same magnification used for the automated scoring analysis. Lower magnification allowed for faster acquisition and richer features to use directly in the scoring scheme, but higher magnification images were used elsewhere in this manuscript for easier viewing. These are sample micrographs from a set of 9 images per well across >20 separate wells (training wells for myofibrillar scoring).
Extended Data Fig. 7 Quality control and additional data from the myofibrillar scoring workflow
(a) Western blot demonstrating that titration of BAG3 siRNA results in decreasing BAG3 and HSPB8 (affected by BAG3 downregulation) levels. N = 2 separate knockdown experiments. (b) BAG3 sarcomere score inversely correlates with BAG3 protein levels. N = 2 biological replicates, with 9 images each. P-value and R2 obtained fitting a linear model. (c) BAG3 sarcomere score when MYBPC3 or cardiac troponin (cTnT) staining was used to train the model to score iPS-CM micrographs. Both myofibrillar protein stains resulted in clear discrimination of BAG3 and Scramble siRNA treated cells. N = 63 images per siRNA from 21 separate knockdown wells. (d) MYBPC3 staining intensity for iPS-CM treated with different doses of BAG3 or MYBPC3 siRNA. N = 2 separate knockdown experiments, with 9 images each. Data is presented as mean values +/− SEM. (e) Plot of the image scores that result from training based on BAG3 or DAPI staining. These stains do not display the same dynamic range as the myofibrillar (MYBPC3) staining. N = 9 for scramble; 18 for the rest. Data is presented as mean values +/− 2x standard deviation. (f) BAG3 Sarcomere Score for the knockdown of selected factors that were not identified in our AP-MS studies. Dots represent mean of 3 replicates from separate wells, each being the median score of 9 images from the same well. Data is presented as mean values +/− SEM. P-val cutoff: 0.05 using a one-way ANOVA with post-hoc Dunnett test. (g, h) Plot of the nuclei count(G) and BAG3 staining(H) intensities for the gene knockdowns used in the siRNA-myofibrillar scoring analyses. Dots represent mean of 3 replicates from separate wells, each being the median score of 9 images from the same well. Data is presented as mean values +/− SEM. P-val cutoff: 0.05 using a one-way ANOVA with post-hoc Dunnett test. (i) Plotting of the APMS intensity ratio for BAG3E455K differential interactors and their BAG3 Sarcomere Score. There is no statistically significant correlation. P-value obtained fitting a linear model. Pearson’s product-moment correlation: −0.27.
Extended Data Fig. 8 Sample images from selected siRNA knockdowns.
(a) HSPB8 knockdown was the only knockdown to significantly reduce BAG3 levels. FLNA, DDB1 and STK38 are BAG3C151R differential interactors whose knockdown resulted in sarcomere scores similar to BAG3 knockdown. ACTN2 and TNNT2 are well known sarcomere components that display low sarcomere scores similar to BAG3 knockdown, possibly due to reduced sarcomeric density and increased disarray and their previously described role as BAG3 client proteins. HSPB7 and DNAJB6 knockdowns displayed high sarcomere scores (similar to Scramble control). (b) Additional micrographs from selected conditions, with stains in separate panels. HSPB8 highlighting reduction of BAG3 levels, and STUB1/CHIP resulting in an increase of BAG3 elongated aggregates. For all images, scale bar = 100 µM. Magenta/Red: BAG3; Green: MYBPC3; Cyan: DAPI. These sample micrographs were selected from a set of randomly acquired images, 9 images per well across 3 separate wells.
Extended Data Fig. 9 BAG3C151R variant does not alter Bortezomib EC50 in undifferentiated iPSc, and BAG3 overexpression rescues bortezomib sensitivity phenotype in BAG3−/− cells.
(a) Bortezomib dose-response curves for the data used for EC50 calculations in iPS-CM. (b, c) Independent replication of the dose-response curve for Bortezomib in iPS-CM expressing the BAG3C151R variant in heterozygosity or homozygosity. N = 3 separate sets of cells. Data is presented as EC50 and error bars denote 95% confidence intervals. ****: P-value < 0.0001 and ***: p-value < 0.01 using one-way ANOVA with post-hoc Zidak correction. (d, e) Plot of calculated EC50 and dose response curves for Bortezomib in undifferentiated iPSc from different cell lines used in this study. N = 3 separate sets of cells. ****: P-value < 0.0001 and **: p-value < 0.01 using one-way ANOVA with post-hoc Zidak correction. The higher EC50 value for the BAG3−/− cell line is explained by the faster growth rate of that iPSc line. Data is presented as EC50 and error bars denote 95% confidence intervals. (f, g) Calculated EC50 with 95% confidence intervals and dose-response curves for Bortezomib in control (WT/WT), and BAG3−/− iPS-CM with and without BAG3 overexpression. N = 3 separate sets of cells. Data is presented as EC50 and error bars denote 95% confidence intervals. ****: P-value < 0.0001 using one-way ANOVA with post-hoc Zidak correction. Connecting line and error bands in panels A, C, E and G are based on prediction from fitted dose-response model and 95% confidence intervals, respectively.
Extended Data Fig. 10 BAG3 variants do not change BAG3 protein levels in iPS-CMs.
Capillary immunoassay (SimpleWestern) quantification of BAG3 protein levels in iPS-CM differentiated from iPSCs heterozygous or homozygous for the indicated BAG3 alleles, untreated or treated with Bortezomib (100 nM, 48 h incubation). N = 3 biological replicates from separate differentiation batches. ***: p-value < 0.01; ****: p-value < 0.0001. Two-way ANOVA with Sidak multiple comparison test. Data is presented as mean values +/− SEM.
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Perez-Bermejo, J.A., Judge, L.M., Jensen, C.L. et al. Functional analysis of a common BAG3 allele associated with protection from heart failure. Nat Cardiovasc Res 2, 615–628 (2023). https://doi.org/10.1038/s44161-023-00288-w
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DOI: https://doi.org/10.1038/s44161-023-00288-w
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