An Unbiased High-Throughput Screen to Identify Novel Effectors That Impact on Cardiomyocyte Aggregate Levels - PubMed (original) (raw)

An Unbiased High-Throughput Screen to Identify Novel Effectors That Impact on Cardiomyocyte Aggregate Levels

Patrick M McLendon et al. Circ Res. 2017.

Abstract

Rationale: Postmitotic cells, such as cardiomyocytes, seem to be particularly susceptible to proteotoxic stimuli, and large, proteinaceous deposits are characteristic of the desmin-related cardiomyopathies and crystallin cardiomyopathic diseases. Increased activity of protein clearance pathways in the cardiomyocyte, such as proteasomal degradation and autophagy, has proven to be beneficial in maintaining cellular and cardiac function in the face of multiple proteotoxic insults, holding open the possibility of targeting these processes for the development of effective therapeutics.

Objective: Here, we undertake an unbiased, total genome screen for RNA transcripts and their protein products that affect aggregate accumulations in the cardiomyocytes.

Methods and results: Primary mouse cardiomyocytes that accumulate aggregates as a result of a mutant CryAB (αB-crystallin) causative for human desmin-related cardiomyopathy were used for a total genome-wide screen to identify gene products that affected aggregate formation. We infected cardiomyocytes using a short hairpin RNA lentivirus library in which the mouse genome was represented. The screen identified multiple candidates in many cell signaling pathways that were able to mediate significant decreases in aggregate levels.

Conclusions: Subsequent validation of one of these candidates, Jak1 (Janus kinase 1), a tyrosine kinase of the nonreceptor type, confirmed the usefulness of this approach in identifying previously unsuspected players in proteotoxic processes.

Keywords: autophagy; desmin; heart failure; lentivirus; mice.

© 2017 American Heart Association, Inc.

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Conflict of interest statement

DISCLOSURES

No potential conflicts of interest were disclosed

Figures

Figure 1

Figure 1. High throughput screen

A, Screening protocol. Primary cardiomyocytes were harvested from 1–1.5 day old neonatal mice and plated (1×104 cells/well) in 96 well plates. Cells were infected with lentivirus overnight and, after 5 hours of recovery, infected with adenoviruses expressing the aggregate reporter (CryABR120G-tGFP) for 2 hours. After 48 hours, puromycin was added to remove uninfected cells and cells were fixed after an additional 48hrs and counterstained for imaging. B, Viability assay in the presence of viral infection and puromycin selection. The neonatal cardiomyocytes were infected with the indicated constructs. Each lentiviral shRNA construct contained a puromycin-resistance gene to gauge infectivity, as cells infected with lentivirus will be resistant to puromycin treatment. Treating uninfected or adenoviral infected cells resulted in 100% cell death, whereas infection with lentivirus largely preserved cell viability. The small (7%) decrease in Alamar Blue Fluorescence as a measure of cell viability (*P< 0.05), represents a small fraction of cells uninfected by lentivirus, which can be eliminated via puromycin selection.

Figure 2

Figure 2. Aggregate induction and validation of the signal:noise ratio for CryABR120G-driven aggregate formation in primary cardiomyocytes

A, High levels of either normal or mutant CryAB expression were driven off of the cytomegalovirus promoter after placement of the indicated construct into adenovirus. B, Quantitation of the relative signal strengths resulting from either CryABWT or CryABR120G was determined using immunofluorescence detected as a result of tGFP expression. C, Primary cardiomyocytes were plated at optimum densities for signal:noise generation, infected with either normal or mutant CryAB and the cells fixed and stained 3–4 days after infection. Cardiomyocytes were identified by immunofluorescent detection of troponin I (red) showing that >90% of the cardiomyocytes contained easily detectable (> 4-fold cytoplasmic CryABWTtGFP signal) CryAB-positive aggregates. D, Using BioTek Cytation 3 software, the algorithm defined aggregated protein on the basis of predefined filters and encircled them, creating yellow masks. To ensure measured aggregates were within cardiomyocytes as opposed to cardiac fibroblasts or endothelial contaminants, hierarchical detection was done using a cardiomyocyte-specific counterstain, TnI (red), such that only those cells where green aggregates were associated with red signal (green-over-red hierarchy) were scored and measured.

Figure 3

Figure 3. Inhibitory RNA knockdown of specific genes leads to decreased aggregate load in cardiomyocytes

A, Each well in each multi-welled plate was sampled in 16 separate locations and only those plates showing uniform cardiomyocyte density and aggregates were selected for further analyses. B, At least 1 well/plate was also treated with Torin1 to induce autophagy and confirm that a lack of aggregates could be scored on the particular plate. C, Typical data for those infections scored as primary “hits” for clones able to decrease aggregate load in cardiomyocytes subjected to the proteotoxic stimulus of CryABR120G expression. Arrows point to examples of regions that the algorithm scored as positive for aggregates. Shown is a plate of mixed cardiomyocytes and fibroblasts in which the “Control” panel was infected with Ad-CryABR120G but empty lentivirus. Shown in the “shRNA” panels are similar wells: an example of a well scored as a hit is shown. Note the staining across the different sampled areas of the well (top) and a striking decrease in aggregate content (yellow staining). Cardiomyocytes are stained red (TnI antibody) and nuclei blue (DAPI).

Figure 4

Figure 4. Systems biology prioritization of proteotoxicity regulators and Jak1 signaling

We first used the OMIM and EntrezGene databases to identify proteotoxicity-annotated genes, and then extended the identified gene sets to related biological processes using ToppGene and AltAnalyze. AltAnalyze NetPerspective network combining proteotoxicity related genes and primary hits with shared direct protein-protein (BIOGRID, grey lines), pathway (WikiPathways, KEGG, grey arrows) or transcriptional regulatory (PAZAR database, red arrows) were visualized. A, A large proportion of primary hits (red dots) have reported interactions with known proteotoxicity genes (blue dots), including 18, which interact with amyloid precursor protein (APP): APP interacting genes are highlighted with a yellow background. A side cluster emerged centered about Jak1 (shown). B, Jak1 knockdown resulted in >75% reduction in aggregates using 3 individual shRNAs directed at the same gene transcript, with no effect to cell viability. A side cluster emerged centered about Jak1 (shown). C, NRVMs were transduced with adenovirus encoding either GFP-CryABWT or GFP-CryABR120G or left untransduced (control; C). Five days after transduction, cells were harvested for protein extraction. A, Representative Western blots stained for p-Stat3 and Stat3. α-actinin was used as a loading control. D, p-Stat3 and Stat3 quantification. Data are presented as mean ± SEM with n=5.

Figure 5

Figure 5. siJak1 knockdown in NRVM

A, siRNA-mediated Jak1 knockdown in NRVMs was done as described in Methods and RNA quantitated via qPCR. B, Protein was isolated from the transfected cultures and assayed for Jak1 via Western blotting as described in Methods. C, The efficacy of Jak1 knockdown by siRNA was assayed with and without transfection of the cells with CryABR120G-containing adenovirus after 48–72 hours. D, Decreased Jak1 decreases aggregate load in NRVMs. Reduction of Jak1 levels resulted in a significant reduction in aggregate content after 48–72 hours. Data are depicted as mean ± SEM with **P<0.01 and ***P<0.001, unpaired Student’s t-test (n=3–4). E, NRVMs were transfected with scrambled siRNA (scr) or siJak1 and transduced with adenovirus (AdV) encoding wildtype CryAB or CryABR120G or left untransduced and aggregate accumulations visualized and F, quantitated after 7 days. G, Cytotoxicity measured 7 days after transduction. In E, nuclei are depicted in blue, GFP-CryAB in green and cardiac troponin I (cardiomyocyte marker) in red. Cell toxicity was measured using the lactate dehydrogenase assay as detailed in Methods. Triton X-100 was used as 100% and untransduced cells as 0%. Data are presented as mean ± SEM with **P<0.01 and ****P<0.0001, one-way ANOVA, Tukey’s post-hoc analysis. The number of images (F) or wells (G) is indicated in the histograms’ bars.

Figure 6

Figure 6. Proteasomal activity is enhanced after Jak1 knockdown

NRVMs were co-infected with adenoviruses containing Flag-CryABR120G and GFPu (a proteasome activity reporter). The cells were then transfected with siJak1 or a scrambled siRNA (scr) for 4 days and treated with the proteasome inhibitor epoxomicin for 15 hours. At 5 days post-infection, the cells were collected. A, Immunofluorescence with the cardiomyocyte marker TnI (red). Nuclei were counterstained with DAPI (blue). B, The relative GFP level in cardiomyocytes was quantitated using NIS-elements software. Control cells (ctr) were transfected with GFPu adenovirus only. *P<0.05, **P<0.01. C, Western blot analysis of GFP expression level. GAPDH was used as a loading control. D, The Western blot was quantitated. *P<0.05, **P<0.01.

Figure 7

Figure 7. Jak1 knockdown does not increase autophagic flux

Cardiomyocytes were transfected with either scr or siJak1 and subsequently infected with CryABR120G-tGFP as detailed in Methods. Forty-eight hours after transfection, cells were treated with 30 nmol/L Bafilomycin A1 for 2 hours before being harvested in SDS lysis buffer (Methods). A, Representative Western blot of indicated proteins. Quantitation of CryABR120G-tGFP in B, p62 in C, and LC3-II in D. Data were normalized to α-actinin and are depicted as mean ± SD with *P<0.05 and ***P<0.001 against scr and +++P<0.001 against siJak1, one-way ANOVA with Tukey’s post-hoc analysis. n=3–4.

Comment in

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