Dynamic map illuminates Hippo to cMyc module crosstalk driving cardiomyocyte proliferation

  1. Bryana N. Harris
  2. Laura A. Woo
  3. R. Noah Perry
  4. Mete Civelek
  5. Matthew J. Wolf
  6. Jeffrey J. Saucerman

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Abstract

Cardiac diseases are characterized by the inability of adult mammalian hearts to overcome the loss of cardiomyocytes (CMs). Current knowledge in cardiac regeneration lacks a clear understanding of the molecular systems determining whether CMs will progress through the cell cycle to proliferate. Here, we developed a computational model of cardiac proliferation signaling that identifies key regulators and provides a systems-level understanding of the cardiomyocyte proliferation regulatory network. This model defines five regulatory networks (DNA replication, mitosis, cytokinesis, growth factor, hippo pathway) of cardiomyocyte proliferation, which integrates 72 nodes and 88 reactions. The model correctly predicts 72 of 76 (94.7%) independent experiments from the literature. Network analysis predicted key signaling regulators of DNA replication (e.g., AKT, CDC25A, Cyclin D/CDK4, E2F), mitosis (e.g., Cyclin B/CDK2, CDC25B/C, PLK1), and cytokinesis, whose functions varied depending on the environmental context. Regulators of DNA replication were found to be highly context-dependent, while regulators of mitosis and cytokinesis were context-independent. We also predicted that in response to the YAP-activating compound TT-10, the Hippo module crosstalks with the growth factor module via PI3K, cMyc, and FoxM1 to drive proliferation. This prediction was validated with inhibitor experiments in primary rat cardiomyocytes and further supported by re-analysis of published data on YAP-stimulated mRNA and open chromatin of Myc from mouse hearts. This study contributes a systems framework for understanding cardiomyocyte proliferation and identifies potential therapeutic regulators that induce cardiomyocyte proliferation.

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    Reply to the reviewers

    We thank the reviewer for their careful evaluation and constructive criticisms of our manuscript. We also appreciate the positive review by all three reviewers. The reviewers noted:

    • "The computational model in this manuscript can be a tool to discover unknown molecular pathways interactions in cardiomyocyte proliferation."
    • "This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression."
    • "The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation." We have responded to all reviewer comments and have outlined the corresponding additions and changes to the manuscript.

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    Summary:

    In the manuscript by Harris et al. titled "Dynamic map illuminates Hippo to cMyc module crosstalk driving cardiomyocyte proliferation," the authors developed a computational model of cardiac proliferation signaling that incorporates various regulatory networks (cytokinesis, mitosis, DNA replication, etc.) to predict molecular drivers (genes) that support cardiomyocyte proliferation. Published research articles on cardiomyocyte proliferation in multiple contexts (different species, ages, in vitro and in vivo, etc) were used to build and validate the computational model. The authors found using their model that different processes during cardiomyocyte proliferation may or may not be context-dependent. For example, DNA replication is regulated differently in conditions with high Neuregulin compared to high YAP, whereas mitosis and cytokinesis regulation is similar in these conditions. To experimentally validate their model, the authors used an in vitro system to test the effects of YAP on 3 connected pathways; in the context of YAP activation, inhibition of PI3K, cMyc, or FoxM1 was combined to assay cell-cycle markers in cultured neonatal rat ventricular cardiomyocytes. Cell-cycle marker expression in cardiomyocytes was attenuated by inhibition of cMyc or PI3K, suggesting that these pathways are involved in YAP-mediated cardiomyocyte proliferation. While this model can be a good tool to gain new insights on interactions between molecular pathways, there are a few questions to be addressed prior to publication.

    We appreciate the Reviewer's positive remarks about important findings in our manuscript and the ability of our model to be a tool to gain insights on interactions between molecular pathways to regulate cardiomyocyte proliferation. We have strived to address their points, as shown below.

    Major Comments:

    1. One of the potential uses for this computational model is to discover new interactions between known pathways that are involved in cardiomyocyte proliferation. However, this would be more powerful if factors such as species, age (neonate vs. adult), and experimental design (in vivo vs. in vitro) were accounted for, as new node inputs or a combination of existing node input activity values. This is very important because cardiomyocyte proliferation can drastically vary depending on these experimental factors. We agree that future extensions of this model accounting for species, age, and experimental design may enable an understanding of how these factors regulate proliferation. While this model's predictions are most relevant to immature cardiomyocytes, we note that it is the first systems model of the molecular network regulating cardiomyocyte proliferation. We extensively validated it against neonatal cardiomyocyte literature and then made new predictions regarding Hippo-cMyc pathways, which we validated in new cardiomyocyte experiments and against data in adult mice. This provides a strong foundation for future extensions. We now address this potential in the Discussion:

    "While our model's predictions are most relevant to immature cardiomyocytes, it is the first systems model of the molecular network regulating cardiomyocytes. In the future, we hope that we and others may extend this model to identify how factors like species, age, and experimental design regulate proliferation. However, these endeavors would span multiple manuscripts, and the field currently lacks sufficient stage-specific data. For example, a previous foundational computational model of cardiomyocyte electrophysiology (Luo and Rudy, Circ Res 1994) focused on adult guinea pigs. This model became the foundation for a range of developmental and species-specific models in electrophysiology (Tusscher et al, AJP 2004,; Courtemanche eta al, AJP 1998; Paci et al, ABME 2013). We believe the open availability of our code will enable similar dissemination and extension for additional factors." Line 651-661

    For reference:

    Luo and Rudy, Circ Res 1994, >2.1k citations; Tusscher et al, AJP 2004, >1.7k citations; Courtemanche et al, AJP 1998, 1.5K citations; Paci et al, ABME 2013, 147 citations

    The finding that cardiomyocyte proliferation is context-dependent is very exciting and warrants further investigation/validation. The authors state that different sets of nodes/modules are affected by neuregulin activation compared to YAP activation. This should be experimentally validated - qPCR/Western blots on sets of genes that are predicted to be differentially regulated in the high neuregulin context vs the high YAP context.

    We agree that the model's prediction of context-dependent cardiomyocyte proliferation is very exciting. To further validate these predictions, we have performed additional experiments to validate context-dependent changes of phospho-ERK treated with Nrg and TT10. Using a high throughput capillary electrophoresis western blot system, we observed that with a short treatment of 30 min, Nrg induces greater phosphor-ERK compared to TT10, which validates our model predictions at short time intervals. Additionally, the model predicted greater p-AKT with 30 min treatment with Nrg compared to TT10. To validate this prediction, we now compare to Western blots from Hara et al. examining p-AKT in Nrg and TT10-treated cells. Validating our model predictions, their data show that Nrg induces greater p-AKT than with TT10. We have added new panels C, D, and E to Figure 4.

    Figure 4: Influence of node knockdowns shifts with context, revealing crosstalk from Hippo to Growth Factor modules.

    (A) Total influence of node knockdowns on the DNA replication, mitosis, and cytokinesis modules, compared across multiple signaling contexts: baseline, high Nrg, and high YAP. Total influence sums the overall effect of a node knockdown on a network module. (B) The total influence of each network module varies depending on whether a basal state, high Nrg, or high YAP signaling context is applied. (C) Capillary electrophoresis western blot for phosphorylated ERK, beta-actin, and GAPDH from neonatal cardiomyocytes treated with Nrg or TT10 for 30 min. (D) Model predictions of AKT and ERK activity of acute response to Nrg or TT10 (time constants for gene expression set to 100). (E) Quantification of effects of Nrg or TT10 treatment on p-ERK (from Western blot in panel C, n = 3) or p-AKT (from Western blot from (Hara et al., 2018), n = 1).

    The overall description of the model can be improved. For example, how are the input and parameters set to validate or predict different experimental observations? What is the steady state activity of each of the nodes and does this make sense biologically? Including a few more sentences to explain the model would help with overall understanding for an uninformed reader.

    We have addressed the following questions provided by the reviewer in the methods and results section of the manuscript:

    How are the input and parameters set to validate or predict different experimental observations?

    __ __"At baseline, input reaction weight parameters (w) were set based on information from the literature describing the baseline state of these inputs in the heart (each input reaction weight can be found in Supplemental File 1). To simulate experiments with biochemical stimuli, input reaction weights were increased to 0.8 or 1. To simulate experiments with inhibition or knockdown, the corresponding maximum species value (ymax) was set to 0.1 or 0. Complete annotations for all validation simulations are provided in Supplemental File 2." Line 154-160

    What is the steady-state activity of each of the nodes and does this make sense biologically?

    "Steady-state activity of model nodes was obtained by running the model until there was a __ __

    Minor Comments:

    Line 124 - The use of "species" and "reactions" is confusing to uninformed readers. Do you mean nodes and interactions/bridges?

    We now further clarify these terms in the manuscript:

    "As in past network models (Zeigler et al., PMID 27017945; Tan et al., PMID 29131824; Kraeutler et al PMID 21087478), species (or nodes) refer to a small molecule, gene, protein, or process. Reactions (or edges) are activating or inhibiting relationships between network species." Line 143-146

    Line 130 - I could not find Supplementary File 2, which includes the references

    We apologize for the error. Supplementary File 2 references articles and resources used to build the model. These files are now attached.

    Line 257 - What is the meaning of the directional arrows in Fig 1A?

    We clarified the Fig 1A legend:

    "Arrows between modules represent one or more reactions that link species from one module to species in another module. " Line 594-595

    Line 301 - Unclear what default values mean here. Please elaborate and provide an example of how this is reasonable.

    We have added further descriptions of default values in reference to the parameters to the manuscript.

    "A previous study identified default values of the parameters (ymax, EC50, W, etc.) that most accurately predict the results of knockdown screens compared to a model where all biochemical parameters were measured experimentally (Kraeutler et al 2010). Subsequent studies started from these default values and further demonstrated that model accuracy was robust to random variation in the parameters (Tan et all 2017, Zeigler et al 2017). Consistent with these prior models, we performed robustness analysis that demonstrates that the CM proliferation model accuracy (compared against 78 experiments) is maintained at >80% with up to 35% variation in ymax, 30% variation with w, and a variation of >50% with EC50 (Figure S4)." Line 305-312

    Supplemental FigS2 - Why would knockdown of PKA, Lats1 or SMAD3 have the exact same effects on node activation? This is seen with multiple other genes was well (IGF and FGF for example).

    PKA, Lats1, and SMAD3 all inhibit cell cycle progression in part through cMyc. Therefore, their knockdown have similar effects on downstream signaling and proliferation. Similarly, IGF and FGF both stimulate Ras and PI3K via similar mechanisms, which is consistent with experimental studies of IGF- and FGF-dependent proliferation.

    Reviewer #1 (Significance (Required)):

    The computational model in this manuscript can be a tool to discover unknown molecular pathway interactions in cardiomyocyte proliferation. The novelty lies in the ability to adjust any parameter or the entire setting/context. While this sounds very exciting, improvement of the model to account for age, experimental conditions (in vivo vs in vitro), and species (human, pig, mouse) could lead to increase prediction accuracy. Additionally, more robust validation of context-dependent interactions between signaling pathways would also increase overall enthusiasm for the manuscript. Readers interested in a systems biology approach to cardiomyocyte proliferation, or researchers probing molecular interactions during cardiomyocyte proliferation would be interested in using such a model to discover novel contexts/combinations in which cardiomyocyte proliferation is more likely.

    The reviewer comes from a varied training background and is qualified to evaluate this manuscript in full - BS in biomedical engineering and mathematics. PhD in biomedical engineering (molecular biology, cardiac electrophysiology). Postdoctoral training in cardiac regeneration and immunity.

    We appreciate the positive comments about our model of the cardiomyocyte proliferation network. As described above, we believe that we have addressed the concerns with additional experimental validation.

    The manuscript submitted by Harris and colleagues collates a molecular map of cardiomyocyte cell cycle activation through mathematical modeling of previously published experimental results. They attempt to validate the constructed model several ways: 1) through testing results compiled from additional literature, 2) through in vitro analysis, and 3) through in vivo supporting data. When validating through additional literature the model proves quite reliable particularly for prediction of effects on synthesis, mitosis, and cytokinetic entry, but was less reliable (or insufficiently tested) at predicting completion of these stages as determined by polyploidization and multinucleation. A potentially novel observation which arose from the model - that hippo nodule connects to the growth factor nodule through PI3K, Myc, and FoxM1 - was partially confirmed with in vitro experiments, though a few experiments are warranted.

    We appreciate the reviewer's recognition of the important contributions of this model of the cardiomyocyte proliferation network. We have addressed the concerns below.

    Major comments:

    • The model is admittedly weakest in its handling of completion of cytokinesis resulting in new daughter cells (i.e. proliferation) versus failure to complete either M phase or cytokinesis resulting in the much more common cellular phenotypes - polyploidy and multinucleation. Notably, very few molecules were "tested" for this output (figure 2) and this proved the least reliable aspect of the model/map. I wonder if the authors consulted the literature on somatic polyploidization at all when building the model (files not provided as indicated, see minor comment 1 below )? And if not, would doing so help strengthen this arm of their map? There are some great reviews on the topic (see PMIDs 25921783, 23849927, 30021843) - while admittedly much of the work is done on other cell types (i.e. trophoblast giant cells and hepatocytes) maybe understanding the molecular intricacies in these cells could be incorporated to strengthen the predictive model in cardiomyocytes. Notably, PMID 23849927 even provides a table of citations about key nodes in the model influencing polyploidy. To validate this model, we used entirely cardiomyocyte specific studies. We appreciate the reviewer's reference to PMID 23849927, which enabled us to add two additional experiments to the validation table in Figure 2. That paper found that overexpression of either cMyc or cyclin D increases polyploidy, which both matched our new simulations in the updated Figure 2.

    Motivated by the reviewer's citation of PMID 23849927, we further validated the model against polyploidization data from multiple cell types, finding an 85.7% accuracy (6 of 7 experiments) as now shown in Supplementary Figure S7.

    We included an additional discussion of polyploidization in the manuscript.

    "Our model validation is notably weakest in predicting experiments on polyploidization, indicating a need to better characterize polyploidy and cytokinesis pathways. Because such data are limited in cardiomyocytes, we performed an additional validation against polyploidization experiments from other cell types as summarized in Pandit et al. Our CM proliferation model predicted 85% (6 of 7) experiments. Future experiments are needed to identify conserved or differential mechanisms of polyploidization and cytokinesis in cardiomyocytes." Line 587-594

    • Paragraph on the cytokinesis module (lines 364-377) is confusing - not sure what the takeaway message is. Also, while progression through G1/S and G2/M are "required" for cytokinesis they on their own are not sufficient (lines 366-368), this perhaps goes back to major comment 1. We agree this sentence was confusing, it was meant to be introductory rather than stating a particular result. We removed that sentence and further revised our description of the output module to clarify the model structure:

    "The output module interlinks the phenotypic outputs of the other modules, representing how experimentally measured aspects of cell cycle activity (DNA replication by EdU or Ki67), mitosis by phospho-Histone 3 (pHH3), abscission by cytokinetic midbody converge on polyploidy, binucleation, or cytokinesis (e.g. completed proliferation) (Figure 1G)." Line 283-286

    Minor comments:

    • Use of the word "Proliferation" should be reserved for situations where the authors can clearly say a new daughter cell was born. In many instances, "cell cycle activation" or "cell cycle progression" might be better terms. As suggested by the reviewer, we now use "cell cycle progression" in 7 instances, reserving "proliferation" for cell cycle progression through cytokinesis. In the remaining 90 instances, we refer to proliferation based on the model's predictions of completed cell division based on the combined DNA replication, mitosis, and cytokinesis pathways in the "output module". We retain "proliferation" in the title because the model encompasses the entire proliferation process from cell cycle entry through cytokinesis.

    • Supplementary Files 1 & 2 or Supplementary Document 2 were not provided or not found during review, thus we were unable to confirm which literature were used to build and validate the model. Thank you, we have included Supplementary Files 1 and 2 along with supplementary document 2 in the submission.

    • Figures are too small, particular Figure 1 We have enlarged Figure 1.

    • "E2F" should be specified as E2F1-3 yield quite distinct results from E2F7/8. We have changed "E2F" to "E2F123"

    • Text corresponding to Figure 5 does not reference most of the panels in the Figure. i.e. figures are not "cited" in the text We have made sure that each panel in Figure 5 is referenced in the text addressing the figure. We have also bolded all references to Figure 5.

    • Figure 5C - why is there no bars for PI3K. Text claims it was predicted by the model, but the data are missing? We apologize for the confusion regarding Figure 5C, in which the bar for PI3K was near zero. We now clarify this in the legend.

    "Predicted DNA replication and mitosis activity is close to zero when PI3K is inhibited alone and when PI3K is inhibited in combination with TT10 treatment."

    • Data provided in figure 5D & E are insufficient on their own to claim "proliferation". Perhaps adding total cardiomyocyte numbers, where one would expect expansion compared to control. We agree that Ki67 and pH3 are not sufficient to claim "proliferation", so we modified the Figure 5 legend to:

    "Prediction and experimental validation of cardiomyocyte cell cycle progression mediated by the Hippo pathway via PI3K, cMyc, and FoxM1."

    We previously found that cardiomyocyte numbers without live tracking are not sufficient to robustly measure proliferation (Woo et al, J Mol Cardiol, 2019).

    • Consider adding a details about the p-values to the figure legend in figure 5. Thank you for this suggestion p-value information has been added to the legend of Figure 5. We use *** Our literature-based validation in Figure 2 focused on 78 experiments that examined well-established and corroborated aspects of cardiomyocyte proliferation. Later in the paper, we focused on a newly predicted mechanism of cardiomyocyte proliferation involving small number of comparisons that would naturally have a lower a priori probability of validation in vitro neonatal experiments (Figure 5) and adult mouse experiments (Figure 6). Therefore, in the revised text we focus on the specific comparisons rather than statistics.

    "Based on predictions from this validated model, we hypothesized that YAP drove proliferation via PI3K, cMyc and FoxM1. To test this model-driven hypothesis, we accurately predicted TT10-induced DNA replication that is suppressed by inhibition of PI3K, cMyc and to a lesser extent FoxM1 (Figure 5D). These model predictions were further validated using RNA-seq and ATAC-seq data from adult mouse hearts showing that constitutively active YAPS5A induces expression of Myc and FoxM1 as well as increased chromatin accessibility at PI3Kca and Myc." Line 454-468

    In the discussion, we add:

    "Further model revision is needed based on these molecular mechanisms of YAP-TEAD-Myc interactions to distinguish between chromatin accessibility, transcription factor binding, and gene expression." Line 649-651

    As it stands now, the generated map largely constitutes already known details offering few if any new insights; however, if updated as new results arise AND made available as a public tool, the model could prove to be a highly valuable resource to the field.

    We thank the reviewer for recognizing our model as a valuable resource and public tool. We have made our model publicly available on GitHub at https://github.com/saucermanlab/Cardiomyocyte-Proliferation-Network.

    The virtual knockdown screens in Figure 3, 4 and 5 provide a wide range of new insights, which we clarify in new text.

    "Because this is a literature-based network model, each component or direct interaction has been studied individually. However, our model makes much broader predictions of how these components interact to regulate proliferation, beyond the ~30 papers available for validation on the response of this system to perturbations shown in Figure 3. For example, Supplemental Figure S3 provides ~5000 predictions of how each protein responds to knockdown of every other protein. These predictions led to new insights into how YAP regulates proliferation via cMyc (experimentally validated in Figure 5 in vitro and Figure 6 in vivo), as well as many other insights that can be validated in future studies. These future studies will be aided by the open-source availability of our model on GitHub." Line 563-571

    __ I have expertise in cardiomyocyte cell cycle and polyploidization.__

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    The authors generated a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. Interestingly, the model correctly predicts the outcome of 95% independent experiments from the literature. The model also elucidated crosstalk between the growth factor and Hippo modules and the authors identified key hubs for which the Hippo signaling pathway regulates cardiomyocyte proliferation. The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation.

    Reviewer #3 (Significance (Required)):

    This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. The program may provide a convenient framework prioritizing potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation. However, the overall impact of the study appears modest since it is unclear whether the study allows elucidation of the unique properties of cardiomyocyte proliferation in adult hearts (i.e. they hardly proliferate) and the validation study was conducted only in neonatal myocytes. The field has seen many studies with neonatal myocytes but the findings are not always translatable to adult cardiomyocytes.

    We thank the reviewer for recognizing the importance of our work that provides a framework for prioritizing potential signaling drivers of therapeutic modulators of CM proliferation.

    Neonatal studies are the most prevalent with cardiomyocyte proliferation literature, making it the most robust starting point that allows for rigorous validation. Based on the high performance of the model against neonatal data, in the future we expect this model to be a stepping stone towards adaptions to understand differences in the adult cardiomyocyte proliferation network. We have updated our model discussion on future directions on this point.

    "While our model's predictions are most relevant to immature cardiomyocytes, it is the first molecular network model of cardiomyocyte proliferation. In the future, this model will enable extensions to identify how factors like species, age and experimental design regulate proliferation. However, such endeavors would span multiple manuscripts and the field currently lacks sufficient stage-specific data. For example, the highly influential computational model of Luo and Rudy focused on adult guinea-pig cardiomyocyte electrophysiology (Luo and Rudy, Circ Res 1994). That model became the foundation for a wide range of development- and species-specific models in electrophysiology (Tusscher et al, AJP 2004,; Courtemanche eta al, AJP 1998; Paci et al, ABME 2013). We believe the open availability of our code will enable similar dissemination and extension for additional factors regulating cardiomyocyte proliferation." Line 655-665

    The authors described that "Literature articles used for model development came from multiple cell types due to limited CM data." It is unclear whether this would allow the identification of unique mechanisms present in cardiomyocytes. As the authors admitted, the fact that the model predictions and experimental observations for polyploidization did not match clearly suggests the complexity surrounding the possibility of cell phenotypes in cardiomyocyte populations. The authors could have addressed whether this model allows the identification of unique mechanisms mediating cardiomyocyte proliferation in the adult heart.

    Although we necessarily included literature on other cell types to support network reactions, all of the experimental validation in Figure 2 was with cardiomyocyte data (~33 publications). 80% of experiments were from neonatal CMs, 10% from adult CMs, 5% from in vivo studies, and the other 5% from hiPSC-derived cardiomyocytes as annotated in Supplemental File 3.

    At this time, there is insufficient data from which to make a model focused only on adult CMs. The mode's open-source availability enables future extensions that examine age and species-dependent mechanisms of cardiomyocyte proliferation. We updated the manuscript, addressing the ability of our model to adapt to new information.

    "This model provides an initial network framework for integrating additional discoveries in cardiomyocyte proliferation. As more information becomes available in cardiomyocyte proliferation literature the model can be adapted. Additionally, the field can use our open-sourced model to adapt this model to other developmental stages or species." Line 671-674

    Acknowledging the limited data on cardiomyocyte polyploidy, we performed a new separate validation of 7 experiments in non-myocytes from PMID 23849927, finding an 85.7% accuracy (new Supplementary Figure S7).

    Please provide more information regarding the rationale for having six modules in the authors' model, including the growth factor and the Hippo pathway.

    We revised the text to clarify the motivation for the six modules:

    "Our initial review of the literature indicated multiple complex molecular pathways that regulate cardiomyocyte proliferation, including growth factors, Hippo signaling, G1/S transition, G2/M transition, or cytokinesis pathways (Hashmi and Ahmad, PMID: 31205684; Payan et al, PMID: 30930108; Moral et al., PMID: 35008660; Wang et al., PMID: 30111784; Johnson et al., PMID: 34360531). Several review articles (Zheng et al, PMID: 32664346; Mia and Singh, PMID: 31632964; Diaz Del Moral et al, PMID: 35008660; Besson et al, PMID: 18267085; Wang et al, PMID: 19216791)) also organized the literature based on these distinct pathways or processes, which we used to define the boundaries of the six modules. However, how these molecular pathways work together is not well characterized. Therefore, we designed the model to incorporate each of these established modules and how they work together to drive cardiomyocyte proliferation." Line 550-557

    The extent of cardiomyocyte proliferation at baseline is very low in the adult heart. The model identified 25 nodes that may influence baseline proliferation. Is there any evidence to support the involvement of these mechanisms in baseline cardiomyocyte proliferation in vivo?

    We agree with the reviewer that proliferation at baseline is very low in the adult heart, and also rather low in neonatal cardiomyocytes. As shown in Figure S4A, we performed a virtual knockdown screen under baseline conditions that showed that no genetic knockdowns caused a substantial decrease in DNA replication or cytokinesis, consistent with a low baseline proliferation rate.

    We describe this point about baseline proliferation in revised text:

    "A complete virtual knockdown screen of the model was done under baseline conditions in Figure S4A, which showed that no knockdowns caused substantial decreases in DNA replication or cytokinesis. This is consistent with a low baseline proliferation rate described in cardiomyocyte literature." Line 354-357

    The validation study was conducted with neonatal rat ventricular cardiomyocytes. This study could have been repeated with adult cardiomyocytes since they are more resistant to proliferation and, thus, the Myc may not work as expected. In addition, the authors could have commented on the mechanism through which chromatin opening and YAP allow transcription of Myc in the heart.

    We agree that Myc is likely less proliferative in adult hearts. While our model was extensively validated against neonatal cardiomyocytes (Figure 2 for literature, Figure 5 for new neonatal experiments), only 10% of literature-based validations in Figure 2 are from adult cardiomyocytes due to limited data. However, in Figure 6 we validate YAP-dependent signaling to Myc, PI3K, and FOXM1 using RNA-seq and ATAC-seq data from Monroe et al. from adult mouse cardiomyocytes in vivo. While molecular mechanisms of YAP regulation of Myc are not characterized in the heart, based on the reviewer's suggestion, we add new discussion on YAP-Myc interaction in other cells:

    "Overexpression of Myc induces cardiomyocyte proliferation in vitro and in vivo in several contexts, with open chromatin and Myc binding near mitotic genes (PMID: 32286286). But to our knowledge, crosstalk of YAP with Myc has not been reported in the heart. Our model prediction and experiments in neonatal cardiomyocytes support a YAP-TEAD-Myc pathway for cardiomyocyte proliferation. Further, our analysis of ATAC-seq and RNA-seq data from Monroe et al. validate that YAP induces Myc chromatin availability and gene expression in adult mouse hearts.

    In MDA-MB-231 breast cancer cells, YAP/TAZ/TEAD bind directly to Myc enhancers through chromatin looping, with decreased acetylation of H3K27 and cell proliferation upon YAP/TAZ knockdown (26258633). YAP-TEAD-Myc signaling regulates the proliferation of cancer cells (26258633), tumorigenesis (29416644), and the growth of Drosophila imaginal discs (20951343). In the future, computational models and experiments are needed to better resolve how YAP promotes proliferation via Myc in the adult heart, including regulation by Mycn (30315164), cyclin T1 (32286286)."Line 632-644

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    Referee #3

    Evidence, reproducibility and clarity

    The authors generated a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. Interestingly, the model correctly predicts the outcome of 95% independent experiments from the literature. The model also elucidated crosstalk between the growth factor and Hippo modules and the authors identified key hubs for which the Hippo signaling pathway regulates cardiomyocyte proliferation. The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation.

    Significance

    This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. The program may provide a convenient framework prioritizing potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation. However, the overall impact of the study appears modest since it is unclear whether the study allows elucidation of the unique properties of cardiomyocyte proliferation in adult hearts (i.e. they hardly proliferate) and the validation study was conducted only in neonatal myocytes. The field has seen many studies with neonatal myocytes but the findings are not always translatable to adult cardiomyocytes.

    The authors described that "Literature articles used for model development came from multiple cell types due to limited CM data." It is unclear whether this would allow the identification of unique mechanisms present in cardiomyocytes. As the authors admitted, the fact that the model predictions and experimental observations for polyploidization did not match clearly suggests the complexity surrounding the possibility of cell phenotypes in cardiomyocyte populations. The authors could have addressed whether this model allows the identification of unique mechanisms mediating cardiomyocyte proliferation in the adult heart.

    Please provide more information regarding the rationale for having six modules in the authors' model, including the growth factor and the Hippo pathway.

    The extent of cardiomyocyte proliferation at baseline is very low in the adult heart. The model identified 25 nodes that may influence baseline proliferation. Is there any evidence to support the involvement of these mechanisms in baseline cardiomyocyte proliferation in vivo?

    The validation study was conducted with neonatal rat ventricular cardiomyocytes. This study could have been repeated with adult cardiomyocytes since they are more resistant to proliferation and, thus, the Myc may not work as expected. In addition, the authors could have commented on the mechanism through which chromatin opening and YAP allow transcription of Myc in the heart.

  3. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #2

    Evidence, reproducibility and clarity

    The manuscript submitted by Harris and colleagues collates a molecular map of cardiomyocyte cell cycle activation through mathematical modeling of previously published experimental results. They attempt to validate the constructed model several ways: 1) through testing results compiled from additional literature, 2) through in vitro analysis, and 3) through in vivo supporting data. When validating through additional literature the model proves quite reliable particularly for prediction of effects on synthesis, mitosis, and cytokinetic entry, but was less reliable (or insufficiently tested) at predicting completion of these stages as determined by polyploidization and multinucleation. A potentially novel observation which arose from the model - that hippo nodule connects to the growth factor nodule through PI3K, Myc, and FoxM1 - was partially confirmed with in vitro experiments, though a few experiments are warranted.

    Major comments:

    1. The model is admittedly weakest in its handling of completion of cytokinesis resulting in new daughter cells (i.e. proliferation) versus failure to complete either M phase or cytokinesis resulting in the much more common cellular phenotypes - polyploidy and multinucleation. Notably, very few molecules were "tested" for this output (figure 2) and this proved the least reliable aspect of the model/map. I wonder if the authors consulted the literature on somatic polyploidization at all when building the model (files not provided as indicated, see minor comment 1 below)? And if not, would doing so help strengthen this arm of their map? There are some great reviews on the topic (see PMIDs 25921783, 23849927, 30021843) - while admittedly much of the work is done on other cell types (i.e. trophoblast giant cells and hepatocytes) maybe understanding the molecular intricacies in these cells could be incorporated to strengthen the predictive model in cardiomyocytes. Notably, PMID 23849927 even provides a table of citations about key nodes in the model influencing polyploidy.
    2. Paragraph on the cytokinesis module (lines 364-377) is confusing - not sure what the takeaway message is. Also, while progression through G1/S and G2/M are "required" for cytokinesis they on their own are not sufficient (lines 366-368), this perhaps goes back to major comment 1.

    Minor comments:

    1. Use of the word "Proliferation" should be reserved for situations where the authors can clearly say a new daughter cell was born. In many instances "cell cycle activation" or "cell cycle progression" might be better terms.
    2. Supplementary Files 1 & 2 or Supplementary Document 2 were not provided or not found during review, thus we were unable to confirm which literature were used to build and validate the model.
    3. Figures are too small, particular Figure 1
    4. "E2F" should be specified as E2F1-3 yield quite distinct results from E2F7/8.
    5. Text corresponding to Figure 5 does not reference most of the panels in the Figure. i.e. figures are not "cited" in the text
    6. Figure 5C - why is there no bars for PI3K. Text claims it was predicted by the model, but the data are missing?
    7. Data provided in figure 5D & E are insufficient on their own to claim "proliferation". Perhaps adding total cardiomyocyte numbers, where one would expect expansion compared to control.
    8. Consider adding a details about the p-values to the figure legend in figure 5.
    9. Data presented in figure 6 do not "validate" the model. Rescue experiments as were provided in vitro would be necessary or at minimum YAP/TEAD binding to the promoters (ATAC insufficient). Alternatively, walking back these statements, might be easiest.
    10. Validation studies through the literature suggested ~94% fidelity. The invitro validation suggests 66% reliability of model? The in vivo 33%? Perhaps this should be added as a discussion point - can the authors comment on the loss of fidelity as the rigor/complexity of the experiment increased?

    Significance

    As it stands now, the generated map largely constitutes already known details offering few if any new insights; however, if updated as new results arise AND made available as a public tool, the model could prove to be a highly valuable resource to the field.

    I have expertise in cardiomyocyte cell cycle and polyploidization

  4. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    In the manuscript by Harris et al. titled "Dynamic map illuminates Hippo to cMyc module crosstalk driving cardiomyocyte proliferation," the authors developed a computational model of cardiac proliferation signaling that incorporates various regulatory networks (cytokinesis, mitosis, DNA replication, etc.) to predict molecular drivers (genes) that support cardiomyocyte proliferation. Published research articles on cardiomyocyte proliferation in multiple contexts (different species, ages, in vitro and in vivo, etc) were used to build and validate the computational model. The authors found using their model that different processes during cardiomyocyte proliferation may or may not be context-dependent. For example, DNA replication is regulated differently in conditions with high Neuregulin compared to high YAP, whereas mitosis and cytokinesis regulation is similar in these conditions. To experimentally validate their model, the authors used an in vitro system to test the effects of YAP on 3 connected pathways; in the context of YAP activation, inhibition of PI3K, cMyc, or FoxM1 was combined to assay cell-cycle markers in cultured neonatal rat ventricular cardiomyocytes. Cell-cycle marker expression in cardiomyocytes was attenuated by inhibition of cMyc or PI3K, suggesting that these pathways are involved in YAP-mediated cardiomyocyte proliferation. While this model can be a good tool to gain new insights on interactions between molecular pathways, there are a few questions to be addressed prior to publication.

    Major Comments:

    1. One of the potential uses for this computational model is to discover new interactions between known pathways that are involved in cardiomyocyte proliferation. However, this would be more powerful if factors such as species, age (neonate vs. adult), experimental design (in vivo vs. in vitro) are accounted for, as new node inputs or a combination of existing node input activity values. This is very important because cardiomyocyte proliferation can drastically vary depending on these experimental factors.
    2. The finding that cardiomyocyte proliferation is context-dependent is very exciting and warrants further investigation/validation. The authors state that different sets of nodes/modules are affected by neuregulin activation compared to YAP activation. This should be experimentally validated - qPCR/Western blots on sets of genes that are predicted to be differentially regulated in the high neuregulin context vs the high YAP context.
    3. The overall description of the model can be improved. How are the modules and overarching model built from published results? For example, how are the input and parameters set to validate or predict different experimental observations? What is the steady-state activity of each of the nodes and does this make sense biologically? Includng a few more sentences to explain the model would help with overall understanding for an uninformed reader.

    Minor Comments:

    Line 124 - The use of "species" and "reactions" is confusing to uninformed readers. Do you mean nodes and interactions/bridges?

    Line 130 - I could not find Supplementary File 2, which includes the references?

    Line 251 - "theseanal"

    Line 257 - What is the meaning of the directional arrows in Fig 1A?

    Line 301 - Unclear what default values mean here. Please elaborate and provide an example of how this is reasonable?

    Supplemental Fig S2 - Why would knockdown of PKA, Lats1, or SMAD3 have the exact same effects on node activation? This is seen with multiple other genes as well (IGF and FGF for example).

    Significance

    The computational model in this manuscript can be a tool to discover unknown molecular pathway interactions in cardiomyocyte proliferation. The novelty lies in the ability to adjust any parameter or the entire setting/context. While this sounds very exciting, improvement of the model to account for age, experimental conditions (in vivo vs in vitro), and species (human, pig, mouse) could lead to increase prediction accuracy. Additionally, more robust validation of context-dependent interactions between signaling pathways would also increase overall enthusiasm for the manuscript. Readers interested in a systems biology approach to cardiomyocyte proliferation, or researchers probing molecular interactions during cardiomyocyte proliferation would be interested in using such a model to discover novel contexts/combinations in which cardiomyocyte proliferation is more likely.

    The reviewer comes from a varied training background and is qualified to evaluate this manuscript in full - BS in biomedical engineering and mathematics. PhD in biomedical engineering (molecular biology, cardiac electrophysiology). Postdoctoral training in cardiac regeneration and immunity.

  5. Version published to 10.1101/2022.10.11.511763v1 on bioRxiv