Professor/Faculty Member University of Virginia, United States
Introduction:: Afflicting men, women, and people of most racial and ethnic groups, heart disease is the leading cause of death in the US1. After a cardiac injury, adult mammalian hearts cannot overcome the loss of established cardiomyocytes (CMs). This inability is due to the lack of proliferation capabilities of CMs linked to an exit from the cell cycle and a loss in the ability to undergo cell division. Current knowledge in cardiac regeneration lacks a clear understanding of the molecular mechanisms 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 signaling network. To experimentally validate the model, we developed a high-content experimental system for determining cell cycle phase distributions and inferring cell cycle transition rates.
Materials and Methods:: We created a model of the cardiomyocyte proliferation signaling network using logic-based differential equations. This model defines 5 regulatory networks (DNA replication, mitosis, cytokinesis, growth factor, hippo pathway) of cardiomyocyte proliferation and integrates them to create a model that includes 72 nodes and 88 reactions. We validated the model using published experimental observations, predicted species dynamics, and performed a functional analysis of the model by simulating individual knockdowns for each species.
To test model predictions, we used single-cell data from our proliferation assay along with an ordinary differential equation (ODE) model to predict cell cycle phase distributions and transition rates. Specifically, cell images obtained from experimental conditions undergo an automated analysis pipeline where nuclei stained with DAPI are segmented, and the mean intensities of Ki67 staining are calculated for each segmented object. Using integrated intensity measurements of DAPI-segmented objects, we can quantify the thresholds that determine whether the cells are positive or negative for Ki67 and whether they have low, medium, or high levels of DAPI intensities. The experimental data is run through an algorithm that automatically categorizes the cells into their respective phases based on the thresholding values. The percentage of cells in each phase for each experimental condition is then used to estimate reaction parameters in the ODE model.
Results, Conclusions, and Discussions:: Results and Discussion: Our model predictions achieve 93.6% agreement (73 out of 76) with observations from independent literature. Functional analysis of the model was performed by simulating individual knockdowns for each of the 74 species in the network and predicting the corresponding change in the activity of every other node in the network. From a subset of this analysis, we identified nodes such as Nrg and YAP as influential network hubs (Fig. 1B). Specifically, these nodes when increased have an increasing effect on important model outputs DNA replication, and Mitosis. To further observe the effect these regulators have on the cardiomyocyte cell cycle, we performed a proliferation assay where we treated neonatal rat cardiomyocytes with Nrg1 drug and a drug found to increase YAP activity (TT10). Using the single-cell data from this experiment, we found an increase of cells in the G1 and a decrease in the G0 phase in the Nrg and YAP conditions. Using a two-state model, we inferred rates showing an increase in rates from G0 to G1 in both treatment conditions (Fig. 1C).
Conclusions: We developed a predictive model that predicts how cardiomyocytes respond to stimuli in the proliferation regulatory network and identifies potential therapeutic regulators that induce cardiomyocyte proliferation. In addition, we created an automated experimental system that uses single-cell data to determine cell cycle phase distributions and infer cell cycle transition rates, and we will apply this system to other regulators of proliferation. We expect this work to provide an innovative framework for understanding the dynamics of the proliferation regulatory network as it affects cell cycle transitions and to identify therapeutic targets that will ultimately be effective in enhancing cardiomyocyte proliferation.
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References (Optional): : 1 Eschenhagen T. Circ. 2017. 136.
