What is the clinical evidence from this study?

Study design: Other. Population: Cardiomyocyte hypertrophy and heart failure. Intervention: Computational modeling of mechano-signaling network. Primary outcome: Model prediction accuracy of experimental observations.

November 13, 2017PLoS Computational BiologyOpen Access

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

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Key result

A predictive computational model of the cardiac mechano-signaling network correctly predicted 78% of independent experimental observations and identified calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of stretch-induced hypertrophy.

Population

Computational model of the cardiomyocyte mechano-signaling network, constructed and validated using…

Comparison

Simulation of mechanical stretch and… vs Baseline condition or single-target perturbations.

Design

Preclinical

Authors

PTPhilip M. TanUniversity of Virginia KBKyle S. BuchholzUniversity of California, San Diego JOJeffrey H. OmensHeart Failure / Cardiomyopathy

Discussion

Member takes

Implication

Model of mechano-signal integration in cardiomyocytes may guide experiments; leaves open in vivo validation before any clinical use.

Structured PICO

Population

A computational model of the cardiac mechano-signaling network constructed from 172 published studies and validated against 55 independent studies of in vitro rat cardiomyocytes.

Intervention

Simulation of mechanical stretch and pharmacological perturbations (e.g., valsartan/sacubitril, single and combination target inhibitions).

Comparator

Baseline condition (no stretch) or single-target perturbations.

Outcome

Prediction of stretch-induced cardiomyocyte remodeling, gene expression (e.g., ANP, BNP), and identification of key network regulators.

A predictive computational model of cardiomyocyte mechano-signaling identifies key regulatory hubs and provides a mechanistic rationale for the efficacy of valsartan/sacubitril in attenuating stretch-induced hypertrophy.

Limitations

The scope of the network reconstruction necessitated the use of default parameters.
The model does not incorporate paracrine signaling from mechanically activated fibroblasts or juxtacrine signaling through cadherins.
The model does not integrate biophysical mechanisms such as force propagation, diffusion, and electrophysiology.
Critical gaps remain in understanding which stretch receptors are directly responsive to mechanical strain versus indirectly activated.
Discrepancies in validation due to thresholding (e.g., predicted changes below the 5% threshold)
Missing connections in the model leading to unpredicted inhibitory effects
Inhibitory effects predicted by the model that were not observed in the literature

Cite This Study

Tan et al. (2017) studied Cardiomyocyte hypertrophy and heart failure. Computational modeling of mechano-signaling network was evaluated on Model prediction accuracy of experimental observations. A predictive computational model of the cardiac mechano-signaling network correctly predicted 78% of independent experimental observations and identified calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of stretch-induced hypertrophy.

synapsesocial.com/papers/6a6a85c645e10bb5bcf2bdd7 https://doi.org/10.1371/journal.pcbi.1005854

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Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

Discussion

Member takes

Implication

Structured PICO

Limitations

Cite This Study

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

Discussion

Member takes

Implication

Structured PICO

Limitations

Cite This Study

Also Consider

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