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MRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: A population-based simulation study

ABSTRACT: mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: A population-based simulation study. This is an example of a model that uses parameter uncertainty, which is specified in terms of probability density functions.

SUBMITTER: David Nickerson

PROVIDER: Walmsley_Rodriguez_2013 | Physiome Model Repository |

REPOSITORIES: PMR

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mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.

Walmsley John J Rodriguez Jose F JF Mirams Gary R GR Burrage Kevin K Efimov Igor R IR Rodriguez Blanca B

PloS one 20130220 2

Differences in mRNA expression levels have been observed in failing versus non-failing human hearts for several membrane channel proteins and accessory subunits. These differences may play a causal role in electrophysiological changes observed in human heart failure and atrial fibrillation, such as action potential (AP) prolongation, increased AP triangulation, decreased intracellular calcium transient (CaT) magnitude and decreased CaT triangulation. Our goal is to investigate whether the inform ...[more]

PMID: 23437117

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mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.

Project description:Differences in mRNA expression levels have been observed in failing versus non-failing human hearts for several membrane channel proteins and accessory subunits. These differences may play a causal role in electrophysiological changes observed in human heart failure and atrial fibrillation, such as action potential (AP) prolongation, increased AP triangulation, decreased intracellular calcium transient (CaT) magnitude and decreased CaT triangulation. Our goal is to investigate whether the information contained in mRNA measurements can be used to predict cardiac electrophysiological remodeling in heart failure using computational modeling. Using mRNA data recently obtained from failing and non-failing human hearts, we construct failing and non-failing cell populations incorporating natural variability and up/down regulation of channel conductivities. Six biomarkers are calculated for each cell in each population, at cycle lengths between 1500 ms and 300 ms. Regression analysis is performed to determine which ion channels drive biomarker variability in failing versus non-failing cardiomyocytes. Our models suggest that reported mRNA expression changes are consistent with AP prolongation, increased AP triangulation, increased CaT duration, decreased CaT triangulation and amplitude, and increased delay between AP and CaT upstrokes in the failing population. Regression analysis reveals that changes in AP biomarkers are driven primarily by reduction in I[Formula: see text], and changes in CaT biomarkers are driven predominantly by reduction in I(Kr) and SERCA. In particular, the role of I(CaL) is pacing rate dependent. Additionally, alternans developed at fast pacing rates for both failing and non-failing cardiomyocytes, but the underlying mechanisms are different in control and heart failure.

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