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

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...

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Main Authors: John Walmsley, Jose F Rodriguez, Gary R Mirams, Kevin Burrage, Igor R Efimov, Blanca Rodriguez
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0056359&type=printable
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author John Walmsley
Jose F Rodriguez
Gary R Mirams
Kevin Burrage
Igor R Efimov
Blanca Rodriguez
author_facet John Walmsley
Jose F Rodriguez
Gary R Mirams
Kevin Burrage
Igor R Efimov
Blanca Rodriguez
author_sort John Walmsley
collection DOAJ
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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spelling doaj-art-0e9bdc19275a4f23a27c978f362712fd2025-08-20T02:30:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0182e5635910.1371/journal.pone.0056359mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.John WalmsleyJose F RodriguezGary R MiramsKevin BurrageIgor R EfimovBlanca RodriguezDifferences 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0056359&type=printable
spellingShingle John Walmsley
Jose F Rodriguez
Gary R Mirams
Kevin Burrage
Igor R Efimov
Blanca Rodriguez
mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.
PLoS ONE
title mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.
title_full mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.
title_fullStr mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.
title_full_unstemmed mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.
title_short mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.
title_sort mrna expression levels in failing human hearts predict cellular electrophysiological remodeling a population based simulation study
url https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0056359&type=printable
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