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. 2013;8(2):e56359.
doi: 10.1371/journal.pone.0056359. Epub 2013 Feb 20.

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

John Walmsley  1 , Jose F Rodriguez, Gary R Mirams, Kevin Burrage, Igor R Efimov, Blanca Rodriguez
Affiliations

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

John Walmsley et al. PLoS One. 2013.

Abstract

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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Conflict of interest statement

Competing Interests: One of the authors on this paper, Dr. GM, has received research support from GlaxoSmithKline Ltd. The authors declare this as a competing interest. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Biomarkers calculated from AP and CaT traces.
A) shows APD80, APD30 and APD3080, and B) shows CaTD30, CaTD80, CaTD3080 and CaTMax. AP-CaT delay is the difference between the AP and CaT upstroke times.
Figure 2
Figure 2. Sample AP and CaT traces obtained from the non-failing (blue) and failing (red) populations.
Traces are shown for BCL = 1500 (A,C) and 300 ms (B,D). The AP for the standard ORd endocardial model is shown in black.
Figure 3
Figure 3. Histograms showing biomarker values obtained for the non-failing (blue) and failing (red) cell model populations.
Histograms shown are recorded at BCL = 1500 ms, 600 ms and 300 ms (A–C: APD80, D–F: APD3080, G–I: CaTD80, J–L: CaTD3080, M–O: CaTmax and P-R: AP-CaT delay. In each panel, the circle indicates biomarker values obtained with the standard ORd model whereas the cross indicates the value obtained with the ORd model including 60% downregulation of Gformula image, Gformula image, Jformula image and Gformula image and 60% upregulation of Gformula image.
Figure 4
Figure 4. Correlation plots of one biomarker against another at 600 ms BCL.
Red denotes the failing population, blue denotes the non-failing population, and the darker shade represents cells lying in the intersection between the two populations. Points which develop alternans are removed from these plots. Each biomarker is plotted against each other.
Figure 5
Figure 5. Regression coefficients obtained in the non-failing and failing populations.
Coefficients are plotted at all cycle lengths for A,B) APD80, C,D) APD3080, E,F) CaTD80, G,H) CaTD3080, I,J) CaT max and K,L) AP-CaT delay. For clarity, only regression coefficients with an absolute value greater than 0.2 are plotted, denoted by the grey band.
Figure 6
Figure 6. Occurrence of voltage and calcium alternans in the non-failing and failing populations.
Colour scale indicates the proportion of models displaying alternans in APD80 and/or CaTD80 for each value of each ionic conductance considered. A) There are 36 non-failing alternans cases at 350 ms BCL. B) There are 64 failing alternans cases at 350 ms BCL. C) There are 2241 non-failing cells in alternans at 300 ms BCL. D) There are 2235 failing cell models in alternans at BCL 300 ms.
See this image and copyright information in PMC

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