Cardiac mitochondria exhibit dynamic functional clustering

• Published in: Frontiers in physiology 2014-09, Vol.5, p.329-329
• Main Authors: Kurz, Felix T, Aon, Miguel A, O'Rourke, Brian, Armoundas, Antonis A
• Format: Article
• Language: English
• Subjects: cardiac myocyte; functional connectivity; mitochondrial clustering; mitochondrial oscillator; Physiology; wavelets
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2014.00329

Multi-oscillatory behavior of mitochondrial inner membrane potential ΔΨ m in self-organized cardiac mitochondrial networks can be triggered by metabolic or oxidative stress. Spatio-temporal analyses of cardiac mitochondrial networks have shown that mitochondria are heterogeneously organized in synchronously oscillating clusters in which the mean cluster frequency and size are inversely correlated, thus suggesting a modulation of cluster frequency through local inter-mitochondrial coupling. In this study, we propose a method to examine the mitochondrial network's topology through quantification of its dynamic local clustering coefficients. Individual mitochondrial ΔΨ m oscillation signals were identified for each cardiac myocyte and cross-correlated with all network mitochondria using previously described methods (Kurz et al., 2010a). Time-varying inter-mitochondrial connectivity, defined for mitochondria in the whole network whose signals are at least 90% correlated at any given time point, allowed considering functional local clustering coefficients. It is shown that mitochondrial clustering in isolated cardiac myocytes changes dynamically and is significantly higher than for random mitochondrial networks that are constructed using the Erdös-Rényi model based on the same sets of vertices. The network's time-averaged clustering coefficient for cardiac myocytes was found to be 0.500 ± 0.051 (N = 9) vs. 0.061 ± 0.020 for random networks, respectively. Our results demonstrate that cardiac mitochondria constitute a network with dynamically connected constituents whose topological organization is prone to clustering. Cluster partitioning in networks of coupled oscillators has been observed in scale-free and chaotic systems and is therefore in good agreement with previous models of cardiac mitochondrial networks.