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Cardiac mitochondria exhibit dynamic functional clustering.

Kurz FT, Aon MA, O'Rourke B, Armoundas AA - Front Physiol (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroradiology, Heidelberg University Hospital Heidelberg, Germany ; Cardiovascular Research Center, Harvard Medical School, Massachusetts General Hospital Charlestown, MA, USA.

ABSTRACT
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.

No MeSH data available.


Single mitochondrial signals and wavelet analysis. (A) Mitochondria in cardiac myocytes are densely packed and their individual TMRE signal can be extracted as previously described (Kurz et al., 2010a,b). (B) Two mitochondrial signals with different oscillatory patterns from different locations within the myocyte are shown. (C) Absolute squared wavelet transform over frequency and time of an oscillating mitochondrion. The major frequency component varies between 15 and 20 mHz.
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Figure 1: Single mitochondrial signals and wavelet analysis. (A) Mitochondria in cardiac myocytes are densely packed and their individual TMRE signal can be extracted as previously described (Kurz et al., 2010a,b). (B) Two mitochondrial signals with different oscillatory patterns from different locations within the myocyte are shown. (C) Absolute squared wavelet transform over frequency and time of an oscillating mitochondrion. The major frequency component varies between 15 and 20 mHz.

Mentions: In Figure 1A, three random mitochondria labeled 1, 2, and 3 from an isolated cardiac myocyte are chosen to illustrate individual mitochondrial TMRE signal behavior. Evidently, only mitochondria 1 and 3 show marked oscillatory behavior over time whereas mitochondrion 2 is non-oscillating (see Figures 1B,C). Figure 1C shows the absolute squared wavelet transform of mitochondrion 3 over frequency and time. It can be seen that the main frequency component, depicted by the dark red color, varies between 15 and 20 mHz during the recording. This frequency component corresponds to the time interval between oscillation peaks and troughs observed in the upper panel of Figure 1C, that is approximately between 50 and 65 s.


Cardiac mitochondria exhibit dynamic functional clustering.

Kurz FT, Aon MA, O'Rourke B, Armoundas AA - Front Physiol (2014)

Single mitochondrial signals and wavelet analysis. (A) Mitochondria in cardiac myocytes are densely packed and their individual TMRE signal can be extracted as previously described (Kurz et al., 2010a,b). (B) Two mitochondrial signals with different oscillatory patterns from different locations within the myocyte are shown. (C) Absolute squared wavelet transform over frequency and time of an oscillating mitochondrion. The major frequency component varies between 15 and 20 mHz.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4151091&req=5

Figure 1: Single mitochondrial signals and wavelet analysis. (A) Mitochondria in cardiac myocytes are densely packed and their individual TMRE signal can be extracted as previously described (Kurz et al., 2010a,b). (B) Two mitochondrial signals with different oscillatory patterns from different locations within the myocyte are shown. (C) Absolute squared wavelet transform over frequency and time of an oscillating mitochondrion. The major frequency component varies between 15 and 20 mHz.
Mentions: In Figure 1A, three random mitochondria labeled 1, 2, and 3 from an isolated cardiac myocyte are chosen to illustrate individual mitochondrial TMRE signal behavior. Evidently, only mitochondria 1 and 3 show marked oscillatory behavior over time whereas mitochondrion 2 is non-oscillating (see Figures 1B,C). Figure 1C shows the absolute squared wavelet transform of mitochondrion 3 over frequency and time. It can be seen that the main frequency component, depicted by the dark red color, varies between 15 and 20 mHz during the recording. This frequency component corresponds to the time interval between oscillation peaks and troughs observed in the upper panel of Figure 1C, that is approximately between 50 and 65 s.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroradiology, Heidelberg University Hospital Heidelberg, Germany ; Cardiovascular Research Center, Harvard Medical School, Massachusetts General Hospital Charlestown, MA, USA.

ABSTRACT
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.

No MeSH data available.