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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.


Mean function clustering coefficient. (A) Time- and myocyte-averaged mean clustering coefficient for different cutoffs of the correlation coefficient as the defining threshold of functional connectedness. The red curve is an exponential fit curve of the form f(t) = a · Exp [−t/b] + c (for details and fit parameters see main text). (B) Time evolution of the mean clustering coefficient averaged over different myocytes (N = 9) (red line) and for random networks on the same set of vertices and maximum degree (blue line). Evidently, the mitochondrial network, as opposed to the randomly constructed network, shows significant functional clustering.
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Figure 3: Mean function clustering coefficient. (A) Time- and myocyte-averaged mean clustering coefficient for different cutoffs of the correlation coefficient as the defining threshold of functional connectedness. The red curve is an exponential fit curve of the form f(t) = a · Exp [−t/b] + c (for details and fit parameters see main text). (B) Time evolution of the mean clustering coefficient averaged over different myocytes (N = 9) (red line) and for random networks on the same set of vertices and maximum degree (blue line). Evidently, the mitochondrial network, as opposed to the randomly constructed network, shows significant functional clustering.

Mentions: The mean functional inter-mitochondrial clustering coefficient was determined for different correlation cutoffs (see Figure 3A). Naturally, high correlation coefficient cutoffs only involve few functionally connected mitochondria whereas lower cutoffs involve a higher number of mitochondria that are thought of as being connected. Therefore, the mean clustering coefficient increases for lower cutoffs. The ensuing fitted curve f (red line in Figure 3A, based on equation: f(t) = a · Exp [−t/b] + c) approximately crosses the 50% value of the mean clustering coefficients at correlation coefficients of 90% which also approximately corresponds to the median derivative value of the fitted curve. The fitting curve function parameters were determined as a = −7.81· 10−6 ± 1.33· 10−6, b = −8.64· 10−2 ± 0.13· 10−2 and c = 77.65· 10−2 ± 0.35· 10−2. Accordingly, and throughout the paper, two mitochondria are thought of as being connected, if their correlation exceeds 90%.


Cardiac mitochondria exhibit dynamic functional clustering.

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

Mean function clustering coefficient. (A) Time- and myocyte-averaged mean clustering coefficient for different cutoffs of the correlation coefficient as the defining threshold of functional connectedness. The red curve is an exponential fit curve of the form f(t) = a · Exp [−t/b] + c (for details and fit parameters see main text). (B) Time evolution of the mean clustering coefficient averaged over different myocytes (N = 9) (red line) and for random networks on the same set of vertices and maximum degree (blue line). Evidently, the mitochondrial network, as opposed to the randomly constructed network, shows significant functional clustering.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Mean function clustering coefficient. (A) Time- and myocyte-averaged mean clustering coefficient for different cutoffs of the correlation coefficient as the defining threshold of functional connectedness. The red curve is an exponential fit curve of the form f(t) = a · Exp [−t/b] + c (for details and fit parameters see main text). (B) Time evolution of the mean clustering coefficient averaged over different myocytes (N = 9) (red line) and for random networks on the same set of vertices and maximum degree (blue line). Evidently, the mitochondrial network, as opposed to the randomly constructed network, shows significant functional clustering.
Mentions: The mean functional inter-mitochondrial clustering coefficient was determined for different correlation cutoffs (see Figure 3A). Naturally, high correlation coefficient cutoffs only involve few functionally connected mitochondria whereas lower cutoffs involve a higher number of mitochondria that are thought of as being connected. Therefore, the mean clustering coefficient increases for lower cutoffs. The ensuing fitted curve f (red line in Figure 3A, based on equation: f(t) = a · Exp [−t/b] + c) approximately crosses the 50% value of the mean clustering coefficients at correlation coefficients of 90% which also approximately corresponds to the median derivative value of the fitted curve. The fitting curve function parameters were determined as a = −7.81· 10−6 ± 1.33· 10−6, b = −8.64· 10−2 ± 0.13· 10−2 and c = 77.65· 10−2 ± 0.35· 10−2. Accordingly, and throughout the paper, two mitochondria are thought of as being connected, if their correlation exceeds 90%.

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.