Limits...
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.


Related in: MedlinePlus

Time-dependent mitochondrial “mean field” correlation maps for 9 cardiac myocytes. The left hand side shows the correlation maps at the time point with the highest network mean correlation c(t) (see main text), whereas the right hand side shows those maps at the time point with the lowest mean correlation. Mitochondrial mean correlation values range between −0.4 and 0.8, the time-averaged mean correlation coefficient was found to be 0.43 ± 0.07. Also, some myocytes [myocytes (d) and (g)] show a prominent difference in overall correlation, while myocyte (i) does not show too pronounced correlation variability.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4151091&req=5

Figure 2: Time-dependent mitochondrial “mean field” correlation maps for 9 cardiac myocytes. The left hand side shows the correlation maps at the time point with the highest network mean correlation c(t) (see main text), whereas the right hand side shows those maps at the time point with the lowest mean correlation. Mitochondrial mean correlation values range between −0.4 and 0.8, the time-averaged mean correlation coefficient was found to be 0.43 ± 0.07. Also, some myocytes [myocytes (d) and (g)] show a prominent difference in overall correlation, while myocyte (i) does not show too pronounced correlation variability.

Mentions: Mean-field coupling in complex non-linear biological networks of weakly coupled oscillators, such as a network of mitochondrial oscillators (Aon et al., 2006) or in other systems of coupled chemical or physical oscillators, assumes a global, all-to-all-coupling with a respective mean coupling constant (Kiss et al., 2002; Rougemont and Naef, 2006). In analogy to such a “mean field” approach, inter-mitochondrial correlation properties were examined at time t through signal correlations ci(m) (t) of individual mitochondrion m with each of its Nm topological network neighbors mi (Figure 2). The average correlation coefficient ∑ ici(m) (t)/Nm for each mitochondrion m was then again averaged over all network constituents to determine the overall dynamic inter-mitochondrial mean correlation c(t) as c(t) = ∑ m ∑ ici(m)(t)/Nm.


Cardiac mitochondria exhibit dynamic functional clustering.

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

Time-dependent mitochondrial “mean field” correlation maps for 9 cardiac myocytes. The left hand side shows the correlation maps at the time point with the highest network mean correlation c(t) (see main text), whereas the right hand side shows those maps at the time point with the lowest mean correlation. Mitochondrial mean correlation values range between −0.4 and 0.8, the time-averaged mean correlation coefficient was found to be 0.43 ± 0.07. Also, some myocytes [myocytes (d) and (g)] show a prominent difference in overall correlation, while myocyte (i) does not show too pronounced correlation variability.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Time-dependent mitochondrial “mean field” correlation maps for 9 cardiac myocytes. The left hand side shows the correlation maps at the time point with the highest network mean correlation c(t) (see main text), whereas the right hand side shows those maps at the time point with the lowest mean correlation. Mitochondrial mean correlation values range between −0.4 and 0.8, the time-averaged mean correlation coefficient was found to be 0.43 ± 0.07. Also, some myocytes [myocytes (d) and (g)] show a prominent difference in overall correlation, while myocyte (i) does not show too pronounced correlation variability.
Mentions: Mean-field coupling in complex non-linear biological networks of weakly coupled oscillators, such as a network of mitochondrial oscillators (Aon et al., 2006) or in other systems of coupled chemical or physical oscillators, assumes a global, all-to-all-coupling with a respective mean coupling constant (Kiss et al., 2002; Rougemont and Naef, 2006). In analogy to such a “mean field” approach, inter-mitochondrial correlation properties were examined at time t through signal correlations ci(m) (t) of individual mitochondrion m with each of its Nm topological network neighbors mi (Figure 2). The average correlation coefficient ∑ ici(m) (t)/Nm for each mitochondrion m was then again averaged over all network constituents to determine the overall dynamic inter-mitochondrial mean correlation c(t) as c(t) = ∑ m ∑ ici(m)(t)/Nm.

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.


Related in: MedlinePlus