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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.


Major Cluster Properties in Relation to Mean Mitochondrial Clustering. (A) Mean clustering coefficient vs. major cluster coherence. Though cluster coherence variability is limited, mean clustering still increases naturally for increasing cluster coherence. (B,D) Mean clustering coefficient vs. major cluster frequency and normalized cluster area. Clustering is shown to increase with cluster area and, therefore, number of cluster mitochondria. In analogy to Figure 2 in Kurz et al. (2010a), clustering then decreases with increasing cluster frequencies. (C) Mean clustering coefficient vs. normalized cluster amplitude. Clustering reaches a plateau for normalized amplitudes below about 4/5 of the maximum amplitude, whereas clustering decreases toward zero for maximum amplitudes. The latter effect might be explained by a more prolonged and incomplete repolarization in between oscillations when the relative size of the cluster increases whereas the plateau might signify a maximum of mitochondrial clustering. However, further amplitude decrease might be due to a failure of the mitochondrial network to reenergize.
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Figure 4: Major Cluster Properties in Relation to Mean Mitochondrial Clustering. (A) Mean clustering coefficient vs. major cluster coherence. Though cluster coherence variability is limited, mean clustering still increases naturally for increasing cluster coherence. (B,D) Mean clustering coefficient vs. major cluster frequency and normalized cluster area. Clustering is shown to increase with cluster area and, therefore, number of cluster mitochondria. In analogy to Figure 2 in Kurz et al. (2010a), clustering then decreases with increasing cluster frequencies. (C) Mean clustering coefficient vs. normalized cluster amplitude. Clustering reaches a plateau for normalized amplitudes below about 4/5 of the maximum amplitude, whereas clustering decreases toward zero for maximum amplitudes. The latter effect might be explained by a more prolonged and incomplete repolarization in between oscillations when the relative size of the cluster increases whereas the plateau might signify a maximum of mitochondrial clustering. However, further amplitude decrease might be due to a failure of the mitochondrial network to reenergize.

Mentions: Individual mitochondrial signals are non-stationary in time and, therefore, wavelet transforms provide adequate means to examine the mitochondrion's signal temporal evolution (Grossmann et al., 1985). Following a recently described methodology (Kurz et al., 2010a), mitochondria were sorted according to the dynamic behavior of their frequencies such that clusters of mitochondria with similar frequencies could be identified. Mitochondria from the major frequency cluster were subsequently sampled and their mean temporal cluster area, amplitude and coherence determined (Kurz et al., 2010a). These spatio-temporal mitochondrial network properties are related to the functional topological network properties through the mean time-dependent clustering coefficient (see Figure 4).


Cardiac mitochondria exhibit dynamic functional clustering.

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

Major Cluster Properties in Relation to Mean Mitochondrial Clustering. (A) Mean clustering coefficient vs. major cluster coherence. Though cluster coherence variability is limited, mean clustering still increases naturally for increasing cluster coherence. (B,D) Mean clustering coefficient vs. major cluster frequency and normalized cluster area. Clustering is shown to increase with cluster area and, therefore, number of cluster mitochondria. In analogy to Figure 2 in Kurz et al. (2010a), clustering then decreases with increasing cluster frequencies. (C) Mean clustering coefficient vs. normalized cluster amplitude. Clustering reaches a plateau for normalized amplitudes below about 4/5 of the maximum amplitude, whereas clustering decreases toward zero for maximum amplitudes. The latter effect might be explained by a more prolonged and incomplete repolarization in between oscillations when the relative size of the cluster increases whereas the plateau might signify a maximum of mitochondrial clustering. However, further amplitude decrease might be due to a failure of the mitochondrial network to reenergize.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4151091&req=5

Figure 4: Major Cluster Properties in Relation to Mean Mitochondrial Clustering. (A) Mean clustering coefficient vs. major cluster coherence. Though cluster coherence variability is limited, mean clustering still increases naturally for increasing cluster coherence. (B,D) Mean clustering coefficient vs. major cluster frequency and normalized cluster area. Clustering is shown to increase with cluster area and, therefore, number of cluster mitochondria. In analogy to Figure 2 in Kurz et al. (2010a), clustering then decreases with increasing cluster frequencies. (C) Mean clustering coefficient vs. normalized cluster amplitude. Clustering reaches a plateau for normalized amplitudes below about 4/5 of the maximum amplitude, whereas clustering decreases toward zero for maximum amplitudes. The latter effect might be explained by a more prolonged and incomplete repolarization in between oscillations when the relative size of the cluster increases whereas the plateau might signify a maximum of mitochondrial clustering. However, further amplitude decrease might be due to a failure of the mitochondrial network to reenergize.
Mentions: Individual mitochondrial signals are non-stationary in time and, therefore, wavelet transforms provide adequate means to examine the mitochondrion's signal temporal evolution (Grossmann et al., 1985). Following a recently described methodology (Kurz et al., 2010a), mitochondria were sorted according to the dynamic behavior of their frequencies such that clusters of mitochondria with similar frequencies could be identified. Mitochondria from the major frequency cluster were subsequently sampled and their mean temporal cluster area, amplitude and coherence determined (Kurz et al., 2010a). These spatio-temporal mitochondrial network properties are related to the functional topological network properties through the mean time-dependent clustering coefficient (see Figure 4).

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.