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Magnetite-Amyloid-β deteriorates activity and functional organization in an in vitro model for Alzheimer's disease.

Teller S, Tahirbegi IB, Mir M, Samitier J, Soriano J - Sci Rep (2015)

Bottom Line: Recent studies have shown that other agents, in particular magnetite, can also play a pivotal role.Our work suggests that magnetite nanoparticles have a more prominent role in AD than previously thought, and may bring new insights in the understanding of the damaging action of magnetite-amyloid-β complex.Our experimental system also offers new interesting perspectives to explore key biochemical players in neurological disorders through a controlled, model system manner.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Estructura i Constituents de la Matèria, Universitat de Barcelona, Barcelona, E-08028, Spain.

ABSTRACT
The understanding of the key mechanisms behind human brain deterioration in Alzheimer' disease (AD) is a highly active field of research. The most widespread hypothesis considers a cascade of events initiated by amyloid-β peptide fibrils that ultimately lead to the formation of the lethal amyloid plaques. Recent studies have shown that other agents, in particular magnetite, can also play a pivotal role. To shed light on the action of magnetite and amyloid-β in the deterioration of neuronal circuits, we investigated their capacity to alter spontaneous activity patterns in cultured neuronal networks. Using a versatile experimental platform that allows the parallel monitoring of several cultures, the activity in controls was compared with the one in cultures dosed with magnetite, amyloid-β and magnetite-amyloid-β complex. A prominent degradation in spontaneous activity was observed solely when amyloid-β and magnetite acted together. Our work suggests that magnetite nanoparticles have a more prominent role in AD than previously thought, and may bring new insights in the understanding of the damaging action of magnetite-amyloid-β complex. Our experimental system also offers new interesting perspectives to explore key biochemical players in neurological disorders through a controlled, model system manner.

No MeSH data available.


Related in: MedlinePlus

Cluster’s activity and network coherence.(A) Difference in cluster’s firing rate between the perturbed (φP) and the unperturbed (φ0) activities, and comparing a control cavity (left, 32 clusters) with one targeted with M-Aβ complex (right, 36 clusters). Data corresponds to the experiments shown in Fig. 2A. Each bar represents a cluster of the network. Clusters are color coded and ordered in the horizontal axis according to their participation in a co-activated group. White bars depict clusters that fire independently. Bars marked with asterisks indicate the clusters that became silent after perturbation, and the ones marked with arrowheads highlight those that boosted in activity. The top horizontal color boxes show the structure of co-activations before perturbation, and color coded according to the sequences shown in Fig. 2A. Grey boxes are sequences that were not indicated in Fig. 2A. (B) Distributions of the normalized firing rate differences  for 15 experimental realizations upon action of the different chemical agents. The red curve shows a Gaussian fit to the distributions, with mean μ and standard deviation σ. The value γ provides the skewness of the distributions.
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f3: Cluster’s activity and network coherence.(A) Difference in cluster’s firing rate between the perturbed (φP) and the unperturbed (φ0) activities, and comparing a control cavity (left, 32 clusters) with one targeted with M-Aβ complex (right, 36 clusters). Data corresponds to the experiments shown in Fig. 2A. Each bar represents a cluster of the network. Clusters are color coded and ordered in the horizontal axis according to their participation in a co-activated group. White bars depict clusters that fire independently. Bars marked with asterisks indicate the clusters that became silent after perturbation, and the ones marked with arrowheads highlight those that boosted in activity. The top horizontal color boxes show the structure of co-activations before perturbation, and color coded according to the sequences shown in Fig. 2A. Grey boxes are sequences that were not indicated in Fig. 2A. (B) Distributions of the normalized firing rate differences for 15 experimental realizations upon action of the different chemical agents. The red curve shows a Gaussian fit to the distributions, with mean μ and standard deviation σ. The value γ provides the skewness of the distributions.

Mentions: To deepen in the understanding of the M-Aβ damage on network dynamics we analyzed in detail the representative experiment of Fig. 2A, and computed the difference in spontaneous activity before and after perturbation for each individual cluster, Δφ = φP − φ0 (see Methods). As shown in Fig. 3A, clusters in the control case slightly varied in activity due to the natural fluctuations in such a biological system, but the overall population activity along the two measurements remained stable, with . The structure of clusters’ co-activations was characterized by the two major communities outlined in the raster plot of Fig. 2A together with few clusters that fired independently. This dynamical organization was the same for both measurements, and illustrates the stability of the clusters’ coherence in control conditions.


Magnetite-Amyloid-β deteriorates activity and functional organization in an in vitro model for Alzheimer's disease.

Teller S, Tahirbegi IB, Mir M, Samitier J, Soriano J - Sci Rep (2015)

Cluster’s activity and network coherence.(A) Difference in cluster’s firing rate between the perturbed (φP) and the unperturbed (φ0) activities, and comparing a control cavity (left, 32 clusters) with one targeted with M-Aβ complex (right, 36 clusters). Data corresponds to the experiments shown in Fig. 2A. Each bar represents a cluster of the network. Clusters are color coded and ordered in the horizontal axis according to their participation in a co-activated group. White bars depict clusters that fire independently. Bars marked with asterisks indicate the clusters that became silent after perturbation, and the ones marked with arrowheads highlight those that boosted in activity. The top horizontal color boxes show the structure of co-activations before perturbation, and color coded according to the sequences shown in Fig. 2A. Grey boxes are sequences that were not indicated in Fig. 2A. (B) Distributions of the normalized firing rate differences  for 15 experimental realizations upon action of the different chemical agents. The red curve shows a Gaussian fit to the distributions, with mean μ and standard deviation σ. The value γ provides the skewness of the distributions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4660300&req=5

f3: Cluster’s activity and network coherence.(A) Difference in cluster’s firing rate between the perturbed (φP) and the unperturbed (φ0) activities, and comparing a control cavity (left, 32 clusters) with one targeted with M-Aβ complex (right, 36 clusters). Data corresponds to the experiments shown in Fig. 2A. Each bar represents a cluster of the network. Clusters are color coded and ordered in the horizontal axis according to their participation in a co-activated group. White bars depict clusters that fire independently. Bars marked with asterisks indicate the clusters that became silent after perturbation, and the ones marked with arrowheads highlight those that boosted in activity. The top horizontal color boxes show the structure of co-activations before perturbation, and color coded according to the sequences shown in Fig. 2A. Grey boxes are sequences that were not indicated in Fig. 2A. (B) Distributions of the normalized firing rate differences for 15 experimental realizations upon action of the different chemical agents. The red curve shows a Gaussian fit to the distributions, with mean μ and standard deviation σ. The value γ provides the skewness of the distributions.
Mentions: To deepen in the understanding of the M-Aβ damage on network dynamics we analyzed in detail the representative experiment of Fig. 2A, and computed the difference in spontaneous activity before and after perturbation for each individual cluster, Δφ = φP − φ0 (see Methods). As shown in Fig. 3A, clusters in the control case slightly varied in activity due to the natural fluctuations in such a biological system, but the overall population activity along the two measurements remained stable, with . The structure of clusters’ co-activations was characterized by the two major communities outlined in the raster plot of Fig. 2A together with few clusters that fired independently. This dynamical organization was the same for both measurements, and illustrates the stability of the clusters’ coherence in control conditions.

Bottom Line: Recent studies have shown that other agents, in particular magnetite, can also play a pivotal role.Our work suggests that magnetite nanoparticles have a more prominent role in AD than previously thought, and may bring new insights in the understanding of the damaging action of magnetite-amyloid-β complex.Our experimental system also offers new interesting perspectives to explore key biochemical players in neurological disorders through a controlled, model system manner.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Estructura i Constituents de la Matèria, Universitat de Barcelona, Barcelona, E-08028, Spain.

ABSTRACT
The understanding of the key mechanisms behind human brain deterioration in Alzheimer' disease (AD) is a highly active field of research. The most widespread hypothesis considers a cascade of events initiated by amyloid-β peptide fibrils that ultimately lead to the formation of the lethal amyloid plaques. Recent studies have shown that other agents, in particular magnetite, can also play a pivotal role. To shed light on the action of magnetite and amyloid-β in the deterioration of neuronal circuits, we investigated their capacity to alter spontaneous activity patterns in cultured neuronal networks. Using a versatile experimental platform that allows the parallel monitoring of several cultures, the activity in controls was compared with the one in cultures dosed with magnetite, amyloid-β and magnetite-amyloid-β complex. A prominent degradation in spontaneous activity was observed solely when amyloid-β and magnetite acted together. Our work suggests that magnetite nanoparticles have a more prominent role in AD than previously thought, and may bring new insights in the understanding of the damaging action of magnetite-amyloid-β complex. Our experimental system also offers new interesting perspectives to explore key biochemical players in neurological disorders through a controlled, model system manner.

No MeSH data available.


Related in: MedlinePlus