Stochastic Model of Gap Junctions Exhibiting Rectification and Multiple Closed States of Slow Gates

Šnipas, Mindaugas; Kraujalis, Tadas; Paulauskas, Nerijus; Maciunas, Kestutis; Bukauskas, Feliksas F.

doi:10.1016/j.bpj.2016.01.035

Cited by 14 publications

(20 citation statements)

References 52 publications

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“…Particularly, the gap junction resistance has been shown to increase as w increases [72, 73, 74, 75, 76]. Several models of voltage gated gap junctions have been proposed (e.g., [77, 78, 72, 73, 79, 80]), and such a model could easily be incorporated into the EMI model by, for example, considering a gap junction model of the form where g ∈ [0, 1] is a dynamic gating variable for the gap junctions (see e.g., [29]).…”

Section: Discussionmentioning

confidence: 99%

Properties of cardiac conduction in a cell-based computational model

et al. 2019

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The conduction of electrical signals through cardiac tissue is essential for maintaining the function of the heart, and conduction abnormalities are known to potentially lead to life-threatening arrhythmias. The properties of cardiac conduction have therefore been the topic of intense study for decades, but a number of questions related to the mechanisms of conduction still remain unresolved. In this paper, we demonstrate how the so-called EMI model may be used to study some of these open questions. In the EMI model, the extracellular space, the cell membrane, the intracellular space and the cell connections are all represented as separate parts of the computational domain, and the model therefore allows for study of local properties that are hard to represent in the classical homogenized bidomain or monodomain models commonly used to study cardiac conduction. We conclude that a non-uniform sodium channel distribution increases the conduction velocity and decreases the time delays over gap junctions of reduced coupling in the EMI model simulations. We also present a theoretical optimal cell length with respect to conduction velocity and consider the possibility of ephaptic coupling (i.e. cell-to-cell coupling through the extracellular potential) acting as an alternative or supporting mechanism to gap junction coupling. We conclude that for a non-uniform distribution of sodium channels and a sufficiently small intercellular distance, ephaptic coupling can influence the dynamics of the sodium channels and potentially provide cell-to-cell coupling when the gap junction connection is absent.

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Section: Discussionmentioning

confidence: 99%

Properties of cardiac conduction in a cell-based computational model

et al. 2019

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“…Junctional conductance of electrical synapses was evaluated using a Markov chain 36-state model of voltage gating, which is detailed in [ 19 ]. The model describes the probabilistic behavior of gap junction channels in response to the transjunctional voltage.…”

Section: Methodsmentioning

confidence: 99%

“…Then, overall instantaneous junctional conductance, g j , is estimated as a weighted average of all 36 state conductances. A more detailed description is presented in [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, experimental data from our and other groups [ 17 , 18 ] allowed us to suggest that the slow gate operates between open (o), initial-closed (c 1 ) and deep-closed (c 2 ) states. Such a suggestion was implemented in a 36-state model (36SM) of voltage gating [ 19 ]. The 36SM allowed us to reproduce experimentally observed gating behavior of gap junction channels more adequately than 16SM, especially regarding the kinetics of conductance recovery, or a low fraction of functional channels clustered in junctional plaques.…”

Section: Introductionmentioning

confidence: 99%

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Functional asymmetry and plasticity of electrical synapses interconnecting neurons through a 36-state model of gap junction channel gating

et al. 2017

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We combined the Hodgkin–Huxley equations and a 36-state model of gap junction channel gating to simulate electrical signal transfer through electrical synapses. Differently from most previous studies, our model can account for dynamic modulation of junctional conductance during the spread of electrical signal between coupled neurons. The model of electrical synapse is based on electrical properties of the gap junction channel encompassing two fast and two slow gates triggered by the transjunctional voltage. We quantified the influence of a difference in input resistances of electrically coupled neurons and instantaneous conductance–voltage rectification of gap junctions on an asymmetry of cell-to-cell signaling. We demonstrated that such asymmetry strongly depends on junctional conductance and can lead to the unidirectional transfer of action potentials. The simulation results also revealed that voltage spikes, which develop between neighboring cells during the spread of action potentials, can induce a rapid decay of junctional conductance, thus demonstrating spiking activity-dependent short-term plasticity of electrical synapses. This conclusion was supported by experimental data obtained in HeLa cells transfected with connexin45, which is among connexin isoforms expressed in neurons. Moreover, the model allowed us to replicate the kinetics of junctional conductance under different levels of intracellular concentration of free magnesium ([Mg2+]i), which was experimentally recorded in cells expressing connexin36, a major neuronal connexin. We demonstrated that such [Mg2+]i-dependent long-term plasticity of the electrical synapse can be adequately reproduced through the changes of slow gate parameters of the 36-state model. This suggests that some types of chemical modulation of gap junctions can be executed through the underlying mechanisms of voltage gating. Overall, the developed model accounts for direction-dependent asymmetry, as well as for short- and long-term plasticity of electrical synapses. Our modeling results demonstrate that such complex behavior of the electrical synapse is important in shaping the response of coupled neurons.

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“…Rectification of unitary conductance was evaluated using the same method as originally proposed in [8], and later applied in our modelling studies [13,18]. That is, we presumed that the unitary conductance (γ) of an open or a closed hemichannel is a function of voltage (V) according to:…”

Section: Rectification Of Unitary Conductancementioning

confidence: 99%

4-state model for simulating kinetic and steady-state voltagedependent gating of gap junctions

Šnipas

Kraujalis

Maciunas

et al. 2019

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Gap junction (GJ) channels, formed of connexin (Cx) proteins, provide a direct pathway for metabolic and electrical cell-to-cell communication. These specialized channels are not just passive conduits for the passage of ions and metabolites, but have been shown to gate robustly in response to transjunctional voltage, Vj, the voltage difference between two coupled cells and are regulated by various chemical factors. Voltage gating of GJs may play a physiological role, particularly in excitable cells which can exhibit large transients in membrane potential during the generation of an action potential. We present a mathematical/computational model of GJ channel voltage gating to assess properties of GJ channels that takes into account contingent gating of two series hemichannels and the distribution of Vj across each hemichannel. From electrophysiological recordings in cell cultures transfected with Cx43 and Cx45, isoforms that are expressed in cardiac tissue, data sets were fit simultaneously using global optimization. The results showed that the model is capable of describing both steady-state and kinetic properties of homotypic and heterotypic GJ channels composed of these connexins. Moreover, mathematical analyses showed that the model can be simplified to a reversible two-state system and solved analytically, using a rapid equilibrium assumption. Given that excitable cells are arranged in interconnected networks, the equilibrium assumption allows for a substantial reduction in computation time, which is useful in simulations of large clusters of coupled cells. Overall, this model can serve not just as a modeling tool, but also to provide a means of testing GJ channel gating behavior. SignificanceGap junction (GJ) channels gate in response to transjunctional voltage which provides the capacity for dynamic regulation of intercellular coupling. Kinetic properties of GJs in modeling studies have been infrequently addressed and we present a computational model of voltage gating that can account for both kinetic and steady-state changes in junctional conductance, gj. Although GJs possess two gating mechanisms, our analysis indicates that changes in gj for each voltage polarity can be adequately described by a kinetic scheme describing a single mechanism in each of the hemichannels, suggesting functional dominance of one mechanism over a substantial voltage range. This property allowed for model simplification that can be applied for efficient simulation of sizeable cell clusters and analyses of electrophysiological data.

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Stochastic Model of Gap Junctions Exhibiting Rectification and Multiple Closed States of Slow Gates

Cited by 14 publications

References 52 publications

Properties of cardiac conduction in a cell-based computational model

Properties of cardiac conduction in a cell-based computational model

Functional asymmetry and plasticity of electrical synapses interconnecting neurons through a 36-state model of gap junction channel gating

4-state model for simulating kinetic and steady-state voltagedependent gating of gap junctions

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