CML_Oral_Presentation
-
Upload
chris-locandro -
Category
Documents
-
view
22 -
download
0
Transcript of CML_Oral_Presentation
The Effect of the Epilepsy-Associated R1648H Sodium
Channel Mutation on Neuronal Excitability: A Model Study
Chris Locandro & Robert Clewley
Neuroscience Institute, Department of Mathematics & Statistics, Georgia State University
Introduction: The Utility of Modeling in Neuroscience
• Why Model?– Explore pathological parameter values (e.g. effect of
mutations)– Explore the effects of drugs/environmental conditions– Understand a complex system or a particular mechanism– Predict short-term future of a system– Ethical constraints, limits on human experimentation
•Examples• IBM’s Blue Brain Project
Experimental Question/Goals
• How does the R1648H sodium channel mutation affect the excitability of a CA3 neuron model and why?
• Understand how the mutant sodium channel interacts with other currents to give rise to epileptiform activity (in progress)
CA3 Hippocampal Neuron Model
€
CdV
dt= I stim − INa − IK − ILeak − IA − IM − IAHP − IKC − ICaL − ICaN − ICaT
Xu & Clancy 2008 PLoS ONE
•Hyperexcitability of neurons in the hippocampus has been implicated in forms of epilepsy
The Hodgkin-Huxley (HH) Model
• Quantitative model of action potential generation in single neurons
• Membrane as an equivalent circuit
• Ohm’s Law, Kirchhoff’s Law, Charging of a Capacitor
€
CdV
dt= Iapplied − INa − IK − ILeak
Ion Channel Kinetics
€
INa = m3h V − ENa( )m = Activationh = Inactivation
€
dx
dt=α x (V ) 1− x( ) − β x (V )x
High Voltages: Large m, Small hLow Voltages: Small m, Large h
The R1648H Mutation
• Neuronal NaV1.1 channel• Missense mutation (Domain IV):
R1648H
From Avanzini 2003 Lancet Neurol.
Clancy & Kass 2004 Biophys J
Markov Chains & The Clancy Model• Channel can reside in 1 of 14 hypothetical states• Each state has a probability (0-1), which changes as a function of incoming
and outgoing rates• Na current is a function of the probability of the channel being in the
open state
€
INa = g NaPO (V − ENa )
Wild-Type States
(Upper)Mutant States
(Upper & Lower)
Clancy & Kass 2004 Biophys J
Methods
• Computer simulation using Python/PyDSTool• Embedding Markov models into full, single-
compartment neuron models• Reproducing output of Clancy/Xu models for
validation• f-I curves and spike/burst metrics to
characterize excitability• Derivative event detection for simple HH model
Clewley 2004
Methods (cont.)
• Simple Neuron Embedding:
€
CdV
dt= Iapplied − g NaPO V − ENa( ) + g K n4 V − EK( ) + g l V − El( )( )
€
dV
dt= 0.5
Clancy & Kass 2004 Biophys J
• Inserting an ion channel model into a previously developed full-neuron model is not trivial, so we manually control potassium to ensure a proper spike:
Results: Effects of the Mutation on Ion Channel Function
1) Increase in Peak Current:
2) Impaired/Incomplete Inactivation:
Results: Simple HH Model Embedding• f-I Curves: frequency response of a neuron to constant stimulus currents (could be input from a pre-synaptic neuron) of different magnitudes
•Effective measure of neuronal excitability
Iapp = 5 pA
Results: CA3 Hippocampal Neuron
•Apply transient (5 ms) stimulus current of 0.5 pA:
•Mutant neuron responds with much higher frequency and continues firing, even though the applied stimulus is gone
Conclusions
• The mutation induces subtle changes in spike metrics of the simple HH model, but does not significantly alter excitability
• The mutation causes drastic dynamical changes when embedded into a complex, physiologically relevant neuron model
• These findings illustrate that the interplay between the sodium current and other currents in the complex neuron model gives rise to unpredictable emergent properties
• We’ve also shed light on a mechanism of hyperexcitability that may underlie seizure generation/propagation in epilepsy
Future Directions
• Use dynamical system reduction techniques to understand how the Na+ current is interacting with other currents to cause the macroscopic burst change
• Incorporate ion channel model into another previously developed model of CA1/CA3 neurons and compute “excitability measure”
• Develop protocol for integration of ion channel models into complex neuron models with different time scales
Nowacki et al. 2011 Prog Biophys Mol Biol
References
• Clancy CE, Kass RS (2004) Theoretical investigation of the neuronal Na channel SCN1A: abnormal gating and epilepsy. Biophys J 86:2606 –2614.
• Xu J, Clancy CE (2008) Ionic Mechanisms of Endogenous Bursting in CA3 Hippocampal Pyramidal Neurons: A Model Study. PLoS ONE 3(4): e2056. doi:10.1371/journal.pone.0002056
• Nowacki J, Osinga HM, Brown JT, Randall AD, Tsaneva-Atanasova K (2011) A unified model of CA1/3 pyramidal cells: an investigation into excitability.Prog Biophys Mol Biol, 105(1-2):34-48.
• RH Clewley, WE Sherwood, MD Lamar, JM Guckenheimer (2004). PyDSTool: a software environment for dynamical systems modeling. http://pydstool.sourceforge.net
• Avanzini G., Franceschetti S. (2003). Cellular biology of epileptogenesis. Lancet Neurol. 2, 33–42. doi: 10.1016/S1474-4422(03)00265-5.
• http://bluebrain.epfl.ch/