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Discovering the Mysteries of Epilepsy Using pClamp Analysis


Epileptic seizures are rapid and rhythmic electrical bursts in the brain that disrupt normal activity. More then 2 million Americans have epilepsy, a disorder which can be caused by problems such as abnormal brain development, brain damage from illness or injury and genetic mutations.

Inherited syndromes account for about 5% of all epilepsy cases. In this later category is a syndrome called generalized epilepsy with febrile (fever) seizures (GEFS+). The cause of this syndrome seems to involve a genetic mutation of the sodium ion (Na+) channels in certain cells. These channels are donut-like pores that act like gates that let electrical current in the form of ions flow into the cell and then close. The closing, or inactivation, occurs improperly in mutant channels.

In 1998, researchers at Vanderbilt and elsewhere were able to identify the first Na+ channel gene mutation for inherited epilepsy. Later, additional mutations were identified. The researchers then replicated "healthy" as well as mutant versions of this Na+ channel in the laboratory to study the functional effects of the mutation at the molecular level.

The experimental data produced during the studies at Vanderbilt consisted of normalized current measurements from excited tissues resembling mutant and non-mutant ion channels as they were subjected to three different pulse protocols. From the resulting data, the researchers built an accurate model that characterized the Na+ channels. The model allowed a clear understanding of this malfunction and became a significant system to test drugs to restore proper function to these channels.

How Origin Was Used

The researchers at Vanderbilt took advantage of Origin's ability to import and plot pClamp data, as well its ability to perform nonlinear curve fitting with the Nonlinear Least Squares Fitter (NLSF) (Fig 3).

To import the data, Origin's built-in pClamp import routine was used.

Figure 1:
Origin's pClamp Options dialog

Then, the data was plotted as a function of pre-pulse duration (Fig. 2a), pre-pulse potential (Fig. 2b), recovery period (Fig. 2c). This showed the response of the ion channels during the pClamp experiment.

Figure 2 (a, b, c):
This figure, a three-panel graph, shows the electrophysiological response of normal (wild type WT) and mutant (I1656M and R1656C) human neuronal voltage-gated Na+ channel SCN1A (Nav1.1).

But characterizing the model graphically is only qualitative. Implicit parametric information had to be extracted. This was done using nonlinear regression analysis. Each of the three sets of data was fit using the appropriate fitting model in Origin's NLSF.

Figure 3:
Origin's NLSF depicting a generic version of the Boltzmann fitting model

  1. Data from onset of slow inactivation experiments was plotted versus the length of the conditioning pulse and fitted to a second order exponential decay function:

    where Ai describes the fraction of the channels entering slow inactivation with time constant τi, and y0 describes fraction of the channels that remains active.

  2. Steady-state slow inactivation data was fitted to the Boltzmann function:

    where V1/2 is the voltage where half-maximal slow inactivation occurs, k is the slope factor of the fit, and y0 is the fraction of the channels that remains active.

  3. Recovery from slow inactivation was fitted to the second order exponential function (referred to as the Exponential Associate function in Origin):

    where Ai refers the fraction of the channels recovering with time constant τi.

The NLSF interface allows complete control over the fitting process and is used to generate the published fit curves above (Fig.2) and the published parameters that clarify the Na+ channel functions. Results are presented as means ± standard error.

This work is part of a series of experiments reported in J Neurosci. 2003 Dec 10;23(36):11289-95.


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