White Paper Research Proposal
The following paper was submitted as an early draft of a mock white paper developed in my Scientific Communications course.

A Proposal to Examine the Role of N- and P- type Voltage-Dependent Calcium Channels in Spontaneous Bursting Activity of Cultured Neuronal Networks.
The University of North Texas requests $[to be determined] from the National Science Foundation (NSF) to explore the role of N- and P-type voltage-dependent calcium channels (VDCCs) in bursting of cultured neuronal networks.  Spontaneous bursts are a ubiquitous feature of the activity of cultured neuronal networks as well as in vivo systems.
Bursts may be a more fundamental method of interneuronal signaling than single action potentials.
Endogenous bursting is frequently associated with and believed to play a significant role in central pattern generators (CPGs), the circuitry that produces stereotypic movements such as those used in walking or swimming (Wenner, 2001).

Voltage Dependent Calcium Channels
Presently N- and P-type VDCCs are unique among the six known classes of VDCCs blocked by specific agents.  Many other VDCC antagonists exist, but act over a broader spectrum of channels.  Specifically, omega-Conotoxin GVIA is a peptide neurotoxin which selectively and reversibly blocks N-type VDCCs. Omega-Agatoxin IVA selectively and reversibly blocks P-type VDCCs.

Rhoades and Gross (1994) established a dependence on extracellular calcium in bursting using more broad spectrum VDCC antagonists such as veratridine and ditiazem.  The greater specificity of the compounds employed in the proposed study will greatly resolve the roles of two calcium channels.

Both channels possess different gating characteristics and tissue specificity.  For example, N-type VDCCs are activated by high voltage, while P-type VDCCs are activated by low voltage.  Further, N-type VDCCs are opened transiently while T-type VDCCs remain open for a greater duration.  The differences between these and other types of VDCCs almost certainly to convey neuronal networks the ability of modulate various parameters of their bursts.

Activity of the networks will be recorded using micro-electrode arrays, glass plates onto which electrodes are photoetched (Rhoades and Gross, 1994).  Frontal cortex and spinal cord tissue will be cultured from embryonic murine tissue on these arrays.

The data files are then processed by our in-house software, IBurst, which derives a number of parameters of activity such as spike and burst production, burst duration, interburst interval, frequency of spikes in a burst, and so on.  Bursts lend themselves to a number of more complex analyses than spike production alone.  Whereas spike production is limited to relatively few measures (primarily frequencies counts), a number of parameters may be analyzed from bursting neurons.  These parameters may be influenced directly by mechanisms tied to specific subtypes of VDCCs.

Further, relationships may exist between apparently independent measures of activity which can be teased out by correlation analyses such as factor or principal components analysis.  Attempts at these analyses will lay the groundwork for additional applications of statistical inference on other data sets collected in this laboratory outside the scope of this study.

The tremendous advances in computing power over the past decade have allowed the shift from primarily qualitative research (comparisons of strip chart recordings) to considerably more quantitative work.  For example, the number of parameters our software may derive from the activity of a neuron has increased from roughly half a dozen to more than 40 variables.  Additional, biologically relevant ratios between these variables may be revealed in this study.

While these innovations increase the quantity of data, they simultaneously create a greater workload for the researcher in the analysis stage of the research.  It is imperative that data analysis be refined and automated as much as possible.  A programmer will be required to develop macros for sorting and analyzing this vast quantity of values.  Since data will be collected for the same series of activity parameters for all experiments, regardless of their aim, these macros will be utilized for additional studies, including previously collected data.

The Center for Network Neuroscience
This research will be conducted in the laboratories of the Center for Network Neuroscience (CNNS), which includes a reliable infrastructure of equipment and software and its own staffed cell culture facility.

Since its inception in 1987 the CNNS has worked to develop a library of activity profiles of neuroactive compounds.  Such "fingerprints" have assisted in the prediction of the clinical effects of compounds through the comparison of blind samples with the effects of previously tested compounds (Keefer, 2001).  However, the existing library contains few examples of calcium channel antagonists and no examples with the specificity of the agents described above.

Another goal of this work is the development of new approaches for summarizing data into readily identifiable profiles of influences on network activity.  Because preliminary results indicate that VDCCs preferentially influence burst production, this study represents a unique opportunity to refine techniques in data analysis and data.  As such, this work may provide guidance in optimizing the existing compound library.

Budget Information
  •  Programmer: SPSS macros
  •  Software: SPSS
  •  Cell Culture: technician and materials (medium, mice, pipettes, etc.)

Institutional Contacts:
removed for individuals' privacy.

Keefer EW, Gramowski A, Stenger DA, Pancrazio JJ, Gross GW. (2001) Characterization of acute neurotoxic effects of trimethylolpropane phosphate via neuronal network biosensors. Biosens Bioelectron 16:513-25.
Rhoades, BK and Gross, GW. (1994) Potassium and calcium channel dependence of bursting in cultured neuronal networks. Brain Research 643: 310-318.
Wenner P, O'Donovan MJ. (2001) Mechanisms that initiate spontaneous network activity in the developing chick spinal cord.  J Neurophysiol 86:1481-98.

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