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AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of caenorhabditis elegans

AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of caenorhabditis elegans
AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of caenorhabditis elegans
Background
The pharyngeal microcircuit of the nematode Caenorhabditis elegans serves as a model for analysing neural network activity and is amenable to electrophysiological recording techniques. One such technique is the electropharyngeogram (EPG) which has provided insight into the genetic basis of feeding behaviour, neurotransmission and muscle excitability. However, the detailed manual analysis of the digital recordings necessary to identify subtle differences in activity that reflect modulatory changes within the underlying network is time consuming and low throughput. To address this we have developed an automated system for the high-throughput and discrete analysis of EPG recordings (AutoEPG).

Methodology/Principal Findings
AutoEPG employs a tailor made signal processing algorithm that automatically detects different features of the EPG signal including those that report on the relaxation and contraction of the muscle and neuronal activity. Manual verification of the detection algorithm has demonstrated AutoEPG is capable of very high levels of accuracy. We have further validated the software by analysing existing mutant strains with known pharyngeal phenotypes detectable by the EPG. In doing so, we have more precisely defined an evolutionarily conserved role for the calcium-dependent potassium channel, SLO-1, in modulating the rhythmic activity of neural networks.

Conclusions/Significance
AutoEPG enables the consistent analysis of EPG recordings, significantly increases analysis throughput and allows the robust identification of subtle changes in the electrical activity of the pharyngeal nervous system. It is anticipated that AutoEPG will further add to the experimental tractability of the C. elegans pharynx as a model neural circuit.
1932-6203
1-13
Dillon, James
f406e30a-3ad4-4a53-80db-6694bab5e3ed
Andrianakis, Ioannis
130365dc-7914-4b33-87b2-92eca9da10a5
Bull, Kate
299fd485-3dd0-413d-a033-af6cfd69b77b
Glautier, Steve
964468b2-3ad7-40cc-b4be-e35c7dee518f
O'Connor, Vincent
8021b06c-01a0-4925-9dde-a61c8fe278ca
Holden-Dye, L.
8032bf60-5db6-40cb-b71c-ddda9d212c8e
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
Dillon, James
f406e30a-3ad4-4a53-80db-6694bab5e3ed
Andrianakis, Ioannis
130365dc-7914-4b33-87b2-92eca9da10a5
Bull, Kate
299fd485-3dd0-413d-a033-af6cfd69b77b
Glautier, Steve
964468b2-3ad7-40cc-b4be-e35c7dee518f
O'Connor, Vincent
8021b06c-01a0-4925-9dde-a61c8fe278ca
Holden-Dye, L.
8032bf60-5db6-40cb-b71c-ddda9d212c8e
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52

Dillon, James, Andrianakis, Ioannis, Bull, Kate, Glautier, Steve, O'Connor, Vincent, Holden-Dye, L. and James, C.J. (2009) AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of caenorhabditis elegans. PLoS ONE, 4 (12), 1-13. (doi:10.1371/journal.pone.0008482).

Record type: Article

Abstract

Background
The pharyngeal microcircuit of the nematode Caenorhabditis elegans serves as a model for analysing neural network activity and is amenable to electrophysiological recording techniques. One such technique is the electropharyngeogram (EPG) which has provided insight into the genetic basis of feeding behaviour, neurotransmission and muscle excitability. However, the detailed manual analysis of the digital recordings necessary to identify subtle differences in activity that reflect modulatory changes within the underlying network is time consuming and low throughput. To address this we have developed an automated system for the high-throughput and discrete analysis of EPG recordings (AutoEPG).

Methodology/Principal Findings
AutoEPG employs a tailor made signal processing algorithm that automatically detects different features of the EPG signal including those that report on the relaxation and contraction of the muscle and neuronal activity. Manual verification of the detection algorithm has demonstrated AutoEPG is capable of very high levels of accuracy. We have further validated the software by analysing existing mutant strains with known pharyngeal phenotypes detectable by the EPG. In doing so, we have more precisely defined an evolutionarily conserved role for the calcium-dependent potassium channel, SLO-1, in modulating the rhythmic activity of neural networks.

Conclusions/Significance
AutoEPG enables the consistent analysis of EPG recordings, significantly increases analysis throughput and allows the robust identification of subtle changes in the electrical activity of the pharyngeal nervous system. It is anticipated that AutoEPG will further add to the experimental tractability of the C. elegans pharynx as a model neural circuit.

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Published date: 2009

Identifiers

Local EPrints ID: 141496
URI: http://eprints.soton.ac.uk/id/eprint/141496
ISSN: 1932-6203
PURE UUID: f56b9e82-3277-4814-8abc-957cd9ab0aa0
ORCID for James Dillon: ORCID iD orcid.org/0000-0003-3244-7483
ORCID for Steve Glautier: ORCID iD orcid.org/0000-0001-8852-3268
ORCID for Vincent O'Connor: ORCID iD orcid.org/0000-0003-3185-5709
ORCID for L. Holden-Dye: ORCID iD orcid.org/0000-0002-9704-1217

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Date deposited: 29 Mar 2010 14:31
Last modified: 14 Mar 2024 02:48

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Contributors

Author: James Dillon ORCID iD
Author: Ioannis Andrianakis
Author: Kate Bull
Author: Steve Glautier ORCID iD
Author: L. Holden-Dye ORCID iD
Author: C.J. James

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