A genetic manipulation of motor neuron excitability does not alter locomotor output in Drosophila larvae
- Published
- Accepted
- Subject Areas
- Neuroscience, Anatomy and Physiology
- Keywords
- ion channels, motor neurons, motor pattern, locomotion, excitability, intrinsic properties, drosophila, eag, shaker, EKI
- Copyright
- © 2015 McKiernan
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. A genetic manipulation of motor neuron excitability does not alter locomotor output in Drosophila larvae. PeerJ PrePrints 3:e469v3 https://doi.org/10.7287/peerj.preprints.469v3
Abstract
Are motor neurons just passive relayers of the signals they receive? Or, do motor neurons shape the signals before passing them on to the muscles, thereby influencing the timing of motor behavior? Few direct tests of the role of motor neuron intrinsic properties in shaping motor behavior have been carried out, and many questions remain about the role of specific ion channel genes in motor neuron function. In this study, two potassium channel transgenes were expressed in Drosophila larval motor neurons to increase their excitability. Mosaic animals were created in which some identified motor neurons expressed the transgenes while others did not. Motor output underlying crawling was compared in muscles innervated by control and experimental neurons in the same animals. No effect of the transgenic manipulation on motor output was seen. Possible explanations for these results are discussed, and future experiments are outlined that could shed light on how the larval nervous system produces normal motor output in the face of altered motor neuron excitability.
Author Comment
This is version 3 of a submission to PeerJ PrePrints. Version 2 of this manuscript was previously submitted for publication in PeerJ, but was withdrawn by the author after receiving a decision of major revisions. The author was unable at the time to perform additional experiments requested by the reviewers. Although the manuscript is no longer under consideration at PeerJ, the text has been revised to address several other concerns raised by the reviewers. The reviewer comments and the author's responses are included in a supplemental document. All data and analysis code associated with this study are now publicly available on GitHub (https://github.com/emckiernan/eki-study).