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Computational Model of the Insect Pheromone Transduction Cascade
A biophysical model of receptor potential generation in the male moth olfactory receptor neuron is presented. It takes into account all pre-effector processes—the translocation of pheromone molecules from air to sensillum lymph, their deactivation and interaction with the receptors, and the G-protei...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649447/ https://www.ncbi.nlm.nih.gov/pubmed/19300479 http://dx.doi.org/10.1371/journal.pcbi.1000321 |
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author | Gu, Yuqiao Lucas, Philippe Rospars, Jean-Pierre |
author_facet | Gu, Yuqiao Lucas, Philippe Rospars, Jean-Pierre |
author_sort | Gu, Yuqiao |
collection | PubMed |
description | A biophysical model of receptor potential generation in the male moth olfactory receptor neuron is presented. It takes into account all pre-effector processes—the translocation of pheromone molecules from air to sensillum lymph, their deactivation and interaction with the receptors, and the G-protein and effector enzyme activation—and focuses on the main post-effector processes. These processes involve the production and degradation of second messengers (IP(3) and DAG), the opening and closing of a series of ionic channels (IP(3)-gated Ca(2+) channel, DAG-gated cationic channel, Ca(2+)-gated Cl(−) channel, and Ca(2+)- and voltage-gated K(+) channel), and Ca(2+) extrusion mechanisms. The whole network is regulated by modulators (protein kinase C and Ca(2+)-calmodulin) that exert feedback inhibition on the effector and channels. The evolution in time of these linked chemical species and currents and the resulting membrane potentials in response to single pulse stimulation of various intensities were simulated. The unknown parameter values were fitted by comparison to the amplitude and temporal characteristics (rising and falling times) of the experimentally measured receptor potential at various pheromone doses. The model obtained captures the main features of the dose–response curves: the wide dynamic range of six decades with the same amplitudes as the experimental data, the short rising time, and the long falling time. It also reproduces the second messenger kinetics. It suggests that the two main types of depolarizing ionic channels play different roles at low and high pheromone concentrations; the DAG-gated cationic channel plays the major role for depolarization at low concentrations, and the Ca(2+)-gated Cl(−) channel plays the major role for depolarization at middle and high concentrations. Several testable predictions are proposed, and future developments are discussed. |
format | Text |
id | pubmed-2649447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-26494472009-03-20 Computational Model of the Insect Pheromone Transduction Cascade Gu, Yuqiao Lucas, Philippe Rospars, Jean-Pierre PLoS Comput Biol Research Article A biophysical model of receptor potential generation in the male moth olfactory receptor neuron is presented. It takes into account all pre-effector processes—the translocation of pheromone molecules from air to sensillum lymph, their deactivation and interaction with the receptors, and the G-protein and effector enzyme activation—and focuses on the main post-effector processes. These processes involve the production and degradation of second messengers (IP(3) and DAG), the opening and closing of a series of ionic channels (IP(3)-gated Ca(2+) channel, DAG-gated cationic channel, Ca(2+)-gated Cl(−) channel, and Ca(2+)- and voltage-gated K(+) channel), and Ca(2+) extrusion mechanisms. The whole network is regulated by modulators (protein kinase C and Ca(2+)-calmodulin) that exert feedback inhibition on the effector and channels. The evolution in time of these linked chemical species and currents and the resulting membrane potentials in response to single pulse stimulation of various intensities were simulated. The unknown parameter values were fitted by comparison to the amplitude and temporal characteristics (rising and falling times) of the experimentally measured receptor potential at various pheromone doses. The model obtained captures the main features of the dose–response curves: the wide dynamic range of six decades with the same amplitudes as the experimental data, the short rising time, and the long falling time. It also reproduces the second messenger kinetics. It suggests that the two main types of depolarizing ionic channels play different roles at low and high pheromone concentrations; the DAG-gated cationic channel plays the major role for depolarization at low concentrations, and the Ca(2+)-gated Cl(−) channel plays the major role for depolarization at middle and high concentrations. Several testable predictions are proposed, and future developments are discussed. Public Library of Science 2009-03-20 /pmc/articles/PMC2649447/ /pubmed/19300479 http://dx.doi.org/10.1371/journal.pcbi.1000321 Text en Gu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Gu, Yuqiao Lucas, Philippe Rospars, Jean-Pierre Computational Model of the Insect Pheromone Transduction Cascade |
title | Computational Model of the Insect Pheromone Transduction Cascade |
title_full | Computational Model of the Insect Pheromone Transduction Cascade |
title_fullStr | Computational Model of the Insect Pheromone Transduction Cascade |
title_full_unstemmed | Computational Model of the Insect Pheromone Transduction Cascade |
title_short | Computational Model of the Insect Pheromone Transduction Cascade |
title_sort | computational model of the insect pheromone transduction cascade |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649447/ https://www.ncbi.nlm.nih.gov/pubmed/19300479 http://dx.doi.org/10.1371/journal.pcbi.1000321 |
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