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Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision
Electrophysiological studies indicate altered contrast processing in some Parkinson’s Disease (PD) patients. We recently demonstrated that vision is altered in Drosophila PD models and hypothesised that different types of genetic and idiopathic PD may affect dopaminergic visual signalling pathways d...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657034/ https://www.ncbi.nlm.nih.gov/pubmed/26597171 http://dx.doi.org/10.1038/srep16933 |
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author | West, Ryan J.H. Elliott, Christopher J.H. Wade, Alex R. |
author_facet | West, Ryan J.H. Elliott, Christopher J.H. Wade, Alex R. |
author_sort | West, Ryan J.H. |
collection | PubMed |
description | Electrophysiological studies indicate altered contrast processing in some Parkinson’s Disease (PD) patients. We recently demonstrated that vision is altered in Drosophila PD models and hypothesised that different types of genetic and idiopathic PD may affect dopaminergic visual signalling pathways differently. Here we asked whether visual responses in Drosophila could be used to identify PD mutations. To mimic a clinical setting a range of flies was used. Young flies from four control lines were compared to three early-onset PD mutations (PINK1, DJ-1α and DJ-1β), and to two other neurodegenerative mutations, one in the fly LRRK2 orthologue (dLRRK) the other in eggroll, a model of general neurodegeneration in Drosophila. Stimuli were contrast reversing gratings spanning 64 spatiotemporal frequency combinations. We recorded the steady-state visually-evoked response amplitude across all combinations. We found that the pattern of neuronal responses differed between genotypes. Wild-type and early-onset PD flies formed separate clusters; the late-onset mutation is an outlier. Neuronal responses in early-onset PD flies were stronger than in wild-types. Multivariate pattern analysis grouped flies by PD/non-PD genotype with an accuracy >85%. We propose that machine learning algorithms may be useful in increasing the diagnostic specificity of human electrophysiological measurements in both animal models and PD patients. |
format | Online Article Text |
id | pubmed-4657034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46570342015-11-30 Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision West, Ryan J.H. Elliott, Christopher J.H. Wade, Alex R. Sci Rep Article Electrophysiological studies indicate altered contrast processing in some Parkinson’s Disease (PD) patients. We recently demonstrated that vision is altered in Drosophila PD models and hypothesised that different types of genetic and idiopathic PD may affect dopaminergic visual signalling pathways differently. Here we asked whether visual responses in Drosophila could be used to identify PD mutations. To mimic a clinical setting a range of flies was used. Young flies from four control lines were compared to three early-onset PD mutations (PINK1, DJ-1α and DJ-1β), and to two other neurodegenerative mutations, one in the fly LRRK2 orthologue (dLRRK) the other in eggroll, a model of general neurodegeneration in Drosophila. Stimuli were contrast reversing gratings spanning 64 spatiotemporal frequency combinations. We recorded the steady-state visually-evoked response amplitude across all combinations. We found that the pattern of neuronal responses differed between genotypes. Wild-type and early-onset PD flies formed separate clusters; the late-onset mutation is an outlier. Neuronal responses in early-onset PD flies were stronger than in wild-types. Multivariate pattern analysis grouped flies by PD/non-PD genotype with an accuracy >85%. We propose that machine learning algorithms may be useful in increasing the diagnostic specificity of human electrophysiological measurements in both animal models and PD patients. Nature Publishing Group 2015-11-24 /pmc/articles/PMC4657034/ /pubmed/26597171 http://dx.doi.org/10.1038/srep16933 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article West, Ryan J.H. Elliott, Christopher J.H. Wade, Alex R. Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision |
title | Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision |
title_full | Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision |
title_fullStr | Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision |
title_full_unstemmed | Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision |
title_short | Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision |
title_sort | classification of parkinson’s disease genotypes in drosophila using spatiotemporal profiling of vision |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657034/ https://www.ncbi.nlm.nih.gov/pubmed/26597171 http://dx.doi.org/10.1038/srep16933 |
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