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dc.contributor.authorLessios, Nicolas
dc.date.accessioned2017-09-14T22:08:54Z
dc.date.available2017-09-14T22:08:54Z
dc.date.issued2017-07-21
dc.identifier.citationUsing electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levels 2017, 5:e3595 PeerJen
dc.identifier.issn2167-8359
dc.identifier.pmid28740757
dc.identifier.doi10.7717/peerj.3595
dc.identifier.urihttp://hdl.handle.net/10150/625519
dc.description.abstractUnderstanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike’s information criterion (AIC c ) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis , the branchiopod water flea, Daphnia magna , normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus , which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei . The modeling approach presented here will be useful in selecting the most likely alternative hypotheses of opsin-based spectral photoreceptor classes, using relative opsin expression and extracellular electroretinography.
dc.description.sponsorshipNational Science Foundation Graduate Research Fellowship [DGE-0802261]; NIH IRACDA PERT fellowship through the Center for Insect Science at the University of Arizona [K12 GM000708]en
dc.language.isoenen
dc.publisherPEERJ INCen
dc.relation.urlhttps://peerj.com/articles/3595en
dc.rightsCopyright © 2017 Lessios. Distributed under Creative Commons CC-BY 4.0.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectColor visionen
dc.subjectElectrophysiologyen
dc.subjectVisual systemen
dc.subjectSpectral sensitivityen
dc.subjectPhotoreceptoren
dc.subjectOpsin expressionen
dc.subjectVisual pigmentsen
dc.titleUsing electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levelsen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Neuroscien
dc.identifier.journalPeerJen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionSchool of Life Sciences, Arizona State University, Tempe, AZ, USA
refterms.dateFOA2018-08-14T05:00:29Z
html.description.abstractUnderstanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike’s information criterion (AIC c ) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis , the branchiopod water flea, Daphnia magna , normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus , which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei . The modeling approach presented here will be useful in selecting the most likely alternative hypotheses of opsin-based spectral photoreceptor classes, using relative opsin expression and extracellular electroretinography.


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Copyright © 2017 Lessios. Distributed under Creative Commons CC-BY 4.0.
Except where otherwise noted, this item's license is described as Copyright © 2017 Lessios. Distributed under Creative Commons CC-BY 4.0.