A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna.
Action Potentials
/ physiology
Animals
Arthropod Antennae
/ metabolism
Drosophila Proteins
/ metabolism
Drosophila melanogaster
/ metabolism
Models, Molecular
Models, Theoretical
Odorants
Olfactory Receptor Neurons
/ metabolism
Protein Binding
Receptors, Odorant
/ metabolism
Signal Transduction
Smell
/ physiology
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
06
06
2019
accepted:
27
02
2020
revised:
24
04
2020
pubmed:
15
4
2020
medline:
15
7
2020
entrez:
15
4
2020
Statut:
epublish
Résumé
Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.
Identifiants
pubmed: 32287275
doi: 10.1371/journal.pcbi.1007751
pii: PCOMPBIOL-D-19-00920
pmc: PMC7182276
doi:
Substances chimiques
Drosophila Proteins
0
Receptors, Odorant
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1007751Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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