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Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists

Caballero Vidal, Gabriela and Bouysset, Cedric and Gevar, Jeremy and Mbouzid, Hayat and Nara, Celine and Delaroche, Julie and Golebiowski, Jerome and Montagne, Nicolas and Fiorucci, Sebastien and Jacquin-Joly, Emmanuelle (2021). Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists. Cellular and Molecular Life Sciences. 78 , 6593-6603
[Research article]

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Abstract

The concept of reverse chemical ecology (exploitation of molecular knowledge for chemical ecology) has recently emerged in conservation biology and human health. Here, we extend this concept to crop protection. Targeting odorant receptors from a crop pest insect, the noctuid moth Spodoptera littoralis, we demonstrate that reverse chemical ecology has the potential to accelerate the discovery of novel crop pest insect attractants and repellents. Using machine learning, we first predicted novel natural ligands for two odorant receptors, SlitOR24 and 25. Then, electrophysiological validation proved in silico predictions to be highly sensitive, as 93% and 67% of predicted agonists triggered a response in Drosophila olfactory neurons expressing SlitOR24 and SlitOR25, respectively, despite a lack of specificity. Last, when tested in Y-maze behavioral assays, the most active novel ligands of the receptors were attractive to caterpillars. This work provides a template for rational design of new eco-friendly semiochemicals to manage crop pest populations.

Authors/Creators:Caballero Vidal, Gabriela and Bouysset, Cedric and Gevar, Jeremy and Mbouzid, Hayat and Nara, Celine and Delaroche, Julie and Golebiowski, Jerome and Montagne, Nicolas and Fiorucci, Sebastien and Jacquin-Joly, Emmanuelle
Title:Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists
Series Name/Journal:Cellular and Molecular Life Sciences
Year of publishing :2021
Volume:78
Page range:6593-6603
Number of Pages:11
Publisher:SPRINGER BASEL AG
ISSN:1420-682X
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Ecology
Keywords:Semiochemicals, Insects, Spodoptera littoralis, Behavior, Crop protection, Machine learning
URN:NBN:urn:nbn:se:slu:epsilon-p-113452
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-113452
Additional ID:
Type of IDID
DOI10.1007/s00018-021-03919-2
Web of Science (WoS)000689685800001
ID Code:26071
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Department of Plant Protection Biology
Deposited By: SLUpub Connector
Deposited On:08 Nov 2021 09:25
Metadata Last Modified:08 Nov 2021 09:31

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