Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists - Archive ouverte HAL Access content directly
Journal Articles Cellular and Molecular Life Sciences Year : 2021

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

Gabriela Caballero-Vidal
  • Function : Author
Cédric Bouysset
Jérémy Gévar
  • Function : Author
Hayat Mbouzid
  • Function : Author
Céline Nara
  • Function : Author
Julie Delaroche
  • Function : Author
Jérôme Golebiowski
Nicolas Montagné
Sébastien Fiorucci

Abstract

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.

Dates and versions

hal-03635144 , version 1 (08-04-2022)

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Gabriela Caballero-Vidal, Cédric Bouysset, Jérémy Gévar, Hayat Mbouzid, Céline Nara, et al.. Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists. Cellular and Molecular Life Sciences, 2021, 78 (19-20), pp.6593-6603. ⟨10.1007/s00018-021-03919-2⟩. ⟨hal-03635144⟩
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