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Cell Mol Life Sci

Title:		Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists
Author(s):		Caballero-Vidal G; Bouysset C; Gevar J; Mbouzid H; Nara C; Delaroche J; Golebiowski J; Montagne N; Fiorucci S; Jacquin-Joly E;
Address:		"INRAE, Sorbonne Universite, CNRS, IRD, UPEC, Universite de Paris, Institute of Ecology and Environmental Sciences of Paris, 78000, Versailles, France. Disease Vector Group, Chemical Ecology Unit, Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden. Max Planck Centre Next Generation Chemical Ecology, Uppsala, Sweden. Universite Cote d'Azur, CNRS, Institut de Chimie de Nice UMR7272, 28 avenue Valrose, 06108, Nice, France. Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, 711-873, South Korea. INRAE, Sorbonne Universite, CNRS, IRD, UPEC, Universite de Paris, Institute of Ecology and Environmental Sciences of Paris, 78000, Versailles, France. nicolas.montagne@sorbonne-universite.fr. Universite Cote d'Azur, CNRS, Institut de Chimie de Nice UMR7272, 28 avenue Valrose, 06108, Nice, France. sebastien.fiorucci@univ-cotedazur.fr. INRAE, Sorbonne Universite, CNRS, IRD, UPEC, Universite de Paris, Institute of Ecology and Environmental Sciences of Paris, 78000, Versailles, France. emmanuelle.joly@inrae.fr"
Journal Title:		Cell Mol Life Sci
Year:		2021
Volume:		20210827
Issue:		19-20
Page Number:		6593 - 6603
DOI:		10.1007/s00018-021-03919-2
ISSN/ISBN:		1420-9071 (Electronic) 1420-682X (Print) 1420-682X (Linking)
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"
Keywords:		"Animals Drosophila/drug effects/metabolism Insect Proteins/metabolism Insect Repellents/pharmacology Machine Learning Moths/*drug effects/*metabolism Odorants Pheromones/pharmacology Receptors, Odorant/*metabolism Smell/drug effects Spodoptera/drug effect;"
Notes:		"MedlineCaballero-Vidal, Gabriela Bouysset, Cedric Gevar, Jeremy Mbouzid, Hayat Nara, Celine Delaroche, Julie Golebiowski, Jerome Montagne, Nicolas Fiorucci, Sebastien Jacquin-Joly, Emmanuelle eng ANR-16-CE21-0002/Agence Nationale de la Recherche/ ANR-15-IDEX-01/Agence Nationale de la Recherche/ Switzerland 2021/08/28 Cell Mol Life Sci. 2021 Oct; 78(19-20):6593-6603. doi: 10.1007/s00018-021-03919-2. Epub 2021 Aug 27"