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

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.

AlepPBP2, but not AlepPBP3, may involve in the recognition of sex pheromones and maize volatiles inAthetis lepigone

“…Usually, the interaction between olfactory proteins and different odorants are studied in vitro to screen out the odorants that can highly bind to olfactory proteins, and then conduct behavioral experiments to further determine which highly bound odorants have behavioral attraction or repellent activity. This has been successfully applied in different insects, such as Culex quinquefasciatus (Choo et al ., 2018), Rhodnius prolixus (Franco et al ., 2018), Aenasius bambawalei (Li et al ., 2018a), and Spodoptera littoralis (Caballero-Vidal et al ., 2021). Therefore, the assays on the binding affinity of AlepPBPs and volatiles in our study belongs to a part of reverse chemical ecology, the results will provide important help for us to develop attractants or repellents in the future.…”

“…Therefore, it is necessary to strengthen dynamic monitoring, timely make pest predictions to reduce the damage or develop novel green pest control strategies in modern pest management (Sarfraz et al ., 2005; Choo et al ., 2018; Hackett and Bonsall, 2019). The development of novel green behavioral inhibitors based on olfactory genes has become a hot spot research field for pest control (Pelosi et al ., 2018; Caballero-Vidal et al ., 2021). In order to fully understand the concept of insect chemical communication, we have recently identified two female sex pheromone of A. lepigone as (Z)-7-dodecenyl acetate (Z7-12:Ac) and (Z)-9-tetradecenyl acetate (Z9-14:Ac) with a ratio of 1:5 by analyzing the extracts of the female sex pheromone gland (Yan et al ., 2018), and obtained three antennae-enriched PBP genes (AlepPBP1-3) in A. lepigone by antennal transcriptome analysis (Zhang et al ., 2017).…”

Behavioural regulator and molecular reception of a double‐edge‐sword hunter beetle

“…Computational biology has improved dramatically, revealing many inspiring results and potential products for insect‐targeted volatile ligands 53–55 . However, compared with insect OBPs, it is still hard to model OR homologues and conduct simulated docking because of a lack of background databases 56,57 .…”

“…However, we do not exclude the existence of potential agonists or artificial odorants that may have better performance or a fit better with key CmaxORs of C. maximoviczi adults. Although we can now achieve a swift screen for natural products, it would be beneficial to overcome the selection of potential bioactive volatiles from millions of chemicals using ligand‐based approaches 54 . Nevertheless, the current work could benefit from larger replicates, indoor bioassays and more importantly, functional deorphanization of receptors with heterologous systems ( Xenopus oocyte or Drosophila empty neuron) and in vivo approaches (RNA interference or CRISPR–Cas9) 61 .…”

“…Computational biology has improved dramatically, revealing many inspiring results and potential products for insect-targeted volatile ligands. [53][54][55] However, compared with insect OBPs, it is still hard to model OR homologues and conduct simulated docking because of a lack of background databases. 56,57 The advanced modelling tool has provided us with a chance to carry out reliable predictions of OR structures from scratch and obtain complete docking results for further analysis.…”