Biology subjects

Lalis, M.

Publications and source records attributed to Lalis, M..

3 recordsLinked to original sources

bioRxiv2026

Single-point mutation alters odorant receptor sensitivity associated with host plant specialization in Spodoptera moths

Host specialization in herbivorous insects is often associated with divergence in chemosensory abilities. Here, we investigated the possible contribution of odorant receptors (ORs) in host plant restriction in the lily moth Spodoptera picta, a species specialized on Amaryllidaceae. Manual annotation of S. picta ORs in its genome revealed a repertoire similar in size and composition to those of its polyphagous sister species, S. littoralis and S. litura, suggesting that specialization did not involve major gene loss or expansion in the lily moth. To assess functional divergence beyond gene number, we applied a large scaled structure-based virtual screening approach to the entire OR repertoires of these three Spodoptera species, generating ligand-binding profiles for 120,591 volatile compounds. Among 69 1:1:1 OR orthologs, 24 exhibited divergent predicted binding spectra. We pinpointed OR29 that we also found to be highly expressed in both male and female antennae of S. picta through a RNAseq approach. Functional assays demonstrated that S. picta OR29 acquired heightened sensitivity to limonene enantiomers, volatiles emitted by host Amaryllidaceae inflorescences. Site-directed mutagenesis revealed that a single amino acid substitution within the predicted binding region underlies this shift in sensitivity. These results show that host specialization in S. picta has not been accompanied by significant OR repertoire remodeling, but rather by subtle molecular changes that fine-tune receptor sensitivity to host-derived volatiles.

molecular biology↗

bioRxiv2025

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual responses. While such mappings are well characterized in vision and audition, they remain poorly defined in olfaction, limiting progress toward understanding the representations of smell. Predicting perceptual similarity between odor mixtures offers a promising route to formalize these relationships. To advance this effort, the DREAM (Dialogue for Reverse Engineering Assessment and Methods) Olfactory Mixtures Prediction Challenge assembled a curated, cross-study dataset describing the similarity of 507 mixture pairs and an unpublished test set of 46 mixture pairs. Teams competed to predict the perceptual similarity of mixture pairs, and then collaborated post-challenge to create an ensemble combining top-performing models that notably improves predictions over the existing state-of-the-art models. Moreover, ensemble model maintains high predictive accuracy in novel validation set. Our model provides a reproducible framework for neuroscientists, chemists, and engineers to compare odor mixtures and provides a foundation for future efforts towards better understanding the olfactory properties of mixtures.

neuroscience↗

bioRxiv2024

Accelerating Ligand Discovery for Insect Odorant Receptors

Odorant receptors (ORs) are main actors of the insects peripheral olfactory system, making them prime targets for pest control through olfactory disruption. Traditional methods employed in the context of chemical ecology for identifying OR ligands rely on analyzing compounds present in the insects environment or screening molecules with structures similar to known ligands. However, these approaches can be time-consuming and constrained by the limited chemical space they explore. Recent advances in OR structural understanding, coupled with scientific breakthroughs in protein structure prediction, have facilitated the application of structure-based virtual screening (SBVS) techniques for accelerated ligand discovery. Here, we report the first successful application of SBVS to insect ORs. We developed a unique workflow that combines molecular docking predictions, in vivo validation and behavioral assays to identify new behaviorally active volatiles for non-pheromonal receptors. This work serves as a proof of concept, laying the groundwork for future studies and highlighting the need for improved computational approaches. Finally, we propose a simple model for predicting receptor response spectra based on the hypothesis that the binding pocket properties partially encode this information, as suggested by our results on Spodoptera littoralis ORs.

bioinformatics↗