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      Heterologous expression and functional characterization of Drosophila suzukii OR69a transcript variants unveiled response to kairomones and to a candidate pheromone

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          Abstract

          Drosophila suzukii is a main pest of berries on all continents. Population control relies on insecticides and a current research challenge is to develop alternative strategies. Methods based on behavior-modifying semiochemicals are widely used against other horticultural insects, and the functional characterization of chemoreceptors facilitates the identification of active compounds. Following heterologous expression of single olfactory receptors (ORs) in ab3A olfactory sensory neurons of D. melanogaster, we screened three transcript variants from the OR69a-locus (DsuzOR69aA, DsuzOR69aB, DsuzOR69aC), demonstrating binding to two possible kairomones (3-octanol and R-carvone) and to a possible fly-emitted volatile compound [(Z)-4-nonenal], although with different pharmacological qualities. By coupling Gas Chromatography to SSR (GC-SSR), these ligands enhanced ab3A-spiking at nanogram-aliquots in a complementary fashion among the different OR69a-variants, and we identified another possible kairomone, methyl salicylate, as the most active and specific ligand for the sole DsuzOR69aB. In testing headspaces collected from D. suzukii females and from the fly-associated yeast Hanseniaspora uvarum we did not observe activation from female headspaces but activation from yeast headspaces. In situ hybridization analysis on D. suzukii antennae suggests unique expression of OR69a-subunits in specific neurons, and points toward co-expression within the same neurons. The OR69a-subunits of D. suzukii constitute cation channels, which binding suggests kairomone specificity, even if effects coexist for a complementary binding of (Z)-4-nonenal. Methyl salicylate is the most active ligand and is specific to the sole DsuzOR69aB, inspiring future investigation to validate potentials of this compound for D. suzukii control strategies.

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          Most cited references78

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          New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

          PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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            MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization

            Abstract This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.
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              SMS: Smart Model Selection in PhyML

              Abstract Model selection using likelihood-based criteria (e.g., AIC) is one of the first steps in phylogenetic analysis. One must select both a substitution matrix and a model for rates across sites. A simple method is to test all combinations and select the best one. We describe heuristics to avoid these extensive calculations. Runtime is divided by ∼2 with results remaining nearly the same, and the method performs well compared with ProtTest and jModelTest2. Our software, “Smart Model Selection” (SMS), is implemented in the PhyML environment and available using two interfaces: command-line (to be integrated in pipelines) and a web server (http://www.atgc-montpellier.fr/phyml-sms/).
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Pest Science
                J Pest Sci
                Springer Science and Business Media LLC
                1612-4758
                1612-4766
                June 2023
                December 14 2022
                June 2023
                : 96
                : 3
                : 1149-1171
                Article
                10.1007/s10340-022-01585-2
                2ea3618e-c695-4fe2-97b5-49beb2816481
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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