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The codling moth, Cydia pomonella, is a major pest of apple and pear orchards. However, with intensive use of pesticide, an increasing number of resistances has been reported. Detoxification is one of the main mechanisms involved in resistance in Lepidoptera, and there is still much to discover about the molecular mechanisms underlying it. This study was conducted to decipher the molecular mechanisms involved in the insecticide resistance of C. pomonella and to identify genetic markers associated with resistance upon exposure to several pesticides. To that aim, an experimental evolution was carried out from a wild population resistant to several active substances. We generated three lines by combining genetic crosses and experimental selection using chlorantraniliprole, deltamethrin and spinosad. High throughput RNA-sequencing (RNA-seq) was performed on the three lines to verify whether similar mechanisms were involved in pesticide resistance across lines and to assess the role of detoxification and other mechanisms in resistance. In total, we found 592 mutations and 157 differentially expressed genes. Of the candidate genes identified as involved in resistance, a very limited number were shared between lines. We found in total 20 genes belonging to already known candidate genes, with 2 of them known to be involved in C. pomonella resistance (CYP4C1 and CYP6B2) and identified several genes that have never been linked with resistance before (mainly associated with odorant perception and stress).

The utilization of insect pest pathogens as biocontrol agents can serve as an effective alternative to chemical pesticides. However, pests can develop resistance to these pathogens, similar to resistance observed with chemicals. Identifying the genetic bases of resistance is a crucial step in comprehending the co-evolution mechanisms between insect hosts and their pathogens, thereby enhancing the durability of these biocontrol methods. Cydia pomonella, a major pest of apple and pear trees found across all continents, can be effectively controlled in commercial orchards through the use of the granulovirus, CpGV. However, since 2005, resistance to the CpGV-M isolate has been reported in Europe, leading to significant challenges in managing C. pomonella in organic orchards. Studies conducted on CPGV-M resistance have revealed that viral replication is blocked in resistant phenotypes, and resistance is linked to a major gene located on the Z sex chromosome. Nevertheless, this major gene appears to be insufficient in explaining resistance to CPGV-M, as other forms of resistance involving autosomal genes have been identified subsequently. The objective of this study was to gain insights into the genetic architecture of C. pomonella resistance to CpGV-M by conducting a genome-wide association study using newly generated whole-genome resequencing data from pools of individuals (Pool-Seq) sampled from 13 susceptible and resistant populations across four regions in France and Italy. Through this analysis, eight candidate regions were identified, including one located on the Z sex chromosome. These eight regions encompass a total of 27 lncRNAs (long non-coding RNAs) and 40 genes, of which three are known to be involved in insects immune pathways.

Cydia pomonella (L.) and Cydia molesta (Busck) (Lepidoptera: Tortricidae) are two important lepidopteran pests that may co-occur in apple orchards and are difficult to differentiate in the larval stage. We investigate the possibility of using near-infrared spectroscopy (NIRS) coupled with partial least squares analysis to distinguish the larvae of the two species. We further assess whether wild individuals can be differentiated using laboratory strains of the two species for model calibration. The NIRS spectra of C. molesta and C. pomonella differed most in the wavelengths between 1,142 and 1,338 nm. Using these wavelengths, partial least squares analysis allowed the differentiation of C. molesta and C. pomonella at the larval stage with very low error, but only as long as both the calibration and prediction sets for individuals had the same origin (either both from the laboratory or both from the field). Errors that appeared when using laboratory individuals for calibration were owing to the divergence of the C. pomonella laboratory strain, most likely following evolution during rearing. Thus, NIRS appears to be a promising tool for the easy and rapid identification of individuals in the field, provided that it is calibrated based on a subset of field individuals.