bioRxivPhysiologyPreprint
Exploring the connections between digestion and detoxification in microevolution of insecticide resistance of the tea tortrix moth, Adoxophyes honmai
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Abstract
While the evolution of insecticide resistance is often assumed to come with a fitness cost, there are instances where insect populations that acquired resistance fail to show an evolutionary cost to maintaining the trait in the absence of insecticide. In comparing two populations of tea tortrix moth, Adoxophyes honmai, we found the absence of a cost of resistance but also noted differences in digestive enzyme gene expression. This raised the possibility that insecticide resistance coevolved with enhanced digestive capabilities, potentially offsetting putative costs of resistance. This study explored gene transcript patterns that may influence how traits manifest spatially and temporally, evaluating potential connections between digestion and the costs of resistance. We found that our resistant larvae had constitutively greater transcript levels of multiple putative digestive genes as well as a marker of resistance, CYP9A170. The putative digestive genes were expressed mostly in the digestive tract, whereas the tissue-specific pattern of CYP9A170 expression was strain-dependent. For most genes, the difference in expression between susceptible and resistant larvae remained consistent throughout development. Interestingly, the expression of an ABC transporter was upregulated in response to tebufenozide exposure, but only in the resistant larvae. A comparison of A. honmai transcriptomes suggests that the majority of differentially expressed genes between populations may not be directly contributing to resistance, but rather microevolutionary variations specific to individual populations. Future studies on fitness costs of resistance should consider other physiological systems and their interactions with direct mechanisms of resistance.
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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