Role of CYP9E2 and a long non-coding RNA gene in resistance to a spinosad insecticide in the Colorado potato beetle, Leptinotarsa decemlineata
Bibliographic record
Abstract
Spinosads are insecticides used to control insect pests, especially in organic farming where limited tools for pest management exist. However, resistance has developed to spinosads in economically important pests, including Colorado potato beetle (CPB), Leptinotarsa decemlineata. In this study, we used bioassays to determine spinosad sensitivity of two field populations of CPB, one from an organic farm exposed exclusively to spinosad and one from a conventional farm exposed to a variety of insecticides, and a reference insecticide naïve population. We found the field populations exhibited significant levels of resistance compared with the sensitive population. Then, we compared transcriptome profiles between the two field populations to identify genes associated primarily with spinosad resistance and found a cytochrome P450, CYP9E2, and a long non-coding RNA gene, lncRNA-2, were upregulated in the exclusively spinosad-exposed population. Knock-down of these two genes simultaneously in beetles of the spinosad-exposed population using RNA interference (RNAi) resulted in a significant increase in mortality when gene knock-down was followed by spinosad exposure, whereas single knock-downs of each gene produced smaller effects. In addition, knock-down of the lncRNA-2 gene individually resulted in significant reduction in CYP9E2 transcripts. Finally, in silico analysis using an RNA-RNA interaction tool revealed that CYP9E2 mRNA contains multiple binding sites for the lncRNA-2 transcript. Our results imply that CYP9E2 and lncRNA-2 jointly contribute to spinosad resistance in CPB, and lncRNA-2 is involved in regulation of CYP9E2 expression. These results provide evidence that metabolic resistance, driven by overexpression of CYP and lncRNA genes, contributes to spinosad resistance in CPB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".