Transcriptomics-Based Approach Identifies Spinosad-Associated Targets in the Colorado Potato Beetle, Leptinotarsa decemlineata
Bibliographic record
Abstract
is an insect pest that threatens potato crops globally. The primary method to control its damage on potato plants is the use of insecticides, including imidacloprid, chlorantraniliprole and spinosad. However, insecticide resistance has been frequently observed in Colorado potato beetles. The molecular targets and the basis of resistance to imidacloprid and chlorantraniliprole have both been previously quantified. This work was undertaken with the overarching goal of better characterizing the molecular changes associated with spinosad exposure in this insect pest. Next-generation sequencing was conducted to identify transcripts that were differentially expressed between Colorado potato beetles exposed to spinosad versus control insects. Results showed several transcripts that exhibit different expression levels between the two conditions, including ones coding for venom carboxylesterase-6, chitinase 10, juvenile hormone esterase and multidrug resistance-associated protein 4. In addition, several microRNAs, such as miR-12-3p and miR-750-3p, were also modulated in the investigated conditions. Overall, this work reveals a molecular footprint underlying spinosad response in Colorado potato beetles and provides novel leads that could be targeted as part of RNAi-based approaches to control this insect pest.
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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) | 0.001 | 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".