Exposure to Temperature and Insecticides Modulates the Expression of Small Noncoding RNA-Associated Transcripts in the Colorado Potato Beetle, <i>Leptinotarsa decemlineata</i> (Coleoptera: Chrysomelidae)
Bibliographic record
Abstract
The Colorado potato beetle (Leptinotarsa decemlineata (Say)) is an insect that can adapt to various challenges, including temperature fluctuations or select insecticide treatments. This pest is also an ongoing threat to the potato industry. Small noncoding RNAs such as miRNAs, which can control posttranscriptionally the expression of various genes, and piRNAs, which can notably impact mRNA turnover, are modulated in insects under different conditions. Unfortunately, information regarding the expression status of key players involved in their synthesis and function is for the most part lacking. The current study thus aims at assessing the levels of such targets in L. decemlineata exposed to hot and cold temperatures as well as treated to the insecticides chlorantraniliprole, clothianidin, imidacloprid, and spinosad. Transcript expression levels of Ago1, Ago2, Ago3, Dcr2a, Dcr2b, Expo-5, Siwi-1, and Siwi-2, components of pathways associated with small noncoding RNA production or function, were measured by qRT-PCR and revealed modulation of select transcripts in response to temperature challenges and to select insecticides. RNAi-mediated reduction of Ago2 transcript levels in L. decemlineata injected with Ago2-targeting dsRNA and exposed to cold and warm temperatures was also conducted. Changes in survival rates were observed for the latter condition in dsRNA- versus saline-injected insects. These results showcase the differential expression of select targets involved in small noncoding RNA homeostasis and provide leads for the subsequent assessment of their involvement during stress response in L. decemlineata using RNAi-based approaches.
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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".