Expression status of heat shock proteins in response to cold, heat, or insecticide exposure in the Colorado potato beetle Leptinotarsa decemlineata
Bibliographic record
Abstract
The Colorado potato beetle (Leptinotarsa decemlineata (Say)) is an agricultural pest that threatens the potato industry worldwide. This insect is widely regarded as one of the most difficult-to-control pests, as it can thrive in a wide range of temperature conditions and routinely develops resistance towards various insecticides. The molecular changes associated with response to these challenges have not been fully investigated in L. decemlineata. While differential expression and characterization of heat shock proteins (HSPs) in response to stress have been conducted in several insects, data regarding HSPs in L. decemlineata are limited. The overarching objective of this study consisted of evaluating the expression of various HSPs in L. decemlineata exposed to different temperatures or treated with the insecticides imidacloprid and chlorantraniliprole. Expression levels of HSP60, HSP70, HSP90, and HSP Beta-1 were evaluated by qRT-PCR and insect mortality was assessed using dsRNAs aimed at select HSP targets. Elevated HSP70 and HSP90 transcript levels were observed in heat-exposed L. decemlineata while downregulation of HSP70 transcript levels was measured in insects submitted to cold conditions. Chlorantraniliprole exposure was associated with reduced HSP Beta-1 transcript levels while no change in expression was monitored in insects exposed to imidacloprid. RNAi-based knockdown of HSP60 levels correlated with significant insect mortality 14 days after dsRNA injection. These results highlight the modulation of HSPs that occur in L. decemlineata exposed to fluctuating temperatures and position HSPs as interesting candidates in the identification of novel molecular leads that could be targeted to control this insect.
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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 | 0.001 |
| 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".