Tissue- and temperature-dependent expression, enzyme activity, and RNAi knockdown of <i>Catalase</i> in a freeze-tolerant insect
Bibliographic record
Abstract
Organisms that overwinter in temperate climates may experience freezing and freezing-induced oxidative stress during winter. While many insect species can survive freezing, molecular tools such as RNA interference (RNAi) or CRISPR have not been used to understand the physiological mechanisms underlying freeze tolerance. The spring field cricket Gryllus veletis can survive freezing following a 6-week fall-like acclimation. We used RNAi of an antioxidant enzyme in G. veletis to test the hypothesis that minimizing oxidative stress is important for freeze tolerance. In fat body tissue, Catalase mRNA abundance and enzyme activity increased during the acclimation that induces freeze tolerance. Other tissues such as midgut and Malpighian tubules had more stable or lower Catalase expression and activity during acclimation. In unacclimated (freeze-intolerant) crickets, RNA interference (RNAi) effectively knocked down production of the Catalase mRNA and protein in fat body and midgut, but not Malpighian tubules. In acclimated (freeze-tolerant) crickets, RNAi efficacy was temperature-dependent, functioning well at warm (c. 22°C) but not cool (15°C or lower) temperatures. This highlights a challenge of using RNAi in cold-acclimated organisms, as they may need to be warmed up for RNAi to work, potentially affecting their stress physiology. Knockdown of Catalase via RNAi in acclimated crickets also had no effect on the ability of the crickets to survive a mild freeze treatment, suggesting that Catalase may not be necessary for freeze tolerance. Our study is the first to demonstrate that RNAi is possible in a freeze-tolerant insect, but further research is needed to examine whether other genes and antioxidant molecules are important in freeze tolerance of G. veletis .
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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".