Proteostasis in ice: The role of heat shock proteins and ubiquitin in the freeze tolerance of the intertidal mussel, <i>Mytilus trossulus</i>
Bibliographic record
Abstract
Abstract The bay mussel, Mytilus trossulus , is one of the few animals that can survive internal ice formation. Freeze tolerant intertidal animals, like M. trossulus , may freeze and thaw many times during the winter, depending on air and ocean temperatures. Freezing can cause protein denaturation, leading to an induction of the heat shock response with expression of proteins like HSP70, and an increase in ubiquitin conjugated proteins. There has been little work on the mechanisms of freeze tolerance in intertidal species, limiting our understanding of this survival strategy. Additionally, this limited research has focused solely on the effects of single freezing events, but the act of repeatedly crossing the freezing threshold may present novel physiological or biochemical stressors that have yet to be discovered. We predicted that repeated freeze exposures would increase mortality, upregulate HSP70 expression, and increase ubiquitin conjugates in mussels, relative to single, prolonged freeze exposures. Mytilus trossulus from Vancouver, Canada were repeatedly frozen for a combination of 1 × 8 hours, 4 × 2 hours, or 2 × 4 hours. We then compared mortality, HSP70 expression, and ubiquitin quantity across experimental groups. We found a single 8-hour freeze caused significantly more mortality than repeated freeze-thaw cycles. We also found that HSP70 and ubiquitin expression was upregulated exclusively after freeze-thaw cycles, suggesting that freeze-thaw cycles offer a period of damage repair between freezes. This indicates that freeze-thaw cycles, which happen naturally in the intertidal, are crucial for M. trossulus survival in sub-zero temperatures.
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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".