Ribosomal genes and heat shock proteins as putative markers for chronic, sublethal heat stress in Arctic charr: applications for aquaculture and wild fish
Bibliographic record
Abstract
Arctic charr thrive at high densities and can live in freshwater year round, making this species especially suitable for inland, closed containment aquaculture. However, it is a cold-water salmonid, which both limits where the species can be farmed and places wild populations at particular risk to climate change. Previously, we identified genes associated with tolerance and intolerance to acute, lethal temperature stress in Arctic charr. However, there remained a need to examine the genes involved in the stress response to more realistic temperatures that could be experienced during a summer heat wave in grow-out tanks that are not artificially cooled, or under natural conditions. Here, we exposed Arctic charr to sublethal heat stress of 15-18°C for 72 h, and gill tissues extracted before, during (i.e., at 72 h), immediately after cooling and after 72 h of recovery at ambient temperature (6°C) were used for gene expression profiling by microarray and qPCR analyses. The results revealed an expected pattern for heat shock protein expression, which was highest during heat exposure, with significantly reduced expression (approaching control levels) quickly thereafter. We also found that the expression of numerous ribosomal proteins was significantly elevated immediately and 72 h after cooling, suggesting that the gill tissues were undergoing ribosome biogenesis while recovering from damage caused by heat stress. We suggest that these are candidate gene targets for the future development of genetic markers for broodstock development or for monitoring temperature stress and recovery in wild or cultured conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".