Identification of genes associated with heat tolerance in Arctic charr exposed to acute thermal stress
Bibliographic record
Abstract
Arctic charr is an especially attractive aquaculture species given that it features the desirable tissue traits of other salmonids and is bred and grown at inland freshwater tank farms year round. It is of interest to develop upper temperature tolerant (UTT) strains of Arctic charr to increase the robustness of the species in the face of climate change and to enable production in more southern regions. We used a genomics approach that takes advantage of the well-studied Atlantic salmon genome to identify genes that are associated with UTT in Arctic charr. Specifically, we conducted an acute temperature trial to identify temperature tolerant and intolerant Arctic charr individuals, which were subject to microarray and qPCR analysis to identify candidate UTT genes. These were compared with genes annotated in a quantitative trait locus (QTL) region that was previously identified as associated with UTT in rainbow trout and Arctic charr and that we sequenced in Atlantic salmon. Our results suggest that small heat shock proteins as well as HSP-90 genes are associated with UTT. Furthermore, hemoglobin expression was significantly downregulated in tolerant compared with intolerant fish. Finally, QTL analysis and expression profiling identified COUP-TFII as a candidate UTT gene, although its specific role is unclear given the identification of two transcripts, which appear to have different expression patterns. Our results highlight the importance of using more than one approach to identify candidate genes, particularly when examining a complicated trait such as UTT in a highly complex genome for which there is no reference genome.
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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".