An integrated transcriptomic and comparative genomic analysis of differential gene expression in Arctic charr ( <i>Salvelinus alpinus</i> ) following seawater exposure
Bibliographic record
Abstract
High-throughput RNA sequencing was used to compare expression profiles in two Arctic charr (Salvelinus alpinus) families post-seawater exposure to identify genes and biological processes involved in hypo-osmoregulation and regulation of salinity tolerance. To further understand the genetic architecture of hypo-osmoregulation, the genomic organization of differentially expressed (DE) genes was also analysed. Using a de novo gill transcriptome assembly we found over 2300 contigs to be DE. Major transporters from the seawater mitochondrion-rich cell (MRC) complex were up-regulated in seawater. Expression ratios for 257 differentially expressed contigs were highly correlated between families, suggesting they are strictly regulated. Based on expression profiles and known molecular pathways we inferred that seawater exposure induced changes in methylation states and elevated peroxynitrite formation in gill. We hypothesized that concomitance between DE immune genes and the transition to a hypo-osmoregulatory state could be related to Cl(-) sequestration by antimicrobial defence mechanisms. Gene ontology analysis revealed that cell division genes were up-regulated, which could reflect the proliferation of ATP1α1b-type seawater MRCs. Comparative genomics analyses suggest that hypo-osmoregulation is influenced by the relative proximities among a contingent of genes on Arctic charr linkage groups AC-4 and AC-12 that exhibit homologous affinities with a region on stickleback chromosome Ga-I. This supports the hypothesis that relative gene location along a chromosome is a property of the genetic architecture of hypo-osmoregulation. Evidence of non-random structure between hypo-osmoregulation candidate genes was found on AC-1/11 and AC-28, suggesting that interchromosomal rearrangements played a role in the evolution of hypo-osmoregulation in Arctic charr.
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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".