Transcriptomics of salinity tolerance capacity in Arctic charr (<i>Salvelinus alpinus</i>): a comparison of gene expression profiles between divergent QTL genotypes
Bibliographic record
Abstract
Osmoregulatory capabilities have played an important role in the evolution, dispersal, and diversification of vertebrates. To better understand the genetic architecture of hypo-osmoregulation in fishes and to determine which genes and biological processes affect intraspecific variation in salinity tolerance, we used mRNA sequence libraries from Arctic charr gill tissue to compare gene expression profiles in fish exhibiting divergent salinity tolerance quantitative trait locus (QTL) genotypes. We compared differentially expressed genes with QTL positions to gain insight about the nature of the underlying polymorphisms and examined gene expression within the context of genome organization to gain insight about the evolution of hypo-osmoregulation in fishes. mRNA sequencing of 18 gill tissue libraries produced 417 million reads, and the final reduced de novo transcriptome assembly consisted of 92,543 contigs. Families contained a similar number of differentially expressed contigs between high and low salinity tolerance capacity groups, and log2 expression ratios ranged from 10.4 to -8.6. We found that intraspecific variation in salinity tolerance capacity correlated with differential expression of immune response genes. Some differentially expressed genes formed clusters along linkage groups. Most clusters comprised gene pairs, though clusters of three, four, and eight genes were also observed. We postulated that conserved synteny of gene clusters on multiple ancestral and teleost chromosomes may have been preserved via purifying selection. Colocalization of QTL with differentially expressed genes suggests that polymorphisms in cis-regulatory elements are part of a majority of QTL.
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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".