Unraveling the Tissue-Specific Gene Signatures of Gilthead Sea Bream (Sparus aurata L.) after Hyper- and Hypo-Osmotic Challenges
Bibliographic record
Abstract
A custom microarray was used for the transcriptomic profiling of liver, gills and hypothalamus in response to hypo- (38‰ → 5‰) or hyper- (38‰ → 55‰) osmotic challenges (7 days after salinity transfer) in gilthead sea bream (Sparus aurata) juveniles. The total number of differentially expressed genes was 777. Among them, 341 and 310 were differentially expressed in liver after hypo- and hyper-osmotic challenges, respectively. The magnitude of changes was lower in gills and hypothalamus with around 131 and 160 responsive genes in at least one osmotic stress condition, respectively. Regardless of tissue, a number of genes were equally regulated in either hypo- and hyper-osmotic challenges: 127 out of 524 in liver, 11 out of 131 in gills and 19 out of 160 in hypothalamus. In liver and gills, functional analysis of differentially expressed genes recognized two major clusters of overlapping canonical pathways that were mostly related to "Energy Metabolism" and "Oxidative Stress". The later cluster was represented in all the analyzed tissues, including the hypothalamus, where differentially expressed genes related to "Cell and tissue architecture" were also over-represented. Overall the response for "Energy Metabolism" was the up-regulation, whereas for oxidative stress-related genes the type of response was highly dependent of tissue. These results support common and different osmoregulatory responses in the three analyzed tissues, helping to load new allostatic conditions or even to return to basal levels after hypo- or hyper-osmotic challenges according to the different physiological role of each tissue.
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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".