Interaction between <i>ω</i> 6 and <i>ω</i> 3 fatty acids of different chain lengths regulates Atlantic salmon hepatic gene expression and muscle fatty acid profiles
Bibliographic record
Abstract
Atlantic salmon smolts (approx. 20-months old) were fed experimental diets with different combinations of omega-6:omega-3 fatty acids (FAs) (high-ω6, high-ω3, or balanced) and eicosapentaenoic acid plus docosahexaenoic acid (EPA + DHA) levels (0.3, 1.0 or 1.4%) for 12 weeks. Muscle FA (% total FA) reflected dietary C 18 -polyunsaturated FA; however, muscle EPA per cent and content (mg g −1 ) were not different in salmon fed high-ω3 or balanced diets. Muscle DHA per cent was similar among treatments, while DHA content increased in fish fed 1.4% EPA + DHA, compared with those fed 0.3–1.0% EPA + DHA combined with high-ω6 FA. Muscle 20:3 ω 6 (DGLA) content was highest in those fed high-ω6 with 0.3% EPA + DHA. Quantitative polymerase chain reaction analyses on liver RNA showed that the monounsaturated FA synthesis-related gene, scdb, was upregulated in fish fed 1.0% EPA + DHA with high-ω6 compared to those fed 0.3% EPA + DHA. In high-ω3-fed salmon, liver elovl2 transcript levels were higher with 0.3% EPA + DHA than with 1.0% EPA + DHA. In high-ω6-fed fish, elovl2 did not vary with EPA + DHA levels, but it was positively correlated with muscle ARA, 22:4 ω 3 and DGLA. These results suggest dietary 18:3 ω 3 elongation contributed to maintaining muscle EPA + DHA levels despite a two- to threefold change in dietary proportions, while 18:2 ω 6 with 0.3% EPA + DHA increased muscle DGLA more than arachidonic acid (ARA). Positive correlations between hepatic elovl2 and fabp10a with muscle ω 6: ω 3 and EPA + DHA + ARA, respectively, were confirmed by reanalysing data from a previous salmon trial with lower variations in dietary EPA + DHA and ω 6: ω 3 ratios. This article is part of the theme issue ‘The next horizons for lipids as ‘trophic biomarkers’: evidence and significance of consumer modification of dietary fatty acids’.
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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 | 0.001 |
| 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".