Dietary Immunostimulant CpG Modulates MicroRNA Biomarkers Associated with Immune Responses in Atlantic Salmon (Salmo salar)
Bibliographic record
Abstract
(ASAL) on miRNA expression in Atlantic salmon fed a commercial diet with and without immunostimulant CpG. To this end, first, we performed small RNA deep sequencing and qPCR analyses to identify and confirm pIC- and/or ASAL-responsive miRNAs in the head kidney of salmon fed a control diet. DESeq2 analyses identified 12 and 18 miRNAs differentially expressed in pIC and ASAL groups, respectively, compared to the controls. Fifteen of these miRNAs were studied by qPCR; nine remained significant by qPCR. Five miRNAs (miR-27d-1-2-5p, miR-29b-2-5p, miR-146a-5p, miR-146a-1-2-3p, miR-221-5p) were shown by qPCR to be significantly induced by both pIC and ASAL. Second, the effect of CpG-containing functional feed on miRNA expression was investigated by qPCR. In pre-injection samples, 6 of 15 miRNAs (e.g., miR-181a-5-3p, miR-462a-3p, miR-722-3p) had significantly lower expression in fish fed CpG diet than control diet. In contrast, several miRNAs (e.g., miR-146a-1-2-3p, miR-192a-5p, miR-194a-5p) in the PBS- and ASAL-injected groups had significantly higher expression in CpG-fed fish. Multivariate statistical analyses confirmed that the CpG diet had a greater impact on miRNA expression in ASAL-injected compared with pIC-injected fish. This study identified immune-relevant miRNA biomarkers that will be valuable in the development of diets to combat infectious diseases of salmon.
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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".