Characterization of the impact of dietary immunostimulant CpG on the expression of mRNA biomarkers involved in the immune responses in Atlantic salmon (Salmo salar)
Bibliographic record
Abstract
Infectious diseases have significantly impacted Atlantic salmon aquaculture worldwide. Modulating fish immunity with immunostimulant-containing functional feeds could be an effective strategy in mitigating disease problems. Previously, we characterized the impact of polyriboinosinic polyribocytidylic acid (pIC) and formalin-killed typical Aeromonas salmonicida bacterin on miRNA expression in Atlantic salmon fed a commercial diet with and without immunostimulant CpG. A set of miRNA biomarkers of Atlantic salmon head kidney responding to pIC and/or bacterin immune stimulations was identified (Xue et al., 2019) [1]. Herein, we report a complementary qPCR study that investigated the impact of the pIC, bacterin and dietary CpG on the expression of immune-relevant mRNAs (n = 31) using the same samples as in the previous study (Xue et al., 2019) [1]. Twenty-six of these genes were predicted target transcripts of the pIC- and/or bacterin-responsive miRNAs identified in the earlier study. The current data showed that pIC and/or bacterin stimulations significantly modulated the majority of the qPCR-analyzed genes involved in various immune pathways. Some genes responded to both stimulations (e.g. tnfa , il10rb , ifng , irf9 , cxcr3 , campb ) while others appeared to be stimulation specific [e.g. irf3 , irf7a , il1r1 , mxa , mapk3 (pIC only); clra (bacterin only)]. A. salmonicida bacterin stimulation produced a strong inflammatory response (e.g. higher expression of il1b , il8a and tnfa ), while salmon stimulated with pIC showed robust interferon responses (both type I and II). Furthermore, the current data indicated significant down-regulation of immune-relevant transcripts (e.g. tlr9 , irf5 , il1r1 , hsp90ab1 , itgb2 ) by dietary immunostimulant CpG, especially among pre-injection and PBS-injected fish. Together with our prior miRNA study, the present research provided complementary information on Atlantic salmon anti-viral and anti-bacterial immune responses and on how dietary CpG may modulate these responses. • Viral mimic pIC and A. salmonicida bacterin stimulations altered predicted miRNA target genes. • A. salmonicida bacterin induced strong inflammatory responses, while pIC triggered significant interferon responses. • Dietary CpG significantly down-regulated immune-relevant transcripts, particularly in pre-injection and PBS-injected fish. • Both opposing and concordant regulation among various pairs of miRNAs and their predicted targets were identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".