Functional feed ingredients modulate the immune response of RTgutGC cells to LPS-induced inflammation
Bibliographic record
Abstract
Functional feed ingredients can enhance the fish gut integrity and immune resilience during challenging situations in the aquaculture industry. This study used the RTgutGC cell line – derived from rainbow trout intestinal epithelium, to evaluate the immunomodulatory and barrier effects of selected ingredients. These included β-glucan from Saccharomyces cerevisiae (BG40 and BG60), laminarin extracted from Laminaria hyperborea (Lam60 and Lam90), and bioactive peptides with antioxidative and immunomodulatory potential; carnosine (Carn100 and Carn120) and salmon hydrolysate (FPH300 and FPH600). Cells were exposed for 24 hours at two concentrations (maintaining 100 % and 80 % viability), and effects on transepithelial resistance (TEER), permeability (Papp) and gene expression (qPCR) were assessed before and after a 6-hour lipopolysaccharide (LPS) challenge. High-dose laminarin and both salmon hydrolysate concentrations elevated mRNA encoding for pro-inflammatory cytokines (il6, il8, il1b and tnfa; p < 0.05). All ingredients except carnosine significantly reduced TEER (p < 0.05) often with downregulation of barrier genes. Low-dose carnosine and laminarin reduced Papp of Lucifer yellow, indicating less barrier disruption. LPS induced inflammation, barrier dysfunction and reduced proliferation. These effects were modulated by high-dose β-glucan and both laminarin concentrations, which significantly reduced il6 expression (p < 0.05). High-dose salmon hydrolysate also tended to reduce il6 (p = 0.05) and increased pcna expression (p < 0.001), suggesting tissue recovery. Low-dose laminarin and both carnosine concentrations upregulated cldn3 post-challenge (p < 0.05). These findings support the RTgutGC model as a valuable screening tool and provides new insights into the biological activity and immunomodulatory effects of various functional feed ingredients.
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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".