Lactic acid bacteria (LAB) strains modulate inflammatory genes in RTgutGC cells, and triploid Chinook salmon (Oncorhynchus tshawytscha) challenged with V. anguillarum
Bibliographic record
Abstract
Lactic acid bacteria (LAB) are known for their probiotic benefits, including enhancing fish growth, stress tolerance, and immune responses. While multi-strain probiotics have shown promise in improving survival and modulating inflammation in V. anguillarum -infected Chinook salmon, the effects of individual or dual-strain formulations on immune modulation and gut barrier integrity in less commonly farmed salmonids like Chinook salmon ( Oncorhynchus tshawytscha ) remain understudied. This research evaluated the potential of Limosilactobacillus reuteri LRE2, L. reuteri 830, and a mixture of L. reuteri RC-14 and L. rhamnosus GR-1 as oral immunostimulants. Using the RTgutGC intestinal epithelial cell line, cytotoxicity assays confirmed that none of the LAB strains affected cell viability at concentrations up to 10 ^7 CFU. Gene expression analysis revealed that L. reuteri LRE2 significantly downregulated il1b and il6 transcripts compared to L. reuteri 830 and the strain mixture, while tnfa expression was reduced in L. reuteri 830-treated cells. In vivo , juvenile Chinook salmon were supplemented with the LAB strains for four months, but no changes in weight were observed. Following V. anguillarum challenge, the probiotics did not improve survival. However, il8 expression increased significantly in the hindgut of fish supplemented with L. reuteri 830 and the strain mixture at 7 days post-infection. Tight junction gene expression remained unchanged across treatments. This study highlights strain-specific differences in immune modulation, though the tested LAB strains did not enhance survival during V. anguillarum infection. Further research is needed to optimize probiotic formulations for disease resistance in Chinook 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 | 0.000 |
| 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".