Hepatic Gene Expression Changes of Zebrafish Fed Yeast Prebiotic, Yeast Probiotic, Black Soldier Fly Meal, and Butyrate
Bibliographic record
Abstract
As global fish consumption rises, improving fish health through immunomodulatory feed ingredients shows promise while also supporting growth performance. This study investigated the effects of yeast prebiotics, probiotics, a postbiotic (butyrate), and black soldier fly larvae (BSFL) meal on fish immune responses. Zebrafish were fed diets containing these ingredients for 63 days and then exposed to either Pseudomonas aeruginosa lipopolysaccharide (LPS) or live Flavobacterium psychrophilum to assess hepatic candidate gene expression and weight gain. No mortalities were observed post-immune challenges, and weight gains were not significantly different across treatments. Liver samples were collected for mRNA analysis, and real-time qPCR was used to evaluate the expression of immune-related genes such as TNF-α, IL-1β, hepcidin, and NF-κB/p65. NF-κB/p65 was upregulated in response to immune challenges, indicating a reaction to both LPS and pathogen exposure. Fish on the BSFL diet showed decreased NF-κB/p65 expression after the pathogen challenge, while probiotic-fed fish had reduced angiopoietin-like 4 (angptl4) levels following LPS exposure. Butyrate supplementation had the most significant impact, downregulating pro-inflammatory cytokines and other immune-related genes, suggesting a protective effect. These findings support the health benefits of BSFL and sodium butyrate during an immune challenge.
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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".