Dietary live yeast and increased water temperature influence the gut microbiota of rainbow trout
Bibliographic record
Abstract
AIMS: The objective was to determine the effects of dietary substitution of fishmeal (FM) with live yeast and increasing water temperature on the diversity and composition of gut microbiota in rainbow trout. METHODS AND RESULTS: Fish were fed either FM or yeast (Saccharomyces cerevisiae) and reared in water temperatures of either 11°C (cold) or 18°C (warm) for 6 weeks. Luminal content and mucosa were collected from the distal gut and the load, diversity and species abundance of yeast and bacteria were analysed using agar plating, MALDI-TOF and rRNA gene amplicon sequencing. Yeast in the gut of fish fed FM were represented by S. cerevisiae, Rhodotorula spp. and Debaryomyces hansenii, while fish fed yeast contained 4-5 log higher CFU per g of yeast that were entirely represented by S. cerevisiae. For gut bacteria, sequencing of 16S rRNA gene amplicons using Illumina MiSeq showed lower bacterial diversity and abundance of lactic acid bacteria, especially Lactobacillus, in fish reared in warm rather than cold water. Fish fed yeast had similar bacterial diversity and lower abundance of Leuconostocaceae and Photobacterium compared with fish fed FM. CONCLUSIONS: Feeding live yeast mainly increased yeast load in the gut, while increased water temperature significantly altered the gut microbiota of rainbow trout in terms of bacterial diversity and abundance. SIGNIFICANCE AND IMPACT OF THE STUDY: Live yeast can replace 40% of FM without disrupting bacteria communities in the gut of rainbow trout, while increased water temperature due to seasonal fluctuations and/or climate change may result in a gut dysbiosis that may jeopardize the health of farmed fish.
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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".