Interactions of erythromycin and an antibiotic mixture with the gut microbiome of juvenile rainbow trout
Bibliographic record
Abstract
Erythromycin (ERY) is a commonly used antibiotic found in wastewater effluents and the environment globally. Due to the bioactivity by which they kill and prevent bacterial growth, ERY and other antibiotics may have significant unwanted impacts on the gut microbiome of fishes. The overall objective of this project was to assess effects on the gut microbiome in response to exposure to ERY alone or in a mixture with other common antibiotics, which was accomplished in two experiments. The objectives of experiment 1 as a pilot study were to understand uptake and depuration of ERY in juvenile rainbow trout (RBT) over a 7-d exposure to three concentrations of ERY followed by a 7-d depuration period. Furthermore, throughout the study changes in gut microbiome were assessed. In experiment 2, an identical experimental design was used to assess the effects of a mixture of antibiotics containing, in addition to ERY, 100 μg/g each of ampicillin, metronidazole, and ciprofloxacin. In that study, three matrices were analyzed, with gut collected for 16S rRNA metabarcoding, blood plasma for non-targeted metabolomics, and brain tissue for mRNA-seq analysis. ERY was relatively quickly depurated from fish and gut microbiome dysbiosis was observed at 7 d after exposure, with a slight recovery after the 7-d depuration period. A greater number of plasma metabolites was dysregulated at 14 d compared to 7 d revealing distinct temporal dynamics compared to gut microbiome dysbiosis. Furthermore, several transformation products of antibiotics and biomarker metabolites were observed in plasma due to antibiotic exposure. The transcriptome of the brain was only slightly altered due to antibiotic exposure. Results of these studies will help inform aquaculture practitioners and risk assessors when assessing the potential impacts of antibiotics present in fish feed and the environment, with implications for host health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".