Novel insights on microbiome dynamics during a gill disease outbreak in farmed rainbow trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
The generic term "Gill disease" refers to a wide range of disorders that affect the gills and severely impact salmonid aquaculture systems worldwide. In rainbow trout freshwater aquaculture, various etiological agents causing gill diseases have been described, particularly Flavobacterium and Amoeba species, but research studies suggest a more complex and multifactorial aetiology. Here, a cohort of rainbow trout affected by gill disease is monitored both through standard laboratory techniques and 16S rRNA Next-Generation Sequencing (NGS) analysis during a natural disease outbreak and subsequent antibiotic treatment with Oxytetracycline. NGS results show a clear clustering of the samples between pre- and post-treatment based on the microbial community of the gills. Interestingly, the three main pathogenic bacteria species in rainbow trout (Yersinia ruckeri, Flavobacterium psychrophilum, and Flavobacterium branchiophilum) appear to be weak descriptors of the diversity between pre-treatment and post-treatment groups. In this study, the dynamics of the gill microbiome during the outbreak and subsequent treatment are far more complex than previously reported in the literature, and environmental factors seem of the utmost importance in determining gill disease. These findings present a potential novel perspective on the diagnosis and management of gill diseases, showing the limitations of conventional laboratory methodologies in elucidating the complexity of this disease in rainbow trout. To the authors' knowledge, this work is the first to describe the microbiome of rainbow trout gills during a natural outbreak and subsequent antibiotic treatment. The results of this study suggest that NGS can play a critical role in the analysis and comprehension of gill pathology. Using NGS in future research is highly recommended to gain deeper insights into such diseases correlating gill's microbiome with other possible cofactors and establish strong prevention guidelines.
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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 | 0.001 |
| 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".