Expanding the use of circulating microbiome in fish: contrast between the gut and blood microbiome of <i>Sebastes fasciatus</i>
Bibliographic record
Abstract
Abstract The study of microbiomes in fish populations offers vital insights for ecological and fisheries management, particularly in responses to environmental changes. Although traditional studies have concentrated on the gut microbiome, the emerging concept of a circulating blood microbiome suggests it may act as an early indicator of dysbiosis and various health conditions by reflecting transient bacterial DNA presence. In this study, we examined the gut and blood microbiomes of Sebastes fasciatus (Storer, 1854), a species of redfish of significant economic and ecological importance in the Gulf of St. Lawrence, to obtain critical information for health monitoring, pathogen detection, and ecological management in fisheries. Our results revealed that the gut and blood microbiomes of S. fasciatus have distinct bacterial DNA signatures, with significant differences in microbial diversity. Notably, although both microbiomes exhibited similar dominant genera, specific amplicon sequence variants varied significantly. Through a controlled experimental design, we found that the dietary impacts on microbiome composition were statistically significant yet minimal, suggesting that environmental factors play a more substantial role in shaping microbial communities. Finally, we report the presence of potential pathogens and opportunistic bacteria found exclusively in the blood microbiome. Our results highlight the blood microbiome's value as a sensitive health and environmental stress indicator, essential for sustainable fish population management. Integrating microbiome indicators can improve fisheries management and ecosystem sustainability, offering a model applicable to various marine species and environments.
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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".