Bacterial <scp>16S</scp> Ribosomal Gene Fingerprints as a Tool to Diagnose and Mitigate Fish Larvae Gut Dysbiosis
Bibliographic record
Abstract
Dysbiosis is associated with shifts in the diversity or relative abundance of beneficial versus harmful bacteria, leading to health issues in organisms. This study investigated gut bacterial dysbiosis associated with larval quality using 16S rRNA gene sequencing. The gut microbiome of gilthead sea bream and European sea bass, key commercial species and vertebrate models, was examined in high- and low-quality larvae batches from several European hatcheries. Larval quality, hatchery site and species influenced bacterial diversity in the gut. Individuals from larval batches that performed well had higher microbial diversity in the gut and individuals from batches that performed poorly had a gut microbiota dominated by pathogenic Vibrio (e.g., V. aestuarianus and V. cortegadensis). The bacterial dysbiosis index revealed a notable predominance of Fusobacteriota and Firmicutes phyla, Thermoanaerobacteria class and Lactobacillaceae, Moritellaceae, Clostridiaceae, Thiotrichaceae and Shewanellaceae families in good-quality larvae batches, and a prevalence of the Proteobacteria phylum, Gammaproteobacteria class, Sphingomonadaceae and Vibrionaceae families in the gut of individuals from poor-quality larvae batches. A positive dysbiosis index (cutoff > 0.4) was associated with a high risk of decreased larval performance and quality. Additionally, the abundance of Clostridium_sensu_stricto_15, Shewanellaceae_unclassified, Cetobacterium, Psychrilyobacter, Moritella and Latilactobacillus genera in the gut of good production batches, and the Vibrio genus in the gut of poor production batches, identified these genus as potential markers for diagnosing and mitigating bacterial dysbiosis in fish and potentially other vertebrates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".