Identifying sex-linked metabolomic biomarkers in fish gonads after bacterial infection
Bibliographic record
Abstract
PURPOSE: Fish aquaculture faces sustainable production challenges. Among them are the pathogenic outbreaks that can compromise the health of the stocks from various perspectives, including broodstock reproduction. This study focused on identifying the metabolite alterations produced after a bacterial infection by Vibrio anguillarum in the gonads of European seabass (Dicentrarchus labrax). Sex-related response to the infection challenge was studied using a metabolomics approach. METHOD: The metabolome of testes and ovaries of adult fish were extracted and analyzed after 48 h of bacterial exposure by ultra-high-performance liquid chromatography-mass spectrometer using negative-mode electrospray ionization (ESI) (UHPLC-MS, Vanquish Horizon UHPLC coupled to a Thermo Fisher Scientific Q-Exactive HF). To further decipher the molecular events, metabolomic and transcriptomic data were interconnected. RESULTS: In total, 97 metabolites were identified. In the ovary, uric acid, O-phosphoethanolamine, allantoin, and acetoacetic acid were more represented. By contrast, nine metabolites were altered after the infection in testes, including uridine, N-acetylglucosamine-6-Phosphate, and Gamma-aminobutyric acid (GABA). The most abundant metabolic cascades triggered by infection in ovaries were related to glyoxylate and dicarboxylate metabolism, nitrogen metabolism, and purine metabolism, while in testes, we observed changes in glycerolipid metabolism, glycerophospholipid metabolism, and galactose metabolism. CONCLUSION: The present results demonstrate, for the first time in fish, that changes in metabolic pathways induced following infection are sex-dependent. The findings will help develop sex-specific immune therapies, identify resistant phenotypes, and improve aquaculture infection protocols.
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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".