Identification and characterization of a novel peptide from rainbow trout (Oncorhynchus mykiss) with antimicrobial activity against Streptococcus iniae
Bibliographic record
Abstract
The overuse and misuse of antibiotics has led to the emergence of antibiotic-resistant bacterial species which remain a challenge to treat therapeutically. Novel and efficacious drugs are desperately needed to combat pathogens. One method to facilitate these discoveries is the use of in silico methods. Computational biology has the power to scan large data sets and screen for potential molecules with antibacterial function. In the current study, an in silico approach was used to identify an antimicrobial peptide (AMP) derived from rainbow trout von Willebrand Factor. The AMP was tested against a panel of aquatic bacterial pathogens and was found to possess antibacterial activity against Streptococcus iniae (S. iniae). Since S. iniae is a zoonotic pathogen, this may be useful in other species as well. The peptide was non-hemolytic and non-cytotoxic at the concentrations tested in rainbow trout cells. Pre-treatment of rainbow trout cells with the peptide did not result in an upregulation of immune genes but stimulating the rainbow trout macrophage/monocyte-like cell line, RTS11, with heat-killed S. iniae, did result in a significant upregulation of the tumor necrosis factor alpha (tnfa) gene. In this study, a new AMP has been identified but its expression, synthesis and role in vivo remains unknown. Nevertheless, the findings presented improve our understanding of fish gill and macrophage responses towards this important zoonotic pathogen.
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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".