Use of shotgun immunoproteomics for the development of protein vaccines against Edwardsiella piscicida
Bibliographic record
Abstract
Edwardsiella piscicida is an important emerging pathogen in various cultured fish species. This study aimed to identify immunogenic E. piscicida proteins and evaluate these antigens as protein vaccines for use in aquaculture. Shotgun immunoproteomics using anti-E. piscicida serum from rainbow trout (Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus) (♀) × blue catfish (Ictalurus furcatus) (♂) hybrids inoculated with formalin-killed whole-bacteria preparations identified 36 candidate immunogenic E. piscicida proteins. The chaparonin GroEL, the glycine 2TM zipper domain-containing protein (GlyZip), and coproporphyrinogen-III oxidase (COPIII) were used to orally (PO) and intra-coelomically (IC) immunize Chinook salmon (Oncorhynchus tshawytscha). Fish IC vaccinated with either GlyZip or COPIII demonstrated a slight, but non-significant, improvement in survival post-challenge with E. piscicida S11-285. Surprisingly, fish IC or PO vaccinated with GroEL displayed an anti-protective effect (RPS = -184 % and RPS = -76 %, respectively) against subsequent challenge. All IC vaccinated fish generated a strong specific antibody response against the immunizing protein, and sham vaccinated fish challenged with E. piscicida S11-285 generated a significantly higher specific antibody response to the GroEL and GlyZip proteins than negative control fish, suggesting that shotgun immunoproteomics was effective for detection of immunogenic bacterial proteins that can stimulate humoral immune responses in the host fish.
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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) | 0.000 | 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".