Putative antigenic proteins identified by comparative and subtractive reverse vaccinology in necrotic enteritis-causing Clostridium perfringens isolated from broiler chickens
Bibliographic record
Abstract
BACKGROUND: Avian necrotic enteritis (NE) caused by Clostridium perfringens is a disease with a major economic impact, generating losses estimated to 6 billion of dollars annually for the poultry industry worldwide. The incidence of the disease is particularly on the rise in broiler chicken flocks eliminating the preventive use of antibiotics. To date, no alternative allows for the efficient prevention of NE and a control of the disease using a vaccinal strategy would be mostly prized. For this purpose, comparative and subtractive reverse vaccinology identifying putative immunogenic bacterial surface proteins is one of the most promising approaches. RESULTS: A comparative genomic study was performed on 16 C. perfringens strains isolated from healthy broiler chickens and from broilers affected with necrotic enteritis. Results showed that the analyzed genomes were composed of 155,700 distinct proteins from which 13% were identified as extracellular, 65% as cytoplasmic and 22% as part of the bacterial membrane. The evaluation of the immunogenicity of these proteins was determined using the prediction software VaxiJen®. CONCLUSIONS: For the most part, proteins with the highest scores were associated with an extracellular localisation. For all the proteins analyzed, the combination of both the immunogenicity score and the localisation prediction led to the selection of 12 candidate proteins that were mostly annotated as hypothetical proteins. We describe 6 potential candidates of higher interest due to their antigenic potential, their extracellular localisation, and their possible role in virulence of C. perfringens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".