Purification and Immunogenicity of a Recombinant<i>Bordetella pertussis</i>S1S3FHA Fusion Protein Expressed by<i>Streptococcus gordonii</i>
Bibliographic record
Abstract
Acellular pertussis vaccines typically consist of antigens isolated from Bordetella pertussis, and pertussis toxin (PT) and filamentous hemagglutinin (FHA) are two prominent components. One of the disadvantages of a multiple-component vaccine is the cost associated with the production of the individual components. In this study, we constructed an in-frame fusion protein consisting of PT fragments (179 amino acids of PT subunit S1 and 180 amino acids of PT subunit S3) and a 456-amino-acid type I domain of FHA. The fusion protein was expressed by the commensal oral bacterium Streptococcus gordonii. The fusion protein was secreted into the culture medium as an expected 155-kDa protein, which was recognized by a polyclonal anti-PT antibody, a monoclonal anti-S1 antibody, and a monoclonal anti-FHA antibody. The fusion protein was purified from the culture supernatant by affinity and gel permeation chromatography. The immunogenicity of the purified fusion protein was assessed in BALB/c mice by performing parenteral and mucosal immunization experiments. When given parenterally, the fusion protein elicited a very strong antibody titer against the FHA type I domain, a moderate titer against native FHA, and a weak titer against PT. When given mucosally, it elicited a systemic response and a mucosal response to FHA and PT. In Western blots, the immune sera recognized the S1, S3, and S2 subunits of PT. These data collectively indicate that fragments of the pertussis vaccine components can be expressed in a single fusion protein by S. gordonii and that the fusion protein is immunogenic. This multivalent fusion protein approach may be used in designing a new generation of acellular pertussis vaccines.
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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 | 0.001 |
| 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".