Protection induced by a Francisella tularensis subunit vaccine delivered by glucan particles
Bibliographic record
Abstract
Francisella tularensis is an intracellular pathogen causing the disease tularemia, and an organism of concern to biodefence. There is no licensed vaccine available. Subunit approaches have failed to induce protection, which requires both humoral and cellular immune memory responses, and have been hampered by a lack of understanding as to which antigens are immunoprotective. We undertook a preliminary in silico analysis to identify candidate protein antigens. These antigens were then recombinantly expressed and encapsulated into glucan particles (GPs), purified Saccharomyces cerevisiae cell walls composed primarily of β-1,3-glucans. Immunological profiling in the mouse was used to down-selection to seven lead antigens: FTT1043 (Mip), IglC, FTT0814, FTT0438, FTT0071 (GltA), FTT0289, FTT0890 (PilA) prior to transitioning their evaluation to a Fischer 344 rat model for efficacy evaluation. F344 rats were vaccinated with the GP protein antigens co-delivered with GP-loaded with Francisella LPS. Measurement of cell mediated immune responses and computational epitope analysis allowed down-selection to three promising candidates: FTT0438, FTT1043 and FTT0814. Of these, a GP vaccine delivering Francisella LPS and the FTT0814 protein was able to induce protection in rats against an aerosol challenge of F. tularensis SchuS4, and reduced organ colonisation and clinical signs below that which immunisation with a GP-LPS alone vaccine provided. This is the first report of a protein supplementing protection induced by LPS in a Francisella vaccine. This paves the way for developing an effective, safe subunit vaccine for the prevention of inhalational tularemia, and validates the GP platform for vaccine delivery where complex immune responses are required for prevention of infections by intracellular pathogens.
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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".