The Francisella Tularensis Proteome and its Recognition by Antibodies
Bibliographic record
Abstract
Francisella tularensis is the causative agent of a spectrum of diseases collectively known as tularemia. The extreme virulence of the pathogen in humans, combined with the low infectious dose and the ease of dissemination by aerosol have led to concerns about its abuse as a bioweapon. Until recently, nothing was known about the virulence mechanisms and even now, there is still a relatively poor understanding of pathogen virulence. Completion of increasing numbers of Francisella genome sequences, combined with comparative genomics and proteomics studies, are contributing to the knowledge in this area. Tularemia may be treated with antibiotics, but there is currently no licensed vaccine. An attenuated strain, the Live Vaccine Strain (LVS) has been used to vaccinate military and at risk laboratory personnel, but safety concerns mean that it is unlikely to be licensed by the FDA for general use. Little is known about the protective immunity induced by vaccination with LVS, in humans or animal models. Immunoproteomics studies with sera from infected humans or vaccinated mouse strains, are being used in gel-based or proteome microarray approaches to give insight into the humoral immune response. In addition, these data have the potential to be exploited in the identification of new diagnostic or protective antigens, the design of next generation live vaccine strains, and the development of subunit vaccines. Herein, we briefly review the current knowledge from Francisella comparative proteomics studies and then focus upon the findings from immunoproteomics approaches.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".