Discovery and characterization of an essential <i>Francisella tularensis</i> protein required for tularemia disease development (538.5)
Bibliographic record
Abstract
Potential bioterrorism agent Francisella tularensis subspecies tularensis ( F. tularensis ) causes tularemia; a disease that can cause up to 60% mortality. An essential step to developing tularemia is the ability of F. tularensis to escape the vacuole that it initially occupies to replicate in the cytosol of epithelial cells. We hypothesize F. tularensis possesses virulence factors (VFs) that facilitate this process. In this study, we identified a crucial VF for tularemia development. To identify this VF, we screened a 3,050 Francisella mutant library for microbes deficient in bacterial replication once inside host cells. By using subsequent bioinformatics analysis we identified proteins in a subset of mutated bacteria that fit traits of VFs and we tested their ability to cause tularemia‐induced death in mice. Mice infected with bacteria inactivated in 1 novel gene appeared uninfected whereas wild‐type infected mice died within 2 days. Electron and immunofluorescence micrographs of infected hepatocytes showed that this gene aids the bacteria in exiting lysosome‐associated membrane protein‐1 (LAMP‐1) positive membrane vacuoles leading to bacterial replication in the cytosol. Our findings demonstrate this novel VF enables F. tularensis to escape into the cytosol and ultimately cause disease.
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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".