Identification of genes essential for Francisella invasion of non‐phagocytic cells
Bibliographic record
Abstract
The intracellular bacterium Francisella tularensis subspecies tularensis ( F. tularensis ) is the causative agent of tularemia, a disease that can result in 30–35% mortality if left untreated. F. tularensis host cell invasion is crucial for their pathogenesis as without invasion disease does not occur. Upon host entry, the bacteria colonize both macrophages and epithelial cells. We hypothesize F. tularensis possesses unique virulence factors specific for the invasion of epithelial cells. To identify F. tularensis genes involved in this process, we utilized both in vitro and in vivo experimental approaches using a biosafety level 2 bacterial surrogate, F. tularensis subspecies novicida ( F. novicida ). A F. novicida mutant library was screened for bacteria with reduced invasion and intracellular growth using gentamycin‐based survival assays in hepatocytes in vitro . Select candidate mutants were tested in mice for their ability to colonize murine livers. We discovered that bacteria containing mutations in at least 6 previously uncharacterized genes had significantly reduced bacterial loads, resulting in mice that survived for extended durations as compared to wild‐type infections. Our findings demonstrate that novel bacterial proteins are crucial for Francisella virulence and these findings open a new door to the discovery of novel targets for the development of therapeutics and prophylactics. Grant Funding Source : NSERC and CIHR
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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".