Genome‐wide screen identifies genes required for Francisella invasion in 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. Due to F. tularensis ' high infectivity and lethality the Centers for Disease Control and Prevention has classified it as a Biosafety Level 3 pathogen and a Category A Select agent. F. tularensis host cell invasion is crucial for their pathogenesis as without invasion disease does not occur. Upon entry into their host, the bacteria colonize both macrophages and epithelial cells. Despite significant research advances in macrophage infections, the study of epithelial infections has lagged behind. To investigate F. tularensis genes involved in epithelial cell invasion, we utilized a biosafety level 2 bacterial surrogate called F. tularensis subspecies novicida ( F. novicida ). A F. novicida transposon mutant library was screened to assess their invasion in hepatocytes. We selected for mutants with reduced intracellular growth using gentamycin‐based invasion/survival assays and confirmed our results microscopically using a subset of mutants. We discovered 232 mutants had significantly reduced invasion/survival levels as compared to wild type F. novicida infections. Ultimately our findings open a new door to the discovery of novel targets for the development of therapeutics and prophylactics. Grant Funding Source : CIHR and NSERC
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".