Dissecting virulence: Systematic and functional analyses of a pathogenicity island
Bibliographic record
Abstract
Bacterial pathogenicity islands (PAI) often encode both effector molecules responsible for disease and secretion systems that deliver these effectors to host cells. Human enterohemorrhagic Escherichia coli (EHEC), enteropathogenic E. coli, and the mouse pathogen Citrobacter rodentium (CR) possess the locus of enterocyte effacement (LEE) PAI. We systematically mutagenized all 41 CR LEE genes and functionally characterized these mutants in vitro and in a murine infection model. We identified 33 virulence factors, including two virulence regulators and a hierarchical switch for type III secretion. In addition, 7 potential type III effectors encoded outside the LEE were identified by using a proteomics approach. These non-LEE effectors are encoded by three uncharacterized PAIs in EHEC O157, suggesting that these PAIs act cooperatively with the LEE in pathogenesis. Our findings provide significant insights into bacterial virulence mechanisms and disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".