Sec24 interaction is essential for localization and virulence‐associated function of the bacterial effector protein NleA
Bibliographic record
Abstract
Enteropathogenic and enterohaemorrhagic Escherichia coli (EPEC and EHEC) are food-borne pathogens that cause severe diarrhoeal disease in humans. Citrobacter rodentium is a related mouse pathogen that serves as a small animal model for EPEC and EHEC infections. EPEC, EHEC and C. rodentium translocate bacterial virulence proteins directly into host cells via a type III secretion system (T3SS). Non-LEE-encoded effector A (NleA) is a T3SS effector that is common to EPEC, EHEC and C. rodentium and is required for bacterial virulence. NleA localizes to the host cell secretory pathway and inhibits vesicle trafficking by interacting with the Sec24 subunit of mammalian coatamer protein II complex (COPII). Mammalian cells express four paralogues of Sec24 (Sec24A-D), which mediate selection of cargo proteins for transport and possess distinct, but overlapping cargo specificities. Here, we show that NleA binds Sec24A-D with two distinct mechanisms. An NleA protein variant with greatly diminished interaction with all Sec24 paralogues does not properly localize, does not inhibit COPII-mediated vesicle budding, and does not confer virulence in the mouse infection model. Together, this work provides strong evidence that the interaction and inhibition of COPII by NleA is an important aspect of EPEC- and EHEC-mediated 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) | 0.001 | 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".