Regulation of Type III Secretion Hierarchy of Translocators and Effectors in Attaching and Effacing Bacterial Pathogens
Bibliographic record
Abstract
Human enteropathogenic Escherichia coli (EPEC), enterohemorrhagic E. coli (EHEC), and the mouse pathogen Citrobacter rodentium (CR) belong to the family of attaching and effacing (A/E) bacterial pathogens. They possess the locus of enterocyte effacement (LEE) pathogenicity island, which encodes a type III secretion system. These pathogens secrete a number of proteins into culture media, including type III effector proteins and translocators that are required for the translocation of effectors into host cells. Preliminary evidence indicated that the LEE-encoded SepL and Rorf6/SepD may form a molecular switch that controls the secretion of translocators and effectors in CR. Here, we show that SepL and SepD indeed perform this function in A/E pathogens such as EHEC and EPEC. Their sepL and sepD mutants do not secrete translocators but exhibit enhanced secretion of effectors. We demonstrate that SepL and SepD interact with each other and that both SepL and SepD are localized to the bacterial membranes. Furthermore, we demonstrate that culture media influence the type III secretion profile of EHEC, EPEC, and CR and that low-calcium concentrations inhibit secretion of translocators but promote the secretion of effectors, similar to effects on type III secretion by mutations in sepL and sepD. However, the secretion profile of the sepD and sepL mutants is not affected by these culture conditions. Collectively, our results suggest that SepL and SepD not only are necessary for efficient translocator secretion in A/E pathogens but also control a switch from translocator to effector secretion by sensing certain environmental signals such as low calcium.
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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 | 0.001 | 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".