OmpT Outer Membrane Proteases of Enterohemorrhagic and Enteropathogenic Escherichia coli Contribute Differently to the Degradation of Human LL-37
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) and enteropathogenic E. coli (EPEC) are food-borne pathogens that cause serious diarrheal diseases. To colonize the human intestine, these pathogens must overcome innate immune defenses such as antimicrobial peptides (AMPs). Bacterial pathogens have evolved various mechanisms to resist killing by AMPs, including proteolytic degradation of AMPs. To examine the ability of the EHEC and EPEC OmpT outer membrane (OM) proteases to degrade α-helical AMPs, ompT deletion mutants were generated. Determination of MICs of various AMPs revealed that both mutant strains are more susceptible than their wild-type counterparts to α-helical AMPs, although to different extents. Time course assays monitoring the degradation of LL-37 and C18G showed that EHEC cells degraded both AMPs faster than EPEC cells in an OmpT-dependent manner. Mass spectrometry analyses of proteolytic fragments showed that EHEC OmpT cleaves LL-37 at dibasic sites. The superior protection provided by EHEC OmpT compared to EPEC OmpT against α-helical AMPs was due to higher expression of the ompT gene and, in turn, higher levels of the OmpT protein in EHEC. Fusion of the EPEC ompT promoter to the EHEC ompT open reading frame resulted in decreased OmpT expression, indicating that transcriptional regulation of ompT is different in EHEC and EPEC. We hypothesize that the different contributions of EHEC and EPEC OmpT to the degradation and inactivation of LL-37 may be due to their adaptation to their respective niches within the host, the colon and small intestine, respectively, where the environmental cues and abundance of AMPs are different.
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 | 0.000 | 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".