Defining enteric bacterial pathogenesis using organoids: <i>Citrobacter rodentium</i> uses EspC, an atypical mucinolytic protease, to penetrate mouse colonic mucus
Bibliographic record
Abstract
Enteric bacterial pathogens pose significant threats to human health; however, the mechanisms by which they infect the mammalian gut in the face of daunting host defenses remain to be fully defined. For the attaching and effacing (A/E) bacterial family member and murine pathogen Citrobacter rodentium, its virulence strategy appears to involve penetration of the colonic mucus barrier to reach the underlying epithelium. To better define these interactions, we grew colonoids under air-liquid interface (ALI) conditions, producing a thick mucus layer that mimicked in vivo mucus composition and glycosylation. C. rodentium’s penetration of ALI-derived mucus was dramatically enhanced upon exposure to sialic acid, in concert with the secretion of two serine protease autotransporter of Enterobacteriaceae (SPATE) proteins, Pic and EspC. Despite Pic being a class II SPATE, and already recognized as a mucinase, it was EspC, a class I SPATE family member, that degraded ALI-derived mucus, despite class I SPATEs not previously shown to possess mucinase activity. Confirming this finding, E. coli DH5α carrying a plasmid that expresses C. rodentium-derived EspC was able to degrade the mucus. Moreover, recombinant EspC alone also displayed mucinolytic activity in a dose-dependent manner. Collectively, our results reveal the utility of ALI-derived mucus in modeling microbe-host interactions at the intestinal mucosal surface, as well as identify EspC as an atypical class I SPATE that shows significant mucinolytic activity toward ALI-derived mucus.
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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".