Tricellular Tight Junction Protein Tricellulin Is Targeted by the Enteropathogenic Escherichia coli Effector EspG1, Leading to Epithelial Barrier Disruption
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC)-induced diarrhea is often associated with disruption of intestinal epithelial tight junctions. Although studies have shown alterations in the expression and localization of bicellular tight junction proteins during EPEC infections, little is known about whether tricellular tight junction proteins (tTJs) are affected. Using Caco-2 cell monolayers, we investigated if EPEC is capable of targeting the tTJ protein tricellulin. Our results demonstrated that at 4 h postinfection, EPEC induced a significant reduction in tricellulin levels, accompanied by a significant loss of transepithelial resistance (TEER) and a corresponding increase in paracellular permeability. Conversely, cells overexpressing tricellulin were highly resistant to EPEC-induced barrier disruption. Confocal microscopy revealed the distribution of tricellulin into the plasma membrane of infected epithelial cells and confirmed the localization of EPEC aggregates in close proximity to tTJs. Moreover, infections with EPEC strains lacking genes encoding specific type III secreted effector proteins demonstrated a crucial role for the effector EspG1 in modulating tricellulin expression. Complementation studies suggest that the EspG-induced depletion of tricellulin is microtubule dependent. Overall, our results show that EPEC-induced epithelial barrier dysfunction is mediated in part by EspG1-induced microtubule-dependent depletion of tricellulin.
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".