Effects of Claudin-1 on the Action of Clostridium perfringens Enterotoxin in Caco-2 Cells
Bibliographic record
Abstract
enterotoxin (CPE) contributes to diarrhea and an often-lethal enterotoxemia. CPE action starts when it binds to claudin receptors, forming a small complex (90 kDa). Six small complexes then oligomerize to create prepores, followed by insertion of beta-hairpins from CPE to form beta-barrel pores named CH-1 or CH-2. Of the ~27 members of the human claudin protein family, only some bind CPE. However, both receptor claudins and the nonreceptor claudin-1 (CLDN-1) are associated with the small and CH-1/CH-2 CPE complexes. Therefore, this study evaluated whether claudin-1 affects CPE action by generating a CLDN-1 null mutant in Caco-2 cells using CRISPR-Cas9. Compared to wild-type Caco-2 cells, paracellular permeability of the CLDN-1 mutant was significantly enhanced, suggesting that claudin-1 may reduce CPE absorption during enterotoxemia. The CLDN-1 mutant was also markedly more sensitive than wild-type Caco-2 cells to apically-applied CPE. The mechanism behind this increased sensitivity involved higher CPE binding by the CLDN-1 mutant vs. wild-type Caco-2 cells, which led to more CH-1/CH-2 complex formation. However, the CH-1/CH-2 complexes formed by the CLDN-1 mutant were less stable or trypsin resistant than those of wild-type cells. These results indicate that, although a nonreceptor, CLDN-1 positively and negatively influences CPE action.
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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 | 0.001 |
| 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".