Cathelicidins Induce Toll-Interacting Protein Synthesis to Prevent Apoptosis in Colonic Epithelium
Bibliographic record
Abstract
Cathelicidin peptides secreted by leukocytes and epithelial cells are microbicidal but also regulate pathogen sensing via toll-like receptors (TLRs) in the colon by mechanisms that are not fully understood. Herein, analyses with the attaching/effacing pathogen Citrobacter rodentium model of colitis in cathelicidin-deficient (Camp-/-) mice, and colonic epithelia demonstrate that cathelicidins prevent apoptosis by sustaining post-transcriptional synthesis of a TLR adapter, toll-interacting protein (TOLLIP). Cathelicidins induced phosphorylation-activation of epidermal growth factor receptor (EGFR)-kinase, which phosphorylated-inactivated miRNA-activating enzyme Argonaute 2 (AGO2), thus reducing availability of the TOLLIP repressor miRNA-31. Cathelicidins promoted stability of TOLLIP protein via a proteosome-dependent pathway. This cathelicidin-induced TOLLIP upregulation prevented apoptosis in the colonic epithelium by reducing levels of caspase-3 and poly (ADP-ribose) polymerase (PARP)-1 in response to the proinflammatory cytokines, interferon-γ (IFNγ) and tumor necrosis factor-α (TNFα). Further, Camp-/- colonic epithelial cells were more susceptible to apoptosis during C. rodentium infection than wild-type cells. This antiapoptotic effect of cathelicidins, maintaining epithelial TOLLIP protein in the gut, provides insight into cathelicidin's ability to regulate TLR signaling and prevent exacerbated inflammation.
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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".