Protease‐activated receptor 2 (PAR2) inactivates the pro‐apoptotic protein, BAD, via ERK1/2 and PI3K activity to decrease apoptosis in colonic epithelial cells
Bibliographic record
Abstract
Colonic biopsies from inflamed colon have higher levels of epithelial apoptosis than biopsies from healthy individuals. Therapies that decrease intestinal epithelial apoptosis may decrease the inflammation observed in inflammatory bowel disease. We previously showed that signaling by PAR2, a GPCR activated by serine proteases, delayed IFN‐γ & TNF‐α‐induced apoptosis in colonic epithelial (HT‐29) cells. We aimed to determine the mechanism responsible for PAR2‐induced survival. The PAR2 agonists 2‐furoyl‐LIGRLO (2fLI), SLIGKV and trypsin all significantly reduced cleavage of caspase‐8, 9, 3 as well as PARP in response to IFNγ/TNFα. 2fLI increased phosphorylation of ERK1/2, p90RSK and Akt. 2fLI treatment stimulated the phosphorylation of pro‐apoptotic BAD at Ser112 and Ser136. Selective MAPK pathway inhibitors (U0126 & SL0101) blocked BAD Ser112 phosphorylation, whereas the PI3K‐selective inhibitor (LY294002) blocked BAD Ser136 phosphorylation. Pre‐treatment with 2fLI prior to the addition of IFNγ/TNFα increased BAD phosphorylation at both residues. PAR2 signaling reduces epithelial apoptosis in response to inflammatory cytokines via activation of pathways that inactivate the pro‐apoptotic BAD protein. Our findings may represent a mechanism whereby proteases facilitate epithelial cell survival and colonic healing after inflammation. Funding: Crohn's and Colitis Foundation of Canada
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".