The Inflammatory Bowel Disease Drug Azathioprine Induces Autophagy via mTORC1 and the Unfolded Protein Response Sensor PERK
Bibliographic record
Abstract
BACKGROUND: Genetic studies have strongly linked autophagy to Crohn's disease (CD), and stimulating autophagy in CD patients may be therapeutically beneficial. The aim of this study was to evaluate the effect of current inflammatory bowel disease (IBD) drugs on autophagy and investigate molecular mechanisms of action and functional outcomes in relation to this cellular process. METHODS: Autophagy marker LC3 was evaluated by confocal fluorescence microscopy and flow cytometry. Drug mechanism of action was investigated by polymerase chain reaction (PCR) array with changes in signaling pathways examined by immunoblot and quantitative reverse transcription PCR (RT-qPCR). Clearance of adherent-invasive Escherichia coli (AIEC) and levels of pro-inflammatory cytokine tumor necrosis factor alpha (TNFα) were evaluated by gentamicin protection assays and RT-qPCR, respectively. The marker LC3 was analyzed in peripheral blood mononuclear cells (PBMCs) from pediatric patients by flow cytometry. RESULTS: Azathioprine induces autophagy via mechanisms involving modulation of mechanistic target of rapamycin (mTORC1) signaling and stimulation of the unfolded protein response (UPR) sensor PERK. Induction of autophagy with azathioprine correlated with the enhanced clearance of AIEC and dampened AIEC-induced increases in TNFα. Azathioprine induced significant increase in autophagosome bound LC3-II in PBMC populations ex vivo, supporting in vitro findings. In patients, the CD-associated ATG16L1 T300A single-nucleotide polymorphism did not attenuate azathioprine induction of autophagy. CONCLUSIONS: Modulation of autophagy via mTORC1 and the UPR may contribute to the therapeutic efficacy of azathioprine in IBD.
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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 | 0.000 |
| 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".