Regulation of ULK1 Expression and Autophagy by STAT1
Bibliographic record
Abstract
Autophagy involves the lysosomal degradation of cytoplasmic contents for regeneration of anabolic substrates during nutritional or inflammatory stress. Its initiation occurs rapidly after inactivation of the protein kinase mammalian target of rapamycin (mTOR) (or mechanistic target of rapamycin), leading to dephosphorylation of Unc-51-like kinase 1 (ULK1) and autophagosome formation. Recent studies indicate that mTOR can, in parallel, regulate the activity of stress transcription factors, including signal transducer and activator of transcription-1 (STAT1). The current study addresses the role of STAT1 as a transcriptional suppressor of autophagy genes and autophagic activity. We show that STAT1-deficient human fibrosarcoma cells exhibited enhanced autophagic flux as well as its induction by pharmacological inhibition of mTOR. Consistent with enhanced autophagy initiation, ULK1 mRNA and protein levels were increased in STAT1-deficient cells. By chromatin immunoprecipitation, STAT1 bound a putative regulatory sequence in the ULK1 5'-flanking region, the mutation of which increased ULK1 promoter activity, and rendered it unresponsive to mTOR inhibition. Consistent with an anti-apoptotic effect of autophagy, rapamycin-induced apoptosis and cytotoxicity were blocked in STAT1-deficient cells but restored in cells simultaneously exposed to the autophagy inhibitor ammonium chloride. In vivo, skeletal muscle ULK1 mRNA and protein levels as well as autophagic flux were significantly enhanced in STAT1-deficient mice. These results demonstrate a novel mechanism by which STAT1 negatively regulates ULK1 expression and autophagy.
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".