PML nuclear bodies contribute to the basal expression of the mTOR inhibitor DDIT4
Bibliographic record
Abstract
Abstract The promyelocytic leukemia (PML) protein is an essential component of PML nuclear bodies (PML NBs) frequently lost in cancer. PML NBs coordinate chromosomal regions via modification of nuclear proteins that in turn may regulate genes in the vicinity of these bodies. However, few PML NB-associated genes have been identified. PML and PML NBs can also regulate mTOR and cell fate decisions in response to cellular stresses. We now demonstrate that PML depletion in U2OS cells or TERT-immortalized normal human diploid fibroblasts results in decreased expression of the mTOR inhibitor DDIT4 (REDD1). DNA and RNA immuno-FISH reveal that PML NBs are closely associated with actively transcribed DDIT4 loci, implicating these bodies in regulation of basal DDIT4 expression. Although PML silencing did reduce the sensitivity of U2OS cells to metabolic stress induced by metformin, PML loss did not inhibit the upregulation of DDIT4 in response to metformin, hypoxia-like (CoCl2) or genotoxic stress. Analysis of publicly available cancer data also revealed a significant correlation between PML and DDIT4 expression in several cancer types (e.g. lung, breast, prostate). Thus, these findings uncover a novel mechanism by which PML loss may contribute to mTOR activation and cancer progression via dysregulation of basal DDIT4 gene expression.
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 | 0.001 | 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".