Faculty Opinions recommendation of PTEN interacts with the transcription machinery on chromatin and regulates RNA polymerase II-mediated transcription.
Bibliographic record
Abstract
Regulation of RNA polymerase II (RNAPII)-mediated transcription controls cellular phenotypes such as cancer. Phosphatase and tensin homologue deleted on chromosome ten (PTEN), one of the most commonly altered tumor suppressors in cancer, affects transcription via its role in antagonizing the PI3K/AKT signaling pathway. Using co-immunoprecipitations and proximal ligation assays we provide evidence that PTEN interacts with AFF4, RNAPII, CDK9, cyclin T1, XPB and CDK7. Using ChIP-seq, we show that PTEN co-localizes with RNAPII and binds to chromatin in promoter and putative enhancer regions identified by histone modifications. Furthermore, we show that loss of PTEN affects RNAPII occupancy in gene bodies and further correlates with gene expression changes. Interestingly, PTEN binds to promoters and negatively regulates the expression of genes involved in transcription including AFF4 and POL2RA, which encodes a subunit of RNAPII. Loss of PTEN also increased cells' sensitivity to transcription inhibition via small molecules, which could provide a strategy to target PTEN-deficient cancers. Overall, our work describes a previously unappreciated role of nuclear PTEN, which by interacting with the transcription machinery in the context of chromatin exerts an additional layer of regulatory control on RNAPII-mediated transcription.© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. PMID: 31169889 Funding information This work was supported by: NIGMS NIH HHS, United States Grant ID: T32 GM008224 NIH HHS, United States Grant ID: S10 OD018522 NCI NIH HHS, United States Grant ID: R35 CA220491
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.711 | 0.501 |
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".