PHF6 functions as a tumor suppressor by recruiting methyltransferase SUV39H1 to nucleolar region and offers a novel therapeutic target for PHF6-muntant leukemia
Bibliographic record
Abstract
Mutations in the plant homeodomain-like finger protein 6 ( PHF6 ) gene are strongly associated with acute myeloid (AML) and T-cell acute lymphoblastic leukemia (T-ALL). In this study, we demonstrated that PHF6 can bind to H3K9me3 and H3K27me1 on the nucleolar chromatin and recruit histone methyltransferase SUV39H1 to the rDNA locus. The deletion of PHF6 caused a decrease in the recruitment of SUV39H1 to rDNA gene loci , resulting in a reduction in the level of H3K9me3 and the promotion of rDNA transcription. The knockdown of either SUV39H1 or PHF6 significantly attenuated the effects of increase in H3K9me3 and suppressed the transcription of rDNA induced by the overexpression of the other interacting partner, thereby establishing an interdependent relationship between PHF6 and SUV39H1 in their control of rRNA transcription . The PHF6 clinical mutants significantly impaired the ability to bind and recruit SUV39H1 to the rDNA loci, resulting in an increase in rDNA transcription activity, the proliferation of in vitro leukemia cells , and the growth of in vivo mouse xenografts . Importantly, significantly elevated levels of pre-rRNA were observed in clinical AML patients who possessed a mutated version of PHF6 . The specific rDNA transcription inhibitor CX5461 significantly reduced the resistance of U937 AML cells deficient in PHF6 to cytarabine , the drug that is most commonly used to treat AML. Collectively, we revealed a novel molecular mechanism by which PHF6 recruits methyltransferase SUV39H1 to the nucleolar region in leukemia and provided a potential therapeutic target for PHF6 -mutant leukemia.
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) | 0.001 | 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".