ERK signaling controls productive HIF‐1 binding to chromatin and cancer cell adaptation to hypoxia through HIF‐1α interaction with NPM1
Bibliographic record
Abstract
The hypoxia-inducible factor HIF-1 is essential for oxygen homeostasis. Despite its well-understood oxygen-dependent expression, regulation of its transcriptional activity remains unclear. We show that phosphorylation by extracellular signal-regulated kinases1/2 (ERK1/2), in addition to promoting HIF-1α nuclear accumulation, also enhances its interaction with chromatin and stimulates direct binding to nucleophosmin (NPM1), a histone chaperone and chromatin remodeler. NPM1 is required for phosphorylation-dependent recruitment of HIF-1 to hypoxia response elements, its interaction with acetylated histones, and high expression of HIF-1 target genes under hypoxia. Transcriptome analysis revealed a significant number of hypoxia-related genes commonly regulated by NPM1 and HIF-1. These NPM1/HIF-1α co-upregulated genes are enriched in three different cancer types, and their expression correlates with hypoxic tumor status and worse patient prognosis. In concert, silencing of NPM1 expression or disruption of its association with HIF-1α inhibits metabolic adaptation of cancer cells and triggers apoptotic death upon hypoxia. We suggest that ERK-mediated phosphorylation of HIF-1α regulates its physical interaction with NPM1, which is essential for the productive association of HIF-1 with hypoxia target genes and their optimal transcriptional activation, required for survival under low oxygen or tumor growth.
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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".