HIF-1 activated by PIM1 assembles a pathological transcription complex and regulon that drives JAK2V617F MPN disease
Bibliographic record
Abstract
Abstract Hypoxia-inducible factors (HIFs) are master transcriptional regulators, central to cellular survival under limited oxygen (hypoxia) and frequently activated within malignancy. Malignant context affects the role of HIFs within oncogenesis; however, the mechanisms regulating HIF context-specificities are not well characterised. Applying the JAK2V617F (JVF) model of myeloproliferative neoplasms (MPNs), in which HIF-1 is active in normoxia (20% O 2 ), we sought to determine whether the modality of HIF-1 activation directs its function. We identify that HIF-1 is stabilised in JVF cells downstream of STAT1/5 signalling and upregulation of PIM1: PIM1 mediates phosphorylation of HIF-1 (Thr498/Ser500) in JVF cells that inhibits proteasomal degradation. PIM1 inhibition eradicates HIF-1 from JVF cells. Applying a single-input dual-omics output chromatin interactome methodology (DOCIA), we define JVF-specific transcription cofactors and genomic redistribution of HIF-1, and a JVF-HIF-1 regulon in primary haematopoietic stem/progenitor cells. In a cohort of 172 JVF-MPN patients, we observe significant association of the JVF-HIF-1 regulon (but strikingly, not canonical HIF-1 genes) with disease severity, progression, and patient survival. Finally, we identify a core set of JVF-HIF-1 targets significantly associated with spontaneous transformation of MPNs to AML. Our findings identify that HIF-1 activation by the JVF-PIM1 axis substantially alters its function, and that this reprogramming drives MPN disease progression, restoring the potential for targeted therapies that delineate HIF-1 activity co-opted by malignancy from essential roles within physiological oxygen homeostasis. Key Points HIF-1 activation via PIM1 in JAK2V617F-MPNs drives non-canonical transcription complex formation/function. The JAK2V617F-HIF-1 regulon drives MPN disease progression, transformation to AML and worse patient outcomes.
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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".