The human GFI136N variant induces epigenetic changes at the Hoxa9 locus and accelerates K-RAS driven myeloproliferative disorder in mice
Bibliographic record
Abstract
The coding single nucleotide polymorphism GFI136N in the human gene growth factor independence 1 (GFI1) is present in 3%-7% of whites and increases the risk for acute myeloid leukemia (AML) by 60%. We show here that GFI136N, in contrast to GFI136S, lacks the ability to bind to the Gfi1 target gene that encodes the leukemia-associated transcription factor Hoxa9 and fails to initiate histone modifications that regulate HoxA9 expression. Consistent with this, AML patients heterozygous for the GFI136N variant show increased HOXA9 expression compared with normal controls. Using ChipSeq, we demonstrate that GFI136N specific epigenetic changes are also present in other genes involved in the development of AML. Moreover, granulomonocytic progenitors, a bone marrow subset from which AML can arise in humans and mice, show a proliferative expansion in the presence of the GFI136N variant. In addition, granulomonocytic progenitors carrying the GFI136N variant allele have altered gene expression patterns and differ in their ability to grow after transplantation. Finally, GFI136N can accelerate a K-RAS driven fatal myeloproliferative disease in mice. Our data suggest that the presence of a GFI136N variant allele induces a preleukemic state in myeloid precursors by deregulating the expression of Hoxa9 and other AML-related genes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".