USING A TRANSGENIC ZEBRAFISH MODEL TO IDENTIFY DOWNSTREAM THERAPEUTIC TARGETS IN HIGH-RISK, NUP98-HOXA9-INDUCED MYELOID DISEASE
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a genetic disease whereby sequential genetic\naberrations alter essential white blood cell development leading to differentiation arrest\nand hyperproliferation. Pertinent animal models serve as essential intermediaries between\nin vitro molecular studies and the use of new agents in clinical trials. We previously\ngenerated a transgenic zebrafish model expressing human NUP98-HOXA9 (NHA9), a\nfusion oncogene found in high-risk AML. This expression yields a pre-leukemic state in\nboth embryos and adults. Using this model, we have identified the overexpression of\ndnmt1 and the Wnt/β-catenin pathway as downstream contributors to the\nmyeloproliferative phenotype. Targeted dnmt1 morpholino knockdown and\npharmacological inhibition with methyltransferase inhibitors rescues NHA9 embryos.\nSimilarly, inhibition of β-catenin with COX inhibitors partially restores normal\nhematopoiesis. Interestingly, concurrent treatment with a histone deacetylase inhibitor\nand either a methyltransferase inhibitor or a COX inhibitor, synergistically inhibits the\neffects of NHA9 on embryonic hematopoiesis. Thus, we have identified potential\npharmacological targets in NHA9-induced myeloid disease that may offer a highly\nefficient therapy with limited toxicity – addressing a major long-term goal of AML\nresearch.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".