The leukemogenic fusion gene MLL-AF9 alters microRNA expression pattern and inhibits monoblastic differentiation via miR-511 repression
Bibliographic record
Abstract
BACKGROUND: In this study we explored the role of microRNAs (miRNAs) as mediators of leukemogenic effects of the fusion gene MLL-AF9, which results from a frequent chromosomal translocation in infant and monoblastic acute myeloid leukemia (AML). METHODS: We performed a specific and efficient knockdown of endogenous MLL-AF9 in the human monoblastic AML cell line THP1. RESULTS: The knockdown associated miRNA expression profile revealed 21 MLL-AF9 dependently expressed miRNAs. Gene ontology analyses of target genes suggested an impact of these miRNAs on downstream gene regulation via targeting of transcriptional modulators as well as involvement in many functions important for leukemia maintenance as e.g. myeloid differentiation, cell cycle and stem cell maintenance. Furthermore, we identified one of the most intensely repressed miRNAs, miR-511, to raise CCL2 expression (a chemokine ligand important for immunosurveillance), directly target cyclin D1, inhibit cell cycle progression, increase cellular migration and promote monoblastic differentiation. With these effects, miR-511 may have a therapeutic potential as a pro-differentiation agent as well as in leukemia vaccination approaches. CONCLUSIONS: Our study provides new insights into the understanding of miRNAs as functional mediators of the leukemogenic fusion gene MLL-AF9 and opens new opportunities to further investigate specific therapeutic options for AML via the miRNA level.
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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".