A non-canonical enzymatic function of PIWIL4 maintains genomic integrity and leukemic growth in AML
Bibliographic record
Abstract
RNA-binding proteins (RBPs) form a large and diverse class of factors, many members of which are overexpressed in hematologic malignancies. RBPs participate in various processes of messenger RNA (mRNA) metabolism and prevent harmful DNA:RNA hybrids or R-loops. Here, we report that PIWIL4, a germ stem cell-associated RBP belonging to the RNase H-like superfamily, is overexpressed in patients with acute myeloid leukemia (AML) and is essential for leukemic stem cell function and AML growth, but dispensable for healthy human hematopoietic stem cells. In AML cells, PIWIL4 binds to a small number of known piwi-interacting RNA. Instead, it largely interacts with mRNA annotated to protein-coding genic regions and enhancers that are enriched for genes associated with cancer and human myeloid progenitor gene signatures. PIWIL4 depletion in AML cells downregulates the human myeloid progenitor signature and leukemia stem cell (LSC)-associated genes and upregulates DNA damage signaling. We demonstrate that PIWIL4 is an R-loop resolving enzyme that prevents R-loop accumulation on a subset of AML and LSC-associated genes and maintains their expression. It also prevents DNA damage, replication stress, and activation of the ATR pathway in AML cells. PIWIL4 depletion potentiates sensitivity to pharmacological inhibition of the ATR pathway and creates a pharmacologically actionable dependency in AML cells.
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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) | 0.002 | 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".