The ubiquitin-activating enzyme E1 as a therapeutic target for the treatment of leukemia and multiple myeloma
Bibliographic record
Abstract
The proteasomal pathway of protein degradation involves 2 discrete steps: ubiquitination and degradation. Here, we evaluated the effects of inhibiting the ubiquitination pathway at the level of the ubiquitin-activating enzyme UBA1 (E1). By immunoblotting, leukemia cell lines and primary patient samples had increased protein ubiquitination. Therefore, we examined the effects of genetic and chemical inhibition of the E1 enzyme. Knockdown of E1 decreased the abundance of ubiquitinated proteins in leukemia and myeloma cells and induced cell death. To further investigate effects of E1 inhibition in malignancy, we discovered a novel small molecule inhibitor, 3,5-dioxopyrazolidine compound, 1-(3-chloro-4-fluorophenyl)-4-[(5-nitro-2-furyl)methylene]-3,5-pyrazolidinedione (PYZD-4409). PYZD-4409 induced cell death in malignant cells and preferentially inhibited the clonogenic growth of primary acute myeloid leukemia cells compared with normal hematopoietic cells. Mechanistically, genetic or chemical inhibition of E1 increased expression of E1 stress markers. Moreover, BI-1 overexpression blocked cell death after E1 inhibition, suggesting ER stress is functionally important for cell death after E1 inhibition. Finally, in a mouse model of leukemia, intraperitoneal administration of PYZD-4409 decreased tumor weight and volume compared with control without untoward toxicity. Thus, our work highlights the E1 enzyme as a novel target for the treatment of hematologic malignancies.
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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) | 0.001 | 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".