Ubash3b promotes TPA-mediated suppression of leukemogenesis through accelerated downregulation of PKCδ protein
Bibliographic record
Abstract
Acquired drug-resistance, often involving downregulation or mutations in the target protein, is a major caveat in precision medicine. Understanding mechanisms of resistance to therapeutic drugs may unravel strategies to overcome or prevent them. We previously identified phorbol ester (PE) compounds such as TPA that induce Protein Kinase δ (PKCδ), thereby suppressing leukemogenesis. Here we identified erythroleukemia cell lines that resist PEs and showed that reduced PKCδ protein expression underlies drug resistance. Reduced level of PKCδ in resistant cell lines was due to its phosphorylation followed by protein degradation. Indeed, proteasome inhibition prevented PE-induced loss of PKCδ. Accordingly, a combination of TPA and the proteasome inhibitor ALLN significantly suppressed leukemia in a mouse model of leukemia. PKCδ downregulation by TPA was independent of the downstream MAPK/ERK/P38/JNK pathway. Instead, expression of ubiquitin-associated and SH3 domain-containing protein b (Ubash3b) was induced by TPA, which leads to PKCδ protein dephosphorylation and degradation. This specific degradation was blocked by RNAi-mediated depletion of Ubash3b. In drug-sensitive leukemic cells, TPA did not induce Ubash3b, and consequently, PKCδ levels remained high. A PE-resistant cell line derived from PE-treated sensitive cells exhibited very low PKCδ expression. In these drug resistance cells, a Ubash3b independent mechanism led to PKCδ degradation. Thus, PE compounds in combination with proteasome or specific inhibitors for Ubash3b, or other factors can overcome resistance to TPA, leading to durable suppression of leukemic growth. These results identify Ubash3b as a potential target for drug development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".