The ubiquitin ligase HERC4 suppresses MafA transcriptional activity triggered by GSK3β in myeloma by atypical K63-linked polyubiquitination
Bibliographic record
Abstract
MafA and c-Maf are close members of the Maf transcription factor family and indicators of poor prognosis of multiple myeloma (MM). Our previous study finds that the ubiquitin ligase HERC4 induces c-Maf degradation but stabilizes MafA, and the mechanism is elusive. In the present study, we find that HERC4 interacts with MafA and mediates its K63-linked polyubiquitination at K33. Moreover, HERC4 inhibits MafA phosphorylation and its transcriptional activity triggered by glycogen synthase kinase 3β (GSK3β). The K33R MafA variant prevents HERC4 from inhibiting MafA phosphorylation and increases MafA transcriptional activity. Further analyses reveal that MafA can also activate the STAT3 signaling, but it is suppressed by HERC4. Lastly, we demonstrate that lithium chloride, a GSK3β inhibitor, can upregulate HERC4 and synergizes dexamethasone, a typical anti-MM drug, in inhibiting MM cell proliferation and xenograft growth in nude mice. These findings thus highlight a novel regulation of MafA oncogenic activity in MM and provide the rationale by targeting HERC4/GSK3β/MafA for the treatment of MM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".