Ligand-inducible interaction of the DRIP/TRAP coactivator complex with retinoid receptors in retinoic acid–sensitive and –resistant acute promyelocytic leukemia cells
Bibliographic record
Abstract
Retinoic acid (RA) signaling is mediated by its nuclear receptors RXR and RAR, which bind to their cognate response elements as a heterodimer, RXR/RAR, and act in concert with coregulatory factors to regulate gene transcription on ligand binding. To identify specific cofactors that interact with the RXR/RAR heterodimer in acute promyelocytic leukemia (APL) cells, a double cistronic construct was used that allowed coexpression of the RXR LBD (ligand binding domain) with the RAR LBD as an affinity matrix to pull down interacting proteins from nuclear extracts prepared from a human APL cell line, NB4. A group of proteins was detected whose interaction with RXR/RAR is ligand inducible. The molecular weight pattern of these proteins is similar to that of a complex of proteins previously identified as DRIP or TRAP, which are ligand-dependent transcription activators of VDR and TR, respectively. The RXR/RAR-interacting proteins from NB4 were confirmed to be identical to the DRIP subunits by comparative electrophoresis, Western blot analysis, and in vitro protein interaction assay. In addition to RXR/RAR, the DRIP component can interact directly with the APL-specific PML-RARalpha fusion protein. The same DRIP complex is present in RA-resistant APL cells and in a variety of cancer cell lines, supporting its global role in transcriptional regulation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".