The QA repeat domain of TCERG1 mediates its inhibitory effect towards C/EBPα and the ability of TCERG1 to be relocalized in the nucleus (946.6)
Bibliographic record
Abstract
Previously we showed that TCERG1 inhibits the growth arrest and transactivation functions of C/EBPα, through a mechanism that requires the relocalization of TCERG1 to sites of C/EBPα occupancy in the nucleus. TCERG1 contains a unique QA repeat domain, for which no function has been assigned, as well as 3 WW domains within the N‐terminal half of TCERG1 which has been shown previously to mediate its relocalization. These domains were explored for their roles in mediating the inhibitory and relocalization activities of TCERG1. While mutations of the WW domains were without effect, deletion of the QA repeat domain in TCERG1 prevented its relocalization in the nucleus (assessed by confocal microscopy), and abrogated its ability to repress both the growth arrest and transactivation functions of C/EBPα. Reducing the number of QA repeats from 38 to 20 had no impact on the activities of TCERG1, while further reduction to 10 resulted in a significant loss of relocalization ability and inhibitory activity towards C/EBPα. Interestingly, deletion of the QA repeat domain did not alter the ability of TCERG1 to interact with C/EBPα in a co‐IP assay. Based on these data, we speculate that one function of the QA repeat domain is to allow relocalization of TCERG1 from its nuclear speckle compartment. (Note ‐ the first two authors contributed equally to this work)
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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) | 0.002 | 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".