Inhibition of ATF4 Transcriptional Activity by FIAT/γ‐Taxilin Modulates Bone Mass Accrual
Bibliographic record
Abstract
The basic domain-leucine zipper protein, activating transcription factor 4 (ATF4), was recently shown to control key aspects of osteoblast biology. ATF4 regulates the timely onset of osteoblast differentiation, the synthesis of type I collagen, and the transcription of the osteocalcin and RANKL (receptor activator of NFkappa-B ligand) genes. Accordingly, the levels and activity of ATF4 are under tight control through mechanisms that include protein stability and phosphorylation. We have uncovered yet another mode of inhibition of ATF4 through its interaction with the leucine zipper protein FIAT (Factor Inhibiting ATF4-mediated Transcription, also described as gamma-taxilin). FIAT/gamma-taxilin localizes to the nucleus in osteoblasts and dimerizes with ATF4 to form inactive dimers, because it does not contain a DNA-binding basic domain moiety. The interaction of FIAT/gamma-taxilin with ATF4 thus inhibits ATF4-mediated transcription. Transgenic mice overexpressing FIAT/gamma-taxilin show osteopenia and reduced expression of the ATF4 target gene, osteocalcin. Interestingly, FIAT/gamma-taxilin also interacts with the transcriptional co-activator alphaNAC (Nascent polypeptide associated complex And Coactivator alpha), suggesting alternative, non-mutually exclusive mechanisms contributing to the inhibition of ATF4-dependent osteocalcin gene transcription by FIAT/gamma-taxilin.
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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 | 0.000 |
| 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".