TGF-<b><i>β</i></b>Signaling Cooperates with AT Motif-Binding Factor-1 for Repression of the<b><i>α</i></b>-Fetoprotein Promoter
Bibliographic record
Abstract
α-Fetoprotein (AFP) is known to be highly produced in fetal liver despite its barely detectable level in normal adult liver. On the other hand, hepatocellular carcinoma often shows high expression of AFP. Thus, AFP seems to be an oncogenic marker. In our present study, we investigated how TGF-β signaling cooperates with AT motif-binding factor-1 (ATBF1) to inhibit AFP transcription. Indeed, the expression of AFP mRNA in HuH-7 cells was negatively regulated by TGF-β signaling. To further understand how TGF-β suppresses the transcription of the AFP gene, we analyzed the activity of the AFP promoter in the presence of TGF-β. We found that the TGF-β signaling and ATBF1 suppressed AFP transcription through two ATBF1 binding elements (AT-motifs). Using a heterologous reporter system, both AT-motifs were required for transcriptional repression upon TGF-β stimulation. Furthermore, Smads were found to interact with ATBF1 at both its N-terminal and C-terminal regions. Since the N-terminal (ATBF1N) and C-terminal regions of ATBF1 (ATBF1C) lack the ability of DNA binding, both truncated mutants rescued the cooperative inhibitory action by the TGF-β signaling and ATBF1 in a dose-dependent manner. Taken together, these findings indicate that TGF-β signaling can act in concert with ATBF1 to suppress the activity of the AFP promoter through direct interaction of ATBF1 with Smads.
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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".