Osteoprotegerin Ligand Induces β-Casein Gene Expression through the Transcription Factor CCAAT/Enhancer-binding Protein β
Bibliographic record
Abstract
Osteoprotegerin ligand (OPGL, also known as RANKL), a member of the tumor necrosis factor superfamily, is essential for mammary gland development during pregnancy in addition to key roles in the immune system and bone development. Here we show that OPGL induces beta-casein transcription through the CCAAT/enhancer-binding protein beta (C/EBPbeta). In both HC11 cell lines and primary mammary epithelial cells, OPGL stimulation triggers rapid nuclear translocation of C/EBPbeta, which is critical for the expression of the beta-casein gene. Mutation of C/EBbeta binding sites in the beta-casein gene promoter completely abrogated OPGL-induced beta-casein promoter activity. By contrast, OPGL stimulation did not result in STAT5 phosphorylation. In vivo immunohistochemistry studies further demonstrated defective nuclear translocation of C/EBPbeta, but normal STAT5 activation, in OPGL-deficient mice. These data show that OPGL is a critical activator of beta-casein gene expression via the transcription factor C/EBPbeta. Our data provide new insights into the understanding of the molecular events involved in milk protein gene expression.
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".