Estrogen receptor-related receptor α (ERRα) regulates osteopontin expression through a non-canonical ERRα response element in a cell context-dependent manner
Bibliographic record
Abstract
We previously demonstrated that the orphan nuclear receptor, estrogen receptor-related receptor alpha (ERRalpha) is highly expressed in osteoblasts and osteoclasts, regulates osteogenesis and expression of osteoblast-associated markers in the rat calvaria cell differentiation system, and is dysregulated in the rat ovariectomy model of postmenopausal osteoporosis. There are conflicting published data on the transcriptional regulation by ERRalpha of the gene for osteopontin (OPN), an extracellular matrix protein required in bone remodeling, and a potential direct target mediating ERRalpha effects in bone. We therefore readdressed OPN gene regulation by ERRalpha in both osteoblastic (rat osteosarcoma ROS17/2.8 cells) and non-osteoblastic (HeLa) cell lines using a mouse proximal 2 kb OPN promoter fragment. A minimal OPN promoter fragment spanning from -56 to +9 bp is activated in HeLa cells but repressed it in ROS17/2.8 cells. Adenine scanning mutagenesis revealed the presence of a non-canonical ERRalpha response element in this minimal promoter. Surprisingly, prototypical inactivating mutations in the activation function 2 (AF2) domain or a naturally occurring allelic variant of ERRalpha (ERRalphaH408) were all better activators than wild-type ERRalpha in HeLa cells, activities that were generally paralleled by repression in ROS17/2.8 cells. Finally, we found that the N-terminus of ERRalpha harbors a repressor domain that acts in a cell context-dependent manner. We conclude that OPN is an ERRalpha target gene whose promoter is regulated by ERRalpha in a cell context-dependent manner and that a predicted silencing mutation in AF2 or a more flexible helix 12 increases ERRalpha transcriptional activity, effects with implications for ERRalpha as a therapeutic target in bone.
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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 | 0.001 |
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".