Membrane and nuclear estrogen receptor a collaborate to suppress adipogenesis but not triglyceride content
Bibliographic record
Abstract
Estrogen and estrogen receptor (ER)-α suppress visceral fat development through actions in several organs via unclear mechanisms that we sought to identify. Using mice that express only nuclear ER-α [nuclear-only ER-α (NOER) mice] or plasma membrane ER-α [membrane-only ER-α (MOER) mice], we found that 10-wk-old mice that lacked either receptor pool showed extensive abdominal visceral fat deposition and weight gain compared with wild-type (WT) mice. Differentiation of cultured bone marrow stem cells (BMSCs) into the adipocyte lineage was suppressed by 17-β-estradiol (E2) in WT female mice but not in NOER or MOER mice. This finding correlated with E2 inhibition of prominent differentiation genes in WT BMSCs. In contrast, triglyceride content in differentiated BMSCs or 3T3-L1 cells was suppressed as a result of membrane ER-α signaling through several kinases to inhibit carbohydrate response element-binding protein-α and -β. We concluded that extranuclear and nuclear ER-α collaborate to suppress adipocyte development, but inhibition of lipid synthesis in mature cells does not involve nuclear ER-α.
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".