Collagen-IV and laminin-1 regulate estrogen receptor α expression and function in mouse mammary epithelial cells
Bibliographic record
Abstract
The expression level and functional activity of estrogen receptor alpha is an important determinant of breast physiology and breast cancer treatment. However, it has been difficult to identify the signals that regulate estrogen receptor because cultured mammary epithelial cells generally do not respond to estrogenic signals. Here, we use a combination of two- and three-dimensional culture systems to dissect the extracellular signals that control endogenous estrogen receptor alpha. Its expression was greatly reduced when primary mammary epithelial cells were placed on tissue culture plastic; however, the presence of a reconstituted basement membrane in combination with lactogenic hormones partially prevented this decrease. Estrogen receptor alpha expression in primary mammary fibroblasts was not altered by these culture conditions, indicating that its regulation is cell type specific. Moreover, estrogen receptor-dependent reporter gene expression, as well as estrogen receptor alpha levels, were increased threefold in a functionally normal mammary epithelial cell line when reconstituted basement membrane was added to the medium. This regulatory effect of reconstituted basement membrane was reproduced by two of its components, collagen-IV and laminin-1, and it was blocked by antibodies against alpha2, alpha6 and beta1 integrin subunits. Our results indicate that integrin-mediated response to specific basement membrane components, rather than cell rounding or cell growth arrest induced by reconstituted basement membrane, is critical in the regulation of estrogen receptor alpha expression and function in mammary epithelial cells.
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 | 0.001 |
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".