Profile of estrogen‐responsive genes in an estrogen‐specific mammary gland outgrowth model
Bibliographic record
Abstract
Both ovarian and pituitary hormones are required for the pubertal development of the mouse mammary gland. Estradiol directs ductal elongation and branching, while progesterone leads to tertiary branching and alveolar development. The purpose of this investigation was to identify estrogen-responsive genes associated with pubertal ductal growth in the mouse mammary gland in the absence of other ovarian hormones and at different stages of development. We hypothesized that the estrogen-induced genes and their associated functions at early stages of ductal elongation would be distinct from those induced after significant ductal elongation had occurred. Therefore, ovariectomized prepubertal mice were exposed to 17beta-estradiol from two to 28 days, and mammary gland global gene expression analyzed by microarray analysis at various times during this period. We found that: (a) gene expression changes in our estrogen-only model mimic those changes that occur in normal pubertal development in intact mice, (b) both distinct and overlapping gene profiles were observed at varying extents of ductal elongation, and (c) cell proliferation, the immune response, and metabolism/catabolism were the most common functional categories associated with mammary ductal growth. Particularly striking was the novel observation that genes active during carbohydrate metabolism were rapidly and robustly decreased in response to estradiol. Lastly, we identified mammary estradiol-responsive genes that are also co-expressed with estrogen receptor alpha in human breast cancer. In conclusion, our genomic data support the physiological observation that estradiol is one of the primary hormonal signals driving ductal elongation during pubertal mammary development.
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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 | 0.001 | 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".