An <i>in utero</i> exposure to the synthetic estrogen diethylstilbestrol affects the fat pad composition in post-natal mammary glands
Bibliographic record
Abstract
Abstract In utero exposure to the synthetic estrogen diethylstilbestrol (DES) has been linked to developmental abnormalities and elevated breast cancer risk in adulthood in human and rodent models. While the impact of DES on the mammary epithelium has been thoroughly investigated, its effect on the other cell types of the mammary gland remains understudied. Here, given that the mammary gland development is strongly associated with its microenvironment, we aimed to investigate how in utero DES exposure alters the mammary gland’s stromal and immune function across key developmental stages. To achieve this aim, timed-pregnant rats were gavaged daily with DES or vehicle from gestation days 16-21, and female offspring mammary glands were analyzed at pre-puberty (postnatal day 21 (PDN21)), puberty (PND46), and adulthood (PND90). We assessed morphological and extracellular matrix changes, performed transcriptomic cell-type enrichment analysis, measured cytokine expression, and quantified immune cell populations. DES-exposed mammary glands exhibited pronounced stromal remodeling, including increased collagen deposition and orientation by adulthood. Gene expression profiling indicated DES-induced stage-specific immune alterations: immune cell signatures were enriched at PND21 and PND90 but diminished at PND46. Correspondingly, DES increased macrophage populations at PND21 while reducing T-lymphocyte numbers at PND46 and PND90. DES exposure also dysregulated inflammatory cytokine/chemokine expression in adult glands, suggesting a persistent inflammatory environment. In conclusion, in utero exposure to an estrogenic compound can reprogram mammary development, inducing long-term changes in the extracellular matrix and immune landscape. These disruptions to stromal-immune homeostasis may impair normal mammary morphogenesis and increase susceptibility to breast pathologies later in life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".