Loss of Igfbp7 Causes Precocious Involution in Lactating Mouse Mammary Gland
Bibliographic record
Abstract
BACKGROUND: Insulin like growth factors (IGFs) and their binding proteins (IGFBPs) are secreted peptides that play major roles in regulating the normal development and maturation of mammary gland. While Igfbp7 has been shown to decrease breast tumor growth, its role in regulating the normal mammary gland development has not been studied. To this end, we generated Igfbp7-null mice and examined the development and maturation of mammary glands in the virgin, pregnant and lactating animals. RESULTS: We report here that loss of Igfbp7 significantly retards mammary gland development in the virgin animals. More significantly, the pregnant Igfpb7-null glands contained fewer alveolar structures and that during lactation these glands exhibit the morphological changes that are associated with involution. The transcriptome profile of the Igfbp7-null glands on the lactation day 3 revealed a distinct involution-related gene signature compared to the lactating WT glands. Interestingly, we found that the lactating Igfbp7-null glands exhibit increased expression of Stat3 and enhanced activation of (phosphorylated) Stat3, combined with decreased expression of Stat5 suggesting that the absence of Igfbp7 accelerates the onset of involution. We also found that in absence of Igfpb7, the lactating glands contain increased Igfbp5 protein along with decreased expression of IGF-1 Receptor and Akt activation. Finally, we show that during the normal course of involution, Igfbp7 expression is significantly decreased in the mammary gland. CONCLUSION: Our data suggest that loss of Igfbp7 induces precocious involution possibly through diminished cell survival signals. Our findings identify Igfbp7 as major regulator of involution in the mammary gland.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".