Stromal Adipocyte Enhancer-binding Protein (AEBP1) Promotes Mammary Epithelial Cell Hyperplasia via Proinflammatory and Hedgehog Signaling
Bibliographic record
Abstract
Disruption of mammary stromal-epithelial communication leads to aberrant mammary gland development and induces mammary tumorigenesis. Macrophages have been implicated in carcinogenesis primarily by creating an inflammatory microenvironment, which promotes growth of the adjacent epithelial cells. Adipocyte enhancer-binding protein 1 (AEBP1), a novel proinflammatory mediator, promotes macrophage inflammatory responsiveness by inducing NF-κB activity, which has been implicated in tumor cell growth and survival by aberrant sonic hedgehog (Shh) expression. Here, we show that stromal macrophage AEBP1 overexpression results in precocious alveologenesis in the virgin AEBP1 transgenic (AEBP1(TG)) mice, and the onset of ductal hyperplasia was accelerated in AEBP1(TG) mice fed a high fat diet, which induces endogenous AEBP1 expression. Transplantation of AEBP1(TG) bone marrow cells into non-transgenic (AEBP1(NT)) mice resulted in alveolar hyperplasia with up-regulation of NF-κB activity and TNFα expression as displayed in the AEBP1(TG) mammary macrophages and epithelium. Shh expression was induced in AEBP1(TG) macrophages and RAW264.7 macrophages overexpressing AEBP1. The Shh target genes Gli1 and Bmi1 expression was induced in the AEBP1(TG) mammary epithelium and HC11 mammary epithelial cells co-cultured with AEBP1(TG) peritoneal macrophages. The conditioned AEBP1(TG) macrophage culture media promoted NF-κB activity and survival signal, Akt activation, in HC11 cells, whereas such effects were abolished by TNFα neutralizing antibody treatment. Furthermore, HC11 cells displayed enhanced proliferation in response to AEBP1(TG) macrophages and their conditioned media. Our findings highlight the role of AEBP1 in the signaling pathways regulating the cross-talk between mammary epithelium and stroma that could predispose the mammary tissue to tumorigenesis.
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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 | 0.000 | 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".