RelB/p52 NF-κB Complexes Rescue an Early Delay in Mammary Gland Development in Transgenic Mice with Targeted Superrepressor IκB-α Expression and Promote Carcinogenesis of the Mammary Gland
Bibliographic record
Abstract
Classical NF-kappaB (p65/p50) transcription factors display dynamic induction in the mammary gland during pregnancy. To further elucidate the role of NF-kappaB factors in breast development, we generated a transgenic mouse expressing the IkappaB-alpha S32/36A superrepressor (SR) protein under control of the mouse mammary tumor virus (MMTV) long terminal repeat promoter. A transient delay in mammary ductal branching was observed in MMTV-SR-IkappaB-alpha mice early during pregnancy at day 5.5 (d5.5) and d7.5; however, development recovered by mid- to late pregnancy (d14.5). Recovery correlated with induction of nuclear cyclin D1 and RelB/p52 NF-kappaB complexes. RelB/p52 complexes induced cyclin D1 and c-myc promoter activities and failed in electrophoretic mobility shift assay to interact with IkappaB-alpha-glutathione S-transferase, indicating that their weak interaction with IkappaB-alpha can account for the observed recovery of mammary gland development. Activation of IKKalpha and NF-kappaB-inducing kinase was detected by d5.5, implicating the alternative NF-kappaB signaling pathway in RelB/p52 induction. Constitutively active IKKalpha induced p52, RelB, and cyclin D1 in untransformed mammary epithelial cells. Moreover, mouse mammary tumors induced by 7,12-dimethylbenz(a)anthracene treatment displayed increased RelB/p52 activity. Inhibition of RelB in breast cancer cells repressed cyclin D1 and c-Myc levels and growth in soft agar. These results implicate RelB/p52 complexes in mammary gland development and carcinogenesis.
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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 | 0.000 |
| 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 | 0.001 |
| 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".