RANK drives structured intestinal epithelial expansion during pregnancy
Bibliographic record
Abstract
Abstract During reproduction, multiple species such as insects and all mammals undergo extensive physiological and morphological adaptions to ensure health and survival of the mother and optimal development of the offspring. Here we report that the intestinal epithelium undergoes expansion during pregnancy and lactation in mammals. This enlargement of the intestinal surface area results in a novel geometry of expanded villi. Receptor activator of nuclear factor-κΒ (RANK, encoded by TNFRSF11A ) and its ligand RANKL were identified as a molecular pathway involved in this villous expansion of the small intestine in vivo in mice and in intestinal mouse and human organoids. Mechanistically, RANK–RANKL protects gut epithelial cells from cell death and controls the intestinal stem cell niche through BMP receptor signalling, resulting in the elongation of villi and a prominent increase in the intestinal surface. As a transgenerational consequence, babies born to female mice that lack Rank in the intestinal epithelium show reduced weight and develop glucose intolerance after metabolic stress. Whereas gut epithelial remodelling in pregnancy/lactation is reversible, constitutive expression of an active form of RANK is sufficient to drive intestinal expansion followed by loss of villi and stem cells, and prevents the formation of Apc min -driven small intestinal stem cell tumours. These data identify RANK–RANKL as a pathway that drives intestinal epithelial expansion in pregnancy/lactation, one of the most elusive and fundamental tissue remodelling events in mammalian life history and evolution.
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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 | 0.001 |
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".