GLP-2, EGF, and the Intestinal Epithelial IGF-1 Receptor Interactions in the Regulation of Crypt Cell Proliferation
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2) is an intestinotrophic hormone that promotes intestinal growth and proliferation through downstream mediators, including epidermal growth factor (EGF) and insulin-like growth factor-1 (IGF-1). EGF synergistically enhances the proliferative actions of IGF-1 in intestinal cell lines, and both of these factors are known to be essential for the trophic effects of GLP-2 in vivo. However, whether EGF and IGF-1 interact to mediate the proliferative actions of GLP-2 in vivo remains unknown. Normal and knockout (KO) mice lacking the intestinal epithelial IGF-1 receptor (IE-IGF-1R) were therefore treated chronically with EGF and/or long-acting human hGly2GLP-2, followed by determination of intestinal growth parameters. Intestines from control and IE-IGF-1R KO mice were also used to generate organoids (which lack the GLP-2 receptor) and were treated with EGF and/or IGF-1. Combination treatment with EGF and hGly2GLP-2 increased small intestinal weight and crypt-villus height in C57Bl/6 mice in an additive manner, whereas only hGly2GLP-2 treatment increased crypt cell proliferation. However, although combination treatment also increased small intestinal weight and crypt-villus height in IE-IGF-1R KO mice, the proliferative responses to hGly2GLP-2 alone or with EGF were diminished in these animals. Finally, IGF-1 treatment of organoids undergoing EGF withdrawal was not additive to the effect of EGF replacement on proliferation, but could restore normal proliferation in the absence of EGF. Together, these findings demonstrate that the intestinal proliferative effects of hGly2GLP-2 are augmented by exogenous EGF in a manner that is partially dependent upon IE-IGF-1R signaling.
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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 | 0.000 | 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 | 0.001 |
| 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".