The roles of glucagon-like peptide-2 and the intestinal epithelial insulin-like growth factor-1 receptor in regulating microvillus length
Bibliographic record
Abstract
Abstract Microvilli are tiny projections on the apical end of enterocytes, aiding in the digestion and absorption of nutrients. One of their key features is uniform length, but how this is regulated is poorly understood. Glucagon-like peptide-2 (GLP-2) has been shown to increase microvillus length but, the requirement of its downstream mediator, the intestinal epithelial insulin-like growth factor-1 receptor (IE-IGF-1R), and the microvillus proteins acted upon by GLP-2, remain unknown. Using IE-IGF-1R knockout (KO) mice, treated with either long-acting human (h) (GLY2)GLP-2 or vehicle for 11d, it was found that the h(GLY2)GLP-2-induced increase in microvillus length required the IE-IGF-1R. Furthermore, IE-IGF-1R KO alone resulted in a significant decrease in microvillus length. Examination of the brush border membrane proteome as well as of whole jejunal mucosa demonstrated that villin was increased with h(GLY2)GLP-2 treatment in an IE-IGF-1R-dependent manner. Under both basal conditions and with h(GLY2)GLP-2 treatment of the IE-IGF-1R KO mice, changes in villin, IRTKS-1, harmonin, β-actin, and myosin-1a did not explain the decrease in microvillus length, in either the brush border or jejunal mucosa of KO animals. Collectively, these studies define a new role for the IE-IGF-1R within the microvillus, in both the signaling cascade induced by GLP-2, as well as endogenously.
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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 | 0.001 |
| 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".