Localization of Glucagon-Like Peptide-2 Receptor Expression in the Mouse
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2), secreted from enteroendocrine cells, attenuates gut motility, enhances barrier function, and augments nutrient absorption, actions mediated by a single GLP-2 receptor (GLP-2R). Despite extensive analyses, the precise distribution and cellular localization of GLP-2R expression remains controversial, confounded by the lack of suitable GLP-2R antisera. Here, we reassessed murine Glp2r expression using regular and real-time quantitative PCR (qPCR), in situ hybridization (ISH), and a Glp2rLacZ reporter mouse. Glp2r mRNA expression was detected from the stomach to the rectum and most abundant in the jejunum. Glp2r transcripts were also detected in cerebral cortex, mesenteric lymph nodes, gallbladder, urinary bladder, and mesenteric fat. Surprisingly, Glp2r mRNA was found in testis by qPCR at levels similar to jejunum. However, the testis Glp2r transcripts, detected by different primer pairs and qPCR, lacked 5' mRNA coding sequences, and only a minute proportion of them corresponded to full-length Glp2r mRNA. Within the gut, Glp2r-driven LacZ expression was localized to enteric neurons and lamina propria stromal cells, findings confirmed by ISH analysis of the endogenous Glp2r mRNA. Unexpectedly, vascular Glp2rLacZ expression was localized to mesenteric veins and not arteries. Moreover, mesenteric fat Glp2rLacZ expression was detected within blood vessels and not adipocytes. Reporter LacZ expression was not detected in all tissues expressing an endogenous Glp2r transcript, such as gallbladder, urinary bladder, and mesenteric lymph nodes. Collectively, these findings extend our understanding of the cellular domains of Glp2r expression and highlight limitations inherent in application of commonly used technologies to infer analysis of gene expression.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".