Intestinal adaptation to cold-induced metabolic demand and feeding requires GLP-1R and GLP-2R signalling
Bibliographic record
Abstract
Chronic cold exposure in mice increases metabolic demand and food intake; the gut correspondingly expands its absorptive surface area. Gut enteroendocrine cells produce peptide hormones including glucagon-like peptide-1 (GLP-1), GLP-2, and glucose-dependent insulinotropic polypeptide (GIP) in response to a meal to facilitate nutrient absorption and post-prandial metabolism. The requirement of GLP-1, GLP-2, and GIP receptor signaling for small intestinal adaptations to chronic cold stress has not been investigated. Here, we show that male and female wild-type, double incretin receptor knockout (Glp1r-/-Gipr-/-; DIRKO), and glucagon-like peptide double receptor knockout (Glp1r-/-Glp2r-/-; GLPDRKO) mice consume significantly more food over five weeks in cold (6⁰C) compared to thermoneutral (27 ⁰C; TN) conditions. Jejunal circumference, villi length, and crypt depth are significantly greater with cold-stress in WT and DIRKO mice, but not GLPDRKO mice, compared to TN controls. We show that the GLP-2R is required for jejunal villi length expansion upon cold stress despite significantly elevated plasma active GLP-1 levels. In line with this, GLPDRKO mice fail to gain body weight over the five-week experiment compared to WT controls. Therefore, while GLP-2R action is required for cold stress-induced jejunal villi lengthening, this adaptation is dispensable for body weight gain in the presence of GLP-1R signaling. Chronic cold stress in Glp1r-/-Gipr-/-, Glp1r-/-Glp2r-/-, and Glp2r-/- mice revealed distinct and overlapping roles for GLP-1R and GLP-2R in the expansion of the intestinal absorptive surface area expansion in response to chronic cold stress.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".