The GIPR is Predominantly Localized to Non-Adipocyte Cell Types Within White Adipose Tissue
Bibliographic record
Abstract
The incretin hormone glucose-dependent insulinotropic polypeptide (GIP) augments glucose-dependent insulin secretion through its receptor expressed on islet b-cells. GIP also acts on adipose tissue, yet paradoxically, both enhanced and reduced GIP receptor (GIPR) signaling reduce adipose tissue mass and attenuate weight gain in response to nutrient excess. Moreover, the precise cellular localization of GIPR expression within white adipose tissue (WAT) remains uncertain. Here, we used mouse genetics to target Gipr expression within adipocytes. Surprisingly, targeting Cre expression to adipocytes using the Adiponectin (Adipoq) promoter did not produce meaningful reduction of WAT Gipr expression in Adipoq-Cre:Giprflx/flx mice. In contrast, adenoviral expression of Cre under the control of the CMV promoter, or transgenic expression of Cre using non-adipocyte-selective promoters (Ap2/Fabp4 and Ubc) markedly attenuated WAT Gipr expression. Analysis of single nucleus RNA-seq adipose tissue data sets localized Gipr/GIPR expression predominantly to pericytes and mesothelial cells rather than to adipocytes. Together, these observations reveal that adipocytes are not the major GIPR+ cell type within WAT, findings with mechanistic implications for understanding how GIP and GIP-based co-agonists control adipose tissue biology.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".