Alterations in mouse visceral adipose tissue <scp>mRNA</scp> expression of islet G‐protein‐coupled receptor ligands in obesity
Bibliographic record
Abstract
BACKGROUND: Adipose tissue mass expansion in obesity leads to alterations in expression and secretion of adipokines, some of which may alter islet function by binding to G-protein-coupled receptors (GPCRs) expressed by islets. We have therefore quantified expression of mRNAs encoding islet GPCR ligands in visceral adipose tissue retrieved from lean and diet-induced obese mice to determine alterations in islet GPCR ligand mRNAs in obesity. METHODS: Epididymal adipose tissue was retrieved from C57BL/6 mice that had been maintained on a control-fat diet (10% fat) or high-fat diet (60% fat) for 16 weeks and RT-qPCR was used to quantify mRNAs encoding ligands for islet GPCRs. RESULTS: Of the 155 genes that encode ligands for islet GPCRs, 45 and 40 were expressed in visceral adipose tissue retrieved from lean and obese mice respectively. The remaining mRNAs were either expressed at trace level (0.0001% to 0.001% relative to Actb expression) or absent (<0.0001%). Obesity was associated with significant alterations in GPCR ligand mRNA expression in visceral adipose tissue, some of which encode for peptides with established effects on islet function (e.g. neuropeptide Y), or for GPCR ligands that have not previously been investigated for their effects on islets (e.g. (C-C motif) ligand 4; Ccl4). CONCLUSION: Mouse visceral adipose tissue showed significant alterations in expression of mRNAs encoding islet GPCR ligands in obesity. Our data point to ligands of interest for future research on adipose-islet crosstalk via secreted ligands acting at islet GPCRs. Such research may identify islet GPCRs with therapeutic potential for T2D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".