Control of human pancreatic beta cell kinome by glucagon‐like peptide‐1 receptor biased agonism
Bibliographic record
Abstract
AIM: To determine the kinase activity profiles of human pancreatic beta cells downstream of glucagon-like peptide-1 receptor (GLP-1R) balanced versus biased agonist stimulations. MATERIALS AND METHODS: This study analysed the kinomic profiles of human EndoC-βh1 cells following vehicle and GLP-1R stimulation with the pharmacological agonist exendin-4, as well as exendin-4-based biased derivatives exendin-phe1 and exendin-asp3 for acute (10-minute) versus sustained (120-minute) responses, using PamChip protein tyrosine kinase and serine/threonine kinase assays. The raw data were filtered and normalized using BioNavigator. The kinase analyses were conducted with R, mainly including kinase-substrate mapping and Kyoto Encyclopedia of Genes and Genomes pathway analysis. RESULTS: The present analysis reveals that kinomic responses are distinct for acute versus sustained GLP-1R agonist exposure, with individual responses associated with agonists presenting specific bias profiles. According to pathway analysis, several kinases, including JNKs, PKCs, INSR and LKB1, are important GLP-1R signalling mediators, constituting potential targets for further research on biased GLP-1R downstream signalling. CONCLUSION: The results from this study suggest that differentially biased exendin-phe1 and exendin-asp3 can modulate distinct kinase interaction networks. Further understanding of these mechanisms will have important implications for the selection of appropriate anti-type 2 diabetes therapies with optimized downstream kinomic profiles.
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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 | 0.000 |
| 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 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".