G protein–coupled receptor kinase 6 (GRK6) regulates insulin processing and secretion via effects on proinsulin conversion to insulin
Bibliographic record
Abstract
Recent studies identified a missense mutation in the gene coding for G protein-coupled receptor kinase 6 (GRK6) that segregates with type 2 diabetes (T2D). To better understand how GRK6 might be involved in T2D, we used pharmacological inhibition and genetic knockdown in the mouse β-cell line, MIN6, to determine whether GRK6 regulates insulin dynamics. We show inhibition of GRK5 and GRK6 increased insulin secretion but reduced insulin processing while GRK6 knockdown revealed these same processing defects with reduced levels of cellular insulin. GRK6 knockdown cells also had attenuated insulin secretion but enhanced proinsulin secretion consistent with decreased processing. In support of these findings, we demonstrate GRK6 rescue experiments in knockdown cells restored insulin secretion after glucose treatment. The altered insulin profile appears to be caused by changes in the proprotein convertases, the enzymes responsible for proinsulin to insulin conversion, as GRK6 knockdown resulted in significantly reduced convertase expression and activity. To identify how the GRK6-P384S mutation found in T2D patients might affect insulin processing, we performed biochemical and cell biological assays to study the properties of the mutant. We found that while GRK6-P384S was more active than WT GRK6, it displayed a cytosolic distribution in cells compared to the normal plasma membrane localization of GRK6. Additionally, GRK6 overexpression in MIN6 cells enhanced proinsulin processing, while GRK6-P384S expression had little effect. Taken together, our data show that GRK6 regulates insulin processing and secretion in a glucose-dependent manner and provide a foundation for understanding the contribution of GRK6 to T2D.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".