Proinsulin Intermolecular Interactions during Secretory Trafficking in Pancreatic β Cells
Bibliographic record
Abstract
Classically, exit from the endoplasmic reticulum (ER) is rate-limiting for secretory protein trafficking because protein folding/assembly occurs there. In this study, we have exploited "hPro-CpepSfGFP," a human proinsulin bearing "superfolder" green fluorescent C-peptide expressed in pancreatic β cells where it is processed to human insulin and CpepSfGFP. Remarkably, steady-state accumulation of hPro-CpepSfGFP and endogenous proinsulin is in the Golgi region, as if final stages of protein folding/assembly were occurring there. The Golgi regional distribution of proinsulin is dynamic, influenced by fasting/refeeding, and increased with β cell zinc deficiency. However, coexpression of ER-entrapped mutant proinsulin-C(A7)Y shifts the steady-state distribution of wild-type proinsulin to the ER. Endogenous proinsulin coprecipitates with hPro-CpepSfGFP and even more so with hProC(A7)Y-CpepSfGFP. Using Cerulean and Venus-tagged proinsulins, we find that both WT-WT and WT-mutant proinsulin pairs exhibit FRET. The data demonstrate that wild-type proinsulin dimerizes within the ER but accumulates at a poorly recognized slow step within the Golgi region, reflecting either slow kinetics of proinsulin hexamerization, steps in formation of nascent secretory granules, or other unknown molecular events. However, in the presence of ongoing misfolding of a subpopulation of proinsulin in β cells, the rate-limiting step in transport of the remaining proinsulin shifts to the ER. Background: Proinsulin assembly is linked to its intracellular transport. Results: Proinsulin self-associates in the endoplasmic reticulum but, surprisingly, accumulates at a rate-limiting transport step in the Golgi region. Conclusion: Proinsulin transport is a dynamic process, and its perturbation may be measured under steady-state conditions. Significance: Proinsulin distribution may be a useful tool to characterize proinsulin trafficking in disease states.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".