The sorting of proglucagon to secretory granules is mediated by carboxypeptidase E and intrinsic sorting signals
Bibliographic record
Abstract
Proglucagon is expressed in pancreatic alpha cells, intestinal L cells and brainstem neurons. Tissue-specific processing of proglucagon yields the peptide hormones glucagon in the alpha cell and glucagon-like peptide (GLP)-1 and GLP-2 in L cells. Both glucagon and GLP-1 are secreted in response to nutritional status and are critical for regulating glycaemia. The sorting of proglucagon to the dense-core secretory granules of the regulated secretory pathway is essential for the appropriate secretion of glucagon and GLP-1. We examined the roles of carboxypeptidase E (CPE), a prohormone sorting receptor, the processing enzymes PC1/3 and PC2 and putative intrinsic sorting signals in proglucagon sorting. In Neuro 2a cells that lacked CPE, PC1/3 and PC2, proglucagon co-localised with the Golgi marker p115 as determined by quantitative immunofluorescence microscopy. Expression of CPE, but not of PC1/3 or PC2, enhanced proglucagon sorting to granules. siRNA-mediated knockdown of CPE disrupted regulated secretion of glucagon from pancreatic-derived alphaTC1-6 cells, but not of GLP-1 from intestinal cell-derived GLUTag cells. Mutation of the PC cleavage site K70R71, the dibasic R17R18 site within glucagon or the alpha-helix of glucagon, all significantly affected the sub-cellular localisation of proglucagon. Protein modelling revealed that alpha helices corresponding to glucagon, GLP-1 and GLP-2, are arranged within a disordered structure, suggesting some flexibility in the sorting mechanism. We conclude that there are multiple mechanisms for sorting proglucagon to the regulated secretory pathway, including a role for CPE in pancreatic alpha cells, initial cleavage at K70R71 and multiple sorting signals.
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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 | 0.001 |
| 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".