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 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 | 0.000 | 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) | 0.000 | 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".