Two Dipolar α-Helices within Hormone-encoding Regions of Proglucagon Are Sorting Signals to the Regulated Secretory Pathway
Bibliographic record
Abstract
Proglucagon is expressed in pancreatic α cells, intestinal L cells, and some hypothalamic and brainstem neurons. Tissue-specific processing of proglucagon yields three major peptide hormones as follows: glucagon in the α cells and glucagon-like peptides (GLP)-1 and -2 in the L cells and neurons. Efficient sorting and packaging into the secretory granules of the regulated secretory pathway in each cell type are required for nutrient-regulated secretion of these proglucagon-derived peptides. Our previous work suggested that proglucagon is directed into granules by intrinsic sorting signals after initial processing to glicentin and major proglucagon fragment (McGirr, R., Guizzetti, L., and Dhanvantari, S. (2013) J. Endocrinol. 217, 229-240), leading to the hypothesis that sorting signals may be present in multiple domains. In the present study, we show that the α-helices within glucagon and GLP-1, but not GLP-2, act as sorting signals by efficiently directing a heterologous secretory protein to the regulated secretory pathway. Biophysical characterization of these peptides revealed that glucagon and GLP-1 each encode a nonamphipathic, dipolar α-helix, whereas the helix in GLP-2 is not dipolar. Surprisingly, glicentin and major proglucagon fragment were sorted with different efficiencies, thus providing evidence that proglucagon is first sorted to granules prior to processing. In contrast to many other prohormones in which sorting is directed by ordered prodomains, the sorting determinants of proglucagon lie within the ordered hormone domains of glucagon and GLP-1, illustrating that each prohormone has its own sorting "signature."
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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 | 0.000 |
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".