Plasticity in the Glucagon Interactome Reveals Novel Proteins That Regulate Glucagon Secretion in α-TC1-6 Cells
Bibliographic record
Abstract
Glucagon is stored within the secretory granules of pancreatic alpha cells until stimuli trigger its release. The alpha cell secretory responses to the stimuli vary widely, possibly due to differences in experimental models or microenvironmental conditions. We hypothesized that the response of the alpha cell to various stimuli could be due to plasticity in the network of proteins that interact with glucagon within alpha cell secretory granules. We used tagged glucagon with Fc to pull out glucagon from the enriched preparation of secretory granules in α-TC1-6 cells. Isolation of secretory granules was validated by immunoisolation with Fc-glucagon and immunoblotting for organelle-specific proteins. Isolated enriched secretory granules were then used for affinity purification with Fc-glucagon followed by liquid chromatography/tandem mass spectrometry to identify secretory granule proteins that interact with glucagon. Proteomic analyses revealed a network of proteins containing glucose regulated protein 78 KDa (GRP78) and histone H4. The interaction between glucagon and the ER stress protein GRP78 and histone H4 was confirmed through co-immunoprecipitation of secretory granule lysates, and colocalization immunofluorescence confocal microscopy. Composition of the protein networks was altered at different glucose levels (25 vs. 5.5 mM) and in response to the paracrine inhibitors of glucagon secretion, GABA and insulin. siRNA-mediated silencing of a subset of these proteins revealed their involvement in glucagon secretion in α-TC1-6 cells. Therefore, our results show a novel and dynamic glucagon interactome within α-TC1-6 cell secretory granules. We suggest that variations in the alpha cell secretory response to stimuli may be governed by plasticity in the glucagon "interactome."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".