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Record W4414348069 · doi:10.1126/sciadv.ady8048

α cells use both PC1/3 and PC2 to process proglucagon peptides and control insulin secretion

2025· article· en· W4414348069 on OpenAlexaff
Canqi Cui, Danielle C. Leander, Sarah M. Gray, Kimberley El, A. Y. Chen, Paul A. Grimsrud, Jessica O. Becker, Austin J. Taylor, Guofang Zhang, Kyle W. Sloop, C. Bruce Verchere, Andrew N. Hoofnagle, David A. D’Alessio, Jonathan E. Campbell

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsBC Children's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsProglucagonProhormone convertaseProhormoneGlucagon-like peptide-1GlucagonInsulinSecretionIncretinParacrine signalling

Abstract

fetched live from OpenAlex

α cells secrete proglucagon peptides to regulate nutrient metabolism. Recent findings support an α cell–to–β cell axis that is mediated by paracrine signaling through the glucagon receptor and glucagon-like peptide 1 (GLP-1) receptor in β cells. To address which proglucagon peptides stimulate insulin secretion, we developed an assay to quantify levels of GLP-1(7–36)NH 2 . We also generated three transgenic mouse lines that allow α cell-specific, inducible deletion of the genes for the two prohormone convertase enzymes that process proglucagon . Our studies reveal that both mouse and human islets contain GLP-1(7–36)NH2, but glucagon mediates α cell–to–β cell communication in mice. However, in the absence of normal production of glucagon, α cells up-regulate prohormone convertase 1 (PC1/3) to generate GLP-1 and enhance glucose tolerance. Human islets have substantially higher levels of GLP-1 than mice, which positively correlate with rates of insulin secretion. These studies show plasticity in proglucagon processing to support α cell–to–β cell communication.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.009
GPT teacher head0.288
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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