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Record W2732519908 · doi:10.1101/158642

Type 2 Diabetes Risk Alleles Reveal a Role for Peptidylglycine Alpha-amidating Monooxygenase in Beta Cell Function

2017· preprint· en· W2732519908 on OpenAlexaff
Anne Raimondo, Soren K. Thomsen, Benoît Hastoy, Mahesh M. Umapathysivam, Xiao-Qing Dai, Jocelyn E. Manning Fox, Amy Barrett, Christopher J. Groves, Austin Bautista, Nicola L. Beer, Anne Clark, Patrick E. MacDonald, Patrik Rorsman, Anna L. Gloyn

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersMedical Research Council
KeywordsAlleleExocytosisBiologyCell biologyGeneticsSecretionGeneEndocrinology

Abstract

fetched live from OpenAlex

ABSTRACT Molecular mechanisms underpinning the genetic risk for type 2 diabetes (T2D) remain poorly understood, hindering translation into new therapies. Recently, genome-wide studies identified two coding variants in Peptidylglycine Alpha-amidating Monooxygenase ( PAM ) associated with T2D risk and measures of beta cell dysfunction. Here, we demonstrate that both risk alleles impact negatively on overall PAM activity, but via distinct effects on expression and catalytic function. In a human beta cell model, PAM silencing caused decreased insulin content and altered dynamics of granule exocytosis. Analysis of primary human beta cells from cadaveric donors confirmed an effect on exocytosis in carriers of the p.D563G T2D-risk allele. Finally, we show that the granular packaging protein Chromogranin A is a PAM substrate and a strong candidate for mediating downstream effects on insulin secretion. Taken together, our results establish a role for PAM in beta cell function, and uncover a novel mechanism for T2D-associated PAM alleles.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.015
GPT teacher head0.232
Teacher spread0.217 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicPancreatic function and diabetes→French-language works237,207→