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Record W2775716088 · doi:10.1073/pnas.1713736114

Islet cells share promoter hypomethylation independently of expression, but exhibit cell-type–specific methylation in enhancers

2017· article· en· W2775716088 on OpenAlexaff
Daniel Neiman, Joshua Moss, Merav Hecht, Judith Magenheim, Sheina Piyanzin, A. M. James Shapiro, Eelco J.P. de Koning, Aharon Razin, Howard Cedar, Ruth Shemer, Yuval Dor

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesSeventh Framework ProgrammeBIRAXIsrael Science FoundationStichting Diabetes Onderzoek NederlandRosetrees TrustEuropean CommissionLeona M. and Harry B. Helmsley Charitable Trust
KeywordsDNA methylationPromoterBiologyReprogrammingEnhancerMethylationEpigeneticsEpigenetics of physical exerciseEpigenomicsMolecular biologyGene expressionGeneGenetics

Abstract

fetched live from OpenAlex

embryonic endocrine progenitors, regardless of the specific endocrine cell-type chosen. Similar methylation dynamics were observed in transgenic mice containing a human insulin promoter fragment, pointing to the responsible cis element. Whole-methylome comparison of human α- and β-cells revealed generality of the findings: genes active in one cell type and silent in the other tend to share demethylated promoters, while methylation differences between α- and β-cells are concentrated in enhancers. These findings suggest an epigenetic basis for the observed plastic identity of islet cell types, and have implications for β-cell reprogramming in diabetes and diagnosis of β-cell death using methylation patterns of circulating DNA.

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: Observational · Consensus signal: none
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)0.0000.000
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)0.0020.001

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 head0.055
GPT teacher head0.315
Teacher spread0.261 · 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 designObservational
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

Citations60
Published2017
Admission routes1
Has abstractyes

Explore more

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