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Record W3011038351 · doi:10.1172/jci.insight.136907

Loss of Glp2r signaling activates hepatic stellate cells and exacerbates diet-induced steatohepatitis in mice

2020· article· en· W3011038351 on OpenAlexafffund
Shai Fuchs, Bernardo Yusta, Laurie L. Baggio, Elodie M. Varin, Dianne Matthews, Daniel J. Drucker

Bibliographic record

VenueJCI Insight · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health ResearchHospital for Sick ChildrenNovo NordiskPediatric Endocrine SocietyNovo Nordisk FondenDiabetes Canada
KeywordsHepatic stellate cellDownregulation and upregulationSteatohepatitisInternal medicineEndocrinologyInflammationHepatic fibrosisFatty liverFibrosisBiologyMedicineDiseaseBiochemistryGene

Abstract

fetched live from OpenAlex

A glucagon-like peptide-2 (GLP-2) analog is used in individuals with intestinal failure who are at risk for liver disease, yet the hepatic actions of GLP-2 are not understood. Treatment of high-fat diet-fed (HFD-fed) mice with GLP-2 did not modify the development of hepatosteatosis or hepatic inflammation. In contrast, Glp2r-/- mice exhibited increased hepatic lipid accumulation, deterioration in glucose tolerance, and upregulation of biomarkers of hepatic inflammation. Both mouse and human liver expressed the canonical GLP-2 receptor (GLP-2R), and hepatic Glp2r expression was upregulated in mice with hepatosteatosis. Cell fractionation localized the Glp2r to hepatic stellate cells (HSCs), and markers of HSC activation and fibrosis were increased in livers of Glp2r-/- mice. Moreover, GLP-2 directly modulated gene expression in isolated HSCs ex vivo. Taken together, these findings define an essential role for the GLP-2R in hepatic adaptation to nutrient excess and unveil a gut hormone-HSC axis, linking GLP-2R signaling to control of HSC activation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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 teacher head, 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

Citations27
Published2020
Admission routes2
Has abstractyes

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