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Record W2884479282 · doi:10.1530/jme-17-0262

Glucolipotoxic conditions induce β-cell iron import, cytosolic ROS formation and apoptosis

2018· article· en· W2884479282 on OpenAlexaff
Jakob Bondo Hansen, Laila R.B. Santos, Ying Liu, Kacey J. Prentice, Frederik Teudt, Morten Tonnesen, Jean‐Christophe Jonas, Michael B. Wheeler, Thomas Mandrup‐Poulsen

Bibliographic record

VenueJournal of Molecular Endocrinology · 2018
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReactive oxygen speciesCytosolChemistryCell biologyApoptosisBeta cellMitochondrionIsletBiochemistryInsulinEndocrinologyBiologyEnzyme

Abstract

fetched live from OpenAlex

Type 2 diabetes (T2D) arises when the pancreatic beta-cell fails to compensate for increased insulin needs due to insulin resistance. Glucolipotoxicity (GLT) has been proposed to induce beta-cell dysfunction in T2D by formation of reactive oxygen species (ROS). Here, we examined if modeling glucolipotoxic conditions by high glucose-high free fatty acid (FFA) exposure (GLT) regulates beta-cell iron transport, by increasing the cytosolic labile iron pool (LIP). In isolated mouse islets, the GLT-induced increase in the LIP catalyzed cytosolic ROS formation and induced apoptosis. We show that GLT-induced ROS production is regulated by an increased LIP associated with elevated expression of genes regulating iron import. Using pharmacological and transgenic approaches, we show that iron reduction and decreased iron import protects from GLT-induced ROS production, prevents impairment of the mitochondrial membrane potential (MMP) and inhibits apoptosis. This study identifies a novel pathway underlying GLT-induced apoptosis involving increased iron import, generation of hydroxyl radicals from hydrogen peroxide through the Fenton reaction in the cytosolic compartment associated with dissipation of the MMP and beta-cell apoptosis.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.368

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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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

Citations41
Published2018
Admission routes1
Has abstractyes

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