Lack of ZnT8 protects pancreatic islets from hypoxia- and cytokine-induced cell death
Bibliographic record
Abstract
Pancreatic β-cells depend on the well-balanced regulation of cytosolic zinc concentrations, providing sufficient zinc ions for the processing and storage of insulin, but avoiding toxic effects. The zinc transporter ZnT8, encoded by SLC30A8,is a key player regarding islet cell zinc homeostasis, and polymorphisms in this gene are associated with altered type 2 diabetes susceptibility in man. The objective of this study was to investigate the role of ZnT8 and zinc in situations of cellular stress as hypoxia or inflammation. Isolated islets of WT and global ZnT8-/- mice were exposed to hypoxia or cytokines and cell death was measured. To explore the role of changing intracellular Zn2+ concentrations, WT islets were exposed to different zinc concentrations using zinc chloride or the zinc chelator N,N,N',N'-tetrakis(2-pyridinylmethyl)-1,2-ethanediamine (TPEN). Hypoxia or cytokine (TNF-α, IFN-γ, IL1-β) treatment induced islet cell death, but to a lesser extent in islets from ZnT8-/- mice, which were shown to have a reduced zinc content. Similarly, chelation of zinc with TPEN reduced cell death in WT islets treated with hypoxia or cytokines, whereas increased zinc concentrations aggravated the effects of these stressors. This study demonstrates a reduced rate of cell death in islets from ZnT8-/- mice as compared to WT islets when exposed to two distinct cellular stressors, hypoxia or cytotoxic cytokines. This protection from cell death is, in part, mediated by a reduced zinc content in islet cells of ZnT8-/- mice. These findings may be relevant for altered diabetes burden in carriers of risk SLC30A8 alleles in man.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".