Endoplasmic Reticulum Redox State Is Not Perturbed by Pharmacological or Pathological Endoplasmic Reticulum Stress in Live Pancreatic β-Cells
Bibliographic record
Abstract
Accumulation of unfolded, misfolded and aggregated proteins in the endoplasmic reticulum (ER) causes ER stress. ER stress can result from physiological situations such as acute increases in secretory protein biosynthesis or pathological conditions that perturb ER homeostasis such as alterations in the ER redox state. Here we monitored ER redox together with transcriptional output of the Unfolded Protein Response (UPR) in INS-1 insulinoma cells stably expressing eroGFP (ER-redox-sensor) and mCherry protein driven by a GRP78 promoter (UPR-sensor). Live cell imaging, flow cytometry and biochemical characterization were used to examine these parameters in response to various conditions known to induce ER stress. As expected, treatment of the cells with the reducing agent dithiothreitol caused a decrease in the oxidation state of the ER accompanied by an increase in XBP-1 splicing. Unexpectedly however, other treatments including tunicamycin, thapsigargin, DL-homocysteine, elevated free fatty acids or high glucose had essentially no influence on the ER redox state, despite inducing ER stress. Comparable results were obtained with dispersed rat islet cells expressing eroGFP. Thus, unlike in yeast cells, ER stress in pancreatic β-cells is not associated with a more reducing ER environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".