Modeling Acute ER Stress in Vivo and in Vitro
Bibliographic record
Abstract
The endoplasmic reticulum (ER) is a critical organelle that synthesizes secretory proteins and serves as the main calcium storage site of the cell. The accumulation of unfolded proteins at the ER results in ER stress. Although the association between ER stress and the pathogenesis of many metabolic conditions have been well characterized using both in vivo and in vitro models, no standardized model concerning ER stress exists. Here, we report a standardized model of ER stress using two well-characterized ER stress-inducing agents, thapsigargin and tunicamycin. Our aim in this current study was 2-fold: to characterize and establish which agent is optimal for in vitro use to model acute ER stress and to evaluate which agent is optimal for in vivo use. To study the first aim we used two well-established metabolic cell lines; human hepatocellular carcinoma (HepG2s) and differentiated mouse adipocytes (3T3-L1). In the second aim we utilized C57BL/6J mice that were randomized into three treatment groups of sham, thapsigargin, and tunicamycin. Our in vitro results showed that tunicamycin worked as a rapid and efficacious inducer of ER stress in adipocytes consistently, whereas thapsigargin and tunicamycin were equally effective in inducing ER stress in hepatocytes. In regards to our in vivo results, we saw that tunicamycin was superior in not only inducing ER stress but also recapturing the metabolic alterations associated with ER stress. Thus, our findings will help guide and inform researchers as to which ER stress agent is appropriate with regards to their model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".