The gene TDAG51 facilitates the phosphorylation of eIF2α in mouse embryonic fibroblasts
Bibliographic record
Abstract
Unfolded protein accumulation in the endoplasmic reticulum (ER) results in ER stress. This causes the binding of GRP78 to unfolded proteins, auto‐phosphorylation of PERK and the phosphorylation of eIF2α, leading to changes in the rate and characteristics of protein synthesis, including an upregulation of CHOP/GADD153 expression. TDAG51 is an ER stress response gene and has been shown to regulate translation. We hypothesized that TDAG51 influences protein translation by mediating the phosphorylation of eIF2α. Mouse embryonic fibroblasts (MEFs) treated with ER stressors, tunicamycin (Tm; 5 μg/ml) or thapsigargin (Tg; 1 μM), or a phosphorylated eIF2α dephosphorylation inhibitor, salubrinal (30 μM), or combined treatments demonstrated an increase in eIF2α phosphorylation. TDAG51 −/− MEFs showed a decrease in eIF2α phosphorylation to drug vehicle, Tm plus salubrinal, Tg and Tg plus salubrinal treatment. It was determined that TDAG51 −/− MEFs have reduced CHOP/GADD153 expression compared to wild type cells. Therefore, TDAG51 knockout may regulate CHOP/GADD153 expression by reducing the phosphorylation of eIF2α, which in turn may lead to decreased expression of GADD34, essentially inhibiting the GADD34/PP1 dephosphorylation complex. This mechanism is also being investigated through the use of siRNA‐mediated knockdown. Supported by CIHR MOP‐67116.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".