Evidence for eIF2α phosphorylation-independent effects of GSK2656157, a novel catalytic inhibitor of PERK with clinical implications
Bibliographic record
Abstract
The endoplasmic reticulum (ER)-resident protein kinase PERK is a major component of the unfolded protein response (UPR), which promotes the adaptation of cells to various forms of stress. PERK phosphorylates the α subunit of the translation initiation factor eIF2 at serine 51, a modification that plays a key role in the regulation of mRNA translation in stressed cells. Several studies have demonstrated that the PERK-eIF2α phosphorylation pathway maintains insulin biosynthesis and glucose homeostasis, facilitates tumor formation and decreases the efficacy of tumor treatment with chemotherapeutic drugs. Recently, a selective catalytic PERK inhibitor termed GSK2656157 has been developed with anti-tumor properties in mice. Herein, we provide evidence that inhibition of PERK activity by GSK2656157 does not always correlate with inhibition of eIF2α phosphorylation. Also, GSK2656157 does not always mimic the biological effects of the genetic inactivation of PERK. Furthermore, cells treated with GSK2656157 increase eIF2α phosphorylation as a means to compensate for the loss of PERK. Using human tumor cells impaired in eIF2α phosphorylation, we demonstrate that GSK2656157 induces ER stress-mediated death suggesting that the drug acts independent of the inhibition of eIF2α phosphorylation. We conclude that GSK2656157 might be a useful compound to dissect pathways that compensate for the loss of PERK and/or identify PERK pathways that are independent of eIF2α phosphorylation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".