Polycystin-1 inhibits eIF2α phosphorylation and cell apoptosis through a PKR-eIF2α pathway
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in PKD1 or PKD2 which encodes polycystin-1 (PC1) and polycystin-2, respectively. PC1 was previously shown to slow cell proliferation and inhibit apoptosis but the underlying mechanisms remain elusive or controversial. Here we showed in cultured mammalian cells and Pkd1 knockout mouse kidney epithelial cells that PC1 and its truncation mutant comprising the last five transmembrane segments and the intracellular C-terminus (PC1-5TMC) down-regulate the phosphorylation of protein kinase R (PKR) and its substrate eukaryotic translation initiation factor 2 alpha (eIF2α). PKR is known to be activated by interferons and dsRNAs, inhibits protein synthesis and induces apoptosis. By co-immunoprecipitation experiments we found that PC1 truncation mutants associate with PKR, or with PKR and its activator PACT. Further experiments showed that PC1 and PC1-5TMC reduce phosphorylation of eIF2α through inhibiting PKR phosphorylation. Our TUNEL experiments using tunicamycin, an apoptosis inducer, and GADD34, an inhibitor of eIF2α phosphorylation, demonstrated that PC1-5TMC inhibits apoptosis of HEK293T cells in a PKR-eIF2α-dependent manner, with concurrent up- and down-regulation of Bcl-2 and Bax, respectively, revealed by Western blotting. Involvement of PC1-regulated eIF2α phosphorylation and a PKR-eIF2α pathway in cell apoptosis may be an important part of the mechanism underlying ADPKD pathogenesis.
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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 | 0.000 | 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".