Translational up‐regulation of polycystic kidney disease protein PKD2 by endoplasmic reticulum stress
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in PKD1 or PKD2, and it affects over 10 million people worldwide. It is characterized by cyst formation in the kidney, liver and pancreas. Dosage changes in PKD1/PKD2 are important in ADPKD pathogenesis; therefore, their expression and function has to be strictly regulated. However, how they are regulated remain poorly understood. Recent studies have linked PKD2 regulation to endoplasmic reticulum (ER) stress that is implicated in neuronal, cardiac, and renal diseases. One major ER stress downstream is phosphorylation of eukaryotic initiation factor eIF2α by kinase PERK, which attenuates global protein translation and enhances translation of selected proteins. Here, we showed in several mammalian cell lines that PKD2 protein expression is up-regulated by different stresses that all increase phosphorylated eIF2α (P-eIF2α). Increasing P-eIF2α by overexpression or inhibiting the phosphatase activity resulted in increased PKD2. PCR and polysome-binding assays showed that ER stress does not affect the PKD2 mRNA level but increase its binding with ribosomes, indicating that P-eIF2α translationally up-regulates PKD2. By mutation analysis, we found that the upstream open reading frame (uORF) in the 5'-untranslated region of PKD2 mRNA represses PKD2 translation. Thus, ER stress and P-eIF2α translationally up-regulates PKD2 through bypassing the inhibitory uORF.
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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 | 0.000 |
| 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".