Analysis of Protein Kinase D1’s role during reactive oxygen species-mediated signaling
Bibliographic record
Abstract
Protein Kinase D1 (PKD1) is a critical component of cellular responses to reactive oxygen species (ROS)-mediated apoptosis. Previous studies demonstrated that upon oxidative stress, PKD1 is activated through a signaling pathway mediated by the Src/Abl non-receptor tyrosine kinases. Subsequently, PKD1’s translocation to different cellular compartments can regulate gene expression and promote cell survival through the NF-kB signaling pathway. Additionally, PKD1 deficiency increases ROS sensitivity in mouse embryonic fibroblasts (MEFs), leading to mitochondrial membrane depolarization and apoptosis when exposed to oxidative stress. Our study examined PKD1’s protective role against oxidative stress-induced apoptosis and its impact on gene expression. Using wild-type (WT) and PKD1-deficient (PKD1-/-) MEFs, we found that the absence of PKD1 significantly increases oxidative stressinduced apoptosis, as evidenced by H₂O₂ stimulation and serum starvation experiments. To better understand the molecular mechanisms underlying PKD1's protective function, RNASeq analysis was performed, comparing gene expression levels between WT and PKD1-/- MEFs. RNA-Seq analysis revealed differentially expressed genes (DEGs) between WT and PKD1-/- genotypes, with 383 DEGs in primary and 1705 DEGs in immortalized MEFs. Notably, BCL2L11 (BIM), a pro-apoptotic protein, was more than two-fold down-regulated in both primary and immortalized PKD1-/- MEFs, suggesting its interaction with PKD1 on the mitochondrial outer membrane through the mitochondrial apoptosis pathway. In addition, KEGG pathway analysis highlighted enriched differentially expressed pathways, including those associated with apoptosis, cell survival, and response to oxidative stress. This study sheds light on how PKD1 defends against oxidative stress and influences gene expression, potentially contributing to the development of treatments for oxidative stress-linked diseases.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".