Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways
Bibliographic record
Abstract
Kinases are critical components of intracellular signaling pathways and have been extensively investigated with regard to their roles in cancer. p21-activated kinase-1 (PAK1) is a serine/threonine kinase that has been previously implicated in numerous biological processes, such as cell migration, cell cycle progression, cell motility, invasion, and angiogenesis, in glioma and other cancers. However, the signaling network linked to PAK1 is not fully defined. We previously reported a large-scale yeast genetic interaction screen using toxicity as a readout to identify candidate PAK1 genetic interactions. En masse transformation of the PAK1 gene into 4,653 homozygous diploid Saccharomyces cerevisiae yeast deletion mutants identified ∼400 candidates that suppressed yeast toxicity. Here we selected 19 candidate PAK1 genetic interactions that had human orthologs and were expressed in glioma for further examination in mammalian cells, brain slice cultures, and orthotopic glioma models. RNAi and pharmacological inhibition of potential PAK1 interactors confirmed that DPP4 , KIF11 , mTOR , PKM2 , SGPP1 , TTK , and YWHAE regulate PAK1-induced cell migration and revealed the importance of genes related to the mitotic spindle, proteolysis, autophagy, and metabolism in PAK1-mediated glioma cell migration, drug resistance, and proliferation. AKT1 was further identified as a downstream mediator of the PAK1 - TTK genetic interaction. Taken together, these data provide a global view of PAK1-mediated signal transduction pathways and point to potential new drug targets for glioma therapy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".