Pifithrin-µ Induces Stress Granule Formation, Regulates Cell Survival, and Rewires Cellular Signaling
Bibliographic record
Abstract
(1) Background: Stress granules (SGs) are cytoplasmic protein-RNA condensates that assemble in response to various insults. SG production is driven by signaling pathways that are relevant to human disease. Compounds that modulate SG characteristics are therefore of clinical interest. Pifithrin-µ is a candidate anti-tumor agent that inhibits members of the hsp70 chaperone family. While hsp70s are required for granulostasis, the impact of pifithrin-µ on SG formation is unknown. (2) Methods: Using HeLa cells as model system, cell-based assays evaluated the effects of pifithrin-µ on cell viability. Quantitative Western blotting assessed cell signaling events and SG proteins. Confocal microscopy combined with quantitative image analyses examined multiple SG parameters. (3) Results: Pifithrin-µ induced bona fide SGs in the absence of exogenous stress. These SGs were dynamic; their properties were determined by the duration of pifithrin-µ treatment. The phosphorylation of eIF2α was mandatory to generate SGs upon pifithrin-µ exposure. Moreover, the formation of pifithrin-µ SGs was accompanied by profound changes in cell signaling. Pifithrin-µ reduced the activation of 5′-AMP-activated protein kinase, whereas the pro-survival protein kinase Akt was activated. Long-term pifithrin-µ treatment caused a marked loss of cell viability. (4) Conclusions: Our study identified stress-related changes in cellular homeostasis that are elicited by pifithrin-µ. These insights are important knowledge for the appropriate therapeutic use of pifithrin-µ and related compounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".