Disabling Mitochondrial Peroxide Metabolism via Combinatorial Targeting of Peroxiredoxin 3 as an Effective Therapeutic Approach for Malignant Mesothelioma
Bibliographic record
Abstract
Dysregulation of signaling pathways and energy metabolism in cancer cells enhances production of mitochondrial hydrogen peroxide that supports tumorigenesis through multiple mechanisms. To counteract the adverse effects of mitochondrial peroxide many solid tumor types up-regulate the mitochondrial thioredoxin reductase 2--thioredoxin 2 (TRX2)--peroxiredoxin 3 (PRX3) antioxidant network. Using malignant mesothelioma cells as a model, we show that thiostrepton (TS) irreversibly disables PRX3 via covalent crosslinking of peroxidatic and resolving cysteine residues in homodimers, and that targeting the oxidoreductase TRX2 with the triphenylmethane gentian violet (GV) potentiates adduction by increasing levels of disulfide-bonded PRX3 dimers. Due to the fact that activity of the PRX3 catalytic cycle dictates the rate of adduction by TS, immortalized and primary human mesothelial cells are significantly less sensitive to both compounds. Moreover, stable knockdown of PRX3 reduces mesothelioma cell proliferation and sensitivity to TS. Expression of catalase in shPRX3 mesothelioma cells restores defects in cell proliferation but not sensitivity to TS. In a SCID mouse xenograft model of human mesothelioma, administration of TS and GV together reduced tumor burden more effectively than either agent alone. Because increased production of mitochondrial hydrogen peroxide is a common phenotype of malignant cells, and TS and GV are well tolerated in mammals, we propose that targeting PRX3 is a feasible redox-dependent strategy for managing mesothelioma and other intractable human malignancies.
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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) | 0.000 | 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".