Pharmacological inhibition of MALT1 (mucosa-associated lymphoid tissue lymphoma translocation protein 1) induces ferroptosis in vascular smooth muscle cells
Bibliographic record
Abstract
Abstract MALT1 (mucosa-associated lymphoid tissue lymphoma translocation protein 1) is a human paracaspase protein with proteolytic activity via its caspase-like domain. The pharmacological inhibition of MALT1 by MI-2, a specific chemical inhibitor, diminishes the response of endothelial cells to inflammatory stimuli. However, it is largely unknown how MALT1 regulates the functions of vascular smooth muscle cells (SMCs). This study aims to investigate the impact of MALT1 inhibition by MI-2 on the functions of vascular SMCs, both in vitro and in vivo. MI-2 treatment led to concentration- and time-dependent cell death of cultured aortic SMCs, which was rescued by the iron chelator deferoxamine (DFO) or ferrostatin-1 (Fer-1), a specific inhibitor of ferroptosis, but not by inhibitors of apoptosis (Z-VAD-fmk), pyroptosis (Z-YVAD-fmk), or necrosis (Necrostatin-1, Nec-1). MI-2 treatment downregulated the expression of glutathione peroxidase 4 (GPX4) and ferritin heavy polypeptide 1 (FTH1), which was prevented by pre-treatment with DFO or Fer-1. MI-2 treatment also activated autophagy, which was inhibited by Atg7 deficiency or bafilomycin A1 preventing MI-2-induced ferroptosis. MI-2 treatment reduced the cleavage of cylindromatosis (CYLD), a specific substrate of MALT1. Notably, MI-2 treatment led to a rapid loss of contractility in mouse aortas, which was prevented by co-incubation with Fer-1. Moreover, local application of MI-2 significantly reduced carotid neointima lesions and atherosclerosis in C57BL/6J mice and apolipoprotein-E knockout (ApoE−/−) mice, respectively, which were both ameliorated by co-treatment with Fer-1. In conclusion, the present study demonstrated that MALT1 inhibition induces ferroptosis of vascular SMCs, likely contributing to its amelioration of proliferative vascular 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 | 0.000 | 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".