Selective Induction of Cell Death in Human M1 Macrophages by Smac Mimetics Is Mediated by cIAP-2 and RIPK-1/3 through the Activation of mTORC
Bibliographic record
Abstract
Inflammatory macrophages have been implicated in many diseases, including rheumatoid arthritis and inflammatory bowel disease. Therefore, targeting macrophage function and activation may represent a potential strategy to treat macrophage-associated diseases. We have previously shown that IFN-γ-induced differentiation of human M0 macrophages toward proinflammatory M1 state rendered them highly susceptible to the cytocidal effects of second mitochondria-derived activator of caspases mimetics (SMs), antagonist of the inhibitors of apoptosis proteins (IAPs), whereas M0 and anti-inflammatory M2c macrophages were resistant. In this study, we investigated the mechanism governing SM-induced cell death during differentiation into M1 macrophages and in polarized M1 macrophages. IFN-γ stimulation conferred on M0 macrophages the sensitivity to SM-induced cell death through the Jak/STAT, IFN regulatory factor-1, and mammalian target of rapamycin complex-1 (mTORC-1)/ribosomal protein S6 kinase pathways. Interestingly, mTORC-1 regulated SM-induced cell death independent of M1 differentiation. In contrast, SM-induced cell death in polarized M1 macrophages is regulated by the mTORC-2 pathway. Moreover, SM-induced cell death is regulated by cellular IAP (cIAP)-2, receptor-interacting protein kinase (RIPK)-1, and RIPK-3 degradation through mTORC activation during differentiation into M1 macrophages and in polarized M1 macrophages. In contrast to cancer cell lines, SM-induced cell death in M1 macrophages is independent of endogenously produced TNF-α, as well as the NF-κB pathway. Collectively, selective induction of cell death in human M1 macrophages by SMs may be mediated by cIAP-2, RIPK-1, and RIPK-3 degradation through mTORC activation. Moreover, blocking cIAP-1/2, mTORC, or IFN regulatory factor-1 may represent a promising therapeutic strategy to control M1-associated 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 | 0.000 |
| 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".