Die another way: necroptosis as alternative route to cell death
Bibliographic record
Abstract
Introduction: The deregulation of pro- and anti-survival signalling pathways leads to escape from cell death and contributes to a poor response to chemotherapy and relapse in malignancies. Exploiting recently identified novel cell death mechanisms such as necroptosis represents an attractive strategy to eradicate such resistant tumor cells. Necroptosis occurs without caspase activation and relies on distinct protein-protein interactions regulated by receptor-interacting protein kinase 1, RIP1. RIP1 is held in check by the inhibitor of apoptosis proteins (IAPs). Depletion of IAPs using small-molecule SMAC mimetics (SM) can potently induce a switch from RIP1-controlled survival to cell death. Methods: We use a combination of CRIPSR/Cas9-based genome editing methodologies, cell death assays, correlative gene expression analyses and xenograft models. Results: We have recently found that a subgroup of acute lymphoblastic leukemia (ALL) samples including refractory cases responds to SM with simultaneous activation of apoptosis and necroptosis. Using a multicolor lentiCRISPR-based functional genomic approach, we identified RIP1 as key regulator of both cell death mechanisms upon cIAP depletion. Accordingly, deletion of both apopotic and necroptotic genes is required to rescue cell viability after SM treatment. Comparative gene expression analyses combined with functional CRISPR-based genome editing approaches suggest that upstream of RIP1, TNF receptor 2 is required for the composition of a RIP1/TNF receptor 1 containing death signalling complex. Conclusion: Thus, our data indicate that necroptosis is a relevant stress management pathway that specifies a targetable vulnerability in resistant disease.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".