Inactivation of necroptosis-promoting protein MLKL creates a therapeutic vulnerability in colorectal cancer cells
Bibliographic record
Abstract
Mortality from colorectal cancer (CRC) is significant, and novel CRC therapies are needed. A pseudokinase MLKL typically executes necroptotic cell death, and MLKL inactivation protects cells from such death. However, we found unexpectedly that MLKL gene knockout enhanced CRC cell death caused by a protein synthesis inhibitor homoharringtonine used for chronic myeloid leukemia treatment. In an effort to explain this finding, we observed that MLKL gene knockout reduces the basal CRC cell autophagy and renders such autophagy critically dependent on the presence of VPS37A, a component of the ESCRT-I complex. We further found that the reason why homoharringtonine enhances CRC cell death caused by MLKL gene knockout is that homoharringtonine activates p38 MAP kinase and thereby prevents VPS37A from supporting autophagy in MLKL-deficient cells. We observed that the resulting inhibition of the basal autophagy in CRC cells triggers their parthanatos, a cell death type driven by poly(ADP-ribose) polymerase hyperactivation. Finally, we discovered that a pharmacological MLKL inhibitor necrosulfonamide strongly cooperates with homoharringtonine in suppressing CRC cell tumorigenicity in mice. Thus, while MLKL promotes cell death during necroptosis, MLKL supports the basal autophagy in CRC cells and thereby protects them from death. MLKL inactivation reduces such autophagy and renders the cells sensitive to autophagy inhibitors, such as homoharringtonine. Hence, MLKL inhibition creates a therapeutic vulnerability that could be utilized for CRC treatment.
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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".