NF-κB regulated expression of A20 controls IKK dependent repression of RIPK1 induced cell death in activated T cells
Bibliographic record
Abstract
IKK signalling is essential for survival of thymocytes by repressing RIPK1 induced cell death rather than its canonical function of activating NF-κB. The role of IKK signalling in activated T cells is unclear. To investigate this, we analysed activation of IKK2 deficient T cells. While TCR triggering was normal, proliferation and expansion was profoundly impaired. This was not due to defective cell cycle progression, rather dividing T cells became sensitised to TNF induced cell death, since inhibition of RIPK1 kinase activity rescued cell survival. Gene expression analysis of activated IKK2 deficient T cells revealed defective expression of Tnfaip3, that encodes A20, a negative regulator of NF-κB. To test whether A20 expression was required to protect IKK2 deficient T cells from cell death, we generated mice with T cells lacking both A20 and IKK2. Doing this resulted in near complete loss of peripheral T cells, in contrast to mice lacking one or other gene. Strikingly, this phenotype was completely reversed by inactivation of RIPK1 kinase activity in vivo. Together, our data show that IKK signalling in activated T cells protects against RIPK1 dependent death, both by direct phosphorylation of RIPK1 and through NF-κB mediated induction of A20, that we identify for the first time as a key modulator of RIPK1 activity in T cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".