The regulation of sequence specific NF-κB DNA binding and transcription by IKKβ phosphorylation of NF-κB p50 at serine 80
Bibliographic record
Abstract
Phosphorylation of the NF-κB transcription factor is an important regulatory mechanism for the control of transcription. Here we identify serine 80 (S80) as a phosphorylation site on the p50 subunit of NF-κB, and IKKβ as a p50 kinase. Transcriptomic analysis of cells expressing a p50 S80A mutant reveals a critical role for S80 in selectively regulating the TNFα inducible expression of a subset of NF-κB target genes including pro-inflammatory cytokines and chemokines. S80 phosphorylation regulates the binding of p50 to NF-κB binding (κB) sites in a sequence specific manner. Specifically, phosphorylation of S80 reduces the binding of p50 at κB sites with an adenine at the -1 position. Our analyses demonstrate that p50 S80 phosphorylation predominantly regulates transcription through the p50:p65 heterodimer, where S80 phosphorylation acts in trans to limit the NF-κB mediated transcription of pro-inflammatory genes. The regulation of a functional class of pro-inflammatory genes by the interaction of S80 phosphorylated p50 with a specific κB sequence describes a novel mechanism for the control of cytokine-induced transcriptional responses.
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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".