Identification of an N-terminal Truncation of the NF-κB p65 Subunit That Specifically Modulates Ribosomal Protein S3-dependent NF-κB Gene Expression
Bibliographic record
Abstract
NF-κB is a pleiotrophic transcription factor that plays a prominent regulatory role in various cellular processes. Although previous efforts have focused on its activation, how NF-κB selects specific target genes in response to discrete signals remains puzzling. In addition to the well defined Rel protein components of NF-κB, the ribosomal protein S3 (RPS3) was identified to be an essential component of specific NF-κB complexes. RPS3 synergistically interacts with the NF-κB p65 subunit to achieve optimal binding and transactivation of a subset of NF-κB target genes, thus providing regulatory specificity. Emerging evidence suggests an important role for the RPS3-p65 interaction in context-specific NF-κB gene transcription. The food-borne pathogen Escherichia coli O157:H7 impacts the transcription of a subset of NF-κB target genes encoding proinflammatory cytokines and chemokines in host cells by preventing the nuclear translocation of RPS3, but not p65. The N terminus of p65 is crucial for RPS3 binding. Although several p65 N-terminal fragments are generated by either protease cleavage or alternative mRNA splicing under certain pathophysiological conditions, the role of these fragments in modulating NF-κB signaling, in particular RPS3-dependent selective gene transcription, has not been fully characterized. Here we report that an N-terminal fragment of p65 (amino acids 21-186) can selectively modulate NF-κB gene transcription by competing for RPS3 binding to p65. This 21-186 fragment preferentially localizes in the cytoplasm where it delays stimuli-induced RPS3 nuclear translocation, without affecting the nuclear translocation of p65. Our findings thus uncover a new cytoplasmic function for the N-terminal domain of p65 and provide a novel strategy for selective inhibition of NF-κB gene transcription.
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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 | 0.001 |
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".