Teamwork of clustered low-affinity κB sites and accessory factors regulates transcriptional strength of NF-κB RelA dimers
Bibliographic record
Abstract
Non-consensus binding sites of transcription factors (TFs) are often observed within the regulatory elements of genes; however, their effect on transcriptional strength is unclear. Within the promoters and enhancers of NF-κB-responsive genes, we identified clusters of non-consensus κB DNA sites, many exhibiting low affinity for NF-κB in vitro. Deletion of these sites demonstrated their collective critical role in transcription. We explored how these "weak" κB sites exert their influence, especially given the typically low nuclear concentrations of NF-κB. Using proteomics approaches, we identified additional nuclear factors, including other DNA-binding TFs, that could interact with κB site-bound NF-κB RelA. ChIP-seq and RNA-seq analyses suggest that these accessory TFs, referred to as the TF-cofactors of NF-κB, facilitate dynamic recruitment of NF-κB to the clustered weak κB sites. Overall, the occupancy of NF-κB at promoters and enhancers appears to be defined by a collective contribution from all κB sites, both weak and strong, in association with specific cofactors. This congregation of multiple factors within dynamic transcriptional complexes is likely a common feature of transcriptional programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".