Abstract 673: Discovering and Dissecting the Function of Conserved NF-κB Binding Events in the Human Genome
Bibliographic record
Abstract
The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription factor plays a prominent role in inflammation and contributes to the development of atherosclerosis. Genome-wide DNA binding assays of the human NF-κB subunit RELA (p65) have revealed tens of thousands of NF-κB binding sites and hundreds of target genes. However, the function of individual RELA binding sites and the extent to which NF-κB occupancy and function is conserved across mammals are not well understood. To better understand the function of NF-κB we characterized the genome-wide binding of RELA in primary vascular endothelial cells (ECs) isolated from the aortas of human, mouse and cow. ECs were stimulated acutely with the pro-inflammatory cytokine tumor necrosis factor alpha (TNFA) and we profiled RELA occupancy, open chromatin, select histone modifications, and RNA expression. We found ~5000 RELA binding events conserved across all three species and these highly conserved human binding events were enriched for genes controlling vascular development, apoptosis, and pro-inflammatory responses. Approximately 2000 of these highly conserved RELA binding events were also shared across multiple human cell types, revealing a conserved core of robustly bound NF-κB sites. These NF-κB binding sites were also prominent components of ~40 inflammation-induced super-enhancers (SE) common to several tissues. To gain insight into the function of individual conserved NF-κB binding sites we focused on the inflammation-induced SE proximal to the monocyte recruiting chemokine CCL2 , which we detected as a SE in all three species and across multiple cell types. We tested the functional significance of six conserved RELA binding sites comprising this SE using CRISPR/Cas9 genome editing. We found that only deletion of the most proximal upstream RELA binding site could abolish the induction of CCL2 upon TNFA treatment. This site also contains a disease associated variant that can modulate CCL2 induction. Overall, our comparative genomics assessment of NF-κB binding gives new insight into NF-κB biology and the function of conserved transcription factor binding events within mammalian super-enhancers.
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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 | 0.001 |
| 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) | 0.005 | 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".