Multi-species analysis of inflammatory response elements reveals ancient and lineage-specific contributions of transposable elements to NF-κB binding
Bibliographic record
Abstract
Transposable elements (TEs) provide a source of transcription factor binding sites that can rewire conserved gene regulatory networks. NF-κB is an evolutionary conserved transcription factor complex primarily involved in innate immunity and inflammation. The extent to which TEs have contributed to NF-κB responses during mammalian evolution is not well established. Here we performed a multi-species analysis of TEs bound by the NF-κB subunit RELA (also known as p65) in response to the proinflammatory cytokine TNF. By comparing RELA ChIP-seq data from TNF-stimulated primary aortic endothelial cells isolated from human, mouse and cow, we found that 55 TE subfamilies were associated with RELA bound regions. These RELA-bound transposons possess active epigenetic features and reside near TNF-responsive genes. A prominent example of lineage-specific contribution of transposons comes from the bovine SINE subfamilies Bov-tA1/2/3 which collectively contributed over 14,000 RELA bound regions in cow. By comparing RELA binding data across species, we also found several examples of RELA motif-bearing TEs that colonized the genome prior to the divergence of the three species and contributed to species-specific RELA binding. For example, we found human RELA bound MER81 instances were enriched for the interferon gamma pathway and demonstrated that one RELA bound MER81 element can control the TNF-induced expression of Interferon Gamma Receptor 2 ( IFNGR2 ). Using ancestral reconstructions, we found that RELA containing MER81 instances rapidly decayed during early primate evolution (> 50 million years ago (MYA)) before stabilizing since the separation of Old World monkeys (< 50 MYA). Taken together, our results suggest ancient and lineage-specific transposon subfamilies contributed to mammalian NF-κB regulatory networks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".