Transposable elements have contributed human regulatory regions that are activated upon bacterial infection
Bibliographic record
Abstract
ABSTRACT Transposable elements (TEs) are increasingly recognized as important contributors to mammalian regulatory systems. For instance, they have been shown to play a role in the human interferon response. However, their involvement in other mechanisms of immune cell activation remains poorly understood. We investigated the profile of accessible chromatin enhanced in stimulated human macrophages using ATAC-Seq to assess the role of different TE subfamilies in regulating the immune response. We found that both previously identified and new repeats belonging to the MER44, THE1, Tigger3 and MLT1 families provide 14 subfamilies that are enriched in differentially accessible chromatin and found near differentially expressed genes. These TEs also harbour binding motifs for several candidate transcription factors, including important immune regulators AP-1 and NF-kB, present in 96% of accessible MER44B and 83% of THE1C instances, respectively. To more directly assess their regulatory potential, we evaluated their presence in regions putatively affecting gene expression, as defined by quantitative trait locus (QTL) analysis, and find that repeats are also contributing to accessible elements near QTLs. Together, these results suggest that a number of TE families have contributed to the regulation of the immunogenomic response to infection in humans.
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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) | 0.004 | 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".