High constitutive activity of a broad panel of housekeeping and tissue-specific <i>cis</i>-regulatory elements depends on a subset of ETS proteins
Bibliographic record
Abstract
Enhancers and promoters that control the transcriptional output of terminally differentiated cells include cell type-specific and broadly active housekeeping elements. Whether the high constitutive activity of these two groups of cis-regulatory elements relies on entirely distinct or instead also on shared regulators is unknown. By dissecting the cis-regulatory repertoire of macrophages, we found that the ELF subfamily of ETS proteins selectively bound within 60 base pairs (bp) from the transcription start sites of highly active housekeeping genes. ELFs also bound constitutively active, but not poised, macrophage-specific enhancers and promoters. The role of ELFs in promoting high-level constitutive transcription was suggested by multiple evidence: ELF sites enabled robust transcriptional activation by endogenous and minimal synthetic promoters, ELF recruitment was stabilized by the transcriptional machinery, and ELF proteins mediated recruitment of transcriptional and chromatin regulators to core promoters. These data suggest that the co-optation of a limited number of highly active transcription factors represents a broadly adopted strategy to equip both cell type-specific and housekeeping cis-regulatory elements with the ability to efficiently promote 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".