Cis and trans effects differentially contribute to the evolution of promoters and enhancers
Bibliographic record
Abstract
BACKGROUND: Gene expression differences between species are driven by both cis and trans effects. Whereas cis effects are caused by genetic variants located on the same DNA molecule as the target gene, trans effects are due to genetic variants that affect diffusible elements. Previous studies have mostly assessed the impact of cis and trans effects at the gene level. However, how cis and trans effects differentially impact regulatory elements such as enhancers and promoters remains poorly understood. Here, we use massively parallel reporter assays to directly measure the transcriptional outputs of thousands of individual regulatory elements in embryonic stem cells and measure cis and trans effects between human and mouse. RESULTS: Our approach reveals that cis effects are widespread across transcribed regulatory elements, and the strongest cis effects are associated with the disruption of motifs recognized by strong transcriptional activators. Conversely, we find that trans effects are rare but stronger in enhancers than promoters and are associated with a subset of transcription factors that are differentially expressed between human and mouse. While we find that cis-trans compensation is common within promoters, we do not see evidence of widespread cis-trans compensation at enhancers. Cis-trans compensation is inversely correlated with enhancer redundancy, suggesting that such compensation may often occur across multiple enhancers. CONCLUSIONS: Our results highlight differences in the mode of evolution between promoters and enhancers in complex mammalian genomes and indicate that studying the evolution of individual regulatory elements is pivotal to understand the tempo and mode of gene expression evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".