Differences in enhancer activity in mouse and zebrafish reporter assays are often associated with changes in gene expression
Bibliographic record
Abstract
BACKGROUND: Phenotypic evolution in animals is thought to be driven in large part by differences in gene expression patterns, which can result from sequence changes in cis-regulatory elements (cis-changes) or from changes in the expression pattern or function of transcription factors (trans-changes). While isolated examples of trans-changes have been identified, the scale of their overall contribution to regulatory and phenotypic evolution remains unclear. RESULTS: Here, we attempt to examine the prevalence of trans-effects and their potential impact on gene expression patterns in vertebrate evolution by comparing the function of identical human tissue-specific enhancer sequences in two highly divergent vertebrate model systems, mouse and zebrafish. Among 47 human conserved non-coding elements (CNEs) tested in transgenic mouse embryos and in stable zebrafish lines, at least one species-specific expression domain was observed in the majority (83%) of cases, and 36% presented dramatically different expression patterns between the two species. Although some of these discrepancies may be due to the use of different transgenesis systems in mouse and zebrafish, in some instances we found an association between differences in enhancer activity and changes in the endogenous gene expression patterns between mouse and zebrafish, suggesting a potential role for trans-changes in the evolution of gene expression. CONCLUSIONS: In total, our results: (i) serve as a cautionary tale for studies investigating the role of human enhancers in different model organisms, and (ii) suggest that changes in the trans environment may play a significant role in the evolution of gene expression in vertebrates.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".