Systematic Dissection of Coding Exons at Single Nucleotide Resolution Supports an Additional Role in Cell-Specific Transcriptional Regulation
Bibliographic record
Abstract
In addition to their protein coding function, exons can also serve as transcriptional enhancers. Mutations in these exonic-enhancers (eExons) could alter both protein function and transcription. However, the functional consequence of eExon mutations is not well known. Here, using massively parallel reporter assays, we dissect the enhancer activity of three liver eExons (SORL1 exon 17, TRAF3IP2 exon 2, PPARG exon 6) at single nucleotide resolution in the mouse liver. We find that both synonymous and non-synonymous mutations have similar effects on enhancer activity and many of the deleterious mutation clusters overlap known liver-associated transcription factor binding sites. Carrying a similar massively parallel reporter assay in HeLa cells with these three eExons found differences in their mutation profiles compared to the liver, suggesting that enhancers could have distinct operating profiles in different tissues. Our results demonstrate that eExon mutations could lead to multiple phenotypes by disrupting both the protein sequence and enhancer activity and that enhancers can have distinct mutation profiles in different cell types.
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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".