Adaptive evolution of transcription factor binding affinities
Bibliographic record
Abstract
Understanding the molecular basis of gene expression evolution is a central problem in evolutionary biology. However, connecting changes in gene expression to increased fitness, and identifying the functional basis of those changes, remains challenging. To study adaptive evolution of gene expression in real time, we performed long term experimental evolution (LTEE) of Saccharomyces cerevisiae (budding yeast) in ammonium-limited chemostats. Following several hundred generations of continuous selection we found significant divergence of nitrogen-responsive gene expression in lineages with increased fitness. In multiple independent lineages we found repeated selection for non-synonymous mutations in the zinc finger DNA binding domain of the activating transcription factor (TF), GAT1, that operates within incoherent feedforward loops to control expression of the nitrogen catabolite repression (NCR) regulon. Missense mutations in the DNA binding domain of GAT1 reduce its binding affinity for the GATAA consensus sequence in a promoter-specific manner, resulting in increased expression of ammonium permease genes via both direct and indirect effects, thereby conferring increased fitness. We find that altered transcriptional output of the NCR regulon results in antagonistic pleiotropy in alternate environments and that the DNA binding domain of GAT1 is subject to purifying selection in natural populations. Our study shows that adaptive evolution of gene expression can entail tuning expression output by quantitative changes in TF binding affinities while maintaining the overall topology of a gene regulatory network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".