Fitness costs and benefits of gene expression plasticity in rice under drought
Bibliographic record
Abstract
Abstract Genome-wide gene expression changes in response to environmental variability have been widely documented, but we lack detailed and comprehensive understanding of the interplay between this form of phenotypic plasticity and natural selection. Selection on expression plasticity may be limited by environment-specific costs, and plasticity may in turn affect selection on baseline expression levels. Here, we address this fundamental issue by measuring selection on drought-induced plasticity of leaf transcripts in field-grown rice populations. Selection disfavored switching off housekeeping genes under drought. This stress-induced dysregulation did not constrain selection on baseline transcript levels, suggesting compensatory evolution may be possible. Selection rarely acted strongly on individual transcripts but worked polygenically on gradual (continuous) plasticity of co-expressed gene modules regulating photosynthesis via known drought-responsive transcription factors. Finally, selection was tied to inefficient gene architectural features and metabolic costs of expression. Our study provides a genome-wide view of costs and benefits of gene expression plasticity.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".