Adaptive Divergence in Experimental Populations of <i>Pseudomonas fluorescens</i>. III. Mutational Origins of Wrinkly Spreader Diversity
Bibliographic record
Abstract
Understanding the connections among genotype, phenotype, and fitness through evolutionary time is a central goal of evolutionary genetics. Wrinkly spreader (WS) genotypes evolve repeatedly in model Pseudomonas populations and show substantial morphological and fitness differences. Previous work identified genes contributing to the evolutionary success of WS, in particular the di-guanylate cyclase response regulator, WspR. Here we scrutinize the Wsp signal transduction pathway of which WspR is the primary output component. The pathway has the hallmarks of a chemosensory pathway and genetic analyses show that regulation and function of Wsp is analogous to the Che chemotaxis pathway from Escherichia coli. Of significance is the methyltransferase (WspC) and methylesterase (WspF) whose opposing activities form an integral feedback loop that controls the activity of the kinase (WspE). Deductions based on the regulatory model suggested that mutations within wspF were a likely cause of WS. Analyses of independent WS genotypes revealed numerous simple mutations in this single open reading frame. Remarkably, different mutations have different phenotypic and fitness effects. We suggest that the negative feedback loop inherent in Wsp regulation allows the pathway to be tuned by mutation in a rheostat-like manner.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".