Multifaceted Evolution of the <scp>PhoPQ</scp> Two‐Component System in <scp><i>Salmonella enterica</i></scp> Enhanced the Expression of Horizontally Acquired Virulence Genes
Bibliographic record
Abstract
For a bacterium to adapt to a new environmental niche, its regulatory networks must evolve to effectively sense and respond to cues within that niche. For bacterial pathogens, which encounter harsh and dynamic host niches that require efficient coordination between detecting host cues and regulating virulence genes, this process is a key aspect of how virulence properties evolve. Here, we investigate how a widely conserved two-component regulatory system (TCS), PhoP/PhoQ (PhoPQ), evolved in Salmonella enterica to adopt a new role as a master regulator of gene expression within its species-specific intracellular niche: the Salmonella-containing vacuole (SCV). By comparing Salmonella PhoPQ with the closely related Escherichia coli PhoPQ ortholog, we demonstrate that optimizing virulence gene expression in Salmonella required a multifaceted evolution of several PhoPQ functional domains and establish that distinct genetic differences and mechanisms enhance virulence gene expression for different inducing cues. Interestingly, we find that the increased activity of the Salmonella PhoPQ system has a much more profound impact on the expression of H-NS-repressed, horizontally acquired virulence genes than on the ancestral members of the PhoP regulon. We observe that the PhoPQ systems of other related bacteria exhibit activity levels similar to the E. coli system, suggesting that the differences we observe are the result of Salmonella-specific adaptations that produced a more active PhoPQ system when encountering SCV conditions. Collectively, this study offers a window into the evolutionary adaptations of a TCS that enable it to assume an expanded regulatory role in a unique environment.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".