Thermosensing Coordinates a Cis-regulatory Module for Transcriptional Activation of the Intracellular Virulence System in Salmonella enterica Serovar Typhimurium
Bibliographic record
Abstract
The expression of bacterial virulence genes is tightly controlled by the convergence of multiple extracellular signals. As a zoonotic pathogen, virulence gene regulation in Salmonella enterica serovar Typhimurium must be responsive to multiple cues from the general environment as well as from multiple niches within animal and human hosts. Previous work has identified combined magnesium and phosphate limitation as an environmental cue that activates genes required for intracellular virulence. One unanswered question is how virulence genes that are expressed within the host are inhibited in non-host environments that satisfy the phosphate and magnesium limitation cues. We report here that thermosensing is the major mechanism controlling incongruous activation of the intracellular virulence phenotype. Bacteria grown at 30 degrees C or lower were unable to activate the intracellular type III secretion system even under strong inducing signals such as synthetic medium, contact with macrophages, and exposure to the murine gut. Thermoregulation was fully recapitulated in a Salmonella bongori strain engineered to contain the intracellular virulence genes of S. enterica sv. Typhimurium, suggesting that orthologous thermoregulators were available. Accordingly, virulence gene repression at the nonpermissive temperature required Hha and H-NS, two nucleoid-like proteins involved in virulence gene control. The use of combined environmental cues to control transcriptional "logic gates" allows for transcriptional selectivity of virulence genes that would otherwise be superfluous if activated in the non-host environment. Thus, thermosensing by Salmonella provides integrated control of host niche-specific virulence factors.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".