Transcriptomic analysis of iron transport mutants reveals serovar-specific adaptations in <i>Salmonella enterica</i>
Bibliographic record
Abstract
Iron is an essential mineral for almost all pathogenic bacteria, including Salmonella enterica serovar Typhimurium and Enteritidis. We have investigated the effect of the deletion of iroN, fepA, and fhu genes on the transcriptomic profiles of S. Typhimurium and S. Enteritidis strains with double ( Δ iroN Δ fepA) and triple ( Δ fhu Δ iroN Δ fepA) gene deletions and grown under iron-deficient conditions. Significant changes were observed in the expression of genes involved in virulence, stress-response, and energy metabolism in both Salmonella serovars. The pathways most affected were the tricarboxylic acid cycle, electron transport chain, and stress responses all of which were downregulated while cysteine biosynthesis was upregulated. In general, common and/or related genes were affected in the double and triple mutant strains which indicated that the additional deletion of the fhu gene affected a limited number of genes, although S. Typhimurium showed more differences than S. Enteritidis. Nevertheless, the main difference observed between the mutants of the two serovars grown under iron-depleted conditions is the enhanced upregulation of the flagellum genes in S. Enteritidis. This study reveals evidence of novel interactions between metabolic pathways and virulence under iron limitation, providing insights into adaptive strategies that may contribute to the enhanced virulence of these Salmonella serovars.
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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) | 0.000 | 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".