Genomic Profiling of Iron-Responsive Genes in <i>Salmonella enterica</i> Serovar Typhimurium by High-Throughput Screening of a Random Promoter Library
Bibliographic record
Abstract
The importance of iron to bacteria is shown by the presence of numerous iron-scavenging and transport systems and by many genes whose expression is tightly regulated by iron availability. We have taken a global approach to gene expression analysis of Salmonella enterica serovar Typhimurium in response to iron by combining efficient, high-throughput methods with sensitive, luminescent reporting of gene expression using a random promoter library. Real-time expression profiles of the library were generated under low- and high-iron conditions to identify iron-regulated promoters, including a number of previously identified genes. Our results indicate that approximately 7% of the genome may be regulated directly or indirectly by iron. Further analysis of these clones using a Fur titration assay revealed three separate classes of genes; two of these classes consist of Fur-regulated genes. A third class was Fur independent and included both negatively and positively iron-responsive genes. These may reflect new iron-dependent regulons. Iron-responsive genes included iron transporters, iron storage and mobility proteins, iron-containing proteins (redox proteins, oxidoreductases, and cytochromes), transcriptional regulators, and the energy transducer tonB. By identifying a wide variety of iron-responsive genes, we extend our understanding of the global effect of iron availability on gene expression in the bacterial cell.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".