Identification and Characterization of <i>fhuD1</i> and <i>fhuD2</i> , Two Genes Involved in Iron-Hydroxamate Uptake in <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Staphylococcus aureus can utilize several hydroxamate siderophores for growth under iron-restricted conditions. Previous findings have shown that S. aureus possesses a cytoplasmic membrane-associated traffic ATPase that is involved in the specific transport of iron(III)-hydroxamate complexes. In this study, we have identified two additional genes, termed fhuD1 and fhuD2, whose products are involved in this transport process in S. aureus. We have shown that fhuD2 codes for a posttranslationally modified lipoprotein that is anchored in the cytoplasmic membrane, while the deduced amino acid sequence predicts the same for fhuD1. The predicted FhuD1 and FhuD2 proteins share 41.0% identity and 56.4% total similarity with each other, 45.9 and 49.1% total similarity with the FhuD homolog in Bacillus subtilis, and 29.3 and 24.6% total similarity with the periplasmic FhuD protein from Escherichia coli. Insertional inactivation and gene replacement of both genes showed that while FhuD2 is involved in the transport of iron(III) in complex with ferrichrome, ferrioxamine B, aerobactin, and coprogen, FhuD1 shows a more limited substrate range, capable of only iron(III)-ferrichrome and iron(III)-ferrioxamine B transport in S. aureus. Nucleotide sequences present upstream of both fhuD1 and fhuD2 predict the presence of consensus Fur binding sequences. In agreement, transcription of both genes was negatively regulated by exogenous iron levels through the activity of the S. aureus Fur protein.
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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 | 0.001 |
| 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 | 0.000 |
| 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 | 0.001 |
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".