Coordination of the Fe-S cluster biogenesis network by the sRNA RyhB in <i>E. coli</i>
Bibliographic record
Abstract
Iron (Fe) plays critical roles as enzyme cofactor involved in key biological processes but can also lead to toxicity by catalysing the formation of highly damaging reactive oxygen species. To stabilize Fe and perform catalysis, most organisms rely on Fe-S clusters, which are fundamental and evolutionary ancient cofactors. In E. coli, two distinct pathways for the biosynthesis of Fe-S cluster exist: the three-part iscR-SUA-hscBA-fdx-iscX (ISC-HSC) operon and the sufABCDSE (SUF) operon. The iscR-SUA section of the ISC-HSC operon is regulated at the promoter level by the IscR transcription factor and post-transcriptionally by the small RNA (sRNA) RyhB. The SUF operon is regulated by a combination of transcription factors, including the Fe-sensing Fur, the Fe-S using IscR, and the oxidative stress responsive OxyR. Here, we show evidence that the sRNA RyhB regulates the hscBA-fdx-iscX part of the ISC-HSC operon as well as part of the SUF operon. RyhB orchestrates a complex pattern of expression of the iscR-SUA-hscBA-fdx-iscX operon during Fe starvation. This results in increased level of iscR and constant expression of iscSUA, encoding the scaffold for Fe-S cluster formation. However, the third part of the operon, hscBA-fdx-iscX, encoding a chaperone that facilitates Fe-S cluster transfer, is repressed by RyhB during Fe starvation. Furthermore, RyhB represses part of the sufABCDSE transcript, which counteracts Fur derepression. Overall, RyhB represses both ISC and SUF systems under iron starvation, to reduce Fe-S biogenesis under such limiting conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".