Stress-Induced Iron-Sulfur Cluster Damage as a Conserved Trigger of the Stringent Response
Bibliographic record
Abstract
Abstract Pathogenic bacteria rely on the stringent response to adapt to the complex and fluctuating conditions encountered within the host. However, the mechanisms by which the stringent response senses host-induced stress remain poorly understood. Here, we identify iron–sulfur (Fe–S) cluster damage as a conserved trigger of the stringent response in major Gram-negative pathogens, including Salmonella enterica , Enterobacter cloacae , and Klebsiella pneumoniae . We demonstrate that Fe–S cluster disruption—caused by oxidative stress or metal imbalance—restricts the intracellular pools of sulfur-containing and branched-chain amino acids, thereby activating the ribosome-associated (p)ppGpp synthetase RelA. Furthermore, we show that iron availability governs recovery from Fe–S cluster damage, modulating the dynamics of the stringent response. Finally, we emphasize the dual role of (p)ppGpp in transcriptional regulation, enhancing bacterial fitness during Fe–S cluster stress while simultaneously promoting virulence by upregulating the SPI-2 type III secretion system. Together, these findings uncover a conserved mechanism by which pathogenic bacteria integrate metabolic stress into adaptive gene regulation and virulence, positioning Fe–S cluster integrity as a pivotal node linking environmental sensing to transcriptional control during infection.
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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 | 0.000 |
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".