Interdependency of regulatory effects of iron and riboflavin in the foodborne pathogen <i>Shigella flexneri</i> determined by integral transcriptomics
Bibliographic record
Abstract
Shigella flexneri is the causative agent of dysentery. For pathogens, iron is a critical micronutrient as its bioavailability is usually low in bacterial niches. This metal is involved in critical physiological processes mainly as a component of important metabolic molecules involved in redox reactions. Usually bacteria respond to fluctuations in iron availability to regulate iron acquisition and other iron-related functions. Recently the close metabolic feedback between iron and riboflavin, another pivotal biological redox agent, began to draw attention in bacteria. This is a widespread biological phenomenon, partly characterized by the coordination of regulatory responses to iron and riboflavin, probably owed to the involvement of these cofactors in common processes. Nonetheless, no systematic analyses to determine the extent of this regulatory effect have been performed in any species. Here, the transcriptomics responses to iron, riboflavin, iron in the presence of riboflavin and riboflavin in the presence of iron were assessed and compared in S. flexneri . The riboflavin regulon had a 43% overlap with the iron regulon. Notably, the presence of riboflavin highly increased the number of iron-responsive genes. Reciprocally, iron drastically changed the pool of riboflavin-responsive genes. Gene ontology (GO) functional terms enrichment analysis showed that biological processes were distinctively enriched for each subgroup of responsive genes. Among the biological processes regulated by iron and riboflavin were iron uptake, amino acids metabolism and electron transfer for ATP synthesis. Thus, iron and riboflavin highly affect the transcriptomics responses induced by each other in S. flexneri . GO terms analysis suggests that iron and riboflavin coordinately regulate specific physiological functions involving redox metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".