Regulating iron storage and metabolism with RNA: an overview of posttranscriptional controls of intracellular iron homeostasis
Bibliographic record
Abstract
Abstract Iron (Fe) is a double‐edged sword for most living organisms. Although it is essential for the catalytic activity of a large number of enzymes, ferrous iron (Fe2+) becomes cytotoxic in the presence of normal respiratory by‐products such as H2O2. Because of this toxicity, intracellular iron concentrations ought to be regulated by elaborated homeostasis systems that, despite decades of extensive studies, have not yet revealed all of their surprising arrays of mechanistic details. Within the last few years, our understanding of iron metabolism has revealed that posttranscriptional regulation represents a major contribution to iron homeostasis in a host of organisms. While the small RNA RyhB regulates iron homeostasis in bacteria, its functional homolog protein Cth2 performs a similar task in yeasts. Recent advances in the elucidation of the mechanism of action and functions of RyhB have been made in Escherichia coli. In addition, other RyhB‐like small RNAs have been identified in several bacterial species, such as Pseudomonas aeruginosa, Salmonella enterica, Vibrio cholerae, Neisseria meningitidis, and Shigella spp. These recent findings have shed light on the complexity of iron homeostasis. WIREs RNA 2012, 3:26–36. doi: 10.1002/wrna.102 This article is categorized under: Translation > Translation Regulation RNA Turnover and Surveillance > Regulation of RNA Stability Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA in Disease and Development > RNA in Disease
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".