Fep1, an Iron Sensor Regulating Iron Transporter Gene Expression in Schizosaccharomyces pombe
Bibliographic record
Abstract
Schizosaccharomyces pombe cells acquire iron under high affinity conditions through the action of a cell surface ferric reductase encoded by the frp1(+) gene and a two-component iron-transporting complex encoded by the fip1(+) and fio1(+) genes. When cells are grown in the presence of iron, transcription of all three genes is blocked. A conserved regulatory element, 5'-(A/T)GATAA-3', located upstream of the frp1(+), fip1(+), and fio1(+) genes, is necessary for iron repression. We have cloned a novel gene, termed fep1(+), which encodes an iron-sensing transcription factor. Binding studies reveal that the putative DNA binding domain of Fep1 expressed as a fusion protein in Escherichia coli specifically interacts with the 5'-(A/T)GATAA-3' sequence in an iron-dependent manner. In a fep1 Delta mutant strain, the fio1(+) gene is highly expressed and is unregulated by iron. Furthermore, the fep1 Delta mutation increases activity of the cell surface iron reductase and renders cells hypersensitive to the iron-dependent free radical generator phleomycin. Mutations in the transcriptional co-repressors tup11(+) and tup12(+) are phenocopies to fep1(+). Indeed, strains with both tup11 Delta and tup12 Delta deletions fail to sense iron. This suggests that in the presence of iron and Fep1, the Tup11 and Tup12 proteins may act as co-repressors for down-regulation of genes encoding components of the reductive iron transport machinery.
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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 | 0.001 |
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".