Fra2 Is a Co-Regulator of Fep1 Inhibition in Response to Iron Starvation
Bibliographic record
Abstract
Iron is required for several metabolic functions involved in cellular growth. Although several players involved in iron transport have been identified, the mechanisms by which iron-responsive transcription factors are controlled are still poorly understood. In Schizosaccharomyces pombe, the Fep1 transcription factor represses genes involved in iron acquisition in response to high levels of iron. In contrast, when iron levels are low, Fep1 becomes inactive and loses its ability to associate with chromatin. Although the molecular basis by which Fep1 is inactivated under iron starvation remains unknown, this process requires the monothiol glutaredoxin Grx4. Here, we demonstrate that Fra2 plays a role in the negative regulation of Fep1 activity. Disruption of fra2+ (fra2Δ) led to a constitutive repression of the fio1+ gene transcription. Fep1 was consistently active and constitutively bound to its target gene promoters in cells lacking fra2+. A constitutive activation of Fep1 was also observed in a php4Δ fra2Δ double mutant strain in which the behavior of Fep1 is freed of its transcriptional regulation by Php4. Microscopic analyses of cells expressing a functional Fra2-Myc13 protein revealed that Fra2 localized throughout the cells with a significant proportion of Fra2 being observed within the nuclei. Further analysis by coimmunoprecipitation showed that Fra2, Fep1 and Grx4 are associated in a heteroprotein complex. Bimolecular fluorescence complementation experiments brought further evidence that an interaction between Fep1 and Fra2 occurs in the nucleus. Taken together, results reported here revealed that Fra2 plays a role in the Grx4-mediated pathway that inactivates Fep1 in response to iron deficiency.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".