Grx4 Monothiol Glutaredoxin Is Required for Iron Limitation-Dependent Inhibition of Fep1
Bibliographic record
Abstract
The expression of iron transport genes in Schizosaccharomyces pombe is controlled by the Fep1 transcription factor. When iron levels exceed those needed by the cells, Fep1 represses iron transport genes. In contrast, Fep1 is unable to bind chromatin under low-iron conditions, and that results in activation of genes involved in iron acquisition. Studies of fungi have revealed that monothiol glutaredoxins are required to inhibit iron-dependent transcription factors in response to high levels of iron. Here, we show that the monothiol glutaredoxin Grx4 plays an important role in the negative regulation of Fep1 activity in response to iron deficiency. Deletion of the grx4(+) gene led to constitutive promoter occupancy by Fep1 and caused an invariable repression of iron transport genes. We found that Grx4 and Fep1 physically interact with each other. Grx4 contains an N-terminal thioredoxin (TRX)-like domain and a C-terminal glutaredoxin (GRX)-like domain. Deletion mapping analysis revealed that the TRX domain interacts strongly and constitutively with the C-terminal region of Fep1. As opposed to the TRX domain, the GRX domain associates weakly and in an iron-dependent manner with the N-terminal region of Fep1. Further analysis showed that Cys35 of Grx4 is required for the interaction between the Fep1 C terminus and the TRX domain, whereas Grx4 Cys172 is necessary for the association between the Fep1 N terminus and the GRX domain. Our results describe the first example of a monothiol glutaredoxin that acts as an inhibitory partner for an iron-regulated transcription factor under conditions of low iron levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".