Nuclear transport of the <i>Neurospora crassa</i> NIT-2 transcription factor is mediated by importin-α
Bibliographic record
Abstract
The Neurospora crassa NIT-2 transcription factor belongs to the GATA transcription factor family and plays a fundamental role in the regulation of nitrogen metabolism. Because NIT-2 acts by accessing DNA inside the nucleus, understanding the nuclear import process of NIT-2 is necessary to characterize its function. Thus, in the present study, NIT-2 nuclear transport was investigated using a combination of biochemical, cellular, and biophysical methods. A complemented strain that produced an sfGFP–NIT-2 fusion protein was constructed, and nuclear localization assessments were made under conditions that favored protein translocation to the nucleus. Nuclear translocation was also investigated using HeLa cells, which showed that the putative NIT-2 nuclear localization sequence (NLS; 915TISSKRQRRHSKS927) was recognized by importin-α and that subsequent transport occurred via the classical import pathway. The interaction between the N. crassa importin-α (NcImpα) and the NIT-2 NLS was quantified with calorimetric assays, leading to the observation that the peptide bound to two sites with different affinities, which is typical of a monopartite NLS sequence. The crystal structure of the NcImpα/NIT-2 NLS complex was solved and revealed that the NIT-2 peptide binds to NcImpα with the major NLS-binding site playing a primary role. This result contrasts other recent studies that suggested a major role for the minor NLS-binding site in importin-α from the α2 family, indicating that both sites can be used for different cargo proteins according to specific metabolic requirements.
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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 | 0.001 | 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".