The PSF·p54nrb Complex Is a Novel Mnk Substrate That Binds the mRNA for Tumor Necrosis Factor α
Bibliographic record
Abstract
To identify new potential substrates for the MAP kinase signal-integrating kinases (Mnks), we employed a proteomic approach. The Mnks are targeted to the translational machinery through their interaction with the cap-binding initiation factor complex. We tested whether proteins retained on cap resin were substrates for the Mnks in vitro, and identified one such protein as PSF (the PTB (polypyrimidine tract-binding protein)-associated splicing factor). Mnks phosphorylate PSF at two sites in vitro, and our data show that PSF is an Mnk substrate in vivo. We also demonstrate that PSF, together with its partner, p54(nrb), binds RNAs that contain AU-rich elements (AREs), such as those for proinflammatory cytokines (e.g. tumor necrosis factor alpha (TNFalpha)). Indeed, PSF associates specifically with the TNFalpha mRNA in living cells. PSF is phosphorylated at two sites by the Mnks. Our data show that Mnk-mediated phosphorylation increases the binding of PSF to the TNFalpha mRNA in living cells. These findings identify a novel Mnk substrate. They also suggest that the Mnk-catalyzed phosphorylation of PSF may regulate the fate of specific mRNAs by modulating their binding to PSF.p54(nrb).
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) | 0.000 | 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) | 0.002 | 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".