Translational control of human neutrophil responses by MNK1
Bibliographic record
Abstract
A growing number of inflammatory and immune processes in vivo have been shown to be influenced by neutrophil-derived cytokines. Whereas the underlying transcriptional mechanisms are increasingly well understood, the translational regulation of this neutrophil response remains largely unexplored. Here, we show that the MNK1, which participates in translational control in several cell types, is activated in response to physiological neutrophil agonists (LPS, TNF-α) in the cytoplasmic and nuclear compartments. With the use of various pharmacological inhibitors, we found that MNK1 activation takes place downstream of the TAK1-p38 MAPK axis in neutrophils, whereas the MEK/ERK, JNK, PI3K, and PKC pathways are not involved. Pharmacological blockade of MNK1, as well as overexpression experiments, established that cytokine protein synthesis (but not gene expression) is under the control of MNK1 in neutrophils. Likewise, MNK1 inhibition reversed the antiapoptotic effect of LPS and TNF-α in neutrophils, and this was accompanied by a decreased expression of the antiapoptotic protein Mcl-1. Thus, MNK1 appears to be an important regulator of neutrophil responses. Although MNK1 inhibition did not affect protein recruitment to mRNA caps, it decreased the phosphorylation of molecules implicated in translation initiation control, such as S6K, S6, and hyperphosphorylated 4E-BP1. These molecular targets of MNK1 are shared with those of PI3K in neutrophils, and accordingly, MNK1 inhibition partially impaired the belated PI3K/Akt activation elicited by LPS or TNF in these cells. Given the importance of neutrophils and their products in numerous chronic inflammatory disorders, MNK1 could represent an attractive therapeutic target.
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) | 0.001 | 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 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".