MNK-driven eIF4E phosphorylation regulates the fibrogenic transformation of mesenchymal cells and chronic lung allograft dysfunction
Bibliographic record
Abstract
Tissue fibrosis remains unamenable to meaningful therapeutic interventions and is the primary cause of chronic graft failure after organ transplantation. Eukaryotic translation initiation factor (eIF4E), a key translational regulator, serves as convergent target of multiple upstream profibrotic signaling pathways that contribute to mesenchymal cell (MC) activation. Here, we investigate the role of MAP kinase-interacting serine/threonine kinase-induced (MNK-induced) direct phosphorylation of eIF4E at serine 209 (Ser209) in maintaining fibrotic transformation of MCs and determine the contribution of the MNK/eIF4E pathway to the pathogenesis of chronic lung allograft dysfunction (CLAD). MCs from patients with CLAD demonstrated constitutively higher eIF4E phosphorylation at Ser209, and eIF4E phospho-Ser209 was found to be critical in regulating key fibrogenic protein autotaxin, leading to sustained β-catenin activation and profibrotic functions of CLAD MCs. MNK1 signaling was upregulated in CLAD MCs, and genetic or pharmacologic targeting of MNK1 activity inhibited eIF4E phospho-Ser209 and profibrotic functions of CLAD MCs in vitro. Treatment with an MNK1/2 inhibitor (eFT-508) abrogated allograft fibrosis in an orthotopic murine lung-transplant model. Together these studies identify what we believe is a previously unrecognized MNK/eIF4E/ATX/β-catenin signaling pathway of fibrotic transformation of MCs and present the first evidence, to our knowledge, for the utility of MNK inhibitors in fibrosis.
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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) | 0.000 | 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".