Role of hypusine signaling in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease. Co-morbid pulmonary hypertension (PH) is common in patients with IPF and contributes to a worse clinical prognosis. Exaggerated resistance to apoptosis, enhanced proliferation and excessive extracellular matrix (ECM) deposition are key endophenotypes observed in both lung fibroblasts (LFs) and pulmonary artery smooth muscle cells (PASMCs) from IPF patients resulting in the development and perpetuation of fibrotic scarring and vascular obliteration. Eukaryotic translation initiation factor 5A (eIF5A) is known to facilitate translation of mRNAs with oncogenic proprieties and containing consecutive proline residues. Strikingly, eIF5A is the only protein that contains the post-translational modification hypusine, which is required for its function. Hypusine formation is catalyzed by the sequential actions of Deoxyhypusine synthase (DHPS) and Deoxyhypusine hydroxylase (DOHH). We hypothesized that hypusine signaling is increased in IPF and promotes parenchymal and vascular remodeling. We demonstrated that DHPS, DOHH, total and hypusinated forms of eIF5A are overexpressed in lungs and isolated LFs of IPF patients and animal models (bleomycin (BLM)-treated mice, BLM+MCT-treated rats). We found that pharmacological inhibition of DHPS (GC7) reduced IPF-LF and IPF-PASMC survival and proliferation (Annexin V, Ki67, EdU incorporation), activation (aSMA, FN, COL1) and migratory capacity (scratch-wound and transwell assays). GC7 or genetic deletion of DHPS reduced TGFb1-induced activation of control LFs (Survivin, aSMA and COL1). Inhibition of DHPS improved fibrosis in human IPF-PCLS. Our data show that hypusine signaling may represent a new therapeutic target in IPF.
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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 | 0.001 |
| 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".