Signal Transducers and Activators of Transcription-3/Pim1 Axis Plays a Critical Role in the Pathogenesis of Human Pulmonary Arterial Hypertension
Bibliographic record
Abstract
BACKGROUND: Pulmonary artery hypertension (PAH) is a proliferative disorder associated with enhanced pulmonary artery smooth muscle cell proliferation and suppressed apoptosis. The sustainability of this phenotype required the activation of a prosurvival transcription factor like signal transducers and activators of transcription-3 (STAT3) and nuclear factor of activated T cell (NFAT). Because these factors are implicated in several physiological processes, their inhibition in PAH patients could be associated with detrimental effects. Therefore, a better understanding of the mechanism accounting for their expression/activation in PAH pulmonary artery smooth muscle cells is of great therapeutic interest. METHODS AND RESULTS: Using multidisciplinary and translational approaches, we demonstrated that STAT3 activation in both human and experimental models of PAH accounts for the expression of both NFATc2 and the oncoprotein kinase Pim1, which trigger NFATc2 activation. Because Pim1 expression correlates with the severity of PAH in humans and is confined to the PAH pulmonary artery smooth muscle cell, Pim1 was identified as an attractive therapeutic target for PAH. Indeed, specific Pim1 inhibition in vitro decreases pulmonary artery smooth muscle cell proliferation and promotes apoptosis, all of which are sustained by NFATc2 inhibition. In vivo, tissue-specific inhibition of Pim1 by nebulized siRNA reverses monocrotaline-induced PAH in rats, whereas Pim1 knockout mice are resistant to PAH development. CONCLUSION: We demonstrated for the first time that inhibition of the inappropriate activation of STAT3/Pim1 axis is a novel, specific, and attractive therapeutic strategy to reverse PAH.
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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) | 0.002 | 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".