Shear stress unveils patient-specific transcriptional signatures in PAH: Towards personalized molecular diagnostics
Bibliographic record
Abstract
Rationale: Pulmonary arterial hypertension (PAH) is a life-threatening disorder characterized by increased pulmonary blood pressures and regional inhomogeneities in flows, with diagnostic and treatment challenges arising from diverse underlying pathogenic mechanisms.Conventional in vitro models often obscure the mechanistic nuances of PAH by failing to replicate the dynamic mechanical environment of the diseased lung, limiting the identification of specific molecular patterns.To address this, we employed an in vitro shear stress model simulating physiological or pathological conditions to explore the transcriptional heterogeneity of human pulmonary microvascular endothelial cells (hPMECs) from PAH patients and healthy controls within their respective biomechanical context.Methods & Results: hPMECs from PAH patients and controls were exposed to static, low shear stress (LSS), and high shear stress (HSS) conditions, followed by bulk RNA-sequencing.While increasing shear stress resulted in a greater number of differentially expressed genes, traditional grouped analysis showed minimal overall transcriptional differences.Further, pathway enrichment analysis indicated common shear-induced responses in both groups, suggesting that standard analysis methods may mask meaningful disease-specific changes.Crucially, detailed dimensionality reduction analyses revealed pronounced inter-patient variability among PAH donors in response to increasing shear stress, facilitating the identification of 398 genes driving this transcriptional heterogeneity.Unsupervised clustering of these high-variability genes enabled the sub-classification of patients based on their unique transcriptomic profiles, each linked to specific combinations of PAH associated pathogenic pathways such as mesenchymal transition, inflammation, metabolism, extracellular matrix remodeling, and cell cycle/DNA damage signaling.Importantly, re-analysis of published peripheral blood mononuclear cell (PBMC) omics data from PAH patients Ivyspring International
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".