Critical Genomic Networks and Vasoreactive Variants in Idiopathic Pulmonary Arterial Hypertension
Bibliographic record
Abstract
RATIONALE: Idiopathic pulmonary arterial hypertension (IPAH) is usually without an identified genetic cause, despite clinical and molecular similarity to bone morphogenetic protein receptor type 2 mutation-associated heritable pulmonary arterial hypertension (PAH). There is phenotypic heterogeneity in IPAH, with a minority of patients showing long-term improvement with calcium channel-blocker therapy. OBJECTIVES: We sought to identify gene variants (GVs) underlying IPAH and determine whether GVs differ in vasodilator-responsive IPAH (VR-PAH) versus vasodilator-nonresponsive IPAH (VN-PAH). METHODS: We performed whole-exome sequencing (WES) on 36 patients with IPAH: 17 with VR-PAH and 19 with VN-PAH. Wnt pathway differences were explored in human lung fibroblasts. MEASUREMENTS AND MAIN RESULTS: We identified 1,369 genes with 1,580 variants unique to IPAH. We used a gene ontology approach to analyze variants and identified overrepresentation of several pathways, including cytoskeletal function and ion binding. By mapping WES data to prior genome-wide association study data, Wnt pathway genes were highlighted. Using the connectivity map to define genetic differences between VR-PAH and VN-PAH, we found enrichment in vascular smooth muscle cell contraction pathways and greater genetic variation in VR-PAH versus VN-PAH. Using human lung fibroblasts, we found increased stimulated Wnt activity in IPAH versus controls. CONCLUSIONS: A pathway-based analysis of WES data in IPAH demonstrated multiple rare GVs that converge on key biological pathways, such as cytoskeletal function and Wnt signaling pathway. Vascular smooth muscle contraction-related genes were enriched in VR-PAH, suggesting a potentially different genetic predisposition for VR-PAH. This pathway-based approach may be applied to next-generation sequencing data in other diseases to uncover the contribution of unexpected or multiple GVs to a phenotype.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".