Critical Genomic Networks and Vasoreactive Variants in Idiopathic Pulmonary Arterial Hypertension

特发性肺动脉高压中的关键基因组网络和血管反应性变异

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作者:Anna R Hemnes, Min Zhao, James West, John H Newman, Stuart Rich, Stephen L Archer, Ivan M Robbins, Timothy S Blackwell, Joy Cogan, James E Loyd, Zhongming Zhao, Christa Gaskill, Christopher Jetter, Jonathan A Kropski, Susan M Majka, Eric D Austin

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.

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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