Multi-omics dissection of high TWAS-active endothelial pathogenesis in pulmonary arterial hypertension: bridging single-cell heterogeneity, machine learning-driven biomarkers, and developmental reprogramming.

Abstract

Background: Pulmonary Arterial Hypertension (PAH) is a leading cause of cardiovascular-related mortality worldwide. The emergence of single-cell RNA sequencing (scRNA-seq) has enhanced the ability to dissect cellular heterogeneity in PAH at a granular level. Transcriptome-wide association studies (TWAS) leverage expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) data to identify novel susceptibility genes whose genetically predicted expression correlates with disease risk. However, no study has systematically integrated TWAS with scRNA-seq to unravel the pathogenesis of PAH at single-cell resolution.

Methods: Using TWAS analysis, we identified a set of candidate genes genetically associated with PAH. We then evaluated the differential activity of these genes across PAH cell types at single-cell resolution using AUCell, Ucell, ssGSEA, and AddModuleScore algorithms. A subset of endothelial cells exhibiting elevated TWAS activity was identified via quartile-based stratification and designated as the high TWAS activity state (HTS) group. Multi-dimensional analyses, including observed-to-expected ratio (RO/E), CellChat, CytoTRACE, and scMetabolism, were employed to characterize the functional and communicative properties of HTS cells. Machine learning algorithms were integrated to identify signature genes of the HTS subpopulation, and a benchmarked random forest model was trained to predict HTS status. We performed immunohistochemistry and qRT-PCR validation of the signature genes (KLF2, RASIP1 and DEPP1) in PAH and control lung tissues to support their expression patterns.

Results: We demonstrated that HTS endothelial cells are strongly associated with PAH pathogenesis, exhibiting significant tissue tropism, enhanced roles in intercellular communication, and a progenitor-like function in endothelial differentiation. Machine learning-based feature selection revealed three robust signature genes: KLF2, RASIP1, and DEPP1. These genes demonstrated exceptional predictive power for identifying HTS cells, suggesting their potential as drivers of endothelial dysfunction in PAH. The random forest model, benchmarked against multiple algorithms, achieved high accuracy in predicting PAH progression using these genes. Immunohistochemical analysis of pulmonary artery and qRT-PCR result of lung tissues addressed the elevated expression of KLF2, RASIP1 and DEPP1 in arterial wall post-PAH.

Conclusion: This study elucidates endothelial cell heterogeneity in PAH and establishes the central role of HTS cells in disease progression, cellular crosstalk, and developmental reprogramming. Our findings bridge the gap between GWAS and scRNA-seq methodologies and provide a transformative framework for understanding PAH mechanisms.