bioRxivCell BiologyPreprint
The Liver is an Inflammatory Mediator of Pulmonary Arterial Hypertension
Navneet SinghiD, Jordan Lawson, Ashok RagavendraniD, Somanshu BanerjeeiD, Andy Hon, Alejandro Vega, Jason S. HongiD, Christopher J. Mullin, Mandy G. PereiraiD, Allyson Sherman-Roe, Alexander T. Jorrin, Tiffaney Cayton, Gregory A. FishbeiniD, James R. KlingeriD, William M. OldhamiD, Zhiyu DaiiD, Michael Fallon, Elizabeth O. HarringtoniD, Olin D. LiangiD, Soban UmariD, Corey E. VentetuoloiD
Abstract
The livers contribution to pulmonary arterial hypertension (PAH) pathogenesis remains unclear. We hypothesized that the liver promotes inflammatory injury to the pulmonary endothelium. PAH patients without liver disease with pulmonary artery endothelial cell (PAEC) biopsies were included. Liver serologies and imaging were analyzed by unsupervised classification and regression tree (CART) to identify subclinical liver dysfunction clusters. Two machine-learning models predicted cluster assignment and informed differential expression. PAEC transcriptomes were compared to liver and lung data from monocrotaline and Sugen-Hypoxia rats. Liver fibrosis was assessed in rat and human PAH livers. Among 25 PAH patients (76% female, median age 61 [30 - 84] years), CART identified clusters distinguished by Model for End-Stage Liver Disease Sodium (MELD-Na) [≥]12, predicting higher pulmonary vascular resistance ({beta}=0.5 Wood units per point increase in MELD-Na, 95% CI 0.2-0.8, p=0.005) after adjustment for right atrial pressure. Subjects with MELD-Na [≥]12 had decreased 6-minute walk distance (353 [120 - 576] m vs. 411[300 - 600] m, p=0.03), with upregulation of apelin, beta-catenin, and immune signaling. Rat lung ECs demonstrated survival and hepatic growth-factor signaling, while rat livers showed immune activation. Rat (20.8 vs 16.6 % area stained, p=0.09) and human PAH livers revealed fibrosis despite absent right ventricular failure, supporting a pathogenic lung-liver axis in PAH.
- organism: human
- organism: rat
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: machine learning (classical)
- Citations
- 2
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