ABSTRACT
BACKGROUND: Risk assessment in pulmonary arterial hypertension (PAH) is fundamental to guiding treatment and improved outcomes. Clinical models are excellent at identifying high-risk patients but leave uncertainty amongst moderate risk patients. RESEARCH QUESTION: Can a multiple blood biomarker model of PAH, using previously described biomarkers, improve risk discrimination over current models? STUDY DESIGN AND METHODS: Using multiplex ELISA, we measured NT-proBNP, ST2, IL-6, Endostatin, Galectin-3, HDGF, and IGF binding proteins (IGFBP1-7) in train (n=1623), test (n=696) and validation (n=237) cohorts. Clinical variables, biomarkers were evaluated by principal component analysis. NT-proBNP was not included to develop an NT-proBNP independent model. Unsupervised k-means clustering classified subjects into clusters. Transplant-free survival by cluster was examined using Kaplan-Meier and Cox proportional hazard regressions. Hazard by cluster was compared to NT-proBNP, REVEAL, and ESC/ERS Risk models alone, and combined clinical and biomarker models. RESULTS: The algorithm generated 5 clusters with good risk discrimination using 6 biomarkers, weight, height, and age at PAH diagnosis. In the test and validation cohorts the biomarker model alone performed equivalent to REVEAL (AUC 0.74). Adding the biomarker model to the ESC/ERS, and REVEAL scores improved the ESC/ERS and REVEAL scores. The best overall model was the biomarker model adjusted for NT-proBNP with the best C-statistic, AIC, and calibration for the adjusted model compared to either the biomarker or NT-proBNP model alone. INTERPRETATION: A multi-biomarker model alone was equivalent to current PAH clinical mortality risk prediction models and improved performance when combined, and added to NT-proBNP. Clinical risk scores offer excellent predictive models but require multiple tests; adding blood biomarkers to models can improve prediction or enable more frequent, non-invasive monitoring of risk in PAH to support therapeutic decision making.
ABSTRACT
BACKGROUND: The ubiquitin-proteasome system regulates protein degradation and the development of pulmonary arterial hypertension (PAH), but knowledge about the role of deubiquitinating enzymes in this process is limited. UCHL1 (ubiquitin carboxyl-terminal hydrolase 1), a deubiquitinase, has been shown to reduce AKT1 (AKT serine/threonine kinase 1) degradation, resulting in higher levels. Given that AKT1 is pathological in pulmonary hypertension, we hypothesized that UCHL1 deficiency attenuates PAH development by means of reductions in AKT1. METHODS: Tissues from animal pulmonary hypertension models as well as human pulmonary artery endothelial cells from patients with PAH exhibited increased vascular UCHL1 staining and protein expression. Exposure to LDN57444, a UCHL1-specific inhibitor, reduced human pulmonary artery endothelial cell and smooth muscle cell proliferation. Across 3 preclinical PAH models, LDN57444-exposed animals, Uchl1 knockout rats (Uchl1-/-), and conditional Uchl1 knockout mice (Tie2Cre-Uchl1fl/fl) demonstrated reduced right ventricular hypertrophy, right ventricular systolic pressures, and obliterative vascular remodeling. Lungs and pulmonary artery endothelial cells isolated from Uchl1-/- animals exhibited reduced total and activated Akt with increased ubiquitinated Akt levels. UCHL1-silenced human pulmonary artery endothelial cells displayed reduced lysine(K)63-linked and increased K48-linked AKT1 levels. RESULTS: Supporting experimental data, we found that rs9321, a variant in a GC-enriched region of the UCHL1 gene, is associated with reduced methylation (n=5133), increased UCHL1 gene expression in lungs (n=815), and reduced cardiac index in patients (n=796). In addition, Gadd45α (an established demethylating gene) knockout mice (Gadd45α-/-) exhibited reduced lung vascular UCHL1 and AKT1 expression along with attenuated hypoxic pulmonary hypertension. CONCLUSIONS: Our findings suggest that UCHL1 deficiency results in PAH attenuation by means of reduced AKT1, highlighting a novel therapeutic pathway in PAH.