Walejko, Jacquelyn M.

Objectives: Pulmonary arterial hypertension (PAH) is a deadly disease without any effective cure. Identification of the PAH-associated gut microbiome may shift conceptual paradigms of the disease that could lead to new therapeutic advancement. We tested the hypothesis that PAH patients have distinct gut microbiome composition contributing to PAH that predicts the disease. Methods: A total of 71 participants were recruited. Fecal microbiomes of 18 type 1 PAH patients (mean pulmonary arterial pressure [mPAP] 57.4±16.7 mmHg) and 13 reference subjects were compared by shotgun metagenomics methods. Plasma from 22 PAH patients (mPAP 48.3± 18.4 mmHg) and 18 reference subjects was used in untargeted metabolomic analysis. Results: Significant taxonomic and functional changes in the microbiome of the PAH cohort were observed. PAH patients had increased gut barrier dysfunction and inflammation as evidenced by plasma markers and metagenomics. A random forest model predicted PAH from gut microbiome composition with 82.6% accuracy. Changes in the gut microbiome were associated with purine metabolism, arginine biosynthesis, trimethylamine production and tryptophan biosynthesis, all previously associated with PAH. Area under the curve of the multi-variate receiver operating characteristics (ROC) plot for metabolites of these pathways had a predictive accuracy of 0.965. Conclusions: The gut microbiome/metabolites of PAH patients predict disease in random forest models and ROC analyses. The unique PAH gut microbiome influences carnitine, arginine and purine metabolisms contributing to pathophysiology. These highlight previously unknown roles of gut bacteria and their metabolites in PAH that could lead to new therapeutic or management paradigms.

Objectives: Pulmonary arterial hypertension (PAH) is a cardiopulmonary disease without effective cure. The objective of this study is to test the hypothesis that PAH patients have a distinct gut microbiome composition acting as a driving force for the increased PAH metabolic biomarkers, acyl carnitines and urate. Methods: Fecal samples from 18 type 1 PAH patients (mean pulmonary arterial pressure [mPAP] 57.4±16.7 mmHg) and 13 reference subjects in Brazil were collected for comparison of microbiome using shotgun metagenomics. Plasma samples from 33 type I PAH patients (mPAP 51.7± 13.5 mmHg) and 19 reference subjects in the USA were used for untargeted metabolomic analysis. Results: More than 80 bacterial taxa were differentially enriched in the microbiomes of the PAH cohort. Random forest machine-learning algorithm predicted PAH patients with 82.4% accuracy from their gut microbiome composition. The most increased taxa in the PAH microbiome were trimethylamine (TMA) producing bacteria like Collinsella (P=0.0037). The PAH metabolome also showed increases of gut microbiome-derived short chain acyl carnitines like isubutyrylcarnitine (P=0.010) and valerylcarnitine (P=0.030), which are the precursors of TMA. In addition, increased urate (P=0.0004) and allantoin (P=0.027) in PAH plasma correlated with significantly increased enzymes producing urate in the PAH microbiome (purine metabolism (P=0.017) and xanthine oxidase (P<0.05)). Conclusion: These findings suggest that the random forest modeling of the gut microbiome composition can be used for prediction of PAH and that the unique gut microbiome of PAH influences acyl carnitines and urate metabolism to contribute to pathophysiology.