Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma

• Published in: Communications medicine Vol. 3; no. 1; p. 35
• Main Authors: Srinath, Abhinav, Xie, Bingqing, Li, Ying, Sone, Je Yeong, Romanos, Sharbel, Chen, Chang, Sharma, Anukriti, Polster, Sean, Dorrestein, Pieter C, Weldon, Kelly C, DeBiasse, Dorothy, Moore, Thomas, Lightle, Rhonda, Koskimäki, Janne, Zhang, Dongdong, Stadnik, Agnieszka, Piedad, Kristina, Hagan, Matthew, Shkoukani, Abdallah, Carrión-Penagos, Julián, Bi, Dehua, Shen, Le, Shenkar, Robert, Ji, Yuan, Sidebottom, Ashley, Pamer, Eric, Gilbert, Jack A, Kahn, Mark L, D'Souza, Mark, Sulakhe, Dinanath, Awad, Issam A, Girard, Romuald
• Format: Journal Article
• Language: English
• Published: England Springer Nature B.V 03.03.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Biomarkers; Chromatography; Disease; Endothelium; Hemorrhage; Kinases; Mass spectrometry; Metabolites; MicroRNAs; Mutation; Plasma; Proteins; Scientific imaging; United States > US
• ISSN: 2730-664X; 2730-664X
• DOI: 10.1038/s43856-023-00265-1

Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage. The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis (p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage. Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity). Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.