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
Neurovascular Surgery Program, Department of Neurological Surgery, The University of Chicago, 5841S. Maryland Avenue, Chicago, IL, 60637, USA.
Department of Medicine, University of Chicago, Chicago, IL, 60637, USA.
Commun Med (Lond). 2023 Mar 3;3(1):35. 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.
海绵状血管瘤（CA）影响0.5%的人群，易因脑出血导致严重的神经后遗症。在患CA的患者中发现肠道上皮渗漏与宽松的肠道微生物群有关，有利于产生脂质多糖的细菌种类。微小核糖核酸以及反映血管生成和炎症的血浆蛋白水平此前也与CA及有症状性出血的CA相关。
使用液相色谱质谱法评估CA患者和有症状性出血的CA患者的血浆代谢组。使用偏最小二乘判别分析鉴定差异代谢物（p<0.05，经FDR校正）。查询这些代谢物与先前建立的CA转录组、微生物组和差异蛋白之间的相互作用以确定其机制相关性。然后在一个独立的、倾向匹配的队列中验证有症状性出血的CA患者中的差异代谢物。采用机器学习实现的贝叶斯方法整合蛋白质、微小RNA和代谢物，以开发有症状性出血的CA患者的诊断模型。
在此我们鉴定出血浆代谢物，包括区分CA患者的胆酸和次黄嘌呤，而花生四烯酸和亚油酸区分有症状性出血的患者。血浆代谢物与宽松的微生物组基因以及先前涉及的疾病机制相关。区分有症状性出血的CA的代谢物在一个独立的倾向匹配队列中得到验证，并且它们与循环微小RNA水平的整合提高了血浆蛋白生物标志物的性能（敏感性高达85%，特异性高达80%）。
血浆代谢物反映CA及其出血活性。它们的多组学整合模型适用于其他病理情况。
