BACKGROUNDCerebral cavernous angiomas (CAs) with a symptomatic hemorrhage (CASH) have a high risk of recurrent hemorrhage and serious morbidity.METHODSEighteen plasma molecules with mechanistic roles in CA pathobiology were investigated in 114 patients and 12 healthy subjects. The diagnostic biomarker of a CASH in the prior year was derived as that minimizing the Akaike information criterion and validated using machine learning, and was compared with the prognostic CASH biomarker predicting bleeding in the subsequent year. Biomarkers were longitudinally followed in a subset of cases. The biomarkers were queried in the lesional neurovascular unit (NVU) transcriptome and in plasma miRNAs from CASH and non-CASH patients.RESULTSThe diagnostic CASH biomarker included a weighted combination of soluble CD14 (sCD14), VEGF, C-reactive protein (CRP), and IL-10 distinguishing CASH patients with 76% sensitivity and 80% specificity (P = 0.0003). The prognostic CASH biomarker (sCD14, VEGF, IL-1β, and sROBO-4) was confirmed to predict a bleed in the subsequent year with 83% sensitivity and 93% specificity (P = 0.001). Genes associated with diagnostic and prognostic CASH biomarkers were differentially expressed in CASH lesional NVUs. Thirteen plasma miRNAs were differentially expressed between CASH and non-CASH patients.CONCLUSIONShared and unique biomarkers of recent symptomatic hemorrhage and of future bleeding in CA are mechanistically linked to lesional transcriptome and miRNA. The biomarkers may be applied for risk stratification in clinical trials and developed as a tool in clinical practice.FUNDINGNIH, William and Judith Davis Fund in Neurovascular Surgery Research, Be Brave for Life Foundation, Safadi Translational Fellowship, Pritzker School of Medicine, and Sigrid Jusélius Foundation.

Cavernous angiomas (CA) are common vascular anomalies causing brain hemorrhage. Based on mouse studies, roles of gram-negative bacteria and altered intestinal homeostasis have been implicated in CA pathogenesis, and pilot study had suggested potential microbiome differences between non-CA and CA individuals based on 16S rRNA gene sequencing. We here assess microbiome differences in a larger cohort of human subjects with and without CA, and among subjects with different clinical features, and conduct more definitive microbial analyses using metagenomic shotgun sequencing. Relative abundance of distinct bacterial species in CA patients is shown, consistent with postulated permissive microbiome driving CA lesion genesis via lipopolysaccharide signaling, in humans as in mice. Other microbiome differences are related to CA clinical behavior. Weighted combinations of microbiome signatures and plasma inflammatory biomarkers enhance associations with disease severity and hemorrhage. This is the first demonstration of a sensitive and specific diagnostic microbiome in a human neurovascular disease.

The purpose of this study was to determine important genes, functions, and networks contributing to the pathobiology of cerebral cavernous malformation (CCM) from transcriptomic analyses across 3 species and 2 disease genotypes. Sequencing of RNA from laser microdissected neurovascular units of 5 human surgically resected CCM lesions, mouse brain microvascular endothelial cells, Caenorhabditis elegans with induced Ccm gene loss, and their respective controls provided differentially expressed genes (DEGs). DEGs from mouse and C. elegans were annotated into human homologous genes. Cross-comparisons of DEGs between species and genotypes, as well as network and gene ontology (GO) enrichment analyses, were performed. Among hundreds of DEGs identified in each model, common genes and 1 GO term (GO:0051656, establishment of organelle localization) were commonly identified across the different species and genotypes. In addition, 24 GO functions were present in 4 of 5 models and were related to cell-to-cell adhesion, neutrophil-mediated immunity, ion transmembrane transporter activity, and responses to oxidative stress. We have provided a comprehensive transcriptome library of CCM disease across species and for the first time to our knowledge in Ccm1/Krit1 versus Ccm3/Pdcd10 genotypes. We have provided examples of how results can be used in hypothesis generation or mechanistic confirmatory studies.

Background

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.

Methods

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.

Results

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).

Conclusions

Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.