Microenvironment of ruptured cerebral aneurysms discovered using data driven analysis of gene expression

Abstract

Background: It is well known that ruptured intracranial aneurysms are associated with substantial morbidity and mortality, yet our understanding of the genetic mechanisms of rupture remains poor. We hypothesize that applying novel techniques to the genetic analysis of aneurysmal tissue will yield key rupture-associated mechanisms and novel drug candidates for the prevention of rupture.

Methods: We applied weighted gene co-expression networks (WGCNA) and population-specific gene expression analysis (PSEA) to transcriptomic data from 33 ruptured and unruptured aneurysm domes. Mechanisms were annotated using Gene Ontology, and gene network/population-specific expression levels correlated with rupture state. We then used computational drug repurposing to identify plausible drug candidates for the prevention of aneurysm rupture.

Results: Network analysis of bulk tissue identified multiple immune mechanisms to be associated with aneurysm rupture. Targeting these processes with computational drug repurposing revealed multiple candidates for preventing rupture including Btk inhibitors and modulators of hypoxia inducible factor. In the macrophage-specific analysis, we identify rupture-associated mechanisms MHCII antigen processing, cholesterol efflux, and keratan sulfate catabolism. These processes map well onto several of highly ranked drug candidates, providing further validation.

Conclusions: Our results are the first to demonstrate population-specific expression levels and intracranial aneurysm rupture, and propose novel drug candidates based on network-based transcriptomics.

Fig 1. Module-based analysis reveals differences between immune processes in ruptured vs unruptured aneurysms.

A: Dendrogram demonstrating the taxonomic relationship between genes with the y-axis representing gene dissimilarity based on the TOM metric. Colour bar representing genes grouped into modules. Grey interspacing represents unclassified genes. B: Boxplots of module meta-gene expression for significant modules which annotate to immune function. Both the blue and red modules (320 and 164 genes, respectively) annotate to inflammation/innate immunity. Similarly, the brown module (29 genes) annotates mostly strongly to Il-1 and TNF, while the lavender module (4 genes) enriches in B cells and phagocytosis predominantly. The horizontal lines represent median value, with the box representing interquartile range (first and third quartile represented by the bottom and top of the box, respectively). Whiskers represent the data range, with a maximum extension of 1.5 times the interquartile range. Values falling outside this range are considered outliers and represented by small circles.

Fig 2. PSEA genes map to expected biological processes in DAVID.

Barplots of the top 5 biological processes from each reference cell type, ranked by Bonferroni p-value. Endothelial and mast cell references did not yield any significant annotations in either cohort.

Fig 3. GoSemSim reveals differences in biological mechanisms between ruptured and unruptured aneurysms.

A and B: Balloon plots of significant GO term similarity measure (S) for macrophage (A) and T cell (B) populations, with terms of ruptured and unruptured aneurysms represented as columns and rows, respectively. Dot size is proportional to S, and green dots represent high similarity (S > 0.8) while red dots represent low similarity (S<0.2). Red boxes highlight processes which have low association with the GO terms of the opposite phenotype (i.e. C_i for which S(A_i,x)<0.2 for any x in D, where C and D represent phenotype).