Survivin as a mediator of stiffness-induced cell cycle progression and proliferation of vascular smooth muscle cells.

Stiffened arteries are a pathology of atherosclerosis, hypertension, and coronary artery disease and a key risk factor for cardiovascular disease events. The increased stiffness of arteries triggers a phenotypic switch, hypermigration, and hyperproliferation of vascular smooth muscle cells (VSMCs), leading to neointimal hyperplasia and accelerated neointima formation. However, the mechanism underlying this trigger remains unknown. Our analyses of whole-transcriptome microarray data from mouse VSMCs cultured on stiff hydrogels simulating arterial pathology identified 623 genes that were significantly and differentially expressed (360 upregulated and 263 downregulated) relative to expression in VSMCs cultured on soft hydrogels. Functional enrichment and gene network analyses revealed that these stiffness-sensitive genes are linked to cell cycle progression and proliferation. Importantly, we found that survivin, an inhibitor of apoptosis protein, mediates stiffness-dependent cell cycle progression and proliferation as determined by gene network and pathway analyses, RT-qPCR, immunoblotting, and cell proliferation assays. Furthermore, we found that inhibition of cell cycle progression did not reduce survivin expression, suggesting that survivin functions as an upstream regulator of cell cycle progression and proliferation in response to ECM stiffness. Mechanistically, we found that the stiffness signal is mechanotransduced via the FAK-E2F1 signaling axis to regulate survivin expression, establishing a regulatory pathway for how the stiffness of the cellular microenvironment affects VSMC behaviors. Overall, our findings indicate that survivin is necessary for VSMC cycling and proliferation and plays a role in regulating stiffness-responsive phenotypes.

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood.

After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were "growing" (PAT ≥ 4.6) and 33 were more "stable". After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in "growing" and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish "growing" and "stable" IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology.

BACKGROUND: Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic. OBJECTIVE: To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes. METHODS: We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end RNAseq, and transcriptomes with less than 10 million unique reads were excluded from analysis. Significant differentially expressed genes (DEGs) between subtypes (defined by the Trial of Org 10 172 in Acute Stroke Treatment) were identified by expression analysis in edgeR. Gene ontology enrichment analysis was used to study the biologic differences between stroke etiologies. RESULTS: In all, 38 clot transcriptomes were analyzed; 6 from large artery atherosclerosis (LAA), 21 from cardioembolism (CE), 5 from strokes of other determined origin, and 6 from cryptogenic strokes. Among all comparisons, there were 816 unique DEGs, 174 of which were shared by at least two comparisons, and 20 of which were shared by all three. Gene ontology analysis showed that CE clots reflected high levels of inflammation, LAA clots had greater oxidoreduction and T-cell processes, and clots of other determined origin were enriched for aberrant platelet and hemoglobin-related processes. Principal component analysis indicated separation between these subtypes and showed cryptogenic samples clustered among several different groups. CONCLUSIONS: Expression profiles of stroke clots were identified between stroke etiologies and reflected different biologic responses. Cryptogenic thrombi may be related to multiple etiologies.

Profiling of Circulating Gene Expression Reveals Molecular Signatures Associated with Intracranial Aneurysm Rupture Risk.

BACKGROUND: Following detection, rupture risk assessment for intracranial aneurysms (IAs) is critical. Towards molecular prognostics, we hypothesized that circulating blood RNA expression profiles are associated with IA risk. METHODS: We performed RNA sequencing on 68 blood samples from IA patients. Here, patients were categorized as either high or low risk by assessment of aneurysm size (≥ 5 mm = high risk) and Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site (PHASES) score (≥ 1 = high risk). Modified F-statistics and Benjamini-Hochberg false discovery rate correction was performed on transcripts per million-normalized gene counts. Protein-coding genes expressed in ≥ 50% of samples with a q value < 0.05 and an absolute fold-change ≥ 2 were considered significantly differentially expressed. Bioinformatics in Ingenuity Pathway Analysis was performed to understand the biology of risk-associated expression profiles. Association was assessed between gene expression and risk via Pearson correlation analysis. Linear discriminant analysis models using significant genes were created and validated for classification of high-risk cases. RESULTS: We analyzed transcriptomes of 68 IA patients. In these cases, 31 IAs were large (≥ 5 mm), while 26 IAs had a high PHASES score. Based on size, 36 genes associated with high-risk IAs, and two were correlated with the size measurement. Alternatively, based on PHASES score, 76 genes associated with high-risk cases, and nine of them showed significant correlation to the score. Similar ontological terms were associated with both gene profiles, which reflected inflammatory signaling and vascular remodeling. Prediction models based on size and PHASES stratification were able to correctly predict IA risk status, with > 80% testing accuracy for both. CONCLUSIONS: Here, we identified genes associated with IA risk, as quantified by common clinical metrics. Preliminary classification models demonstrated feasibility of assessing IA risk using whole blood expression.

Analysis of chromatin data supports a role for CD14+ monocytes/macrophages in mediating genetic risk for juvenile idiopathic arthritis.

Introduction: Genome wide association studies (GWAS) have identified multiple regions that confer genetic risk for the polyarticular/oligoarticular forms of juvenile idiopathic arthritis (JIA). However, genome-wide scans do not identify the cells impacted by genetic polymorphisms on the risk haplotypes or the genes impacted by those variants. We have shown that genetic variants driving JIA risk are likely to affect both innate and adaptive immune functions. We provide additional evidence that JIA risk variants impact innate immunity. Materials and methods: We queried publicly available H3K4me1/H3K27ac ChIP-seq data in CD14+ monocytes to determine whether the linkage disequilibrium (LD) blocks incorporating the SNPs that tag JIA risk loci showed enrichment for these epigenetic marks. We also queried monocyte/macrophage GROseq data, a functional readout of active enhancers. We defined the topologically associated domains (TADs) encompassing enhancers on the risk haplotypes and identified genes within those TADs expressed in monocytes. We performed ontology analyses of these genes to identify cellular processes that may be impacted by these variants. We also used whole blood RNAseq data from the Genotype-Tissue Expression (GTEx) data base to determine whether SNPs lying within monocyte GROseq peaks influence plausible target genes within the TADs encompassing the JIA risk haplotypes. Results: The LD blocks encompassing the JIA genetic risk regions were enriched for H3K4me1/H3K27ac ChIPseq peaks (p=0.00021 and p=0.022) when compared to genome background. Eleven and sixteen JIA were enriched for resting and activated macrophage GROseq peaks, respectively risk regions (p=0.04385 and p=0.00004). We identified 321 expressed genes within the TADs encompassing the JIA haplotypes in human monocytes. Ontological analysis of these genes showed enrichment for multiple immune functions. Finally, we found that SNPs lying within the GROseq peaks are strongly associated with expression levels of plausible target genes in human whole blood. Conclusions: These findings support the idea that both innate and adaptive immunity are impacted by JIA genetic risk variants.

Chromatin architecture around stroke haplotypes provides evidence that genetic risk is conferred through vascular cells.

Introduction: Genome-wide association studies (GWAS) have identified numerous stroke-associated SNPs. To understand how SNPs affect gene expression related to increased stroke risk, we studied epigenetic landscapes surrounding 26 common, validated stroke-associated loci. Methods: We mapped the SNPs to linkage disequilibrium (LD) blocks and examined H3K27ac, H3K4me1, H3K9ac, and H3K4me3 histone marks and transcription-factor binding-sites in pathologically relevant cell types (hematopoietic and vascular cells). Hi-C data were used to identify topologically associated domains (TADs) encompassing the LD blocks and overlapping genes. Results: Fibroblasts, smooth muscle, and endothelial cells showed significant enrichment for enhancer-associated marks within stroke-associated LD blocks. Genes within encompassing TADs reflected vessel homeostasis, cellular turnover, and enzymatic activity. Conclusions: Stroke-associated genetic variants confer risk predominantly through vascular cells rather than hematopoietic cell types.

Previous studies have found several variations in the DNA sequence (known as single nucleotide polymorphisms) linked to higher stroke risk. But the mechanisms behind how they increase risk is unknown. One hypothesis is that they affect non-coding DNA elements (i.e., epigenetics), which in turn drive abnormal changes in gene expression leading to increased stroke risk. To investigate this potential mechanism, we mined publicly available, cell-type specific databases. We searched for overlap between the regions with polymorphisms and regions where DNA transcription machinery bind (i.e., enhancers, transcription factor binding sites). We found that fibroblasts and smooth muscle cells (cells in vessel walls) had more of these DNA elements in regions associated with stroke risk. Bioinformatics analyses of genes that could be affected by changes in these elements were linked to stroke-related mechanisms.

Isolation of RNA from Acute Ischemic Stroke Clots Retrieved by Mechanical Thrombectomy.

Mechanical thrombectomy (MT) for large vessel acute ischemic stroke (AIS) has enabled biologic analyses of resected clots. While clot histology has been well-studied, little is known about gene expression within the tissue, which could shed light on stroke pathophysiology. In this methodological study, we develop a pipeline for obtaining useful RNA from AIS clots. A total of 73 clot samples retrieved by MT were collected and stored in RNALater and in 10% phosphate-buffered formalin. RNA was extracted from all samples using a modified Chemagen magnetic bead extraction protocol on the PerkinElmer Chemagic 360. RNA was interrogated by UV-Vis absorption and electrophoretic quality control analysis. All samples with sufficient volume underwent traditional qPCR analysis and samples with sufficient RNA quality were subjected to next-generation RNA sequencing on the Illumina NovaSeq platform. Whole blood RNA samples from three patients were used as controls, and H&E-stained histological sections of the clots were used to assess clot cellular makeup. Isolated mRNA was eluted into a volume of 140 µL and had a concentration ranging from 0.01 ng/µL to 46 ng/µL. Most mRNA samples were partially degraded, with RNA integrity numbers ranging from 0 to 9.5. The majority of samples (71/73) underwent qPCR analysis, which showed linear relationships between the expression of three housekeeping genes (GAPDH, GPI, and HPRT1) across all samples. Of these, 48 samples were used for RNA sequencing, which had moderate quality based on MultiQC evaluation (on average, ~35 M reads were sequenced). Analysis of clot histology showed that more acellular samples yielded RNA of lower quantity and quality. We obtained useful mRNA from AIS clot samples stored in RNALater. qPCR analysis could be performed in almost all cases, while sequencing data could only be performed in approximately two-thirds of the samples. Acellular clots tended to have lower RNA quantity and quality.

Aberrant Whole Blood Gene Expression in the Lumen of Human Intracranial Aneurysms.

The rupture of an intracranial aneurysm (IA) causes devastating hemorrhagic strokes. Yet, most IAs remain asymptomatic and undetected until they rupture. In the search for circulating biomarkers of unruptured IAs, we previously performed transcriptome profiling on whole blood and identified an IA-associated panel of 18 genes. In this study, we seek to determine if these genes are also differentially expressed within the IA lumen, which could provide a mechanistic link between the disease and the observed circulating gene expression patterns. To this end, we collected blood from the lumen of 37 IAs and their proximal parent vessels in 31 patients. The expression levels of 18 genes in the lumen and proximal vessel were then measured by quantitative polymerase chain reaction. This analysis revealed that the expression of 6/18 genes (CBWD6, MT2A, MZT2B, PIM3, SLC37A3, and TNFRSF4) was significantly higher in intraluminal blood, while the expression of 3/18 genes (ST6GALNAC1, TCN2, and UFSP1) was significantly lower. There was a significant, positive correlation between intraluminal and proximal expression of CXCL10, MT2A, and MZT2B, suggesting local increases of these genes is reflected in the periphery. Expression of ST6GALNAC1 and TIFAB was significantly positively correlated with IA size, while expression of CCDC85B was significantly positively correlated with IA enhancement on post-contrast MRI, a metric of IA instability and risk. In conclusion, intraluminal expression differences in half of the IA-associated genes observed in this study provide evidence for IA tissue-mediated transcriptional changes in whole blood. Additionally, some genes may be informative in assessing IA risk, as their intraluminal expression was correlated to IA size and aneurysmal wall enhancement.

RNA Sequencing Data from Human Intracranial Aneurysm Tissue Reveals a Complex Inflammatory Environment Associated with Rupture.

BACKGROUND: Intracranial aneurysm (IA) rupture leads to deadly subarachnoid hemorrhages. However, the mechanisms leading to rupture remain poorly understood. Altered gene expression within IA tissue is linked to the pathobiology of aneurysm development and progression. Here, we analyzed expression patterns of control tissue samples and compared them to those of unruptured and ruptured IA tissue samples using data from the Gene Expression Omnibus (GEO). METHODS: FASTQ files for 21 ruptured IAs, 21 unruptured IAs, and 16 control tissue samples were accessed from the GEO database. DESeq2 was used for differential expression analysis in three comparisons: unruptured IA versus control, ruptured IA versus control, and ruptured versus unruptured IA. Genes that were differentially expressed in multiple comparisons were evaluated to find those progressively increasing/decreasing from control to unruptured to ruptured. Significance was tested by either analysis of variance/Gabriel or Brown-Forsythe/Games Howell (p < 0.05 was considered significant). We used additional RNA sequencing and proteomics datasets to evaluate if our differentially expressed genes (DEGs) were present in other studies. Bioinformatics analyses were performed with g:Profiler and Ingenuity Pathway Analysis. RESULTS: In total, we identified 1768 DEGs, of which 318 were found in multiple comparisons. Unruptured versus control reflected vascular remodeling processes, while ruptured versus control reflected inflammatory responses and cell activation/signaling. When comparing ruptured to unruptured IAs, we found massive activation of inflammation, inflammatory responses, and leukocyte responses. Of the 318 genes in multiple comparisons, 127 were found to be significant in the multi-cohort correlation analysis. Those that progressively increased (70 genes) were associated with immune system processes, while those that progressively decreased (38 genes) did not return any gene ontology terms. Many of our DEGs were also found in the other IA tissue sequencing studies. CONCLUSIONS: We found unruptured IAs relate more to remodeling processes, while ruptured IAs reflect more inflammatory and immune responses.

Identification of intima-to-media signals for flow-induced vascular remodeling using correlative gene expression analysis.

Changes in blood flow can induce arterial remodeling. Intimal cells sense flow and send signals to the media to initiate remodeling. However, the nature of such intima-media signaling is not fully understood. To identify potential signals, New Zealand white rabbits underwent bilateral carotid ligation to increase flow in the basilar artery or sham surgery (n = 2 ligated, n = 2 sham). Flow was measured by transcranial Doppler ultrasonography, vessel geometry was determined by 3D angiography, and hemodynamics were quantified by computational fluid dynamics. 24 h post-surgery, the basilar artery and terminus were embedded for sectioning. Intima and media were separately microdissected from the sections, and whole transcriptomes were obtained by RNA-seq. Correlation analysis of expression across all possible intima-media gene pairs revealed potential remodeling signals. Carotid ligation increased flow in the basilar artery and terminus and caused differential expression of 194 intimal genes and 529 medial genes. 29,777 intima-media gene pairs exhibited correlated expression. 18 intimal genes had > 200 medial correlates and coded for extracellular products. Gene ontology of the medial correlates showed enrichment of organonitrogen metabolism, leukocyte activation/immune response, and secretion/exocytosis processes. This demonstrates correlative expression analysis of intimal and medial genes can reveal novel signals that may regulate flow-induced arterial remodeling.

Identification of Circulating Gene Expression Signatures of Intracranial Aneurysm in Peripheral Blood Mononuclear Cells.

Peripheral blood mononuclear cells (PBMCs) play an important role in the inflammation that accompanies intracranial aneurysm (IA) pathophysiology. We hypothesized that PBMCs have different transcriptional profiles in patients harboring IAs as compared to IA-free controls, which could be the basis for potential blood-based biomarkers for the disease. To test this, we isolated PBMC RNA from whole blood of 52 subjects (24 with IA, 28 without) and performed next-generation RNA sequencing to obtain their transcriptomes. In a randomly assigned discovery cohort of n = 39 patients, we performed differential expression analysis to define an IA-associated signature of 54 genes (q < 0.05 and an absolute fold-change ≥ 1.3). In the withheld validation dataset, these genes could delineate patients with IAs from controls, as the majority of them still had the same direction of expression difference. Bioinformatics analyses by gene ontology enrichment analysis and Ingenuity Pathway Analysis (IPA) demonstrated enrichment of structural regulation processes, intracellular signaling function, regulation of ion transport, and cell adhesion. IPA analysis showed that these processes were likely coordinated through NF-kB, cytokine signaling, growth factors, and TNF activity. Correlation analysis with aneurysm size and risk assessment metrics showed that 4/54 genes were associated with rupture risk. These findings highlight the potential to develop predictive biomarkers from PBMCs to identify patients harboring IAs.

Epigenetic landscapes of intracranial aneurysm risk haplotypes implicate enhancer function of endothelial cells and fibroblasts in dysregulated gene expression.

BACKGROUND: Genome-wide association studies have identified many single nucleotide polymorphisms (SNPs) associated with increased risk for intracranial aneurysm (IA). However, how such variants affect gene expression within IA is poorly understood. We used publicly-available ChIP-Seq data to study chromatin landscapes surrounding risk loci to determine whether IA-associated SNPs affect functional elements that regulate gene expression in cell types comprising IA tissue. METHODS: We mapped 16 significant IA-associated SNPs to linkage disequilibrium (LD) blocks within human genome. Using ChIP-Seq data, we examined these regions for presence of H3K4me1, H3K27ac, and H3K9ac histone marks (typically associated with latent/active enhancers). This analysis was conducted in several cell types that are present in IA tissue (endothelial cells, smooth muscle cells, fibroblasts, macrophages, monocytes, neutrophils, T cells, B cells, NK cells). In cell types with significant histone enrichment, we used HiC data to investigate topologically associated domains (TADs) encompassing the LD blocks to identify genes that may be affected by IA-associated variants. Bioinformatics were performed to determine the biological significance of these genes. Genes within HiC-defined TADs were also compared to differentially expressed genes from RNA-seq/microarray studies of IA tissues. RESULTS: We found that endothelial cells and fibroblasts, rather than smooth muscle or immune cells, have significant enrichment for enhancer marks on IA risk haplotypes (p < 0.05). Bioinformatics demonstrated that genes within TADs subsuming these regions are associated with structural extracellular matrix components and enzymatic activity. The majority of histone marked TADs (83% fibroblasts [IMR90], 77% HUVEC) encompassed at least one differentially expressed gene from IA tissue studies. CONCLUSIONS: These findings provide evidence that genetic variants associated with IA risk act on endothelial cells and fibroblasts. There is strong circumstantial evidence that this may be mediated through altered enhancer function, as genes in TADs encompassing enhancer marks have also been shown to be differentially expressed in IA tissue. These genes are largely related to organization and regulation of the extracellular matrix. This study builds upon our previous (Poppenberg et al., BMC Med Genomics, 2019) by including a more diverse set of data from additional cell types and by identifying potential affected genes (i.e. those in TADs).

Broadening our understanding of genetic risk for scleroderma/systemic sclerosis by querying the chromatin architecture surrounding the risk haplotypes.

BACKGROUND: Genetic variants in the human leukocyte antigen (HLA) locus contribute to the risk for developing scleroderma/systemic sclerosis (SSc). However, there are other replicated loci that also contribute to genetic risk for SSc, and it is unknown whether genetic risk in these non-HLA loci acts primarily on the vasculature, immune system, fibroblasts, or other relevant cell types. We used the Cistrome database to investigate the epigenetic landscapes surrounding 11 replicated SSc associated loci to determine whether SNPs in these loci may affect regulatory elements and whether they are likely to impact a specific cell type. METHODS: We mapped 11 replicated SNPs to haplotypes and sought to determine whether there was significant enrichment for H3K27ac and H3K4me1 marks, epigenetic signatures of enhancer function, on these haplotypes. We queried pathologically relevant cell types: B cells, endothelial cells, fibroblasts, monocytes, and T cells. We then identified the topologically associated domains (TADs) that encompass the SSc risk haplotypes in primary T cells to identify the full range of genes that may be influenced by SSc causal SNPs. We used gene ontology analyses of the genes within the TADs to gain insight into immunologic functions that might be affected by SSc causal SNPs. RESULTS: The SSc-associated haplotypes were enriched (p value < 0.01) for H3K4me1/H3K27ac marks in monocytes. Enrichment of one of the two histone marks was found in B cells, fibroblasts, and T cells. No enrichment was identified in endothelial cells. Ontological analyses of genes within the TADs encompassing the risk haplotypes showed enrichment for regulation of transcription, protein binding, activation of T lymphocytes, and proliferation of immune cells. CONCLUSIONS: The 11 non-HLA SSc risk haplotypes queried are highly enriched for H3K4me1/H3K27ac-marked regulatory elements in a broad range of immune cells and fibroblasts. Furthermore, in immune cells, the risk haplotypes belong to larger chromatin structures encompassing genes that regulate a wide array of immune processes associated with SSc pathogenesis. Though importance of the vasculature in the pathobiology of SSc is widely accepted, we were unable to find evidence for genetic influences on endothelial cell function in these regions.

Endogenous animal models of intracranial aneurysm development: a review.

The pathogenesis and natural history of intracranial aneurysm (IA) remains poorly understood. To this end, animal models with induced cerebral vessel lesions mimicking human aneurysms have provided the ability to greatly expand our understanding. In this review, we comprehensively searched the published literature to identify studies that endogenously induced IA formation in animals. Studies that constructed aneurysms (i.e., by surgically creating a sac) were excluded. From the eligible studies, we reported information including the animal species, method for aneurysm induction, aneurysm definitions, evaluation methods, aneurysm characteristics, formation rate, rupture rate, and time course. Between 1960 and 2019, 174 articles reported endogenous animal models of IA. The majority used flow modification, hypertension, and vessel wall weakening (i.e., elastase treatment) to induce IAs, primarily in rats and mice. Most studies utilized subjective or qualitative descriptions to define experimental aneurysms and histology to study them. In general, experimental IAs resembled the pathobiology of the human disease in terms of internal elastic lamina loss, medial layer degradation, and inflammatory cell infiltration. After the early 2000s, many endogenous animal models of IA began to incorporate state-of-the-art technology, such as gene expression profiling and 9.4-T magnetic resonance imaging (MRI) in vivo imaging, to quantitatively analyze the biological mechanisms of IA. Future studies aimed at longitudinally assessing IA pathobiology in models that incorporate aneurysm growth will likely have the largest impact on our understanding of the disease. We believe this will be aided by high-resolution, small animal, survival imaging, in situ live-cell imaging, and next-generation omics technology.

Whole blood transcriptome biomarkers of unruptured intracranial aneurysm.

BACKGROUND: The rupture of an intracranial aneurysm (IA) causes devastating subarachnoid hemorrhages, yet most IAs remain undiscovered until they rupture. Recently, we found an IA RNA expression signature of circulating neutrophils, and used transcriptome data to build predictive models for unruptured IAs. In this study, we evaluate the feasibility of using whole blood transcriptomes to predict the presence of unruptured IAs. METHODS: We subjected RNA from peripheral whole blood of 67 patients (34 with unruptured IA, 33 without IA) to next-generation RNA sequencing. Model genes were identified using the least absolute shrinkage and selection operator (LASSO) in a random training cohort (n = 47). These genes were used to train a Gaussian Support Vector Machine (gSVM) model to distinguish patients with IA. The model was applied to an independent testing cohort (n = 20) to evaluate performance by receiver operating characteristic (ROC) curve. Gene ontology and pathway analyses investigated the underlying biology of the model genes. RESULTS: We identified 18 genes that could distinguish IA patients in a training cohort with 85% accuracy. This SVM model also had 85% accuracy in the testing cohort, with an area under the ROC curve of 0.91. Bioinformatics reflected activation and recruitment of leukocytes, activation of macrophages, and inflammatory response, suggesting that the biomarker captures important processes in IA pathogenesis. CONCLUSIONS: Circulating whole blood transcriptomes can detect the presence of unruptured IAs. Pending additional testing in larger cohorts, this could serve as a foundation to develop a simple blood-based test to facilitate screening and early detection of IAs.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

BACKGROUND: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Characterization of Long Non-coding RNA Signatures of Intracranial Aneurysm in Circulating Whole Blood.

BACKGROUND AND OBJECTIVE: Long non-coding RNAs (lncRNAs) may serve as biomarkers for complex disease states, such as intracranial aneurysms. In this study, we investigated lncRNA expression differences in the whole blood of patients with unruptured aneurysms. METHODS: Whole blood RNA from 67 subjects (34 with aneurysm, 33 without) was used for next-generation RNA sequencing. Differential expression analysis was used to define a signature of intracranial aneurysm-associated lncRNAs. To estimate the signature's ability to classify aneurysms and to identify the most predictive lncRNAs, we implemented a nested cross-validation pipeline to train classifiers using linear discriminant analysis. Ingenuity pathway analysis was used to study potential biological roles of differentially expressed lncRNAs, and lncRNA ontology was used to investigate ontologies enriched in signature lncRNAs. Co-expression correlation analysis was performed to investigate associated differential protein-coding messenger RNA expression. RESULTS: Of 4639 detected lncRNAs, 263 were significantly different (p < 0.05) between the two groups, and 84 of those had an absolute fold-change ≥ 1.5. An eight-lncRNA signature (q < 0.35, fold-change ≥ 1.5) was able to separate patients with and without aneurysms on principal component analysis, and had an estimated accuracy of 70.9% in nested cross-validation. Bioinformatics analyses showed that networks of differentially expressed lncRNAs (p < 0.05) were enriched for cell death and survival, connective tissue disorders, carbohydrate metabolism, and cardiovascular disease. Signature lncRNAs shared ontologies that reflected regulation of gene expression, signaling, ubiquitin processing, and p53 signaling. Co-expression analysis showed correlations with messenger RNAs related to inflammatory responses. CONCLUSIONS: Differential expression in whole blood lncRNAs is detectable in patients harboring aneurysms, and reflects expression/signaling regulation, and ubiquitin and p53 pathways. Following validation in larger cohorts, these lncRNAs may be potential diagnostic targets for aneurysm detection by blood testing.

Epigenetic landscapes suggest that genetic risk for intracranial aneurysm operates on the endothelium.

BACKGROUND: Genetics play an important role in intracranial aneurysm (IA) pathophysiology. Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) that are linked to IA but how they affect disease pathobiology remains poorly understood. We used Encyclopedia of DNA Elements (ENCODE) data to investigate the epigenetic landscapes surrounding genetic risk loci to determine if IA-associated SNPs affect functional elements that regulate gene expression and if those SNPs are most likely to impact a specific type of cells. METHODS: We mapped 16 highly significant IA-associated SNPs to linkage disequilibrium (LD) blocks within the human genome. Within these regions, we examined the presence of H3K4me1 and H3K27ac histone marks and CCCTC-binding factor (CTCF) and transcription-factor binding sites using chromatin immunoprecipitation-sequencing (ChIP-Seq) data. This analysis was conducted in several cell types relevant to endothelial (human umbilical vein endothelial cells [HUVECs]) and inflammatory (monocytes, neutrophils, and peripheral blood mononuclear cells [PBMCs]) biology. Gene ontology analysis was performed on genes within extended IA-risk regions to understand which biological processes could be affected by IA-risk SNPs. We also evaluated recently published data that showed differential methylation and differential ribonucleic acid (RNA) expression in IA to investigate the correlation between differentially regulated elements and the IA-risk LD blocks. RESULTS: The IA-associated LD blocks were statistically significantly enriched for H3K4me1 and/or H3K27ac marks (markers of enhancer function) in endothelial cells but not in immune cells. The IA-associated LD blocks also contained more binding sites for CTCF in endothelial cells than monocytes, although not statistically significant. Differentially methylated regions of DNA identified in IA tissue were also present in several IA-risk LD blocks, suggesting SNPs could affect this epigenetic machinery. Gene ontology analysis supports that genes affected by IA-risk SNPs are associated with extracellular matrix reorganization and endopeptidase activity. CONCLUSION: These findings suggest that known genetic alterations linked to IA risk act on endothelial cell function. These alterations do not correlate with IA-associated gene expression signatures of circulating blood cells, which suggests that such signatures are a secondary response reflecting the presence of IA rather than indicating risk for IA.

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches.

BACKGROUND: The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population. METHODS: Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry. RESULTS: In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis. CONCLUSIONS: This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.

Morphologic Parameters and Location Associated with Rupture Status of Intracranial Aneurysms in Elderly Patients.

BACKGROUND: Treatment of unruptured intracranial aneurysms (IAs) in elderly patients is associated with a high risk of morbidity and mortality, necessitating a thorough understanding of the potential rupture risk. The aim of this study was to identify morphologic parameters and anatomic locations that could discriminate ruptured IAs in patients ≥70 years old. METHODS: Retrospective analysis was performed of three-dimensional angiograms and medical records of 344 patients with 411 saccular IAs. Patients ≥70 years old were defined as elderly. IAs were subdivided into ruptured and unruptured. Morphologic parameters and anatomic locations were compared in elderly and younger (<70 years old) patients with ruptured and unruptured IAs. RESULTS: The study included 266 patients <70 years old and 78 patients ≥70 years old with 411 aneurysms (102 ruptured and 309 unruptured). In the elderly group, 22 of 95 aneurysms were ruptured (23.15%) compared with 80 of 316 (25.3%) in the younger group. Size ratio and aspect ratio were higher in ruptured IAs, but only in the younger group. Undulation index, indicating IA shape irregularity, was significantly different between ruptured and unruptured IAs in younger and elderly groups. The only variables associated with rupture in the elderly group were undulation index (0.11 ± 0.07 vs. 0.07 ± 0.06, P = 0.02) and location (P = 0.001). CONCLUSIONS: Aneurysm size, size ratio, and aspect ratio may not be reliable discriminants of rupture in elderly patients. Unruptured IAs in elderly patients should be evaluated on the basis of shape irregularity and anatomic location.