The role of inflammation and potential pharmacological therapy in intracranial aneurysms

Metabolic alterations of peripheral blood immune cells and heterogeneity of neutrophil in intracranial aneurysms patients

BACKGROUND

Intracranial aneurysms (IAs) represent a severe cerebrovascular disease that can potentially lead to subarachnoid haemorrhage. Previous studies have demonstrated the involvement of peripheral immune cells in the formation and progression of IAs. Nevertheless, the impact of metabolic alterations in peripheral immune cells and changes in neutrophil heterogeneity on the occurrence and progression of IAs remains uncertain.

METHODS

Single-cell Cytometry by Time-of-Flight (CyTOF) technology was employed to profile the single-cell atlas of peripheral blood mononuclear cells (PBMCs) and polymorphonuclear cells (PMNs) in 72 patients with IAs. In a matched cohort, metabolic shifts in PBMC subsets of IA patients were investigated by contrasting the expression levels of key metabolic enzymes with their respective counterparts in the healthy control group. Simultaneously, compositional differences in peripheral blood PMNs subsets between the two groups were analysed to explore the impact of altered heterogeneity in neutrophils on the initiation and progression of IAs. Furthermore, integrating immune features based on CyTOF analysis and clinical characteristics, we constructed an aneurysm occurrence model and an aneurysm growth model using the random forest method in conjunction with LASSO regression.

RESULTS

Different subsets exhibited distinct metabolic characteristics. Overall, PBMCs from patients elevated CD98 expression and increased proliferation. Conversely, CD36 was up-regulated in T cells, B cells and monocytes from the controls but down-regulated in NK and NKT cells. The comparison also revealed differences in the metabolism and function of specific subsets between the two groups. In terms of PMNs, the neutrophil landscape within patients group revealed a pronounced shift towards heightened complexity. Various neutrophil subsets from the IA group generally exhibited lower expression levels of anti-inflammatory functional molecules (IL-4 and IL-10). By integrating clinical and immune features, the constructed aneurysm occurrence model could precisely identify patients with IAs with high prediction accuracy (AUC = 0.987). Furthermore, the aneurysm growth model also exhibited superiority over ELAPSS scores in predicting aneurysm growth (lower prediction errors and out-of-bag errors).

CONCLUSION

These findings enhanced our understanding of peripheral immune cell participation in aneurysm formation and growth from the perspectives of immune metabolism and neutrophil heterogeneity. Moreover, the predictive model based on CyTOF features holds the potential to aid in diagnosing and monitoring the progression of human IAs.

Molecular Subtypes and Machine Learning-Based Predictive Models for Intracranial Aneurysm Rupture

BACKGROUND

The determination of biological mechanisms and biomarkers related to intracranial aneurysm (IA) rupture is of utmost significance for the development of effective preventive and therapeutic strategies in the clinical field.

METHODS

GSE122897 and GSE13353 datasets were downloaded from Gene Expression Omnibus. Data extracted from GSE122897 were used for analyzing differential gene expression, and consensus clustering was performed to identify stable molecular subtypes. Clinical characteristics were compared between subgroups, and fast gene set enrichment analysis and weighted gene coexpression network analysis were performed. Hub genes were identified via least absolute shrinkage and selection operator analysis. Predictive models were constructed based on hub genes using the Light Gradient Boosting Machine, eXtreme Gradient Boosting, and logistic regression algorithm. Immune cell infiltration in IA samples was analyzed using Microenvironment Cell Population counter, CIBERSORT, and xCell algorithm. The correlation between hub genes and immune cells was analyzed. The predictive model and immune cell infiltration were validated using data from the GSE13353 dataset.

RESULTS

A total of 43 IA samples were classified into 2 subgroups based on gene expression profiles. Subgroup I had a higher risk of rupture, while 70% of subgroup II remained unruptured. In subgroup I, specific genes were associated with inflammation and immunity, and weighted gene coexpression network analysis revealed that the black module genes were linked to IA rupture. We identified 4 hub genes (spermine synthase, macrophage receptor with collagenous structure, zymogen granule protein 16B, and LIM and calponin-homology domains 1), which constructed predictive models with good diagnostic performance in differentiating between ruptured and unruptured IA samples. Monocytic lineage was found to be a significant factor in IA rupture, and the 4 hub genes were linked to monocytic lineage (P < 0.05).

CONCLUSIONS

We reveal a new molecular subtype that can reflect the actual pathological state of IA rupture, and our predictive models constructed by machine learning algorithms can efficiently predict IA rupture.

Impact of spatial and temporal stability of flow vortices on vascular endothelial cells

PURPOSE

Intracranial aneurysms (IAs) are pathological dilations of cerebrovascular vessels due to degeneration of the mechanical strength of the arterial wall, precluded by altered cellular functionality. The presence of swirling hemodynamic flow (vortices) is known to alter vascular endothelial cell (EC) morphology and protein expression indicative of IAs. Unfortunately, less is known if vortices with varied spatial and temporal stability lead to differing levels of EC change. The aim of this work is to investigate vortices of varying spatial and temporal stability impact on ECs.

METHODS

Vortex and EC interplay was investigated by a novel combination of parallel plate flow chamber (PPFC) design and computational analysis. ECs were exposed to laminar (7.5 dynes/[Formula: see text] wall shear stress) or low (<1 dynes/[Formula: see text]) stress vortical flow using PPFCs. Immunofluorescent imaging analyzed EC morphology, while ELISA tests quantified VE-cadherin (cell-cell adhesion), VCAM-1 (macrophage-EC adhesion), and cleaved caspase-3 (apoptotic signal) expression. PPFC flow was simulated, and vortex stability was calculated via the temporally averaged degree of (volume) overlap (TA-DVO) of vortices within a given area.

RESULTS

EC morphological changes were independent of vortex stability. Increased stability promoted VE-cadherin degradation (correlation coefficient r = [Formula: see text]0.84) and 5-fold increased cleaved caspase-3 post 24 h in stable (TA-DVO 0.736 ± 0.05) vs unstable (TA-DVO 0.606 [Formula: see text]0.2) vortices. ECs in stable vortices displayed a 4.5-fold VCAM-1 increase than unstable counterparts after 12 h.

CONCLUSION

This work demonstrates highly stable disturbed flow imparts increased inflammatory signaling, degraded cell-cell adhesion, and increased cellular apoptosis than unstable vortices. Such knowledge offers novel insight toward understanding IA development and rupture.

A Two-Gene-Based Diagnostic Signature for Ruptured Intracranial Aneurysms

Background: Ruptured intracranial aneurysm (IA) is a disease with high mortality. Despite the great progress in treating ruptured IA, methods for risk assessment of ruptured IA remain limited. Methods: In this study, we aim to develop a robust diagnostic model for ruptured IA. Gene expression profiles in blood samples of 18 healthy persons and 43 ruptured IA patients were obtained from the Gene Expression Omnibus (GEO). Differential expression analysis was performed using limma Bioconductor package followed by functional enrichment analysis via clusterProfiler Bioconductor package. Immune cell compositions in ruptured IA and healthy samples were assessed through the CIBERSORT tool. Protein-protein interaction (PPI) was predicted based on the STRING database. Logistic regression model was used for the construction of predictive model for distinguishing ruptured IA and healthy samples. Real-time quantitative polymerase chain reaction (RT-qPCR) was performed to validate the gene expression between the ruptured IA and healthy samples. Results: A total of 58 differentially expressed genes (DEGs) were obtained for ruptured IA patients compared with healthy controls. Functional enrichment analysis showed that the DEGs were enriched in biological processes related to neutrophil activation, neutrophil degranulation, and cytokine-cytokine receptor interaction. Notably, immune analysis results proved that the rupture of IA might be related to immune cell distribution. We further identified 24 key genes as hub genes using the PPI networks. The logistic regression model trained based on the 24 key genes ultimately retained two genes, i.e., IL2RB and CCR7, which had great potential for risk assessment for rupture of IA. The RT-qPCR further validated that compared with the healthy samples, the expression levels of IL2RB and CCR7 were decreased in ruptured IA samples. Conclusions: This study might be helpful for cohorts who have a high risk of ruptured IA for early diagnosis and prevention of the disease.

Exploring the association of long noncoding RNA expression profiles with intracranial aneurysms, based on sequencing and related bioinformatics analysis

Background

The present study aims to investigate the complete long non-coding RNA (lncRNA) and messenger RNA (mRNA) expression profiles in Intracranial aneurysm (IA) patients and controls by RNA sequencing, which reveals the lncRNA with predictive value for IA risk.

Methods

The comprehensive lncRNA and mRNA expression profiles were detected by RNA-Seq in human IA walls and superficial temporal arteries (STAs), followed by bioinformatics analyses, such as GO analysis, KEGG pathway analysis, and CNC network construction. Subsequently, qRT-PCR was used to profile the expression levels of selected lncRNA (lncRNA ENST000000576153, lncRNA ENST00000607042, lncRNA ENST00000471220, lncRNA ENST00000478738, lncRNA MALAT1, lncRNA ENST00000508090 and lncRNA ENST00000579688) in 30 (small) or 130 (large) peripheral blood leukocytes, respectively. Multivariate logistic regression was utilized to analyze the effects of lncRNA on IA. Receiver operating characteristic (ROC) curve was further drawn to explore the value of lncRNA in predicting IA.

Results

Totally 900 up-regulated and 293 down-regulated lncRNAs, as well as 1297 up-regulated and 831 down-regulated mRNAs were discovered in sequencing. Enrichment analyses revealed that they were actively involved in immune/inflammatory response and cell adhesion/extracellular matrix. Co-expression analysis and further enrichment analyses showed that five candidate lncRNAs might participate in IA’s inflammatory response. Besides, after controlling other conventional risk factors, multivariate logistic regression analysis disclosed that low expression of lncRNA ENST00000607042, lncRNA ENST00000471220, lncRNA ENST00000478738, lncRNA MALAT1 in peripheral blood leukocytes were independent risk factors for IA. LncRNA ENST00000607042 has superior diagnostic value for IA.

Conclusions

This study reveals the complete lncRNAs expression profiles in IA. The inflammatory response was closely related to IA. Besides, lncRNA ENST00000607042 might be a novel biomarker for IA risk.