Molecular characteristics of arteries in patients with intracranial aneurysm: integration of WES and RNA-seq

doi:10.21203/rs.3.rs-1945671/v1

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Molecular characteristics of arteries in patients with intracranial aneurysm: integration of WES and RNA-seq

https://doi.org/10.21203/rs.3.rs-1945671/v1

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Genetic and vascular morphology research suggested intracranial aneurysm (IA) may not be a local artery disease. IA initiation may be correlated with the histological and molecular changes of arteries. We used whole-exome sequencing (WES) of blood and RNA sequencing (RNA-seq) of superficial temporal artery (STA) to explore the artery molecular characteristics of IA under the genetic background, and try to find the potential mechanism of the IA initiation. Fifty IA patients’ and 40 controls’ blood samples were enrolled in WES; 10 IA patients’ and 5 controls’ STA were included in RNA-seq. Bioinformatic analysis was performed. RT-qPCR was used for validation. We analyze the cell types of STA by the xCell algorithm, and conducted a correlation analysis between the endothelium and mutation genes. We identified 2118 genes via RNA-seq, and via WES 43 genes harboring 54 potential deleterious variants. Four genes (ALDH1L2, KCNJ12, SPNS2, TUBB8) had both variants and differential expressions. Xcell algorithms showed that the expression of mutation genes KCNJ12 and SPSN2 were strongly associated with decreasing endothelial cells on STA and higher IA risks. KCN12 and SPSN2 expressions have been validated by RT-qPCR. In total, we have described the artery molecular characteristics of IAs by RNA-seq and WES. A total of two gene mutations (KCNJ12 and SPSN2) were found potentially to facilitate IA initiation by decreasing EC expression in brain arteries.

Exome

Transcriptome

Intracranial aneurysm

Endothelial cell

Sequencing

Intracranial aneurysm (IA) is pathologically saccular or fusiform dilatations of cerebral arteries, resulting from abnormally weak wall[1]. IA rupture is the most common reason for subarachnoid hemorrhage, with an incidence of 3–5% in the general population, and with a 25% reason for hemorrhagic stroke death[2]. Genetics, hemodynamics, environmental factors involved in IA initiation[3–6]. However, the specific mechanism remains unclear.

Some IAs have genetic features. Approximately 20% of IA patients had a family history[7]. Epidemiological investigations showed that first-degree relatives of IA patients had 7 times higher risk for the disease than the general population[8]. Some large-sample case-control studies found that genetic variants of LOX, ANGPTL6, FBLN2, NOTCH3, PCNT, ARHGEF17 genes were associated with higher risks for IAs[9, 10].

Clinical data showed that approximately 20% of IA patients had multiple IAs, and 95% of IA patients had IA recurrence or de novo formation after neck clipping in 20 years[7, 11]. Kim, Labeyrie and Pei et al. found that IA patients were commonly accompanied by tortuous arteries[12, 13]. Our previous study also found that patients with IAs showed greater brain artery shifting with the age increasing[14]. In addition, some IAs were associated with the incidence of aortic aneurysms[15, 16]. These studies showed that IA may not be a local disease on one cerebral artery, and implied that IA may have a whole-brain or even whole-body artery susceptibility.

The present study was designed to integrally analyze whole-exome sequencing (WES) of blood and RNA sequencing (RNA-seq) of superficial temporal artery (STA) and investigate the arterial molecule feature of IA patients under the genetic susceptibility. We aimed to explore the potential IA initiation mechanism.

Subjects and sample collection

The study was approved by the Xiangya Hospital of Central South University Institute Research Ethics Committee and informed consent was obtained from all participants. Patients with sporadic IAs confirmed by CTA or DSA from June 2020 to December 2020 were enrolled in our study. Exclusion criteria were as follows: 1) IAs associated with arteriovenous malformation; 2) IAs resulting from syndromic disorders such as Autosomal polycystic kidney disease, Ehlers-Danlos syndrome type IV, Loeys–Dietz syndrome, and Marfan syndrome[17]. Besides, health controls without IA from the general population and head trauma patients undergoing craniotomy were also enrolled during the same period. We recruited a total of 105 patients, in which 40 patients with sporadic IAs were assigned to the WES experiment group, 50 without IAs to the WES control group, 10 with IAs to the RNA-seq experiment group, 5 without IAs to the RNA-seq control group. General clinical information was collected, including sex, age, hypertension, hyperlipidemia, diabetes, smoking, and drinking. The rupture, maximum size, and number of IAs were also recorded.

Peripheral blood samples were collected from IA patients and healthy controls for WES, and were stored in ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes with a temperature of -80 ℃. STAs were harvested from IA patients and head trauma patients (controls) during craniotomy surgery for RNA-seq, and were also stored at a temperature of -80 ℃.

WES and bioinformatic analysis

Genomic DNA was extracted from the blood samples of 40 IA patients and 50 healthy controls using a DNA RNA kit (E.Z.N.A). Library construction and WES were performed on the BGISEQ – 500 platform by the HuaDa Gene company (Shenzhen, China). The raw sequencing data were filtered to gain clean data. The sequence reads were aligned to the human reference genome GRCh37/hg19 using BWA. Duplication removing, realignment, variant calling, and annotation of the sequence reads were conducted with Picard, GATK, and SnpEff softwares.

The filtering criteria for single nucleotide variant (SNV) were as follows: 1) removing variants with frequency > 1% in the 1000G all genomes project[18]; 2) removing synonymous variants; 3) selecting deleterious variants by 2 or more prediction tools from SIFT, PolyPhen2, MutationAssessor, and MutationTaster; 4) selecting variants with statistically significant difference between 2 groups. To visualize the filtered SNV landscape, a waterfall plot and Manhattan plot were drawn using the R package “maftools” and “CMplot”, separately.

The filtered SNVs from WES and differentially expressed genes (DEGs) from RNA-seq were integrated to gain rare variants in DEGs. Hardy-Weinberg equilibrium testing was performed for the genotype of these variants in the SPSS software (version 26). We further analyzed the statistical difference of allele- and genotype- frequency and distribution for these variants between IA patients and the controls.

RNA-seq and bioinformatic analysis

Total RNA was extracted from the STA tissues of 10 IA patients and 5 head trauma patients using TRIzol (Invitrogen, Carlsbad, CA, USA). Library construction and RNA-seq were performed on the BGISEQ – 500 platform by the HuaDa Gene company (Shenzhen, China). Single-end 50 base reads were generated. The read quality was inspected using FastQC and MultiQC, and trimmed with Trimmomatic to yield clean reads. Then, for each gene, read counts were calculated, and expression values were further normalized using FPKM.

To visualize the disparity between two groups, principal components analysis (PCA) was performed using the R packages “factoextra” and “FactoMineR”. The gene expression difference analysis was conducted using the “limma” R package (P < 0.05 and log2-fold change > 0.6 or < -0.6). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to detect the function of DEGs.

The vascular cell types were analyzed using the R package “xCell”, which could convert gene expression profiles to enrichment scores of 64 immune and stromal cell types. The association analysis was performed between the vascular cells and target genes defined in both WES and RNA-seq. Endothelial cells (EC) had the highest correlation and were identified as target cells.

To identify endothelial-cell-related genes, the association between individual genes and endothelial-cell densities was quantified by weighted gene co-expression network analysis (WGCNA). A soft threshold β was set at 15 to attain a scale-free topology network. The genes in the module with the strongest correlation were selected (gene significance > 0.5 and module member > 0.8). We used these genes, together with the identified SNV genes to construct SNV-gene-regulated protein-protein interaction (PPI) network in ECs.

Validation with reverse-transcription quantitative polymerase chain reaction (RT-qPCR)

Two representative genes SPSN2 and KCNJ12 were selected and validated by RT-qPCR. The RNA left after RNA-sequencing was reverse transcribed to cDNA using RevertAid First Strand cDNA Synthesis Kit (ThermoFisher). ChamQ Universal SYBR qPCR Master Mix (Vazyme, China) and StepOne Real-time PCR system (Applied Biosystems) were used for RT-qPCR reaction. GAPDH served as the internal control. Reactions were repeated in triplicate for each sample. Expression levels were calculated using the 2^-ΔΔCt method. Primer sequences were listed in Supplemental Table 1.

Statistical analysis

For continuous variables, Student’s t-test and Wilcoxon test were used to compare the two groups. For categorical variables, Chi-squared test and Fisher exact test were conducted to compare the two groups. Spearman correlation was performed to calculate correlation coefficients. Data were visualized using the R package “ggplot2”. Heatmaps were drawn based on the R package “pheatmap”. All statistical analyses were conducted using R software (version 4.0.2) or SPSS software (version 26.0). P < 0.05 was considered statistically significant.

The process of selecting candidate genes in this study was presented in Figure 1.

Demographics

The clinical data of all participants were summarized in Tables 1 and 2. For IA patients undergoing WES, 14 were men and 26 women, with a mean age of 56.03 ± 11.38 years. For controls undergoing WES, 16 were men and 34 women, with a mean age of 38.62 ± 14.10 years. There was no significant difference in susceptibility factors and comorbidities between the two WES groups (Table 1). Of IA patients undergoing RNA-seq, the mean age was 54.70 ± 10.24. 4 cases were men and 6 women. Of controls undergoing RNA-seq, the mean age was 36.00 ± 8.28. 3 cases were men and 2 women. No significant difference in susceptibility factors and comorbidities was found between the two RNA-seq groups (Table 2).

IA-related SNVs and genes by WES

The present study enrolled 40 patients with IA and 50 controls to undergo WES. After annotation of the variants, we gained a total of 99212 SNVs, which included synonymous variants, missense variants, stop gained variants, and other types of variants. After filtering, 54 SNVs in 43 genes were predicted to be damaging to gene function and had a statistically significant difference between the two groups. The waterfall plot displayed the genome landscape of these 54 SNVs on 40 patients with IA (Figure 2A). The Manhattan plot visualized the distribution of these 54 SNVs on the chromosomes (Figure 2B).

IA-related genes and enrichment function determined by RNA-seq

RNA-seq enrolled 10 patients with IA and 5 controls. Transcripts of 18581 genes were detected. PCA showed the IA group could be discriminated from the control group at the transcript level (Figure 3A). The differential analysis showed that a total of 2118 genes were differentially expressed between the IA and control samples, in which 299 genes were up-regulated and 1819 genes were down-regulated (Figure 3B). Functionally, GO enrichment analysis found that the up-regulated genes were concentrated in terms involving muscle contractile, muscle cell differentiation, and down-regulated genes in terms involving synaptic membrane, T cell activation, positive regulation of cell adhesion, and collagen-containing extracellular matrix (Figure 3C). KEGG enrichment analysis showed that the up-regulated genes were enriched in the focal adhesion, ECM-receptor interaction, vascular smooth muscle contraction, PI3K-Akt signaling pathway, and down-regulated genes were was correlated to calcium signaling pathway, axon guidance, cell-adhesion molecules, MAPK signaling pathway (Figure 3D).

Integrative analysis of WES and RNA-seq data

By integrating the 54 SNVs in WES and 2118 DEGs in RNA-seq, we gained 4 rare variants in 4 genes (Figure 3E). The details of the 4 variants (rs9329307, rs75238381, rs201403828, rs201267925) were listed in Table 3. The variants were located in the coding regions of TUBB8 (Tubulin Beta 8 Class VIII), ALDH1L2 (Aldehyde Dehydrogenase 1 Family Member L2), KCNJ12 (Potassium Inwardly Rectifying Channel Subfamily J Member 12), and SPNS2 (Sphingolipid Transporter 2), respectively.

Moreover, we performed genotype analysis for 4 rare variants in 4 genes. The genotype distributions of rs75238381 (ALDH1L2), KCNJ12 (rs201403828), and SPNS2 (rs201267925) were fitted to the Hardy-Weinberg equilibrium (P > 0.05, Table 4). The allele and genotype distributions of TUBB8 (rs9329307), KCNJ12 (rs201403828), SPNS2 (rs201267925) showed a statistically significant difference between IA patients and controls (P < 0.05, Table 5).

IA-related cell expression changes

The expression difference of the levels of 64 cell types was investigated in 10 patients and 5 controls who underwent RNA-seq (Figures 4A). StromaScore and MicroenviromentScore were significantly lower in patients with IA (Figure 4B). For meyloids, basophils were significantly higher expressed, and conventional dendritic cells (DC), DCs, immature DCs were significantly lower expressed in patients with IAs. For stromal cells, ECs, lymphatic ECs, microvascular ECs, pericytes, and preadipocytes were significantly lower expressed in patients with IAs (Figure 4C).

We then performed the correlation analysis between the cell expression and 4 genes identified by WES and RNA-seq (Supplemental figure 1). Both KCNJ12 and SPNS2 were highly associated with the expression level of xCell-defined ECs, lymphatic ECs, and microvascular ECs, which indicated KCNJ12 and SPNS2 mutation may regulate the EC decreasing in IAs (Figures 5A). The down-regulated expressions of SPSN2 and KCNJ12 in IAs were validated by RT-qPCR (Figure 5B). To further detect the endothelium-related genes, we performed WGCNA. No sample was excluded by sample clustering (Figure 5C). A power β = 15 was selected as the software threshold for scale-free network construction (Figure 5D). Twenty-four modules were identified by gene dendrogram (Figure 5E). The correlations between the blue module and xCell-defined ECs (ECs, lymphatic ECs, and microvascular ECs) were 0.97, indicating a concentrated expression of the blue module in ECs (Figure 5F). Once identifying the blue module as the one with the highest significance, we further screened 505 intramodular genes, containing KCNJ12 and SPNS2, with a high correlation to ECs (Figure 5G; gene significance > 0.5 and module membership > 0.8). We subsequently constructed PPI networks targeting SPSN2 and KCNJ2, which may regulate the expression of ECs (Figure 5H).

The mechanisms of IA initiation have not been clear. Multiple factors, including inflammation, genetics, hemodynamics, and so on, contribute together to IA formation[19,20]. Genetic and morphological studies suggested that IA may not be a local disease. In the present study, we compared RNA-seq data of STA and WES data of blood between IA patients and controls, integrated the intersected genes from the two comparisons, used the Xcell R package to analyze the cell types with intersected genes, drawing the results that the down-regulated expression of KCNJ12 and SPSN2 on endothelial cells of STA was the vascular feature of IA.

To visualize the difference of STA between IA patients and controls, we performed the bioinformatic analysis at the transcript level. PCA showed STA of the IA group could be discriminated from the control group. Function enrichment analysis displayed IA patients’ STA had consistently upregulated pathways in smooth muscle and downregulated pathways in immune and cell adhesion. These results confirmed there was a difference in STA between IA patients and controls, which laid the foundation for the subsequent analysis.

The arterial microenvironment was investigated using the consensus-based xCell algorithm. Our result indicated the expression of ECs was down-regulated in the whole brain artery of IA patients. ECs were the major components of the artery wall. Endothelial cell impartment played a crucial role in IA pathogenesis and was the first event in IA formation[21]. Previous studies showed that reduction of endothelial tight junction proteins facilitated IA formation in rats[22]. Currently, the mechanisms behind endothelial cell impairment in IA formation mainly included aberrant hemodynamics, oxidative stress, estrogen imbalance, and hazardous nitric oxide synthase activity[23]. Together with the subsequent WES data, our findings enriched the EC-IA formation mechanisms from the perspective of gene mutation.

By integrating the results of WES and RNA-seq, we found four SNVs with their genes (TUBB8, ALDH1L2, KCNJ12, and SPNS2) might be involved in IA initiation. Two of them did not satisfy the genetic equilibrium or did not show a statistical difference of allele and genotype distribution between IA patients and controls. Therefore, they were excluded; only KCNJ12 and SPNS2 gene mutations were included in the subsequent analysis. Correlation analysis found that the expression level of these two genes was highly associated with EC expression defined by the xCell algorithm.

KCNJ12, encoding potassium inwardly rectifying channel subfamily J member 12, has been related to the occurrence of cardiovascular diseases[24]. Brain vessel ECs expressed a large number of inward rectifying potassium (KIR) channels, which could sense hemodynamic stimuli and drive vascular diastole and contraction responses[25,26]. Previous studies found a genetic deficiency of endothelial KIR channels would impair endothelium-derived hyperpolarization-mediated type of arterial dilation[25]. In addition, KIR channels exerted an important role in the differentiation of endothelial progenitor cells (EPC). Inhibition of KIR channels would depress the EPC differentiation and affect the endothelial integrity[27]. In the present study, we identified a rare missense mutation rs201403828 by WES and down-regulated KCNJ12 expression by RNA-seq in IA patients. The expression level of KCNJ12 was highly associated with down-regulated ECs in brain arteries. Taken together, these observations indicated the KCNJ12 gene mutation may facilitate IA initiation by regulating the down-regulated EC expression.

SPSN2 encoded sphingolipid transporter 2, a transporter for sphingosine 1-phosphate (S1P)[28]. Endothelial SPSN2 regulated vascular tone and maintained endothelial integrity via S1P[29,30]. A recent study showed the disruption of SPSN2 could cause migration defects of cardiac progenitors and be associated with cardiovascular diseases[28]. In our analysis, SPSN2 was down-regulated on brain arteries in IA patients. The population-based survey of SNV rs201267925 in the SPSN2 gene showed a statistically significant difference in the allele and genotype between IA patients and the controls. Correlation analysis showed SPSN2 expression was significantly associated with down-regulated ECs in brain arteries. This finding indicated the potential role of SPSN2 gene mutation on the onset of IA by regulating EC expression.

Our study had some limitations that need to be considered. First, the sample size was small in RNA-seq. Second, the inference of decreased ECs in IA whole-brain artery lacked further detection at more sites. Third, IA was a polygenetic disease. Our findings could only explain part of IA initiation.

We have described vascular molecule characteristics of whole-brain artery susceptibility in IAs by RNA-seq and WES. A total of two gene mutations (KCNJ12 and SPSN2) were found potentially to facilitate IA initiation by decreasing EC expression in brain arteries.

DC = dendritic cells;

DEGs = differentially expressed genes;

EC = endothelial cells;

EDTA = ethylenediaminetetraacetic acid;

EPC = endothelial progenitor cells;

GO = Gene Ontology;

IA = Intracranial aneurysm;

KEGG = Kyoto Encyclopedia of Genes and Genomes;

PCA = principal components analysis;

PPI = protein-protein interaction;

RNA-seq = RNA sequencing;

S1P = sphingosine 1-phosphate;

SNV = single nucleotide variants;

STA = superficial temporal artery;

WES = whole-exome sequencing;

WGCNA = weighted gene co-expression network analysis.

Authorship information:

Bo Chen: [email protected];

Ming Zeng: [email protected];

Langchao Yan: [email protected];

Ying Wang: [email protected];

Laixin Song: [email protected];

Wengui Tao: [email protected];

Zheng Huang: [email protected];

Fenghua Chen: [email protected].

Conflict of interest: None.

Funding information: The Fundamental Research Funds for the Central Universities of Central South University (No.160171016).

Authors’ contributions

F.H. Chen, and Z. Huang designed research protocols; L.C. Yan, M. Zeng, and W. G. Tao collected samples; B. Chen and L.X. Song collected clinical information; B. Chen and M. Zeng performed bioinformatic analysis; Y. Wang supported WES analysis support; B. Chen drew the figures; B. Chen drafted the manuscript; B. Chen and F.H. Chen revised the manuscript.

All authors have read and approved the final manuscript.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities of Central South University (Grant No.160171016 to M.D. Chen).

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval

Ethical approval was completed through Xiangya Hospital of Central South University Institute Research Ethics Committee (202103613).

Consent to participate

Written informed consent was obtained from the patients/participants.

Consent to publish

The authors affirm that human research patients/participants provided informed consent for publication of the sequencing data.

Competing interests

The authors declare no competing interests.

Acknowledgements

We thank all the patients/participants enrolled in this research.

Supplemental materials

Supplemental Figure 1. Correlation analysis between the 4 mutation genes and STA cell expression. * P < 0.05; ** P < 0.01; *** P < 0.001.

Supplemental Table 1. Primer sequences for RT-qPCR.

Frösen J, Tulamo R, Paetau A, Laaksamo E, Korja M, Laakso A, Niemelä M, Hernesniemi J (2012) Saccular intracranial aneurysm: pathology and mechanisms. Acta Neuropathol 123 (6):773-786. doi:10.1007/s00401-011-0939-3
Chalouhi N, Hoh BL, Hasan D (2013) Review of cerebral aneurysm formation, growth, and rupture. Stroke 44 (12):3613-3622. doi:10.1161/strokeaha.113.002390
Bakker MK, Cobyte S, Hennekam FAM, Rinkel GJE, Veldink JH, Ruigrok YM (2022) Genome-wide linkage analysis combined with genome sequencing in large families with intracranial aneurysms. Europ J Hum Genet. doi:10.1038/s41431-022-01059-0
Diagbouga MR, Morel S, Bijlenga P, Kwak BR (2018) Role of hemodynamics in initiation/growth of intracranial aneurysms. Eur J Clin Invest 48 (9):e12992. doi:10.1111/eci.12992
Ho AL, Lin N, Frerichs KU, Du R (2015) Smoking and Intracranial Aneurysm Morphology. Neurosurgery 77 (1):59-66; discussion 66. doi:10.1227/neu.0000000000000735
Chen B, Tao W, Li S, Zeng M, Zhang L, Huang Z, Chen F (2022) Medial Gap: A Structural Factor at the Arterial Bifurcation Aggravating Hemodynamic Insult. J Neuropathol Exp Neurol 81 (4):282-290. doi:10.1093/jnen/nlac017
Brown RD, Jr., Huston J, Hornung R, Foroud T, Kallmes DF, Kleindorfer D, Meissner I, Woo D, Sauerbeck L, Broderick J (2008) Screening for brain aneurysm in the Familial Intracranial Aneurysm study: frequency and predictors of lesion detection. J Neurosurg 108 (6):1132-1138. doi:10.3171/jns/2008/108/6/1132
Ruigrok YM, Rinkel GJ, Wijmenga C (2005) Genetics of intracranial aneurysms. Lancet Neurol 4 (3):179-189. doi:10.1016/s1474-4422(05)01015-x
Luo C, Hu C, Li B, Liu J, Hu L, Dong R, Liao X, Zhou J, Xu L, Liu S, Li Y, Yuan D, Jiang W, Yan J (2021) Polymorphisms in Lysyl Oxidase Family Genes Are Associated With Intracranial Aneurysm Susceptibility in a Chinese Population. Front Endocrinol (Lausanne) 12:642698. doi:10.3389/fendo.2021.642698
Hostettler IC, O'Callaghan B, Bugiardini E, O'Connor E, Vandrovcova J, Davagnanam I, Alg V, Bonner S, Walsh D, Bulters D, Kitchen N, Brown MM, Grieve J, Werring DJ, Houlden H (2021) ANGPTL6 Genetic Variants Are an Underlying Cause of Familial Intracranial Aneurysms. Neurology 96 (6):e947-e955. doi:10.1212/wnl.0000000000011125
Kaminogo M, Yonekura M, Shibata S (2003) Incidence and outcome of multiple intracranial aneurysms in a defined population. Stroke 34 (1):16-21. doi:10.1161/01.str.0000046763.48330.ad
Pei Y, Xu Z, Liang G, Jin H, Duan Y, Yang B, Qiao X, You H, Xing D (2021) Risk Factors of Anterior Circulation Intracranial Aneurysm Rupture: Extracranial Carotid Artery Tortuosity and Aneurysm Morphologic Parameters. Front Neurol 12:693549. doi:10.3389/fneur.2021.693549
Kim HJ, Song HN, Lee JE, Kim YC, Baek IY, Kim YS, Chung JW, Jee TK, Yeon JY, Bang OY, Kim GM, Kim KH, Kim JS, Hong SC, Seo WK, Jeon P (2021) How Cerebral Vessel Tortuosity Affects Development and Recurrence of Aneurysm: Outer Curvature versus Bifurcation Type. J Stroke 23 (2):213-222. doi:10.5853/jos.2020.04399
Zeng M, Huang Z, Tao W, Zeng F, Chen F (2021) A retrospective longitudinal study of age-related shifts and deformations in the basilar artery bifurcation. Neuroradiology 63 (8):1305-1311. doi:10.1007/s00234-021-02644-8
Wilkinson DA, Daou BJ, Nadel JL, Chaudhary N, Gemmete JJ, Thompson BG, Pandey AS (2021) Abdominal aortic aneurysm is associated with subarachnoid hemorrhage. J Neurointerv Surg 13 (8):716-721. doi:10.1136/neurintsurg-2020-016757
Rouchaud A, Brandt MD, Rydberg AM, Kadirvel R, Flemming K, Kallmes DF, Brinjikji W (2016) Prevalence of Intracranial Aneurysms in Patients with Aortic Aneurysms. AJNR Am J Neuroradiol 37 (9):1664-1668. doi:10.3174/ajnr.A4827
Ding X, Zhao S, Zhang Q, Yan Z, Wang Y, Wu Y, Li X, Liu J, Niu Y, Zhang Y, Zhang M, Wang H, Zhang Y, Chen W, Yang XZ, Liu P, Posey JE, Lupski JR, Wu Z, Yang X, Wu N, Wang K (2020) Exome sequencing reveals a novel variant in NFX1 causing intracranial aneurysm in a Chinese family. J Neurointerv Surg 12 (2):221-226. doi:10.1136/neurintsurg-2019-014900
Yang B, Yang Y, Liu Y, Li H, Ren S, Peng Z, Fang K, Yang L, Dong Q (2021) Molecular characteristics of varicocele: integration of whole-exome and transcriptome sequencing. Fertil Steril 115 (2):363-372. doi:10.1016/j.fertnstert.2020.08.004
Salimi Ashkezari SF, Mut F, Chung BJ, Yu AK, Stapleton CJ, See AP, Amin-Hanjani S, Charbel FT, Rezai Jahromi B, Niemelä M, Frösen J, Maiti S, Robertson AM, Cebral JR (2021) Hemodynamics in aneurysm blebs with different wall characteristics. J Neurointerv Surg 13 (7):642-646. doi:10.1136/neurintsurg-2020-016601
Liu H, Guo W, Xiang S, Hu P, Sun F, Gao J, Zhang X, Wang P, Jing W, Zhang L, Yang X, Duan C, He M, Zhang H, Qu Y (2019) The natural course of unruptured intracranial aneurysms in a Chinese cohort: protocol of a multi-center registration study in CIAP. J Transl Med 17 (1):349. doi:10.1186/s12967-019-2092-z
Jamous MA, Nagahiro S, Kitazato KT, Tamura T, Aziz HA, Shono M, Satoh K (2007) Endothelial injury and inflammatory response induced by hemodynamic changes preceding intracranial aneurysm formation: experimental study in rats. J Neurosurg 107 (2):405-411. doi:10.3171/jns-07/08/0405
Li MH, Li PG, Huang QL, Ling J (2014) Endothelial injury preceding intracranial aneurysm formation in rabbits. West Indian Med J 63 (2):167-171. doi:10.7727/wimj.2013.129
Sheinberg DL, McCarthy DJ, Elwardany O, Bryant JP, Luther E, Chen SH, Thompson JW, Starke RM (2019) Endothelial dysfunction in cerebral aneurysms. Neurosurg Focus 47 (1):E3. doi:10.3171/2019.4.Focus19221
Yuan HX, Yan K, Hou DY, Zhang ZY, Wang H, Wang X, Zhang J, Xu XR, Liang YH, Zhao WS, Xu L, Zhang L (2017) Whole exome sequencing identifies a KCNJ12 mutation as a cause of familial dilated cardiomyopathy. Medicine (Baltimore) 96 (33):e7727. doi:10.1097/md.0000000000007727
Köhler R, Ruth P (2010) Endothelial dysfunction and blood pressure alterations in K+-channel transgenic mice. Pflugers Arch 459 (6):969-976. doi:10.1007/s00424-010-0819-z
Fancher IS, Le Master E, Ahn SJ, Adamos C, Lee JC, Berdyshev E, Dull RO, Phillips SA, Levitan I (2020) Impairment of Flow-Sensitive Inwardly Rectifying K(+) Channels via Disruption of Glycocalyx Mediates Obesity-Induced Endothelial Dysfunction. #N/A 40 (9):e240-e255. doi:10.1161/atvbaha.120.314935
Ye G, Guan H, Karush J, Wang F, Xu X, Mao H, Huang X, Yang X, Peng P, Ba Y, Zhou J, Lian J (2014) Effects of Ca2+-activated potassium and inward rectifier potassium channel on the differentiation of endothelial progenitor cells from human peripheral blood. Mol Biol Rep 41 (5):3413-3423. doi:10.1007/s11033-014-3203-9
Hisano Y, Ota S, Takada S, Kawahara A (2013) Functional cooperation of spns2 and fibronectin in cardiac and lower jaw development. Biol Open 2 (8):789-794. doi:10.1242/bio.20134994
Spiegel S, Maczis MA, Maceyka M, Milstien S (2019) New insights into functions of the sphingosine-1-phosphate transporter SPNS2. J Lipid Res 60 (3):484-489. doi:10.1194/jlr.S091959
Del Gaudio I, Rubinelli L, Sasset L, Wadsack C, Hla T, Di Lorenzo A (2021) Endothelial Spns2 and ApoM Regulation of Vascular Tone and Hypertension Via Sphingosine-1-Phosphate. J Am Heart Assoc 10 (14):e021261. doi:10.1161/jaha.121.021261

Table 1 Characteristic of the patients with IA and the controls in WES

Variable	Intracranial aneurysm (n=40)	Control (n=50)	P-value	OR
Age	56.03 ± 11.38	38.62 ± 14.10
Sex (Man)	14	16	0.764	1.144 (0.474 - 2.760)
Hypertension	18	19	0.502	1.335 (0.573 - 3.108)
Hyperlipidemia	7	9	0.951	0.966 (0.325 - 2.871)
Diabetes	6	11	0.399	0.626 (0.209 - 1.872)
Smoking	2	4	0.887	0.605 (0.105 - 3.486)
Alcohol use	5	7	0.835	0.878 (0.256 - 3.006)
Rupture	31
Maximum diameter
diameter < 5	7
5 ≤ diameter < 15	30
15 ≤ diameter < 25	1
diameter ≥ 25	2
Number
1	14
2	16
3	7
≥ 4	3

Table 2 Characteristic of the patients with IA and the controls in RNA-seq

Variable	Intracranial aneurysm (n=10)	Control (n=5)	P-value	OR
Age	54.70 ± 10.24	36.00 ± 8.28
Sex (Man)	4	3	0.608	0.444 (0.050 – 3.976)
Hypertension	5	2	1.000	1.500 (0.170 – 13.225)
Hyperlipidemia	1	2	0.242	0.167 (0.011 – 2.563)
Diabetes	3	1	1.000	1.714 (0.131 – 22.513)
Smoking	2	2	0.560	0.375 (0.035 – 3.999)
Alcohol use	2	2	0.560	0.375 (0.035 - 3.999)
Rupture	5
Maximum diameter
diameter < 5	2
5 ≤ diameter < 15	7
15 ≤ diameter < 25	0
diameter ≥ 25	1
Number
1	4
2	2
3	4
≥ 4	0

Table 3 Details of recurrent rare variants in four genes retrieved from WES and RNAseq

Sequencing	Variant	rs9329307	rs75238381	rs201403828	rs201267925
WES	Gene	TUBB8	ALDH1L2	KCNJ12	SPNS2
	Position	chr10: 94018	chr12: 105440719	chr17: 21318793	chr17: 4436671
	SNV	c.314A>G	c.1715G>A	c.314A>G	c.1222G>T
	Type	MISSENSE	MISSENSE	MISSENSE	MISSENSE
	SIFT	D	D	D	D
	PolyPhen2	-	B	-	-
	MutationAssessor	-	N	-	-
	MutationTaster	D	D	D	D
	1000G_ALL	-	0.0091853	-	0.00259585
	Control variant frequency (variant number/ total number)	15 / 50	8 / 50	0 / 50	0 / 50
	Patient variant frequency (variant number/ total number)	22 / 40	1 / 40	5 / 40	4 / 40
	P-value (α = 0.05)	0.019	0.040	0.015	0.036
RNA-seq
	Control FPKM expression mean	0.064	4.476	2.176	21.110
	Patient FPKM expression mean	0.010	2.919	1.147	11.386
	Log2Foldchange	-2.678	-0.617	-0.924	-0.891
	P-value (α = 0.05)	0.019	0.008	0.013	0.040

Table 4 Testing for Hardy-Weinberg equilibrium of recurrent rare variants in the overall study population

Polymorphism locus	Genotype	Genotype frequency, n (%)	P-value of Hardy-Weinberg equilibrium testing (α = 0.05)
rs9329307 (TUBB8)	TT	53 (58.9)	0.049
rs9329307 (TUBB8)	TC	37 (41.1)
rs75238381 (ALDH1L2)	CC	81 (90.0)
	CT	8 (8.9)	0.349
	TT	1 (1.1)
rs201403828 (KCNJ12)	GG	85 (94.4)	0.964
rs201403828 (KCNJ12)	GA	5 (5.6)
rs201267925 (SPNS2)	GG	86 (95.6)	0.977
rs201267925 (SPNS2)	GT	4 (4.4)

Table 5 Comparisons of the allele and genotype distributions for the recurrent rare variants of genes between the patients with IA and the controls

Gene (variant)	Allele frequency and distribution				Genotype frequency and distribution
Gene (variant)	Allele	Patients, n (%)	Controls, n (%)	P - values (α = 0.05)	Genotype	Patients, n (%)	Controls, n (%)	P - values (α = 0.05)
TUBB8 (rs9329307)	T	58 (72.5)	85 (85.0)	0.043	TT	18 (45.0)	35 (70.0)	0.019
TUBB8 (rs9329307)	C	22 (27.5)	15 (15.0)		TC	22 (55.0)	15 (30.0)
ALDH1L2 (rs75238381)	C	79 (98.8)	91 (91.0)	0.054	CC	39 (97.5)	42 (84.0)	0.102
	T	1 (1.2)	9 (9.0)		CT	1 (2.5)	7 (14.0)
	-	-	-		TT	0 (0.0)	1 (2.0)
KCNJ12 (rs201403828)	G	75 (93.8)	100 (100.0)	0.016	GG	35 (87.5)	50 (100.0)	0.015
KCNJ12 (rs201403828)	A	5 (6.2)	0 (0.0)		GA	5 (12.5)	0 (0.0)
SPNS2 (rs201267925)	G	76 (95.0)	100 (100.0)	0.037	GG	36 (90.0)	50 (100.0)	0.036
SPNS2 (rs201267925)	T	4 (5.0)	0 (0.0)		GT	4 (10.0)	0 (0.0)

Supplementalfigure1.tif
Supplemental Figure 1. Correlation analysis between the 4 mutation genes and STA cell expression. * P < 0.05; ** P < 0.01; *** P < 0.001.
Supplementaltable.docx

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Molecular characteristics of arteries in patients with intracranial aneurysm: integration of WES and RNA-seq

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