Genetic correlations, shared risk genes and immunity landscapes between COVID-19 and venous thromboembolism: evidence from GWAS and bulk transcriptome data.

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) were vulnerable to venous thromboembolism (VTE), which further increases the risk of unfavorable outcomes. However, neither genetic correlations nor shared genes underlying COVID-19 and VTE are well understood. OBJECTIVE: This study aimed to characterize genetic correlations and common pathogenic mechanisms between COVID-19 and VTE. METHODS: We used linkage disequilibrium score (LDSC) regression and Mendelian Randomization (MR) analysis to investigate the genetic associations and causal effects between COVID-19 and VTE, respectively. Then, the COVID-19 and VTE-related datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics and systems biology approaches with R software, including weighted gene co-expression network analysis (WGCNA), enrichment analysis, and single-cell transcriptome sequencing analysis. The miRNA-genes and transcription factor (TF)-genes interaction networks were conducted by NetworkAnalyst. We performed the secondary analysis of the ATAC-seq and Chip-seq datasets to address the epigenetic-regulating relationship of the shared genes. RESULTS: This study demonstrated positive correlations between VTE and COVID-19 by LDSC and bidirectional MR analysis. A total of 26 potential shared genes were discovered from the COVID-19 dataset (GSE196822) and the VTE dataset (GSE19151), with 19 genes showing positive associations and 7 genes exhibiting negative associations with these diseases. After incorporating two additional datasets, GSE164805 (COVID-19) and GSE48000 (VTE), two hub genes TP53I3 and SLPI were identified and showed up-regulation and diagnostic capabilities in both illnesses. Furthermore, this study illustrated the landscapes of immune processes in COVID-19 and VTE, revealing the downregulation in effector memory CD8+ T cells and activated B cells. The single-cell sequencing analysis suggested that the hub genes were predominantly expressed in the monocytes of COVID-19 patients at high levels. Additionally, we identified common regulators of hub genes, including five miRNAs (miR-1-3p, miR-203a-3p, miR-210-3p, miR-603, and miR-124-3p) and one transcription factor (RELA). CONCLUSIONS: Collectively, our results highlighted the significant correlations between COVID-19 and VTE and pinpointed TP53I3 and SLPI as hub genes that potentially link the severity of both conditions. The hub genes and their common regulators might present an opportunity for the simultaneous treatment of these two diseases.

Genetically predicted tobacco consumption and risk of intracranial aneurysm: a Mendelian randomization study.

Several observational studies have suggested that tobacco consumption is a risk factor for intracranial aneurysms (IAs). We here genetically predict the causal association between specific smoking features and biomarkers for smokers and IA risk. The Mendelian randomization (MR) analysis considered summary statistics from the largest current genome-wide association studies of smoking and IA. The inverse-variance weighted (IVW) method, weighted median method, MR-RAPS, and multiple variants Mendelian randomization (MVMR) were performed to estimate the effect of different smoking features and drinking in IA. We observed significant causal effects of smoking on the risk of both aneurysmal subarachnoid hemorrhage (aSAH) and unruptured IA (uIA). The ORs of IAs based on the IVW method were 1.890 (95% CI 1.486-2.405) of ever smoking regularly. MVMR analysis afforded odds ratios of 1.685 (95% CI 1.136-2.501). In the further subgroup analysis, a similar causal relationship was observed in aSAH. Moreover, our analyses suggested that higher blood cotinine level and cadmium increases aSAH risk, and ORs were 1.235 (95%CI 1.009-1.186) and 1.235 (95%CI 1.046-1.458), respectively. Our study suggests that ever smoking regularly is associated with the IA risk, which includes both uIA and aSAH. Besides, higher blood cadmium and cotinine level may increases IA and aSAH risk. Thus, tobacco control should be promoted as primordial prevention for IAs, and screening for patients with a smoking history is emphasized.

Global trends and Frontier topics about vascular smooth muscle cells phenotype switch: A bibliometric analysis from 1999 to 2021.

Objective: Vascular smooth muscle cell phenotype switch (VSMCPS) plays a significant role in vascular remodeling. This study aimed to conduct a bibliometric analysis and visualize the knowledge map of research on VSMCPS. Methods: We retrieved publications focusing on VSMCPS from the Web of Science Core Collection database (SCI-EXPANDED) from 1999 to 2021. Using bibliometric tools, VOSviewer and CiteSpace, we identified the most productive researchers, journals, institutions, and countries. At the same time, the trends, hot topics, and knowledge networks were analyzed and visualized. Results: A total of 2213 publications were included in this analysis. The number of annual publications in the VSMCPS field exhibited an upward trend and could be roughly divided into three phases. Until 2006, the most prolific authors were from the United States. As of 2008, the number of articles published in China increased dramatically to reach 126 papers in 2020. As of 2014, China was the most productive country in this field. The United States ranked first in the number of highly-influential authors, institutions, and literature from 1999 to 2022. Owens GK, Hata, Akiko, and Wen, jin-kun were the most prolific authors. Arteriosclerosis Thrombosis and Vascular Biology, Circulation Research, and Cardiovascular Research were the top-ranked journals in this field. "Vascular remodeling," "atherosclerosis," "neointima," "hypertension", and "inflammation" were the main researched topics. New diseases, new mechanisms, and new phenotype (e.g., micro RNA, macrophage-like-cell, hypoxia, autophagy, long noncoding RNA, oxidative stress, endoplasmic reticulum stress, senescence, aging, abdominal aortic aneurysm, and aortic dissection) represent the trending topics in recent years. Conclusion: This study systematically analyzed and visualized the knowledge map of VSMCPS over the past 2 decades. Our findings provide a comprehensive overview for scholars who want to understand current trends and new research frontiers in this area.

Angioarchitectural features of brain arteriovenous malformation presented with seizures.

Seizures are the second most common manifestations of brain arteriovenous malformations (bAVMs). This study was conducted to investigate the clinical and angioarchitectural features of bAVMs with seizures and provide guidelines for the clinical management of these patients. We collected clinical and radiological data on patients with bAVMs diagnosed by digital subtraction angiography between January 2013 and December 2020 and dichotomized the patients into the seizures and non-seizures groups. We identified differences in demographic and angiographic features. Logistic regression and random forest (RF) models were developed and compared. The diagnostic capacity was assessed using receiver operating characteristic (ROC) curves. A nomogram was constructed, and the clinical impact was determined by decision curve analysis. A total of 414 patients with bAVMs were included in the analysis, of which 78 (18.8%) had bAVM-related seizures. In the multivariable logistic regression model, the location and side of bAVMs were independently associated with seizures. In RF models, the maximal diameter of veins and the cross-sectional area of feeding arteries and draining veins were the most important features. ROC curves showed that the RF model was not better than MLR in predicting seizures. Decision curve analysis revealed that the use of a constructed nomogram to stratify the seizure patients was beneficial at all threshold probabilities in our study. The side and location of bAVMs are specific angioarchitectural features independently associated with the occurrences of seizures with bAVMs.

Molecular feature of arterial remodeling in the brain arteriovenous malformation revealed by arteriovenous shunt rat model and RNA sequencing.

PURPOSE: Morphological research suggested the feeding artery of brain arteriovenous malformation (bAVM) had vascular remodeling under the high blood flow; however, the underlying molecular mechanisms were unclear. METHODS: We constructed 32 simplified AVM rat models in four groups: the control group (n = 6), 1-week high-blood-flow group (n = 9), 3-week high-blood-flow group (n = 7) and 6-week high-blood-flow group (n = 10). The circumference, blood velocity, blood flow, pressure, and wall shear of the feeding artery were measured or calculated. The arterial wall change was observed by Masson staining. RNA sequencing (RNA-seq) of feeding arteries was performed, followed by bioinformatics analysis to detect the potential molecular mechanism for bAVM artery remodeling under the high blood flow. RESULTS: We observed hemodynamic injury and vascular remodeling on the feeding artery under the high blood flow. RNA-seq showed immune/inflammation infiltration and vascular smooth muscle cell (VSMC) phenotype transformation during remodeling. Weighted gene co-expression network analysis (WGCNA) and time series analysis further identified 27 key genes and pathways involved in remodeling. Upstream miRNA and molecular drugs were predicted targeting these key genes. CONCLUSIONS: We depicted molecular change of bAVM arterial remodeling via RNA-seq in high-blood-flow rat models. Twenty-seven key genes may regulate immune/inflammation infiltration and VSMC phenotype transform in bAVM arterial remodeling.

The Transcriptional Landscapes and Key Genes in Brain Arteriovenous Malformation Progression in a Venous Hypertension Rat Model Revealed by RNA Sequencing.

BACKGROUND: Brain arteriovenous malformations (bAVM) are abnormal vascular lesions characterized by direct connections between arteries and veins without an intervening capillary bed. The primary goal for brain AVM treatment is to prevent rupture and hemorrhage; however, the underlying molecular mechanisms are still unknown. METHODS: We constructed venous hypertension (VH) rat model with end-to-end anastomosis of the proximal left common carotid artery and the left distal external jugular vein. Thirty-eight adult rats were randomly assigned to four groups: the 0-week (n=5), the 1-week VH group (n=12), the 3-week VH group (n=9), and the 6-week VH group (n=12). We measured the hemodynamics and diameter of the arterialized veins. An RNA sequencing of arterialized veins was conducted, followed by comprehensive bioinformatics analysis to identify key genes and biological pathways involved in VH progression. The candidate genes from RNA-Seq were validated by RT-qPCR and immunostaining in human tissues. RESULTS: We observed high-flow and low resistance characteristics in VH models. A total of 317 upregulated and 258 downregulated common genes were consistently differentially expressed during VH progression. Thirteen co-expression modules were obtained by WGCNA analysis, and 4 key modules were identified. Thirteen genes: Adamts8, Adamtsl3, Spon2, Adamtsl2, Chad, Itga7, Comp, Itga8, Bmp6, Fst, Smad6, Smad7, Grem1, and Nog with differential expressions were identified using the density of maximum neighborhood component (DMNC) algorithm in Cytohubba. The expression of five potential genes (Adamts8, Adamtsl3, Spon2, Adamtsl2, Itga8) were increased in RT-qPCR, while in human bAVM tissue, the protein levels of Adamtsl2 and Itga8 were significant elevated and Spon2 and Adamtsl3 were decreased. CONCLUSION: The identified gene networks of Adamtsl3, Spon2, Adamtsl2, and Itga8 provided key genes for further intervention.

Comprehensive analysis of immunocyte infiltration and the key genes associated with intraplaque hemorrhage in carotid atherosclerotic plaques.

PURPOSE: To identify key biomarkers associated with intraplaque hemorrhage (IPH). METHODS: We conducted a comprehensive analysis combined with DEGs, xCell, WGCNA, GSEA, and GSVA methods to identify immune infiltration cells and key genes involved in IPH by using GSE163154 from the gene expression omnibus (GEO). E-MTAB-1470 and E-MTAB-2055 from the ArrayExpress database were utilized as the verification datasets. Finally, the candidate hub genes were further validated by RT-qPCR in clinical samples. RESULTS: A total of 280 genes were upregulated and 234 genes were downregulated in GSE163154. Among the upregulated pathways, the lysosome and chemokine signaling pathway were enriched, while the vascular smooth muscle (VSMC) contraction and focal adhesion were downregulated. In addition, ten coexpression modules were obtained by using the WGCNA method and two IPH and immunity-related modules were identified. In total, 454 genes overlapped by DEGs and WGCNA results were imported into Cytoscape to construct a protein-protein network. Eight genes (FCER1G, ITGB2, VAV1, CSF1R, ITGAM, TYROBP, PTK2, and PTPN11) were identified as the IPH-related gene set with area under curves (AUC) of 0.961, 0.905, and 0.857 in GSE163154, E-MTAB-2055, and E-MTAB-1470, respectively. The expression of four genes (ITGB2, VAV1, ITGAM, TYROBP) from our analysis were consistent with RT-qPCR results. CONCLUSION: Eight genes were found to be involved in IPH, and four genes (ITGB2, VAV1, ITGAM, TYROBP) may be an important biomarkers for IPH.

Factors affecting the performance of brain arteriovenous malformation rupture prediction models.

BACKGROUND: In many cases, both the rupture rate of cerebral arteriovenous malformation (bAVM) in patients and the risk of endovascular or surgical treatment (when radiosurgery is not appropriate) are not low, it is important to assess the risk of rupture more cautiously before treatment. Based on the current high-risk predictors and clinical data, different sample sizes, sampling times and algorithms were used to build prediction models for the risk of hemorrhage in bAVM, and the accuracy and stability of the models were investigated. Our purpose was to remind researchers that there may be some pitfalls in developing similar prediction models. METHODS: The clinical data of 353 patients with bAVMs were collected. During the creation of prediction models for bAVM rupture, we changed the ratio of the training dataset to the test dataset, increased the number of sampling times, and built models for predicting bAVM rupture by the logistic regression (LR) algorithm and random forest (RF) algorithm. The area under the curve (AUC) was used to evaluate the predictive performances of those models. RESULTS: The performances of the prediction models built by both algorithms were not ideal (AUCs: 0.7 or less). The AUCs from the models built by the LR algorithm with different sample sizes were better than those built by the RF algorithm (0.70 vs 0.68, p < 0.001). The standard deviations (SDs) of the AUCs from both prediction models with different sample sizes displayed wide ranges (max range > 0.1). CONCLUSIONS: Based on the current risk predictors, it may be difficult to build a stable and accurate prediction model for the hemorrhagic risk of bAVMs. Compared with sample size and algorithms, meaningful predictors are more important in establishing an accurate and stable prediction model.