Browsing by Author "Jiang, Jianjun"

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    Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
    (Hindawi, 2022-02-07) Zhang, Hongliang; Chen, Tingting; Zhang, Yunyan; Lin, Jiangbo; Zhao, Wenjun; Shi, Yangyang; Lau, Huichong; Zhang, Yang; Yang, Minjun; Xu, Cheng; Tang, Lijiang; Xu, Baohui; Jiang, Jianjun; Chen, Xiaofeng; Radiation Oncology, School of Medicine
    Background: Aortic dissection (AD) is a lethal vascular disease with high mortality and morbidity. Though AD clinical pathology is well understood, its molecular mechanisms remain unclear. Specifically, gene expression profiling helps illustrate the potential mechanism of aortic dissection in terms of gene regulation and its modification by risk factors. This study was aimed at identifying the genes and molecular mechanisms in aortic dissection through bioinformatics analysis. Method: Nine patients with AD and 10 healthy controls were enrolled. The gene expression in peripheral mononuclear cells was profiled through next-generation RNA sequencing. Analyses including differential expressed gene (DEG) via DEGseq, weighted gene coexpression network (WGCNA), and VisANT were performed to identify crucial genes associated with AD. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was also utilized to analyze Gene Ontology (GO). Results: DEG analysis revealed that 1,113 genes were associated with AD. Of these, 812 genes were markedly reduced, whereas 301 genes were highly expressed, in AD patients. DEGs were rich in certain categories such as MHC class II receptor activity, MHC class II protein complex, and immune response genes. Gene coexpression networks via WGCNA identified 3 gene hub modules, with one positively and 2 negatively correlated with AD, respectively. Specifically, module 37 was the most strongly positively correlated with AD with a correlation coefficient of 0.72. Within module 37, five hub genes (AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D) displayed high connectivity and may have clinical significance in the pathogenesis of AD. Conclusion: Our analysis provides the possible association of specific genes and gene modules for the involvement of the immune system in aortic dissection. AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D in module M37 were highly connected and strongly linked with AD, suggesting that these genes may help understand the pathogenesis of aortic dissection.
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    Potential influencing factors of aortic diameter at specific segments in population with cardiovascular risk
    (BMC, 2022-02-05) Chen, Tingting; Yang, Xingan; Fang, Xiaoxin; Tang, Lijiang; Zhang, Yang; Weng, Yingzheng; Zhang, Hongliang; Wu, Juntao; Mao, Ping; Xu, Baohui; Jiang, Jianjun; Chen, Xiaofeng; Radiation Oncology, School of Medicine
    Background: Aortic diameter is a critical parameter for the diagnosis of aortic dilated diseases. Aortic dilation has some common risk factors with cardiovascular diseases. This study aimed to investigate potential influence of traditional cardiovascular risk factors and the measures of subclinical atherosclerosis on aortic diameter of specific segments among adults. Methods: Four hundred and eight patients with cardiovascular risk factors were prospectively recruited in the observational study. Comprehensive transthoracic M-mode, 2-dimensional Doppler echocardiographic studies were performed using commercial and clinical diagnostic ultrasonography techniques. The aortic dimensions were assessed at different levels: (1) the annulus, (2) the mid-point of the sinuses of Valsalva, (3) the sinotubular junction, (4) the ascending aorta at the level of its largest diameter, (5) the transverse arch (including proximal arch, mid arch, distal arch), (6) the descending aorta posterior to the left atrium, and (7) the abdominal aorta just distal to the origin of the renal arteries. Multivariable linear regression analysis was used for evaluating aortic diameter-related risk factors, including common cardiovascular risk factors, co-morbidities, subclinical atherosclerosis, lipid profile, and hematological parameters. Results: Significant univariate relations were found between aortic diameter of different levels and most traditional cardiovascular risk factors. Carotid intima-media thickness was significantly correlated with diameter of descending and abdominal aorta. Multivariate linear regression showed potential effects of age, sex, body surface area and some other cardiovascular risk factors on aortic diameter enlargement. Among them, high-density lipoprotein cholesterol had a significantly positive effect on the diameter of ascending and abdominal aorta. Diastolic blood pressure was observed for the positive associations with diameters of five thoracic aortic segments, while systolic blood pressure was only independently related to mid arch diameter. Conclusion: Aortic segmental diameters were associated with diastolic blood pressure, high-density lipoprotein cholesterol, atherosclerosis diseases and other traditional cardiovascular risk factors, and some determinants still need to be clarified for a better understanding of aortic dilation diseases.
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    Single-Cell RNA Sequencing Reveals Smooth Muscle Cells Heterogeneity in Experimental Aortic Dissection
    (Frontiers Media, 2022-08-11) Xu, Cheng; Liu, Xiaowei; Fang, Xiaoxin; Yu, Lei; Lau, Hui Chong; Li, Danlei; Liu, Xiaoman; Li, Haili; Ren, Justin; Xu, Baohui; Jiang, Jianjun; Tang, Lijiang; Chen, Xiaofeng; Radiation Oncology, School of Medicine
    Purpose: This study aims to illustrate the cellular landscape in the aorta of experimental aortic dissection (AD) and elaborate on the smooth muscle cells (SMCs) heterogeneity and functions among various cell types. Methods: Male Apolipoprotein deficient (ApoE-/-) mice at 28 weeks of age were infused with Ang II (2,500 ng/kg/min) to induce AD. Aortas from euthanized mice were harvested after 7 days for 10×Genomics single-cell RNA sequencing (scRNA-seq), followed by the identification of cell types and differentially expressed genes (DEGs). Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted. Results: AD was successfully induced in ApoE-/- mice. scRNA-seq identified 15 cell clusters and nine cell types, including non-immune cells (endothelials, fibroblasts, and SMCs) and immune cells (B cells, natural killer T cell, macrophages, dendritic cells, neutrophils, and mast cells). The relative numbers of SMCs were remarkably changed, and seven core DEGs (ACTA2,IL6,CTGF,BGN,ITGA8,THBS1, and CDH5) were identified in SMCs. Moreover, we found SMCs can differentiate into 8 different subtypes through single-cell trajectory analysis. Conclusion: scRNA-seq technology can successfully identify unique cell composition in experimental AD. To our knowledge, this is the first study that provided the complete cellular landscape in AD tissues from mice, seven core DEGs and eight subtypes of SMCs were identified, and the SMCs have evolution from matrix type to inflammatory type.