Transcriptional (ChIP-Chip) Analysis of ELF1, ETS2, RUNX1 and STAT5 in Human Abdominal Aortic Aneurysm

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Identification of crucial genes involved in pathogenesis of regional weakening of the aortic wall

Background

The diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall.

Methods

The GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets.

Result

A total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression.

Conclusion

We identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00200-1.

Weighted Gene Co-Expression Network Analysis Reveals Key Genes and Potential Drugs in Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is a prevalent aortic disease that causes high mortality due to asymptomatic gradual expansion and sudden rupture. The underlying molecular mechanisms and effective pharmaceutical therapy for preventing AAA progression have not been fully identified. In this study, we identified the key modules and hub genes involved in AAA growth from the GSE17901 dataset in the Gene Expression Omnibus (GEO) database through the weighted gene co-expression network analysis (WGCNA). Key genes were further selected and validated in the mouse dataset (GSE12591) and human datasets (GSE7084, GSE47472, and GSE57691). Finally, we predicted drug candidates targeting key genes using the Drug-Gene Interaction database. Overall, we identified key modules enriched in the mitotic cell cycle, GTPase activity, and several metabolic processes. Seven key genes (CCR5, ADCY5, ADCY3, ACACB, LPIN1, ACSL1, UCP3) related to AAA progression were identified. A total of 35 drugs/compounds targeting the key genes were predicted, which may have the potential to prevent AAA progression.

Identification of Transcriptomic Differences between Lower Extremities Arterial Disease, Abdominal Aortic Aneurysm and Chronic Venous Disease in Peripheral Blood Mononuclear Cells Specimens

Several human tissues are investigated in studies of molecular biomarkers associated with diseases development. Special attention is focused on the blood and its components due to combining abundant information about systemic responses to pathological processes as well as high accessibility. In the current study, transcriptome profiles of peripheral blood mononuclear cells (PBMCs) were used to compare differentially expressed genes between patients with lower extremities arterial disease (LEAD), abdominal aortic aneurysm (AAA) and chronic venous disease (CVD). Gene expression patterns were generated using the Ion S5XL next-generation sequencing platform and were analyzed using DESeq2 and UVE-PLS methods implemented in R programming software. In direct pairwise analysis, 21, 58 and 10 differentially expressed genes were selected from the comparison of LEAD vs. AAA, LEAD vs. CVD and AAA vs. CVD patient groups, respectively. Relationships between expression of dysregulated genes and age, body mass index, creatinine levels, hypertension and medication were identified using Spearman rank correlation test and two-sided Mann-Whitney U test. The functional analysis, performed using DAVID website tool, provides potential implications of selected genes in pathological processes underlying diseases studied. Presented research provides new insight into differences of pathogenesis in LEAD, AAA and CVD, and selected genes could be considered as potential candidates for biomarkers useful in diagnosis and differentiation of studied diseases.

Non-coding RNAs in aortic dissection: From biomarkers to therapeutic targets

Aortic dissection (AD) is the rupture of the aortic intima, causing the blood in the cavity to enter the middle of the arterial wall. Without urgent and proper treatment, the mortality rate increases to 50% within 48 hours. Most patients present with acute onset of symptoms, including sudden severe pain and complex and variable clinical manifestations, which can be easily misdiagnosed. Despite this, the molecular mechanisms underlying AD are still unknown. Recently, non‐coding RNAs have emerged as novel regulators of gene expression. Previous studies have proven that ncRNAs can regulate several cardiovascular diseases; therefore, their potential as clinical biomarkers and novel therapeutic targets for AD has aroused widespread interest. To date, several studies have reported that microRNAs are crucially involved in AD progression. Additionally, several long non‐coding RNAs and circular RNAs have been found to be differentially expressed in AD samples, suggesting their potential roles in vascular physiology and disease. In this review, we discuss the functions of ncRNAs in AD pathophysiology and highlight their potential as biomarkers and therapeutic targets for AD. Meanwhile, we present the animal models previously used for AD research, as well as the specific methods for constructing mouse or rat AD models.

Cellular signaling in Abdominal Aortic Aneurysm

Abdominal aortic aneurysms (AAAs) are highly lethal cardiovascular diseases without effective medications. However, the molecular and signaling mechanisms remain unclear. A series of pathological cellular processes have been shown to contribute to AAA formation, including vascular extracellular matrix remodeling, inflammatory and immune responses, oxidative stress, and dysfunction of vascular smooth muscle cells. Each cellular process involves complex cellular signaling, such as NF-κB, MAPK, TGFβ, Notch and inflammasome signaling. In this review, we discuss how cellular signaling networks function in various cellular processes during the pathogenesis and progression of AAA. Understanding the interaction of cellular signaling networks with AAA pathogenesis as well as the crosstalk of different signaling pathways is essential for the development of novel therapeutic approaches to and personalized treatments of AAA diseases.

Genetic and epigenetic regulation of abdominal aortic aneurysms

Abdominal aortic aneurysms (AAAs) are focal dilations of the aorta that develop from degenerative changes in the media and adventitia of the vessel. Ruptured AAAs have a mortality of up to 85%, thus it is important to identify patients with AAA at increased risk for rupture who would benefit from increased surveillance and/or surgical repair. Although the exact genetic and epigenetic mechanisms regulating AAA formation are not completely understood, Mendelian cases of AAA, which result from pathologic variants in a single gene, have helped provide a basic understanding of AAA pathophysiology. More recently, genome wide associated studies (GWAS) have identified additional variants, termed single nucleotide polymorphisms, in humans that may be associated with AAAs. While some variants may be associated with AAAs and play causal roles in aneurysm pathogenesis, it should be emphasized that the majority of SNPs do not actually cause disease. In addition to GWAS, other studies have uncovered epigenetic causes of disease that regulate expression of genes known to be important in AAA pathogenesis. This review describes many of these genetic and epigenetic contributors of AAAs, which altogether provide a deeper insight into AAA pathogenesis.

Molecular Pharmacological Approaches for Treating Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is considered to be a potent life-threatening disorder in elderly individuals. Although many patients with a small AAA are detected during routine abdominal screening, there is no effective therapeutic option to prevent the progression or regression of AAA in the clinical setting. Recent advances in molecular biology have led to the identification of several important molecules, including microRNA and transcription factor, in the process of AAA formation. Regulation of these factors using nucleic acid drugs is expected to be a novel therapeutic option for AAA. Nucleic acid drugs can bind to target factors, mRNA, microRNA, and transcription factors in a sequence-specific fashion, resulting in a loss of function of the target molecule at the transcriptional or posttranscriptional level. Of note, inhibition of a transcription factor using a decoy strategy effectively suppresses experimental AAA formation, by regulating the expression of several genes associated with the disease progression. This review focuses on recent advances in molecular therapy of using nucleic acid drugs to treat AAA.

Characterization of CD4+ T Cell Subsets in Patients with Abdominal Aortic Aneurysms

BACKGROUND

The mediators produced by CD4+ T lymphocytes are involved in the pathogenesis of aneurysmal lesions in abdominal aortic aneurysm (AAA) patients. The aim of this study was to identify and characterize the CD4+ T cell subsets involved in human AAA.

METHODS

The CD4+ T cell subsets in 30 human aneurysmal lesions were determined using flow cytometry (FC) and immunohistochemistry (IHC). The peripheral blood mononuclear cells (PBMCs) from patients with AAA were also analyzed by FC and compared with control subjects.

RESULTS

Human aneurysmal lesions contained IFN-γ, IL-12p35, IL-4, IL-23p19, IL-17R, and IL-22 positive cells. PBMCs from AAA patients had higher expression levels of IFN-γ, TNF-α, IL-4, and IL-22 when compared to controls.

CONCLUSIONS

Our results show the presence of TH1, TH2, TH17, and TH22 subsets in aneurysmal lesions of AAA patients and suggest that these cells may be mainly activated in situ, where they can induce tissue degradation and contribute to the pathogenesis of AAA.

LncRNA and mRNA interaction study based on transcriptome profiles reveals potential core genes in the pathogenesis of human thoracic aortic dissection

The aim of the present study was to determine the potential core genes in the pathogenesis of human thoracic aortic dissection (TAD) by analyzing microarray profiles of long non‑coding (lnc)‑RNAs between TAD and normal thoracic aorta (NTA). The differentially expressed lncRNA profiles of the aorta tissues between TAD patients (TAD group, n=6) and age‑matched donors with aortic diseases (NTA group, n=6) were analyzed by lncRNAs microarray. Gene ontology (GO), pathway and network analyses were used to further investigate candidate lncRNAs and mRNAs. Differentially expressed lncRNAs and mRNAs were validated by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). In total, the present study identified 765 lncRNAs and 619 mRNAs with differential expression between TAD and NTA (fold change >2.0, P<0.01). GO analysis demonstrated that the differentially upregulated lncRNAs are associated with cell differentiation, homeostasis, cell growth and angiogenesis. Kyoto Encyclopedia of Gene and Genomes pathway analysis demonstrated that the differentially downregulated lncRNAs are mainly associated with arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy and dilated cardiomyopathy. To reduce the lncRNAs for further investigation and to enrich those potentially involved in TAD, a total of 16 candidate lncRNAs with a significant expression (fold change >4, P<0.01) were selected, that were associated with an annotated protein‑coding gene through the GO term and scientific literatures. Then a set of significantly expressed lncRNAs [purinergic receptor P2X7 (P2RX7), hypoxia inducing factor (HIF)‑1A‑AS2, AX746823, RP11‑69I8.3 and RP11‑536K7.5) and the corresponding mRNAs (P2RX7, cyclin dependent kinase inhibitor 2B, HIF‑1A, runt‑related transcription factor 1, connective tissue growth factor and interleukin 2 receptor a chain] were confirmed using RT‑qPCR. The present study revealed that the expression profiles of lncRNAs and mRNAs in aorta tissues from TAD were significantly altered. These results may provide important insights into the pathogenesis of TAD disease.

Lyn regulates mucus secretion and MUC5AC via the STAT6 signaling pathway during allergic airway inflammation

Hypersecretion of mucus is an important component of airway remodeling and contributes to the mucus plugs and airflow obstruction associated with severe asthma phenotypes. Lyn has been shown to down-regulate allergen-induced airway inflammation. However, the role of Lyn in mucin gene expression remains unresolved. In this study, we first demonstrate that Lyn overexpression decreased the mucus hypersecretion and levels of the muc5ac transcript in mice exposed to ovalbumin (OVA). Lyn overexpression also decreased the infiltration of inflammatory cells and the levels of IL-13 and IL-4 in OVA-challenged airways. Whereas Lyn knockdown increased the IL-4 or IL-13-induced MUC5AC transcript and protein levels in the human bronchial epithelial cell line, 16HBE, Lyn overexpression decreased IL-4- or IL-13-induced MUC5AC transcript and protein levels. Overexpression of Lyn also decreased the expression and phosphorylation of STAT6 in OVA-exposed mice, whereas Lyn knockdown increased STAT6 and MUC5AC levels in 16HBE cells. Finally, chromatin immunoprecipitation analysis confirmed that Lyn overexpression decreased the binding of STAT6 to the promoter region of Muc5ac in mice exposed to OVA. Collectively, these findings demonstrated that Lyn overexpression ameliorated airway mucus hypersecretion by down-regulating STAT6 and its binding to the MUC5AC promoter.

Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci

Supplemental Digital Content is available in the text.

Rationale:

Abdominal aortic aneurysm (AAA) is a complex disease with both genetic and environmental risk factors. Together, 6 previously identified risk loci only explain a small proportion of the heritability of AAA.

Objective:

To identify additional AAA risk loci using data from all available genome-wide association studies.

Methods and Results:

Through a meta-analysis of 6 genome-wide association study data sets and a validation study totaling 10 204 cases and 107 766 controls, we identified 4 new AAA risk loci: 1q32.3 (SMYD2), 13q12.11 (LINC00540), 20q13.12 (near PCIF1/MMP9/ZNF335), and 21q22.2 (ERG). In various database searches, we observed no new associations between the lead AAA single nucleotide polymorphisms and coronary artery disease, blood pressure, lipids, or diabetes mellitus. Network analyses identified ERG, IL6R, and LDLR as modifiers of MMP9, with a direct interaction between ERG and MMP9.

Conclusions:

The 4 new risk loci for AAA seem to be specific for AAA compared with other cardiovascular diseases and related traits suggesting that traditional cardiovascular risk factor management may only have limited value in preventing the progression of aneurysmal disease.

Understanding the pathogenesis of abdominal aortic aneurysms

An aortic aneurysm is a dilatation in which the aortic diameter is ≥3.0 cm. If left untreated, the aortic wall continues to weaken and becomes unable to withstand the forces of the luminal blood pressure resulting in progressive dilatation and rupture, a catastrophic event associated with a mortality of 50-80%. Smoking and positive family history are important risk factors for the development of abdominal aortic aneurysms (AAA). Several genetic risk factors have also been identified. On the histological level, visible hallmarks of AAA pathogenesis include inflammation, smooth muscle cell apoptosis, extracellular matrix degradation and oxidative stress. We expect that large genetic, genomic, epigenetic, proteomic and metabolomic studies will be undertaken by international consortia to identify additional risk factors and biomarkers, and to enhance our understanding of the pathobiology of AAA. Collaboration between different research groups will be important in overcoming the challenges to develop pharmacological treatments for AAA.