Inflammation and TGF-β Signaling Differ between Abdominal Aneurysms and Occlusive Disease

Aneurysm Rupture Prediction Based on Strain Energy-CFD Modelling

This paper presents a Patient-Specific Aneurysm Model (PSAM) analyzed using Computational Fluid Dynamics (CFD). The PSAM combines the energy strain function and stress-strain relationship of the dilated vessel wall to predict the rupture of aneurysms. This predictive model is developed by analyzing ultrasound images acquired with a 6-9 MHz Doppler transducer, which provides real-time data on the arterial deformations. The patient-specific cyclic loading on the PSAM is extrapolated from the strain energy function developed using historical stress-strain relationships. Multivariant factors are proposed to locate points of arterial weakening that precede rupture. Biaxial tensile tests are used to calculate the material properties of the artery wall, enabling the observation of the time-dependent material response in wall rupture formation. In this way, correlations between the wall deformation and tissue failure mode can predict the aneurysm's propensity to rupture. This method can be embedded within the ultrasound measures used to diagnose potential AAA ruptures.

Melatonin and TGF-β-Mediated Release of Extracellular Vesicles

The immune system, unlike other systems, must be flexible and able to "adapt" to fully cope with lurking dangers. The transition from intracorporeal balance to homeostasis disruption is associated with activation of inflammatory signaling pathways, which causes modulation of the immunology response. Chemotactic cytokines, signaling molecules, and extracellular vesicles act as critical mediators of inflammation and participate in intercellular communication, conditioning the immune system's proper response. Among the well-known cytokines allowing for the development and proper functioning of the immune system by mediating cell survival and cell-death-inducing signaling, the tumor necrosis factor α (TNF-α) and transforming growth factor β (TGF-β) are noteworthy. The high bloodstream concentration of those pleiotropic cytokines can be characterized by anti- and pro-inflammatory activity, considering the powerful anti-inflammatory and anti-oxidative stress capabilities of TGF-β known from the literature. Together with the chemokines, the immune system response is also influenced by biologically active chemicals, such as melatonin. The enhanced cellular communication shows the relationship between the TGF-β signaling pathway and the extracellular vesicles (EVs) secreted under the influence of melatonin. This review outlines the findings on melatonin activity on TGF-β-dependent inflammatory response regulation in cell-to-cell communication leading to secretion of the different EV populations.

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were "growing" (PAT ≥ 4.6) and 33 were more "stable". After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in "growing" and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish "growing" and "stable" IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets

Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.

Epithelial-mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis

Intracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial-mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes in aneurysms and their pathogenic mechanisms via bioinformatic analysis. The GSE13353, GSE75436, and GSE54083 datasets from Gene Expression Omnibus were analyzed with limma to identify differentially expressed genes (DEGs) among unruptured aneurysms, ruptured aneurysms, and healthy samples. The results revealed that three EMT-related DEGs (ADIPOQ, WNT11, and CCL21) were shared among all groups. Coexpression modules and hub genes were identified via weighted gene co-expression network analysis, revealing two significant modules (red and green) and 14 EMT-related genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that cytokine interactions were closely related. Gene set enrichment analysis revealed that unruptured aneurysms were enriched for the terms "inflammatory response" and "vascular endothelial growth". Protein-protein interaction analysis identified seven key genes, which were evaluated with the GSE54083 dataset to determine their sensitivity and specificity. In the external validation set, we verified the differential expression of seven genes in unruptured aneurysms and normal samples. Together, these findings indicate that FN1, and SPARC may help distinguish normal patients from patients with asymptomatic IAs.

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.

MFAP4 Deficiency Attenuates Angiotensin II-Induced Abdominal Aortic Aneurysm Formation Through Regulation of Macrophage Infiltration and Activity

Objective: Abdominal aortic aneurysm (AAA) is a common age-related vascular disease characterized by progressive weakening and dilatation of the aortic wall. Microfibrillar-associated protein 4 (MFAP4) is an extracellular matrix (ECM) protein involved in the induction of vascular remodeling. This study aimed to investigate if MFAP4 facilitates the development of AAA and characterize the underlying MFAP4-mediated mechanisms.

Approach and Results: Double apolipoprotein E- and Mfap4-deficient (ApoE^−/−Mfap4^−/−) and control apolipoprotein E-deficient (ApoE^−/−) mice were infused subcutaneously with angiotensin II (Ang II) for 28 days. Mfap4 expression was localized within the adventitial and medial layers and was upregulated after Ang II treatment. While Ang II-induced blood pressure increase was independent of Mfap4 genotype, ApoE^−/−Mfap4^−/− mice exhibited significantly lower AAA incidence and reduced maximal aortic diameter compared to ApoE^−/− littermates. The ApoE^−/−Mfap4^−/− AAAs were further characterized by reduced macrophage infiltration, matrix metalloproteinase (MMP)-2 and MMP-9 activity, proliferative activity, collagen content, and elastic membrane disruption. MFAP4 deficiency also attenuated activation of integrin- and TGF-β-related signaling within the adventitial layer of AAA tissues. Finally, MFAP4 stimulation promoted human monocyte migration and significantly upregulated MMP-9 activity in macrophage-like THP-1 cells.

Conclusion: This study demonstrates that MFAP4 induces macrophage-rich inflammation, MMP activity, and maladaptive remodeling of the ECM within the vessel wall, leading to an acceleration of AAA development and progression. Collectively, our findings suggest that MFAP4 is an essential aggravator of AAA pathology that acts through regulation of monocyte influx and MMP production.

POU class 2 homeobox associating factor 1 (POU2AF1) participates in abdominal aortic aneurysm enlargement based on integrated bioinformatics analysis

Abdominal aortic aneurysm (AAA) is life-threatening, its natural course is progressively sac expansion and rupture. Elegant studies have been conducted to investigate the molecular markers associated with AAA growth and expansion, this topic however, still needs to be further elucidated. This study aimed to identify potential genes for AAA growth and expansion based on comprehensive bioinformatics approaches. Firstly, 29 up-regulated genes were identified through DEGs analysis between large AAA and small AAA in GSE57691. Secondly, signed WGCNA analysis was conducted based on GSE57691 and the green module was found to exhibit the topmost correlation with large AAA as well as AAA, 133 WGCNA hub genes were further identified. Merged gene set including 29 up-regulated DEGs and 858 green module genes was subjected to constructing a PPI network where 195 PPI hub genes were identified. Subsequently, 4 crucial genes including POU2AF1, FCRLA, CD79B, HLA-DOB were recognized by Venn plot. In addition, by using GSE7084 and GSE98278 for verification, POU2AF1 showed potential diagnostic value between AAA and normal groups, and exhibited a significant higher expression level in large AAA samples compared with small AAA samples. Furthermore, immunohistochemistry results indicated up-regulation of POU2AF1 in large AAA samples than small AAA samples, which implies POU2AF1 may be a key regulator in AAA enlargement and growth. In summary, this study indicates that POU2AF1 has great predictive value for the expansion of AAA, and may contribute to the further exploration of pathogenesis and progression of AAA.

Single-Cell RNA Sequencing Reveals Heterogeneity of Vascular Cells in Early Stage Murine Abdominal Aortic Aneurysm-Brief Report

OBJECTIVE

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease characterized by smooth muscle cell depletion, ECM (extracellular matrix) degradation, and infiltration of immune cells. The cellular and molecular profiles that govern the heterogeneity of the AAA aorta are yet to be elucidated. Approach and Results: We performed single-cell RNA sequencing on mouse AAA tissues. AAA was induced in C57BL/6J mice by perivascular application of CaCl₂. Unbiased clustering identified 12 distinct populations of 8 cell types. Percentages of each population and gene expression were compared between sham and AAA tissue. Furthermore, we characterized the transcriptional profiles and potential functional features of populations in smooth muscle cells, fibroblasts, and macrophages and revealed the unique regulons in each cell type.

CONCLUSIONS

Together, these data provide high-resolution insight into the complexity and heterogeneity of mouse AAA and indicate that populations within major cell types such as smooth muscle cells, fibroblasts, and macrophages may contribute differently to AAA pathogenesis. Graphic Abstract: A graphic abstract is available for this article.