Programmed cell death-related ABI1 is a critical mediator of abdominal aortic aneurysm

Abstract

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by localized dilation of the abdominal aorta, posing a significant risk of rupture and fatal hemorrhage. While surgical and endovascular repair techniques have advanced, the underlying mechanisms driving AAA development remain unclear, hindering the development of effective preventive and therapeutic strategies. Using bioinformatics analysis of publicly available data sets, the study identified a strong correlation between cell death (CD) score and different types of programmed cell death scores in AAA samples. WGCNA analysis revealed a module enriched in genes related to proteasome-mediated protein degradation, nuclear envelope, and endocytosis, significantly correlated with CD score. Further analysis identified ABI1 as a dominant feature gene, highlighting its potential role in AAA pathogenesis. In vitro validation using an Angiotensin II-induced AAA model in human aortic smooth muscle cells demonstrated that siRNA-mediated knockdown of ABI1 significantly reduced cell apoptosis, migration, and the expression of pro-apoptotic proteins, confirming ABI1's crucial role in promoting CD and AAA progression. The findings suggest that ABI1 may represent a promising therapeutic target for the prevention and treatment of AAA. Further research is warranted to fully understand the role of ABI1 in AAA and to develop targeted therapies based on this promising target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-024-02128-4.

Introduction

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by a localized dilation of the abdominal aorta, posing a significant risk of rupture and potentially fatal hemorrhage [1]. Despite advancements in surgical and endovascular repair techniques, the underlying mechanisms driving AAA development remain incompletely understood [2]. This lack of clarity hinders the development of effective preventive and therapeutic strategies.

Programmed cell death (PCD) is a highly regulated process of cell self-destruction that plays a vital role in maintaining tissue homeostasis and eliminating damaged or unwanted cells [3]. PCD is essential for various physiological processes, including cell development, immune system, tissue homeostasis, and disease prevention [4]. A series of distinct morphological and biochemical changes, including cell shrinkage, nuclear fragmentation, plasma membrane blebbing, DNA degradation, and caspase activation characterize the process of apoptosis [5]. Dysregulation of PCD can contribute to various diseases, including cancer, autoimmune disorders, and neurodegenerative diseases [6]. Therefore, understanding the molecular mechanisms underlying PCD is crucial for developing effective therapeutic strategies for these conditions.

A growing body of evidence points to the crucial role of PCD in AAA pathogenesis. This controlled process of cell elimination contributes to the weakening of the aortic wall, leading to aneurysm formation [7]. While apoptosis is known to play a role in AAA, other forms of PCD, such as necroptosis and ferroptosis, remain less understood. Besides, the specific molecular mediators orchestrating apoptosis in AAA remain largely unknown.

This manuscript focuses on investigating ABI1, a protein involved in cell death (CD) pathways, as a potential critical mediator of AAA. This study aims to elucidate the role of ABI1 in AAA development and progression, providing insights into the molecular mechanisms driving this debilitating disease. By investigating the interplay between ABI1 and CD pathways, we seek to identify novel therapeutic targets for the prevention and treatment of AAA.

Materials and methods

Bioinformatics analysis

GSE47472 (14 AAA samples and eight normal samples) and GSE98278 (48 AAA samples) data sets from the Gene Expression Omnibus (GEO) database were collected using the R package GEOquery for analysis. The gene list of 12 kinds of PCD was collected from the previous literature [8]. 12 kinds of PCD scores and CD score were calculated using the single sample gene set enrichment analysis (ssGSEA) algorithm from the R package GSVA [9]. The R package WGCNA was performed to determine the CD-related module genes [10]. A power of β = 10 was used as the parameter. GO–BP, GO–CC, GO–MF, and KEGG pathways were enriched based on the CD-related module genes using the R package clusterProfiler [11]. The R package limma was used to identify the differentially expressed genes (DEGs) between AAA samples and normal samples [12]. LASSO regression analysis was performed to identify the feature genes. CIBERSORT-based immune cells were calculated [13]. GSEA was performed using the R package clusterProfiler [11].

Cell culture

Immortalized Human Aortic Smooth Muscle Cells—SV40 were purchased from Zhejiang Meisen Cell Technology Co., LTD. Cells were cultured in F12 medium (Meilunbio, China) containing 10% fetal bovine serum (Gibco, USA) and 1% dual antibiotics (Meilunbio, China). Cells were cultured in a 5% CO₂ cell incubator at 37 ℃. After starvation for 12 h, HASMCs treated with 1 µM/L AngII for 24 h were used as an in vitro AAA model.

Western blotting

12.5% SDS–PAGE separated equal amounts of protein extracts, transferred to nitrocellulose membranes, and incubated with the relevant antibodies overnight at 4 °C. The cells were incubated with fluorescent secondary antibodies for 1 h at room temperature. Proteins were visualized using the Odyssey® CLX dual-color infrared laser imaging system. Antibodies were purchased from the following companies: anti-Cleaved-caspase3 (Zenbio,341034), anti-Bcl2 (Zenbio,381702), anti-Vimentin (Proteintech, 10366-1-AP), anti-Bax (Proteintech, 60267-1-Ig), anti-PARP1 (Proteintech, 13371-1-AP), anti-MMP-2 (CST #40994), anti-JNK (Proteintech, 66210-1-Ig), anti-p38 MAPK (Proteintech, 14064-1-AP), and anti-GAPDH (Proteintech, 60004-1-Ig).

Cell transfection

Small interfering RNAs (siRNAs) directed against the human ABI1 gene were obtained from Gemma Genes Technology Co., LTD. (Shanghai, China). Transfection was carried out using Seven HighTrans™ siRNA Non-Lipid Transfection Reagent (Seven Biotech, Beijing, China) in accordance with the manufacturer's instructions.

Flow cytometry

Apoptosis was evaluated following the manufacturer's protocol with the Annexin V-FITC/PI Apoptosis kit (Seven Biotech, Beijing, China). Cells from different groups were harvested, trypsinized (EDTA-free), centrifuged, and suspended in 500 μL of 1 × AnnexinV Buffer. Subsequently, 5 μL of Annexin V-FITC and 5 μL of Propidium Iodide (PI) were added and gently mixed into the cell suspension. The cells were incubated in the dark at room temperature for 15 min before undergoing apoptosis analysis via flow cytometry. Data analysis was performed with FlowJo software.

Transwell assay

Cell migration experiments were performed using a 6-well cell insert (pore size of 0.4 μm, SAINING, China). The transfected HASMCs were resuspended in serum-free F12 and adjusted to a cell density of approximately 5 × 10⁴ cells. Subsequently, 200 μL of this cell suspension is introduced into the Transwell insert's upper compartment, while the lower compartment is loaded with 600 μL of medium containing 10% FBS. Cells were incubated in a 37 °C incubator for 24 h, after which they were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet for 10 min, and finally counted under a microscope.

Results

Identification of CD score-related genes

CD score was calculated based on all the gene lists of 12 kinds of PCD. PCD scores were calculated based on the gene lists of 12 kinds of PCD, respectively. CD score and 12 kinds of PCD scores are highly correlated in GSE98278 (Fig. 1). The relationship between scale-free topology model fit, mean connectivity, and soft threshold by WGCNA is shown in Fig. 2A. The waterfall plot shows the clustering results of WGCNA-derived module genes (Fig. 2B). CD score was most correlated with the turquoise module (Fig. 2C). Gene significance was significantly associated with module membership in the turquoise module (Fig. 2D). The turquoise module genes were extracted for subsequent analysis. Enriched GO–BP, GO–CC, GO–MF, and KEGG pathways showed that the turquoise module genes were highly enriched in proteasome-mediated ubiquitin-dependent protein catabolic process, nuclear envelope, protein–macromolecule adaptor activity, and endocytosis (Fig. 3).

Fig. 1.

Correlation between CD score and 12 kinds of PCD scores in GSE98278

Fig. 2.

WGCNA for CD score-related gene module in GSE98278. A Relationship between scale-free topology model fit, mean connectivity, and soft threshold. B Waterfall plot shows the clustering results of WGCNA-derived module genes. C Correlation between CD score and gene modules. D Correlation between gene significance and module membership in the turquoise module

Fig. 3.

Enriched GO–BP, GO–CC, GO–MF, and KEGG pathways based on turquoise module genes in GSE98278

Identification of AAA-related feature genes

Volcano plot shows the DEGs between AAA and normal samples in GSE47472 (Fig. 4A). Venn plot shows 12 intersected genes between DEGs and turquoise module genes (Fig. 4B). 12 intersected genes, except for DLD, CCDC117, and ABI1, were significantly more expressed in AAA samples than normal samples in GSE47472 (Fig. 4C).

Fig. 4.

Identification of AAA-related feature genes. A Volcano plot shows the DEGs between AAA and normal samples in GSE47472. B Venn plot shows the intersected genes between DEGs and turquoise module genes. C Vlnplot shows the expression patterns of 12 intersected genes in AAA and normal samples in GSE47472

Immune features of ABI1

To determine the most powerful intersected genes, LASSO regression analysis was performed on 12 intersected genes (Fig. 5A). Coefficients of 12 intersected genes in the LASSO model showed that ABI1 was dominant (Fig. 5B). ABI1 was found to have a high correlation with immune cells (Fig. 5C). Besides, ABI1 was highly related to cellular response to interferon-gamma, positive regulation of macroautophagy, ubiqtuitin-dependent protein catabolic process, protein K48-linked ubiquintination, regulation of innate immune response, U2-type prespliceosome assembly, DNA repair, protein polyubiquintination, T cell receptor signaling pathway, G1/S transition of mitotic cell cycle, cellular response to glucose starvation, and T cell activation (Fig. 5D).

Fig. 5.

Immune features of ABI1. A LASSO regression analysis on 12 intersected genes. B Coefficients of 12 intersected genes in the LASSO model. C Heatmap shows the correlation between ABI1 and immune cells. D Enriched GSEA pathways related to ABI1

In vitro validation on ABI1

Western blot assay shows that the protein expression of Vimentin is significantly increased in the Angiotensin II group while significantly downregulated in the Angiotensin II+siRNA–ABI1 group (Fig. 6A). The protein expression of Vimentin was significantly increased in the Angiotensin II group, indicating an enhancement in the epithelial-to-mesenchymal transition (EMT). This transition is often linked to increased fibrotic processes and cellular migration. In contrast, treatment with siRNA–ABI1 led to a significant downregulation of Vimentin, suggesting that targeting ABI1 can effectively mitigate the EMT effects induced by Angiotensin II. The protein expression of BAX is significantly increased in the Angiotensin II group while significantly downregulated in the Angiotensin II+siRNA–ABI1 group (Fig. 6B). The protein expression of BCL-2 is significantly downregulated in the Angiotensin II group while significantly upregulated in the Angiotensin II+siRNA–ABI1 group (Fig. 6C). The expression levels of BAX, a pro-apoptotic protein, were significantly increased in the Angiotensin II group. This is indicative of a shift towards apoptosis, as BAX promotes cell death. Meanwhile, the anti-apoptotic protein BCL-2 was significantly downregulated in the same group. Treatment with siRNA–ABI1 resulted in a significant downregulation of BAX and an upregulation of BCL-2. This change suggests that silencing ABI1 can create a more balanced apoptotic environment, reducing cell death rates. Similarly, Caspase-3, a critical executor of apoptosis, showed significantly increased expression in the Angiotensin II group (Fig. 6D). This reinforces the notion of enhanced apoptosis in response to Angiotensin II. Following siRNA–ABI1 treatment, Caspase-3 levels were significantly downregulated, further supporting the protective role of ABI1 inhibition against cell death. The protein expression of PARP1 (Fig. 6E) and MMP2 (Fig. 6F) was also significantly increased in the Angiotensin II group. PARP1 is involved in DNA repair and cell death, while MMP2 is associated with tissue remodeling and invasion. Both proteins exhibited significant downregulation in the Angiotensin II+siRNA–ABI1 group, indicating that targeting ABI1 can reduce the pathological changes associated with Angiotensin II. Besides, p38 MAPK and JNK was found to be profoundly increased in siRNA–ABI1 group (Figure S1). Apoptosis assay shows that the cell apoptosis rates are significantly increased in the Angiotensin II group while significantly downregulated in the Angiotensin II+siRNA–ABI1 group (Fig. 6G, H). Transwell assay shows the number of migrated cells is significantly increased in the Angiotensin II group while significantly downregulated in the Angiotensin II+siRNA–ABI1 group (Fig. 6I, J).

Fig. 6.

In vitro validation on ABI1. A Western blot assay shows the protein expression of Vimentin in four groups. B Western blot assay shows the protein expression of BAX in four groups. C Western blot assay shows the protein expression of BCL-2 in four groups. D Western blot assay shows the protein expression of Caspase3 in four groups. E Western blot assay shows the protein expression of PARP1 in four groups. F Western blot assay shows the protein expression of MMP2 in four groups. G Apoptosis assay shows the cell apoptosis rates in four groups. H Statistical analysis of apoptosis assay. I Transwell assay shows the migrated cells in four groups. J Transwell assay shows the migrated cells in four groups

Besides, ABI1 was significantly associated with 12 kinds of PCD scores in GSE98278 (Fig. 7).

Fig. 7.

Correlation between ABI1 and 12 kinds of PCD scores in GSE98278

Discussion

This study sheds light on the critical role of ABI1 in the pathogenesis of abdominal aortic aneurysm (AAA). A comprehensive bioinformatics analysis of publicly available gene expression data sets identified ABI1 as a key mediator of cell death (CD) pathways in AAA. This finding was further validated in vitro using an Angiotensin II-induced AAA model in human aortic smooth muscle cells (HASMCs).

The bioinformatics analysis revealed a strong correlation between CD score and 12 different types of PCD scores in the GSE98278 data set, highlighting the importance of PCD in AAA development. WGCNA analysis identified a turquoise module enriched in genes related to proteasome-mediated protein degradation, nuclear envelope, and endocytosis. This module was significantly correlated with CD score, suggesting its involvement in the regulation of CD pathways in AAA.

Further analysis of the turquoise module genes and differentially expressed genes (DEGs) between AAA and normal samples in the GSE47472 data set revealed 12 intersected genes, including ABI1. LASSO regression analysis identified ABI1 as the dominant feature gene, indicating its crucial role in AAA pathogenesis. LASSO regression is a statistical method used for variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces. The 12 intersected genes were input into a LASSO regression model. This model aims to minimize the residual sum of squares subject to a constraint on the sum of the absolute values of the coefficients, effectively shrinking some coefficients to zero. This characteristic allows LASSO to select only the most relevant genes while excluding others. After fitting the LASSO model, the coefficients of the 12 intersected genes were evaluated. The analysis revealed that ABI1 had a significantly higher coefficient compared to the other intersected genes, indicating it plays a more dominant role in the prediction model. This suggests that ABI1 is not only associated with AAA but may also be a key factor influencing its progression.

ABI1, also known as Abl-interactor 1, is a protein that plays a crucial role in various cellular processes, including signal transduction, cytoskeletal organization, and cell growth [14]. While primarily known for its involvement in normal cellular functions, ABI1 has also been implicated in the development and progression of several diseases, including cancer [15] and respiratory disease [16].

The immune features of ABI1 were further investigated, revealing its high correlation with various immune cells and its involvement in multiple pathways related to immune response, autophagy, and cell cycle regulation. These findings suggest that ABI1 may contribute to AAA development by modulating immune cell infiltration and promoting inflammation [17].

In vitro validation experiments using siRNA-mediated knockdown of ABI1 in Angiotensin II-treated HASMCs demonstrated a significant reduction in cell apoptosis, migration, and the expression of cell plasticity protein (Vimentin [18]), pro-apoptotic proteins (BCL-2 [19], BAX [20], Caspase3 [21], PARP1 [22]) and extracellular matrix protein MMP2 [23]. These results confirm the crucial role of ABI1 in promoting CD and AAA progression. ABI1 emerges as a critical mediator in the pathogenesis of AAA, influencing key processes such as EMT, apoptosis, and cell migration.

Our findings provide compelling evidence for ABI1 as a critical mediator of CD pathways in AAA. Targeting ABI1 may offer a novel therapeutic strategy for the prevention and treatment of AAA. Further research is needed to elucidate the precise mechanisms by which ABI1 regulates CD pathways in AAA and to explore the potential of ABI1 as a therapeutic target [24].

This study has some limitations. First, the bioinformatics analysis was based on publicly available data sets, which may not fully reflect the complex heterogeneity of AAA. Second, the data sets utilized, such as GSE47472 and GSE98278, may have a limited number of samples, which could affect the robustness and generalizability of the findings. Thirdly, the in vitro validation was performed using a single cell line, and further studies in animal models are necessary to confirm these findings. Future research should focus on investigating the specific mechanisms by which ABI1 regulates CD pathways in AAA. This could involve examining the interaction of ABI1 with other proteins involved in CD and exploring the downstream signaling pathways activated by ABI1. Additionally, preclinical studies in animal models are needed to evaluate the therapeutic potential of targeting ABI1 for AAA treatment.

In conclusion, this study provides novel insights into the role of ABI1 in AAA pathogenesis and suggests that ABI1 may represent a promising therapeutic target for this debilitating disease. Further research is warranted to fully understand the role of ABI1 in AAA and to develop targeted therapies based on this promising target. Investigate the potential of developing ABI1-targeted therapies, such as small molecule inhibitors or monoclonal antibodies. Preclinical studies should evaluate their efficacy in preventing or treating AAA. Besides, explore the effects of combining ABI1 inhibition with other therapeutic strategies that target different pathways involved in AAA, potentially enhancing treatment efficacy.

Author contributions

Han Wang, Yu Tian, and Shengjie Fu designed the study and provided funding support. Zhihai Xu, Jing Guo, and Lei Li analyzed the data and conducted the experiments. Han Wang wrote the manuscript. Han Wang, Zhihai Xu, Jing Guo, Lei Li, Yu Tian, and Shengjie Fu revised the manuscript.

Funding

None.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.