ALAS2 overexpression alleviates oxidative stress-induced ferroptosis in aortic aneurysms via GATA1 activation

Introduction

The bulging of an aorta caused by the compression, traction, or erosion of surrounding tissues by a tumor is known as an aortic aneurysm (1). It may appear anywhere on the aorta and may be tube-shaped or bulb-shaped (2). It is generally defined as a focal aortic diameter exceeding 50% of the expected normal diameter (3). Clinically, aortic aneurysms are mainly divided into abdominal aortic aneurysms, which occur in the abdominal part of the aorta, and thoracic aortic aneurysms (TAA), which are located in the chest cavity (4,5). Several factors contribute to the pathogenesis of aortic aneurysms, including genetic predisposition, hypertension, atherosclerosis, and connective tissue disease (6,7).

The exact prevalence and death toll of aortic aneurysms varies by region and population; however, extensive research has shown that the incidence of aortic aneurysms is increasing (5). Thus, early detection and intervention are critical to reduce the risk of aortic rupture, dissection, and other life-threatening complications (8). Current treatment strategies primarily include surgical repair and medical management (mostly by blood pressure lowering drugs to mitigate) to mitigate the risk of aneurysm expansion and rupture (9,10). Despite advancements, there remains a pressing demand to identify novel diagnostic biomarkers, therapeutic strategies such as gene therapy and stem cell treatments, as well as prognostic indicators to enhance the precision and efficacy of aortic aneurysm interventions. This necessitates concerted research efforts in emerging fields like gene therapy, stem cell therapy, and novel drug development.

A new type of cell death called ferroptosis is critical in many clinical diseases (11,12). In the context of aortic aneurysms, research into the involvement, mechanisms, and therapeutic potential of ferroptosis has gained prominence. Using bioinformatics technology, Ren et al. identified key ferroptosis-related genes related to abdominal aortic aneurysm formation and rupture, including glutathione peroxidase 4 (GPX4), solute carrier family 2 member 1 (SLC2A1), and phosphatidylethanolamine binding protein 1 (PEBP1) (13). Further, research by Filiberto et al. demonstrated that the immunomodulatory effects of Resolvin D1 are mediated through macrophage formyl peptide receptor 2. This regulation effectively reduced ferroptosis and high-mobility group box 1 release, thereby attenuating aortic inflammation and remodeling in the pathogenesis of abdominal aortic aneurysms (14). Loick et al. found that ferritin is elevated in abdominal aortic aneurysms (AAA) and negatively correlates with lipid-cotransport protein-2 (LCN2). Smooth muscle cell (SMC)-specific ferritin-deficient mice display a more severe AAA phenotype. LCN2-neutralising antibodies restore autophagy, reduce neutrophil infiltration, and prevent the elevation of the AAA phenotype, attenuating the progression of the pathology (15).

5-aminolevulinate synthase 2 (ALAS2) encodes an enzyme responsible for the initial and rate-limiting steps of heme biosynthesis (16). Where heme is a component of the iron within the hemoglobin molecule (17). This suggested that the activity and regulation of ALAS2 is critical for heme synthesis and intracellular iron utilization. The study by Sawicki et al. identified increased oxidative stress and cell death associated with ALAS2 overexpression due to heme accumulation. They further observed that knockdown of ALAS2 reversed the increase in heme content and cell death in cultured cardiomyoblasts exposed to hypoxic conditions. By administering the mitochondrial antioxidant MitoTempo to ALAS2 overexpressing cardiomyoblasts, elevated levels of oxidative stress and cell death could be normalised to baseline, suggesting that the effects of ALAS2 and increased haemoglobin are mediated through elevated mitochondrial oxidative stress (18). A study by Pilling et al. revealed an age- and sex-specific gene expression signature for muscle strength that may be present in the blood. They found that gene expression levels correlated with strength after adjustment for cofactors and several statistical tests, including ALAS2 (19). Peng et al. showed that ALAS2 overexpressing transgenic mice (Tg mice) exhibited muscular dystrophy, which was associated with reductions in atrogin-1 and MuRF-1, as well as a strong association with mitochondrial dysfunction (20). The study by Reinwald et al. identified 222 genes with early responses to specific major modes of action (MoA) in environmental non-target organisms. Many of these genes are associated with three major processes: cardiomyocyte development and function, oxygen transport and hypoxic stress, and neuronal development and plasticity, including ALAS2, which is associated with oxygen transport and hypoxic stress (21). A study by Massaiu et al. found higher expression levels of heme synthesis and externalisation (ALAS2 upregulation) in left ventricular samples from subjects with dilated cardiomyopathy (DCM), which was confirmed by bulk and single nucleus RNA sequencing (RNA-seq) data (22). The findings provided above suggest that ALAS2 plays an important role in several biological processes, including oxidative stress, muscle nutrition, oxygen transport and hypoxic stress, which provides important clues to further understand its role in the development and treatment of related diseases. Currently, the interaction between ALAS2 and ferroptosis is an emerging and important research area, but its mechanism of action in aortic aneurysms is still unclear. Therefore, the relationship between them needs to be explored to improve our understanding of aortic aneurysms and reveal potential new treatment strategies.

By integrating bioinformatics analysis with in vitro research, we sought to gain an understanding of the function of ferroptosis in the etiology of aortic aneurysms. Specifically, the interaction between ALAS2 and the transcription factor GATA binding protein 1 (GATA1) and their effects on ferroptosis and apoptosis of mouse aortic vascular smooth muscle cells (MOVAS) induced by hydrogen peroxide (H₂O₂) were examined. Our findings highlighted the importance of ALAS2 as a potential diagnostic marker and therapeutic target for aortic aneurysms, which could possibly enhance diagnosis and therapy. We present this article in accordance with the MDAR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-370/rc).

Methods

Downloading and processing of the GSE9106 dataset

The R program (version 4.0.1) was used to preprocess a microarray dataset for TAAs (GSE9106) that was retrieved from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds/). The dataset includes TAA samples (n=59) and their corresponding controls (n=34). Using the GEO2R tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/), the probe identifier (probe ID) was converted to the gene symbol and a differential analysis was carried out. The threshold standard for fold change (FC) was >1.5 or <0.67, and the modified P value was <0.05. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Weighted gene co-expression network analysis (WGCNA)

A WGCNA was conducted to analyze the differentially expressed genes (DEGs) in the GSE9106 data set. A gene co-expression network was created using the R language’s “WGCNA” package (version 3.5.1). The soft threshold power was precisely tuned at β =12 to guarantee a scale-free topology. A topological overlap matrix (TOM) was generated from the weighted adjacency matrix, which served as a robust measure of network connectedness after the network had been built. Hierarchical clustering is performed based on TOM to generate gene modules, which are visualized through dendrograms. In this structure, individual branches (shown in different colors) represent different gene modules. Using weighted correlation coefficients, DEGs exhibiting similar expression trajectories were merged into corresponding modules. Ultimately, we examined the association between the gene modules and different samples of GSE9106 to determine the key module.

Analysis of protein-protein interactions (PPIs)

To elucidate the potential PPIs in the turquoise module, we conducted a PPI network analysis using the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) database, along with Cytoscape software (Version 3.8.1). The generated PPI network, comprising the Molecular Complex Detection (MOCODE), Maximal Clique Centrality (MCC), and Maximal Neighborhood Component (MNC) networks, was visualized using the open-source network visualization software platform, Cytoscape. This visualization facilitated a detailed analysis of the PPIs within the module.

Identification and expression analysis of intersecting genes

Bioinformatics and Evolutionary Genomics (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to identify the intersecting genes in the MOCODE, MCC, and MNC networks. After identifying the intersecting genes, the expression levels of these intersecting genes in the TAA and control samples of the GSE9106 dataset were detected. A P value <0.05 was considered statistically significant.

Expression of GATA1 and correlation analysis with ALAS2

To explore the function of GATA1 in TAAs, GATA1 expression in the control and TAA samples in the GSE9106 dataset was first examined. Next, a correlation analysis was performed to examine the expressions of GATA1 and ALAS2 in the Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/) database, and the Pearson correlation coefficients were calculated to evaluate the strength and direction of the linear relationships between the genes. A P value <0.05 was considered statistically significant.

Just Another Spar Promoter Analysis Resource (JASPAR) database

The JASPAR database (http://jaspar.genereg.net/) comprehensively collects transcription factor binding profiles and matrices. It provides valuable insights into potential binding motifs that transcription factors may recognize in gene promoter regions. In this study, the JASPAR database was used to predict the ALAS2 and GATA1 binding locations.

Cell lines and cell culture

The MOVAS were provided by the American Type Culture Collection (ATCC; Manassas, VA, USA). The MOVAS were maintained in Dulbecco’s Modified Eagle Medium with 10% fetal bovine serum (FBS) to which 1% penicillin-streptomycin was added. The cells were then cultivated in a humid environment at 37 °C with 5% carbon dioxide.

Transfection assay

H₂O₂ (800 µmol/L) was used to induce the damage model of the MOVAS. For the purpose of transient transfection, the MOVAS were inoculated into 24-well plates at an adjusted density of 2×10⁵ cells per well. A plasmid encoding ALAS2 was transfected into the MOVAS using an appropriate transfection method to enable the transfected cells to express the ALAS2 protein for a specific period to achieve overexpression. Next, to effectively knock down the expression of GATA1, a particular small interfering RNA targeting GATA1 was transfected into the MOVAS. The cells were then incubated for the required amount of time. Lipofectamine 3000 (Invitrogen, MA, USA) was used to transfect the cells in accordance with the manufacturer’s instructions.

Quantitative real-time-polymerase chain reaction (qRT-PCR) assay

The total RNA of the MOVAS was extracted using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) in accordance with the manufacturer’s instructions. A PrimeScript RT kit (Takara, Tokyo, Japan) was used for the complementary DNA synthesis. SYBR Green PCR Master Mix (Applied Biosystems, CA, USA) was used to perform the qRT-PCR with the StepOnePlus Real-Time PCR System (Applied Biosystems). The 2^−ΔΔCT technique was used to examine the data, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) abundance was used as a standard. The following primer sequences were used in the amplification process: ALAS2 forward: 5'-CTGCCAGGGTGCGAGATT-3', reverse: 5'-TTGGCTGCTCCACTGTTACG-3'. GATA1 forward: 5'-CCTGCTTTGTTGCCAATG-3', reverse: 5'-CTGCTCCACTGTTACGGATAC-3'. The primer sequences used for amplifying GAPDH were: GAPDH forward: 5'-CAAGCTCATTTCCTGGTATGAC-3', reverse: 5'-CAGTGAGGGTCTCTCCTTCCT-3'.

Westtern blotting (WB) assay

Radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo Fisher Scientific, IL, USA) was used to make the cellular protein lysates, which also included protease and phosphatase inhibitors (1:1,000) from the same supplier. The protein concentration (2 mg/mL) was quantified using a bicinchoninic acid (BCA) protein assay kit (Thermo Fisher Scientific, Waltham, MA, USA). The same amounts of protein were separated via sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then put onto a polyvinylidene difluoride (PVDF) membrane (Millipore, MA, USA). Specific primary antibodies against ALAS2, NFE2 like BZIP transcription factor 2 (NRF2), solute carrier family 7 member 11 (SLC7A11), glutathione peroxidase 4 (GPX4), and GATA1 (Abcam, Cambridge, UK; 1:1,000) were used. As an internal reference, GAPDH (Abcam; 1:5,000) was employed. Following this, the membrane underwent an incubation phase with secondary antibodies linked to horseradish peroxidase (provided by Amersham Bioscience, Tokyo, Japan). Next, an advanced chemiluminescence kit was used to visualize the protein bands (Thermo Fisher Scientific, Waltham, MA, USA). A densitometric analysis was executed using a ChemiDoc imaging system (Bio-Rad, Hercules, CA, USA). The intensity was measured using ImageJ software.

Cell counting kit-8 (CCK-8) assay

The viability of the cells was assessed using the CCK-8, (Dojindo, Kumamoto, Japan). The RPE cells were seeded at a density of 5×10³ cells per well in 96-well plates. Following the treatment, each well received CCK-8 reagent. Subsequently, absorbance at 450 nm was measured at 24, 48, 72, and 96 hours using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA).

Flow cytometry

For the flow cytometric analysis, the MOVAS were detached using trypsin- ethylenediaminetetraacetic acid (EDTA) (Gibco, Waltham, MA, USA) and washed with phosphate-buffered saline. In accordance with the manufacturer’s instructions, the cells were stained with Annexin V and propidium iodide to distinguish among the live, apoptotic, and necrotic cells. A flow cytometer (BD Biosciences, CA, USA) was used for the flow cytometry, and the data were analyzed using FlowJo software (FlowJo LLC, Oregon, USA) to quantify the cell apoptosis rate.

Ferrous iron assay

A 24-well plate was used to seed the MOVAS (2×10³ cells/well). Intracellular ferrous iron levels were determined in accordance with the manufacturer’s instructions and using an iron colorimetric assay kit (Abcam).

Enzyme-linked immunosorbent assay (ELISA)

Appropriately diluted samples of the cell culture supernatant were added to the wells of an ELISA plate pre-coated with interleukin (IL)-1β, tumor necrosis factor α (TNF-α), and IL-6 antibodies. After incubation and washing, the enzyme-linked secondary antibody was added, followed by a chromogenic substrate. The reaction was stopped and the absorbance was measured at the appropriate wavelength using a microplate reader. The concentrations of inflammatory factors were determined by comparing the absorbance values to a standard curve generated using corresponding standards of known concentration.

Assessment of malonaldehyde (MDA), reactive oxygen species (ROS), and glutathione (GSH) levels

Following the H₂O₂ treatment and ALAS2 overexpression, the levels of the ROS, MDA, and GSH in the MOVAS were measured. A detection kit based on fluorescence was used to quantify the ROS. A thiobarbituric acid reactive substances test was used to evaluate the MDA levels. A colorimetric assay involving the reaction with 5,5'-Dithiobis (2-nitrobenzoic acid) (DTNB) was used to quantify GSH. Measurements were made using a calibrated microplate reader, and all processes were carried out in accordance with the manufacturer’s instructions.

Chromatin immunoprecipitation (ChIP) assay

A ChIP analysis was conducted to observe the association between proteins and DNA. After the DNA and proteins were cross-linked with formaldehyde, the cells were lysed. Chromatin was fragmented using sonication, and the cells were then incubated with primary antibodies against ALAS2, GATA1, and NC. The cross-links were reversed, DNA was extracted, and a quantitative PCR analysis was performed to identify the binding sites.

Dual-luciferase reporter assay

The cells were allocated into a 96-well format and subsequently transfected with pmirGLO vectors containing the sequences for ALAS2 3' untranslated region wild-type (ALAS2-3'UTR-WT) or ALAS2 3' untranslated region mutant (ALAS2-3'UTR-MT), in conjunction with small interfering RNA targeting GATA1 (si-GATA1). Post 48-hour transfection, luciferase expression was quantified using a dedicated assay kit, with the results normalized to the Renilla luciferase signal to ensure consistency. This procedure was replicated in triplicate to ensure the robustness of the findings.

Statistical analysis

SAS version 9.2 (SAS Institute Inc.) was used to examine the data. Each experiment was carried out in triplicate at least. A two-tailed Student’s t-test was used to evaluate differential expression and correlation coefficients. A P value <0.05 was considered statistically significant. All the tests were conducted in duplicate and were two-sided.

Results

Comprehensive analysis of DEGs and module-based network relationships in aortic aneurysms

In this study, 567 upregulated and 200 downregulated DEGs were identified from the control and TAA samples of the GSE9106 data set (Figure 1A). To determine the optimal soft threshold power for fitting the scale-free topology model, a no-scale fit analysis was conducted, resulting in a power of 12 (Figure 1B). Subsequently, we performed a comprehensive clustering analysis of 93 samples from the GSE9106 data set. The genes were clustered into different modules based on their co-expression patterns using the WGCNA method. Each module was represented by a specific color (Figure 1C). To assess the relationships between the identified modules, we explored the adjacency of hub genes. Notably, the turquoise module exhibited a significant correlation with sample characteristics, with a correlation coefficient of 0.41 (Figure 1D). PPI networks were subsequently constructed. The MCODE network contained 13 nodes and 51 edges (Figure 1E), while the MCC network featured 10 nodes and 44 edges (Figure 1F), and the MNC network included 10 nodes and 20 edges (Figure 1G).

Figure 1 Screening of DEGs in the GSE9106 dataset and comprehensive analysis of module-based network relationships. (A) Volcano maps of DEGs based on the GSE9106 datasets. The gray scatter points represent genes with insignificant differences in expression, while the orange and blue scatter points represent genes with significant differences in expression. (B) The upper figure shows the determination of the optimal soft threshold in the gene co-expression network, and the lower figure shows the mean connectivity for different soft thresholds. (C) Cluster dendrogram of gene modules with different colors. (D) Heatmap of the correlation between gene modules and GSE9106 samples; the numbers in the modules represent the correlation coefficients and P values. (E-G) PPI network representation of the turquoise module. This includes MCODE (E), MCC (F), and MNC (G). The nodes represent proteins or protein domains, while the edges represent the interactions between these proteins. DEGs, differentially expressed genes; PPI, protein-protein interaction; MCODE, Molecular Complex Detection; MCC, Maximal Clique Centrality; MNC, Maximal Neighborhood Component; FC, fold change.

Identification and characterization of intersecting genes

Three intersecting genes (i.e., ALAS2, GYPA, and GYPB) were identified from the MCODE, MCC, and MNC networks (Figure 2A). The subsequent individual expression analysis of these three genes revealed that they were significantly upregulated in the TAA group of the GSE9106 data set (Figures 2B-2D). Thus, these three genes may be potential aortic aneurysm genes. Based on bioinformatics analysis and literature review, ALAS2 was chosen for further investigation.

Figure 2 Identification and characterization of intersecting genes. (A) Venn diagram showing the overlapping DEGs between MCODE, MCC, and MNC. (B-D) Expression analysis of three overlapping genes in the GSE9106 data set. **, P<0.01. DEGs, differentially expressed genes; MCODE, Molecular Complex Detection; MCC, Maximal Clique Centrality; MNC, Maximal Neighborhood Component; TAA, thoracic aortic aneurysms.

Iron content in MOVAS cells increases after H₂O₂ induction

Mitochondrial damage is a prominent feature of iron-dependent cell death. H₂O₂ (800 µmol/L) was employed to induce damage in the MOVAS. The MOVAS were treated with ferroptosis inhibitors [simvastatin (SIM) and ferrostatin-1 (Fer-1)] and catalysts [erastin and iron-saturated holo-transferrin (HTF)] to further examine the function of oxidative stress in the development of ferroptosis. H₂O₂ and Erastin significantly increased the iron levels, and the effect was more pronounced in cells treated with H₂O₂ + HTF. Conversely, Fer-1 and SIM alleviated the H₂O₂-induced increases in iron to a certain extent (Figure 3A). As Figure 3B shows, H₂O₂ reduced the GSH levels in comparison to the control group, and Fer-1 further exacerbated this result. The ferroptosis catalyst reversed the reducing effect of H₂O₂ on GSH levels to a certain extent. The results in Figure 3C indicated that H₂O₂ or erastin treatment resulted in increased ROS levels. After H₂O₂ treatment, the addition of Fer-1 or SIM resulted in a decrease in ROS levels, whereas the addition of HTF resulted in an increase in ROS content. Additionally, in the WB experiment, following the addition of H₂O₂, the level of ALAS2 increased, while the levels of ferroptosis negative regulators showed the opposite result. HTF promoted the H₂O₂-induced reduction of NRF2, SLC7A11, GPX4, and FTH1, while Fer-1 and SIM reversed this effect (Figure 3D,3E). These findings suggested that their effect in the modulation of ferroptosis warrants further investigation.

Figure 3 Oxidative stress promotes ferroptosis in MOVAS. (A-C) Intracellular iron content, GSH, and ROS levels of MOVAS under different treatments. (D,E) WB detection of NRF2, SLC7A11, GPX4, and FTH1 protein levels in the MOVAS cells under different treatments. The bar graph showed the results of the gray-scale detection of proteins. *, P<0.05 and **, P<0.01, vs. control group. ^#, P<0.05. vs. H₂O₂ group and Erastin group. MOVAS, mouse aortic vascular smooth muscle cells; GSH, glutathione; ROS, reactive oxygen species; WB, western blot; Fer-1, ferrostatin-1; SIM, simvastatin; HTF, iron-saturated holo-transferrin; H₂O₂, hydrogen peroxide.

ALAS2 overexpression reverses the H₂O₂-induced inflammatory response and oxidative stress in MOVAS

The qRT-PCR and WB analysis results revealed a significant increase in ALAS2 expression levels after H₂O₂ treatment. Further, the overexpression of ALAS2 led to a more pronounced increase in ALAS2 expression (Figure 4A,4B). The ELISA results showed that the IL-1β, IL-6, and TNF-α levels in the H₂O₂-induced MOVAS were significantly upregulated. The overexpression of ALAS2 reversed these effects, resulting in lower levels of these inflammatory cytokines (Figure 4C-4E). The trends in the ROS and MDA changes corresponded with the alterations in the expression of inflammatory factors, showing a consistent pattern (Figure 4F,4G). Further, the pattern of GSH levels was contrary to that of ROS and MDA; the GSH levels decreased after adding H₂O₂, but when H₂O₂ acted synergistically with ALAS2 overexpression, the GSH levels increased (Figure 4H).

Figure 4 ALAS2 overexpression reduces H₂O₂-induced inflammatory response and oxidative stress in MOVAS. (A,B) qRT-PCR and WB analysis of ALAS2 expression before and after H₂O₂ treatment and after the overexpression of ALAS2. The bar graph showed the results of the gray-scale detection of proteins. (C-E) ELISA measured the levels of inflammatory factors across four experimental groups: control, H₂O₂, H₂O₂ + vector, and H₂O₂ + over-ALAS2. (F-H) Analysis of ROS, MDA, and GSH levels before and after H₂O₂ treatment and after the overexpression of ALAS2. *, P<0.05 vs. control group. ^#, P<0.05 vs. H₂O₂ group. MOVAS, mouse vascular aortic smooth muscle cells; qRT-PCR, quantitative real-time polymerase chain reaction; WB, western blot; ELISA, enzyme linked immunosorbent assay; ROS, reactive oxygen species; MDA, malondialdehyde; GSH, glutathione; H₂O₂, hydrogen peroxide.

ALAS2 overexpression regulates apoptosis and iron concentration in H₂O₂-treated cells

The flow cytometry analysis showed that the H₂O₂ treatment significantly increased the apoptosis rate. However, the interaction with the overexpressed ALAS2 resulted in reduced apoptosis (Figure 5A,5B). Further, H₂O₂ treatment significantly increased cellular iron concentration, which subsequently decreased following the overexpression of ALAS2 (Figure 5C). Further, the H₂O₂ treatment resulted in a significant decrease in the protein levels of the ferroptosis negative regulators. The overexpression of ALAS2 partially restored the protein levels of these factors (Figure 5D,5E). All of these results suggested that the overexpression of ALAS2 may act as a protective mechanism against H₂O₂-induced ferroptosis by regulating the key proteins involved in ferroptosis.

Figure 5 ALAS2 overexpression partially reverses ferroptosis induced by H₂O₂. (A,B) Flow cytometry analysis of MOVAS apoptosis before and after H₂O₂ induction and after the overexpression of ALAS2. (C) Cellular iron content in the MOVAS cells before and after the H₂O₂ induction and after the overexpression of ALAS2. (D,E) WB analysis of ferroptosis negative regulatory factors (NRF2, GPX4, FTH1, and SLC7A11) in the MOVAS before and after H₂O₂ induction and after the overexpression of ALAS2. The bar graph showed the results of the gray-scale detection of proteins. **, P<0.01 and ***, P<0.001 vs. control group; ^#, P<0.05 and ^##, P<0.01 vs. H₂O₂ group. MOVAS, mouse vascular aortic smooth muscle cells; WB, western blot; PI, propidium iodide; FITC, fluorescein isothiocyanate; H₂O₂, hydrogen peroxide.

GATA1 transcriptional activation ALAS2

The expression analysis of the GSE9106 dataset showed that GATA1 was significantly overexpressed in the TAA samples compared with the controls (Figure 6A). The GEPIA database revealed a positive correlation between this gene and ALAS2 expression (Figure 6B). The qRT-PCR and WB results showed that the GATA1 expression levels were significantly increased in the MOVAS after H₂O₂ treatment. Conversely, the knockdown of GATA1 resulted in a significant reduction in GATA1 expression (Figure 6C,6D). As Figure 6E shows, the binding site between GATA1 and ALAS2 was predicted using the JASPAR database (size of promoter fragment about 3,500 bp upstream). This prediction was confirmed by luciferase activity assays, which confirmed their interaction (Figure 6F). Further, GATA1 exhibited a strong correlation with the ALAS2 promoter region (Figure 6G), which suggested that GATA1 exerted a regulatory effect on ALAS2 expression under oxidative stress conditions.

Figure 6 GATA1 transcriptionally activates ALAS2. (A) Expression analysis of GATA1 in the control and TAA samples of the GSE9106 dataset. (B) Spearman correlation analysis between GATA1 and ALAS2. The X-axis in the figure represents gene expression; the Y-axis represents gene scores; and the density curve on the right represents the distribution trend of the gene scores. (C,D) qRT-PCR and WB were used to detect the expression level of GATA1 under different treatments. The groups were as follows: control, H₂O₂, H₂O₂ + si-NC, and H₂O₂ + si-GATA1. The bar graph showed the results of the gray-scale detection of proteins. (E) The DNA binding motif of GATA1 predicted by JASPAR, highlighting specific binding sites for GATA1 interaction on the ALAS2 promoter. The WT sequence is shown as GTCTTATCTTT and the MUT sequence is shown as AGTGGCGTGGG. (F) Luciferase activity assay showed that the knockdown of GATA1 significantly promoted the luciferase activity of the ALAS2 binding site. (G) ChIP analysis of GATA1 high-affinity binding sites in the ALAS2 promoter region. *, P<0.05 vs. control group or si-NC group or anti-NC group; **, P<0.01 vs. control group; ^#, P<0.05 vs. H₂O₂ group. TAA, thoracic aortic aneurysms; qRT-PCR, quantitative real-time polymerase chain reaction; WB, western blot; ChIP, chromatin immunoprecipitation; WT, wild-type; MUT, mutant; si-NC, small interfering RNA negative control; anti-NC, antibody negative control; H₂O₂, hydrogen peroxide.

Effects of ALAS2 and GATA1 on H₂O₂-induced inflammation and oxidative stress in MOVAS

The CCK-8 test revealed a considerable decrease in cell proliferation following H₂O₂ therapy. The overexpression of ALAS2 alleviated the decrease in cell proliferation induced by H₂O₂. However, the knockdown of GATA1 reversed the effect of ALAS2 overexpression and continued to cause a decrease in proliferation activity (Figure 7A). The ELISA results showed that the H₂O₂ treatment significantly upregulated the expression of inflammatory cytokines in the MOVAS. The combined treatment of H₂O₂, overexpressed ALAS2, and si-GATA1 further amplified the expression of these inflammatory markers compared with the expression of overexpressed ALAS2 alone (Figure 7B-7D). The levels of ROS and MDA, increased after H₂O₂ treatment and further increased in the presence of ALAS2 overexpression and GATA1 knockdown (Figure 7E,7F). Conversely, the levels of GSH showed the opposite trend, decreasing after H₂O₂ treatment and further decreasing with the knockdown of GATA1 in the presence of overexpressed ALAS2 (Figure 7G). These findings suggested that ALAS2 and GATA1 interact closely to control oxidative stress and inflammatory reactions in MOVAS.

Figure 7 ALAS2 overexpression alleviates H₂O₂-induced cell damage, while GATA1 knockdown reverses these effects. (A) CCK-8 was used to detect the cell viability of MOVAS in which ALAS2 was overexpressed and GATA1 was knocked down after H₂O₂ treatment. (B-D) ELISA analysis of the expression of inflammatory factors (TNF-α, IL-1β, and IL-6) in the MOVAS under different treatments. (E-G) Changes in ROS, MDA, and GSH levels in the MOVAS following different treatments. *, P<0.05 vs. control group; ^#, P<0.05 vs. H₂O₂ group and H₂O₂ + over-ALAS2 group. OD, optical density; CCK-8, cell counting kit-8; MOVAS, mouse vascular aortic smooth muscle cells; ELISA, enzyme-linked immunosorbent assay; ROS, reactive oxygen species; MDA, malondialdehyde; GSH, glutathione; H₂O₂, hydrogen peroxide.

ALAS2 regulates iron content and ferroptosis in H₂O₂-treated MOVAS by activating GATA1

According to the iron colorimetric assay results, the MOVAS treated with H₂O₂ had a much lower iron content after ALAS2 overexpression and GATA1 knockdown than after H₂O₂ treatment alone. Conversely, the iron levels remained increased in comparison to the controls (Figure 8A). Under the same experimental circumstances, the WB analysis showed that H₂O₂ therapy downregulated the expression of the negative regulators of ferroptosis. Interestingly, after being treated with H₂O₂, the cells that had both ALAS2 overexpression and concomitant GATA1 knockdown showed a partial reversal in the expression levels of these ferroptosis-related proteins (Figure 8B,8C). This finding suggested that GATA1 and ALAS2 are important players in the cellular response to oxidative stress, especially in the regulation of iron levels and the alteration of the expression of important proteins that regulate ferroptosis.

Figure 8 ALAS2 overexpression inhibits H₂O₂-induced ferroptosis in MOVAS via GATA1 knockdown. (A) An iron colorimetric assay was used to assess the iron content in the MOVAS. (B,C) WB analysis of ferroptosis negative regulatory factors (NRF2, GPX4, FTH1, and SLC7A11) in the MOVAS under different treatment conditions. The bar graph showed the results of the gray-scale detection of proteins. **, P<0.01 vs. control group, ^#, P<0.05 vs. H₂O₂ group and H₂O₂ + over-ALAS2 group. MOVAS, mouse vascular aortic smooth muscle cells; WB, western blot; H₂O₂, hydrogen peroxide.

Discussion

An aortic aneurysm is life-threatening due to the pathological dilation of the thoracic aorta, which, if left untreated, can lead to dissections or ruptures with high mortality rates (23,24). Traditional diagnostic techniques such as computed tomography and magnetic resonance imaging are critical to the treatment of clinical patient (25,26); however, they have limitations, including radiation exposure, high costs, and the use of contrast agents. As a result, there is a rising interest in discovering biomarkers, such as genetic markers, proteins, or other molecular indicators for the early, non-invasive detection of aortic aneurysms (27,28).

Advances in our understanding of the genetic underpinnings of aortic aneurysms have paved the way for targeted gene treatments. These therapies aim to address the root causes of aortic aneurysms by modulating key genes involved in aortic aneurysm development, offering the potential for more personalized and effective treatments. Presently, the 5-year survival rate for aortic aneurysm patients depends on factors such as the aneurysm size, location, and timely interventions (29). Surgical and endovascular procedures have shown efficacy in preventing aortic rupture, significantly improving survival rates (30,31). However, innovative therapeutic strategies, including gene-based treatments, may further enhance these rates. In summary, the identification of efficient and precise biomarkers for the diagnosis, treatment, and prognosis of aortic aneurysms is crucial.

In the present study, ALAS2 was identified as one of the key genes with significantly increased expression in the TAA samples, which suggests that this gene could be involved in the etiology of aortic aneurysms. Ferroptosis has emerged as a key factor in the progression of various diseases, highlighting the importance of iron regulation in cellular health and the potential to target ferroptosis in therapeutic strategies (32). The dysregulation of ALAS2 expression and its association with ferroptosis has been demonstrated in other diseases. ALAS2 activity is controlled by intracellular iron levels, which underscores the significance of maintaining adequate iron supplies for hemoglobin synthesis. For example, Ono et al. showed that ALAS2 mutations are associated with the occurrence of ferroptosis in X-linked sideroblastic anemia (33). Using a zebrafish model, Lv et al. (34) showed that insufficient hemoglobin production due to the abnormal function of ALAS2 induced ferroptosis in hematopoietic stem and progenitor cells. Whatley et al. (35) showed that C-terminal deletion of ALAS2 may result in gain-of-function and X-linked dominant protoporphyria without iron excess or anemia. Further, another study reported a link between ALAS2 and ferroptosis. Specifically, Song et al. found that ALAS2 expression was regulated by the ferroptosis response in a rat germinal matrix hemorrhage model, which may affect the molecular mechanisms of brain injury (36). These studies highlight the potential of ALAS2 to serve as an interventional target for a range of iron-related diseases.

Ferroptosis is a mode of controlled cell death closely related to oxidative stress (37). In this complex interaction, MDA functions as a crucial indicator of lipid peroxidation, which indicates cellular damage, and ROS initiate ferroptosis by causing damage to cellular components (38). Conversely, GSH serves as an essential antioxidant, and ferroptosis is characterized by its deficiency (39). Recent research by Zhou et al. showed that cordycepin targets vascular endothelial growth factor to inhibit apoptosis, inflammation, and oxidative stress in TAAs (40). Branchetti et al. discovered a potential link between increased peak wall stress, oxidative stress (ROS accumulation), and a shift in the vascular SMC phenotype towards a synthetic state, indicating the likely involvement of oxidative stress in TAA development (41). Further, Portelli et al. explored the association between oxidative stress and genetically triggered TAA, providing extensive evidence of oxidative stress and ROS imbalance (42).

The ferroptosis negative regulators NRF2, FTH1, GPX4, and SLC7A11 are critical for preserving iron metabolism and cellular redox homeostasis (43). Among them, NRF2 stimulates improved GSH production, facilitates ROS detoxification, and orchestrates the cellular antioxidant response (44). By controlling iron availability and reducing the likelihood of oxidative stress caused by iron, FTH1 suppresses the ferroptosis response (45). The enzyme GPX4 provides an important defense against iron oxidation by directly neutralizing lipid peroxides and shielding the cell membrane from oxidative stress (46). Common uses of SLC7A11 include lipid peroxidation prevention and intracellular GSH maintenance, both of which are essential for shielding cells against ferroptosis (47). In our study, we found that treatment with H₂O₂ not only increased the levels of iron and ROS in the MOVAS but also significantly decreased the GSH levels, highlighting the oxidative damage. NRF2, GPX4, FTH1, and SLC7A11 were among the ferroptosis inhibitors that were restored when ALAS2 overexpression reduced apoptosis and iron buildup.

In light of these findings, the role of GATA1 in aortic aneurysm pathogenesis needs to be examined further. GATA1, a crucial transcription factor known for its involvement in hematopoietic differentiation, has recently garnered attention due to its potential relevance in non-hematopoietic tissues, including the cardiovascular system. Previous studies have explored the significance of GATA1 in different disease contexts (48,49). For example, Zingariello et al. demonstrated that mice with low GATA1 expression can serve as a genuine model for myelofibrosis, as this model reflects the excessive activation of the TPO/MPL/JAK2 axis observed in the megakaryocytes of patients with bone marrow fibrosis. This study illustrated the multifaceted roles of GATA1 in regulating the critical pathways associated with hematological disorders, emphasizing the importance of this transcription factor in disease pathogenesis (50). Moreover, research by Zon et al. revealed the ability of GATA1 to activate the erythropoietin receptor promoter, underscoring its role in promoting red blood cell production (51).

The implications of these findings extended beyond erythropoiesis and encompass broader areas of hematopoiesis, reflecting the diverse functions of GATA1 in various physiological processes. To further complicate matters, our study highlights the role of ALAS2 overexpression and GATA1 knockdown in reducing H₂O₂-induced inflammation via pro-inflammatory cytokines, including IL-1β, TNF-α, and IL-6, exhibited a significant increase, as confirmed by previous study. These markers of inflammation highlighted the relevance of therapeutic techniques in addressing ferroptosis and inflammation, which are critical in the pathogenesis of aortic aneurysms (52).

Limitation

Our study initially explored the role of the ALAS2 gene in SMCs, and despite some findings, we need to acknowledge some limitations of the study. Our study was based on the analysis of bulk microarray datasets, which may have led to a less clear understanding of the major role factors of ALAS2 in SMCs. In order to more accurately determine the role of ALAS2 in SMCs, future studies could consider analyses using single-cell RNA sequencing data or human tissue data to provide more convincing evidence. In the discussion section, we need to explore the role of ALAS2 in more depth. Some literature reports that ALAS2 plays a negative role in muscle diseases, such as in ALAS2 overexpression mouse models. In addition, we need to consider whether ALAS2 overexpression leads to an increase in downstream HO-1 activity and explore whether this mechanism could explain the benefits of ALAS2. The future is needed to further validate the findings in this regard. We should also explicitly mention that the small haemorrhages that may have been present in the TAA tissues we observed may have partially stimulated the appearance of iron treatment in TAA. However, this is different from what may be present in cultured SMCs under experimental conditions. We need to recognize this point.

Conclusions

In summary, our study provides compelling evidence supporting the protective role of ALAS2 against oxidative stress-induced ferroptosis in MOVAS cells. Through ALAS2 overexpression, we observed a significant reduction in both iron accumulation and apoptosis triggered by H₂O₂ induction, underscoring its cytoprotective effects. Importantly, our investigation into the interaction between ALAS2 and GATA1 reveals a critical aspect of this defense mechanism. Specifically, we found that GATA1 knockdown reversed the cytoprotective benefits conferred by ALAS2, further emphasizing the intertwined relationship between these two factors in regulating ferroptosis and iron homeostasis. These findings not only enhance our understanding of the molecular mechanisms underlying ferroptosis but also lay the groundwork for potential therapeutic strategies aimed at mitigating ferroptosis-associated aortic aneurysm progression.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-370/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-370/dss

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-370/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

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