SGK1 contributes to endothelial cell ferroptosis in coronary heart disease through the NEDD4L/NF-κB pathway

doi:10.21203/rs.3.rs-4094468/v1

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SGK1 contributes to endothelial cell ferroptosis in coronary heart disease through the NEDD4L/NF-κB pathway

https://doi.org/10.21203/rs.3.rs-4094468/v1

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The prevalence of coronary heart disease (CHD) has increased significantly with the aging population worldwide. It is unclear whether ferroptosis occurs during CHD. Hence, we aimed to investigate the potential mechanisms associated with ferroptosis in CHD. Bioinformatics was used to characterize differentially expressed genes (DEGs) in CHD-related datasets (GSE21610 and GSE66360), and enrichment analysis was performed via protein‒protein interaction (PPI) network analysis. Proteins that interact with SGK1 as predicted by the String database. Flow cytometry and western blot analysis revealed alterations in lipid peroxidation, Fe accumulation, and ferroptosis-related marker expression in MAECs following lentivirus-mediated modulation of SGK1 and NEDD4L expression. A total of 76 and 689 DEGs were involved in pathways associated with immune and inflammatory responses, respectively. DDX3Y, EIF1AY, KDM5D, RPS4Y1, SGK1, USP9Y, and NSG1 showed intersecting DEGs. The differences in the number of circulating endothelial cells (ECs) between healthy individuals and CHD patients are consistent with the results of bioinformatics analysis. SGK1 may interact with NEDD4L and promote NEDD4L and p-P65 expression in MAECs according to the String database. Additionally, SGK1 knockdown alleviated the Erastin-induced downregulation of SLC7A11, GPX4, GSH, and GSSG, as well as the upregulation of lipid peroxidation, Fe accumulation, p-P65 expression, and mitochondrial damage. NEDD4L and PMA (NF-κB pathway activator) were rescued with overexpression. SGK1 contributes to EC ferroptosis by regulating the NEDD4L-NF-κB pathway. SGK1 could be recognized as a therapeutic target related to ferroptosis in CHD.

Coronary Heart Disease

Endothelial Cells

Ferroptosis

Serum/Glucocorticoid Regulated Kinase 1

NEDD4-Like E3 Ubiquitin Protein Ligase

CHD is characterized by myocardial dysfunction and/or organic lesions resulting from narrowed coronary arteries and insufficient blood supply, leading to conditions such as angina pectoris, sudden coronary death, and myocardial infarction^[1]. Risk factors for CHD include hyperlipidemia, hyperglycemia, insulin resistance, and hypertension. Its pathogenesis involves endothelial cell dysfunction, lipid deposition, macrophage activation, and proliferation with the migration of smooth muscle cells^[1–3]. Current treatments for CHD include pharmacologic therapy drugs, including antiplatelet agents, lipid-lowering agents, and vasodilators, as well as procedures such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG)^[4–6]. PCI involves the use of cardiac catheterization to open narrowed or occluded coronary arteries, while CABG involves the use of veins or arteries to bypass narrowed coronary arteries. Although both strategies are localized and provide temporary relief from myocardial ischemia and hypoxia, they do not prevent disease progression. Due to the persistence of cardiovascular risk factors, patients remain at high risk for recurrent adverse cardiovascular events, such as in-stent stenosis and plaque rupture, which can be life-threatening^{[7, 8]}. Therefore, systemic therapies that simultaneously address all lesions are more advantageous than limited strategies targeting a single aspect. Identification of additional CHD-related molecular mechanisms is needed to support the development of systemic therapies.

Ferroptosis is a novel form of noncaspase-dependent cell death regulated by reactive oxygen species (ROS)-induced lipid peroxidation. It is pathologically characterized by Fe accumulation, mitochondrial damage, and increased lipid peroxide levels ^[9–11]. The main molecular features of ferroptosis include the depletion of GSH and GSSG, reduced activity of GPX4, and the involvement of SCL7A11 and ACSL4 in regulatory processes ^[9–11]. Notably, Zhou et al ^[12] demonstrated the occurrence of ferroptosis in CHD patients, and PTGS2 can serve as a biomarker for CHD-associated ferroptosis. Furthermore, various molecular mechanisms, such as lnc-MRGPRF-6:1/GPX4, KAT5/GPX4, PDSS2/Nrf2, and TRPM4, aggravate the pathological manifestations of CHD by mediating ferroptosis ^[13–16]. Iron chelators such as deferoxamine, desferri-exochelin 772SM, endogenous ferritin, and Q50 can alleviate the progression of CHD by modulating ferroptosis ^[17–21]. This suggests that the development of ferroptosis-related therapeutic strategies may be an effective means of addressing CHD. However, there is currently limited information available on the regulatory mechanisms of ferroptosis in CHD, highlighting the need for further research to support the development of therapeutic strategies targeting ferroptosis.

In summary, the present study aimed to bioinformatically characterize biomarkers related to ferroptosis in a CHD-related dataset from the Gene Expression Omnibus (GEO) database and to explore the effect of this biomarker on endothelial cell ferroptosis and related molecular mechanisms through functional experiments. This study aimed to identify ferroptosis-related targets and new ideas to expand systemic therapies for CHD.

Identification of the Critical Genes in CHD

The CHD-related datasets used for bioinformatics analysis in this study were acquired from GEO and consisted of GSE21610 ^[22] and GSE66360 ^[23]. Information on the GSE21610 and GSE66360 datasets is shown in Table 1. Bioinformatics analysis included differential expression analysis of the GSE21610 and GSE66360 datasets, as well as GO and KEGG enrichment analysis of DEGs, which were performed using NetworkAnalyst ^[24] and ClueGO APP within Cytoscape software ^[25], respectively. Intersecting DEGs from the GSE21610 and GSE66360 datasets were predicted for interaction using the String database, and protein–protein interaction (PPI) network analysis was performed using Cytoscape software.

Table 1

Information on the GSE21610 and GSE66360 datasets
	GSE21610	GSE66360
Organism	Homo sapiens	Homo sapiens
Experiment Type	Array	Array
Platform	GPL570	GPL570
Tissue/Cell Type	Myocard	CD146 + Circulating Endothelial Cells
Group: Sample (n)	Health: GSM545657-GSM545664 (8) CHD: GSM545665-GSM545694 (30)	Health: GSM1620862-GSM1620876 and GSM1620892-GSM1620904 (28) CHD: GSM1620877-GSM1620891 and GSM1620905-GSM1620917 (28)

Isolation and Characterization of Human-derived Circulating Endothelial Cells (CECs)

Peripheral blood samples were obtained from healthy individuals undergoing physical examination and CHD patients attending Yan'an Hospital Affiliated with Kunming Medical University, with six samples for each group. The study was approved (NO. 2021-056-01) by the Ethics Committee of Yan'an Hospital Affiliated with Kunming Medical University. All patients consented to the study and signed an informed consent form. Patients diagnosed with CHD exhibit coronary artery stenosis exceeding 50% or a history of prior myocardial infarction. Briefly, peripheral blood was centrifuged to isolate peripheral blood mononuclear cells (PBMCs) from healthy individuals and CHD patients at a concentration of 1×10⁵ cells/µl in PBS. PBMCs obtained from healthy individuals and CHD patients were incubated with 5 µl of CD146 monoclonal antibody (14-1469-82, Invitrogen, USA) for 30 min. Subsequently, samples from each group were sorted and quantified using a Moflo AstriosEQ ultrahigh-speed flow cytometer (B25982, Beckman, USA).

Culture and infection of endothelial cells

Mouse aortic endothelial cells (MAECs) were obtained from BNCC (BNCC359881, Beijing, China). MAECs were cultured in endothelial cell-specific medium supplemented with ECM, FBS, ECGS and P/S (BNCC364799, Beijing, China). Genomeditech (Shanghai) Co., Ltd., designed, synthesized, and packaged lentiviral vectors for SGK1 knockdown and NEDD4L overexpression. The shRNA sequences targeting SGK1 were GCAACACCTATGCATGCAAAC, GGCAGAAGAAGTATT CTATGC, and GGAATGTTCTGTTGAAGAATG. The vectors used for the SGK1 knockdown lentivirus and NEDD4L overexpression lentivirus were PGMLV-SB3 RNAi and pcDNA3.1-VSVg, respectively. MAECs were randomly divided into Con, Erastin, KD-NC, KD-SGK1, OV-NEDD4L, and PMA groups. MAECs in the KD-SGK1, OV-NEDD4L and PMA groups were transfected with SGK1 knockdown lentiviruses, while those in the OV-NEDD4L group were cotransformed with NEDD4L overexpression lentivirus. MAECs in the Erastin, KD-NC, KD-SGK1, OV-NEDD4L, and PMA groups were treated with 10 µM Erastin (S7242, Selleck, USA) for 48 h, while those in the PMA group received an additional treatment with 20 ng/ml PMA (NF-κB pathway activator; HY-18739, MCE. USA).

Flow Cytometry of Endothelial Cells

A DCFH-DA Kit (HY-D0940, MCE, USA), BODIPY 581/591 C11 Kit (HY-D1301, MCE, USA), and Phen Green SK Diacetate (HY-126823, MCE, USA) were used in conjunction with flow cytometry to assess lipid peroxidation and Fe accumulation in MAECs. Briefly, DCFH-DA, C11-BODIPY, and PGSK probes were adjusted to final concentrations of 10, 5, and 5 nM, respectively, in serum-free culture medium and incubated with MAECs for 20 min. MAECs were assayed using a Cytek Aurora full-spectrum flow cytometer (Cytek Biosciences, USA) to determine the percentage of cells exhibiting DCF+, C11-BODIPY+, and quenched phen green fluorescence. The emission/excitation of the DCFH-DA, C11-BODIPY and PGSK probes were 517/492, 510/500 and 532/507, respectively.

RT‒qPCR Assay for CHD-related DEGs

The expression levels of CHD-related DEGs (DDX3Y, EIF1AY, KDM5D, RPS4Y1, SGK1, USP9Y, and NSG1) were detected by RT‒qPCR in circulating endothelial cells (CECs) obtained from healthy individuals and patients with CHD. Briefly, RNA extraction and qualification were conducted using TRNzol Universal (DP424, TianGen, Germany) and SolidSpec-3700i/3700i DUV spectrophotometers (Shimadzu, Japan) in CECs, respectively. Subsequently, reverse transcription and amplification of the target gene were carried out using the OneStep RT‒qPCR Kit (210212, QIAGEN, Germany) for each sample group, and the cycle threshold (Ct) was detected using the QuantStudio 6 Pro system (A43161, Applied Biosystems, USA). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the internal reference for the target genes (DDX3Y, EIF1AY, KDM5D, RPS4Y1, SGK1, USP9Y, and NSG1), with primer sequences detailed in Table 2.

Table 2

RT‒qPCR primer sequences for the target genes.
Name	Sequence	Length (bp)
GAPDH (H)-F	TTGCCCTCAACGACCACTTT	120
GAPDH (H)-R	TGGTCCAGGGGTCTTACTCC	120
USP9Y (H)-F	TACTGTTCTACGGTCTTC	78
USP9Y (H)-R	TAATGTCACCTCAGGATAA	78
EIF1AY (H)-F	CCGAGACTACCAGGATAACA	78
EIF1AY (H)-R	CCGTATGCCTTCAGACTTC	78
RPS4Y1 (H)-F	GAGACTGGCAAGATTACT	92
RPS4Y1 (H)-R	ATCACACCAATTCTTCCTA	92
KDM5D (H)-F	ATTGAGAAGCATCCAGAG	93
KDM5D (H)-R	CTTATCCTTATCCTTAGCCATA	93
SGK1 (H)-F	AAGACACAAGGCAGAAGAA	101
SGK1 (H)-R	CATTCCGCTCCGACATAA	101
DDX3Y (H)-F	CGCTATATTCCTCCTCATT	102
DDX3Y (H)-R	GCTATACGCATCCTTATCT	102
NSG1 (H)-F	GGATAAGGTGGTCGTGAA	90
NSG1 (H)-R	ACGGTGAACTCAGCAATT	90
Note: H, F and R represent Homo sapiens, forward primer and reverse primer, respectively.

Western blot analysis for ferroptosis-related markers

The protein expression levels of ferroptosis-related markers (SLC7A11, GPX4, and ACSL4), SGK1, NEDD4L, p-P65, and P65 in each group of MAECs were detected using RT‒qPCR. Briefly, protein extraction and determination of the protein concentration in MAECs were performed using a ProteoPrep Kit (PROTTOT-1KT, Merck, USA) and a SolidSpec-3700i/3700i DUV spectrophotometer (Shimadzu, Japan), respectively. Protein samples from MAECs were subjected to SDS‒PAGE and transferred to PVDF membranes in a sandwich format. PVDF membranes were incubated with the corresponding primary and secondary antibodies, which included antibodies against SLC7A11 (ab307601, Abcam, USA; 1:1000), GPX4 (ab125066, Abcam, USA; 1:10000), ACSL4 (66617-1-Ig, Proteintech, USA; 1:10000), SGK1 (ab32374, Abcam, USA; 1:500), NEDD4L (13690-1-AP, Proteintech, USA; 1:5000), p-P65 (bs-3485R, Bioss, China; 1:200), p65 (bs-23217R, Bioss. China; 1:200), β-actin (ab8227, Abcam, USA; 1:1000), AffiniPure™ goat anti-rabbit (111-005-003, Jackson ImmunoResearch, USA; 1:1000), and AffiniPure™ goat anti-mouse IgG (115-005-044, Jackson ImmunoResearch, USA; 1:1000). A ChemiDoc MP System (Bio-Rad, USA) was utilized to capture and acquire representative images of the target proteins.

GSH and GSSG Assays

Briefly, according to the instructions of the GSSG kit (BC1185, Solarbio, China) and GSH kit (BC1175, Solarbio, China), the supernatant of MAECs (20 µl) was supplemented with reagent II (140 µl) and reagent III (40 µl) from the GSH kit and reagent II (1 µl), reagent III (140 µl), reagent IV (20 µl), reagent V (20 µl), and reagent VI (2 µl) from the GSSG kit. The optical densities of GSSG and GSH were measured at 412 nm on a SolidSpec-3700i/3700i DUV (Shimadzu, Japan), and the contents of GSSG and GSH were calculated from a standard curve.

Statistical analysis

Statistical analysis was conducted using GraphPad Prism software. The Mann‒Whitney test or Student’s t test was used for comparisons between two groups, while the Kruskal‒Wallis test or one-way ANOVA was used for comparisons among multiple groups. Two-way ANOVA was used to analyze differences in MAEC activity at different time points among multiple groups. P < 0.05 indicated that the difference was statistically significant. CHD-associated DEGs satisfying both |log2FoldChange (FC)|>1 and adjusted P < 0.05 were identified.

Identification and enrichment analysis of CHD-related DEGs

In this study, we extracted and analyzed the GSE21610 and GSE66360 datasets from the GEO database to identify key genes associated with CHD. Analysis of the GSE21610 dataset revealed the presence of 76 DEGs in patients with CHD-derived myocardial samples, comprising 55 upregulated and 21 downregulated genes (Fig. 1A and Supplementary file 1). The GSE66360 dataset exhibited 689 DEGs in patients with CHD-derived CECs compared to the healthy cohort, including 488 upregulated and 201 downregulated DEGs (Fig. 1B and Supplementary file 1). Notably, the GSE21610 and GSE66360 datasets shared seven intersecting DEGs, namely, DDX3Y, EIF1AY, KDM5D, NSG1, RPS4Y1, SGK1, and USP9Y, resulting in a total of 758 concatenated DEGs (Fig. 1C).

Figure 1D shows that the concatenated DEGs were primarily associated with GO terms such as “cellular response to lipopolysaccharide”, “regulation of tumor necrosis factor production”, “fibrinolysis”, “neutrophil chemotaxis”, “leukocyte differentiation”, and “secretory granule”, with significant crosstalk among these terms. Moreover, the concatenated DEGs may modulate signaling pathways such as the TNF pathway, Toll-like receptor pathway, NOD-like receptor pathway, IL-17 signaling pathway, NF-κB signaling pathway, cytokine‒cytokine receptor interaction and lipid and atherosclerosis pathways (Fig. 1E). This indicated that CHD-associated DEGs primarily participate in biological processes and pathways related to the immune response and inflammatory response.

This study involved constructing PPI networks associated with concatenated DEGs to identify hub genes. Figure 2A displays a PPI network comprising 304 nodes and 1781 relationship pairs. Among these genes, the top 10 hub genes based on degree centrality were TNF, IL-1B, TLR2, TLR4, TYROBP, MMP9, JUN, CCR5, CSF1R and TLR8 (Fig. 2B and Supplementary File 2). These genes are all involved in regulatory factors associated with the immune response and inflammatory response. These findings suggested that CHD-associated DEGs primarily play roles in the immune response and inflammatory response, including the TNF pathway, Toll-like receptor pathway, and NF-κB pathway.

SGK1 is aberrantly highly expressed in CHD-derived CECs

To validate the accuracy of the CHD-related datasets, CHD-derived CECs were isolated, and the differential expression of intersecting DEGs was verified. As revealed in Fig. 3A, 853 and 1,482 CD146⁺ cells (CECs) were sorted from the peripheral blood of healthy individuals and patients with CHD, respectively, and these counts were significantly different. This suggested that CECs were successfully isolated in this study and that patients with CHD have more CECs in their peripheral blood. Moreover, patients with CHD-derived CECs exhibited upregulation of DDX3Y, EIF1AY, KDM5D, RPS4Y1, SGK1, and USP9Y and downregulation of NSG1 compared to healthy individual-derived CECs (Fig. 3B). This finding is consistent with the differential expression analysis of the GSE21610 and GSE66360 datasets (Table 3). Additionally, SGK1, which exhibited a greater fold change, was selected for validation at the protein level in this study. As expected, the SGK1 protein was highly expressed in patients with CHD-derived CECs (Fig. 3C). These findings suggest that the differential expression of these seven DEGs in CECs may be associated with the progression of CHD.

Table 3

Expression of CHD-related intersectional DEGs in the GSE21610 and GSE66360 datasets.
	GSE21610		GSE66360
	Log2 FC	adj.P.Val	Log2 FC	adj.P.Val
DDX3Y	2.70363563	0.0149618	2.20576907	0.000369
EIF1AY	3.97235913	0.0001269	2.47445955	0.000966
KDM5D	3.08257595	0.0011392	2.48763207	0.005060
RPS4Y1	3.79023493	0.0008781	2.52992731	0.005820
SGK1	1.90398204	0.0109039	1.15511344	0.003800
USP9Y	3.70649165	0.0000799	2.34920867	0.005060
NSG1	-1.14107511	0.0417122	-1.02517701	0.001330

SGK1 contributes to lipid peroxidation, Fe accumulation, and ferroptosis-related marker expression in MAECs

This study further investigated the impact of SGK1 on the phenotype of MAECs to characterize its role in CHD. Importantly, prior studies have demonstrated that SGK1 regulates ferroptosis in cancer cells ^[26]. Consequently, this study focused on examining the influence of SGK1 on ferroptosis in MAECs. Infection with the SGK1 lentivirus led to decreased SGK1 mRNA and protein expression in MAECs, especially in those infected with the SGK1 #1 lentivirus (Fig. 3D-3E). As depicted in Fig. 4A, treatment with erastin led to the upregulation of SGK1 protein expression in MAECs, which was attenuated by the SGK1 lentivirus. In addition, infection with the SGK1 lentivirus alleviated the Erastin-induced decrease in cell viability at 24, 36, and 48 h (Fig. 4B). As shown in Fig. 4C-4D, erastin led to a threefold increase in the percentage of DCF⁺ and C11-BODIPY⁺ cells, and SGK1 knockdown alleviated erastin-induced lipid peroxidation. SGK1 lentivirus reversed Eranstin-induced Fe²⁺ accumulation (Fig. 4E). Notably, erastin induced mitochondrial damage in MAECs, characterized by crumpling, reduction or loss of inner cristae, and rupture of the outer membrane. Knockdown of SGK1 alleviated erastin-induced mitochondrial damage in MAECs (Fig. 5A). Among ferroptosis markers, SLC7A11 and GPX4 were expressed at higher levels, while ACSL4 was expressed at lower levels in the KD-SGK1 group than in the KD-NC group (Fig. 5B). As expected, SGK1 knockdown alleviated the Eranstin-induced reduction in GSH and GSSG levels in MAECs (Fig. 5C). These findings indicated that SGK1 knockdown attenuated Eranstin-induced ferroptosis in MAECs.

The NEDD4L and NF-κB pathways are downstream of SGK1-mediated ferroptosis in MAECs

To identify potential mechanisms of SGK1-mediated ferroptosis, this study utilized the String database to predict SGK1-related interactions. As shown in Fig. 5D, with the minimum required interaction score set to the highest confidence level (0.9), SGK1 potentially interacts with 10 proteins, including RICTOR, MAPKAP1, MTOR, TSC22D3, PDPK1, KCNJ1, NEDD4L, FOXO3, FOXO1 and FOXO4. Notably, NEDD4L contributes to ferroptosis in multiple cell types ^[27–29]. It was hypothesized that the SGK1-mediated ferroptosis of MAECs might be associated with NEDD4L. As illustrated in Fig. 5E, Eranstin treatment upregulated the protein expression of NEDD4L in MAECs, but this effect was limited by SGK1 knockdown. Interestingly, the NF-κB pathway is among the downstream pathways regulated by NEDD4L and serves as a key pathway connecting inflammatory signaling and ferroptosis processes in CHD ^{[9, 30, 31]}. As expected, SGK1 knockdown similarly rescued Eranstin-induced P65 phosphorylation (Fig. 5F). This indicated that the promoting effect of SGK1 on ferroptosis might be related to the NEDD4L-mediated NF-κB pathway in MAECs.

To verify the hypotheses of this study, a rescue experiment was conducted. Interestingly, both the overexpression of NEDD4L and treatment with PMA (an NF-κB pathway activator) reversed the downregulation of SGK1 and NEDD4L expression induced by the SGK1 lentivirus (Fig. 6A). In addition, the viability of MAECs was greater in the OV-NEDD4L and PMA groups than in the KD-SGK1 group (Fig. 6B). Overexpression of NEDD4L and treatment with PMA alleviated the reductions in ROS levels, lipid peroxidation, and Fe accumulation induced by SGK1 knockdown (Fig. 6C-6E). TEM revealed that the crumpling, reduction or disappearance of inner cristae and rupture of the outer membrane in MAEC mitochondria were more prominent in the OV-NEDD4L and PMA groups than in the KD-SGK1 group (Fig. 7A). Similarly, compared with those in the KD-SGK1 group, the levels of SLC7A11, GPX4, GSH and GSSG were decreased, while the level of ACSL4 was increased in MAECs in the OV-NEDD4L and PMA groups (Fig. 7B-7C). Overexpression of NEDD4L and treatment with PMA reversed the downregulation of p-P65 caused by SGK1 knockdown (Fig. 7D). These findings suggested that SGK1 promotes ferroptosis in MAECs through the NEDD4L-NF-κB pathway.

This study identified 76 and 689 CHD-associated DEGs in the GSE21610 and GSE66360 datasets, respectively, predominantly associated with pathways mediating the immune response and inflammatory response, such as the TNF, Toll-like receptor, and NF-κB pathways. The PPI network of DEGs further validated the involvement of hub genes associated with the immune response and inflammatory response. These hub genes mediating immune responses and inflammatory reactions play pivotal roles in CHD pathology, validating the reliability of the current analysis. Furthermore, differential expression of intersecting DEGs (DDX3Y, EIF1AY, KDM5D, RPS4Y1, SGK1, USP9Y, and NSG1) in the GSE21610 and GSE66360 datasets was demonstrated, indicating the accuracy of the datasets and analyses. Notably, some of the intersecting DEGs have been proven to play a role in CHD. Multiple bioinformatics studies have identified EIF1AY, KDM5D and RPS4Y1 as regulatory molecules and blood biomarkers of CHD-associated inflammatory pathways ^{[32, 33]}. These findings align closely with the results obtained in the present study. Interestingly, polymorphisms in SGK1 have been implicated in CHD susceptibility ^[34], and SGK1 overexpression enhances thrombosis, neointimal hyperplasia, vascular inflammation, and CHD-associated memory deficits, possibly through modulation of the NF-κB pathway ^[35–37]. These findings indicate that previous studies have extensively documented the contribution of SGK1 to the progression of CHD. Notably, SGK1 regulates ferroptosis in cancer cells ^[26], and the release of its contents during ferroptosis amplifies the immune response and inflammatory reaction, which accelerates the progression of CHD ^[12–16]. This finding suggests that SGK1 may play a role in regulating CHD-associated ferroptosis. In this study, we confirmed this hypothesis and demonstrated that SGK1 contributes to Fe accumulation and lipid peroxidation in MAECs. This finding implies that the promotional effect of SGK1 on thrombosis, neointimal hyperplasia, vascular inflammation, and CHD-associated memory deficits is at least partially attributed to its mediation of ferroptosis. Interestingly, this study revealed that the NEDD4L and NF-κB pathways are downstream targets of SGK1 and that SGK1 enhances their expression in MAECs.

NEDD4L belongs to the NEDD4 subfamily of ubiquitin ligases and targets a wide array of substrates, including ions, neurotransmitter channels, growth factor receptors, signaling molecules, and tight junction molecules ^[38]. Importantly, NEDD4L is associated with cardiovascular disease, especially coronary events ^{[38, 39]}, and contributes to ferroptosis in granulocytes, cardiomyocytes, and cancer cells ^[27–29]. In this study, we observed that overexpression of NEDD4L counteracted the inhibition of ferroptosis caused by SGK1 knockdown, suggesting a regulatory relationship between SGK1 and NEDD4L in MAECs. Interestingly, previous studies have demonstrated that both SGK1 and NEDD4L influence the NF-κB pathway, suggesting a potential interaction in the regulation of inflammation associated with CHD ^{[30, 36]}. This finding suggests a potential association between the regulation of the NF-κB pathway by SGK1 and NEDD4L. The present study confirms this hypothesis and suggests that NEDD4L most likely regulates the NF-κB pathway through ubiquitination. This finding is supported by previous evidence indicating that NEDD4L promotes the ubiquitination and degradation of IκBα, leading to NF-κB pathway activation ^[40]. However, the mechanism by which SGK1 and NEDD4L interact in CHD remains unknown, and this study confirmed only their coexpression. Previous studies have suggested that SGK1 can phosphorylate the S448 site of NEDD4L and serve as a substrate for NEDD4L ubiquitination ^{[26, 41]}. These pathways may represent the mechanism by which SGK1 and NEDD4L interact in MAECs. Additionally, P38 ^[26], PDK ^[42], NGF ^[43], and PI3K ^[44] may act as upstream regulators of the SGK1/NEDD4L/NF-κB pathway.

Unfortunately, this study did not utilize a CHD animal model to verify the role of the SGK1/NEDD4L/NF-κB pathway in ferroptosis. In addition, the interactions among the SGK1, NEDD4L and NF-κB pathways need further exploration and verification. In conclusion, this study revealed for the first time that the SGK1/NEDD4L/NF-κB pathway facilitates ferroptosis in MAECs associated with CHD. These findings will contribute to the development of therapeutic strategies targeting ferroptosis in CHD and systematic therapies for CHD.

Ethics approval and consent to participate

The study was supported by the Ethics Committee of Yan'an Hospital Affiliated with Kunming Medical University (2021-056-01), and all patients provided written informed consent.

Consent for publication

All authors declared their consent for publication.

Availability of data and materials

The author confirms that all the data generated or analyzed during this study are included in this article.

Competing interests

The authors declare no competing interests.

Funding

This work was supported in part by the National Natural Science Foundation of China (No. 82360075), Yunnan Fundamental Research Projects (Nos. 202201AY070001-190, 202102AA310003 and 202201AT070277), and the Health Science and Technology Project of Kunming (No. 2023-04-02-018).

Author contributions

Y.P. and Y.J. designed the project and wrote the original manuscript. Y.P., Y.J. and Q.F.Z. performed the experiments. Q.F.Z. collected the literature and summarized the data. Y.J. and H.T. processed the samples. Y.P. and Z.J. performed the data analysis. Z.J. and H.T. generated the figures, polished the language and supervised the edited manuscript. All authors have read and approved the article.

Acknowledgments

Not applicable.

Authors' information

First authors: Yong Peng^1† and Yu Jiang^1† contributed equally to this work.

Department of Cardiovascular Surgery, Key Laboratory for Cardiovascular Disease of Yunnan Province, Clinical Medicine Center for Cardiovascular Disease of Yunnan Province, Yan’an Hospital Affiliated to Kunming Medical University, Kunming, 650051, China

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No competing interests reported.

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SGK1 contributes to endothelial cell ferroptosis in coronary heart disease through the NEDD4L/NF-κB pathway

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Abstract

Figures

Introduction

Materials and Methods

Identification of the Critical Genes in CHD

Isolation and Characterization of Human-derived Circulating Endothelial Cells (CECs)

Culture and infection of endothelial cells

Flow Cytometry of Endothelial Cells

RT‒qPCR Assay for CHD-related DEGs

Western blot analysis for ferroptosis-related markers

GSH and GSSG Assays

Statistical analysis

Results

Identification and enrichment analysis of CHD-related DEGs

SGK1 is aberrantly highly expressed in CHD-derived CECs

SGK1 contributes to lipid peroxidation, Fe accumulation, and ferroptosis-related marker expression in MAECs

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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