IFIT1 exacerbates atherosclerosis by activating Macrophage Extracellular Traps via the STING-TBK1 pathway

IFIT1 exacerbates atherosclerosis by activating Macrophage Extracellular Traps via the STING-TBK1 pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IFIT1 exacerbates atherosclerosis by activating Macrophage Extracellular Traps via the STING-TBK1 pathway Bingxing Chen, Yuan Qi, Xiaochen Yu, Chao Wang, Peng Jiang, Xiuru Guan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4759187/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Interferon-induced protein with tetratricopeptide repeats 1 (IFIT1)'s role has been shown to drive immune regulation and inflammation in many human diseases. However, the exact mechanism of action of IFIT1 in AS is unclear, and the specific mechanism of action on METs is also unknown. In this study, we will explore the potential mechanisms of IFIT1 in the formation of METs during AS. Methods : We downloaded GSE100927, GSE193336, GSE159677, IRGs, and METs-related genes for analysis and used qRT-PCR, flow cytometry, and immunofluorescence to detect the expression levels of IFIT1 and METs in plaques from AS patients and mice. The potential association of IFIT1 and METs in macrophages was similarly verified in LPS-induced macrophages. After IFIT1 silencing, the expression levels of METs were detected using qRT-PCR, flow cytometry, immunofluorescence, and WB. In addition, we delved into the potential mechanisms to detect the expression of the STING-TBK1 pathway and explored the interaction between IFIT1 and the STING-TBK1 pathway. Results : Our results showed that IFIT1 was upregulated in AS patients, mouse plaque tissues, and LPS-induced macrophages. The same changes were observed in METs.The decrease in METs after IFIT1 silencing suggests that IFIT1 is involved in the regulation of macrophages through METs. Notably, with the decrease in IFIT1 levels, we observed a corresponding decrease in the STING-TBK1 pathway, which decreased accordingly, suggesting some connection between IFIT1, STING-TBK1, and METs. Validation of the effect of STING-TBK1 on a macrophage basis showed that the STING activator SR-717 increased the expression of METs, while the STING inhibitor H-151 had the opposite result. Interestingly, we added SR-717 and H-151 to si-IFIT1, respectively, and the same changes occurred in METs. Conclusion : In summary, our study suggests that IFIT1 activates METs through the STING-TBK1 pathway, thereby aggravating AS. Atherosclerosis IFIT1 STING-TBK1 METs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Atherosclerosis (AS) is a chronic cardiovascular disease that is hazardous to human health, with a high incidence in recent years. Recent studies have linked AS to immune mechanisms. A variety of immune cells are involved in the pathogenesis of AS, such as macrophages, lymphocytes, dendritic cells, and neutrophils, which cause endothelial cell dysfunction through the activation of adhesion molecules and inflammatory cytokines, ultimately resulting in the formation of AS plaques (Hansson et al. 2006 ; Wolf and Ley 2019 ; Wu et al. 2017 ) Macrophages are a significant source of inflammatory cytokines and a key mediator of the innate immune response, playing a critical role in the development of AS (Blagov et al. 2023 ). However, recent research has identified extracellular traps (ETs), a mechanism unrelated to phagocytosis, in innate immune cells, including macrophages (Daniel et al. 2019 ). Macrophage extracellular traps (METs) are extracellular structures released by macrophages that consist of DNA combined with citrullinated histones 3 (H3CIT), myeloperoxidase (MPO), peptidylarginine deiminase 4 (PAD4), nuclear chromatin and elastases, and histones (Rasmussen and Hawkins 2022 ). Macrophages protect the body from infection by releasing DNA fibers and trap-associated proteins to capture and kill pathogenic microorganisms, and are also able to interact with other immune cells via DNA fibers and proteins in the traps to promote the onset and development of inflammatory responses (Boe et al. 2015 ), In addition, the formation of extracellular traps depends on reactive oxygen species (ROS), and its release requires NADPH oxidase and MPO(Kirchner et al. 2012 ). Wu et al. demonstrated that macrophage extracellular traps in hepatic ischemia cause post-hypoxic hepatocyte survival to decrease and aggravate the inflammatory response causing injury (Wu et al. 2021 ), similarly it has been demonstrated that Polystyrene-induced release of METs leads to hepatocyte inflammatory response through activation of the ROS/TGF-β/Smad2/3 signaling axis, which suggests that MTEs amplify the inflammatory response of disease (Wang et al. 2023 ) and that the formation of AS is mainly due to the inflammatory response caused by endothelial cell damage, and the formation and release of METs can promote the expression of endothelial cell adhesion molecules, attracting more monocytes and macrophages into the vascular wall, thus accelerating plaque formation and development. Knight et al. found (Knight et al. 2014 ) that in a mouse model of atherosclerosis, the use of peptidyl arginine deiminase (PAD) inhibitors to chemically inhibit ETs reduced vascular inflammation and inhibited plaque progression. This suggests that METs may negatively regulate AS, but the exact mechanism of action is unclear. IFIT1 is a member of the interferon-stimulated gene family. IFIT1 is located on human chromosome 10. IFIT1 regulates the immune system, cell proliferation, and apoptosis. IFIT1 In most in vitro cultured cells, the gene is usually silenced, but viral infections, double-stranded RNAs, and LPS can induce enhanced IFIT1 transcription (Wang et al. 2020 ). In addition, it has been shown that IFITI is highly expressed in macrophage subpopulations in the aorta of atherosclerotic ApoE-/- mice (Huang et al. 2018 ). In recent years, new insights have been gained into the transcriptional regulation of mouse IFIT1 at the chromatin level, and it has been found that shortly after IFN-β treatment, one of the METs markers, H3CIT variant H3.3, is deposited in the inner part of ISG56, similar to exons, but not in its promoter region (Tamura et al. 2009 ) Importantly, downregulation of H3.3 impairs IFIT1, so we hypothesize that IFIT1 and MTEs are somehow linked. Finally, elevated IFIT1 negatively regulates the antiviral response and also enhances the role of inflammatory pathways NF-κB, CXCL10, and others(Li et al. 2009 ; Shiratori et al. 2020 ). Although the innate immune role of IFIT1 is known, its exact mechanism of action in atherosclerosis is unclear. Hematopoietic cells such as macrophages, NK cells, and T cells mainly express the Stimulator of Interferon genes (STING), a key sensor of cytoplasmic DNA. Structurally, the STING signaling pathway combines DNA sensing and the induction of a robust innate immune defense program (Chen et al. 2021 ) to play a crucial role in the host immune response. TBK1, an atypical IkappaB kinase (IKK), becomes activated after phosphorylation. TBK1 also transmits inflammatory signals and releases a variety of inflammatory factors through downstream pathways, such as NF-κB and IRF3 (Al Hamrashdi and Brady 2022 ). The function of STING-TBK1 in inflammation has been extensively studied (Decout et al. 2021 ). The function of STING-TBK1 in inflammation has been extensively studied (Li et al. 2023 ). It has also been demonstrated that entinostat treatment leads to increased chromatin accessibility to the promoter region of the interferon-inducible protein promoter of the IFIT1 gene, which increases IFIT1 transcript and protein levels, and thus enhances the IFIT1-mediated IRF1, STAT4, and STING pathways (Idso et al. 2020 ). Based on what we have stated above, we hypothesize that IFIT1 may exacerbate atherosclerosis by regulating extracellular traps and inflammatory responses in macrophages. In addition, we investigated the use of si-IFIT1 to reveal whether IFIT1 regulates the formation of METs during AS via the STING/TBK1 pathway. Materials and Methods Patients and controls Between May 2023 and July 2024, our hospital recruited 62 AS patients and 54 healthy volunteers who underwent physical examinations during the same period. We collected peripheral blood from each subject, all subjects signed an informed consent form, the Ethics Committee of the First Affiliated Hospital of Harbin Medical University approved all experimental procedures, and the subjects in the control group had no inflammatory, autoimmune, infectious, or oncological diseases. Neither group of subjects received any systemic corticosteroids or other immunosuppressive treatments. Animal tissue analysis We purchased forty male ApoE-/-mice aged 6–8 weeks from Charles River (China). We randomly divided the mice into two groups: the AS group (high-fat diet: 15% fat, 1.25% cholesterol, and 0.5% sodium cholate) and the control group (normal diet: 4% fat, no cholesterol, and sodium cholate). At 16 weeks, we necropsied the mice and collected their tissues for further analysis. The Medical Ethics Committee of the First Clinical Hospital of Harbin Medical University approved all animal experiments. Data Collection To study the gene expression changes in atherosclerotic plaques, we downloaded the dataset GSE100927 (69 atherosclerotic plaque samples and 35 normal tissue samples) from the gene expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov) database, GSE193336 (4 normal and 4 LPS-stimulated macrophage samples), and the single-cell sequencing dataset GSE159677 (3 patients with matched proximal adjacent (PA) portions of the carotid artery and 3 AS patients with calcified atherosclerotic core (AC) plaques). We obtained immune-related genes (IRGs) from InnateDB (https://www.innatedb.com), an immune-related comprehensive database. Finally, macrophage extracellular trap-related genes were obtained from GeneCards (https://www.genecards.org). Weighted Gene Co-Expression Network Analysis We analyzed the co-expression network of GSE100927 using the Weighted Gene Co-Expression Network Analysis (WGCNA) from the R package, utilizing an unsigned topological overlap matrix for network construction and module detection. Here, we set the soft threshold power to 12 to remove abnormal samples, construct the co-expression network of the gene expression matrix of the remaining samples, identify gene modules, and select the most relevant gene modules. Functional enrichment analysis GO and KEGG enrichment analyses were performed using the R package "org.Hs.egg.db" to determine the functions of differential genes. The p-value <0.05 for GO or KEGG pathways was considered statistically significant. CIBERSORT analysis of immune infiltration patterns The CIBERSORT algorithm, based on gene microarray data, was used to quantify the extent of infiltration in the atherosclerotic cohort at the level of enrichment of 22 immune cell infiltrates. The Wilcox test was used to compare the differences between the two groups. PPI network construction and identification Gene information in the PPI network was downloaded from the STRING database (https://cn.string-db.org). The central genes were filtered in Cytoscape using the MCODE plugin. The default parameters were: degree cutoff = 2, node score cutoff = 0.2, and k-score = 2. All PPI networks were run within Cytoscape (https://cytoscape.org/). Single-cell data processing GSE159677 gene expression substrates were screened and further analyzed with the R package Seurat to find cells that met the quality control criteria of having less than 15% mitochondrial genes, more than 200 nFeature_RNAs, and less than 3000 cells. A total of 42,047 cells met these standards and were used for further analysis, and 6 samples were normalized for variable features. We clustered cells into 19 cell groups using the FindClusters function (resolution = 0.5). Cell type identification was based on specific cell markers obtained from the CellMarker database ( http://bio-bigdata.hrbmu.edu.cn/CellMarker/index.html), aiming to explore potential interactions between immune cells and genes with AS. Differential gene identification We applied the "limma" package to screen for differentially expressed genes between experimental and control groups in GSE100927 and GSE193336. Probe names were converted to gene names. The criteria were P<0.05, |log2FC|≥0.585. Cell culture We placed THP-1 cells in 10% 1640 medium and cultured them at 37 °C with 5% CO2 in a humidified atmosphere. We detached and passaged the cells, keeping the number of cell passages at 15-20 generations, once the cell density in the culture flask reached 70–80%. Cell transfection We cultured THP-1 cells in 12-well plates containing 2% 1640 medium and phorbol myristate acetate (PMA, 100 nmol/L) for 48 hours to enable them to fully adhere to the wall and transform into macrophages. Next, we transfected the cells by adding 1.25 ul of small interfering negative control RNA (RiboBio, Guangzhou, China) or si-IFIT1 (RiboBio, Guangzhou, China) to 27.5 ul of transfection reagent (RiboBio, Guangzhou, China), following the manufacturer's instructions. We treated the THP-1 cells for 24 hours (48 hours for the WB and flow treatments). After transfection, they were treated with 10 μg/mL lipopolysaccharide (LPS) for 24 hours. Subsequently, we added the STING inhibitor H-151 (MCE, New Jersey, USA) and activator SR-717 (MCE, New Jersey, USA) based on the experimental requirements, and treated them for 6 hours. Finally, we used the prepared total RNA and total protein from THP-1 cells for qRT-PCR, WB, flow cytometry, and fluorescence analysis. Cell viability assay THP-1 was cultured in 96-well plates containing 2% 1640 and PMA for 48 h to allow complete wall attachment. Next, different concentrations of LPS were added to the 96-well plates and incubated at 37 °C and 5% CO2 humidity for 24 h. At the end of the treatment, the medium was discarded, and a new solution was configured according to 2% 1640: CCK8 reagent (TargetMol, Boston, USA) = 10:1 per well was placed in the wells and incubated in the dark at 37 °C for 1 h. A microtiter plate was used to read the 450 nm absorbance, and cell activity was calculated. Each experiment was repeated three times. Cytotoxicity assay THP-1 was cultured in 6-well plates containing 2% 1640 and PMA for 48 h to allow complete wall attachment. Next, different concentrations of LPS were added to the 6-well plate, and the supernatant was extracted after incubation at 37 °C and 5% CO2 humidity for 24 h. It was mixed with the supernatant and added to the 96-well plate according to the requirements for the preparation of the LDH kit (Jiancheng, Nanjing, China), and the plate was incubated in the dark at 37°C for 30 min, and the absorbance at 450 nm was read with a microplate, and the OD value of cytotoxicity was calculated. OD value of cytotoxicity, each experiment was repeated three times. Ros assay After cell culture treatment, discard the cell culture medium in the dishes and rinse gently with sterile PBS twice.DCFH-DA was diluted with serum-free 1640 medium to a final concentration of 10 mm, referring to the ROS kit instructions. 1 ml of diluted DCFH-DA was added to each sample and incubated for 20 min at 37°C in an incubator. The cells were then washed three times with serum-free RPMI 1640 to adequately remove DCFH-DA that had not entered the cells. mixing and flow-through. Immunofluorescence THP-1 culture was fixed with 4% paraformaldehyde and permeabilized with methanol after completion of culture (mouse plaques were permeabilized with Triton X-100). Fixed THP-1 and paraffin-embedded arteries were then stained with antibodies: H3CIT (Proteintech, 17168-1-AP, dilution 1:200), MPO (Proteintech, 22225-1-AP, dilution 1:100), and IFIT1 (Abcam, ab305301, dilution 1:200). Atherosclerotic tissues were stained, and after staining was completed, they were then incubated with goat anti-rabbit IgG-labeled (Affinity, S0006, dilution ratio 1:200) secondary antibody for 2 h. Cell nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI) for 5 min. Images were captured by fluorescence microscopy. Collection and extraction of PBMC Peripheral blood from AS patients and normal controls were collected and separated by density gradient centrifugation with the addition of Ficoll reagent (Huake Biotechnology, Tianjin, China), and peripheral blood mononuclear cells (PBMCs) were obtained by rinsing them twice with PBS. At the end of centrifugation, the plasma layer was aspirated and discarded. After centrifugation, the plasma layer was discarded, and the PBMC layer (the white membrane layer) was carefully aspirated and divided equally into two tubes, to which Trizol and saline were added for the subsequent PCR and flow cytometry experiments, respectively. RNA extraction and real-time quantitative polymerase chain reaction (PCR) According to the manufacturer's instructions, we extracted total RNA from THP-1 cells or PBMC using a Trizol reagent (Takara, RNAiso, Japan). We then mixed the extracted RNA into 2ul 5x PrimeScript RT Master Mix (TaKaRa, Japan) for reverse transcription and real-time quantitative polymerase chain reaction (qRT-PCR) in a reaction volume of 20 uL, which included cDNA, dNTPs, primers, 2X SYBR Green qPCR MasterMix II (Universal) (Sevenbio, Beijing, China), and nuclease-free water. We calculated the results using the 2-Ct method. For each experiment, we conducted three replicates. Table 1 displays the primer sequences. Gene Primer sequences GAPDH F: GAGTCAACGGATTTGGTCGT R: GACAAGCTTCCCGTTCTCAG MPO F: CGCCCAACAACATCGACATC R: ATGCTGAACACACCCTCGTT PAD4 F: CAGGGGACATTGATCCGTGTG R: GGGAGGCGTTGATGCTGAA STING F: CCAGAGCACACTCTCCGGTA R: CGCATTTGGGAGGGAGTAGTA TBK1 F：TGGGTGGAATGAATCATCTACGA R: GCTGCACCAAAATCTGTGAGT IFI27 F: TGCTCTCACCTCATCAGCAGT R: CACAACTCCTCCAATCACAACT BST2 F: CACACTGTGATGGCCCTAATG R: GTCCGCGATTCTCACGCTT OAS3 F: GAAGGAGTTCGTAGAGAAGGCG R: CCCTTGACAGTTTTCAGCACC SIGLEC1 F: CCTCGGGGGGGAACATCCTT R: AGGCGTACCCCATCCTTGA IFIT1 F: TTGATGACGATGAAATGCCTGA R: CAGGTCACCAGACTCCTCAC Protein extraction and Western blotting Pre-cooled protein lysates were used: 100 ul RIPA buffer (Solarbio, Beijing, China), 1 ul PMSF (Solarbio, Beijing, China), and 1 ul phosphatase inhibitor (NCM Biotech, Suzhou, China) per well, which were mixed, added, and lysed on ice for 30 After ultrasonic crushing, the supernatant was extracted by centrifugation, and the protein concentration was determined by a BCA protein assay (Beyotime, Shanghai, China). Proportionally, 5× protein buffer (Solarbio, Beijing, China) was added and denatured in a dry bath at 100°C for 10 min. Proteins were separated by SDS-PAGE, and after electrophoresis, they were transferred to polyvinylidene fluoride (PVDF) for transmembrane transfer and closed with antibody dilution (NCM Biotech Ltd., Suzhou, China) diluted with antibodies to incubate the strips overnight at 4°C. Antibody dilution ratios were as follows: GAPDH (Affinity, AF7021, 1:3000), IFIT1 (Abcam, ab305301, dilution ratio 1:1000), H3CIT (Proteintech, 17168-1-AP, dilution ratio 1:3000), MPO (Wanleibio, WL02355, dilution ratio 1:1000), PAD4 (Proteintech, 17373-1-AP, dilution ratio 1:2000), STING (Proteintech, 19851-1-AP, 1:1000), P-TBK1 (CST, 5483T, dilution ratio 1:1000), TBK1 (CST, 3504T, dilution ratio 1:1000), and then incubated with secondary antibody (Affinity, #S0001, dilution ratio 1:10,000) for 1 h. The strips were then incubated with an ultrasensitive ECL chemiluminescence kit (NCM Biotech, Suzhou, China) and finally with a Tanon 5200 imager to expose the bands, and optical density analysis was performed on Image J. Flow cytometry analysis PBMC or THP-1 was added with 1ul CD14 staining for 15 min, and then treated with membrane-breaking agent solution A and solution B for 15 min and 20 min, respectively, and then centrifuged with saline to take the precipitate, and then added with 10ul MPO antibody () staining for 15 min, and then centrifuged to take the precipitate and added with 300ul of physiological saline for mixing on the machine, and all of the flow-binding analyses were based on Kaluza software. Data analysis Statistical analyses were performed using Sendo Academic (https://www.xiantaozi.com/), GraphPad Prism 9.0.0 software, and R and Sangerbox (http://www.sangerbox.com/tool). All experiments were repeated at least three times. Data were expressed as mean ± standard deviation (mean ± SD). Differences between the two groups were analyzed using two-sided, unpaired t-tests with normally distributed variables. Differences between three or more groups were analyzed using one-way ANOVA as well as the non-parametric Wilcoxon rank-sum test, and differences with a p-value < 0.05 were considered statistically significant. Results Identification of atherosclerosis-related genes and functional enrichment analysis The database of atherosclerosis-related expressed genes, GSE100927, was analyzed by WGCNA, and 13 modules in GSE100927 were identified (soft threshold power β was set to 12). By Spearman's correlation coefficient, the "turquoise" module (r = 0.72, p = 2e-17) was the most highly correlated module (Fig. 1A). According to GO analysis (Fig. 1B), the biological processes were mainly enriched in immune system processes, immune response, and cell activation. KEGG analysis showed (Fig. 1C) that these genes were significantly correlated with the chemokine signaling pathway, cytokine-cytokine receptor interaction, and actin cytoskeleton. Taken together, the above results suggest that the genes in the turquoise module are closely associated with immune-related functions. Data set analysis identifies genes In previous GO and KEGG enrichment analyses, we identified genes in the turquoise module that were highly enriched in immune-related pathways. Therefore, the CiberSort algorithm was further performed in GSE100927 to explore immune cell types that may be involved in atherogenesis and progression of atherosclerosis, and most of the immune cells had a higher level of infiltration in atherosclerotic plaques as compared to healthy individuals (Fig. 2A). This was particularly more pronounced with macrophages, and the immune microenvironment of atherosclerosis was also analyzed using the single-cell sequencing dataset GSE159677. Based on the CellMarker database, Figs. 2B and 2C show the annotated results of scRNA-seq data. We subsequently found that a high proportion of macrophages were detected in the AC of AS patients compared to PA-healthy samples. Because of this, we started our study with macrophages in AS, and we selected differential genes from the LPS-induced macrophage dataset GSE193336, the immune-related dataset, the macrophage-associated mechanisms-extra-macrophage traps dataset, and the WGCNA and limma differential genes of GSE100927 to take the intersections, which yielded 58 differential genes (Fig. 2D), and to better understand the interactions that have been identified between them, we constructed a PPI network using the STRING online server (Fig. 2E). Then, the core module was obtained from the PPI network by the MCODE plugin. 5 genes were selected as related genes (Fig. 2F), and a qRT-PCR assay was performed in LPS-induced macrophages (Fig. 2G, Fig. 5A), and one of these 5 genes, IFIT1, will be selected for subsequent experiments in this experiment. IFIT1 and METs are upregulated in atherosclerosis To see if IFIT1 and METs change in atherosclerosis, we took PBMC from the peripheral blood of AS patients and healthy people and measured IFIT1 and METs-related markers (MPO, PAD4, and H3CIT) by qRT-PCR (Fig. 3A) and flow (Fig. 3B). The results showed that IFIT1 and METs-related markers were significantly higher in AS patients than in healthy controls. Similarly, we subjected the mouse plaque sections and normal group sections to immunofluorescence (Fig. 3C), revealing higher MPO, H3CIT, and IFIT1 expression in the AS plaque group compared to the normal group sections. These results concluded that AS significantly increased METs and IFIT1 expression. In macrophages, 10 ug/ml LPS induces the formation of METs and promotes IFIT1 expression The mechanism by which IFIT1 leads to the formation of atherosclerotic METs is unknown. To determine the relationship between IFIT1 and METs, we explored whether LPS induced the formation of METs in macrophages. First, we determined the cytotoxicity of LPS on macrophages and treated macrophages with different concentrations of LPS for 24 h. We found that the cell viability declined with the increase of the LPS concentration (Fig. 4A) and the cytotoxicity increased with the increase of the LPS concentration (Fig. 4B), respectively, with the most pronounced change at 10 ug/ml, indicating that the decrease in cell viability and the increase in cytotoxicity were dose-dependent. Secondly, the results of qRT-PCR (Fig. 4C-E) and WB (Fig. 4F-J) experiments showed that MPO, PAD4, and H3CIT also showed positively correlated and significant changes with increasing LPS concentration as compared to the control group, and similarly, IFIT1 also exhibited a dose-dependent increase with LPS. Therefore, because of these data, we will conduct subsequent experiments with 10 ug/mL LPS. Next, we used 10ug/ml LPS-stimulated macrophages as an in vitro macrophage model of atherosclerosis. qRT-PCR (Fig.5A-C) and WB assay (Fig.5D-H) revealed that IFIT1, MPO, PAD4 and H3CIT were higher than those of the normal group, and immunofluorescence microscopy confirmed (Fig.5I) that METs were formed in the LPS-stimulated THP -1 cells after METs formation, flow results showed (Fig.5J) that MPO expression in experimental group cells was higher than control group after adding 10ug/ml LPS.ROS results showed (Fig.5K) that oxidative stress occurred in the LPS group compared to the Control group when ROS reagents were added. In conclusion, IFIT1 and METs protein and gene levels were significantly altered at 10ug/ml in LPS-treated macrophages. Knockdown of IFIT1 down-regulates LPS-induced METs in macrophages. To verify the function of IFIT1, we synthesized small interfering RNA (si-RNA) systems to de-knockdown IFIT1 expression in macrophages. In addition, qRT-PCR (Fig. 6A) and WB (Fig. 6B) results showed that the si-NC group did not differ from the control group, and the expression of IFIT1 was significantly reduced in the knockdown IFIT1 (si-IFIT1) group. Next, we examined the effect of down-regulation of IFIT1 expression on LPS-induced macrophages by treating LPS-induced macrophages with si-IFIT1 for 24 hr or 48 hr. qRT-PCR results (Fig. 6C-E) showed that MPO and PAD4 were decreased in the si-IFIT1+LPS group compared with the si-NC+LPS group, and the same WB results (Fig. 6F-I) showed that the si-IFIT1+LPS group also exhibited decreased MPO, PAD4, and H3CIT. Finally, fluorescence (Fig. 6J) and flow results (Fig. 6K) also showed different degrees of decrease in MPO and H3CIT, whereas there was no difference between the si-NC+LPS group and the LPS-treated group in all experimental results. These data suggest that IFIT1 may be involved in macrophage regulation through METs. Knockdown of IFIT1 reduces STING-TBK1 signaling pathway expression, while altered STING-TBK1 pathway expression causes alterations in METs We performed KEGG enrichment analysis (Fig. 7A) on the previous 58 differential genes and found that they were highly correlated with the cytoplasmic DNA receptor signaling pathway (STING-TBK1), so we verified the relationship between IFIT1 and the STING-TBK1 pathway. Firstly, both qRT-PCR (Fig. 7B-C) and WB results (Fig. 7D-F) showed that STING and TBK1 were increased in the control group compared with the LPS-treated group. When the si-IFIT1+LPS group was compared with the si-NC+LPS group, both qRT-PCR (Fig. 7B-C) and WB results (Fig. 7D-F) showed a decrease in STING and TBK1, suggesting that IFIT1 may regulate the STING-TBK1 signaling pathway. Next, to determine whether activation or inhibition of the STING-TBK1 signaling pathway could alter the occurrence of METs in LPS-induced macrophages, we treated LPS-induced macrophages with the STING activator SR-717 and the STING inhibitor H-151. The experimental results showed (Fig. 8A-H) that the expression of MPO, PAD4, and H3CIT was elevated in the LPS+SR-717 group compared to the LPS-treated group. On the contrary, the expression of MPO, PAD4, and H3CIT in the LPS+H-151 group was lower than that in the LPS group (Fig. 8A-H). This shows that inhibition of the STING-TBK1 signaling pathway inhibits the occurrence of METs, and activation of the STING-TBK1 signaling pathway enhances the occurrence of METs. The effect of IFIT1 downregulation on METs was enhanced by SR-717 and inhibited by H-151 To determine whether IFIT1 affects METs through the STING-TBK1 signaling pathway, we treated the cells in the si-IFIT1+LPS group by adding SR-717 and H-151, respectively. qRT-PCR (Fig. 9A-B) and WB (Fig. 9C-F) results of the si-IFIT1+LPS+SR-717 group compared to the si-IFIT1+LPS group showed that both MPO (Fig. 9C-F) and H3H1 (Fig. 9C-F) results showed that MPO and H3H1 down-regulation was inhibited by SR-717. and WB (Fig. 9C-F) results showed that MPO, H3CIT, and PAD4 were all increased, while the results of METs were all decreased in the si-IFIT1+LPS+H-151 group. Similarly, the levels of MPO and H3CIT-positive cells were analyzed using immunofluorescence staining, and the mean fluorescence intensity rose in the si-IFIT1+LPS+SR-717 group, while the opposite was true for the si-IFIT1+LPS+H-151 group (Fig. 9G). Finally, a corresponding trend of MPO+/CD14+ was observed in the flow-through (Fig. 9H). In summary, IFIT activates METs through the STING-TBK1 pathway. Discussion Atherosclerosis is a chronic, progressive inflammatory disease, and inflammatory and immune factors play an important role in the pathogenesis and progression of AS (Doran 2022 ; Kong et al. 2022 ). Studies in experimental AS animal models have shown that elevated levels of inflammation and alterations in immune cells in AS exacerbate AS (Jia et al. 2022 ). Currently, a comprehensive study of immune-related genes in the development of AS is lacking. Currently, there is a lack of comprehensive study of immune-related genes in AS development. As a result, we used WGCNA, extensive transcriptomic data, and single-cell sequencing data for analysis, followed by experimental validation, to explore immune mechanisms in the AS disease process. Steenman M et al. (Steenman et al. 2018 ) analyzed 69 atherosclerotic tissues and 35 normal tissues to obtain GSE100927, which we used as the basis for WGCNA analysis to derive a correlation between AS and immune function, followed by immune infiltration analysis to derive a difference in macrophages. In addition, Alsaigh et al.(Alsaigh et al. 2022 ) examined the single-cell transcriptome of whole calcified atherosclerotic core (AC) plaques in patients who underwent carotid endarterectomy and matched proximal adjacent (PA) portions of patients with carotid artery tissue, and similarly, we performed single-cell database analyses that yielded macrophage discrepancies, yielding a high correlation between AS and macrophages. Correlated. Macrophages play a central role in the regulation of inflammation, and advanced atherosclerotic plaques contain a large number of pro-inflammatory macrophages that secrete matrix-degrading enzymes that induce peripheral cell death and lead to plaque instability and rupture (Colin et al. 2014 ; Koelwyn et al. 2018 ; Patterson and Williams 2021 ). Extracellular traps are an important mechanism in macrophages and were first identified in 2004 (Brinkmann et al. 2004) when extracellular traps were first reported by Megens et al. in atherosclerotic lesions and their promotion of thrombus formation (Megens et al. 2012 ) Since then, most studies have focused on the effects of NETs on AS (Mutua and Gershwin 2021 ; Tembhre et al. 2022 ), and relevant studies on METs are scarce and mostly confined to the fields of systemic lupus erythematosus, rheumatoid arthritis, and others (El Shikh et al. 2019 ). Given the important role of macrophages in AS, we selected METs to verify their role in AS, which is a highlight of our study. It was found by Pertiwi et al. (Pertiwi et al. 2019 ) that METs were much more abundant in AS intact lipid plaques compared to the normal group, Our results also showed that METs were more abundant on plaques in mice. In the same way, we found that PBMCs from AS patients had higher levels of METs. They also had a lot more CD14 and MPO double-positive PBMCs and macrophages than controls. An et al. similarly used PBMCs for their experiments (An et al. 2019 ), Still, they used neutrophil-produced NETs to stimulate macrophage production of inflammatory factors that affect AS, and the present experiments used CD14 localized to monocytes or macrophages to exclude the interference of NETs and better validate the role of METs on AS. Finally, Zhai et al. showed that extracellular traps activate smooth muscle cells to aggravate atherosclerosis (Zhai et al. 2022 ). This indicates a connection between macrophage extracellular traps and atherosclerosis, although the precise action mechanism remains unclear. In this study, to make the sample data in this paper more extensive and comprehensive, we used AS dataset GSE100927, LPS-induced macrophage dataset GSE193336 (Li et al. 2022 ), immune-related genes as well as related genes in macrophage extracellular traps to take the intersections, which yielded 58 differential genes. Finally, we derived five genes from PPI protein interactions and correlation scores, selecting IFIT1 for further study through qRT-PCR. We looked at how much IFIT1 was made in mouse AS plaques and in the PBMCs of people who have AS, and we confirmed that more IFIT1 is made in AS. In line with what we found, Zhang's study found that IFIT1 gene expression levels were higher in aortic plaques of pristane-treated ApoE −/− mice compared to PBS-treated ApoE −/− mice (Zhang et al. 2015 ), Dong et al. through the identification of immune-related biomarkers in patients with atherosclerosis and the construction of the regulatory network, it was concluded that IFIT1 is a central immune-related gene in atherosclerosis (Dong et al. 2022 ). Another study demonstrated that LPS upregulated IFIT1 expression in HUVECs through the IFIT1 pathway (Wang et al. 2020 ), but the mechanism by which LPS upregulated IFIT1 expression needs to be further investigated. Next, we conducted further experiments with LPS-induced macrophages and found that knockdown of IFIT1 reduced the expression of METs. We then conducted an enrichment analysis to identify the pathway of these 58 factors, specifically STING-TBK1. This led us to hypothesize that IFIT1 may activate METs by inducing STING-TBK1. It has been shown that activation of the STING-TBK1 pathway plays a key role in NETs release (Zhao et al. 2023 ). However, these studies have not explored or elucidated the role of STING-TBK1 in the formation of METs. We discovered that stopping STING-TBK1 from working and starting it up again made the formation of METs go down and up, respectively. These results suggest that cytoplasmic STING-TBK1 is a key part of the formation of METs. It is not clear whether IFIT1 can act as an intrinsic signal to activate the STING-TBK1 pathway in the AS. Li et al. discovered that IFIT1 is associated with the adapter protein MITA and disrupts the interaction of MITA with VISA or TBK1, leading to inhibition of cellular antiviral responses (Li et al. 2009 ), but did not elucidate the role between IFIT1 and the STING-TBK1 pathway. Our findings revealed that LPS-induced macrophages elevated the STING-TBK1 pathway, while si-IFIT1 also reduced it, indicating that IFIT1 could potentially regulate the STING-TBK1 pathway. Notably, the STING inhibitor H-151 effectively limited the STING-TBK1 pathway and subsequent MET production, while the STING activator SR-717 activated it. These results strongly suggest that IFIT1's ability to activate the STING-TBK1 pathway primarily mediates the formation of IFIT1-dependent METs; however, the present study is only a preliminary investigation of IFIT1's role in atherosclerosis, and it will be necessary to expand on these results after knockdown of IFIT1 in animal models. Overall, our study demonstrates that IFIT1 and METs are elevated in both AS, mouse plaques, and LPS-induced macrophages, and we have identified a novel link between IFIT1 and macrophages in the pathogenesis of AS through the formation of METs and provided the first evidence that IFIT1 activation of the STING-TBK1 pathway induces the generation of METs and results in the AS exacerbation(Fig. 10 ). Declarations Acknowledgments Thank you to all my colleagues at the First Hospital of Harbin Medical University. Author contributions Bingxing Chen conceived and designed experiments and most of the experiments and data analysis. Yuan Qi, XiaoChen Yu, Chao Wang, and Peng Jiang provide technical support and data analysis, Xiuru Guan in concept and design, data acquisition, data analysis, and interpretation, and has made a significant contribution to the access to capital. All authors participated in the drafting of the manuscript, all authors read and approved the final version of the manuscript, and did not use a paper mill. Funding This research by the Harbin Medical University graduate student scientific research and practice innovation project funding (YJSCX2023-183HYD). Data availability All the data used to support this study is obtained according to the reasonable requirements of the corresponding author. This research includes database links as follows: GEO database (http://www.ncbi.nlm.nih.gov/geo), InnateDB database ((https://www.innatedb.com/), GeneCard database (https://www.genecards.org). Ethical Approval Animal tissue collection and experiments were approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University. Human peripheral blood collection and experiments were approved by the Ethics committee of the First Affiliated Hospital of Harbin Medical University, and all experiments involving human specimens were conducted by the Declaration of Helsinki (World Medical Association, 2013). Competing interests The authors declare no conflicts of interest. Consent for publication Informed consent was obtained from all individuals participating in the study. References Al Hamrashdi M, Brady G. Regulation of IRF3 activation in human antiviral signaling pathways. Biochem Pharmacol. 2022;200:115026. https://doi.org/10.1016/j.bcp.2022.115026. Alsaigh T, Evans D, Frankel D, Torkamani A. Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution. Commun Biol. 2022;5(1):1084. https://doi.org/10.1038/s42003-022-04056-7. An Z, Li J, Yu J, Wang X, Gao H, Zhang W, et al. Neutrophil extracellular traps induced by IL-8 aggravate atherosclerosis via activation NF-κB signaling in macrophages. Cell Cycle. 2019;18(21):2928–38. https://doi.org/10.1080/15384101.2019.1662678. Blagov AV, Markin AM, Bogatyreva AI, Tolstik TV, Sukhorukov VN, Orekhov AN. The Role of Macrophages in the Pathogenesis of Atherosclerosis. Cells. 2023;12(4):522. https://doi.org/10.3390/cells12040522. Boe DM, Curtis BJ, Chen MM, Ippolito JA, Kovacs EJ. Extracellular traps and macrophages: new roles for the versatile phagocyte. J Leukoc Biol. 2015;97(6):1023–35. https://doi.org/10.1189/jlb.4RI1014-521R. Chen K, Cagliani J, Aziz M, Tan C, Brenner M, Wang P. Extracellular CIRP activates STING to exacerbate hemorrhagic shock. JCI Insight. 2021;6(14):e143715. https://doi.org/10.1172/jci.insight.143715. Colin S, Chinetti-Gbaguidi G, Staels B. Macrophage phenotypes in atherosclerosis. Immunol Rev. 2014 Nov;262(1):153–66. Daniel C, Leppkes M, Muñoz LE, Schley G, Schett G, Herrmann M. Extracellular DNA traps in inflammation, injury and healing. Nat Rev Nephrol. 2019;15(9):559–75. https://doi.org/10.1038/s41581-019-0163-2. Decout A, Katz JD, Venkatraman S, Ablasser A. The cGAS-STING pathway as a therapeutic target in inflammatory diseases. Nat Rev Immunol. 2021;21(9):548–69. https://doi.org/10.1038/s41577-021-00524-z. Dong R, Jiang G, Tian Y, Shi X. Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis. BMC Med Genomics. 2022;15(1):245. https://doi.org/10.1186/s12920-022-01397-4. Doran AC. Inflammation Resolution: Implications for Atherosclerosis. Circ Res . 2022 Jan 7;130(1):130–48. https://doi.org/10.1161/CIRCRESAHA.121.319822. El Shikh MEM, El Sayed R, Nerviani A, Goldmann K, John CR, Hands R, et al. Extracellular traps and PAD4 released by macrophages induce citrullination and auto-antibody production in autoimmune arthritis. J Autoimmun . 2019;105:102297. https://doi.org/10.1016/j.jaut.2019.06.008. Hansson GK, Robertson A-KL, Söderberg-Nauclér C. Inflammation and atherosclerosis. Annu Rev Pathol . 2006;1:297–329. https://doi.org/10.1146/annurev.pathol.1.110304.100100. Huang C, Lewis C, Borg NA, Canals M, Diep H, Drummond GR, et al. Proteomic Identification of Interferon-Induced Proteins with Tetratricopeptide Repeats as Markers of M1 Macrophage Polarization. J Proteome Res . 2018;17(4):1485–99. https://doi.org/10.1021/acs.jproteome.7b00828. Idso JM, Lao S, Schloemer NJ, Knipstein J, Burns R, Thakar MS, et al. Entinostat augments NK cell functions via epigenetic upregulation of IFIT1-STING-STAT4 pathway. Oncotarget. 2020;11(20):1799–815. https://doi.org/10.18632/oncotarget.27546. Jia M, Li Q, Guo J, Shi W, Zhu L, Huang Y, et al. Deletion of BACH1 Attenuates Atherosclerosis by Reducing Endothelial Inflammation. Circ Res . 2022;130(7):1038–55. https://doi.org/10.1161/CIRCRESAHA.121.319540. Kirchner T, Möller S, Klinger M, Solbach W, Laskay T, Behnen M. The impact of various reactive oxygen species on the formation of neutrophil extracellular traps. Mediators Inflamm. 2012;2012:849136. https://doi.org/10.1007/s12035-021-02635-z. Knight JS, Luo W, O’Dell AA, Yalavarthi S, Zhao W, Subramanian V, et al. Peptidylarginine deiminase inhibition reduces vascular damage and modulates innate immune responses in murine models of atherosclerosis. Circ Res . 2014;114(6):947–56. https://doi.org/10.1161/CIRCRESAHA.114.303312. Koelwyn GJ, Corr EM, Erbay E, Moore KJ. Regulation of macrophage immunometabolism in atherosclerosis. Nat Immunol. 2018 Jun;19(6):526–37. Kong P, Cui Z-Y, Huang X-F, Zhang D-D, Guo R-J, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022 Apr 22;7(1):131. https://doi.org/10.1038/s41392-022-00955-7. Li L, Freitag J, Asbrand C, Munteanu B, Wang B-T, Zezina E, et al. Multi-omics profiling of collagen-induced arthritis mouse model reveals early metabolic dysregulation via SIRT1 axis. Sci Rep . 2022;12(1):11830. https://doi.org/10.1038/s41598-022-16005-9. Li W, Huang Z, Luo Y, Cui Y, Xu M, Luo W, et al. Tetrandrine alleviates atherosclerosis via inhibition of STING-TBK1 pathway and inflammation in macrophages. Int Immunopharmacol. 2023;119:110139. https://doi.org/10.1016/j.intimp.2023.110139. Li Y, Li C, Xue P, Zhong B, Mao A-P, Ran Y, et al. ISG56 is a negative-feedback regulator of virus-triggered signaling and cellular antiviral response. Proc Natl Acad Sci U S A . 2009;106(19):7945–50. https://doi.org/10.1073/pnas.0900818106. Megens RTA, Vijayan S, Lievens D, Döring Y, van Zandvoort MAMJ, Grommes J, et al. Presence of luminal neutrophil extracellular traps in atherosclerosis. Thromb Haemost. 2012;107(3):597–8. https://doi.org/10.1160/TH11-09-0650. Mutua V, Gershwin LJ. A Review of Neutrophil Extracellular Traps (NETs) in Disease: Potential Anti-NETs Therapeutics. Clin Rev Allergy Immunol. 2021;61(2):194–211. https://doi.org/10.1007/s12016-020-08804-7. Patterson MT, Williams JW. Metabolic regulation of macrophage proliferation and function in atherosclerosis. Curr Opin Lipidol. 2021;32(5):293–300. https://doi.org/10.1097/MOL.0000000000000778. Pertiwi KR, de Boer OJ, Mackaaij C, Pabittei DR, de Winter RJ, Li X, et al. Extracellular traps derived from macrophages, mast cells, eosinophils and neutrophils are generated in a time-dependent manner during atherothrombosis. J Pathol . 2019 Apr;247(4):505–12. https://doi.org/10.1002/path.5212. Rasmussen KH, Hawkins CL. Role of macrophage extracellular traps in innate immunity and inflammatory disease. Biochem Soc Trans. 2022;50(1):21–32. https://doi.org/10.1042/BST20210962. Shiratori T, Imaizumi T, Hirono K, Kawaguchi S, Matsumiya T, Seya K, et al. ISG56 is involved in CXCL10 expression induced by TLR3 signaling in BEAS-2B bronchial epithelial cells. Exp Lung Res . 2020;46(6):195–202. https://doi.org/10.1080/01902148.2020.1760965. Steenman M, Espitia O, Maurel B, Guyomarch B, Heymann M-F, Pistorius M-A, et al. Identification of genomic differences among peripheral arterial beds in atherosclerotic and healthy arteries. Sci Rep . 2018;8(1):3940. https://doi.org/10.1038/s41598-018-22292-y. Tamura T, Smith M, Kanno T, Dasenbrock H, Nishiyama A, Ozato K. Inducible deposition of the histone variant H3.3 in interferon-stimulated genes. J Biol Chem. 2009;284(18):12217–25. https://doi.org/10.1074/jbc.M805651200. Tembhre MK, Sriwastva MK, Hote MP, Srivastava S, Solanki P, Imran S, et al. Interleukin-33 Induces Neutrophil Extracellular Trap (NET) Formation and Macrophage Necroptosis via Enhancing Oxidative Stress and Secretion of Proatherogenic Factors in Advanced Atherosclerosis. Antioxidants (Basel). 2022;11(12):2343. https://doi.org/10.3390/antiox11122343. Wang J-L, Cai F, Liu X-H, Li L-M, He X, Hu X-M, et al. Lipopolysaccharide Promotes Inflammatory Response via Enhancing IFIT1 Expression in Human Umbilical Vein Endothelial Cells. DNA Cell Biol. 2020;39(7):1274–81. https://doi.org/10.1089/dna.2020.5454. Wang S, Chen L, Shi X, Wang Y, Xu S. Polystyrene microplastics-induced macrophage extracellular traps contributes to liver fibrotic injury by activating ROS/TGF-β/Smad2/3 signaling axis. Environ Pollut. 2023;324:121388. https://doi.org/10.1016/j.envpol.2023.121388. Wolf D, Ley K. Immunity and Inflammation in Atherosclerosis. Circ Res . 2019 Jan 18;124(2):315–27. https://doi.org/10.1161/CIRCRESAHA.118.313591. Wu M-Y, Li C-J, Hou M-F, Chu P-Y. New Insights into the Role of Inflammation in the Pathogenesis of Atherosclerosis. Int J Mol Sci . 2017;18(10):2034. https://doi.org/10.3390/ijms18102034. Wu S, Yang J, Sun G, Hu J, Zhang Q, Cai J, et al. Macrophage extracellular traps aggravate iron overload-related liver ischaemia/reperfusion injury. Br J Pharmacol . 2021;178(18):3783–96. https://doi.org/10.1111/bph.15518. Zhai M, Gong S, Luan P, Shi Y, Kou W, Zeng Y, et al. Extracellular traps from activated vascular smooth muscle cells drive the progression of atherosclerosis. Nat Commun. 2022;13(1):7500. https://doi.org/10.1038/s41467-022-35330-1. Zhang CY, Qu B, Ye P, Li J, Bao CD. Vulnerability of atherosclerotic plaques is associated with type I interferon in a murine model of lupus and atherosclerosis. Genet Mol Res . 2015;14(4):14871–81. https://doi.org/10.4238/2015.November.18.52. Zhao C, Liang F, Ye M, Wu S, Qin Y, Zhao L, et al. GSDMD promotes neutrophil extracellular traps via mtDNA-cGAS-STING pathway during lung ischemia/reperfusion. Cell Death Discov . 2023;9(1):368. https://doi.org/10.1038/s41420-023-01663-z. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4759187","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332420666,"identity":"c03e1a28-eb35-4dfa-9e02-ac931574de33","order_by":0,"name":"Bingxing Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bingxing","middleName":"","lastName":"Chen","suffix":""},{"id":332420667,"identity":"9b85557f-be17-4436-85d1-eccb03c7e2d4","order_by":1,"name":"Yuan Qi","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Qi","suffix":""},{"id":332420669,"identity":"d7948225-cdb3-43a2-9670-d2bb5d8cb2b1","order_by":2,"name":"Xiaochen Yu","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Yu","suffix":""},{"id":332420671,"identity":"f93ee700-8b2e-4936-8abe-9c4cec1591f6","order_by":3,"name":"Chao Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wang","suffix":""},{"id":332420674,"identity":"37adef68-eaa4-457c-ba1c-4e9cb5d37dae","order_by":4,"name":"Peng Jiang","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Jiang","suffix":""},{"id":332420676,"identity":"17ac337e-bfb7-4bcd-b0bd-871138233512","order_by":5,"name":"Xiuru Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYJCCA5J/JOQYmEnQwfjAssHCmCQtzAaVDRWJDUSrl4/IfSZxc4dE+vx23oMfGGpsoglqMTxz3Exy5hmJ3A2H+ZIlGI6l5RK0zrC9jU1agg2ohZnHQIKx4TARWprZ2KT/sEmkyzfzGP8gSos8exuzgWSbRALDYR4z4mwx4DnG+EDijIThBqAWiwRi/CI/I43hgERFnbx8/xnjGx9qbIiw5QAyL4GQcrAtBA0dBaNgFIyCUQAAr3s5BIeXp/oAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiuru","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2024-07-18 01:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4759187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4759187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62729613,"identity":"375de679-64f5-4929-9c4c-ae5ae368b5b3","added_by":"auto","created_at":"2024-08-18 23:06:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23950422,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of the analysis for WGCNA \u0026nbsp;(A) The analysis focuses on the soft threshold power and the average connectivity of this power for networks that are not scale-dependent. We set the soft-threshold power to a value of 12. The heatmap of module-trait relationships contains correlation coefficients and p-values, along with a scatterplot of correlations. (B) GO analysis of genes in the turquoise module (C) KEGG analysis of genes in the turquoise module\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/926c21b238cd6ea9e6f92f56.png"},{"id":62729618,"identity":"5e0e765c-0a3e-4187-a112-658a250cafde","added_by":"auto","created_at":"2024-08-18 23:06:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50676516,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and validation of five hub genes and immune cell types (A) Comparison of immune cells from AS plaques and normal control samples in GSE100927. (B and C) GSE159677 cell populations in 3 healthy control PAs and 3 ACs from AS patients. (D) Venn diagram showing gene overlap between turquoise modules, differential genes analyzed by GSE100927 limma, GSE193336, immune-related genes, and DEGs in METs. (E) Interaction diagram of 58 PPI protein networks. (F) PPI network maps of the five hub genes of the core module were analyzed using MCODE. (G) RT-qPCR showing mRNA expression levels of 5 genes in control and LPS-induced macrophages. Compared with the group Control, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/a1dd06e8c0a96b48ab003061.png"},{"id":62730297,"identity":"c60413ea-fdda-4dff-9f99-fa25a3105ba7","added_by":"auto","created_at":"2024-08-18 23:14:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10433799,"visible":true,"origin":"","legend":"\u003cp\u003eIFIT1 and METs were involved in the development of atherosclerosis. (A) The mRNA expression levels of IFIT1, MPO, and PAD4D in AS patients and healthy controls were detected by qRT-PCR using the Wilcoxon Rank Sum test.AS = 62, normal = 54 (B) Cells that were double-positive for CD14 and MPO in PBMC using flow-through assays AS = 62, normal = 54 (C) immunofluorescence assay to detect the presence of IFIT1 (pink), MPO (red), H3CIT (green), and DAPI (blue) in atherosclerotic plaques in mice, n = 3, scale bar = 200 μm. Compared with the group Control, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/8d35bc7b83fc558d48c326f4.png"},{"id":62729617,"identity":"308c3c71-decf-4cd5-95eb-94e6d0fc8d4a","added_by":"auto","created_at":"2024-08-18 23:06:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8857979,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different concentrations of LPS on macrophages (A) Macrophage activity at different LPS concentrations was detected by CCK-8. n=3 (B) \u0026nbsp;The effect of macrophage virulence at different concentrations of LPS was detected by LDH. n=3 (C-E) \u0026nbsp;Expression levels of mRNA of IFIT1, MPO, and PAD4 were detected in macrophages at different concentrations of LPS and healthy controls by qRT-PCR. n=3 (F-G) \u0026nbsp;The expression of IFIT1, MPO, PAD4, H3CIT, and GAPDH was detected by Western blot. n=3. Compared with the group Control, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/75e2718dce6a3e9fd715b35a.png"},{"id":62729612,"identity":"127de446-f0cb-4131-95cc-eedc07da5dff","added_by":"auto","created_at":"2024-08-18 23:06:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14418727,"visible":true,"origin":"","legend":"\u003cp\u003eLPS-induced increase in IFIT1 and METs in macrophages (A-C) qRT-PCR to detect mRNA expression levels of IFIT1, MPO, and PAD4 in LPS-induced macrophages, n = 3. (D-H) Western blot to detect the expression of IFIT1, MPO, PAD4, H3CIT, and GAPDH. n=3. \u0026nbsp;(I) Immunofluorescence to detect the presence of IFIT1, MPO, and H3CIT, quantitative data for Gene+/DAPI+, scale bar = 200 μm, n = 3. (J) Flow cytometry assay for double-positive cells of CD14 and MPO in macrophages, result data for MPO+/CD14+, n = 3. (K) ROS levels were measured by flow cytometry. Compared with the group Control, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/5f00d0abdbda583e42c50aa8.png"},{"id":62729615,"identity":"7533c22f-9340-4ca9-8fa2-493be27b042a","added_by":"auto","created_at":"2024-08-18 23:06:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13152372,"visible":true,"origin":"","legend":"\u003cp\u003eIn macrophages, IFIT1 may regulate the occurrence of METs (A) qRT-PCR to confirm the effect of si-IFIT1 and si-NC, n = 3. compared with control group and si-NC, ns. compared with si-NC group and si-IFIT1 group,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05 (B-C) Western blot to verify the effect of si-IFIT1 and si-NC, n = 3 compared with control group and si-NC, ns. compared with si-NC group and si-IFIT1 group,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05 (D-E) Detection of mRNA expression of MPO and PAD4 after si-IFIT1 by qRT-PCR, n = 3. group LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; group si-IFIT1+LPS group compared with group si-NC+LPS,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05 \u0026nbsp;\u0026nbsp;(F-I) Detection of mRNA expression of MPO, PAD4, H3CIT, and GAPDH after si-IFIT1 by Western blot. PAD4 mRNA expression, n = 3. L group LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; group si-IFIT1+LPS group compared with group si-NC+LPS,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05 \u0026nbsp;(F-I) Expression of MPO, PAD4, H3CIT, and GAPDH after si-IFIT1 was detected by Western blot, n = 3. group LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; group si-IFIT1+LPS group compared with group si-NC+LPS,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05 (J) Immunofluorescence to detect the presence of MPO and H3CIT, quantitative data for Gene+/DAPI+, scale bar = 200 μm, n = 3. group LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; group si-IFIT1+LPS group compared with group si-NC+LPS, #p\u0026lt;0.05 (K) Flow cytometry assay of macrophages with double-positive cells for CD14 and MPO, resulting data as MPO+/CD14+, n = 3. group LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; group si-IFIT1+LPS group compared with group si-NC+LPS, #p\u0026lt;0.05\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/c7bd776a7ba5fff7111cf0d0.png"},{"id":62729616,"identity":"e92e0668-5db1-4193-acb3-81c48103352e","added_by":"auto","created_at":"2024-08-18 23:06:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":12685533,"visible":true,"origin":"","legend":"\u003cp\u003eKnockdown of IFIT1 in macrophages reduces the expression of the STING-TBK1 signaling pathway (A) KEGG analysis of 58 differential genes was performed, and bar graphs and chromosome plots were made (B-C) Expression of mRNA for STING and TBK1 after si-IFIT1 was detected by qRT-PCR, n = 3. group \u0026nbsp;LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; si-IFIT1+LPS group was compared with the group si-NC+LPS,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05 (D-F) Expression of STING, TBK1, P-TBK1, and GAPDH after si-IFIT1 was detected by Western blot to detect the expression of STING, TBK1, P-TBK1, and GAPDH after si-IFIT1, n = 3. group \u0026nbsp;LPS compared with the group Control, *p\u0026lt;0.05. group si-NC+LPS compared with the group LPS, ns.\u0026nbsp; si-IFIT1+LPS group was compared with the group si-NC+LPS,\u003csup\u003e #\u003c/sup\u003ep\u0026lt;0.05\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/40c4dffbcc03f02814cd8c1e.png"},{"id":62729621,"identity":"95907de2-2a7a-4813-a369-6f0a1d52a07e","added_by":"auto","created_at":"2024-08-18 23:06:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":16037272,"visible":true,"origin":"","legend":"\u003cp\u003eThe STING-TBK1 signaling pathway regulates METs (A–B) Expression of mRNA for MPO and PAD4 was detected by qRT-PCR after using the STING activator SR-717 and the inhibitor H-151, n = 3. (C-F) Similarly, MPO was detected by Western blot after using SR-717 and H-151, PAD4, H3CIT, and GAPDH expression (n = 3). (G) Immunofluorescence detection of the fluorescence intensity of MPO and H3CIT, quantitative data for Gene+/DAPI+, scale bar = 200 μm, n = 3. (H) Flow cytometry assay of four groups of macrophages with double-positive cells for CD14 and MPO, and the resultant data for MPO+/CD14+, n = 3. Compared with the group LPS, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/a745d99d5da90d1bfdb1d3ad.png"},{"id":62729619,"identity":"20d4358e-ec99-4389-b8d5-356f3063a316","added_by":"auto","created_at":"2024-08-18 23:06:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":21733146,"visible":true,"origin":"","legend":"\u003cp\u003eSilencing of IFIT1-induced METs can be inhibited by H-151 and enhanced by SR-717 (A-B) H-151 and SR-717 were added on top of si-IFIT1, respectively, and the expression of mRNA of MPO and PAD4 was detected by qRT-PCR. n=3.\u0026nbsp; \u0026nbsp;(C-F) The expression of mRNA of MPO and PAD4 was detected by qRT-PCR also using SR-717 and H-151, followed by Western blot to detect the expression of MPO, PAD4, H3CIT, and GAPDH, n = 3. (G) Immunofluorescence detection of the fluorescence intensity of MPO and H3CIT in the five groups, and the quantitative data were Gene+/DAPI+, scale bar = 200 μm, n = 3. (H) Flow cytometry detection of double-positive cells of CD14 and MPO in the five groups of macrophages resultant data are MPO+/CD14+, n = 3. si-NC+LPS group compared with si-NC+control group, *p\u0026lt;0.05. si-NC+LPS group compared with si-IFIT1+LPS, \u003csup\u003e#\u003c/sup\u003ep\u0026lt;0.05.\u0026nbsp; Group si-IFIT1+LPS+SR-717 and group si-IFIT1+LPS+H-151 compared with group si-IFIT1+LPS,\u003csup\u003e $\u003c/sup\u003ep\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/6603a0049ecae16d17d17d39.png"},{"id":62729620,"identity":"563afbac-54f1-48aa-a2e5-ad8c4b328064","added_by":"auto","created_at":"2024-08-18 23:06:36","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":18398199,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the potential mechanism of IFIT1 regulation of atherosclerotic MTEs. IFIT1-induced STING - TBK1 pathways increase, leading to METs activation and aggravating atherosclerosis. The image is drawn using FiGdraw (\u003ca href=\"https://www.figdraw.com/\"\u003ehttps://www.figdraw.com/\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-4759187/v1/82b777741fd878ff1be11f93.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"IFIT1 exacerbates atherosclerosis by activating Macrophage Extracellular Traps via the STING-TBK1 pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtherosclerosis (AS) is a chronic cardiovascular disease that is hazardous to human health, with a high incidence in recent years. Recent studies have linked AS to immune mechanisms. A variety of immune cells are involved in the pathogenesis of AS, such as macrophages, lymphocytes, dendritic cells, and neutrophils, which cause endothelial cell dysfunction through the activation of adhesion molecules and inflammatory cytokines, ultimately resulting in the formation of AS plaques (Hansson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wolf and Ley \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Macrophages are a significant source of inflammatory cytokines and a key mediator of the innate immune response, playing a critical role in the development of AS (Blagov et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, recent research has identified extracellular traps (ETs), a mechanism unrelated to phagocytosis, in innate immune cells, including macrophages (Daniel et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMacrophage extracellular traps (METs) are extracellular structures released by macrophages that consist of DNA combined with citrullinated histones 3 (H3CIT), myeloperoxidase (MPO), peptidylarginine deiminase 4 (PAD4), nuclear chromatin and elastases, and histones (Rasmussen and Hawkins \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Macrophages protect the body from infection by releasing DNA fibers and trap-associated proteins to capture and kill pathogenic microorganisms, and are also able to interact with other immune cells via DNA fibers and proteins in the traps to promote the onset and development of inflammatory responses (Boe et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), In addition, the formation of extracellular traps depends on reactive oxygen species (ROS), and its release requires NADPH oxidase and MPO(Kirchner et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Wu et al. demonstrated that macrophage extracellular traps in hepatic ischemia cause post-hypoxic hepatocyte survival to decrease and aggravate the inflammatory response causing injury (Wu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), similarly it has been demonstrated that Polystyrene-induced release of METs leads to hepatocyte inflammatory response through activation of the ROS/TGF-β/Smad2/3 signaling axis, which suggests that MTEs amplify the inflammatory response of disease (Wang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and that the formation of AS is mainly due to the inflammatory response caused by endothelial cell damage, and the formation and release of METs can promote the expression of endothelial cell adhesion molecules, attracting more monocytes and macrophages into the vascular wall, thus accelerating plaque formation and development. Knight et al. found (Knight et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) that in a mouse model of atherosclerosis, the use of peptidyl arginine deiminase (PAD) inhibitors to chemically inhibit ETs reduced vascular inflammation and inhibited plaque progression. This suggests that METs may negatively regulate AS, but the exact mechanism of action is unclear.\u003c/p\u003e \u003cp\u003eIFIT1 is a member of the interferon-stimulated gene family. IFIT1 is located on human chromosome 10. IFIT1 regulates the immune system, cell proliferation, and apoptosis. IFIT1 In most in vitro cultured cells, the gene is usually silenced, but viral infections, double-stranded RNAs, and LPS can induce enhanced IFIT1 transcription (Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, it has been shown that IFITI is highly expressed in macrophage subpopulations in the aorta of atherosclerotic ApoE-/- mice (Huang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In recent years, new insights have been gained into the transcriptional regulation of mouse IFIT1 at the chromatin level, and it has been found that shortly after IFN-β treatment, one of the METs markers, H3CIT variant H3.3, is deposited in the inner part of ISG56, similar to exons, but not in its promoter region (Tamura et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) Importantly, downregulation of H3.3 impairs IFIT1, so we hypothesize that IFIT1 and MTEs are somehow linked. Finally, elevated IFIT1 negatively regulates the antiviral response and also enhances the role of inflammatory pathways NF-κB, CXCL10, and others(Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Shiratori et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although the innate immune role of IFIT1 is known, its exact mechanism of action in atherosclerosis is unclear.\u003c/p\u003e \u003cp\u003eHematopoietic cells such as macrophages, NK cells, and T cells mainly express the Stimulator of Interferon genes (STING), a key sensor of cytoplasmic DNA. Structurally, the STING signaling pathway combines DNA sensing and the induction of a robust innate immune defense program (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to play a crucial role in the host immune response. TBK1, an atypical IkappaB kinase (IKK), becomes activated after phosphorylation. TBK1 also transmits inflammatory signals and releases a variety of inflammatory factors through downstream pathways, such as NF-κB and IRF3 (Al Hamrashdi and Brady \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The function of STING-TBK1 in inflammation has been extensively studied (Decout et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The function of STING-TBK1 in inflammation has been extensively studied (Li et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It has also been demonstrated that entinostat treatment leads to increased chromatin accessibility to the promoter region of the interferon-inducible protein promoter of the IFIT1 gene, which increases IFIT1 transcript and protein levels, and thus enhances the IFIT1-mediated IRF1, STAT4, and STING pathways (Idso et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on what we have stated above, we hypothesize that IFIT1 may exacerbate atherosclerosis by regulating extracellular traps and inflammatory responses in macrophages. In addition, we investigated the use of si-IFIT1 to reveal whether IFIT1 regulates the formation of METs during AS via the STING/TBK1 pathway.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween May 2023 and July 2024, our hospital recruited 62 AS patients and 54 healthy volunteers who underwent physical examinations during the same period. We collected peripheral blood from each subject, all subjects signed an informed consent form, the Ethics Committee of the First Affiliated Hospital of Harbin Medical University approved all experimental procedures, and the subjects in the control group had no inflammatory, autoimmune, infectious, or oncological diseases. Neither group of subjects received any systemic corticosteroids or other immunosuppressive treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal tissue analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe purchased forty male ApoE-/-mice aged 6\u0026ndash;8 weeks from Charles River (China). We randomly divided the mice into two groups: the AS group (high-fat diet: 15% fat, 1.25% cholesterol, and 0.5% sodium cholate) and the control group (normal diet: 4% fat, no cholesterol, and sodium cholate). At 16 weeks, we necropsied the mice and collected their tissues for further analysis. The Medical Ethics Committee of the First Clinical Hospital of Harbin Medical University approved all animal experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study the gene expression changes in atherosclerotic plaques, we downloaded the dataset GSE100927 (69 atherosclerotic plaque samples and 35 normal tissue samples) from the gene expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov) database, GSE193336 (4 normal and 4 LPS-stimulated macrophage samples), and the single-cell sequencing dataset GSE159677 (3 patients with matched proximal adjacent (PA) portions of the carotid artery and 3 AS patients with calcified atherosclerotic core (AC) plaques). We obtained immune-related genes (IRGs) from InnateDB (https://www.innatedb.com), an immune-related comprehensive database. Finally, macrophage extracellular trap-related genes were obtained from GeneCards (https://www.genecards.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-Expression Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the co-expression network of GSE100927 using the Weighted Gene Co-Expression Network Analysis (WGCNA) from the R package, utilizing an unsigned topological overlap matrix for network construction and module detection. Here, we set the soft threshold power to 12 to remove abnormal samples, construct the co-expression network of the gene expression matrix of the remaining samples, identify gene modules, and select the most relevant gene modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses\u0026nbsp;were performed using the R package \u0026quot;org.Hs.egg.db\u0026quot; to determine the functions of differential genes. The p-value \u0026lt;0.05 for GO or KEGG pathways was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCIBERSORT analysis of immune infiltration patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT algorithm, based on gene microarray data, was used to quantify the extent of infiltration in the atherosclerotic cohort at the level of enrichment of 22 immune cell infiltrates. The Wilcox test was used to compare the differences between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network construction and identification\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene information in the PPI network was downloaded from the STRING database (https://cn.string-db.org). The central genes were filtered in Cytoscape using the MCODE plugin. The default parameters were: degree cutoff = 2, node score cutoff = 0.2, and k-score = 2. All PPI networks were run within Cytoscape (https://cytoscape.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSE159677 gene expression substrates were screened and further analyzed with the R package Seurat to find cells that met the quality control criteria of having less than 15% mitochondrial genes, more than 200 nFeature_RNAs, and less than 3000 cells. A total of 42,047 cells met these standards and were used for further analysis, and 6 samples were normalized for variable features. We clustered cells into 19 cell groups using the FindClusters function (resolution = 0.5). Cell type identification was based on specific cell markers obtained from the CellMarker database ( http://bio-bigdata.hrbmu.edu.cn/CellMarker/index.html), aiming to explore potential interactions between immune cells and genes with AS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied the \u0026quot;limma\u0026quot; package to screen for differentially expressed genes between experimental and control groups in GSE100927 and GSE193336. Probe names were converted to gene names. The criteria were P\u0026lt;0.05, |log2FC|\u0026ge;0.585.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe placed THP-1 cells in 10% 1640 medium and cultured them at 37 \u0026deg;C with 5% CO2 in a humidified atmosphere. We detached and passaged the cells, keeping the number of cell passages at 15-20 generations, once the cell density in the culture flask reached 70\u0026ndash;80%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe cultured THP-1 cells in 12-well plates containing 2% 1640 medium and phorbol myristate acetate (PMA, 100 nmol/L) for 48 hours to enable them to fully adhere to the wall and transform into macrophages. Next, we transfected the cells by adding 1.25 ul of small interfering negative control RNA (RiboBio, Guangzhou, China) or si-IFIT1 (RiboBio, Guangzhou, China) to 27.5 ul of transfection reagent (RiboBio, Guangzhou, China), following the manufacturer\u0026apos;s instructions. We treated the THP-1 cells for 24 hours (48 hours for the WB and flow treatments). After transfection, they were treated with 10 \u0026mu;g/mL lipopolysaccharide (LPS) for 24 hours. Subsequently, we added the STING inhibitor H-151 (MCE, New Jersey, USA) and activator SR-717 (MCE, New Jersey, USA) based on the experimental requirements, and treated them for 6 hours. Finally, we used the prepared total RNA and total protein from THP-1 cells for qRT-PCR, WB, flow cytometry, and fluorescence analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell viability assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTHP-1 was cultured in 96-well plates containing 2% 1640 and PMA for 48 h to allow complete wall attachment. Next, different concentrations of LPS were added to the 96-well plates and incubated at 37 \u0026deg;C and 5% CO2 humidity for 24 h. At the end of the treatment, the medium was discarded, and a new solution was configured according to 2% 1640: CCK8 reagent (TargetMol, Boston, USA) = 10:1 per well was placed in the wells and incubated in the dark at 37 \u0026deg;C for 1 h. A microtiter plate was used to read the 450 nm absorbance, and cell activity was calculated. Each experiment was repeated three times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytotoxicity assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTHP-1 was cultured in 6-well plates containing 2% 1640 and PMA for 48 h to allow complete wall attachment. Next, different concentrations of LPS were added to the 6-well plate, and the supernatant was extracted after incubation at 37 \u0026deg;C and 5% CO2 humidity for 24 h. It was mixed with the supernatant and added to the 96-well plate according to the requirements for the preparation of the LDH kit (Jiancheng, Nanjing, China), and the plate was incubated in the dark at 37\u0026deg;C for 30 min, and the absorbance at 450 nm was read with a microplate, and the OD value of cytotoxicity was calculated. OD value of cytotoxicity, each experiment was repeated three times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRos assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter cell culture treatment, discard the cell culture medium in the dishes and rinse gently with sterile PBS twice.DCFH-DA was diluted with serum-free 1640 medium to a final concentration of 10 mm, referring to the ROS kit instructions. 1 ml of diluted DCFH-DA was added to each sample and incubated for 20 min at 37\u0026deg;C in an incubator. The cells were then washed three times with serum-free RPMI 1640 to adequately remove DCFH-DA that had not entered the cells. mixing and flow-through.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTHP-1 culture was fixed with 4% paraformaldehyde and permeabilized with methanol after completion of culture (mouse plaques were permeabilized with Triton X-100). Fixed THP-1 and paraffin-embedded arteries were then stained with antibodies: H3CIT (Proteintech, 17168-1-AP, dilution 1:200), MPO (Proteintech, 22225-1-AP, dilution 1:100), and IFIT1 (Abcam, ab305301, dilution 1:200). Atherosclerotic tissues were stained, and after staining was completed, they were then incubated with goat anti-rabbit IgG-labeled (Affinity, S0006, dilution ratio 1:200) secondary antibody for 2 h. Cell nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI) for 5 min. Images were captured by fluorescence microscopy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollection and extraction of PBMC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood from AS patients and normal controls were collected and separated by density gradient centrifugation with the addition of Ficoll reagent (Huake Biotechnology, Tianjin, China), and peripheral blood mononuclear cells (PBMCs) were obtained by rinsing them twice with PBS. At the end of centrifugation, the plasma layer was aspirated and discarded. After centrifugation, the plasma layer was discarded, and the PBMC layer (the white membrane layer) was carefully aspirated and divided equally into two tubes, to which Trizol and saline were added for the subsequent PCR and flow cytometry experiments, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction and real-time quantitative polymerase chain reaction (PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the manufacturer\u0026apos;s instructions, we extracted total RNA from THP-1 cells or PBMC using a Trizol reagent (Takara, RNAiso, Japan). We then mixed the extracted RNA into 2ul 5x PrimeScript RT Master Mix (TaKaRa, Japan) for reverse transcription and real-time quantitative polymerase chain reaction (qRT-PCR) in a reaction volume of 20 uL, which included cDNA, dNTPs, primers, 2X SYBR Green qPCR MasterMix II (Universal) (Sevenbio, Beijing, China), and nuclease-free water. We calculated the results using the 2-Ct method. For each experiment, we conducted three replicates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 displays the primer sequences.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003ePrimer sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: GAGTCAACGGATTTGGTCGT\u003c/p\u003e\n \u003cp\u003eR: GACAAGCTTCCCGTTCTCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eMPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: CGCCCAACAACATCGACATC\u003c/p\u003e\n \u003cp\u003eR: ATGCTGAACACACCCTCGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003ePAD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: CAGGGGACATTGATCCGTGTG\u003c/p\u003e\n \u003cp\u003eR: GGGAGGCGTTGATGCTGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eSTING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: CCAGAGCACACTCTCCGGTA\u003c/p\u003e\n \u003cp\u003eR: CGCATTTGGGAGGGAGTAGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eTBK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF：TGGGTGGAATGAATCATCTACGA\u003c/p\u003e\n \u003cp\u003eR: GCTGCACCAAAATCTGTGAGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eIFI27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: TGCTCTCACCTCATCAGCAGT\u003c/p\u003e\n \u003cp\u003eR: CACAACTCCTCCAATCACAACT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eBST2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: CACACTGTGATGGCCCTAATG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eR: GTCCGCGATTCTCACGCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eOAS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: GAAGGAGTTCGTAGAGAAGGCG\u003c/p\u003e\n \u003cp\u003eR: CCCTTGACAGTTTTCAGCACC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eSIGLEC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: CCTCGGGGGGGAACATCCTT\u003c/p\u003e\n \u003cp\u003eR: AGGCGTACCCCATCCTTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.828209764918626%\" valign=\"top\"\u003e\n \u003cp\u003eIFIT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.17179023508137%\" valign=\"top\"\u003e\n \u003cp\u003eF: TTGATGACGATGAAATGCCTGA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eR: CAGGTCACCAGACTCCTCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eProtein extraction and Western blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePre-cooled protein lysates were used: 100 ul RIPA buffer (Solarbio, Beijing, China), 1 ul PMSF (Solarbio, Beijing, China), and 1 ul phosphatase inhibitor (NCM Biotech, Suzhou, China) per well, which were mixed, added, and lysed on ice for 30 After ultrasonic crushing, the supernatant was extracted by centrifugation, and the protein concentration was determined by a BCA protein assay (Beyotime, Shanghai, China). Proportionally, 5\u0026times; protein buffer (Solarbio, Beijing, China) was added and denatured in a dry bath at 100\u0026deg;C for 10 min. Proteins were separated by SDS-PAGE, and after electrophoresis, they were transferred to polyvinylidene fluoride (PVDF) for transmembrane transfer and closed with antibody dilution (NCM Biotech Ltd., Suzhou, China) diluted with antibodies to incubate the strips overnight at 4\u0026deg;C. Antibody dilution ratios were as follows: GAPDH (Affinity, AF7021, 1:3000), IFIT1 (Abcam, ab305301, dilution ratio 1:1000), H3CIT (Proteintech, 17168-1-AP, dilution ratio 1:3000), MPO (Wanleibio, WL02355, dilution ratio 1:1000), PAD4 (Proteintech, 17373-1-AP, dilution ratio 1:2000), STING (Proteintech, 19851-1-AP, 1:1000), P-TBK1 (CST, 5483T, dilution ratio 1:1000), TBK1 (CST, 3504T, dilution ratio 1:1000), and then incubated with secondary antibody (Affinity, #S0001, dilution ratio 1:10,000) for 1 h. The strips were then incubated with an ultrasensitive ECL chemiluminescence kit (NCM Biotech, Suzhou, China) and finally with a Tanon 5200 imager to expose the bands, and optical density analysis was performed on Image J.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow\u003c/strong\u003e \u003cstrong\u003ecytometry analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePBMC or THP-1 was added with 1ul CD14 staining for 15 min, and then treated with membrane-breaking agent solution A and solution B for 15 min and 20 min, respectively, and then centrifuged with saline to take the precipitate, and then added with 10ul MPO antibody () staining for 15 min, and then centrifuged to take the precipitate and added with 300ul of physiological saline for mixing on the machine, and all of the flow-binding analyses were based on Kaluza software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using Sendo Academic (https://www.xiantaozi.com/), GraphPad Prism 9.0.0 software, and R and Sangerbox (http://www.sangerbox.com/tool). All experiments were repeated at least three times. Data were expressed as mean \u0026plusmn; standard deviation (mean \u0026plusmn; SD). Differences between the two groups were analyzed using two-sided, unpaired t-tests with normally distributed variables. Differences between three or more groups were analyzed using one-way ANOVA as well as the non-parametric Wilcoxon rank-sum test, and differences with a p-value \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of atherosclerosis-related genes and functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database of atherosclerosis-related expressed genes, GSE100927, was analyzed by WGCNA, and 13 modules in GSE100927 were identified (soft threshold power \u0026beta; was set to 12). By Spearman\u0026apos;s correlation coefficient, the \u0026quot;turquoise\u0026quot; module (r = 0.72, p = 2e-17) was the most highly correlated module (Fig. 1A). According to GO analysis (Fig. 1B), the biological processes were mainly enriched in immune system processes, immune response, and cell activation. KEGG analysis showed (Fig. 1C) that these genes were significantly correlated with the chemokine signaling pathway, cytokine-cytokine receptor interaction, and actin cytoskeleton. Taken together, the above results suggest that the genes in the turquoise module are closely associated with immune-related functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData set analysis identifies genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn previous GO and KEGG enrichment analyses, we identified genes in the turquoise module that were highly enriched in immune-related pathways. Therefore, the CiberSort algorithm was further performed in GSE100927 to explore immune cell types that may be involved in atherogenesis and progression of atherosclerosis, and most of the immune cells had a higher level of infiltration in atherosclerotic plaques as compared to healthy individuals (Fig. 2A). This was particularly more pronounced with macrophages, and the immune microenvironment of atherosclerosis was also analyzed using the single-cell sequencing dataset GSE159677. Based on the CellMarker database, Figs. 2B and 2C show the annotated results of scRNA-seq data. We subsequently found that a high proportion of macrophages were detected in the AC of AS patients compared to PA-healthy samples. Because of this, we started our study with macrophages in AS, and we selected differential genes from the LPS-induced macrophage dataset GSE193336, the immune-related dataset, the macrophage-associated mechanisms-extra-macrophage traps dataset, and the WGCNA and limma differential genes of GSE100927 to take the intersections, which yielded 58 differential genes (Fig. 2D), and to better understand the interactions that have been identified between them, we constructed a PPI network using the STRING online server (Fig. 2E). Then, the core module was obtained from the PPI network by the MCODE plugin. 5 genes were selected as related genes (Fig. 2F), and a qRT-PCR assay was performed in LPS-induced macrophages (Fig. 2G, Fig. 5A), and one of these 5 genes, IFIT1, will be selected for subsequent experiments in this experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIFIT1 and METs are upregulated in atherosclerosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo see if IFIT1 and METs change in atherosclerosis, we took PBMC from the peripheral blood of AS patients and healthy people and measured IFIT1 and METs-related markers (MPO, PAD4, and H3CIT) by qRT-PCR (Fig. 3A) and flow (Fig. 3B). The results showed that IFIT1 and METs-related markers were significantly higher in AS patients than in healthy controls. Similarly, we subjected the mouse plaque sections and normal group sections to immunofluorescence (Fig. 3C), revealing higher MPO, H3CIT, and IFIT1 expression in the AS plaque group compared to the normal group sections. These results concluded that AS significantly increased METs and IFIT1 expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn macrophages, 10 ug/ml LPS induces the formation of METs and promotes IFIT1 expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mechanism by which IFIT1 leads to the formation of atherosclerotic METs is unknown. To determine the relationship between IFIT1 and METs, we explored whether LPS induced the formation of METs in macrophages. First, we determined the cytotoxicity of LPS on macrophages and treated macrophages with different concentrations of LPS for 24 h. We found that the cell viability declined with the increase of the LPS concentration (Fig. 4A) and the cytotoxicity increased with the increase of the LPS concentration (Fig. 4B), respectively, with the most pronounced change at 10 ug/ml, indicating that the decrease in cell viability and the increase in cytotoxicity were dose-dependent. Secondly, the results of qRT-PCR (Fig. 4C-E) and WB (Fig. 4F-J) experiments showed that MPO, PAD4, and H3CIT also showed positively correlated and significant changes with increasing LPS concentration as compared to the control group, and similarly, IFIT1 also exhibited a dose-dependent increase with LPS. Therefore, because of these data, we will conduct subsequent experiments with 10 ug/mL LPS.\u003c/p\u003e\n\u003cp\u003eNext, \u0026nbsp;we used 10ug/ml LPS-stimulated macrophages as an in vitro macrophage model of atherosclerosis. qRT-PCR (Fig.5A-C) and WB assay (Fig.5D-H) revealed that IFIT1, MPO, PAD4 and H3CIT were higher than those of the normal group, and immunofluorescence microscopy confirmed (Fig.5I) that METs were formed in the LPS-stimulated THP -1 cells after METs formation, flow results showed (Fig.5J) that MPO expression in experimental group cells was higher than control group after adding 10ug/ml LPS.ROS results showed (Fig.5K) that oxidative stress occurred in the LPS group compared to the Control group when ROS reagents were added. In conclusion, IFIT1 and METs protein and gene levels were significantly altered at 10ug/ml in LPS-treated macrophages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnockdown of IFIT1 down-regulates LPS-induced METs in macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the function of IFIT1, we synthesized small interfering RNA (si-RNA) systems to de-knockdown IFIT1 expression in macrophages. In addition, qRT-PCR (Fig. 6A) and WB (Fig. 6B) results showed that the si-NC group did not differ from the control group, and the expression of IFIT1 was significantly reduced in the knockdown IFIT1 (si-IFIT1) group. Next, we examined the effect of down-regulation of IFIT1 expression on LPS-induced macrophages by treating LPS-induced macrophages with si-IFIT1 for 24 hr or 48 hr. qRT-PCR results (Fig. 6C-E) showed that MPO and PAD4 were decreased in the si-IFIT1+LPS group compared with the si-NC+LPS group, and the same WB results (Fig. 6F-I) showed that the si-IFIT1+LPS group also exhibited decreased MPO, PAD4, and H3CIT. Finally, fluorescence (Fig. 6J) and flow results (Fig. 6K) also showed different degrees of decrease in MPO and H3CIT, whereas there was no difference between the si-NC+LPS group and the LPS-treated group in all experimental results. These data suggest that IFIT1 may be involved in macrophage regulation through METs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnockdown of IFIT1 reduces STING-TBK1 signaling pathway expression, while altered STING-TBK1 pathway expression causes alterations in METs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed KEGG enrichment analysis (Fig. 7A) on the previous 58 differential genes and found that they were highly correlated with the cytoplasmic DNA receptor signaling pathway (STING-TBK1), so we verified the relationship between IFIT1 and the STING-TBK1 pathway. Firstly, both qRT-PCR (Fig. 7B-C) and WB results (Fig. 7D-F) showed that STING and TBK1 were increased in the control group compared with the LPS-treated group. When the si-IFIT1+LPS group was compared with the si-NC+LPS group, both qRT-PCR (Fig. 7B-C) and WB results (Fig. 7D-F) showed a decrease in STING and TBK1, suggesting that IFIT1 may regulate the STING-TBK1 signaling pathway.\u003c/p\u003e\n\u003cp\u003eNext, to determine whether activation or inhibition of the STING-TBK1 signaling pathway could alter the occurrence of METs in LPS-induced macrophages, we treated LPS-induced macrophages with the STING activator SR-717 and the STING inhibitor H-151. The experimental results showed (Fig. 8A-H) that the expression of MPO, PAD4, and H3CIT was elevated in the LPS+SR-717 group compared to the LPS-treated group. On the contrary, the expression of MPO, PAD4, and H3CIT in the LPS+H-151 group was lower than that in the LPS group (Fig. 8A-H). This shows that inhibition of the STING-TBK1 signaling pathway inhibits the occurrence of METs, and activation of the STING-TBK1 signaling pathway enhances the occurrence of METs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe effect of IFIT1 downregulation on METs was enhanced by SR-717 and inhibited by H-151\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether IFIT1 affects METs through the STING-TBK1 signaling pathway, we treated the cells in the si-IFIT1+LPS group by adding SR-717 and H-151, respectively. qRT-PCR (Fig. 9A-B) and WB (Fig. 9C-F) results of the si-IFIT1+LPS+SR-717 group compared to the si-IFIT1+LPS group showed that both MPO (Fig. 9C-F) and H3H1 (Fig. 9C-F) results showed that MPO and H3H1 down-regulation was inhibited by SR-717. and WB (Fig. 9C-F) results showed that MPO, H3CIT, and PAD4 were all increased, while the results of METs were all decreased in the si-IFIT1+LPS+H-151 group. Similarly, the levels of MPO and H3CIT-positive cells were analyzed using immunofluorescence staining, and the mean fluorescence intensity rose in the si-IFIT1+LPS+SR-717 group, while the opposite was true for the si-IFIT1+LPS+H-151 group (Fig. 9G). Finally, a corresponding trend of MPO+/CD14+ was observed in the flow-through (Fig. 9H). In summary, IFIT activates METs through the STING-TBK1 pathway.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAtherosclerosis is a chronic, progressive inflammatory disease, and inflammatory and immune factors play an important role in the pathogenesis and progression of AS (Doran \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kong et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies in experimental AS animal models have shown that elevated levels of inflammation and alterations in immune cells in AS exacerbate AS (Jia et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Currently, a comprehensive study of immune-related genes in the development of AS is lacking. Currently, there is a lack of comprehensive study of immune-related genes in AS development. As a result, we used WGCNA, extensive transcriptomic data, and single-cell sequencing data for analysis, followed by experimental validation, to explore immune mechanisms in the AS disease process.\u003c/p\u003e \u003cp\u003eSteenman M et al. (Steenman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) analyzed 69 atherosclerotic tissues and 35 normal tissues to obtain GSE100927, which we used as the basis for WGCNA analysis to derive a correlation between AS and immune function, followed by immune infiltration analysis to derive a difference in macrophages. In addition, Alsaigh et al.(Alsaigh et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the single-cell transcriptome of whole calcified atherosclerotic core (AC) plaques in patients who underwent carotid endarterectomy and matched proximal adjacent (PA) portions of patients with carotid artery tissue, and similarly, we performed single-cell database analyses that yielded macrophage discrepancies, yielding a high correlation between AS and macrophages. Correlated. Macrophages play a central role in the regulation of inflammation, and advanced atherosclerotic plaques contain a large number of pro-inflammatory macrophages that secrete matrix-degrading enzymes that induce peripheral cell death and lead to plaque instability and rupture (Colin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Koelwyn et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Patterson and Williams \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExtracellular traps are an important mechanism in macrophages and were first identified in 2004 (Brinkmann et al. 2004) when extracellular traps were first reported by Megens et al. in atherosclerotic lesions and their promotion of thrombus formation (Megens et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) Since then, most studies have focused on the effects of NETs on AS (Mutua and Gershwin \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tembhre et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and relevant studies on METs are scarce and mostly confined to the fields of systemic lupus erythematosus, rheumatoid arthritis, and others (El Shikh et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the important role of macrophages in AS, we selected METs to verify their role in AS, which is a highlight of our study. It was found by Pertiwi et al. (Pertiwi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) that METs were much more abundant in AS intact lipid plaques compared to the normal group, Our results also showed that METs were more abundant on plaques in mice. In the same way, we found that PBMCs from AS patients had higher levels of METs. They also had a lot more CD14 and MPO double-positive PBMCs and macrophages than controls. An et al. similarly used PBMCs for their experiments (An et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Still, they used neutrophil-produced NETs to stimulate macrophage production of inflammatory factors that affect AS, and the present experiments used CD14 localized to monocytes or macrophages to exclude the interference of NETs and better validate the role of METs on AS. Finally, Zhai et al. showed that extracellular traps activate smooth muscle cells to aggravate atherosclerosis (Zhai et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This indicates a connection between macrophage extracellular traps and atherosclerosis, although the precise action mechanism remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, to make the sample data in this paper more extensive and comprehensive, we used AS dataset GSE100927, LPS-induced macrophage dataset GSE193336 (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), immune-related genes as well as related genes in macrophage extracellular traps to take the intersections, which yielded 58 differential genes. Finally, we derived five genes from PPI protein interactions and correlation scores, selecting IFIT1 for further study through qRT-PCR. We looked at how much IFIT1 was made in mouse AS plaques and in the PBMCs of people who have AS, and we confirmed that more IFIT1 is made in AS. In line with what we found, Zhang's study found that IFIT1 gene expression levels were higher in aortic plaques of pristane-treated ApoE\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice compared to PBS-treated ApoE\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Zhang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Dong et al. through the identification of immune-related biomarkers in patients with atherosclerosis and the construction of the regulatory network, it was concluded that IFIT1 is a central immune-related gene in atherosclerosis (Dong et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another study demonstrated that LPS upregulated IFIT1 expression in HUVECs through the IFIT1 pathway (Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but the mechanism by which LPS upregulated IFIT1 expression needs to be further investigated.\u003c/p\u003e \u003cp\u003eNext, we conducted further experiments with LPS-induced macrophages and found that knockdown of IFIT1 reduced the expression of METs. We then conducted an enrichment analysis to identify the pathway of these 58 factors, specifically STING-TBK1. This led us to hypothesize that IFIT1 may activate METs by inducing STING-TBK1. It has been shown that activation of the STING-TBK1 pathway plays a key role in NETs release (Zhao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these studies have not explored or elucidated the role of STING-TBK1 in the formation of METs. We discovered that stopping STING-TBK1 from working and starting it up again made the formation of METs go down and up, respectively. These results suggest that cytoplasmic STING-TBK1 is a key part of the formation of METs. It is not clear whether IFIT1 can act as an intrinsic signal to activate the STING-TBK1 pathway in the AS. Li et al. discovered that IFIT1 is associated with the adapter protein MITA and disrupts the interaction of MITA with VISA or TBK1, leading to inhibition of cellular antiviral responses (Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), but did not elucidate the role between IFIT1 and the STING-TBK1 pathway. Our findings revealed that LPS-induced macrophages elevated the STING-TBK1 pathway, while si-IFIT1 also reduced it, indicating that IFIT1 could potentially regulate the STING-TBK1 pathway. Notably, the STING inhibitor H-151 effectively limited the STING-TBK1 pathway and subsequent MET production, while the STING activator SR-717 activated it. These results strongly suggest that IFIT1's ability to activate the STING-TBK1 pathway primarily mediates the formation of IFIT1-dependent METs; however, the present study is only a preliminary investigation of IFIT1's role in atherosclerosis, and it will be necessary to expand on these results after knockdown of IFIT1 in animal models.\u003c/p\u003e \u003cp\u003eOverall, our study demonstrates that IFIT1 and METs are elevated in both AS, mouse plaques, and LPS-induced macrophages, and we have identified a novel link between IFIT1 and macrophages in the pathogenesis of AS through the formation of METs and provided the first evidence that IFIT1 activation of the STING-TBK1 pathway induces the generation of METs and results in the AS exacerbation(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments \u0026nbsp;\u003c/strong\u003eThank you to all my colleagues at the First Hospital of Harbin Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e \u0026nbsp;Bingxing Chen conceived and designed experiments and most of the experiments and data analysis. Yuan Qi, XiaoChen Yu, Chao Wang, and Peng Jiang provide technical support and data analysis, Xiuru Guan in concept and design, data acquisition, data analysis, and interpretation, and has made a significant contribution to the access to capital. All authors participated in the drafting of the manuscript, all authors read and approved the final version of the manuscript, and did not use a paper mill.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003eThis research by the Harbin Medical University graduate student scientific research and practice innovation project funding (YJSCX2023-183HYD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e \u0026nbsp;All the data used to support this study is obtained according to the reasonable requirements of the corresponding author. This research includes database links as follows: GEO database (http://www.ncbi.nlm.nih.gov/geo), InnateDB database ((https://www.innatedb.com/), GeneCard database (https://www.genecards.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval \u003c/strong\u003e\u0026nbsp;Animal tissue collection and experiments were approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University. Human peripheral blood collection and experiments were approved by the Ethics committee of the First Affiliated Hospital of Harbin Medical University, and all experiments involving human specimens were conducted by the Declaration of Helsinki (World Medical Association, 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u0026nbsp; The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Informed consent was obtained from all individuals participating in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl Hamrashdi M, Brady G. Regulation of IRF3 activation in human antiviral signaling pathways. Biochem Pharmacol. 2022;200:115026. \u0026nbsp; https://doi.org/10.1016/j.bcp.2022.115026.\u003c/li\u003e\n \u003cli\u003eAlsaigh T, Evans D, Frankel D, Torkamani A. Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution. Commun Biol. 2022;5(1):1084. \u0026nbsp; https://doi.org/10.1038/s42003-022-04056-7.\u003c/li\u003e\n \u003cli\u003eAn Z, Li J, Yu J, Wang X, Gao H, Zhang W, et al. Neutrophil extracellular traps induced by IL-8 aggravate atherosclerosis via activation NF-\u0026kappa;B signaling in macrophages. Cell Cycle. 2019;18(21):2928\u0026ndash;38. \u0026nbsp; https://doi.org/10.1080/15384101.2019.1662678.\u003c/li\u003e\n \u003cli\u003eBlagov AV, Markin AM, Bogatyreva AI, Tolstik TV, Sukhorukov VN, Orekhov AN. The Role of Macrophages in the Pathogenesis of Atherosclerosis. Cells. 2023;12(4):522. \u0026nbsp; https://doi.org/10.3390/cells12040522.\u003c/li\u003e\n \u003cli\u003eBoe DM, Curtis BJ, Chen MM, Ippolito JA, Kovacs EJ. Extracellular traps and macrophages: new roles for the versatile phagocyte. J Leukoc Biol. 2015;97(6):1023\u0026ndash;35. \u0026nbsp; https://doi.org/10.1189/jlb.4RI1014-521R.\u003c/li\u003e\n \u003cli\u003eChen K, Cagliani J, Aziz M, Tan C, Brenner M, Wang P. Extracellular CIRP activates STING to exacerbate hemorrhagic shock. JCI Insight. 2021;6(14):e143715. \u0026nbsp; https://doi.org/10.1172/jci.insight.143715.\u003c/li\u003e\n \u003cli\u003eColin S, Chinetti-Gbaguidi G, Staels B. Macrophage phenotypes in atherosclerosis. Immunol Rev. 2014 Nov;262(1):153\u0026ndash;66.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDaniel C, Leppkes M, Mu\u0026ntilde;oz LE, Schley G, Schett G, Herrmann M. Extracellular DNA traps in inflammation, injury and healing. Nat Rev Nephrol. 2019;15(9):559\u0026ndash;75. \u0026nbsp; https://doi.org/10.1038/s41581-019-0163-2.\u003c/li\u003e\n \u003cli\u003eDecout A, Katz JD, Venkatraman S, Ablasser A. The cGAS-STING pathway as a therapeutic target in inflammatory diseases. Nat Rev Immunol. 2021;21(9):548\u0026ndash;69. \u0026nbsp; https://doi.org/10.1038/s41577-021-00524-z.\u003c/li\u003e\n \u003cli\u003eDong R, Jiang G, Tian Y, Shi X. Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis. BMC Med Genomics. 2022;15(1):245. \u0026nbsp; https://doi.org/10.1186/s12920-022-01397-4.\u003c/li\u003e\n \u003cli\u003eDoran AC. Inflammation Resolution: Implications for Atherosclerosis. Circ Res \u0026nbsp;. 2022 Jan 7;130(1):130\u0026ndash;48. \u0026nbsp; https://doi.org/10.1161/CIRCRESAHA.121.319822.\u003c/li\u003e\n \u003cli\u003eEl Shikh MEM, El Sayed R, Nerviani A, Goldmann K, John CR, Hands R, et al. Extracellular traps and PAD4 released by macrophages induce citrullination and auto-antibody production in autoimmune arthritis. J Autoimmun \u0026nbsp;. 2019;105:102297. \u0026nbsp; https://doi.org/10.1016/j.jaut.2019.06.008.\u003c/li\u003e\n \u003cli\u003eHansson GK, Robertson A-KL, S\u0026ouml;derberg-Naucl\u0026eacute;r C. Inflammation and atherosclerosis. Annu Rev Pathol \u0026nbsp;. 2006;1:297\u0026ndash;329. \u0026nbsp; https://doi.org/10.1146/annurev.pathol.1.110304.100100.\u003c/li\u003e\n \u003cli\u003eHuang C, Lewis C, Borg NA, Canals M, Diep H, Drummond GR, et al. Proteomic Identification of Interferon-Induced Proteins with Tetratricopeptide Repeats as Markers of M1 Macrophage Polarization. J Proteome Res \u0026nbsp;. 2018;17(4):1485\u0026ndash;99. \u0026nbsp; https://doi.org/10.1021/acs.jproteome.7b00828.\u003c/li\u003e\n \u003cli\u003eIdso JM, Lao S, Schloemer NJ, Knipstein J, Burns R, Thakar MS, et al. Entinostat augments NK cell functions via epigenetic upregulation of IFIT1-STING-STAT4 pathway. Oncotarget. 2020;11(20):1799\u0026ndash;815. \u0026nbsp; https://doi.org/10.18632/oncotarget.27546.\u003c/li\u003e\n \u003cli\u003eJia M, Li Q, Guo J, Shi W, Zhu L, Huang Y, et al. Deletion of BACH1 Attenuates Atherosclerosis by Reducing Endothelial Inflammation. Circ Res \u0026nbsp;. 2022;130(7):1038\u0026ndash;55. \u0026nbsp; https://doi.org/10.1161/CIRCRESAHA.121.319540.\u003c/li\u003e\n \u003cli\u003eKirchner T, M\u0026ouml;ller S, Klinger M, Solbach W, Laskay T, Behnen M. The impact of various reactive oxygen species on the formation of neutrophil extracellular traps. Mediators Inflamm. 2012;2012:849136. https://doi.org/10.1007/s12035-021-02635-z.\u003c/li\u003e\n \u003cli\u003eKnight JS, Luo W, O\u0026rsquo;Dell AA, Yalavarthi S, Zhao W, Subramanian V, et al. Peptidylarginine deiminase inhibition reduces vascular damage and modulates innate immune responses in murine models of atherosclerosis. Circ Res \u0026nbsp;. 2014;114(6):947\u0026ndash;56. \u0026nbsp; https://doi.org/10.1161/CIRCRESAHA.114.303312.\u003c/li\u003e\n \u003cli\u003eKoelwyn GJ, Corr EM, Erbay E, Moore KJ. Regulation of macrophage immunometabolism in atherosclerosis. Nat Immunol. 2018 Jun;19(6):526\u0026ndash;37.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKong P, Cui Z-Y, Huang X-F, Zhang D-D, Guo R-J, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022 Apr 22;7(1):131. \u0026nbsp; https://doi.org/10.1038/s41392-022-00955-7.\u003c/li\u003e\n \u003cli\u003eLi L, Freitag J, Asbrand C, Munteanu B, Wang B-T, Zezina E, et al. Multi-omics profiling of collagen-induced arthritis mouse model reveals early metabolic dysregulation via SIRT1 axis. Sci Rep \u0026nbsp;. 2022;12(1):11830. \u0026nbsp; https://doi.org/10.1038/s41598-022-16005-9.\u003c/li\u003e\n \u003cli\u003eLi W, Huang Z, Luo Y, Cui Y, Xu M, Luo W, et al. Tetrandrine alleviates atherosclerosis via inhibition of STING-TBK1 pathway and inflammation in macrophages. Int Immunopharmacol. 2023;119:110139. \u0026nbsp; https://doi.org/10.1016/j.intimp.2023.110139.\u003c/li\u003e\n \u003cli\u003eLi Y, Li C, Xue P, Zhong B, Mao A-P, Ran Y, et al. ISG56 is a negative-feedback regulator of virus-triggered signaling and cellular antiviral response. Proc Natl Acad Sci U S A \u0026nbsp;. 2009;106(19):7945\u0026ndash;50. \u0026nbsp; https://doi.org/10.1073/pnas.0900818106.\u003c/li\u003e\n \u003cli\u003eMegens RTA, Vijayan S, Lievens D, D\u0026ouml;ring Y, van Zandvoort MAMJ, Grommes J, et al. Presence of luminal neutrophil extracellular traps in atherosclerosis. Thromb Haemost. 2012;107(3):597\u0026ndash;8. \u0026nbsp; https://doi.org/10.1160/TH11-09-0650.\u003c/li\u003e\n \u003cli\u003eMutua V, Gershwin LJ. A Review of Neutrophil Extracellular Traps (NETs) in Disease: Potential Anti-NETs Therapeutics. Clin Rev Allergy Immunol. 2021;61(2):194\u0026ndash;211. \u0026nbsp; https://doi.org/10.1007/s12016-020-08804-7.\u003c/li\u003e\n \u003cli\u003ePatterson MT, Williams JW. Metabolic regulation of macrophage proliferation and function in atherosclerosis. Curr Opin Lipidol. 2021;32(5):293\u0026ndash;300. \u0026nbsp; https://doi.org/10.1097/MOL.0000000000000778.\u003c/li\u003e\n \u003cli\u003ePertiwi KR, de Boer OJ, Mackaaij C, Pabittei DR, de Winter RJ, Li X, et al. Extracellular traps derived from macrophages, mast cells, eosinophils and neutrophils are generated in a time-dependent manner during atherothrombosis. J Pathol \u0026nbsp;. 2019 Apr;247(4):505\u0026ndash;12. \u0026nbsp; https://doi.org/10.1002/path.5212.\u003c/li\u003e\n \u003cli\u003eRasmussen KH, Hawkins CL. Role of macrophage extracellular traps in innate immunity and inflammatory disease. Biochem Soc Trans. 2022;50(1):21\u0026ndash;32. \u0026nbsp; https://doi.org/10.1042/BST20210962.\u003c/li\u003e\n \u003cli\u003eShiratori T, Imaizumi T, Hirono K, Kawaguchi S, Matsumiya T, Seya K, et al. ISG56 is involved in CXCL10 expression induced by TLR3 signaling in BEAS-2B bronchial epithelial cells. Exp Lung Res \u0026nbsp;. 2020;46(6):195\u0026ndash;202. \u0026nbsp; https://doi.org/10.1080/01902148.2020.1760965.\u003c/li\u003e\n \u003cli\u003eSteenman M, Espitia O, Maurel B, Guyomarch B, Heymann M-F, Pistorius M-A, et al. Identification of genomic differences among peripheral arterial beds in atherosclerotic and healthy arteries. Sci Rep \u0026nbsp;. 2018;8(1):3940. \u0026nbsp; https://doi.org/10.1038/s41598-018-22292-y.\u003c/li\u003e\n \u003cli\u003eTamura T, Smith M, Kanno T, Dasenbrock H, Nishiyama A, Ozato K. Inducible deposition of the histone variant H3.3 in interferon-stimulated genes. J Biol Chem. 2009;284(18):12217\u0026ndash;25. \u0026nbsp; https://doi.org/10.1074/jbc.M805651200.\u003c/li\u003e\n \u003cli\u003eTembhre MK, Sriwastva MK, Hote MP, Srivastava S, Solanki P, Imran S, et al. Interleukin-33 Induces Neutrophil Extracellular Trap (NET) Formation and Macrophage Necroptosis via Enhancing Oxidative Stress and Secretion of Proatherogenic Factors in Advanced Atherosclerosis. Antioxidants (Basel). 2022;11(12):2343. \u0026nbsp; https://doi.org/10.3390/antiox11122343.\u003c/li\u003e\n \u003cli\u003eWang J-L, Cai F, Liu X-H, Li L-M, He X, Hu X-M, et al. Lipopolysaccharide Promotes Inflammatory Response via Enhancing IFIT1 Expression in Human Umbilical Vein Endothelial Cells. DNA Cell Biol. 2020;39(7):1274\u0026ndash;81. \u0026nbsp; https://doi.org/10.1089/dna.2020.5454.\u003c/li\u003e\n \u003cli\u003eWang S, Chen L, Shi X, Wang Y, Xu S. Polystyrene microplastics-induced macrophage extracellular traps contributes to liver fibrotic injury by activating ROS/TGF-\u0026beta;/Smad2/3 signaling axis. Environ Pollut. 2023;324:121388. \u0026nbsp; https://doi.org/10.1016/j.envpol.2023.121388.\u003c/li\u003e\n \u003cli\u003eWolf D, Ley K. Immunity and Inflammation in Atherosclerosis. Circ Res \u0026nbsp;. 2019 Jan 18;124(2):315\u0026ndash;27. \u0026nbsp; https://doi.org/10.1161/CIRCRESAHA.118.313591.\u003c/li\u003e\n \u003cli\u003eWu M-Y, Li C-J, Hou M-F, Chu P-Y. New Insights into the Role of Inflammation in the Pathogenesis of Atherosclerosis. Int J Mol Sci \u0026nbsp;. 2017;18(10):2034. \u0026nbsp; https://doi.org/10.3390/ijms18102034.\u003c/li\u003e\n \u003cli\u003eWu S, Yang J, Sun G, Hu J, Zhang Q, Cai J, et al. Macrophage extracellular traps aggravate iron overload-related liver ischaemia/reperfusion injury. Br J Pharmacol \u0026nbsp;. 2021;178(18):3783\u0026ndash;96. \u0026nbsp; https://doi.org/10.1111/bph.15518.\u003c/li\u003e\n \u003cli\u003eZhai M, Gong S, Luan P, Shi Y, Kou W, Zeng Y, et al. Extracellular traps from activated vascular smooth muscle cells drive the progression of atherosclerosis. Nat Commun. 2022;13(1):7500. \u0026nbsp; https://doi.org/10.1038/s41467-022-35330-1.\u003c/li\u003e\n \u003cli\u003eZhang CY, Qu B, Ye P, Li J, Bao CD. Vulnerability of atherosclerotic plaques is associated with type I interferon in a murine model of lupus and atherosclerosis. Genet Mol Res \u0026nbsp;. 2015;14(4):14871\u0026ndash;81. \u0026nbsp; https://doi.org/10.4238/2015.November.18.52.\u003c/li\u003e\n \u003cli\u003eZhao C, Liang F, Ye M, Wu S, Qin Y, Zhao L, et al. GSDMD promotes neutrophil extracellular traps via mtDNA-cGAS-STING pathway during lung ischemia/reperfusion. Cell Death Discov \u0026nbsp;. 2023;9(1):368. \u0026nbsp; https://doi.org/10.1038/s41420-023-01663-z.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atherosclerosis , IFIT1 , STING-TBK1 , METs","lastPublishedDoi":"10.21203/rs.3.rs-4759187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4759187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Interferon-induced protein with tetratricopeptide repeats 1 (IFIT1)'s role has been shown to drive immune regulation and inflammation in many human diseases. However, the exact mechanism of action of IFIT1 in AS is unclear, and the specific mechanism of action on METs is also unknown. In this study, we will explore the potential mechanisms of IFIT1 in the formation of METs during AS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We downloaded GSE100927, GSE193336, GSE159677, IRGs, and METs-related genes for analysis and used qRT-PCR, flow cytometry, and immunofluorescence to detect the expression levels of IFIT1 and METs in plaques from AS patients and mice. The potential association of IFIT1 and METs in macrophages was similarly verified in LPS-induced macrophages. After IFIT1 silencing, the expression levels of METs were detected using qRT-PCR, flow cytometry, immunofluorescence, and WB. In addition, we delved into the potential mechanisms to detect the expression of the STING-TBK1 pathway and explored the interaction between IFIT1 and the STING-TBK1 pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Our results showed that IFIT1 was upregulated in AS patients, mouse plaque tissues, and LPS-induced macrophages. The same changes were observed in METs.The decrease in METs after IFIT1 silencing suggests that IFIT1 is involved in the regulation of macrophages through METs. Notably, with the decrease in IFIT1 levels, we observed a corresponding decrease in the STING-TBK1 pathway, which decreased accordingly, suggesting some connection between IFIT1, STING-TBK1, and METs. Validation of the effect of STING-TBK1 on a macrophage basis showed that the STING activator SR-717 increased the expression of METs, while the STING inhibitor H-151 had the opposite result. Interestingly, we added SR-717 and H-151 to si-IFIT1, respectively, and the same changes occurred in METs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: In summary, our study suggests that IFIT1 activates METs through the STING-TBK1 pathway, thereby aggravating AS.\u003c/p\u003e","manuscriptTitle":"IFIT1 exacerbates atherosclerosis by activating Macrophage Extracellular Traps via the STING-TBK1 pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-18 23:06:30","doi":"10.21203/rs.3.rs-4759187/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7d55b4e-fc0a-425c-9f78-0117b7dad6dd","owner":[],"postedDate":"August 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-16T22:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-18 23:06:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4759187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4759187","identity":"rs-4759187","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}