Profiles of Immune Cell Infiltration in Carotid Artery Atherosclerosis Based on Gene Expression Data

Screening of potential regulatory genes in carotid atherosclerosis vascular immune microenvironment

BACKGROUND

Immune microenvironment is one of the essential characteristics of carotid atherosclerosis (CAS), which cannot be reversed by drug therapy alone. Thus, there is a pressing need to develop novel immunoregulatory strategies to delay this pathological process that drives cardiovascular-related diseases. This study aimed to detect changes in the immune microenvironment of vascular tissues at various stages of carotid atherosclerosis, as well as cluster and stratify vascular tissue samples based on the infiltration levels of immune cell subtypes to distinguish immune phenotypes and identify potential hub genes regulating the immune microenvironment of carotid atherosclerosis.

MATERIALS AND METHODS

RNA sequencing datasets for CAS vascular tissue and healthy vascular tissue (GSE43292 and GSE28829) were downloaded from the Gene Expression Omnibus (GEO) database. To begin, the immune cell subtype infiltration level of all samples in both GSE43292 and GSE28829 cohorts was assessed using the ssGSEA algorithm. Following this, consensus clustering was performed to stratify CAS samples into different clusters. Finally, hub genes were identified using the maximum neighborhood component algorithm based on the construction of interaction networks, and their diagnostic efficiency was evaluated.

RESULTS

Compared to the controls, a higher number of immune cell subtypes were enriched in CAS samples with higher immune scores in the GSE43292 cohort. Advanced CAS was characterized by high immune cell infiltration, whereas early CAS was characterized by low immune cell infiltration in the GSE28829 cohort. Moreover, CAS progression may be related to the immune response pathway. Biological processes associated with muscle cell development may impede the progression of CAS. Finally, the hub genes PTPRC, ACTN2, ACTC1, LDB3, MYOZ2, and TPM2 had satisfactory efficacy in the diagnosis and prediction of high and low immune cell infiltration in CAS and distinguishing between early and advanced CAS samples.

CONCLUSION

The enrichment of immune cells in vascular tissues is a primary factor driving pathological changes in CAS. Additionally, CAS progression may be related to the immune response pathway. Biological processes linked to muscle cell development may delay the progression of CAS. PTPRC, ACTN2, ACTC1, LDB3, MYOZ2, and TPM2 may regulate the immune microenvironment of CAS and participate in the occurrence and progression of the disease.

The Evolving Role of Dendritic Cells in Atherosclerosis

Atherosclerosis, a major contributor to cardiovascular morbidity and mortality, is characterized by chronic inflammation of the arterial wall. This inflammatory process is initiated and maintained by both innate and adaptive immunity. Dendritic cells (DCs), which are antigen-presenting cells, play a crucial role in the development of atherosclerosis and consist of various subtypes with distinct functional abilities. Following the recognition and binding of antigens, DCs become potent activators of cellular responses, bridging the innate and adaptive immune systems. The modulation of specific DC subpopulations can have either pro-atherogenic or atheroprotective effects, highlighting the dual pro-inflammatory or tolerogenic roles of DCs. In this work, we provide a comprehensive overview of the evolving roles of DCs and their subtypes in the promotion or limitation of atherosclerosis development. Additionally, we explore antigen pulsing and pharmacological approaches to modulate the function of DCs in the context of atherosclerosis.

An integrative analysis of single-cell and bulk transcriptome and bidirectional mendelian randomization analysis identified C1Q as a novel stimulated risk gene for Atherosclerosis

Background

The role of complement component 1q (C1Q) related genes on human atherosclerotic plaques (HAP) is less known. Our aim is to establish C1Q associated hub genes using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis to diagnose and predict HAP patients more effectively and investigate the association between C1Q and HAP (ischemic stroke) using bidirectional Mendelian randomization (MR) analysis.

Methods

HAP scRNA-seq and bulk-RNA data were download from the Gene Expression Omnibus (GEO) database. The C1Q-related hub genes was screened using the GBM, LASSO and XGBoost algorithms. We built machine learning models to diagnose and distinguish between types of atherosclerosis using generalized linear models and receiver operating characteristics (ROC) analyses. Further, we scored the HALLMARK_COMPLEMENT signaling pathway using ssGSEA and confirmed hub gene expression through qRT-PCR in RAW264.7 macrophages and apoE-/- mice. Furthermore, the risk association between C1Q and HAP was assessed through bidirectional MR analysis, with C1Q as exposure and ischemic stroke (IS, large artery atherosclerosis) as outcomes. Inverse variance weighting (IVW) was used as the main method.

Results

We utilized scRNA-seq dataset (GSE159677) to identify 24 cell clusters and 12 cell types, and revealed seven C1Q associated DEGs in both the scRNA-seq and GEO datasets. We then used GBM, LASSO and XGBoost to select C1QA and C1QC from the seven DEGs. Our findings indicated that both training and validation cohorts had satisfactory diagnostic accuracy for identifying patients with HPAs. Additionally, we confirmed SPI1 as a potential TF responsible for regulating the two hub genes in HAP. Our analysis further revealed that the HALLMARK_COMPLEMENT signaling pathway was correlated and activated with C1QA and C1QC. We confirmed high expression levels of C1QA, C1QC and SPI1 in ox-LDL-treated RAW264.7 macrophages and apoE-/- mice using qPCR. The results of MR indicated that there was a positive association between the genetic risk of C1Q and IS, as evidenced by an odds ratio (OR) of 1.118 (95%CI: 1.013-1.234, P = 0.027).

Conclusion

The authors have effectively developed and validated a novel diagnostic signature comprising two genes for HAP, while MR analysis has provided evidence supporting a favorable association of C1Q on IS.

Identification of immune infiltration-related biomarkers in carotid atherosclerotic plaques

Atherosclerosis is a chronic lipid-driven inflammatory response of the innate and adaptive immune systems, and it is responsible for several cardiovascular ischemic events. The present study aimed to determine immune infiltration-related biomarkers in carotid atherosclerotic plaques (CAPs). Gene expression profiles of CAPs were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the CAPs and control groups were screened by the "limma" package in R software. Immune cell infiltration between the CAPs and control groups was evaluated by the single sample gene set enrichment analysis. Key infiltrating immune cells in the CAPs group were screened by the Wilcoxon test and least absolute shrinkage and selection operator regression. The weighted gene co-expression network analysis was used to identify immune cell-related genes. Hub genes were identified by the protein-protein interaction (PPI) network. Receiver operating characteristic curve analysis was performed to assess the gene's ability to differentiate between the CAPs and control groups. Finally, we constructed a miRNA-gene-transcription factor network of hub genes by using the ENCODE database. Eleven different types of immune infiltration-related cells were identified between the CAPs and control groups. A total of 1,586 differentially expressed immunity-related genes were obtained through intersection between DEGs and immune-related genes. Twenty hub genes were screened through the PPI network. Eventually, 7 genes (BTK, LYN, PTPN11, CD163, CD4, ITGAL, and ITGB7) were identified as the hub genes of CAPs, and these genes may serve as the estimable drug targets for patients with CAPs.

Identification of lipid metabolism-related biomarkers for diagnosis and molecular classification of atherosclerosis

BACKGROUND

Atherosclerosis is now the main cause of cardiac-cerebral vascular diseases around the world. Disturbances in lipid metabolism have an essential role in the development and progression of atherosclerosis. Thus, we aimed to investigate lipid metabolism-related molecular clusters and develop a diagnostic model for atherosclerosis.

METHODS

First, we used the GSE100927 and GSE43292 datasets to screen differentially expressed lipid metabolism-related genes (LMRGs). Subsequent enrichment analysis of these key genes was performed using the Metascape database. Using 101 atherosclerosis samples, we investigated the LMRG-based molecular clusters and the corresponding immune cell infiltration. After that, a diagnostic model for atherosclerosis was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a series of bioinformatics techniques, including CIBERSORT, gene set variation analysis, and single-cell data analysis, were used to analyze the potential mechanisms of the model genes in atherosclerosis.

RESULTS

A total of 29 LMRGs were found to be differentially expressed between atherosclerosis and normal samples. Functional and DisGeNET enrichment analyses indicated that 29 LMRGs are primarily engaged in cholesterol and lipid metabolism, the PPAR signaling pathway, and regulation of the inflammatory response and are also closely associated with atherosclerotic lesions. Two LMRG-related molecular clusters with significant biological functional differences are defined in atherosclerosis. A three-gene diagnostic model containing ADCY7, SCD, and CD36 was subsequently constructed. Receiver operating characteristic curves, decision curves, and an external validation dataset showed that our model exhibits good predictive performance. In addition, three model genes were found to be closely associated with immune cell infiltration, especially macrophage infiltration.

CONCLUSIONS

Our study comprehensively highlighted the intricate association between lipid metabolism and atherosclerosis and created a three-gene model for future clinical diagnosis.

Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and atherosclerosis

Background

Atherosclerosis (AS) is one of the leading causes of the cardio-cerebral vascular incident. The constantly emerging evidence indicates a close association between nonalcoholic fatty liver disease (NAFLD) and AS. However, the exact molecular mechanisms underlying the correlation between these two diseases remain unclear. This study proposed exploring the common signature genes, pathways, and immune cells among AS and NAFLD.

Methods

The common differentially expressed genes (co-DEGs) with a consistent trend were identified via bioinformatic analyses of the Gene Expression Omnibus (GEO) datasets GSE28829 and GSE49541, respectively. Further, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. We utilized machine learning algorithms of lasso and random forest (RF) to identify the common signature genes. Then the diagnostic nomogram models and receiver operator characteristic curve (ROC) analyses were constructed and validated with external verification datasets. The gene interaction network was established via the GeneMANIA database. Additionally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to explore the co-regulated pathways and immune cells.

Results

A total of 11 co-DEGs were identified. GO and KEGG analyses revealed that co-DEGs were mainly enriched in lipid catabolic process, calcium ion transport, and regulation of cytokine. Moreover, three common signature genes (PLCXD3, CCL19, and PKD2) were defined. Based on these genes, we constructed the efficiently predictable diagnostic models for advanced AS and NAFLD with the nomograms, evaluated with the ROC curves (AUC = 0.995 for advanced AS, 95% CI 0.971–1.0; AUC = 0.973 for advanced NAFLD, 95% CI 0.938–0.998). In addition, the AUC of the verification datasets had a similar trend. The NOD-like receptors (NLRs) signaling pathway might be the most crucial co-regulated pathway, and activated CD4 T cells and central memory CD4 T cells were significantly excessive infiltration in advanced NAFLD and AS.

Conclusion

We identified three common signature genes (PLCXD3, CCL19, and PKD2), co-regulated pathways, and shared immune features of NAFLD and AS, which might provide novel insights into the molecular mechanism of NAFLD complicated with AS.

FCGR2C: An emerging immune gene for predicting sepsis outcome

Background

Sepsis is a life-threatening disease associated with immunosuppression. Immunosuppression could ultimately increase sepsis mortality. This study aimed to identify the prognostic biomarkers related to immunity in sepsis.

Methods

Public datasets of sepsis downloaded from the Gene Expression Omnibus (GEO) database were divided into the discovery cohort and the first validation cohort. We used R software to screen differentially expressed genes (DEGs) and analyzed DEGs' functional enrichment in the discovery dataset. Immune-related genes (IRGs) were filtered from the GeneCards website. A Lasso regression model was used to screen candidate prognostic genes from the intersection of DEGs and IRGs. Then, the candidate prognostic genes with significant differences were identified as prognostic genes in the first validation cohort. We further validated the expression of the prognostic genes in the second validation cohort of 81 septic patients recruited from our hospital. In addition, we used four immune infiltration methods (MCP-counter, ssGSEA, ImmuCellAI, and CIBERSORT) to analyze immune cell composition in sepsis. We also explored the correlation between the prognostic biomarker and immune cells.

Results

First, 140 genes were identified as prognostic-related immune genes from the intersection of DEGs and IRGs. We screened 18 candidate prognostic genes in the discovery cohort with the lasso regression model. Second, in the first validation cohort, we identified 4 genes (CFHR2, FCGR2C, GFI1, and TICAM1) as prognostic immune genes. Subsequently, we found that FCGR2C was the only gene differentially expressed between survivors and non-survivors in 81 septic patients. In the discovery and first validation cohorts, the AUC values of FCGR2C were 0.73 and 0.67, respectively. FCGR2C (AUC=0.84) had more value than SOFA (AUC=0.80) and APACHE II (AUC=0.69) in evaluating the prognosis of septic patients in our recruitment cohort. Moreover, FCGR2C may be closely related to many immune cells and functions, such as B cells, NK cells, neutrophils, cytolytic activity, and inflammatory promotion. Finally, enrichment analysis showed that FCGR2C was enriched in the phagosome signaling pathway.

Conclusion

FCGR2C could be an immune biomarker associated with prognosis, which may be a new direction of immunotherapy to reduce sepsis mortality.

Bioinformatic analysis and experimental validation identified DNA methylation–Related biomarkers and immune-cell infiltration of atherosclerosis

Background: DNA methylation is an important form of epigenetic regulation and is closely related to atherosclerosis (AS). The purpose of this study was to identify DNA methylation–related biomarkers and explore the immune-infiltrate characteristics of AS based on methylation data.

Methods: DNA methylation data of 15 atherosclerotic and paired healthy tissues were obtained from Gene Expression Omnibus database. Differential methylation positions (DMPs) and differential methylation regions (DMRs) were screened by the ChAMP R package. The methylation levels of DMPs located on CpG islands of gene promoter regions were averaged. The limma R package was used to screen differentially methylated genes in the CpG islands of the promoter regions. The diagnostic values of the methylation levels were evaluated using the pROC R package. The EpiDISH algorithm was applied to quantify the infiltration levels of seven types of immune cells. Subsequently, three pairs of clinical specimens of coronary atherosclerosis with Stary’s pathological stage III were collected, and the methylation levels were detected by the methylation-specific PCR (MS-PCR) assay. Western blot was performed to detect the protein expression levels of monocyte markers.

Results: A total of 110, 695 DMPs, and 918 DMRs were screened in the whole genome. Also, six genes with significant methylation differences in the CpG islands of the promoter regions were identified, including 49 DMPs. In total, three genes (GRIK2, HOXA2, and HOXA3) had delta beta greater than 0.2. The infiltration level of monocytes was significantly upregulated in AS tissues. MS-PCR assay confirmed the methylation status of the aforementioned three genes in AS samples. The Western blot results showed that the expression levels of the monocyte marker CD14 and M1-type macrophage marker CD86 were significantly increased in AS while M2-type macrophage marker protein CD206 was significantly decreased.

Conclusion: This study identified potential DNA methylation–related biomarkers and revealed the role of monocytes in early AS.

Exploration of the relationship between hippocampus and immune system in schizophrenia based on immune infiltration analysis

Immune dysfunction has been implicated in the pathogenesis of schizophrenia (SZ). Despite previous studies showing a broad link between immune dysregulation and the central nervous system of SZ, the exact relationship has not been completely elucidated. With immune infiltration analysis as an entry point, this study aimed to explore the relationship between schizophrenia and the immune system in more detail from brain regions, immune cells, genes, and pathways. Here, we comprehensively analyzed the hippocampus (HPC), prefrontal cortex (PFC), and striatum (STR) between SZ and control groups. Differentially expressed genes (DEGs) and functional enrichment analysis showed that three brain regions were closely related to the immune system. Compared with PFC and STR, there were 20 immune-related genes (IRGs) and 42 immune pathways in HPC. The results of immune infiltration analysis showed that the differential immune cells in HPC were effector memory T (Tem) cells. The correlation of immune-related DEGs (IDEGs) and immune cells further analysis showed that NPY, BLNK, OXTR, and FGF12, were moderately correlated with Tem cells. Functional pathway analysis indicated that these four genes might affect Tem by regulating the PI3K-AKT pathway and the neuroactive ligand-receptor interaction pathway. The receiver operating characteristic curve (ROC) analysis results indicated that these four genes had a high diagnostic ability (AUC=95.19%). Finally, the disease animal model was successfully replicated, and further validation was conducted using the real-time PCR and the western blot. These results showed that these gene expression changes were consistent with our previous expression profiling. In conclusion, our findings suggested that HPC in SZ may be more closely related to immune disorders and modulate immune function through Tem, PI3K-Akt pathway, and neuroactive ligand-binding receptor interactions. To the best of our knowledge, the Immucell AI tool has been applied for the first time to analyze immune infiltration in SZ, contributing to a better understanding of the role of immune dysfunction in SZ from a new perspective.

Heterogeneous Population of Immune cells Associated with Early Thrombosis in Arteriovenous Fistula

End-Stage Renal Disease (ESRD) is a growing cause of morbidity and mortality in the practice of modern medicine. Advances in medicine have elongated the average life span and subsequently made chronic diseases prevalent. Hemodialysis is the main treatment that is used to treat ESRD and is a clinical procedure that is being re-imagined with novel approaches to improve patient and clinic practicality and effectiveness. Arteriovenous Fistulas (AVF) are now used in place of catheters due to their higher success and lower co-morbidities. The main drawback of AVF is the time gap that is needed from the surgical creation of AVF to its use. During this time, the AVF is susceptible to thrombosis and occlusion rendering the fistula ineffective for treatment. Immune cells play a major role in vascular pathologies and macrophages, dendritic cells, and T-regulatory cells are the main cells seen during the inflammatory and anti-inflammatory phases. However, the role of immune response and immune cells in AVF maturation is poorly understood. This study aimed to investigate the immune response and immune cell expression in femoral vessels after AVF creation in a miniswine model of AVF using immunohistochemistry and qRT-PCR. The results of this study revealed an increased expression of immune cells in AVF vessels and suggest an association of immune response with AVF creation and maturation.

Integrated investigation of DNA methylation, gene expression and immune cell population revealed immune cell infiltration associated with atherosclerotic plaque formation

BACKGROUND

The clinical consequences of atherosclerosis are significant source of morbidity and mortality throughout the world, while the molecular mechanisms of the pathogenesis of atherosclerosis are largely unknown.

METHODS

In this study, we integrated the DNA methylation and gene expression data in atherosclerotic plaque samples to decipher the underlying association between epigenetic and transcriptional regulation. Immune cell classification was performed on the basis of the expression pattern of detected genes. Finally, we selected ten genes with dysregulated methylation and expression levels for RT-qPCR validation.

RESULTS

Global DNA methylation profile showed obvious changes between normal aortic and atherosclerotic lesion tissues. We found that differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were highly associated with atherosclerosis by being enriched in atherosclerotic plaque formation-related pathways, including cell adhesion and extracellular matrix organization. Immune cell fraction analysis revealed that a large number of immune cells, especially macrophages, activated mast cells, NK cells, and Tfh cells, were specifically enriched in the plaque. DEGs associated with immune cell fraction change showed that they were mainly related to the level of macrophages, monocytes, resting NK cells, activated CD4 memory T cells, and gamma delta T cells. These genes were highly enriched in multiple pathways of atherosclerotic plaque formation, including blood vessel remodeling, collagen fiber organization, cell adhesion, collagen catalogic process, extractable matrix assembly, and platelet activation. We also validated the expression alteration of ten genes associated with infiltrating immune cells in atherosclerosis.

CONCLUSIONS

In conclusion, these findings provide new evidence for understanding the mechanisms of atherosclerotic plaque formation, and provide a new and valuable research direction based on immune cell infiltration.

Analysis of Immune and Inflammation Characteristics of Atherosclerosis from Different Sample Sources

Background

Atherosclerosis is the predominant cause of cardiovascular diseases. Existing studies suggest that the development of atherosclerosis is closely related to inflammation and immunity, but whether there are differences and similarities between atherosclerosis occurring at different sites is still unknown. We elucidated the pathological characteristics of peripheral vascular diseases by using bioinformatic analyses on immune cells and inflammation-related gene expression in atherosclerotic arteries and plaques.

Methods

Eight data sets regarding atherosclerosis were downloaded from the Gene Expression Omnibus database. Human immune genes were obtained from the IMMPORT website. The samples were scored and divided into high- and low-immune groups. Then the samples were analysed using weighted gene co-expression network analysis, while the modules were analysed using functional enrichment. The protein–protein interaction network was constructed using the STRING and Cytoscape databases. The hub immune genes were screened, and the correlation between hub immune genes and immune cells was analysed.

Results

Immune cells and their functions were significantly different during atherosclerosis development. The infiltration proportion of immune cells was approximately similar in samples from different sources of patients with carotid atherosclerosis. However, the sensitivity of lower extremity atherosclerosis samples to immune cells is lower than that of carotid atherosclerosis samples.The samples from the plaque and artery were mainly infiltrated by macrophages, T cells and mast cells. After immune cells were assessed, resting NK cells, activated mast cells and M0 macrophages were found to be key immune cells in atherosclerosis and plaque formation. In addition, CCL4, TLR2, IL1B and PTPRC were considered to be immune marker genes in atherosclerosis development. Conclusion. Bioinformatic data analysis confirms the essential role of immune cells in cardiovascular diseases, and also indicates some differences of immune and inflammation characteristics of atherosclerosis between carotid and lower extremity arteries.

Characterization of an Aging-Based Diagnostic Gene Signature and Molecular Subtypes With Diverse Immune Infiltrations in Atherosclerosis

Objective: Advancing age is a major risk factor of atherosclerosis (AS). Nevertheless, the mechanism underlying this phenomenon remains indistinct. Herein, this study conducted a comprehensive analysis of the biological implications of aging-related genes in AS.

Methods: Gene expression profiles of AS and non-AS samples were curated from the GEO project. Differential expression analysis was adopted for screening AS-specific aging-related genes. LASSO regression analysis was presented for constructing a diagnostic model, and the discriminatory capacity was evaluated with ROC curves. Through consensus clustering analysis, aging-based molecular subtypes were conducted. Immune levels were estimated based on the expression of HLAs, immune checkpoints, and immune cell infiltrations. Key genes were then identified via WGCNA. The effects of CEBPB knockdown on macrophage polarization were examined with western blotting and ELISA. Furthermore, macrophages were exposed to 100 mg/L ox-LDL for 48 h to induce macrophage foam cells. After silencing CEBPB, markers of cholesterol uptake, esterification and hydrolysis, and efflux were detected with western blotting.

Results: This study identified 28 AS-specific aging-related genes. The aging-related gene signature was developed, which could accurately diagnose AS in both the GSE20129 (AUC = 0.898) and GSE43292 (AUC = 0.685) datasets. Based on the expression profiling of AS-specific aging-related genes, two molecular subtypes were clustered, and with diverse immune infiltration features. The molecular subtype–relevant genes were obtained with WGCNA, which were markedly associated with immune activation. Silencing CEBPB triggered anti-inflammatory M2-like polarization and suppressed foam cell formation.

Conclusion: Our findings suggest the critical implications of aging-related genes in diagnosing AS and modulating immune infiltrations.

TDP43 Exacerbates Atherosclerosis Progression by Promoting Inflammation and Lipid Uptake of Macrophages

Objective

Atherosclerosis (AS), characterized by cholesterol overloaded-macrophages accumulation and plaque formation in blood vessels, is the major cause of cardiovascular disease. Transactive response DNA-binding protein∼43 kDa (TDP43) has recently been identified as an independent driver of neurodegenerative diseases through triggering inflammatory response. This study investigated whether TDP43 is involved in AS development, especially in macrophages-mediated-foam cell formation and inflammatory responses.

Methods

Transactive response DNA-binding protein∼43 kDa expressions in oxidized low-density lipoprotein (oxLDL)-treated macrophages and peripheral blood mononuclear cells (PBMCs) from patients with coronary artery disease (CAD) were detected by real time-polymerase chain reaction (RT-PCR), Western blot, and immunofluorescence. Gene gain or loss of function was used to investigate the effects of TDP43 on macrophages-mediated lipid untake and inflammation with ELISA, protein immunoprecipitation, RT-PCR, Western blot, and immunofluorescence. Macrophage TDP43 specific knockout mice with ApoE^-/- background were fed with western diet for 12 weeks to establish AS model, and used to explore the role of TDP43 on AS progression.

Results

Transactive response DNA-binding protein∼43 kDa expression increases in oxLDL-treated macrophages and PBMCs from patients with CAD. Furthermore, we find that TDP43 promotes activation of NF-κB to increase inflammatory factor expression in macrophages through triggering mitochondrial DNA release to activate cGAS-STING signaling. Moreover, TDP43 strengthens lipid uptake of macrophages through regulating β-catenin and PPAR-γ complex to promote scavenger receptor gene CD36 transcription. Finally, using macrophage TDP43 specific knockout mice with ApoE^-/- background fed with western diet for 12 weeks to establish AS model, we find that specific knockout of TDP43 in macrophages obviously alleviates western diet-induced AS progression in mice.

Conclusions

Transactive response DNA-binding protein∼43 kDa exacerbates atherosclerosis progression by promoting inflammation and lipid uptake of macrophages, suggesting TDP43 as a potential target for developing atherosclerotic drug.