Identification of key pathways and core genes involved in atherosclerotic plaque progression

Online application for the diagnosis of atherosclerosis by six genes

BACKGROUND

Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS.

METHODS

We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction.

RESULTS

Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples.

CONCLUSIONS

A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

ITGB2 is a central hub-gene associated with inflammation and early fibro-atheroma development in a swine model of atherosclerosis

Background and aim

The complex dynamic interplay between different biological pathways involved in atherosclerosis development has rendered the identification of specific therapeutic targets a challenging quest. We aimed to identify specific genes and mechanistic pathways associated with the early development of fibro-atheromas in a swine model of atherosclerosis.

Methods

The Wisconsin Miniature Swine™ model of Familial Hypercholesterolemia (WMS-FH, n = 11) and genetically related WMS controls (WMS-N, n = 11) were used. The infrarenal aorta was harvested from both groups for histopathologic and transcriptomic profiling at 12 months. Bioinformatic analysis was performed to identify hub genes and pathways central to disease pathophysiology. The expression of ITGB2, the top ranked hub gene, was manipulated in cell culture and the expression of interconnected genes was tested.

Results

Fibro-atheromatous lesions were documented in all WMS-FH aortic tissues and displayed internal elastic lamina (IEL) disruption, significant reduction of myofibroblast presence and disorganized collagen deposition. No fibro-atheromas were observed in the control group. A total of 266 differentially expressed genes (DEGs) were upregulated in WMS-FH aortic tissues, while 29 genes were downregulated. Top identified hub genes included ITGB2, C1QA, LCP2, SPI1, CSF1R, C5AR1, CTSS, MPEG1, C1QC, and CSF2RB. Overexpression of ITGB2 resulted in elevated expression of other interconnected genes expressed in porcine endothelial cells.

Conclusion

In a swine translational model of atherosclerosis, transcriptomic analysis identified ITGB2 as a central hub gene associated inflammation and early fibroatheroma development making it a potential therapeutic target at this stage of disease.

Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges

Atherosclerotic disease is a major cause of acute cardiovascular events. A deeper understanding of its underlying mechanisms will allow advancing personalized and patient-centered healthcare. Transcriptomic research has proven to be a powerful tool for unravelling the complex molecular pathways that drive atherosclerosis. However, low reproducibility of research findings and lack of standardization of procedures pose significant challenges in this field. In this review, we discuss how transcriptomic research can help in understanding the different phenotypes of the atherosclerotic plaque that contribute to the development and progression of atherosclerosis. We highlight the methodological challenges that need to be addressed to improve research outputs, and emphasize the importance of research protocols harmonization. We also discuss recent advances in transcriptomic research, including bulk or single-cell sequencing, and their added value in plaque phenotyping. Finally, we explore how integrated multiomics data and machine learning improve understanding of atherosclerosis and provide directions for future research.

Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

BACKGROUND

Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis.

METHODS

Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman's rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells.

RESULTS

A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis.

CONCLUSIONS

​This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis.

Comprehensive analysis of atherosclerotic plaques reveals crucial genes and molecular mechanisms associated with plaque progression and rupture

Background

Plaque rupture and acute atherothrombosis, resulting from continued progression of atherosclerotic plaques (APs), are major contributors to acute clinical events such as stroke or myocardial infarction. This article aimed to explore the gene signatures and potential molecular mechanisms in the progression and instability of APs and to identify novel biomarkers and interventional targets for AP rupture.

Methods

The microarray data were downloaded from the Gene Expression Omnibus (GEO) database and grouped into discovery and validation cohorts. In the discovery cohort, Weighted Gene Co-Expression Network Analysis was performed for finding co-expression modules, and the Metascape database was used to perform functional enrichment analysis. Differential Expression Genes analysis subsequently was performed in the validation cohort for verification of the obtained results. Common genes were introduced into Metascape database for protein–protein interaction and functional enrichment analysis. We constructed the miRNAs–mRNAs network with the hub genes. Moreover, gene expression profiles of peripheral blood mononuclear cells (PBMCs) from peripheral blood of patients with plaque rupture were analyzed by high-throughput sequencing, and the diagnostic power of hub genes was verified by receiver operating characteristic (ROC) analysis.

Results

In the discovery cohort, the brown module in GSE28829 and the turquoise module in GSE163154 were the most significant co-expression modules. Functional enrichment analysis of shared genes suggested that “Neutrophil degranulation” was the most significantly enriched pathway. These conclusions were also demonstrated by the validation cohort. A total of 16 hub genes were identified. The miRNA–mRNA network revealed that hsa-miR-665 and hsa-miR-512-3p might regulate the “Neutrophil degranulation” pathway through PLAU and SIRPA, which might play a significant role in AP progression and instability. Five hub genes, including PLAUR, FCER1G, PLAU, ITGB2, and SLC2A5, showed significantly increased expression in PBMCs from patients with plaque rupture compared with controls. ROC analysis finally identified three hub genes PLAUR, FCER1G, and PLAU that could effectively distinguish patients with APs rupture from controls.

Conclusions

The present study demonstrated that the “neutrophil degranulation” signaling pathways and identified novel mRNA and miRNA candidates are closely associated with plaque progression and instability. The hub genes FCER1G, PLAUR, and PLAU may serve as biomarkers for the prospective prediction of AP rupture.

Identification of Key Genes from the Visceral Adipose Tissues of Overweight/Obese Adults with Hypertension through Transcriptome Sequencing

Overweight and obese (OW/OB) adults are at increased risk of hypertension due to visceral adipose tissue (VAT) inflammation. In this study, we explored gene level differences in the VAT of hypertensive and normotensive OW/OB patients. VAT samples obtained from six OW/OB adults (three hypertensive, three normotensive) were subjected to transcriptome sequencing analysis. Gene set enrichment analysis was conducted for all gene expression data to identify differentially expressed genes (DEGs) with |log2 (fold change)| ≥ 1 and q < 0.05. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed on the DEGs, and hub genes were identified by constructing a protein-protein interaction (PPI) network. The proposed hub genes were validated using quantitative real-time PCR in ten other samples from five hypertensive and five normotensive patients. In addition, we performed ROC analysis and Spearman correlation analysis. A total of 84 DEGs were identified between VAT samples from OW/OB patients with and without hypertension, among which 21 were significantly upregulated and 63 were significantly downregulated. Bioinformatics analysis revealed that spleen function was related to hypertension in OW/OB adults. Meanwhile, PPI network analysis identified the following top 10 hub genes: CD79A, CR2, SELL, CD22, IL7R, CCR7, TNFRSF13C, CXCR4, POU2AF1, and JAK3. Through qPCR verification, we found that CXCR4, CD22, and IL7R were statistically significant. qPCR verification suggested that RELA was statistically significant. However, qPCR verification indicated that NFKB1 and KLF2 were not statistically significant. These hub genes were mainly regulated by the transcription factor RELA. The AUC of ROC analysis for CXCR4, IL7R, and CD22 was 0.92. What is more, VAT CXCR4 and CD22 were positively related to RELA relative expression levels. Taken together, our research demonstrates that CXCR4, IL7R, and CD22 related to VAT in hypertensive OW/OB adults could serve as future therapeutic targets.

Bioinformatic Analysis Revealed the Essential Regulatory Genes and Pathways of Early and Advanced Atherosclerotic Plaque in Humans

Atherosclerosis (AS) is a lipid-induced, chronic inflammatory, autoimmune disease affecting multiple arteries. Although much effort has been put into AS research in the past decades, it is still the leading cause of death worldwide. The complex genetic network regulation underlying the pathogenesis of AS still needs further investigation to provide effective targeted therapy for AS. We performed a bioinformatic microarray data analysis at different atherosclerotic plaque stages from the Gene Expression Omnibus database with accession numbers GSE43292 and GSE28829. Using gene set enrichment analysis, we further confirmed the immune-related pathways that play an important role in the development of AS. We are reporting, for the first time, that the metabolism of the three branched-chain amino acids (BCAAs; leucine, isoleucine, and valine) and short-chain fatty acids (SCFA; propanoate, and butanoate) are involved in the progression of AS using microarray data of atherosclerotic plaque tissue. Immune and muscle system-related pathways were further confirmed as highly regulated pathways during the development of AS using gene expression pattern analysis. Furthermore, we also identified four modules mainly involved in histone modification, immune-related processes, macroautophagy, and B cell activation with modular differential connectivity in the dataset of GSE43292, and three modules related to immune-related processes, B cell activation, and nuclear division in the dataset of GSE28829 also display modular differential connectivity based on the weighted gene co-expression network analysis. Finally, we identified eight key genes related to the pathways of immune and muscle system function as potential therapeutic biomarkers to distinguish patients with early or advanced stages in AS, and two of the eight genes were validated using the gene expression dataset from gene-deficient mice. The results of the current study will improve our understanding of the molecular mechanisms in the progression of AS. The key genes and pathways identified could be potential biomarkers or new drug targets for AS management.

Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis

Objective

This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets.

Methods

Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks.

Results

A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors.

Conclusions

We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01257-1.

Role and implications of the CXCL12/CXCR4/CXCR7 axis in atherosclerosis: still a debate

Atherosclerosis is one of the leading causes of mortality and morbidity worldwide. Chemokines and their receptors are implicated in the pathogenesis of atherosclerosis. CXCL12 is a member of the chemokine family exerting a myriad role in atherosclerosis through its classical CXCR4 and atypical ACKR3 (CXCR7) receptors. The modulatory and regulatory functional spectrum of CXCL12/CXCR4/ACKR3 axis in atherosclerosis spans from proatherogenic, prothrombotic and proinflammatory to atheroprotective, plaque stabilizer and dyslipidemia rectifier. This diverse continuum is executed in a wide range of biological units including endothelial cells (ECs), progenitor cells, macrophages, monocytes, platelets, lymphocytes, neutrophils and vascular smooth muscle cells (VSMCs) through complex heterogeneous and homogenous coupling of CXCR4 and ACKR3 receptors, employing different downstream signalling pathways, which often cross-talk among themselves and with other signalling interactomes. Hence, a better understanding of this structural and functional heterogeneity and complex phenomenon involving CXCL12/CXCR4/ACKR3 axis in atherosclerosis would not only help in formulation of novel therapeutics, but also in elucidation of the CXCL12 ligand and its receptors, as possible diagnostic and prognostic biomarkers.Key messagesThe role of CXCL12 per se is proatherogenic in atherosclerosis development and progression.The CXCL12 receptors, CXCR4 and ACKR3 perform both proatherogenic and athero-protective functions in various cell typesDue to functional heterogeneity and cross talk of CXCR4 and ACKR3 at receptor level and downstream pathways, regional boosting with specific temporal and spatial modulators of CXCL12, CXCR4 and ACKR3 need to be explored.

Key Chemokine Pathways in Atherosclerosis and Their Therapeutic Potential

The search to improve therapies to prevent or treat cardiovascular diseases (CVDs) rages on, as CVDs remain a leading cause of death worldwide. Here, the main cause of CVDs, atherosclerosis, and its prevention, take center stage. Chemokines and their receptors have long been known to play an important role in the pathophysiological development of atherosclerosis. Their role extends from the initiation to the progression, and even the potential regression of atherosclerotic lesions. These important regulators in atherosclerosis are therefore an obvious target in the development of therapeutic strategies. A plethora of preclinical studies have assessed various possibilities for targeting chemokine signaling via various approaches, including competitive ligands and microRNAs, which have shown promising results in ameliorating atherosclerosis. Developments in the field also include detailed imaging with tracers that target specific chemokine receptors. Lastly, clinical trials revealed the potential of various therapies but still require further investigation before commencing clinical use. Although there is still a lot to be learned and investigated, it is clear that chemokines and their receptors present attractive yet extremely complex therapeutic targets. Therefore, this review will serve to provide a general overview of the connection between various chemokines and their receptors with atherosclerosis. The different developments, including mouse models and clinical trials that tackle this complex interplay will also be explored.