Neuroimmunology of the atherosclerotic plaque: a morphological approach

Decoding marker genes and immune landscape of unstable carotid plaques from cellular senescence

Recently, cellular senescence-induced unstable carotid plaques have gained increasing attention. In this study, we utilized bioinformatics and machine learning methods to investigate the correlation between cellular senescence and the pathological mechanisms of unstable carotid plaques. Our aim was to elucidate the causes of unstable carotid plaque progression and identify new therapeutic strategies. First, differential expression analysis was performed on the test set GSE43292 to identify differentially expressed genes (DEGs) between the unstable plaque group and the control group. These DEGs were intersected with cellular senescence-associated genes to obtain 40 cellular senescence-associated DEGs. Subsequently, key genes were then identified through weighted gene co-expression network analysis, random forest, Recursive Feature Elimination for Support Vector Machines algorithm and cytoHubba plugin. The intersection yielded 3 CSA-signature genes, which were validated in the external validation set GSE163154. Additionally, we assessed the relationship between these CSA-signature genes and the immune landscape of the unstable plaque group. This study suggests that cellular senescence may play an important role in the progression mechanism of unstable plaques and is closely related to the influence of the immune microenvironment. Our research lays the foundation for studying the progression mechanism of unstable carotid plaques and provides some reference for targeted therapy.

Identification of key pyroptosis-related genes and microimmune environment among peripheral arterial beds in atherosclerotic arteries

Atherosclerosis is a chronic inflammatory disease characterized with innate and adaptive immunity but also involves pyroptosis. Few studies have explored the role of pyroptosis in advanced atherosclerotic plaques from different vascular beds. Here we try to identify the different underlying function of pyroptosis in the progression of atherosclerosis between carotid arteries and femoral. arteries. We extracted gene expression levels from 55 advanced carotid or femoral atherosclerotic plaques. The pyroptosis score of each sample was calculated by single-sample-gene-set enrichment analysis (ssGSEA). We then divided the samples into two clusters: high pyroptosis scores cluster (PyroptosisScoreH cluster) and low pyroptosis scores cluster (PyroptosisScoreL cluster), and assessed functional enrichment and immune cell infiltration in the two clusters. Key pyroptosis related genes were identified by the intersection between results of Cytoscape and LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Finally, all key pyroptosis related genes were validated in vitro. We found all but one of the 29 carotid plaque samples belonged to the PyroptosisScoreH cluster and the majority (19 out of 26) of femoral plaques were part of the PyroptosisScoreL cluster. Atheromatous plaque samples in the PyroptosisScoreL cluster had higher proportions of gamma delta T cells, M2 macrophages, myeloid dendritic cells (DCs), and cytotoxic lymphocytes (CTLs), but lower proportions of endothelial cells (ECs). Immune full-activation pathways (e.g., NOD-like receptor signaling pathway and NF-kappa B signaling pathway) were highly enriched in the PyroptosisScoreH cluster. The key pyroptosis related genes GSDMD, CASP1, NLRC4, AIM2, and IL18 were upregulated in advanced carotid atherosclerotic plaques. We concluded that compared to advanced femoral atheromatous plaques, advanced carotid atheromatous plaques were of higher grade of pyroptosis. GSDMD, CASP1, NLRC4, AIM2, and IL18 were the key pyroptosis related genes, which might provide a new sight in the prevention of fatal strokes in advanced carotid atherosclerosis.

Role of neutrophil extracellular trap and immune infiltration in atherosclerotic plaque instability: Novel insight from bioinformatics analysis and machine learning

The instability of atherosclerotic plaques increases the risk of acute coronary syndrome. Neutrophil extracellular traps (NETs), mesh-like complexes consisting of extracellular DNA adorned with various protein substances, have been recently discovered to play an essential role in atherosclerotic plaque formation and development. This study aimed to investigate novel diagnostic biomarkers that can identify unstable plaques for early distinction and prevention of plaque erosion or disruption. Differential expression analysis was used to identify the differentially expressed NET-related genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. We filtered the characteristic genes using machine learning and estimated diagnostic efficacy using receiver operating characteristic curves. Immune infiltration was detected using single-sample gene set enrichment analysis and the biological signaling pathways involved in characteristic genes utilizing gene set enrichment analysis were explored. Finally, miRNAs- and transcription factors-target genes networks were established. We identified 8 differentially expressed NET-related genes primarily involved in immune-related pathways. Four were identified as capable of distinguishing unstable plaques. More immune cells infiltrated unstable plaques than stable plaques, and these cells were predominantly positively related to characteristic genes. These 4 diagnostic genes are involved in immune responses and the modulation of smooth muscle contractility. Several miRNAs and transcription factors were predicted as upstream regulatory factors, providing further information on the identification and prevention of atherosclerotic plaques rupture. We identified several promising NET-related genes (AQP9, C5AR1, FPR3, and SIGLEC9) and immune cell subsets that may identify unstable atherosclerotic plaques at an early stage and prevent various complications of plaque disruption.

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

Objective

The decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy.

Method

Using the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene–drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted.

Results

A significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8⁺ T cells and NK cells were significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, and HLA-DRA), inflammation-related genes (MMP9, CTSL, and IFI30), and fatty acid-binding proteins (CD36 and APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, and MYH11) was decreased in unstable plaques. M1 macrophages had the highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM, and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which were validated by validation datasets and clinical samples. In addition, insulin, nivolumab, indomethacin, and α-mangostin were predicted to be potential therapeutic agents for unstable plaques.

Conclusion

M1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.

Difference of immune cell infiltration between stable and unstable carotid artery atherosclerosis

Atherosclerotic plaque instability contributes to ischaemic stroke and myocardial infarction. This study is to compare the abundance and difference of immune cell subtypes within unstable atherosclerotic tissues. CIBERSORT was used to speculate the proportions of 22 immune cell types based on a microarray of atherosclerotic carotid artery samples. R software was utilized to illustrate the bar plot, heat map and vioplot. The immune cell landscape in atherosclerosis was diverse, dominated by M2 macrophages, M0 macrophages, resting CD4 memory T cells and CD8 T cells. There was a significant difference in resting CD4 memory T cells (p = 0.032), T cells follicular helper (p = 0.033), M0 (p = 0.047) and M2 macrophages (p = 0.012) between stable and unstable atherosclerotic plaques. Compared with stable atherosclerotic plaques, unstable atherosclerotic plaques had a higher percentage of M2 macrophages. Moreover, correlation analysis indicated that the percentage of naïve CD4 T cells was strongly correlated with that of gamma delta T cells (r = 0.93, p < 0.001), while memory B cells were correlated with plasma cells (r = 0.85, p < 0.001). In summary, our study explored the abundance and difference of specific immune cell subgroups at unstable plaques, which would aid new immunotherapies for atherosclerosis.

High CD3+ Cells in Intracranial Thrombi Represent a Biomarker of Atherothrombotic Stroke

Background and Purpose

Approximately 30% of strokes are cryptogenic despite an exhaustive in-hospital work-up. Analysis of clot composition following endovascular treatment could provide insight into stroke etiology. T-cells already have been shown to be a major component of vulnerable atherosclerotic carotid lesions. We therefore hypothesize that T-cell content in intracranial thrombi may also be a biomarker of atherothrombotic origin.

Materials and Methods

We histopathologically investigated 54 consecutive thrombi retrieved after mechanical thrombectomy in acute stroke patients. First, thrombi were classified as fibrin-dominant, erythrocyte-dominant or mixed pattern. We then performed quantitative analysis of CD3+ cells on immunohistochemically-stained thrombi and compared T-cell content between “atherothrombotic”, “cardioembolism” and “other causes” stroke subtypes.

Results

Fourteen (26%) thrombi were defined as fibrin-dominant, 15 (28%) as erythrocyte-dominant, 25 (46%) as mixed. The stroke cause was defined as “atherothrombotic” in 10 (18.5%), “cardioembolism” in 25 (46.3%), and “other causes” in 19 (35.2%). Number of T-cells was significantly higher in thrombi from the “atherothrombotic” group (53.60 ± 28.78) than in the other causes (21.77 ± 18.31; p<0.0005) or the “cardioembolism” group (20.08 ± 15.66; p<0.0003).

Conclusions

The CD3+ T-cell count in intracranial thrombi was significantly higher in “atherothrombotic” origin strokes compared to all other causes. Thrombi with high content of CD3+ cells are more likely to originate from an atherosclerotic plaque.

The first Insubria autumn school on neuroimmunopharmacology: challenging paradigms beyond boundaries

This issue of the Journal of Neuroimmune Pharmacology is dedicated, in part, to the proceedings of the First Insubria Autumn School on Neuroimmunopharmacology, organized by Marco Cosentino, Georges J.M. Maestroni, Franca Marino (University of Insubria, Varese, Italy), Manfred Schedlowsky (University of Duisburg-Essen, Germany) and Rainer H. Straub (University of Regensburg, Germany), which took place in November 2011 in Varese, Italy. The School was attended by 50 graduate students and postdoctoral fellows in medical and biological disciplines from 16 european universities and 25 faculties/departments.