Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis.

BACKGROUND: Ruptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge. METHODS: In this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data. RESULTS: Following rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made. CONCLUSION: Our study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.

PINK1 dominated mitochondria associated genes signature predicts abdominal aortic aneurysm with metabolic syndrome.

Abdominal aortic aneurysm (AAA) is typically asymptomatic but a devastating cardiovascular disorder, with overall mortality exceeding 80 % once it ruptures. Some patients with AAA may also have comorbid metabolic syndrome (MS), suggesting a potential common underlying pathogenesis. Mitochondrial dysfunction has been reported as a key factor contributing to the deterioration of both AAA and MS. However, the intricate interplay between metabolism and mitochondrial function, both contributing to the development of AAA, has not been thoroughly explored. In this study, we identified candidate genes related to mitochondrial function in AAA and MS. Subsequently, we developed a nomoscore model comprising hub genes (PINK1, ACSL1, CYP27A1, and SLC25A11), identified through the application of two machine learning algorithms, to predict AAA. We observed a marked disparity in immune infiltration profiles between high- and low-nomoscore groups. Furthermore, we confirmed a significant upregulation of the expression of the four hub genes in AAA tissues. Among these, ACSL1 showed relatively higher expression in LPS-treated RAW264.7 cell lines, while CYP27A1 exhibited a notable decrease. Moreover, SLC25A11 displayed a significant upregulation in AngII-treated VSMCs. Conversely, the expression level of PINK1 declined in LPS-stimulated RAW264.7 cell lines but significantly increased in AngII-treated VSMCs. In vivo experiments revealed that the activation of PINK1-mediated mitophagy inhibited the development of AAA in mice. In this current study, we have innovatively identified four mitochondrial function-related genes through integrated bioinformatic analysis. This discovery sheds light on the regulatory mechanisms and unveils promising therapeutic targets for the comorbidity of AAA and MS.

Novel insight of N6-methyladenosine modified subtypes in abdominal aortic aneurysm.

Background: N6-methyladenosine (m6A) is the most prevalent non-cap reversible modification present in messenger RNAs and long non-coding RNAs, and its dysregulation has been linked to multiple cardiovascular diseases, including cardiac hypertrophy and atherosclerosis. Although limited studies have suggested that m6A modification contributes to abdominal aortic aneurysm (AAA) development, the full landscape of m6A regulators that mediate modification patterns has not been revealed. Methods: To distinguish the m6A methylation subtypes in AAA patients, an unsupervised clustering method was carried out, based on the mRNA levels of 17 m6A methylation regulators. Differentially expressed genes were identified by comparing clusters. An m6Ascore model was calculated using principal component analysis and structured to assess the m6A methylation patterns of single samples. Subsequently, the relationship between the m6Ascore and immune cells and the hallmark gene set was analyzed. Finally, pairs of circRNA-m6A regulators and m6A regulators-m6A related genes were used to establish a network. Results: We identified three m6A methylation subtypes in the AAA samples. The m6Acluster A and C were characterized as more immunologically activated because of the higher abundance of immune cells than that in m6Acluster B. The m6Acluster B was less enriched in inflammatory pathways and more prevalent in pathways related to extracellular matrix stability. Subsequently, we divided the individual samples into two groups according to the m6Ascore, which suggested that a high m6Ascore predicted more active inflammatory pathways and higher inflammatory cell infiltration. A network consisting of 9 m6A regulators and 37 circRNAs was constructed. Conclusion: This work highlighted that m6A methylation modification was highly correlated with immune infiltration of AAA, which may promote the progression of AAA. We constructed an individualized m6Ascore model to provide evidence for individualized treatments in the future.