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.

Identification of necroptosis-related gene TRAF5 as potential target of diagnosing atherosclerosis and assessing its stability.

BACKGROUND: Atherosclerosis (AS) is a leading cause of morbidity and mortality in older patients and features progressive formation of plaques in vascular tissues. With the progression of atherosclerosis, plaque rupture may occur and cause stroke, myocardial infarction, etc. Different forms of cell death promote the formation of a necrotic core of the plaque, leading to rupture. Necroptosis is a type of programmed cell death that contributes to the development of cardiovascular disease. However, the role of necroptosis in AS has not yet been investigated. METHODS: The Gene Expression Omnibus (GEO) database was used to obtain gene expression profiles. Differentially expressed genes (DEGs) and necroptosis gene sets were used to identify necroptosis-related differentially expressed genes (NRDEGs). The NRDEGs were used to construct a diagnostic model and were further screened using least absolute shrinkage selection operator (LASSO) regression and random forest (RF) analysis. The discriminatory capacity of the NRDEGs was evaluated using receiver operating characteristic (ROC) curves. Immune infiltration levels were estimated based on CIBERSORTx analysis. The GSE21545 dataset, containing survival information, was used to determine prognosis-associated genes. Univariate and multivariate Cox regression analyses combined with survival analysis determined gene prognostic values. RNA and protein levels were detected by RT-qPCR and western blotting in arteriosclerosis obliterans(ASO) and normal vascular tissues. Vascular smooth muscle cells (VSMCs) were treated with oxidized low-density lipoprotein (ox-LDL) to develop cell models of advanced AS. The effects of protein knockdown on necroptosis were assessed by western blotting and flow cytometry. EdU and Cell Counting Kit-8 assays were used to examine cell proliferation. RESULTS: TNF Receptor Associated Factor 5 (TRAF5) was identified as a diagnostic marker for AS based on the AUC value in both the GSE20129 and GSE43292 datasets. According to differential expression analysis, LASSO regression analysis, RF analysis, univariate analysis, multivariate analysis, and gene-level survival analysis, TRAF5 was markedly associated with necroptosis in AS. Silencing TRAF5 promotes necroptosis and attenuates the proliferation of ox-LDL-induced cell models of advanced AS. CONCLUSIONS: This study identified a diagnostic marker of necroptosis-related atherosclerosis, TRAF5, which can also be used to diagnose and assess atherosclerotic plaque stability. This novel finding has important implications in the diagnosis and assessment of plaque stability in atherosclerosis.

A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays.

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation.

Nested arrays are considered attractive due to their hole-free performance, and have the ability to resolve O ( N 2 ) sources with O ( N ) physical sensors. Inspired by nested arrays, two kinds of three-parallel nested subarrays (TPNAs), which are composed of three parallel sparse linear subarrays with different inter-element spacings, are proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation in this paper. We construct two cross-correlation matrices and combine them as one augmented matrix in the first step. Then, by vectorizing the augmented matrix, a hole-free difference coarray with larger degrees of freedom (DOFs) is achieved. Meanwhile, sparse representation and the total least squares technique are presented to transform the problem of 2-D DOA searching into 1-D searching. Accordingly, we can obtain the paired 2-D angles automatically and improve the 2-D DOA estimation performance. In addition, we derive closed form expressions of sensor positions, as well as number of sensors for different subarrays of two kinds of TPNA to maximize the DOFs. In the end, the simulation results verify the superiority of the proposed TPNAs and 2-D DOA estimation method.