The macrophage C-type lectin receptor CLEC5A (MDL-1) expression is associated with early plaque progression and promotes macrophage survival

REEP5 mediates the function of CLEC5A to alleviate myocardial infarction by inhibiting endoplasmic reticulum stress-induced apoptosis

MI (myocardial infarction) often triggers severe heart failure and is one of the leading causes of death worldwide. Receptor expression-enhancing protein 5 (REEP5), a member of REEPs, acts as regulators of endoplasmic reticulum (ER) affecting cardiac functions. Based on GSE114695 profile data, REEP5 was decreased in the left ventricle of MI mice. However, its role and potential mechanism in MI remain to be investigated. In the present study, the mouse MI model was established by ligation of the left anterior descending artery. REEP5 expression was downregulated in the infarct penumbra area of MI mice. Next, its role during MI was explored by gain-of-function. Interestingly, REEP5 overexpression improved left ventricular function of mice with MI, accompanied with reduced infarct size. In cardiomyocytes, REEP5 overexpression inhibited ER stress, accompanied with repressive phosphorylation of PERK and IRE1α, and the decreased nuclear translocation of ATF6. Subsequently, REEP5 overexpression downregulated the levels of Chop and cleaved caspase-12, further alleviating ER stress-induced apoptosis, which was consistent with the in vivo results. Moreover, REEP5 was found to bind to C-type lectin member 5 A (CLEC5A), a protein that triggers cardiac dysfunction. CLEC5A, whose expression was elevated in hypoxia-induced cell models, led to cardiomyocyte apoptosis. Noteworthily, REEP5 overexpression markedly abolished the effects of CLEC5A on ER stress-induced apoptosis. Taken together, REEP5 mediated the function of CLEC5A to relieve MI via inhibiting ER stress-induced apoptosis in vivo and in vitro. REEP5 may be a promising target for treating MI.

Bioinformatics strategies to identify differences in molecular biomarkers for ischemic stroke and myocardial infarction

Ischemic strokes (ISs) are commonly treated by intravenous thrombolysis using a recombinant tissue plasminogen activator; however, successful treatment can only occur within 3 hours after the stroke. Therefore, it is crucial to determine the causes and underlying molecular mechanisms, identify molecular biomarkers for early diagnosis, and develop precise preventive treatments for strokes. We aimed to clarify the differences in gene expression, molecular mechanisms, and drug prediction approaches between IS and myocardial infarction (MI) using comprehensive bioinformatics analysis. The pathogenesis of these diseases was explored to provide directions for future clinical research. The IS (GSE58294 and GSE16561) and MI (GSE60993 and GSE141512) datasets were downloaded from the Gene Expression Omnibus database. IS and MI transcriptome data were analyzed using bioinformatics methods, and the differentially expressed genes (DEGs) were screened. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape, and the candidate genes with high confidence scores were identified using Degree, MCC, EPC, and DMNC in the cytoHubba plug-in. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using the database annotation, visualization, and integrated discovery database. Network Analyst 3.0 was used to construct transcription factor (TF) - gene and microRNA (miRNA) - gene regulatory networks of the identified candidate genes. The DrugBank 5.0 database was used to identify gene-drug interactions. After bioinformatics analysis of IS and MI microarray data, 115 and 44 DEGS were obtained in IS and MI, respectively. Moreover, 8 hub genes, 2 miRNAs, and 3 TFs for IS and 8 hub genes, 13 miRNAs, and 2 TFs for MI were screened. The molecular pathology between IS and MI presented differences in terms of GO and KEGG enrichment pathways, TFs, miRNAs, and drugs. These findings provide possible directions for the diagnosis of IS and MI in the future.

A Pan-Cancer Analysis Reveals CLEC5A as a Biomarker for Cancer Immunity and Prognosis

Background

CLEC5A is a member of the C-type lectin superfamily. It can activate macrophages and lead to a series of immune-inflammation reactions. Previous studies reveal the role of CLEC5A in infection and inflammation diseases.

Method

We acquire and analyze data from The Cancer Genome Atlas (TCGA) database, Genotype-Tissue Expression (GTEx) database, and other comprehensive databases via GSCALite, cBioPortal, and TIMER 2.0 platforms or software. Single-cell sequencing analysis was performed for quantifying the tumor microenvironment of several types of cancers.

Results

CLEC5A is differentially expressed in a few cancer types, of which overexpression accompanies low overall survival of patients. DNA methylation mainly negatively correlates with CLEC5A expression. Moreover, CLEC5A is positively related to immune infiltration, including macrophages, cancer-associated fibroblasts (CAFs), and regulatory T cells (Tregs). Immune checkpoint genes are significantly associated with CLEC5A expression in diverse cancers. In addition, CLEC5A expression correlates with mismatch repair (MMR) in several cancers. Tumor mutation burden (TMB), microsatellite instability (MSI), and neoantigens show a positive association with CLEC5A expression in several cancers. Furthermore, CLEC5A in cancer correlates with signal transduction, the immune system, EMT, and apoptosis process. The drug sensitivity analysis screens out potential therapeutic agents associated with CLEC5A expression, including FR-180204, Tivozanib, OSI-930, Linifanib, AC220, VNLG/124, Bexarotene, omacetaxine mepesuccinate, narciclasine, leptomycin B, PHA-793887, LRRK2-IN-1, and CR-1-31B.

Conclusion

CLEC5A overexpresses in multiple cancers in contrast to normal tissues, and high CLEC5A expression predicts poor prognosis of patients and immune infiltration. CLEC5A is a potential prognostic biomarker of diverse cancers and a target for anti-tumor therapy.

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods

Background

Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis.

Methods

All statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to identify differentially expressed genes (DEGs) between CAD samples and healthy controls. The WGCNA package was conducted to recognize CAD-related gene modules and hub genes, followed by recursive feature elimination analysis to select the optimal features genes (OFGs). The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. Further validation and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the classification performance.

Results

In total, 374 DEGs, eight gene modules, 33 hub genes and 12 OFGs (HTR4, KISS1, CA12, CAMK2B, KLK2, DDC, CNGB1, DERL1, BCL6, LILRA2, HCK, MTF2) were identified. ROC curve analysis showed that the accuracy of SVM, RF and LR were 75.58%, 63.57% and 63.95% in validation; with area under the curve of 0.813 (95% confidence interval, 95% CI 0.761–0.866, P < 0.0001), 0.727 (95% CI 0.665–0.788, P < 0.0001) and 0.783 (95% CI 0.725–0.841, P < 0.0001), respectively.

Conclusions

In conclusion, this study found 12 gene signatures involved in the pathogenic mechanism of CAD. Among the CAD classifiers constructed by three machine learning methods, the SVM model has the best performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02481-4.

Exploration and Validation of a Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration

Background

Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature.

Methods

The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan–Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry.

Results

The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients’ samples revealed the same result.

Conclusion

The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.

CLEC5A knockdown protects against the cardiac dysfunction after Myocardial infarction by suppressing macrophage polarization, NLRP3 inflammasome activation and pyroptosis

Increasing evidence has shown that NOD-like receptor protein 3 (NLRP3) inflammasome and pyroptotic cell death play vital roles in the pathophysiology of myocardial infarction (MI), a common cardiovascular disease characterized with cardiac dysfunction. C-type lectin member 5A (CLEC5A) is reported to strongly associate with activation of NLRP3 inflammasome and pyroptosis. In this study, in vivo MI model was established by the ligation of left anterior descending coronary artery on male C57BL/6 mice, and CLEC5A knockdown was further achieved by intra-myocardial injection of adenovirus delivering shRNA-CLEC5A. CLEC5A was found to be highly expressed in left ventricular of MI mice, while CLEC5A knockdown conversely alleviated the cardiac dysfunction in MI mice. Besides, MI-induced classical activation of macrophages was significantly inhibited after CLEC5A silencing. Additionally, CLEC5A knockdown dramatically inhibited MI-triggered activation of NLRP3 inflammasome, pyroptosis and NF-κB signaling in left ventricular of mice. In vitro experiment further validated that CLEC5A knockdown suppressed M1 polarization in LPS/IFNγ-stimulated RAW264.7 cells, and inhibited the polarized RAW264.7-induced activation of NLRP3 inflammasome/pyroptosis signaling in co-cultured cardiomyocytes. In conclusion, CLEC5A knockdown protects against the MI-induced cardiac dysfunction by regulating macrophage polarization, NLRP3 inflammasome and cell pyroptosis.

Antibody-induced vascular inflammation skews infiltrating macrophages to a novel remodeling phenotype in a model of transplant rejection

HLA donor-specific antibodies (DSAs) binding to vascular endothelial cells of the allograft trigger inflammation, vessel injury, and antibody-mediated rejection (AMR). Accumulation of intragraft-recipient macrophages is a histological characteristic of AMR, which portends worse outcome. HLA class I (HLA I) DSAs enhance monocyte recruitment by activating endothelial cells and engaging FcγRs, but the DSA-activated donor endothelial influence on macrophage differentiation is unknown. In this study, we explored the consequence of DSA-activated endothelium on infiltrating monocyte differentiation. Here we show that cardiac allografts from murine recipients treated with MHC I DSA upregulated genes related to monocyte transmigration and Fc receptor stimulation. Human monocytes co-cultured with HLA I IgG-stimulated primary human endothelium promoted monocyte differentiation into CD68⁺ CD206⁺ CD163⁺ macrophages (M(HLA I IgG)), whereas HLA I F(ab')₂ stimulated endothelium solely induced higher CD206 (M(HLA I F(ab')₂ )). Both macrophage subtypes exhibited significant changes in discrete cytokines/chemokines and unique gene expression profiles. Cross-comparison of gene transcripts between murine DSA-treated cardiac allografts and human co-cultured macrophages identified overlapping genes. These findings uncover the role of HLA I DSA-activated endothelium in monocyte differentiation, and point to a novel, remodeling phenotype of infiltrating macrophages that may contribute to vascular injury.

Specific macrophage subsets accumulate in human subcutaneous and omental fat depots during obesity

Obesity is a chronic inflammatory disease associated with adipose tissue macrophage (ATM) activation. ATMs from lean mice contribute to tissue homeostasis by their M2-oriented polarization, whereas obesity leads to an increase of M1 inflammatory ATMs that underlies obesity-related metabolic disorders. In humans, studies characterizing ATMs and their functional status are limited. Here we investigated ATM phenotype in visceral (VAT) and subcutaneous (SAT) adipose tissue from healthy lean and obese individuals using two molecules previously identified as markers of M1-like and M2-like/tissue-resident macrophages, the C-type lectin CLEC5A and the scavenger receptor CD163L1, respectively. CD163L1 was expressed by the majority of ATMs, and CD163L1⁺ ATM density was greater with respect to cells expressing the pan-macrophage markers CD68 or CD11b. ATM counts in SAT, but not in VAT, increased in obese compared to lean individuals, measured with the three markers. Accordingly, CD163L1, CD68 and ITGAM gene expression was significantly enhanced in obese with respect to control individuals only in SAT. CLEC5A⁺ ATMs had a proinflammatory profile and were abundant in the lean VAT, but their density diminished in obesity. The only ATM subset that increased its counts in the obese VAT had a mixed M1-like (CD11c⁺ CD163^- CD209^- ) and M2-like (CLEC5A^- CD206⁺ ) phenotype. ATM expansion was dominated by a subset of M2-like macrophages (CD11c^- CLEC5A^- CD163⁺ CD206⁺ CD209⁺ ) in the obese SAT, with a minor contribution of a CD11c⁺ CLEC5A^- ATM subpopulation. Thus, both SAT and VAT seems to limit inflammation during obesity by differentially altering their ATM subset composition.

Identification of a Specific Gene Module for Predicting Prognosis in Glioblastoma Patients

Introduction: Glioblastoma (GBM) is the most common and malignant variant of intrinsic glial brain tumors. The poor prognosis of GBM has not significantly improved despite the development of innovative diagnostic methods and new therapies. Therefore, further understanding the molecular mechanism that underlies the aggressive behavior of GBM and the identification of appropriate prognostic markers and therapeutic targets is necessary to allow early diagnosis, to develop appropriate therapies and to improve prognoses.

Methods: We used a weighted gene co-expression network analysis (WGCNA) to construct a gene co-expression network with 524 glioblastoma samples from The Cancer Genome Atlas (TCGA). A risk score was then constructed based on four module genes and the patients' overall survival (OS) rate. The prognostic and predictive accuracy of the risk score were verified in the GSE16011 cohort and the REMBRANDT cohort.

Results: We identified a gene module (the green module) related to prognosis. Then, multivariate Cox analysis was performed on 4 hub genes to construct a Cox proportional hazards regression model from 524 glioblastoma patients. A risk score for predicting survival time was calculated with the following formula based on the top four genes in the green module: risk score = (0.00889 × EXP_CLEC5A) + (0.0681 × EXP_FMOD) + (0.1724 × EXP_FKBP9) + (0.1557 × EXP_LGALS8). The 5-year survival rate of the high-risk group (survival rate: 2.7%, 95% CI: 1.2–6.3%) was significantly lower than that of the low-risk group (survival rate: 8.8%, 95% CI: 5.5–14.1%).

Conclusions: This study demonstrated the potential application of a WGCNA-based gene prognostic model for predicting the survival outcome of glioblastoma patients.

Transcription factor CEBPB inhibits the proliferation of osteosarcoma by regulating downstream target gene CLEC5A

Objective

To screen the gene related to osteosarcoma (OS) metastasis and the molecular mechanism.

Methods

GEO database and R2 chip analysis platform were used to screen genes related to OS metastasis. UCSC gene browser was used to find the transcription factor (TF) of CLEC5A. The mRNA level and protein expression of CLEC5A in OS tissues and normal tissues were determined by RT‐PCR and Western blotting, respectively. OS cell lines MG‐63 were transfected with CEBPB recombinant plasmid. After transfection, the expression of CLEC5A in MG‐63 cells was determined and the cell proliferation situation was determined by clone formation assay.

Results

Three genes CLEC5A, ALOX5AP, and RNASE3 were obtained, and CLEC5A has the highest correlation with OS. CLEC5A has screened the gene related to OS metastasis, and CEBPB can be taken as TF regulating downstream gene CLEC5A. CEBPB can regulate the downstream CLEC5A as transcription factor. The relative mRNA level and protein expression of CLEC5A in OS tissues were significantly higher than those in normal tissues. CLEC5A can prevent OS metastasis. Transfection of CEBPB increased the expression of CLEC5A in MG‐63 cells and also inhibited the proliferation of OS.

Conclusion

CEBPB can inhibit the proliferation of OS cells via regulating the expression of CLEC5A.