Underlying Genes Involved in Atherosclerotic Macrophages: Insights from Microarray Data Mining

The MEK-ERK-Egr-1 axis and its regulation in cardiovascular disease

Cardiovascular disease (CVD) is the primary cause of morbidity and mortality in the Western world. Multiple molecular and cellular processes underpinning the pathogenesis of CVD are regulated by the zinc finger transcription factor and product of an immediate-early gene, early growth response-1 (Egr-1). Egr-1 regulates multiple pro-inflammatory processes that underpin the manifestation of CVD. The activity of Egr-1 itself is influenced by a range of post-translational modifications including sumoylation, ubiquitination and acetylation. Egr-1 also undergoes phosphorylation by protein kinases, such as extracellular-signal regulated kinase (ERK) which is itself phosphorylated by MEK. This article reviews recent progress on the MEK-ERK-Egr-1 cascade, notably regulation in conjunction with factors and agents such as TET2, TRIB2, MIAT, SphK1, cAMP, teneligliptin, cholinergic drugs, red wine and flavonoids, wogonin, febuxostat, docosahexaenoic acid and AT1R blockade. Such insights should provide new opportunity for therapeutic intervention in CVD.

Integrated bioinformatics analysis of core regulatory elements involved in keloid formation

BACKGROUND

Keloid is a benign fibro-proliferative dermal tumor formed by an abnormal scarring response to injury and characterized by excessive collagen accumulation and invasive growth. The mechanism of keloid formation has not been fully elucidated, especially during abnormal scarring. Here, we investigated the regulatory genes, micro-RNAs (miRNAs) and transcription factors (TFs) that influence keloid development by comparing keloid and normal scar as well as keloid and normal skin.

METHODS

Gene expression profiles (GSE7890, GSE92566, GSE44270 and GSE3189) of 5 normal scar samples, 10 normal skin samples and 18 keloid samples from the Gene Expression Omnibus (GEO) database were interrogated. Differentially expressed genes (DEGs) were identified between keloid and normal skin samples as well as keloid and normal scar samples with R Project for Statistical Computing. Gene Ontology (GO) functional enrichment analysis was also performed with R software. DEG-associated protein-protein interaction (PPI) network was constructed by STRING, followed by module selection from the PPI network based on the MCODE analysis. Regulatory relationships between TF/miRNA and target genes were predicted with miRnet and cytoscape. Core regulatory genes were verified by RT-qPCR.

RESULTS

We identified 628 DEGs, of which 626 were up-regulated and 2 were down-regulated. Seven core genes [neuropeptide Y(NPY), 5-hydroxytryptamine receptor 1A(HTR1A), somatostatin (SST), adenylate cyclase 8 (ADCY8), neuromedin U receptor 1 (NMUR1), G protein subunit gamma 3 (GNG3), and G protein subunit gamma 13 (GNG13)] all belong to MCODE1 and were enriched in the "G protein coupled receptor signaling pathway" of the GO biological process category. Furthermore, nine core miRNAs (hsa-mir-124, hsa-let-7, hsa-mir-155, hsa-mir-26a, hsa-mir-941, hsa-mir-10b, hsa-mir-20, hsa-mir-31 and hsa-mir-372), and two core TFs (SP1 and TERT) were identified to play important roles in keloid formation. In the TF/miRNA-target gene network, both hsa-mir-372 and hsa-mir-20 had a regulatory effect on GNG13, ADCY8 was predicted to be target by hsa-mir-10b, and HTR1A and NPY were potentially by SP1. Furthermore, the expression of core regulatory genes (GNG13, ADCY8, HTR1A and NPY) was validated in clinical samples.

CONCLUSIONS

GNG13, ADCY8, NPY and HTR1A may act as core genes in keloid formation and these core genes establish relationship with SP1 and miRNA (hsa-mir-372, hsa-mir-20, hsa-mir-10b), which may influence multiple signaling pathways in the pathogenesis of keloid.

Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein-protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

Genome-wide transcriptome study using deep RNA sequencing for myocardial infarction and coronary artery calcification

Background

Coronary artery calcification (CAC) is a noninvasive measure of coronary atherosclerosis, the proximal pathophysiology underlying most cases of myocardial infarction (MI). We sought to identify expression signatures of early MI and subclinical atherosclerosis in the Framingham Heart Study (FHS). In this study, we conducted paired-end RNA sequencing on whole blood collected from 198 FHS participants (55 with a history of early MI, 72 with high CAC without prior MI, and 71 controls free of elevated CAC levels or history of MI). We applied DESeq2 to identify coding-genes and long intergenic noncoding RNAs (lincRNAs) differentially expressed in MI and high CAC, respectively, compared with the control.

Results

On average, 150 million paired-end reads were obtained for each sample. At the false discovery rate (FDR) < 0.1, we found 68 coding genes and 2 lincRNAs that were differentially expressed in early MI versus controls. Among them, 60 coding genes were detectable and thus tested in an independent RNA-Seq data of 807 individuals from the Rotterdam Study, and 8 genes were supported by p value and direction of the effect. Immune response, lipid metabolic process, and interferon regulatory factor were enriched in these 68 genes. By contrast, only 3 coding genes and 1 lincRNA were differentially expressed in high CAC versus controls. APOD, encoding a component of high-density lipoprotein, was significantly downregulated in both early MI (FDR = 0.007) and high CAC (FDR = 0.01) compared with controls.

Conclusions

We identified transcriptomic signatures of early MI that include differentially expressed protein-coding genes and lincRNAs, suggesting important roles for protein-coding genes and lincRNAs in the pathogenesis of MI.

Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles

Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD.

LncRNA PVT1 Suppresses the Progression of Renal Fibrosis via Inactivation of TGF-β Signaling Pathway

BACKGROUND

Renal fibrosis is a frequent pathway leading to end-stage kidney dysfunction. In addition, renal fibrosis is the ultimate manifestation of chronic kidney diseases (CKD). Long noncoding RNAs (lncRNAs) are known to be involved in occurrence of renal fibrosis, and lncRNA plasmacytoma variant translocation 1 (PVT1) has been reported to act as a key biomarker in renal diseases. However, the role of PVT1 in renal fibrosis remains unclear.

MATERIALS AND METHODS

HK-2 cells were treated with TGF-β1 to mimic renal fibrosis in vitro. Gene and protein expressions in HK-2 cells were measured by qRT-PCR and Western-blot, respectively. ELISA was used to test the level of creatinine (CR) and blood urea nitrogen (BUN) in serum of mice. Additionally, unilateral ureteral obstruction (UUO)-induced renal fibrosis mice model was established to investigate the effect of PVT1 on renal fibrosis in vivo.

RESULTS

PVT1 was upregulated in TGF-β1-treated HK-2 cells. In addition, TGF-β1-induced upregulation of α-SMA and fibronectin in HK-2 cells was significantly reversed by PVT1 knockdown. Meanwhile, PVT1 bound to miR-181a-5p in HK-2 cells. Moreover, miR-181a-5p directly targeted TGF-βR1. Furthermore, miR-181a-5p antagonist could significantly reverse the anti-fibrotic effect of PVT1 knockdown. Besides, knockdown of PVT1 notably attenuated the symptom of renal fibrosis in vivo.

CONCLUSION

Knockdown of PVT1 significantly inhibited the progression of renal fibrosis in vitro and in vivo. Thus, PVT1 may serve as a potential target for the treatment of renal fibrosis.

Cellular Mechanisms of Human Atherogenesis: Focus on Chronification of Inflammation and Mitochondrial Mutations

Atherosclerosis is one of the most common diseases of the cardiovascular system that leads to the development of life-threatening conditions, such as heart attack and stroke. Arthrosclerosis affects various arteries in the human body, but is especially dangerous in the arteries alimenting heart and brain, aorta, and arteries of the lower limbs. By its pathophysiology, atherosclerosis is an inflammatory disease. During the pathological process, lesions of arterial intima in the form of focal thickening are observed, which form atherosclerotic plaques as the disease progresses further. Given the significance of atherosclerosis for the global health, the search for novel effective therapies is highly prioritized. However, despite the constant progress, our understanding of the mechanisms of atherogenesis is still incomplete. One of the remaining puzzles in atherosclerosis development is the focal distribution of atherosclerotic lesions in the arterial wall. It implies the existence of certain mosaicism within the tissue, with some areas more susceptible to disease development than others, which may prove to be important for novel therapy development. There are many hypotheses explaining this phenomenon, for example, the influence of viruses, and the spread in the endothelium of the vessel multinucleated giant endothelial cells. We suggest the local variations of the mitochondrial genome as a possible explanation of this mosaicism. In this review, we discuss the role of genetic variations in the nuclear and mitochondrial genomes that influence the development of atherosclerosis. Changes in the mitochondrial and nuclear genome have been identified as independent factors for the development of the disease, as well as potential diagnostic markers.