MiR-513a-5p Aggravates Atherosclerosis Phenotype by Downregulating TFPI2 in Vitro

Background: Atherosclerosis (AS) induced cardiology disease is largely associated with morbidity and mortality. The dysfunction of vascular smooth muscle cells (VSMCs) is considered to contribute to the etiology of AS. However, the mechanism underlying VSMCs dysfunction remains largely unclear. Our study aimed to explore novel molecules mediating VSMCs function. Methods: Bioinformatical analysis was applied to identify the key miRNAs that was aberrantly expressed in AS mouse and potentially targeted TFPI2. The AS-like cell model was generated by treating VSMCs with ox-LDL. The expression level of miR-513a-5p and TFPI2 in VSMCs and the serum of AS patients was evaluated by RT-qPCR, and the expression level of TFPI2 and PCNA was measured by western blot. The cell viability and migration capacity of VSMCs were determined by CCK-8 and wound healing assay, respectively. The target relationship between miR-513a-5p and TFPI2 was validated by dual-luciferase assay. Results: MiR-513a-5p was highly expressed while TFPI2 presented a low expression in AS patient serum. Treatment with 100 μg/mL ox-LDL overtly facilitated the cell viability and migration of VSMCs, also promoted miR-513a-5p expression while limit the expression of TFPI2. Moreover, silencing miR-513a-5p inhibited the cell viability, migration and the expression of proliferative marker in ox-LDL treated VSMCs, while inhibition of TFPI2 enhanced that. It was further found that miR-513a-5p could target TFPI2 and silencing miR-513a-5p compromised the aggresive effect of TFPI2 inhibition on the viability and migration ox-LDL treated VSMCs. Conclusion: miR-513a-5p could contribute to the dysfunction of VSMCs in AS through targeting and inhibiting TFPI2.

Applications and Restrictions of Integrated Genomic and Metabolomic Screening: An Accelerator for Drug Discovery from Actinomycetes?

Since the golden age of antibiotics in the 1950s and 1960s actinomycetes have been the most prolific source for bioactive natural products. However, the number of discoveries of new bioactive compounds decreases since decades. New procedures (e.g., activating strategies or innovative fermentation techniques) were developed to enhance the productivity of actinomycetes. Nevertheless, compound identification remains challenging among others due to high rediscovery rates. Rapid and cheap genome sequencing as well as the advent of bioinformatical analysis tools for biosynthetic gene cluster identification in combination with mass spectrometry-based molecular networking facilitated the tedious process of dereplication. In recent years several studies have been dedicated to accessing the biosynthetic potential of Actinomyces species, especially streptomycetes, by using integrated genomic and metabolomic screening in order to boost the discovery rate of new antibiotics. This review aims to present the various possible applications of this approach as well as the newly discovered molecules, covering studies between 2014 and 2021. Finally, the effectiveness of this approach with regard to find new bioactive agents from actinomycetes will be evaluated.

Identification of Significant Genes With Poor Colorectal Cancer Prognosis In Via Bioinformatical Analysis

Purpose:Identification of significant genes with poor colorectal cancer prognosis in via bioinformatical analysis.Method:Gene expression profiles of GSE74602、 GSE110223、GSE113513 and GSE 141174 were available from GEO database. There are 65 CRC tissues and 65 normal tissues in the four profile datasets. Differentially expressed genes (DEGs) between CRC tissues and normal tissues were picked out by GEO2R tool and Venn diagram software. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO). Then protein-protein interaction (PPI) of these DEGs with Search Tool for the Retrieval of Interacting Genes (STRING).Results:There were total of 171 consistently expressed genes in the four datasets, including 148 up-regulated and 23 down-regulated genes. up-regulated DEGs were particularly enriched in oxidation-reduction process, in extracellular exosome, in zinc ion binding, in Metabolic pathways, Mineral absorption; and down-regulated DEGs in positive regulation of cell proliferation, in cytosol, in One carbon pool by folate. Furthermore, for the analysis of overall survival among those genes, Kaplan–Meier analysis was implemented and 30 of 88 genes had a significantly worse prognosis. For validation in Gene Expression Profiling Interactive Analysis (GEPIA), 13 of 30 genes were discovered highly expressed in CRC tissues compared to normal tissues. Furthermore, MYC 和 FGFR3 markedly enriched in the Bladder cancer pathway.Conclusion: We have identified two significant up-regulated DEGs with poor prognosis in CRC , which could be potential therapeutic targets for CRC patients.

Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis

Background. Breast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective targets for breast cancer patients via bioinformatical analysis. Methods. GSE86374, GSE5364, and GSE70947 were chosen from the GEO database. DEGs between breast cancer tissues and normal breast tissues were picked out by GEO2R and Venn diagram software. Then, DAVID (Database for Annotation, Visualization, and Integrated Discovery) was used to analyze these DEGs in gene ontology (GO) including molecular function (MF), cellular component (CC), and biological process (BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. Next, STRING (Search Tool for the Retrieval of Interacting Genes) was used to investigate potential protein-protein interaction (PPI) relationships among DEGs and these DEGs were analyzed by Molecular Complex Detection (MCODE) in Cytoscape. After that, UALCAN, GEPIA (gene expression profiling interactive analysis), and KM (Kaplan–Meier plotter) were used for the prognostic information and core genes were qualified. Results. There were 96 upregulated genes and 98 downregulated genes in this study. 55 upregulated genes were selected as hub genes in the PPI network. For validation in UALCAN, GEPIA, and KM, 5 core genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) were found to highly expressed in breast cancer tissues with poor prognosis. They differentially expressed between different subclasses of breast cancer. Conclusion. These five genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) could be potential targets for therapy in breast cancer and prediction of prognosis on the basis of bioinformatical analysis.