Transcriptional regulation of the ER stress-inducible gene Sec16B in Neuro2a cells

Characterizing the impact of simvastatin co-treatment of cell specific TCDD-induced gene expression and systemic toxicity

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is associated with metabolic syndrome (MetS) in humans and elicits pathologies in rodents that resemble non-alcoholic fatty liver disease (NAFLD) in humans through activation of the aryl hydrocarbon receptor (AHR) pathway. Dysregulation of cholesterol homeostasis, an aspect of MetS, is linked to NAFLD pathogenesis. TCDD exposure is also linked to the suppression of genes that encode key cholesterol biosynthesis steps and changes in serum cholesterol levels. In a previous experiment, treating mice with TCDD in the presence of simvastatin, a 3-Hydroxy-3-Methylglutaryl-CoA Reductase competitive inhibitor, altered lipid and glycogen levels, AHR-battery gene expression, and liver injury in male mice compared to TCDD alone. The aim of this study was to deduce a possible mechanism(s) for the metabolic changes and increased injury using single-nuclei RNA sequencing in mouse liver. We demonstrated that co-treated mice experienced wasting and increased AHR activation compared to TCDD alone. Furthermore, relative proportions of cell (sub)types were different between TCDD alone and co-treated mice including important mediators of NAFLD progression like hepatocytes and immune cell populations. Analysis of non-overlapping differentially expressed genes identified several pathways where simvastatin co-treatment significantly impacted TCDD-induced changes, which may explain the differences between treatments. Overall, these results demonstrate a connection between dysregulation of cholesterol homeostasis and toxicant-induced metabolic changes.

Comprehensive Bioinformatics Analysis to Reveal Key RNA Targets and Hub Competitive Endogenous RNA Network of Keratoconus

Keratoconus (KC) is the most common corneal ectatic disease, with its pathological mechanisms unclear. We mainly performed bioinformatics approaches to reveal core RNA targets and hub competitive endogenous RNA (ceRNA) network and explored the potential regulatory mechanisms of ceRNA in KC. The high-throughput sequencing datasets GSE77938 and GSE151631 were downloaded from the Gene Expression Omnibus (GEO) database. The differential expression of mRNAs and lncRNAs was identified using the DESeq2 package. Functional enrichment analyses and protein–protein interaction (PPI) were executed. Then, the hub genes were filtered and molecular docking analysis was performed. Moreover, we predicted miRNAs through a website database and validated them using quantitative PCR (qPCR). Eventually, the lncRNA–miRNA–mRNA regulatory network was constructed by Cytoscape. We revealed that 428 intersected differentially expressed mRNA (DEGs) and 68 intersected differentially expressed lncRNA (DELs) were shared between the two datasets. Functional enrichment results innovatively showed that the ubiquitin-dependent protein catabolic process was upregulated in KC. The pathway enrichment showed that DEGs were mainly involved in NF-kB signaling and neurodegenerative diseases. In addition, we uncovered the top 20 hub genes in which FBXW11, FBXO9, RCHY1, and CD36 were validated by qPCR. Particularly, a small-molecule drug triptolide was predicted by molecular docking to be a candidate drug for treating KC. Moreover, we innovatively predicted and validated four core miRNAs (miR-4257, miR-4494, miR-4263, and miR-4298) and constructed a ceRNA network that contained 165 mRNA, eight lncRNAs, and four core miRNAs. Finally, we proposed a potential regulatory mechanism for KC. Overall, we uncovered a hub ceRNA network that might underlie a critical posttranslational regulatory mechanism in KC, in which miR-4257, miR-4494, miR-4263, and miR-4298 could be valuable biomarkers and provided core RNAs therapeutic targets for KC.