miR-15a-3p Protects Against Isoniazid-Induced Liver Injury via Suppressing N-Acetyltransferase 2 Expression

The Arylamine N-Acetyltransferases as Therapeutic Targets in Metabolic Diseases Associated with Mitochondrial Dysfunction

In humans, there are two arylamine N-acetyltransferase genes that encode functional enzymes (NAT1 and NAT2) as well as one pseudogene, all of which are located together on chromosome 8. Although they were first identified by their role in the acetylation of drugs and other xenobiotics, recent studies have shown strong associations for both enzymes in a variety of diseases, including cancer, cardiovascular disease, and diabetes. There is growing evidence that this association may be causal. Consistently, NAT1 and NAT2 are shown to be required for healthy mitochondria. This review discusses the current literature on the role of both NAT1 and NAT2 in mitochondrial bioenergetics. It will attempt to relate our understanding of the evolution of the two genes with biologic function and then present evidence that several major metabolic diseases are influenced by NAT1 and NAT2. Finally, it will discuss current and future approaches to inhibit or enhance NAT1 and NAT2 activity/expression using small-molecule drugs. SIGNIFICANCE STATEMENT: The arylamine N-acetyltransferases (NATs) NAT1 and NAT2 share common features in their associations with mitochondrial bioenergetics. This review discusses mitochondrial function as it relates to health and disease, and the importance of NAT in mitochondrial function and dysfunction. It also compares NAT1 and NAT2 to highlight their functional similarities and differences. Both NAT1 and NAT2 are potential drug targets for diseases where mitochondrial dysfunction is a hallmark of onset and progression.

Carbon dots induce pathological damage to the intestine via causing intestinal flora dysbiosis and intestinal inflammation

BACKGROUND

Carbon dots (CDs), as excellent antibacterial nanomaterials, have gained great attention in treating infection-induced diseases such as periodontitis and stomatitis. Given the eventual exposure of CDs to the intestine, elucidating the effect of CDs on intestinal health is required for the safety evaluation of CDs.

RESULTS

Herein, CDs extracted from ε-poly-L-lysine (PL) were chosen to explore the modulation effect of CDs on probiotic behavior in vitro and intestinal remodeling in vivo. Results verify that PL-CDs negatively regulate Lactobacillus rhamnosus (L. rhamnosus) growth via increasing reactive oxygen species (ROS) production and reducing the antioxidant activity, which subsequently destroys membrane permeability and integrity. PL-CDs are also inclined to inhibit cell viability and accelerate cell apoptosis. In vivo, the gavage of PL-CDs is verified to induce inflammatory infiltration and barrier damage in mice. Moreover, PL-CDs are found to increase the Firmicutes to Bacteroidota (F/B) ratio and the relative abundance of Lachnospiraceae while decreasing that of Muribaculaceae.

CONCLUSION

Overall, these evidences indicate that PL-CDs may inevitably result in intestinal flora dysbiosis via inhibiting probiotic growth and simultaneously activating intestinal inflammation, thus causing pathological damage to the intestine, which provides an effective and insightful reference for the potential risk of CDs from the perspective of intestinal remodeling.

Roles of H19/miR-29a-3p/COL1A1 axis in COE-induced lung cancer

Occupational lung cancer caused by coke oven emissions (COE) has attracted increasing attention, but the mechanism is not clear. Many evidences show ceRNA (competing endogenous RNA) networks play important regulatory roles in cancers. In this study, we aimed to construct and verify the ceRNA regulatory network in the occurrence of COE-induced lung squamous cell carcinoma (LUSC). We performed RNA sequencing with lung bronchial epithelial cell (16HBE) and COE induced malignant transformed cell (Rf). Furthermore, we analyzed RNA sequencing data of LUSC and adjacent tissues in the cancer genome atlas (TCGA) database. Combined our data and TCGA data to determine the differentially expressed lncRNAs, miRNAs, mRNAs. lncBASE, miRDB and miRTarBase were used to predict the binding relationship between lncRNA and miRNA, miRNA and mRNA. Based on these, we construct the ceRNA network. FREMSA, dual-luciferase reporter assay, quantitative real-time PCR (qRT-PCR), western-blot were used to verify the regulatory axis. CCK8 assay, phalloidin staining, p53 detection were used to explore the roles of this axis in the COE induced malignant transformation. Results showed 7 lncRNAs, 7 miRNAs and 146 mRNAs were identified. Among these, we constructed a ceRNA network including 1 lncRNA, 2 miRNAs and 9 mRNAs. Further verification confirmed the trend of lncRNA H19, miR-29a-3p and COL1A1 were consistent with sequencing results. H19 and COL1A1 were significantly higher in Rf than in 16HBE and miR-29a-3p was reverse. Regulatory investigation revealed H19 increased COL1A1 expression by sponging miR-29a-3p. Knockdown of H19, COL1A1 or overexpression of miR-29a-3p in Rf cells could inhibit cell proliferation, increased cell adhesion and p53 level. However, knockdown of H19 while suppressing the miR-29a-3p partially rescue the malignant phenotype of Rf caused by H19. In conclusion, all these indicated H19 functioned as a ceRNA to increase COL1A1 by sponging miR-29a-3p and promoted COE-induced cell malignant transformation.

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

This study aimed to investigate the immune landscape in hepatoblastoma (HB) based on deconvolution methods and identify a biomarkers panel for diagnosis based on a machine learning algorithm. Firstly, we identified 277 differentially expressed genes (DEGs) and differentiated and functionally identified the modules in DEGs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GO (gene ontology) were used to annotate these DEGs, and the results suggested that the occurrence of HB was related to DNA adducts, bile secretion, and metabolism of xenobiotics by cytochrome P450. We selected the top 10 genes for our final diagnostic panel based on the random forest tree method. Interestingly, TNFRSF19 and TOP2A were significantly down-regulated in normal samples, while other genes (TRIB1, MAT1A, SAA2-SAA4, NAT2, HABP2, CYP2CB, APOF, and CFHR3) were significantly down-regulated in HB samples. Finally, we constructed a neural network model based on the above hub genes for diagnosis. After cross-validation, the area under the ROC curve was close to 1 (AUC = 0.972), and the AUC of the validation set was 0.870. In addition, the results of single-sample gene-set enrichment analysis (ssGSEA) and deconvolution methods revealed a more active immune responses in the HB tissue. In conclusion, we have developed a robust biomarkers panel for HB patients.