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MicroRNAs (miRNAs) are a class of small RNAs that regulate gene expression. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs) are persistent organic pollutants that exist as complex mixtures in vivo. When humans are simultaneously exposed to these compounds, the development of atherosclerosis is known to be enhanced. However, the roles of miRNA in TCDD- and PCB-induced atherosclerosis are largely unknown. Therefore, the present study is aimed at elucidating the possible dysregulation of miRNAs in atherogenesis induced by coexposure to TCDD and PCBs. Eight-week-old male ApoE-/- mice were coexposed to TCDD (15 μg/kg) and Aroclor1254 (55 mg/kg, a representative mixture of PCBs) by intraperitoneal injection four times over a 6-week period. Microarray analysis of miRNAs and mRNAs in the liver of ApoE-/- mice with or without TCDD and Aroclor1254 coexposure was performed. We discovered that 68 miRNAs and 1312 mRNAs exhibited significant expression changes in response to TCDD and PCB coexposure and revealed that both changed miRNAs and mRNAs are involved in cardiovascular disease processes. An integrated miRNA-mRNA approach indicated that miRNA-26a-5p, miRNA-193a-3p, and miRNA-30c-5p participated in specific TCDD and Aroclor1254 coresponsive networks which are relevant to the cardiovascular system development and function network. Furthermore, our results also indicated that miRNA-130a-3p and miRNA-376a-3p were novel players in the regulation of TCDD- and Aroclor1254-induced atherosclerosis pathways. In summary, our finding provided new insights into the mechanism of atherosclerosis in response to TCDD and PCB coexposure. [ABSTRACT FROM AUTHOR]

The genetic pathogenesis of selective intrauterine growth restriction (sIUGR) remains elusive, with evidence suggesting an important role of epigenetic factors such as microRNAs. In this study, we explored the relevance of miR-373-3p to the occurrence of sIUGR. Hypoxia enhanced the levels of miR-373-3p and hypoxia-inducible factor (HIF)-1α, while HIF-1α knockdown not only boosted the migration and proliferation of HTR8 cells but also suppressed the hypoxia-induced upregulation of miR-373-3p and SLC38A1. By contrast, HIF-1α overexpression induced miR-373-3p downregulation and SLC38A1 upregulation, reducing cell growth and migration, which could be reversed by a miR-373-3p inhibitor. Importantly, the miR-373-3p inhibitor and mimic reproduced phenomena similar to those induced by HIF-1α downregulation and overexpression, respectively (including altered SLC38A1 expression, mTOR activation, cell growth, and migration). Mechanistically, the miRNA regulated cell behaviors and related mTOR signaling by targeting SLC38A1 expression through an interaction with the 3 ′ -untranslated region of SLC38A1. The placental tissues of smaller sIUGR fetuses exhibited miR-373-3p and HIF-1α upregulation, SLC38A1 downregulation, and activated mTOR. Overall, miR-373-3p appears to restrict the growth and migration of HTR8 trophoblast cells by targeting SLC38A1, as observed in the placental tissues associated with smaller sIUGR fetuses, and it could have utility in the diagnosis and treatment of this disorder. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis. [ABSTRACT FROM AUTHOR]