Cytochrome P450 2D6 (CYP2D6) participates in the metabolism of approximately 20–25% of prescribed drugs. Genetic polymorphisms influence the expression and/or activity of CYP2D6, and inter-individual differences in drug activation and elimination caused by CYP2D6 genetic variants were reported. However, little is known about the potential modulation of CYP2D6 expression by microRNAs (miRNAs). In the current study, by using in silico prediction of the stabilities of miRNA/mRNA complexes, we screened 38 miRNA candidates that may interact with the transcript of CYP2D6. An inverse correlation between the expression of miRNA hsa-miR-370-3p and the expression of CYP2D6 was observed in human liver tissue samples. Electrophoretic mobility shift assays confirmed that hsa-miR-370-3p was able to directly bind to its cognate target within the coding region of the CYP2D6 transcript. The transfection of hsa-miR-370-3p mimics into the HepG2CYP2D6 cell line, a genetically modified cell line that overexpresses exogenous CYP2D6, was able to suppress the expression of CYP2D6 significantly at both mRNA and protein levels. The transfection of hsa-miR-370-3p mimics was also able to inhibit endogenous mRNA expression and/or protein production of CYP2D6 in HepaRG cells. Furthermore, in HepaRG, HepG2, and Huh7 cells, dexamethasone-induced expression of CYP2D6 was inhibited by hsa-miR-370-3p mimics. To investigate whether the miRNA mediated suppression is caused by inhibiting protein translation or promoting mRNA degradation, an actinomycin D assay was used to measure the stability of CYP2D6 transcripts. The results indicated that hsa-miR-370-3p mimics facilitated significantly the degradation of CYP2D6 mRNA. In addition, proteomics analyses of proteins isolated from the miRNA/mRNA/protein complex suggested that a group of multifunctional proteins facilitated the interaction between hsa-miR-370-3p and CYP2D6, thereby promoting mRNA degradation.
Acetaminophen (APAP) overdose is the leading cause of acute liver failure. Yet the mechanisms underlying adaptive tolerance toward APAP-induced liver injury are not fully understood. To better understand molecular mechanisms contributing to adaptive tolerance to APAP is an underpinning foundation for APAP-related precision medicine. In the current study, the mRNA and microRNA (miRNA) expression profiles derived from next generation sequencing data for APAP-treated (5 and 10 mM) Hep-aRG cells and controls were analyzed systematically. Putative miRNAs targeting key dysregulated genes involved in APAP hepatotoxicity were selected using in silico prediction algorithms, un-biased gene ontology, and network analyses. Luciferase reporter assays, RNA electrophoresis mobility shift assays, and miRNA pull-down assays were performed to investigate the role of miRNAs affecting the expression of dysregulated genes. Levels of selected miRNAs were measured in serum samples obtained from children with APAP overdose (58.6–559.4 mg/kg) and from healthy controls. As results, 2758 differentially expressed genes and 47 miRNAs were identified. Four of these miRNAs (hsa-miR-224-5p, hsa-miR-320a, hsa-miR-449a, and hsa-miR-877-5p) suppressed drug metabolizing enzyme (DME) levels involved in APAP-induced liver injury by downregulating HNF1A, HNF4A and NR1I2 expression. Exogenous transfection of these miRNAs into HepaRG cells effectively rescued them from APAP toxicity, as indicated by decreased alanine aminotransferase levels. Importantly, hsa-miR-320a and hsa-miR-877-5p levels were significantly elevated in serum samples obtained from children with APAP overdose compared to health controls. Collectively, these data indicate that hsa-miR-224-5p, hsa-miR-320a, hsa-miR-449a, and hsa-miR-877-5p suppress DME expression involved in APAP-induced hepatotoxicity and they contribute to an adaptive response in hepatocytes.
The investigation of the complex processes involved in cellular differentiation must be based on unbiased, high throughput data processing methods to identify relevant biological pathways. A number of bioinformatics tools are available that can generate lists of pathways ranked by statistical significance (i.e. by p-value), while ideally it would be desirable to functionally score the pathways relative to each other or to other interacting parts of the system or process. We describe a new computational method (Network Activity Score Finder - NASFinder) to identify tissue-specific, omics-determined sub-networks and the connections with their upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. Adipogenesis of human SBGS pre-adipocyte cells in vitro was monitored with a transcriptomic data set comprising six time points (0, 6, 48, 96, 192, 384 hours). To elucidate the mechanisms of adipogenesis, NASFinder was used to perform time-point analysis by comparing each time point against the control (0 h) and time-lapse analysis by comparing each time point with the previous one. NASFinder identified the coordinated activity of seemingly unrelated processes between each comparison, providing the first systems view of adipogenesis in culture. NASFinder has been implemented into a web-based, freely available resource associated with novel, easy to read visualization of omics data sets and network modules.
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