China
Africa
Explore chapters and articles related to this topic
Regulation of Human CYP2D6
Published in Shufeng Zhou, Cytochrome P450 2D6, 2018
Jiang et al. (2013) have developed a novel mediation analysis approach to identify new expression quantitative trait loci (eQTLs) driving CYP2D6 activity by combining genotype, gene expression, and enzyme activity data. The authors have found 389,573 and 1,214,416 SNP–transcript–CYP2D6 activity trios that are strongly associated for two different genotype platforms, namely, Affymetrix and Illumina, respectively. In the Affymetrix data set, 295 SNPs correlate with at least 20 genes, which are used to check for overlapping with the results of mediation analysis. A total of 289 eQTL hotspots are found to correlate with 1542 gene expression profiles. The Illumina data set has found that 724 SNPs correlate with at least 20 genes, and 719 of the hotspots are significantly correlated with 2444 genes in mediation analysis. Nine hundred thirty-nine and 1420 genes are successfully mapped in the Ingenuity database for two platforms. The majority of eQTLs are trans-SNPs. Five (CCL16, CCL20, CMTM5, IL-6, and SPP1) and 7 (CCL16, CCL20, CKLF, CKLFSF5, EPO, FAM3C, and SPP1) cytokines, 5 (AR, NR1I2/PXR, NR1I3/CAR, NR2F6, and PPARα) and 7 (AR, ESR1, NR1I2/PXR, NR1I3/CAR, PPARα, RORα/NR1F1, and RORγ) nuclear receptors, and 80 and 113 transcription regulators are found to mediate the relationship between genetic variant and CYP2D6 activity for Affymetrix and Illumina data sets. Overlapped eQTL hotspots with the mediators lead to the identification of 64 transcription factors that can regulate CYP2D6 (Jiang et al. 2013). These transcription factors include AATF, ALYREF, ARHGAP35, ASB8, ATF4, CBX4, CEBPG, CSDA, DDIT3, E2F5, ETV7, FOXN3, FOXN3, FUBP1, GPS2, HDAC10, HMGN1, ID1, INVS, IRF9, KANK1, KAT2B, KHDRBS1, KLF12, MAF, MAML2, MEIS2, MLXIPL, MXD4, MYBBP1A, MYCL1, NCOA7, NCOR1, NFIA, NFKB2, NFYA, NOLC1, NPM1, PEX14, PYCARD, SAP18, SATB1, SIM2, SLC2A4RG, SMARCC1, SNAI3, SNW1, SOX5, TCERG1, TCF7L2, TEAD3, TEAD4, TFDP2, TFEB, TOB1, p53, YWHAB, YY1, ZGPAT, ZHX3, ZKSCAN1, ZNF132, ZNF256, and ZNF263 (Jiang et al. 2013). Among them, YY1 has been reported to putatively bind to human CYP2D6 or rat Cyp2d4 promoter and regulate the expression of CYP2D6 (Gong et al. 2013) and Cyp2d4 (Mizuno et al. 2003). This study has provided new insights into the complex regulatory network for hepatic CYP2D6. Addition of the p53 inhibitor cyclic PFT-α in HepG2 cells dose-dependently enhances CYP2D6 and 3A4 activity, whereas addition of the p53 activator NSC 66811 dose-dependently inhibits CYP2D6 and 3A4 activity (Xiao et al. 2015). Further functional and validation studies are certainly needed to verify the regulation of CYP2D6 by these genes.
Long non-coding RNA NEAT1 promotes steatosis via enhancement of estrogen receptor alpha-mediated AQP7 expression in HepG2 cells
Published in Artificial Cells, Nanomedicine, and Biotechnology, 2019
Xiaohua Fu, Jing Zhu, Lin Zhang, Jing Shu
LncRNA is a large and diverse group of RNA transcripts longer than 200 bases and without evident protein-coding capacity [19]. LncRNAs can be transcribed from antisense, intronic, intergenic, divergent of genes and numerously from enhancers and other regulatory elements in the genome [20]. Most lncRNAs are nuclear localization and are likely to exert their functions via chromatin modifiers recruitment or scaffolds within ribonucleoprotein complexes to repress or activate gene transcription [21]. Recently, the number of functional lncRNAs identified in a variety of diseases including cancer, neuron disease, cardiac disorder, obesity and endocrine physiology is increasing. NEAT1 was demonstrated to play an oncogenic role in breast and prostate cancers [22,23]. And, NEAT1 was one of the non-coding transcriptome signature driven by ERα and was required for the formation of FOXN3-SIN3A repressor complex to facilitate epithelial-to-mesenchymal transition process for tumour progression. Furthermore, NEAT1 was also determined to be involved in adipogenesis including lipolysis, lipid uptake and low-density lipoprotein oxidization [24–26]. In addition to this, LINC01116 [27], MIAT [28] and ElncRNA1 [29] displayed an obviously differential expression upon E2 treatment and ERα silencing of HepG2 cells. Here, we confirmed the connection between NEAT1 and ERα in regulatory network of steatosis.
Clinical and genetic predictors of diabetes drug’s response
Published in Drug Metabolism Reviews, 2019
Adriana Fodor, Angela Cozma, Ramona Suharoschi, Adela Sitar-Taut, Gabriela Roman
A recent combined non-linear mixed effect model with computational genetic techniques identified predictors of metformin response in 1056 T2D patients (Goswami et al. 2016). Nine variants in 8 genes accounted for about one-third of the variability in the progression of HbA1c levels on metformin. Of the 9 variants, CSMD1 (rs2954625), VPS13C (rs12907856), and SLC22A2 (rs3160009) had greater effects on HbA1c levels than the clinical parameters, accounting for about 5%, 6%, and 8% of the HbA1c variability. The model predicted that the HbA1c levels start to increase in patients on metformin at about 321 days, at a rate of 0.1% per year, for the next 3 years; while in patients not treated with metformin, HbA1c levels increase at a steady-state rate of about 0.16% per year (Goswami et al. 2016). Two SNPs (rs2617102, rs2954625) in the CSMD1 gene (CUB and Sushi multiple domains 1) had the worst impact on disease progression, although the mechanism is unknown. Polymorphisms in genes SLC22A2, FOXN3, EMILIN2, and WWOX were associated with lower HbA1c progression.
Circular RNA-CDR1as is involved in lung injury induced by long-term formaldehyde inhalation
Published in Inhalation Toxicology, 2021
Qiu-Ping Liu, Pan Ge, Qian-Nan Wang, Shu-Yu Zhang, Yan-Qi Yang, Mo-Qi Lv, Ye Lu, Man-Xiang Li, Dang-Xia Zhou
More importantly, circRNAs were reported to regulate gene expression at transcriptional or after transcriptional level by circRNA-miRNA-mRNA axis (Cai et al. 2020). For example, Yao et al. reported that CDR1as could sponge miR-7 to release TGFBR2, which plays an essential role during pulmonary fibrosis stimulated by silica (Yao et al. 2018). Cai et al. found that CDR1as-targeting miR-135a-5p promotes neuropathic pain by upregulating the expression level of autophagy and inflammation in chronic constriction injury rats (Cai et al. 2020). Zhang et al. demonstrated that CDR1as functions as a sponge of miR-641 to promote osteoarthritis progression in humans by regulating ECM homeostasis and inflammation via FGF-2-mediated MEK/ERK signaling pathway (Zhang et al. 2020). In our study, we found that the putative target genes of CDR1as-targeting miRNAs included many autophagy-related genes such as Atg7, Atg2b, ULK1, and FOXN3, et al. Autophagy, one type of cell death, which is the key to removing ‘garbage’ in cells, preventing abnormal cell death, and maintaining normal cell functions (Mizushima and Komatsu 2011). The process of autophagy requires the participation of dozens of autophagy-related genes (Ichimiya et al. 2020). An increasing number of studies have found that autophagy is involved in the pathogenesis of many diseases, such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, pulmonary hypertension, acute lung injury, and other pulmonary diseases (Liao et al. 2019). Our previous study demonstrated that FA inhalation triggered autophagy in alveolar epithelial cells, subsequently, autophagy regulated inflammation activation and oxidative stress during acute lung injury (Liu et al. 2018). Among autophagy-related genes, Atg7 has been verified as target genes of miR-7 (Gu et al. 2017). Also, in this study, the expression of Atg7 increased in the lung tissue exposed to FA. Therefore, we speculated that CDR1as participated in lung injury induced by FA through suppressing rno-miR-7b and elevating the level of autophagy, consequently resulting in significant lung damage, including obviously capillary congestion and hemorrhage, alveolar collapse or edema, thickening of the alveolar wall with interstitial cell hyperplasia, and inflammatory cells infiltration.