China
Africa
Explore chapters and articles related to this topic
Regulation of C-Reactive Protein, Haptoglobin, and Hemopexin Gene Expression
Published in Andrzej Mackiewicz, Irving Kushner, Heinz Baumann, Acute Phase Proteins, 2020
Dipak P. Ramji, Riccardo Cortese, Gennaro Ciliberto
IL-6DBP belongs to the C/EBP class of transcription factors. These proteins are positive regulators of gene transcription and bind DNA as dimers. Their DNA binding domain consists of a dimerization interface, termed the leucine zipper, and a DNA contact surface containing clusters of basic amino acid residues. Several members of this family have recently been cloned and show substantial homology in their DNA binding domain (Figure 15). These include: C/EBP (C/EBPα),79,87 IL-6DBp (NF-IL-6,77 LAP,88 AGP/EBP,89 CRP2,90 and C/EBPβ85), and Ig/EBP-1104 (C/EBPγ), CRP1,90 and C/EBPδ.85) With the exception of Ig/EBP-1, which is expressed ubiquitously,104 the other members show a restricted, but partially overlapping, expression pattern in different tissues.85,88,90 The different members are capable of forming heterodimers in all intrafamilial combinations.85,88,90,104 Thus, heterodimeric interactions between the different members may potentially play an important role in the modulation of AP gene transcription. Indeed, functional interaction between IL-6DBP and C/EBP has been demonstrated (Section III.H). In the presence of equimolar amounts of IL-6DBPand C/EBP, the latter, which normally activates transcription in an IL-6-independent manner, is recruited into heterodimers whose activity is regulated by IL-6.
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.
Targeting Autophagy In Disease: Recent Advances In Drug Discovery
Published in Expert Opinion on Drug Discovery, 2020
Dasol Kim, Hui-Yun Hwang, Ho Jeong Kwon
C/EBP family members (C/EBPα, C/EBPβ, C/EBPγ, C/EBPδ, C/EBPε, and C/EBPζ) are characterized by a conserved b-Zip domain at the C-terminus that mediates dimerization and DNA binding [36]. C/EBP family members are known to regulate cell proliferation and differentiation at the transcriptional level [37]. Recent studies have revealed that they also have a role in autophagic regulation. Among them, C/EBPβ was reported to control the expression of autophagy genes, including Beclin-1, ATG5, and ATG4 [38,39]. More recently, C/EBPδ (CEBPD) was found to activate transcription of the autophagy genes LC3B and ATG3 in hepatocellular carcinoma (HCC) [40]. Another transcription factor, C/EBPζ (C/EBP-homologous protein/CHOP), alone or in combination with ATF4, was revealed to bind the promoter regions of a set of autophagy genes through eIF2α/ATF4 pathway activation under ER stress or starvation conditions, thereby regulating autophagy [41]. Thus, the C/EBP family can regulate cellular homeostasis as autophagy master genes, supporting their utility as molecular targets for related diseases.
Association of rs2620381 polymorphism in miR-627 and gastric cancer
Published in British Journal of Biomedical Science, 2020
M Raad, Z Salehi, M Habibzaadeh Baalsini, F Mashayekhi, H Saeidi Saedi
rs2620381 polymorphism A > C [with global minor allele frequency (Global MAF) 0.08] was located in the first nucleotide of miR-627-5p seed site, which can change U nucleotide (in the wild-type form) to G nucleotide (in the variant form) in the miRNA sequence. Validated targets of miR-627-5p as the main mature variant of miR-627 have been identified by using miRWalk and miRTarBase databases. It has been identified that miR-627-5p has 70 validated targets including USP42, ESCO2, DUSP5, WEE1, BTF3L4, KDM3A, NUTM2E, METRN, SEPHS1, NKX6-1, HSBP1, LUZP1, OGT, CD1D, RNF165, ERP44, XPO7, ST6GAL1, FAM168A, ZNF623, CENPN, PTPRB, SHMT2, P3H2, PHF5A, FOS, CARNMT1, APH1A, KANSL1, NAV1, AGO2, C17orf105, EFNB2, HSPA1B, TMEM98, FLVCR1, SURF4, CEBPG, PLD5, RPS16, Slfn5, SLFN5, KLHL12, ZNF548, OGFRL1, ANTXR2, JMJD1C, CLIC6, PPP1R16B, CYB561A3, MSRB1, ICOSLG, CORO2A, TMX4, CDKAL1, COX6B1, GEMIN4, USP15, ATXN7L3B, TFRC, INSIG1, IPPK, ANKRD46, CBX8, RBM20, BTG2, CKAP2L, FGFR1OP, KNSTRN, and ST18. To better understand the connection between mir-627-5p targets with gastric cancer, by searching through previous researches, we found that 11 of these genes (BTG2, AGO2, USP42, ESCO2, DUSP5, CD1D, EFNB2, SLFN5, INSIG1, FOS, and WEE1) were linked with gastric cancer
Molecular and clinical characterization of IDH associated immune signature in lower-grade gliomas
Published in OncoImmunology, 2018
Zenghui Qian, Yiming Li, Xing Fan, Chuanbao Zhang, Yinyan Wang, Tao Jiang, Xing Liu
Considering the different immune status between IDHWT and IDHMUT LGGs, we sought to evaluate the prognostic value of these differentially expressed genes in the training cohort. The univariate Cox regression analyses revealed that 132 out of the 196 differential genes were significantly correlated with the OS. Among these, 114 (86.4%) genes upregulated in IDHWT group were associated with a hazard ration > 1 for death, suggesting an enhanced immune system-related risk in IDHWT LGGs (Fig. 2A). To further generate an IDH associated immune signature for prognostication, we then applied a LASSO Cox regression model to select the genes with the largest prognostic value. According to the minimum criteria and one SE of the minimum criteria, 11 genes (APLN, BCL10, CCRL1, CD276, CEBPG, COLEC12, HAMP, HDAC4, LIG1, MADCAM1, and SECTM1) and 5 genes (BCL10, CD276, SECTM1, HDAC4, MADCAM1) were identified, respectively (Fig. 2B, C, D, E). Using parallel analyses, similar clinical and biological functions were revealed in these two selected gene sets, which indicated that either of those two criteria of gene selection was appropriate. Two heatmaps were used to investigate the correlations between the 11 genes in CGGA and TCGA cohorts (Fig. 3A, B), respectively. The correlation patterns of CGGA and TCGA cohorts were similar, indicating that correlations between the selected genes were relatively stable in different databases. Subsequently, the expression levels of these genes and corresponding Coef were used to calculate the risk score. Patients in the training cohort were assigned into two groups (high-risk and low-risk groups) according to the median risk score. As shown in Fig. 3C, E, patients in the high-risk group had significantly shorter OS than low-risk counterparts.