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Optimizing Reporter Gene Expression for Molecular Magnetic Resonance Imaging
Published in Shoogo Ueno, Bioimaging, 2020
Qin Sun, Frank S. Prato, Donna E. Goldhawk
Genetic encoding of reporter genes enables the tracking of several features of cellular activity, regardless of platform used to detect the encoded proteins. For example, although all somatic cells contain the full complement of an organism’s DNA (the genotype), not all of these genes are expressed at once. Each cell type regulates the expression of individual genes to obtain specific cell functionality (the phenotype). The transcription factors that regulate gene expression thus perform a key role in determining what function a cell has, for example, cardiac, hepatic, neural, or other activity. The same transcription factor (TF) regulation applies to reporter genes that are introduced for the purpose of monitoring cellular activity.
General Introductory Topics
Published in Vadim Backman, Adam Wax, Hao F. Zhang, A Laboratory Manual in Biophotonics, 2018
Vadim Backman, Adam Wax, Hao F. Zhang
Transcription factors are DNA-binding proteins. Some DNA-binding proteins are activators, while others are repressors. Their names tell it all: Activators increase the rate of gene transcription, while repressors do the opposite. These factors are critically important in transcription, as without their help, only a very low level of transcriptional activity would be possible. Other modulator molecules include coactivators—proteins that assist transcription factors to increase the rate of gene transcription—and corepressors—proteins that work with transcription factors to decrease the rate of transcription.
Recovering what was lost: Can morphogens scale to enable regeneration?
Published in David M. Gardiner, Regenerative Engineering and Developmental Biology, 2017
Transcription factors are proteins that bind to specific DNA sequences called enhancers, which interact with the promoter regions of nearby genes, turning them on or off. The first clues to how morphogen gradients are interpreted by cells came from studies in fruit flies. Due to a rather unconventional early development, in which the embryo contains no cell membranes, the fruit fly transcription factor bicoid can act like a morphogen. It forms a gradient from anterior to posterior (head to tail) and activates anterior genes at high concentrations and posterior genes at low concentrations. Bicoid is not a typical morphogen, since it does not act outside cells, but it does demonstrate how cell fate can be altered through the differential activation of transcription factors. Then, transcription factors can act as the morphogen readout, changing cell fate accordingly through their interactions with gene targets (Figure 9.2b). The developmental toolkit of animals with more typical, cellularized development also contains a number of transcription factor families, which, though have no direct physical contact with the external morphogen, can respond differently at different concentration gradient thresholds. This is possible because the morphogens interact with cell surface receptors, proteins that sit in the plasma membranes of cells and have an external binding domain and a cytoplasmic effector domain. Binding of the receptor to morphogen ligand initiates a conformational change in the protein, which is relayed to the inside of the cell, often triggering a cascade of cytoplasmic proteins, ultimately resulting in interaction with a transcription factor (reviewed in Ashe and Briscoe 2006; Christian 2011).
The roles of membrane transporters in arsenic uptake, translocation and detoxification in plants
Published in Critical Reviews in Environmental Science and Technology, 2021
Transcriptional regulation is an early event during gene expression, which is largely controlled by transcription factors (Carroll, 2005; Castrillo et al., 2011). Although the uptake and translocation of As(III) by aquaporins have been well studied in plants, the underlying mechanisms of how aquaporins respond to As(III) in plants are largely unknown. Recently, a R2R3 MYB transcription factor, OsARM1, has been suggested as a negative-regulator for As(III) transport in rice (Wang, Chen et al., 2017). OsARM1 was thought to be able to directly bind to the promoter regions of OsLsi1, OsLsi2, and OsLsi6 in rice as well as AtNIP1;1, AtNIP3;1, and AtNIP5;1 in Arabidopsis and regulate the uptake and root-to-shoot translocation of As(III) by weakly suppressing the expression of these genes (Wang, Chen et al., 2017). Knocking out OsARM1 resulted in enhanced As translocation from roots to shoots in rice while overexpression of OsARM1 showed reduced As translocation after exposure to As(III) (Wang, Chen et al., 2017).
Variance-constrained filtering for discrete-time genetic regulatory networks with state delay and random measurement delay
Published in International Journal of Systems Science, 2019
Dongyan Chen, Weilu Chen, Jun Hu, Hongjian Liu
It is well known that the process of cell division involves a large number of substances, such as messenger RNA (mRNA), proteins and other small molecules. The process of gene expression from genes to proteins is mainly composed of transcription and translation (Chen & Aihara, 2002; Chen, Chen, Hu, Liang, & Dobaie, 2018). During transcription, mRNAs are synthesised from genes through the regulation of transcription factors (proteins). In translation, nucleotide sequences in mRNAs are used to synthesise proteins (Vembarasan, Nagamani, Balasubramaniam, & Park, 2013; Wan, Wang, Wu, & Liu, 2018a). The mechanisms of regulating the gene expression are called the GRNs, which are actually biochemically dynamical systems. Over the past two decades, the GRNs have gained a lot of research attention in the field of biological and biomedical sciences. A variety of models have been presented to describe the GRNs, such as the Boolean model (Somogyi & Sniegoski, 1996), the differential equation model (Smolen, Baxter, & Byrne, 2000), the Bayesian model (Friedman, Linial, Nachman, & Pe'er, 2000) and the state-space model (Wu, Zhang, & Kusalik, 2004). As we know, owing to the slow reaction processes of transcription, translation and the finite switching speed of the amplifier, the time-delay is inevitable in the GRNs (Wan, Wang, Han, & Wu, 2018; Zhang, Wu, & Cui, 2015). So far, a great number of analysis and synthesis issues for delayed GRNs have been investigated, such as stability analysis (Tu & Lu, 2006; Wan, Xu, Fang, & Yang, 2014; Wang, Wang, Nguang, Zhong, & Liu, 2016) and synchronisation problems (Jiang, Liu, Yu, & Shen, 2015; Yue et al., 2017). To be specific, some methods have been provided in Chen Aihara (2002) to analyse the local stability of GRNs with time-invariant delay. In Wang et al. (2016), the stability analysis problem has been studied for a class of GRNs with parameter uncertainties and time-varying delays, where some sufficient criteria have been presented to guarantee the robust asymptotic stability of the GRNs by using Jensen inequality and convex combination approach. In addition, the finite-time synchronisation problem has been considered in Jiang et al. (2015) for stochastic GRNs, and sufficient conditions have been given to ensure that the designed controller can synchronise the concentrations of gene products (i.e. mRNAs and proteins) under the finite-time criterion.
Targeting gap junctional intercellular communication by hepatocarcinogenic compounds
Published in Journal of Toxicology and Environmental Health, Part B, 2020
Kaat Leroy, Alanah Pieters, Andrés Tabernilla, Axelle Cooreman, Raf Van Campenhout, Bruno Cogliati, Mathieu Vinken
In addition to regulation at the cell plasma membrane, GJIC is also modulated at the expression level (Solan and Lampe 2018). The first type of expression control is epigenetic mechanisms, such as histone acetylation, DNA methylation and microRNA interactions. Histone acetylation is carried out by histone acetyltransferases. These enzymes stimulate transcriptional activation through chromatin decondensation, while gene suppression is achieved by actions of histone deacetylases that condensate chromatin. Various inhibitors of histone deacetylases were linked to enhanced connexin production and gap junction functionality (Vinken 2016). In this respect, trichostatin A reestablishes GJIC in rat liver epithelial cells (Jung et al. 2006) and downregulates Cx43 production in human liver cancer, while leaving Cx26 and Cx32 unaffected (Yamashita et al. 2004). This has been associated with suppression of tumor aggressiveness due to reduction of tumor invasiveness and proliferation mediated by Cx43 (Menezes et al. 2019). Another epigenetic mechanism is facilitated by DNA methyltransferase enzymes. These enzymes typically hypermethylate gene promotors leading to inhibition of gene transcription. In liver cancer, the decrease of Cx26 expression was associated with enhanced levels of DNA methyltransferase mRNA (Shimizu et al. 2007) and methylated CpG dinucleotides located within the Cx26 gene promotor (Piechocki, Burk, and Ruch 1999). Similarly, Cx32 and Cx43 gene promotor sites are methylated in liver epithelial cells lacking Cx32 expression and a rat hepatoma cell line missing Cx43, respectively (Piechocki, Burk, and Ruch 1999). MicroRNA-related mechanisms constitute another level of epigenetic control. MicroRNAs are small complementary sequences that bind mRNA target molecules, thereby inhibiting translation or cleavage of mRNA (Vinken 2016; Wang et al. 2020, 2019a). Cx43 may be regulated by microRNA-206 amongst many others, thereby influencing metastasis (Lin et al. 2016) and differentiation (Anderson, Catoe, and Werner 2006). Connexin gene transcription is also modulated by conventional cis/trans mechanisms. Both general and tissue-specific transcription factors are involved in this process. In liver, Cx32 gene expression is mediated, at least in part, by the ubiquitous transcription factor specificity protein 1 as well as by the liver-enriched transcription factor hepatocyte nuclear factor 1α (Koffler et al. 2002; Plante, Charbonneau, and Cyr 2006).