Expression Analysis In Vitro

Abstract

Expression analysis in vitro is a constantly evolving field, consolidated in the fourth quarter of the past century and still expanding at a fast pace. It is essentially based on the use of a template of ribonucleic acid (RNA) for a translation reaction, or of deoxyribonucleic acid (DNA) in a coupled transcription–translation system. Traditional applications of expression analysis in vitro cover a wide range of structural and functional studies on proteins and nucleotides using methodologies such as yeast one‐, two‐ and three‐hybrid systems; reporter genes; phage display; DNase footprinting; methylation interference assays and gel‐shift assays. In the past decades, in vitro expression analyses benefitted from substantial advancements associated with the use of refined cell‐free protein synthesis methods, microarrays and nanodevices. Moreover, the recent and massive accumulation of raw data on bacterial and eukaryotic genomes indicates that in vitro expression studies may require and inspire sophisticated regulation mechanisms of general validity. In this framework, it is important to realise the importance of testing the significance and efficacy of the associated mechanistic models by, respectively, specific statistical methods and simulation techniques.

Key Concepts

  • In vitro expression systems can (1) be used for the expression of toxic, proteolytically sensitive or unstable proteins; (2) incorporate unnatural amino acids and (3) allow the addition of exogenous factors to study enzymatic activity and of microsomal membranes to study posttranslational modifications.
  • Application of in vitro expression systems includes (1) site‐specific methods that utilise tRNA charged with any number of unnatural amino acids; (2) the use of putative DNA‐binding proteins such as transcription factors and (3) improving particular features of preexisting molecules such as specificity, affinity and reaction rate.
  • The efficacy of in vitro expression analyses heavily depends on refined cell‐free protein synthesis (CFPS) methods, microarrays (MA) and nanodevices (ND), whose evolution occurs at a remarkably fast pace.
  • The extraction and exploitation of the massive data flow produced by in vitro expression studies demands rigorous, quantitative descriptions as well as specific statistical methods to assess the significance of the associated dynamic models.
  • The transition from descriptive to predictive targets of most in vitro expression studies may be favoured by the systematic use of simulation techniques.

Keywords: reporter gene studies; DNase footprinting; methylation interference assays; gel‐shift assay; yeast one‐, two‐ and three‐hybrid system; phage display; microarrays

Figure 1. Schematic overview of yeast one‐, two‐ and three‐hybrid systems. DNA‐BP and DNA‐AD (deoxyribonucleic acid activation domain) are, respectively, the DNA‐binding domain and the activation domain, identified in many eukaryotic transcriptional activators as functionally and physically independent units. (a) In one‐hybrid systems, the two domains must be present in the same chimaeric protein to allow generation of the transcriptional signal by the reporter gene, generally consisting of growth or colour selection; (b) in two‐hybrid systems, they are coupled to proteins P1 and P2, whose physical interaction is a necessary prerequisite for a successful transcription of the reporter gene; (c) in three‐hybrid systems, a third hybrid molecule acts to bring together the DNA‐BP fused to the receptor for one ligand with the DNA‐AD fused to the receptor for the second ligand, thus reconstituting a functional transcriptional activator.
Figure 2. Static models of regulated gene expression networks. (a) Forty‐five nodes corresponding to genes or functional genetic modules (the numbering order is arbitrary) are distributed into four structural clusters within which links of different colours represent activation (red) or deactivation (green). Grey, green and red nodes qualitatively represent different levels of functional activity. Manual, straightforward changes in each single node/link can be easily carried out almost in real time with experimental results. (Picture obtained by the igraph software package included in the free Bioconductor suite – see also Table). (b) Four instances of a nine‐node network including (III) or not ((I), (II), (IV)) subnetworks and heterogeneous links (II). Nodes' numbering is only indicated in (I) for the sake of simplicity. The links' weights are unitary everywhere, but in (II). The table in the bottom right contains the quantitative descriptors values: APL, average path length; CC, clustering coefficient; APD, average physical distance. See the text for details.
Figure 3. Dynamic model of regulated gene expression networks. (a) (I) Sequentially ordered network of 21 similar nodes, including 19 bidirectional activation links between linearly ordered couples of 20 nodes. The 21st node is functionally disconnected. The circular arrangement is for the sake of clarity. (II, III) Specific activation of node 16 by node 3 and the other way round, respectively. (IV, V) More than one synchronous activation including the initially disconnected node (0); directions indicated by the blue arrows. (b) Time‐dependent activity levels of representative nodes associated to transitions between different activation patterns. (Drawn on the basis of the Models Library included in the NetLogo (Wilensky, ) programming environment.)
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Colosimo, Alfredo(Jun 2018) Expression Analysis In Vitro. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0005678.pub3]