Functional characterization of DNA regulatory elements through quantitative measurement of transcription dynamics

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Wilbert Copeland, Bryan A. Bartley, Herbert M. Sauro

University of Washington, United States

Mathematical models often inform the experimental design of synthetic gene networks. However, poor characterization of network parameters reduces the predictive power of models and yields imprecise or undesirable cell behaviors. In certain cases, poor characterization is the result of indirect measurement techniques. For example, DNA components, such as promoters and terminators affect RNA expression; yet, their biological function is quantified by measuring protein expression. This is not ideal since fast RNA dynamics are obscured and protein and mRNA expression do not necessarily correlate due to post-transcriptional processes. Methods that easily detect intracellular RNA levels with high temporal resolution should increase researchers’ ability to accurately describe the function of many poor characterized synthetic gene network components. Here, we describe an RNA-based fluorescence reporter to accurately and precisely measure RNA concentration and RNA synthesis rates in E. coli. The reporter consists of an aptamer that interacts with a cell membrane-diffusible organic dye to produce measureable levels of fluorescence within living cells. A model describing the molecular interactions was developed and kinetic parameters for processes relevant to transcription were determined. Fluorescence was correlated with intracellular aptamer concentration and the RNA-based reporter was shown to produce a specific, sensitive, and accurate signal. The technique was applied to observe changes in RNA expression in growing cells, and to quantify promoter activity and terminator efficiency in a high-throughput manner. Furthermore, the effects of cellular context on DNA component function were investigated. Preliminary results suggest that accounting for state-specific parameters, including cell growth rate and copy number, yield a more robust description of promoter activity. The methods presented in this work can be used to improve the predictive power of computational models, to meaningfully share characterization data of synthetic gene network components between research groups, and to create large repositories of standardized DNA components.