Tracking, tuning, and terminating microbial physiology using synthetic riboregulators

Abstract

The development of biomolecular devices that interface with biological systems to reveal new insights and produce novel functions is one of the defining goals of synthetic biology. Our lab previously described a synthetic, riboregulator system that affords for modular, tunable, and tight control of gene expression in vivo. Here we highlight several experimental advantages unique to this RNA-based system, including physiologically relevant protein production, component modularity, leakage minimization, rapid response time, tunable gene expression, and independent regulation of multiple genes. We demonstrate this utility in four sets of in vivo experiments with various microbial systems. Specifically, we show that the synthetic riboregulator is well suited for GFP fusion protein tracking in wild-type cells, tight regulation of toxic protein expression, and sensitive perturbation of stress response networks. We also show that the system can be used for logic-based computing of multiple, orthogonal inputs, resulting in the development of a programmable kill switch for bacteria. This work establishes a broad, easy-to-use synthetic biology platform for microbiology experiments and biotechnology applications.

Fig. 1.

The riboregulator and its features. Our riboregulator system regulates gene expression posttranscriptionally through the specific interaction between the crRNA and taRNA (9). The cis-repressed mRNA (crRNA) is composed of the cis-repressive sequence (CR, yellow), the RBS (blue), a stem loop (light gray), and the target gene (red). The trans-activating RNA (black) contains the trans-activating sequence (TA, green) and the stem loop binding sequence (dark gray). To initiate translation, the taRNA binds the crRNA and grants the ribosome (brown circles) access to the RBS.

Fig. 2.

Fluorescent imaging of GFP-TonB. All images are representative fluorescent micrographs taken at 90 min postinduction. (A) Riboregulator system. (B) Micrographs of GFP-TonB fluorescence in MG1655Pro and MG1655ProΔtonB cells. (C) Micrographs of GFP-TonB expression from P_LfurO and pTonB in MG1655Pro. Additional micrographs are presented in Fig. S1.

Fig. 3.

Transcriptome response to CcdB expression. (A) Riboregulator system. (B) Portion of the time-course gene expression response of CcdB-expressing MG1655 and MG1063 cells. Yellow and blue represent up-regulation and down-regulation, respectively. See SI Text for details concerning our analysis methods and Dataset S1 for the full microarray results.

Fig. 4.

Effect of LexA3 expression on the SOS response. All samples, except for untreated controls, were treated with norfloxacin at various expression levels of LexA3. Graphs depict the triplicate mean ± SEM. (A) Riboregulator system. (B) Log % survival of cells. (C) Fold changes in relative mRNA concentrations for selected SOS genes. See Dataset S2 for the full qPCR data set.

Fig. 5.

Independent regulation of λ-phage lysis proteins in a bacterial kill switch. Graphs depict the triplicate mean ± SEM. (A) Riboregulator system. (B) Effect of λR-R_Z and λS expression on OD₆₀₀. Addition of glucose was necessary to further reduce P_BAD leakage and prevent lysis (see SI Text and Fig. S5 for details). (C) Images illustrating cell lysis were obtained from a movie file (Movie S1) that was recorded at 45 min postinduction. (D) Variable λS expression at constant, medium λR-R_Z expression. See Fig. S6 for full dataset. (E) Variable λR-R_Z expression at constant, medium λS expression.