Rationally designed logic integration of regulatory signals in mammalian cells

Abstract

Molecular-level information processing is essential for 'smart' in vivo nanosystems. Natural molecular computing, such as the regulation of messenger RNA (mRNA) synthesis by special proteins called transcription factors, has inspired engineered systems that can control the levels of mRNA with certain combinations of transcription factors. Here, we show an alternative approach to achieving general-purpose control of mRNA and protein levels by logic integration of transcription factor input signals in mammalian cells. The transcription factors regulate synthetic genes coding for small regulatory RNAs (called microRNAs), which, in turn, control the mRNA of interest (the output) via an RNA interference pathway. The simplicity of these modular interactions makes it possible, in theory, to implement any arbitrary logic relation between the transcription factors and the output. We construct, test and optimize increasingly complex circuits with up to three transcription factor inputs, establishing a platform for in vivo molecular computing.

Figure 1. Design elements of synthetic circuits

a, An example of a logic circuit with multiple transcription factor inputs and a fluorescent ZsYellow protein output. Three different system modules are shown. Transcription factor inputs A through F, promoters P_A through P_F, miRs miR-a to miR-f and output-encoding mRNA transcripts containing miR targets T_a to T_f are indicated. Pointed arrows denote activation, and blunt arrows represent repression. Elements in gray denote potential directions for circuit scale-up. b, Detailed structure and shorthand notation for the constructs used in this report. ‘Ex’ denotes exons, and other structural elements are as indicated.

Figure 2. Experimental implementation of two-input regulatory programs

Plasmid amounts are given in Supplementary Table 1. Red and green bars (mean ± s.d.) correspond to the predicted Off and On states, respectively. a–c, Regulatory program ‘ZsYellow = NOT(rtTA) AND LacI-Krab’. a, From left to right: circuit schematics; representative microscopy snapshots and quantitative performance of the circuit with CMV-driven output. b, Surface plot of control-corrected ZsYellow output as a function of miR-FF3 and FF4 levels judged by the levels of DsRed and AmCyan, respectively. c, Images and quantitative analysis of the circuit with EF1a-driven output. d, Regulatory program ‘ZsYellow = NOT(rtTA) AND NOT (Rheo)’ implemented with EF1a-driven output. Left: circuit schematics; right: anticipated circuit behavior, microscopy images and quantitative analysis.

Figure 3. Experimental implementation of a three-input regulatory program

Plasmid amounts are given in Supplementary Table 1. From left to right: circuit schematics; table of TF input states, the expected outputs and fluorescent output levels of ZsYellow; microscopy images of ZsYellow output; and quantitative output intensity as obtained by FACS analysis. Red and green bars (mean ± s.d.) indicate anticipated Off and On states, respectively.