Rationally designed logic integration of regulatory signals in mammalian cells

Journal name:
Nature Nanotechnology
Volume:
5,
Pages:
666–670
Year published:
DOI:
doi:10.1038/nnano.2010.135
Received
Accepted
Published online

Abstract

Molecular-level information processing1, 2 is essential for ‘smart’ in vivo nanosystems. Natural molecular computing, such as the regulation of messenger RNA (mRNA) synthesis by special proteins called transcription factors3, 4, has inspired engineered systems5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 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 output16. We construct, test and optimize increasingly complex circuits with up to three transcription factor inputs, establishing a platform for in vivo molecular computing.

At a glance

Figures

  1. Design elements of the synthetic circuits.
    Figure 1: Design elements of the synthetic circuits.

    a, Example of a logic circuit with multiple TF inputs and a fluorescent ZsYellow protein output. Three different system modules are shown. TF inputs A–F, promoters PA–PF, miRs miR-a to miR-f and output-encoding mRNA transcripts containing miR targets Ta–Tf are indicated. Arrows with arrowheads denote activation, and blunt arrows represent repression. Elements in grey denote potential opportunities 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.

  2. Experimental implementation of two-input regulatory programs.
    Figure 2: Experimental implementation of two-input regulatory programs.

    Plasmid amounts are given in Supplementary Table S1. Red and green bars (mean ± s.d.) correspond to the predicted OFF and ON states, respectively. ac, Regulatory program ‘ZsYellow = NOT(rtTA) AND LacI-Krab’. From left to right in a are circuit schematics, representative microscopy snapshots, quantitative performance of the circuit with a CMV-driven output. Surface plot (b) of control-corrected ZsYellow output as a function of miR-FF3 and FF4 levels judged by levels of DsRed and AmCyan, respectively. Images and quantitative analysis of the circuit with an EF1a-driven output (c). d, Regulatory program ‘ZsYellow = NOT(rtTA) AND NOT(Rheo)’ implemented with an EF1a-driven output. From left to right are circuit schematics and anticipated circuit behaviour, microscopy images and quantitative analysis.

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

    Plasmid amounts are given in Supplementary Table S1. From left to right are shown circuit schematics, a 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.

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Affiliations

  1. FAS Centre for Systems Biology, Harvard University, 52 Oxford Street, Cambridge Massachussetts 02138 USA

    • Madeleine Leisner,
    • Leonidas Bleris,
    • Jason Lohmueller,
    • Zhen Xie &
    • Yaakov Benenson
  2. Department of Biological and Biomedical Sciences, Harvard Medical School, 25 Shattuck Street, Boston, Massachussetts 02115 USA

    • Jason Lohmueller
  3. Present address: Electrical Engineering Department, University of Texas at Dallas, NSERL 4.708, 800 West Campbell Road, Richardson, Texas, 75080 USA (L.B.); Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, CH-4048 Switzerland (Y.B.)

    • Leonidas Bleris &
    • Yaakov Benenson

Contributions

Y.B. designed the research and supervised the project. M.L., L.B., J.L., Z.X. and Y.B. performed the research. M.L., Y.B., J.L. and L.B. wrote the manuscript.

Competing financial interests

The authors have an application pending for a US patent.

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