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      Binary addition in a living cell based on riboregulation

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      PLoS Genetics
      Public Library of Science

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          Abstract

          Synthetic biology aims at (re-)programming living cells like computers to perform new functions for a variety of applications. Initial work rested on transcription factors, but regulatory RNAs have recently gained much attention due to their high programmability. However, functional circuits mainly implemented with regulatory RNAs are quite limited. Here, we report the engineering of a fundamental arithmetic logic unit based on de novo riboregulation to sum two bits of information encoded in molecular concentrations. Our designer circuit robustly performs the intended computation in a living cell encoding the result as fluorescence amplitudes. The whole system exploits post-transcriptional control to switch on tightly silenced genes with small RNAs, together with allosteric transcription factors to sense the molecular signals. This important result demonstrates that regulatory RNAs can be key players in synthetic biology, and it paves the way for engineering more complex RNA-based biocomputers using this designer circuit as a building block.

          Author summary

          In this work, we have engineered a distinctive genetic system, based on regulatory RNAs that control the process of protein translation, that is able to perform arithmetic logic computations (additions) in a single bacterial cell. The system expresses as output fluorescent proteins according to the molecular concentrations of the inputs (binary code). In the future, this circuitry might be instrumental to develop smart bacterial cells that can make appropriate decisions after certain computation for biomedical applications.

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          Most cited references26

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          FACS-optimized mutants of the green fluorescent protein (GFP).

          We have constructed a library in Escherichia coli of mutant gfp genes (encoding green fluorescent protein, GFP) expressed from a tightly regulated inducible promoter. We introduced random amino acid (aa) substitutions in the twenty aa flanking the chromophore Ser-Tyr-Gly sequence at aa 65-67. We then used fluorescence-activated cell sorting (FACS) to select variants of GFP that fluoresce between 20-and 35-fold more intensely than wild type (wt), when excited at 488 nm. Sequence analysis reveals three classes of aa substitutions in GFP. All three classes of mutant proteins have highly shifted excitation maxima. In addition, when produced in E. coli, the folding of the mutant proteins is more efficient than folding of wt GFP. These two properties contribute to a greatly increased (100-fold) fluorescence intensity, making the mutants useful for a number of applications.
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            Gene regulation at the single-cell level.

            The quantitative relation between transcription factor concentrations and the rate of protein production from downstream genes is central to the function of genetic networks. Here we show that this relation, which we call the gene regulation function (GRF), fluctuates dynamically in individual living cells, thereby limiting the accuracy with which transcriptional genetic circuits can transfer signals. Using fluorescent reporter genes and fusion proteins, we characterized the bacteriophage lambda promoter P(R) in Escherichia coli. A novel technique based on binomial errors in protein partitioning enabled calibration of in vivo biochemical parameters in molecular units. We found that protein production rates fluctuate over a time scale of about one cell cycle, while intrinsic noise decays rapidly. Thus, biochemical parameters, noise, and slowly varying cellular states together determine the effective single-cell GRF. These results can form a basis for quantitative modeling of natural gene circuits and for design of synthetic ones.
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              Synthetic gene networks that count.

              Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here, we report two complementary synthetic genetic counters in Escherichia coli that can count up to three induction events: the first, a riboregulated transcriptional cascade, and the second, a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.
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                Author and article information

                Contributors
                Role: InvestigationRole: MethodologyRole: Validation
                Role: InvestigationRole: Methodology
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                19 July 2018
                July 2018
                : 14
                : 7
                : e1007548
                Affiliations
                [001]Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas–Universidad Politécnica de Valencia, Valencia, Spain
                Universidad de Sevilla, SPAIN
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Instituto de Biología Integrativa de Sistemas, Consejo Superior de Investigaciones Científicas—Universidad de Valencia, Paterna, Spain

                Author information
                http://orcid.org/0000-0002-1871-9617
                Article
                PGENETICS-D-18-00497
                10.1371/journal.pgen.1007548
                6067762
                30024870
                9770a29f-689a-4750-8f1d-3ca1efc8aaff
                © 2018 Rosado et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 March 2018
                : 9 July 2018
                Page count
                Figures: 5, Tables: 0, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100007406, Fundación BBVA;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: BFU2015-66894-P
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: BIO2014-54269-R
                Work supported by the Banco Bilbao Vizcaya Argentaria Foundation (grant for investigators and cultural creators) and the Spanish Ministry of Economy and Competitiveness (BFU2015-66894-P and BIO2014-54269-R, cofounded by European Regional Development Funds). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Electronics Engineering
                Logic Circuits
                Biology and Life Sciences
                Biotechnology
                Genetic Engineering
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Transcriptional Control
                Biology and Life Sciences
                Synthetic Biology
                Synthetic Genetic Systems
                Synthetic Genetic Networks
                Engineering and Technology
                Synthetic Biology
                Synthetic Genetic Systems
                Synthetic Genetic Networks
                Biology and Life Sciences
                Genetics
                Gene Expression
                Protein Translation
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Messenger RNA
                Engineering and Technology
                Signal Processing
                Engineering and Technology
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-07-31
                All relevant data are within the paper and its Supporting Information files.

                Genetics
                Genetics

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