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. 2017 Aug 3;548(7665):117-121.
doi: 10.1038/nature23271. Epub 2017 Jul 26.

Complex cellular logic computation using ribocomputing devices

Alexander A Green  1   2 , Jongmin Kim  1   3 , Duo Ma  2 , Pamela A Silver  1   3 , James J Collins  1   4   5 , Peng Yin  1   3
Affiliations

Complex cellular logic computation using ribocomputing devices

Alexander A Green et al. Nature. .

Abstract

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability, oscillation, feedback, and logic capabilities. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our 'ribocomputing' systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings.

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Figures

Extended Data Figure 1
Extended Data Figure 1. Design, activation mechanism, and characterization of AND-computing toehold switches (ACTS)
a, Nucleotide-level schematics of the Type I and Type II ACTS systems (see Supplementary Information Section 1.6 for discussion). Green and orange bases specify the output GFP sequence and the common 21-nt linker sequence used, respectively. Black bases mark biologically conserved sequences, such as the RBS, start codon, and transcriptional terminator. White bases represent those that can adopt any sequence subject to secondary structure conditions in NUPACK. Programmed hybridization domains between different strands are specified by color. b, The proposed ACTS activation mechanism in which the trigger RNA partially unwinds the switch RNA stem. The remaining weak stem, with low GC content, can interact with the ribosome to initiate translation. c, ON/OFF GFP levels measured for the ACTS systems employed in this study. ON/OFF GFP levels were determined from the geometric mean fluorescence of cells measured via flow cytometry 3 hours after induction with 0.1 mM IPTG. Relative errors for the switch ON/OFF ratios were obtained by adding the relative errors of the switch ON and OFF fluorescence measurements in quadrature. Relative errors for ON and OFF states are from the SD of three biological replicates. Flow cytometry data were produced using the same procedure in subsequent Extended Data Figures.
Extended Data Figure 2
Extended Data Figure 2. Nucleotide-level schematics of ribocomputing devices
a, Secondary structure of the two-input OR gate used in Fig. 2a–d. b, Secondary structure of the six-input OR gate RNA used for circuits in Fig. 3d–f and Extended Data Figs. 7 and 8. c, Schematic of a two-input AND gate using a Type I ACTS system. A1 and A2 domains are 14-nt halves of a 28-nt-long complete trigger RNA. d, Schematic of the A AND (NOT B) circuit design. The A AND (NOT B) system design features nearly perfectly complementary trigger (input A) and deactivating (input B) RNA strands used in Fig. 2i–l. For all panels, black bases mark biologically conserved sequences, such as the RBS and start codon. White bases represent those that can adopt any sequence subject to secondary structure conditions in NUPACK. Gray bases are those whose sequences were originally determined based on secondary structure considerations for the parental toehold switches and were left constant during design of RNA circuit elements. The remaining programmed hybridization domains between different strands are specified by color. Input RNA schematics are truncated just before the transcriptional terminator sequence.
Extended Data Figure 3
Extended Data Figure 3. Systematic study of AND gate circuit overlap domain lengths and comparison of two-input AND ribocomputing devices in different strains
a, An early two-input AND gate was constructed from a standard toehold switch by dividing the trigger evenly into two 15-nt domains A1 and A2. Overlap domains u and u* were designed to cause the two input RNAs to hybridize and form an active trigger. b, A domain u’ was used to vary the region complementary to u* and measure its effect on expression levels. ON/OFF GFP ratios (left axis) vary as a function of the u’ domain length. The onset of substantial GFP expression coincides with the melting temperature of u’-u* hybridization rising above 37°C (right axis). c-f, Comparison of two-input AND ribocomputing devices in RNase-deficient E. coli BL21 Star DE3 and non-RNase-deficient E. coli BL21 DE3. c-d, ON/OFF GFP on linear (c) and logarithmic (d) scales measured for the two-input AND gate from Figure 2e–h. e-f, ON/OFF GFP on linear (e) and logarithmic (f) scales measured for a second two-input AND gate with an identical design but different RNA sequences.
Extended Data Figure 4
Extended Data Figure 4. Three-, four-, and five-input AND gate systems
a, General schematic for a three-input AND gate with GFP output. b, Nucleotide-level schematic of the activated trigger complex for the three-input AND logic circuits. c, Flow cytometry measurements from the three-input AND gate with the truth table shown in panel d. d-g, The truth tables of four different three-input AND gates. h, General schematic for a four-input AND gate with GFP output. i, Nucleotide-level schematic of the activated trigger complex for the four-input AND logic circuits. j, The truth table of an additional four-input AND gate. k, General schematic for the five-input AND gate with GFP output. l, Nucleotide-level schematic of the activated trigger complex for the five-input AND logic circuit. m, Linear-scale truth table for the five-input AND gate that displays a statistically significant difference between logical TRUE and logical FALSE conditions. n, Logarithmic truth table for the five-input AND gate. Insets of d, e, f, g, j: Logarithmic-scale plots of ON/OFF GFP for the devices.
Extended Data Figure 5
Extended Data Figure 5. Systematic study of gate RNA performance as a function of secondary structure
a, Nucleotide-level schematics of three four-input OR gate versions featuring small changes in secondary structure and sequence. Version 1 adopts the original secondary structures of the ACTS switch RNAs. Version 2 differs from the first gate RNA at the six positions marked in red, which weakened the hairpin secondary structure. Version 3 has an additional mismatch in the hairpin lower stem marked in blue. All other bases remained the same across the three gate RNAs. b-c, GFP fluorescence levels measured for the gate RNA versions for a panel of eight RNA triggers shown in linear (b) and logarithmic (c) scales. d, ON/OFF GFP ratios calculated for the three gate RNAs. Gate RNA version 2 provides the best combination of low leakage and high ON state GFP expression. Inset: Logarithmic-scale plot of circuit ON/OFF levels.
Extended Data Figure 6
Extended Data Figure 6. Four- and five-input OR gate systems
a, Linear- and logarithmic-scale plot of ON/OFF levels of a four-input OR gate constructed from ACTS hairpin modules. b, Linear- and logarithmic-scale plot of ON/OFF levels of a five-input OR gate constructed from ACTS devices. Both OR logic gates were measured 3 hours after induction of T7 RNA polymerase expression.
Extended Data Figure 7
Extended Data Figure 7. Gate RNA regulation of mCherry and cerulean outputs with five-input OR gates and an 11-input dual OR gate circuit
a-b, ON/OFF mCherry ratio for a five-input ACTS-based OR gate on a linear (a) and a logarithmic (b) scale. c-d, ON/OFF cerulean ratio for a five-input ACTS-based OR gate on a linear (c) and a logarithmic (d) scale. e, A six-input OR gate was used to regulate GFP and a five-input ACTS-based OR gate was used to regulate mCherry. f-g, ON/OFF ratios of the gate RNAs on linear (f) and logarithmic (g) scales. Combinations of one or two input or decoy RNAs were expressed as specified by the filled-in green (GFP inputs), red (mCherry inputs), and black (decoys) circles below each panel. All circuit responses were measured via flow cytometry 4 hours after IPTG induction.
Extended Data Figure 8
Extended Data Figure 8. Comparison of six-input OR gate ribocomputing devices measured in RNase-deficient E. coli (BL21 Star DE3) and non-RNase-deficient E. coli (BL21 DE3, MG1655Pro)
a-b, ON/OFF GFP ratios measured for the device using T7 RNA polymerase in BL21 Star DE3 and BL21 DE3 cells on linear (a) and logarithmic (b) scales. Gate and input RNAs were expressed using the T7 RNA polymerase and measured 4 hours after induction with IPTG. c-d, ON/OFF GFP ratios obtained from the OR gate using E. coli RNA polymerase in MG1655Pro cells on linear (c) and logarithmic (d) scales. Gate and input RNAs were expressed using the E. coli RNA polymerase and measured 4 hours after induction of the gate RNA with IPTG. Input and decoy RNAs were expressed using a constitutive PN25 promoter.
Extended Data Figure 9
Extended Data Figure 9. Evaluation of an eight-input DNF circuit
a, The eight-input DNF circuit features four two-input ANDs coupled to the four-input OR gate RNA tested in Extended Data Fig. 6a. b, GFP fluorescence histograms obtained from flow cytometry measurements of the circuit under 16 different combinations of input RNAs. c-d, ON/OFF GFP levels obtained from flow cytometry on a linear (c) and a logarithmic (d) scale.
Extended Data Figure 10
Extended Data Figure 10. Evaluation of a 10-input DNF circuit
a, The 10-input DNF circuit features five two-input ANDs coupled to the five-input OR gate RNA tested in Extended Data Fig. 6b. b, GFP fluorescence histograms obtained from flow cytometry measurements of the circuit under 20 different combinations of input RNAs. c-d, ON/OFF GFP levels obtained from flow cytometry on a linear (c) and a logarithmic (d) scale.
Figure 1
Figure 1. In vivo computation using synthetic ribocomputing devices
a, Ribocomputing devices employ RNAs as input signals and protein as the output signal. Signal processing is carried out by a gate RNA that co-localizes sensing and output modules. AND, OR, and NOT logic results from self-assembly of input and gate RNAs in the device. b, Schematic of the toehold switches that form the RNA sensing elements of the gate RNA. The ribosomal binding site (RBS) and start codon (AUG) of the switch RNA are exposed upon trigger RNA binding to activate translation. X and X* are complementary sequences. c, Schematic of toehold switches optimized for AND logic. These toehold switches retain a weak hairpin upon activation by the trigger RNA that still allows efficient translation by ribosome.
Figure 2
Figure 2. Two-input ribocomputing logic circuits
a, Two-input OR gate RNA composed of two switch RNA hairpins. Each switch module has an input RNA recognition site and its own RBS and start codon. Input RNA binding unwinds the corresponding switch stem to activate translation. b, Flow cytometry measurements of the two-input OR gate circuit for cognate and non-cognate inputs. c-d, ON/OFF GFP for the two-input OR gate on linear (c) and logarithmic (d) scales. e, A two-input AND gate constructed from two input RNAs that bind to yield a complete trigger RNA. f, Flow cytometry measurements of the two-input AND circuit under four combinations of input RNAs. g-h, The truth table for the AND computation on linear (g) and logarithmic (h) scales. i, Operating mechanism of the A AND (NOT B) circuit in which a deactivating RNA (input B) employs direct hybridization or strand displacement to abolish trigger RNA (input A) activity. j, Flow cytometry histograms of the A AND (NOT B) circuit with chemical inducers aTc and IPTG. k-l, ON/OFF GFP levels for the A AND (NOT B) circuit on linear (k) and logarithmic (l) scales. ON/OFF GFP was determined from the geometric mean fluorescence of cells measured via flow cytometry 4 hours after induction of RNA expression. Relative errors for ON/OFF GFP were obtained by adding the relative errors of ON and OFF fluorescence in quadrature. Errors for ON and OFF states are the SD of three biological replicates. OFF states were taken from the null-input case with no cognate RNAs expressed.
Figure 3
Figure 3. Multi-input ribocomputing AND and OR circuits
a, Schematic of the input RNA interaction used in a four-input AND gate. b, Flow cytometry measurements of the four-input AND gate. c, ON/OFF GFP for the AND gate truth table showing nine-fold signal increase upon expression of all four required inputs. (Inset) ON/OFF GFP on a logarithmic scale. d, Schematic of the six-input OR gate RNA with six sensor modules. e, Flow cytometry measurements of the six-input OR gate. f, ON/OFF GFP for the OR gate show that cognate inputs provide at least a 120-fold increase in GFP expression. (Inset) ON/OFF GFP on a logarithmic scale. ON/OFF GFP was determined from the geometric mean fluorescence of cells measured via flow cytometry 4 hours after induction of RNA expression. Relative errors for ON/OFF GFP were obtained by adding the relative errors of the ON and OFF fluorescence in quadrature. Errors for ON and OFF states are the SD of three biological replicates.
Figure 4
Figure 4. 12-input disjunctive normal form (DNF) ribocomputing circuit
a, Schematic of the 12-input DNF expression evaluated in E. coli. b, Flow cytometry measurements show low GFP output for 23 logical FALSE states and at least 10-fold increases in GFP for the five logical TRUE states. c, ON/OFF GFP from the DNF circuit under 28 different input RNA combinations. (Inset) ON/OFF GFP on a logarithmic scale. ON/OFF GFP was determined from the geometric mean fluorescence of cells measured via flow cytometry 6 hours after induction of RNA expression. Relative errors for ON/OFF GFP were obtained by adding the relative errors of the ON and OFF fluorescence in quadrature. Errors for ON and OFF states are the SD of three biological replicates. OFF states were taken from the null-input case of the A1 AND A2 AND NOT A1* clause.
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