CTRSD GATE AND PERFORMING CO-TRANSCRIPTIONAL ENCODING

A method for the production and use of scalable co-transcriptional RNA strand displacement (ctRSD) circuits using RNA toehold exchange gates is described. The ctRSD circuits described address the limitations of existing DNA-based strand displacement circuits by isothermally producing circuit components via transcription.

SEQUENCE LISTING

This application contains a Sequence Listing which has been filed electronically in compliance with ST.26 format and is hereby incorporated by reference in its entirety. The Sequence Listing, created on Jun. 26, 2024 is named 21-053US1_Sequence_Listing.xml and is 58 kilobytes in size.

For the simulations, kpof 0.019 s−1was used, and the krsdreaction rate constants were calibrated to the experimental kinetics. The simulation results in (D and E) used the krsdvalues tabulated in (B). Schematics with sequences of the input and gate variants are presented inFIG.16.

FIG.41shows, according to some embodiments, total template concentration and T7 RNAP concentration influence transcriptional load. (A) Schematic of the loading and reporting reactions. The Io template was added at different concentrations to change the total transcriptional load, and the O2r strand was constitutively produced from the 1_2r HDV cau template. The 1_2r HDV cau template was chosen to match the total number of bases in the 1_2r gate template. (B) Experimental (solid lines) and simulated (dashed lines) reporter signal during transcription of the 1_2r HDV cau RNA with different Io template concentrations and T7 RNAP concentrations. The kpvalues used in the simulations are tabulated for the different Io template concentrations and T7 RNAP concentrations. Increasing the Io template concentration (transcriptional load) decreases the effective kpvalue and increasing the T7 RNAP concentration increases the effective kpvalue. 25 nmol/L of the 1_2r HDV cau template and 500 nmol/L of the DNA reporter were used in each experiment.

FIG.42shows, according to some embodiments, representative examples showing calibration of the transcription rate constant for experiments with different transcriptional loads and/or T7 RNAP concentrations. The 1_2r HDV cau template, which constitutively produces the O2r strand, was used as a reference sample for each experiment. The Io template was added to bring the total template concentration in the reference sample to the total concentration used in the experimental samples. The reference sample was then used to calibrate the first order transcription rate constant, kp, for simulation of the experimental samples.

FIG.43shows, according to some embodiments, variation across independently prepared technical replicates for ctRSD circuit reactions using the 1_2r gate (A) or the 5&4_1+3&1_2r gates (B). Left plots show data for three independently prepared replicates of the same ctRSD circuit reaction. The replicates were prepared from the same stock solutions on the same day. The right plots show the mean of the three replicates; error bars represent one standard deviation. The standard deviation was <1.5% from the mean value at each time point for (A) and <5% from the mean value at each time point for (B). In (A), reactions included 25 nmol/L of the 1_2r gate template, 12.5 nmol/L of the 11 template, and Io template to bring the total template to 50 nmol/L in both reactions. In (B), reactions included 25 nmol/L of the 5&4_1, 25 nmol/L of the 3&1_2r gate, and 25 nmol/L of the input templates. For the samples containing I3 only, 50 nmol/L of the Io template was added to bring the total template concentration to 75 nmol/L. In all experiments, 500 nmol/L of the DNA reporter and 2 U/μL of T7 RNAP was used.

FIG.44shows, according to some embodiments, variation across experiments performed on different days for the same set of conditions. Left plots show data for three independent replicates of the same ctRSD circuit reaction. Reactions included 25 nmol/L of the 1_2r gate template, 500 nmol/L of the DNA reporter, 1 U/μL of T7 RNAP, and 50 nmol/L of the 11 or Io templates. The three individual replicates were prepared independently and tested on separate days, with the second and third replicates conducted 5 and 19 days after the first replicate, respectively. Dark colored lines represent the oldest replicate; data also presented inFIG.30B, middle panel. Medium colored lines represent the second oldest replicate;. Light colored lines represent the newest replicate; data also presented inFIG.28B. The right plot shows the mean of the three replicates; error bars represent one standard deviation. The standard deviation is <3% from the mean value at each time point.

DETAILED DESCRIPTION

A detailed description of one or more embodiments is presented herein and the accompanying parts of the specification by way of exemplification and not limitation.

Engineered molecular circuits process information in biological systems and can address emerging human health and biomanufacturing needs. A scalable co-transcriptional RNA strand displacement (ctRSD) circuit is rationally programmed via base pairing interactions. Conventional DNA-based strand displacement circuits can be computationally powerful molecular circuits but are limited in biological systems due to difficulty in genetically encoding components. The ctRSD overcomes this limitation of such conventional technology by isothermally producing circuit components via transcription. The programmability of ctRSD in vitro occurs by designing logic and amplification elements and multi-layer signaling cascades. Further, kinetics of ctRSD are predicted by a model of coupled transcription and strand displacement. The ctRSD provides rational design of molecular circuits that operate in biological systems, including living cells.

It is contemplated that co-transcriptional RNA strand displacement (ctRSD) circuits are scalable and programmable. In ctRSD, circuit components isothermally self-assemble and execute programmed computations in a single transcription reaction. This is achieved through an HDV self-cleaving ribozyme to isothermally prepare kinetically trapped RNA strand displacement intermediates via transcription, and a set of nucleic acid sequence design rules that allow mutiple RNA strand displacement sequences with similar performance to be readily created. The ctRSD overcomes limitations of conventional DNA-based strand displacement such as degradation in biological environments and single-use operation. Moreover, ctRSD provides nucleic acid strand displacement circuits that are genetically encoded into living cells for cellular engineering applications.

Conventional DNA-based strand displacement circuits are a molecular computing paradigm. However, conventional DNA circuits are susceptible to degradation in biological systems. Further, conventional DNA-based circuits are only single-use, wherein they can only execute one computation unless their components are replenished via external perturbation. Finally, there is currently no mechanism to produce these state-of-the-art circuits in the same sample where they operate.

Advantageously and unexpectedly, co-transcriptional RNA strand displacement circuits provide powerful computing features of DNA-based circuits and can be genetically encoded to overcome limitations of conventional DNA-based circuits in biological systems. Co-transcriptional RNA strand displacement circuits can be encoded into living cells for the same programmability and functionality of DNA-based circuits for cellular engineering applications. Co-transcriptional RNA strand displacement circuits provide real-time cell state monitoring through recognition of differential RNA expression patterns. Co-transcriptional RNA strand displacement circuits can provide real-time monitoring of cell-state to improve biomanufacturing processes or for real-time detection of cellular disease states. Nucleic acid pattern recognition has occurred with DNA-based circuits in vitro but has never been demonstrated in living cells, something which co-transcriptional RNA strand displacement circuits can provide for engineering cellular sensing and response.

Co-transcriptional RNA strand displacement circuits can be applied in in vitro environments. Co-transcriptional RNA strand displacement circuits can be used in an in vitro transcription-based biosensor for detecting water contaminants, wherien such biosensors provide more sophisticated computations to be executed than conventional technology.

Although DNA-based strand displacement components can expand computational capabilities, such biosensors are often freeze-dried for long-term storage and transport, and a limitation of using DNA-based components in these sensors is that the DNA strand displacement components result in much shorter shelf-lives when freeze dried compared to longer transcription templates or plasmids. For example, the DNA-based components showed significant decrease in performance only one week after freeze drying. In contrast, long linear DNA templates have been shown to be stable for over a month and DNA plasmids containing transcription templates have been shown to be stable for 2 years after freeze drying. Thus, encoding co-transcriptional RNA strand displacement components in long linear templates or in plasmids offers the same functionality as existing DNA circuits but with improved stability in freeze dried samples.

Certain in vitro sensors for detecting viral infections and other diseases operate by detecting specific RNA sequences that then trigger the production of a fluorescent output. Co-transcriptional RNA strand displacement circuits can be an upstream information processing layer in such diagnostics.

The use of co-transcriptional RNA strand displacement circuits in these diagnostics could enable more complex computations, such as mathematical operations or neural network pattern recognition. These capabilities could enable more robust and reliable diagnostics by integrating more input information before making a diagnosis.

Co-transcriptional RNA strand displacement provides sophisticated DNA-based diagnostics to be robustly operated in biological systems. Certain conventional DNA-based molecular neural networks recognize differential gene expression levels associated with cancers, but that circuit has been operated in a pure in vitro setting. Using co-transcriptional RNA strand displacement provides this sophisticated diagnostic circuit to robustly operate in blood or fecal samples where DNA-based circuits would be limited by degradation.

The ctRSD provides a predictive engineering of biology and programmable cellular engineering. Beneficially, modular RNA gates are isothermally produced in a kinetically trapped form in the same reaction vessel. This has not been achieved in conventional DNA-based systems.

In an embodiment, a ctRSD gate (200) for performing co-transcriptional encoding comprises: an output strand (201) comprising: an input branch migration domain (206); an output branch migration domain (204) sequentially connected to the input branch migration domain (206); and an output toehold domain (205) sequentially interposed between the input branch migration domain (206) and the output branch migration domain (204); and a gate prime strand (202) electrostatically associated with the output strand (201) and comprising; a self-cleaving ribozyme (209); an output toehold sequester domain (213) sequentially connected to the self-cleaving ribozyme (209); a substrate domain (211) sequentially interposed between the self-cleaving ribozyme (209) and the output toehold sequester domain (213), such that a portion of the substrate domain (211) is sequentially complementary to a portion of the input branch migration domain (206) that results in the gate prime strand (202) being electrostatically associated with the output strand (201); and an input toehold domain (210) sequentially interposed between the self-cleaving ribozyme (209) and the substrate domain (211),wherein the output strand (201) and the gate prime strand (202) indepedently consist essentially of RNA.

In an embodiment, the output strand (201) further comprises: a hairpin-forming sequence (203) sequentially connected to the output branch migration domain (204) such that output branch migration domain (204) is sequentially interposed between the hairpin-forming sequence (203) and the output toehold domain (205).

In an embodiment, the output strand (201) further comprises: an output wobble domain (207) sequentially connected to the input branch migration domain (206) such that the output wobble domain (207) is sequentially interposed between a first portion of the input branch migration domain (206) and a second portion of the input branch migration domain (206).

In an embodiment, the output strand (201) further comprises: a linker sequence (208) sequentially connected to the input branch migration domain (206) such that input branch migration domain (206) is sequentially interposed between the linker sequence (208) and the linker sequence (208).

In an embodiment, the gate prime strand (202) further comprises: a transcription termination sequence (214) sequentially connected to the output toehold sequester domain (213) such that output toehold sequester domain (213) is sequentially interposed between the transcription termination sequence (214) and the substrate domain (211).

In an embodiment, the gate prime strand (202) further comprises: a gate prime wobble domain (212) sequentially connected to the substrate domain (211) such that the gate prime wobble domain (212) is sequentially interposed between a first portion of the substrate domain (211) and a second portion of the substrate domain (211).

In an embodiment, the output strand (201) produces a strand displacement product (216) in response to contact with an input template strand (215).

In an embodiment, the output strand (201) further comprises a second input branch migration domain (206).2sequentially connected to the input branch migration domain (206).

In an embodiment, the gate prime strand (202) further comprises a second substrate domain (211).2sequentially connected to the substrate domain (211).

In an embodiment, a process for producing a strand displacement product (216) comprises: providing a ctRSD gate (200); contacting the ctRSD gate (200) with a input template strand (215); and producing the strand displacement product (216) from the ctRSD gate (200) in response to contacting the ctRSD gate (200) with the input template strand (215).

In an embodiment of the process for producing the strand displacement product (216), the ctRSD gate (200) comprises: an output strand (201) comprising: an input branch migration domain (206); an output branch migration domain (204) sequentially connected to the input branch migration domain (206); and an output toehold domain (205) sequentially interposed between the input branch migration domain (206) and the output branch migration domain (204); and a gate prime strand (202) electrostatically associated with the output strand (201) and comprising; a self-cleaving ribozyme (209); an output toehold sequester domain (213) sequentially connected to the self-cleaving ribozyme (209); a substrate domain (211) sequentially interposed between the self-cleaving ribozyme (209) and the output toehold sequester domain (213), such that a portion of the substrate domain (211) is sequentially complementary to a portion of the input branch migration domain (206) that results in the gate prime strand (202) being electrostatically associated with the output strand (201); and an input toehold domain (210) sequentially interposed between the self-cleaving ribozyme (209) and the substrate domain (211), wherein the output strand (201) and the gate prime strand (202) indepedently consist essentially of RNA.

In an embodiment of the process for producing the strand displacement product (216), the output strand (201) further comprises: a hairpin-forming sequence (203) sequentially connected to the output branch migration domain (204) such that output branch migration domain (204) is sequentially interposed between the hairpin-forming sequence (203) and the output toehold domain (205).

In an embodiment of the process for producing the strand displacement product (216), the output strand (201) further comprises: an output wobble domain (207) sequentially connected to the input branch migration domain (206) such that the output wobble domain (207) is sequentially interposed between a first portion of the input branch migration domain (206) and a second portion of the input branch migration domain (206).

In an embodiment of the process for producing the strand displacement product (216), the output strand (201) further comprises: a linker sequence (208) sequentially connected to the input branch migration domain (206) such that input branch migration domain (206) is sequentially interposed between the linker sequence (208) and the linker sequence (208).

In an embodiment of the process for producing the strand displacement product (216), the gate prime strand (202) further comprises: a transcription termination sequence (214) sequentially connected to the output toehold sequester domain (213) such that output toehold sequester domain (213) is sequentially interposed between the transcription termination sequence (214) and the substrate domain (211).

In an embodiment of the process for producing the strand displacement product (216), the gate prime strand (202) further comprises: a gate prime wobble domain (212) sequentially connected to the substrate domain (211) such that the gate prime wobble domain (212) is sequentially interposed between a first portion of the substrate domain (211) and a second portion of the substrate domain (211).

The articles and processes herein are illustrated further by the following Example, which is non-limiting.

Example

Engineered molecular circuits that process information in biological systems could address emerging human health and biomanufacturing needs. However, such circuits can be difficult to rationally design and scale. DNA-based strand displacement reactions have demonstrated the largest and most computationally powerful molecular circuits to date but are limited in biological systems due to the difficulty in genetically encoding components. Here, this Example describes scalable co-transcriptionally encoded RNA strand displacement (ctRSD) circuits that are rationally programmed via base pairing interactions. ctRSD circuits address the limitations of DNA-based strand displacement circuits by isothermally producing circuit components via transcription. We demonstrate circuit programmability in vitro by implementing logic and amplification elements, and multi-layer cascades. Further, we show circuit kinetics are accurately predicted by a simple model of coupled transcription and strand displacement, enabling model-driven design. It is contemplated that ctRSD circuits provide rational design of molecular circuits that operate in biological systems, including living cells.

This Example describes self-assembling RNA circuits for synthetic biology that execute programmable logic, amplification, and cascades.

A goal of synthetic biology is developing programmable molecular circuits that can be rationally engineered to process information in biological systems. Such circuits have the potential to address emerging challenges in human health and disease, agriculture, and biomanufacturing. To meet these diverse needs, molecular circuits must be scalable, modular, and rationally programmable to execute operations like logic, signal amplification, and multi-layer cascades. Further, circuits capable of a wide range of computations beyond Boolean logic, such as molecular pattern recognition, could greatly expand existing capabilities. A key challenge to developing such circuits is identifying molecular components that not only meet the above criteria, but also behave predictably to enable model-driven design.

The predictable and programmable Watson-Crick base pairing interactions of nucleic acids has led to their adoption as versatile components for molecular circuit programming. In particular, in vitro circuits based on toehold-mediated strand displacement (TMSD) reactions have demonstrated sophisticated digital computations and mathematical operations, molecular pattern recognition, signal cascades and amplifiers, and complex dynamics. In TMSD reactions, a single-stranded input binds to a double-stranded nucleic acid gate via a single-stranded toehold domain and displaces an output strand with a new exposed toehold that can facilitate further TMSD reactions (FIG.6A). Interactions between inputs and gates are programmed through sequence complementarity, and the combinatorial nucleic acid sequence space allows TMSD reaction networks to be scaled up to >100 components. Additionally, reaction kinetics can be tuned over six orders of magnitude by simply changing the length of the toehold. These properties have enabled predictive models of TMSD circuit behavior that allow circuit design abstraction.

Because TMSD circuits are composed of nucleic acids, they have great potential for integration with biological systems. However, these circuits have primarily been implemented in vitro using DNA components that are not easily genetically encoded. This restricts their applications in synthetic biology, particularly in vivo. A challenge to operating TMSD circuits in biological systems is developing a method to isothermally prepare all circuit components in a single reaction. Typically, TMSD components are thermally annealed separately to prevent spurious reactions between gates and then mixed to make a circuit. Thus, these circuits currently cannot be continuously produced in the same place they are operated. Although TMSD circuits can be prepared and then added to biological samples or transfected into cells at fixed concentrations, these implementations are only single use and circuit lifetime is limited by component degradation. A method to continuously produce TMSD circuits in situ can greatly expand their capabilities. Genetically encoded RNA-based circuits that utilize strand displacement have been developed and other transcription-based circuits have achieved some of the capabilities of TMSD circuits. However, these systems have yet to demonstrate the predictive design and scale up seen in state-of-the-art DNA-based circuits.

This Examples describes scalable and programmable co-transcriptionally encoded RNA strand displacement (ctRSD) circuits. In ctRSD circuits, components isothermally self-assemble during transcription and execute programmed computations in the same reaction. We validate ctRSD circuit performance in vitro by building circuits that execute logic, signal amplification, and multi-layer cascades. We demonstrate the scalability and modularity of the ctRSD by successfully implementing 13 ctRSD gates in 8 different circuit topologies. We find ctRSD kinetics are well predicted by a simple model of coupled transcription and strand displacement that assumes uniform kinetic behavior across gates, facilitating predictive circuit engineering. Further, ctRSD circuits are designed so that state-of-the-art DNA-based circuits capable of neural network computations and pattern recognition could be directly adopted. ctRSD should enable the power of TMSD circuits to be realized in biological systems for smart diagnostics or sensors. Ultimately, ctRSD circuits could be genetically encoded and continuously operated inside living cells.

Design of Co-Transcriptionally Encoded RNA Strand Displacement (ctRSD) Circuit Components

To develop ctRSD circuits, a system includes modular and programmable strand displacement circuit components that can be isothermally produced via transcription. In TMSD circuits, modularity is achieved by designing toehold exchange gates that allow any input sequence to be converted into any output sequence through a gate. For example, inFIG.6A, the input domain is composed of the a′-toehold and the 1:1′-domain duplex, both of which are complementary to the input, 11. The output domain is composed of the sequestered b-toehold and the 2-domain overhang, neither of which share complementarity with 11. Thus, input and output domain sequences are completely independent. We adopted an analogous modular gate design for ctRSD (FIG.6B). In these toehold exchange gates, the b-toehold of the output is sequestered in a duplex, kinetically precluding a reaction downstream unless the gate input is present. In DNA-based circuits, the toehold exchange gates are thermally annealed in separate test tubes to kinetically trap the outputs before circuit components are mixed. In ctRSD circuits, the RNA toehold exchange gates must isothermally assemble into kinetically trapped intermediates in a single pot during transcription. Simply transcribing the two gate strands separately and allowing them to hybridize to form a gate is not a viable option as the output strand of the gate can also react with downstream gates, introducing significant leak (FIG.17).

To transcriptionally encode kinetically trapped RNA toehold exchange gates, we inserted a self-cleaving RNA ribozyme motif between the two strands of the gate (FIG.6C). This motif allows us to encode RNA gates as single transcripts that fold into hairpins and then cleave to yield reactive gates (FIG.6D). Co-transcriptional folding is at least one order of magnitude faster than transcription, so the RNA gates should fold before they have time to react downstream. The self-cleaving ribozyme also ensures 1:1 stoichiometry between the gate strands, further reducing the potential for leaks. Inclusion of the ribozyme motif is critical, as the co-transcriptionally folded RNA gate exhibited >7-fold lower downstream leak rate than transcribing the two strands of the RNA gate separately (FIG.18). A 5′ hairpin motif and a 3′ hairpin terminator for T7 RNAP were also appended to gates and inputs (FIG.6C). The 5′ hairpin contains the T7 RNAP consensus initiation sequence to facilitate efficient and uniform transcription across components. Additionally, the 5′ hairpin ensures that short abortive transcripts produced during transcription initiation will not have sequence overlap with other circuit elements. The terminator hairpin reduces unwanted products associated with runoff transcription and enables incorporation into plasmids.

FIG.6Cshows the final selection for our ctRSD gate design, however, there are alternative implementations that would embody the same general features. To optimize gate performance, we analyzed four considerations when selecting the final design: 1) directionality of the single-stranded toehold, 2) domain sequence identity, 3) domain transcription order, and 4) self-cleaving ribozyme choice. We designed the ctRSD gates with 5′ toeholds because a 5′ toehold on an RNA gate allows the invading strand to participate in co-axial base stacking, increasing the binding strength compared to a 3′ toehold. We restricted the gate output sequences to cytosine (C), adenine (A), or uracil (U) bases. This sequence constraint reduces unwanted secondary structure or dimerization of single-stranded components. A G-U wobble pair was also introduced in the middle of the hybridized portion of the gate to reduce DNA template synthesis errors and to drive the forward ctRSD reaction with inputs that convert the G-U wobble to a G-C pair. The 5′ end of the output strand of the gate was selected as the starting point for transcription so that the first sequence produced would only possess C, A, and U bases, preventing co-transcriptional folding into undesired secondary structure. This transcription order ensures that the G, A, and U restricted sequence of the strand that hybridizes to the output strand (i.e. the gate′ strand) is transcribed after its complementary sequence to promote folding of the RNA gate stem over alternative structures with G-U wobbles. For the self-cleaving ribozyme, we selected a variant of the hepatitis delta virus (HDV) ribozyme This ribozyme has no upstream or downstream sequence constraints, has a very stable fold, and has been reported to cleave itself with a rate constant of nearly 1 s−1in vivo.

We used native and denaturing agarose gel electrophoresis to confirm the ctRSD gate fold and cleave as designed. On a native gel, the ctRSD gate (lane4,FIG.6E) was the same size as a control sample in which the two strands of the gate were transcribed from separate templates (lane5,FIG.6E), indicating full length gate production and folding. On a denaturing gel, the primary product from the ctRSD gate (lane4,FIG.6E) migrated faster than the uncleaved control transcript (lane2,FIG.6E) and was the same size as the gate′ strand alone (lane3,FIG.6E), indicating ribozyme cleavage. Importantly, the cleavage reaction is efficient and fast, we observed >90% cleavage in less than 15 min with an estimated cleavage rate constant of 0.25 min−1(FIG.22).

Experimental Characterization and Modeling of ctRSD Circuits

We next sought to characterize the reaction in which a ctRSD gate and its corresponding input are co-transcribed and react via strand displacement to release an output strand (FIG.6B). The 11:gate′ product of the strand displacement reaction is a higher molecular weight than the unreacted gate, so we first analyzed the reaction with native gel electrophoresis (FIG.7A). Increasing concentrations of 11 template increased the percentage of I1:gate′ product on the gel, with a 2:1 mixture of the I1 and 1_2 gate templates yielding 100% product (lane3to lane7,FIG.7B). Assuming the transcription rates of 11 and the 1_2 gate are approximately equal, the fraction of I1:gate′ produced with increasing 11 template concentration provides information about the thermodynamics of the reaction. We found the unreacted 1_2 gate percentages across input concentrations in experiments were within ˜12% of the thermodynamic predictions from NUPACK 3.2.2 (FIG.7B).

The results in lane3to lane7inFIG.7Bwere obtained from simultaneous transcription of I1 and the 1_2 gate, so the observed reaction between the two transcripts could result from I1 binding to the 1_2 gate prior to folding, rather than strand displacement. To rule out this potential reaction pathway, we transcribed the I1 and the 1_2 gate RNAs separately and then mixed them together after degradation of the DNA templates. Separate transcription followed by mixing yielded similar results to co-transcription (lane8to lane10,FIG.7BandFIG.24), wherein I1 and the 1_2 gate react via the designed strand displacement mechanism. We further confirmed experimentally that the correct a-toehold sequence and 1-domain branch migration sequence were required for strand displacement (FIG.25).

To explore ctRSD circuit kinetics, we co-transcribed the input and gate templates alongside a DNA reporter complex designed to release a fluorescent signal upon reaction with the gate output strand (FIG.7C). We opted to use a DNA-based reporter, rather than an RNA aptamer-based reporter, because the DNA reporter is easily calibrated to output concentration for modeling. To be stable at 37° C., we designed the reporter with a 16 base duplex. The 5′ end of the 1_2 gate was extended to include the full complement of the reporter (1_2r gate) to ensure an irreversible reaction. We fixed the 1_2r gate template concentration and varied the 11 template concentration. To ensure the same transcriptional load for comparison, a template that produced an unreactive input (Io) was added to maintain the same total input template concentration across samples (Methods). As expected from mass action kinetics, increasing concentrations of the 11 template resulted in faster reaction kinetics (FIG.7D). Further, separate transcription of the gate and input followed by mixing and addition of the DNA reporter exhibited kinetics consistent with strand displacement (FIG.26). A gate with a mutant ribozyme that cannot cleave resulted in >3-fold slower output production (FIG.28). Transcription of the 1_2r gate with only Io resulted in ˜20% of the maximum DNA reporter signal, indicating a slow leak reaction (FIG.7D). The magnitude of this leak depended on T7 RNAP and total template concentrations (FIG.30).

We next investigated whether a mass action kinetic model of coupled transcription, ribozyme cleavage, and RNA strand displacement could recapitulate the kinetics observed in ctRSD circuit experiments. For model parameters, we used the ribozyme cleavage rate that we measured (FIG.22) and estimated order of magnitude strand displacement rate constants consistent with previous literature. We calibrated the transcription rate constant for each experiment with a control sample. Our initial model did not include any terms to describe the leak observed when the 1_2r gate was transcribed without the correct input and thus could not capture that effect (FIGS.31, A and B).

To investigate the source of the leak, we evaluated how well incorporating plausible leak pathways into the model recapitulated the experimental leak kinetics. We first evaluated a leak pathway in which the cleaved 1_2r gate could directly react with the DNA reporter via a 0 base toehold. In simulations, this model exhibited a lag time before the leak was observed, inconsistent with experiments (FIGS.31, C and D). We next introduced a leak pathway in which the 1_2r gate could react with the DNA reporter prior to folding. In simulations, this leak pathway closely recapitulated the observed leak kinetics using a folding rate constant consistent with T7 RNAP transcription (FIGS.31, C and E). To experimentally investigate the presence of this leak pathway, we transcribed the 1_2r gate in the absence of DNA reporter, heat denatured T7 RNAP, and then added the DNA reporter to the solution containing the folded 1_2r gate. If the leak pathway involved the unfolded 1_2r gate, no signal should be observed upon reporter addition. We found that reporter addition resulted in an instantaneous jump in fluorescence, and the magnitude of the leak signal increased with increasing 1_2r gate transcription time (FIG.32). From these results, we reasoned the leak is not due to a reaction with the 1_2r gate prior to folding, but rather due to the presence of a 1_2r gate product that is highly reactive. Such an unintended side product could be the result of premature termination or gate misfolding events that leave the b-toehold of the gate exposed to rapidly react with the DNA reporter. We modeled this leak reaction by assuming the 1_2r gate template directly produced output at a leak transcription rate. In the model, a leak transcription rate of 3% the gate transcription rate recapitulated the experimental kinetics (FIGS.31, C and F). We included this leak term in all subsequent simulations. With the inclusion of this leak term, the kinetic model exhibited good agreement with experimental ctRSD circuit kinetics (FIG.7D).

Using the same design as the 1_2r gate, we created three more ctRSD gate sequences with corresponding inputs. We reused the same input toehold sequence across gates to facilitate similar strand displacement kinetics. These gate sequences cleaved with similar efficiency as the 1_2r gate (FIG.33) and exhibited nearly identical ctRSD circuit kinetics as the 1_2r gate (FIG.8A). Importantly, 11, 13, 14, 15 only reacted with their designed gate (FIG.8B), demonstrating orthogonality.

ctRSD Logic and Signal Amplification Elements

We next investigated whether ctRSD components could be programed to execute logic, signal amplification, and multi-layer cascades. To assess the predictability of ctRSD circuit design, for each circuit we built we evaluated how well our kinetic model predicted behavior. Our model assumes all ctRSD components are transcribed at the same rate and all gates cleave at the same rate. Further, we assume ctRSD components with the same toehold sequence have the same strand displacement rate constants.

With respect to designing OR and AND logic elements, an OR element was composed of two gates that react with different inputs but release the same output (FIG.9A). We confirmed OR functionality with native gel electrophoresis (FIG.9B) and the DNA reporter assay (FIG.9C). Importantly, OR element kinetics closely matched model predictions (FIG.9C). The AND element was a gate composed of two input domains separated by an internal loop (FIG.9D). In this design, I3 reacts with the gate to expose the toehold for I1 in the internal loop. We tested AND gates with internal loops composed of (3, 4, 5, or 6) bases of the a′-toehold. The 5 base and 6 base variants resulted in complete gate reaction with 2× input template (FIG.34). To reduce the chance of the gate reacting with 11 alone, we chose the 5 base internal loop design. Native gel electrophoresis confirmed the AND gate reacted with I3 and I3+I1 but not with I1 alone (FIG.9E). Similar results were observed with the DNA reporter assay, and the kinetics of output release aligned with model predictions (FIG.9F). A second AND gate with I4 and I5 as inputs behaved similarly (FIG.35). Our simulations suggested the AND gates exhibited 6% leak transcription compared to 3% for the single input gates. This could arise because AND gates possess two input domains in series, which may increase the likelihood of truncated or misfolded transcripts compared to single input gates.

A powerful component in strand displacement circuits is the seesaw element, which facilitates signal amplification in larger circuits. In a seesaw element, a single-stranded fuel component reacts with a 1:gate′ complex to displace the input, thus allowing multiple rounds of catalytic signal release (FIG.9G). In DNA-based circuits, which have fixed gate and input concentrations, a seesaw element enables a gate to react completely even when the input is at a lower concentration than the gate. In ctRSD circuits, output release will eventually saturate the DNA reporter signal regardless of the input concentration. However, simulations indicated a seesaw element should decrease the time required to reach reporter saturation for low input template concentrations (FIG.9H). When the input template was 0.05× or 0.1× the concentration of the gate template, inclusion of the fuel strand template (amplified,FIG.9I) reduced the time to reach reporter saturation≈3-fold and ≈4-fold, respectively, compared to samples with the input template but without the fuel template (unamplified,FIG.9I).

Strand displacement circuits capable of complex digital logic, pattern recognition, or temporal signal release require cascades of multi-layer signal transduction, so we next investigated whether we could program ctRSD cascades. We began by designing circuits with one to four ctRSD reaction layers in which the input and gate of the highest layer produce an output that triggers the next layer until the reporting reaction is triggered (FIG.10A). All four multi-layer cascades exhibited kinetics in good agreement with model predictions (FIG.10B). We next integrated ctRSD logic elements into a four-input OR circuit (FIG.10C), a cascade of two AND gates (FIG.10D), and two permutations of AND+OR cascades (FIGS.10, E and F). These cascades successfully executed the designed logic operations, and the experimental kinetics generally agreed with model predictions. However, there were two minor deviations in experimental kinetics compared to model predictions.

In the first deviation from the model, the two cascades in which the first layer was the 3&1_2r gate exhibited less leak than predicted when only I3 was present (FIGS.10Dand E). I3 opens the 3&1_2r gate to react with any leak products from the upstream layer in the cascades. Presumably, the 3&1_2r gate and upstream leak products reacted less than anticipated. Our model assumes leak products react with the same rate constant as their corresponding output products, but leak products are likely misfolded gates that are bulkier than single-stranded outputs. For a 13:3&1_2r complex, the region upstream of the toehold the leak product reacts with is a duplex. Thus, steric hindrance between the 13:3&1_2r complex and a leak product could result in lower leak than predicted in simulations (FIGS.36A-C). Similar steric hindrance between the ctRSD gate ribozyme and an upstream leak product could explain why the observed leak in multi-layer ctRSD cascades was less than predicted (FIGS.36, A-C andFIG.10B). In support of this hypothesis, we found the rate constant for a strand displacement reaction using an input with a hairpin directly adjacent to its toehold was nearly 100-fold lower than with a single-stranded input (FIGS.36, D-F).

In the second deviation from the model, the I3+I4 reaction in the OR+AND cascade (FIG.10E) was slower than predicted. This could be due to a slower strand displacement reaction for the 41 gate. The 4_1 gate itself appears to fold, cleave, and react with 14 similarly to other gates (FIG.8andFIG.33), so the difference in kinetics is not due to the gate misfolding. While all gates reuse the same toehold sequence, the kinetics of the branch migration process can vary over an order of magnitude depending on the sequence. The initial branch migration sequence of the 41 gate contains a weak UA tract (FIG.13) that could result in slower strand displacement kinetics. This mechanism is consistent with the 4_2r gate reaction being slower than gate reactions with the other three input sequences (FIG.8A) and the four-layer ctRSD cascade being slower than predicted (FIG.10B). Consistent with this hypothesis, reducing the 14 RNA strand displacement rate constant 2.5-fold aligned the model predictions more closely to experimental results (FIG.37). Although these hypotheses regarding model deviations are plausible, we present analyses using the model that assumes uniform gate performance.

Varying the Toehold Lengths in ctRSD Circuits

In toehold-mediated strand displacement, kinetics can be precisely controlled by varying toehold length and sequence. Such kinetic control has been demonstrated for both DNA and RNA strand displacement. In ctRSD circuits, toehold length could also influence gate folding or ribozyme cleavage kinetics. Further, in our gate designs, the bulky ribozyme is directly adjacent to the toehold and could sterically hinder input binding. Thus, extending the gate toehold alone could influence kinetics by introducing a single-stranded spacer between the ribozyme and the sequence the input binds.

To explore the influence of toehold length on ctRSD circuit performance, we analyzed 1_2r gates with (6, 8, 10, or 12) base toeholds. These gates cleaved with similar efficiency (FIG.38) and exhibited similar leak in the DNA reporter assay (FIG.39), indicating proper folding and cleavage. To explore the influence of toehold length and spacer length on kinetics, we designed I1 variants possessing (4, 6, 8, or 10) base toeholds and combinatorially transcribed each input alongside a 1_2r gate possessing either a (6, 8, 10, or 12) base toehold. Varying both the toehold and spacer length allowed us to tune the strand displacement rate constant over four orders of magnitude (FIG.40). Increasing toehold length without spacers increased the strand displacement rate. Inclusion of spacers adjacent to the ribozyme increased strand displacement kinetics for inputs with (4, 6, or 8) base toeholds. With sufficiently long spacers the reaction rate constants for all input toehold lengths aligned with predictions from DNA-based circuits, and (6, 8, or 10) base input toehold rate constants approached the theoretical maximum.

We developed scalable co-transcriptionally encoded RNA strand displacement circuits that were rationally programmed to execute logic, signal amplification, and multi-layer cascades. Integral to the development of these circuits was encoding RNA gates that co-transcriptionally folded into kinetically trapped intermediates, allowing all circuit components to be produced where they execute computations. We demonstrated the scalability and modularity of ctRSD circuits by implementing 11 single input gates and 2 AND gates in 8 different circuit topologies, all of which exhibited kinetics in agreement with our model that assumed uniform kinetic parameters. Taken together, these results indicate the robustness of our ctRSD gate design choices. Although other designs were not investigated experimentally, we believe three design choices contributed to the scalability and modularity of ctRSD circuits: 1) selecting the stable and cleavage sequence agnostic HDV ribozyme, 2) restricting the input and output sequences to C, A, or U bases, and 3) transcribing the output strand of the gates first. These choices likely reduced the chances of misfolding during transcription and facilitated proper ribozyme function across gate sequences.

We implemented the ctRSD gates with the same modular toehold exchange design (FIGS.6, A and B) and C, A, U sequence constraints employed in state-of-the-art DNA-based circuits. In DNA computing, these designs have enabled circuits composed of >100 components to be programmed to execute complex pattern recognition tasks and implement arbitrary chemical reaction networks, functionalities not accessible with current genetically encodable RNA circuits. Thus, ctRSD circuits are poised to achieve the same scalability and functionality as the most advanced DNA-based TMSD circuits, while potentially offering improved component purity and stability at comparable costs.

Our design choices also introduce practical limitations. The C, A, U sequence constraint restricts the use of cellular RNAs composed of all four bases as inputs. Simply redesigning gates with a four letter code could make it difficult to predictably design sequences that fold correctly. To address this limitation, we envision building upstream ctRSD translation gates that modularly convert RNA inputs with a four-letter code into outputs with a three-letter code that are processed in ctRSD circuits with our prescribed design rules. In this manner, the same robust information processing circuits may be used, and translation gates with four-letter codes that function correctly could be identified by testing sequences spanning a cellular RNA of interest.

Another limitation of our design is the bulky HDV ribozyme motif left on the gates after cleavage. We found this motif influenced strand displacement kinetics unless a single-stranded spacer between the ribozyme and the toehold binding sequence was inserted. Recently, a scheme was reported for transcriptionally encoding strand displacement circuits that used a dual hammerhead ribozyme motif that excised itself after folding, and a similar multi-ribozyme strategy could be applied to ctRSD gates to remove the HDV ribozyme motif during gate production. However, in contrast to the ctRSD circuits presented here, the alternative scheme used a four-letter code and found gate performance varied with sequence. Further, toeholds switched from 5′ to 3′ between circuit layers, reducing modularity and composability. Ultimately, merging ideas from both these implementations offers routes for further optimizing ctRSD circuits.

We envision ctRSD circuits enabling many new applications in nucleic acid computing and synthetic biology. For example, the inclusion of RNases in ctRSD circuits would allow continuous circuit turnover. Circuits could then respond multiple times to changing input signals, overcoming a current limitation in DNA computing. Additionally, regulating input production with allosteric transcription factors would allow ctRSD circuits that process non-nucleic acid inputs to be readily developed for smart diagnostics. Finally, the ability to transcriptionally encode strand displacement components on DNA plasmids would allow nucleic acid computing to be employed in a number of new environments where DNA computing is limited due to degradation, e.g. in blood samples, cell-free lysates, or inside living cells. In vivo, fluorescent RNA aptamers or RNA regulators that transduce RNA signals into fluorescent protein production could track ctRSD circuit dynamics. Further, ctRSD circuit outputs could regulate protein expression through existing RNA technologies, allowing ctRSD circuits to control cellular function. Adopting ctRSD circuits for these diverse applications will require overcoming challenges in controlling expression, degradation, and cleavage rates in vivo. These issues could be addressed by optimizing 5′ hairpins to tune expression levels or increase RNA stability, as well as exploring HDV ribozyme variants. Ultimately, ctRSD circuits are poised to be a versatile, enabling technology across many synthetic biology platforms.

Materials and Methods

DNA and Materials

DNA transcription templates were ordered as gBlock gene fragments from Integrated DNA Technologies (IDT), amplified via polymerase chain reaction (PCR) with Phusion High-Fidelity PCR Master Mix (Cat #: F531 L) from ThermoFisher Scientific, and purified using Qiagen PCR clean-up kits. All DNA oligo primers were ordered from IDT with standard desalting. For in vitro transcription experiments T7 RNA polymerase (RNAP) and ribonucleotide triphosphates (NTPs) were ordered from ThermoFisher Scientific (Cat #: R0481). DNase I (Cat #: M0303S) was purchased from New England Biolabs (NEB). 4% agarose EX E-gels were purchased from ThermoFisher Scientific (Cat #: G401004). All chemicals were purchased from Sigma Aldrich.

Transcription Template Preparation

All transcription templates were prepared by PCR of 0.2 ng/μL of gBlock DNA with Phusion High-Fidelity PCR Master Mix and 0.5 μmol/L of forward and reverse primers. PCR was conducted for 30 cycles with a 30 s 98° C. denaturing step, a 30 s 60° C. primer annealing step, and a 30 s 72° C. extension step. A 3 min 72° C. final extension step was executed at the end of the program. Following PCR amplification, the samples were purified with Qiagen PCR clean-up kits and eluted in Qiagen Buffer EB (10 mmol/L Tris-HCl, pH 8.5).

RNA Agarose Gel Electrophoresis

4% agarose EX E-gels were used for all RNA gel electrophoresis experiments. These gels are pre-stained with SYBR Gold for fluorescence imaging. Electrophoresis was conducted on a E-gel powerbase, and all E-gels were imaged using the E-gel power snap camera (ThermoFisher Scientific, Cat #: G8200). Unless otherwise stated, to prepare RNA for gel electrophoresis, DNA templates were transcribed at 37° C. for 30 min in transcription conditions (seeCharacterization of RNA strand displacement with in vitro transcription) with 0.6 U/μL T7 RNAP. To stop transcription, CaCl2(final concentration (1 to 1.5) mmol/L) and DNase I (final concentration (0.1 to 0.2) U/μL) were added to degrade the DNA templates. After DNase I addition, the samples were left at 37° C. for (0.5 to 2) h, and subsequently analyzed with gel electrophoresis. For native gels, the gels were sandwiched between icepacks to keep the gels cool during electrophoresis and were run for (45 to 60) min prior to imaging. Integrated band intensities were quantified in gel images using the Gel Analysis Tool in ImageJ as previously described. For denaturing gels, prior to electrophoresis, a solution of 100% formamide, 36 mmol/L EDTA was mixed 1:1 by volume with the samples and the samples were heated to 90° C. for 5 min. The samples were then immediately loaded on gels for electrophoresis and run for (20 to 30) min before imaging. Gel images were not post processed, any brightness and contrast adjustments were executed during image acquisition and were thus applied uniformly to the images to aid visualization.

Characterization of RNA Strand Displacement with a Fluorescence DNA Reporter

The in vitro transcription reactions with DNA reporter complexes were conducted in transcription buffer prepared in house (40 mmol/L Tris-HCl-pH 7.9, 6 mmol/L MgCl2, 10 mmol/L dithiothreitol (DTT), 10 mmol/L NaCl, and 2 mmol/L spermidine) supplemented with 2 mmol/L final concentration of each NTP type (ATP, UTP, CTP, GTP). All transcription reactions were conducted at 37° C. Unless otherwise stated, 500 nmol/L of DNA reporter was used. For in vitro transcription reactions, all components other than T7 RNAP were mixed and tracked in the plate reader for 15 min to 60 min prior to adding T7 RNAP. Addition of T7 RNAP, followed by mixing, corresponded to t=0 min in in vitro transcription experiments. The time to mix T7 RNAP into all samples for an experiment was less than one min. In our experiments, the T7 RNAP concentration varied depending on the total concentration of DNA templates present. To compare the response of a given ctRSD circuit to different input template concentrations or a different number of input templates, the same total template concentration was used across all reactions to ensure the same transcriptional load across samples. An input template (Io) that produces an RNA that does not interact with the gates was added to maintain the template concentration across samples. TABLE 4 contains the concentrations of DNA templates (including Io) and T7 RNAP used in each experiment.

Transcription Rate Calibration and Sample Variability

In these experiments, the transcription rate depended on the concentration of T7 RNAP and the total concentration of DNA templates (FIG.41). Further, variability of T7 RNAP activity across manufacturer lots was expected to be the primary source of variation in our experiments. To calibrate for these effects, we developed a transcription rate reference sample (FIG.41). This reference sample tracked transcription with a template that constitutively expressed the 1_2r strand and contained the same T7 RNAP lot and concentration as the experimental samples on a given day. Additionally, the Io template was added so the total template concentration equaled that of the experimental samples. The reference sample calibrated the first order transcription rate constant chosen for simulations (FIG.42), thus accounting for variation in T7 RNAP activity when assessing how well experimental results agreed with model predictions. To estimate the variability in ctRSD reaction measurements introduced during sample preparation, we conducted reactions between the 1_2r gate and either I1 or Io in triplicate in the DNA reporter assay. Each reaction was prepared independently using the same transcription template, NTP, buffer, and T7 RNAP stocks. These replicates exhibited a standard deviation of <1.5% from the mean value at each time point (FIG.43). A variability of <5% standard deviation was observed for the AND gate cascade inFIG.10D(FIG.43). Additionally, reactions between the 1_2r gate and either I1 or Io performed on different days exhibited <3% standard deviation (FIG.44). We therefore assumed a conservative variability of <5% generalized to ctRSD circuits. For the small circuits studied here, we do not expect this level of variability to influence our conclusions and, unless otherwise stated, DNA reporter experiments were conducted with a single experimental replicate.

Fluorescence Data Acquisition and Normalization

BioTek Synergy Neo2 plate readers were used track in vitro transcription reactions. Reactions were typically conducted in 70 μL volumes in Greiner μClear 96-well plates (Cat #: 655096) read from the bottom. The DNA reporter complex was labeled with a HEX dye which was tracked with excitation: 524 nm (20 nm bandwidth), emission: 565 nm (20 nm bandwidth), and a gain of 85. Fluorescence readings were taken every 46 s. In a typical experiment, fluorescence readings were taken for (25 to 45) min before T7 RNAP was added to initiate the reactions. At the end of most experiments, an excess (2.5 μmol/L) of a DNA version of the O2r strand was added to each sample to obtain an internal maximum DNA reporter fluorescence value. Fluorescence data was then normalized as:

If the DNA O2r strand was not added, a control well in which the ctRSD reaction had saturated the reporter signal served as a max value for normalization.

Sequences, Schematics, and Control Transcripts

TABLE 1 shows DNA sequences used for this Example. All transcription templates were ordered as gBlock gene fragments from IDT. All primers were ordered without purification from IDT. For the input and fuel templates the last 30 lower case bases were added to bring the sequence above 125 bases to order as gBlocks. The PCR product resulting from the T7fwd and T7rev primers does not include this sequence. The T7 RNAP promoter sequence is underlined in all sequences. Black highlighted bases indicate bases that were mutated from a C to a T to render the HDV ribozyme catalytically inactive. Two terminators that differ in their first base were used to prevent undesired secondary structure.

Different design considerations were analyzed during development of the ctRSD gates. Two methods to transcriptionally encode RNA strand displacement gates include: transcription of the two gate strands from separate transcription templates or transcription of an RNA hairpin with a ribozyme that cleaves the hairpin after folding to produce a dsRNA gate. The former method introduces a significant downstream leak reaction and was not used. Below provides analysis of four different transcription paths for producing ctRSD gates. In principle, these different transcription paths are conceptually equivalent but depend on the selected toehold directionality (5′ vs 3′) and the position of the ribozyme within the transcript. Analysis of three different self-cleaving ribozyme options for the ctRSD gates is described below.

The Co-Transcriptional Folding Pathway

Considering the directionality of the single-stranded RNA (ssRNA) toehold that facilitates strand displacement and the placement of the self-cleaving ribozyme within the RNA transcript, there are four possible designs for ctRSD gates (FIG.20). Previous work indicates that 5′ toeholds on RNA strand displacement gates perform better than 3′ toeholds, so we focused on designs with 5′ toeholds (FIGS.20, A and B). The placement of the self-cleaving ribozyme influences which domains of the gate are transcribed first. For example, placing the ribozyme adjacent to the 5′ toehold results in transcription of the output region (2-, b-, and 1-domains) of the gate first, while placing the ribozyme on the opposite side of the transcript results in transcription of the gate′ strand first. In many DNA strand displacement circuits the output sequences of the DNA gates are constrained to a 3 letter code (C, A, or, T). This constraint reduces the possibility of unwanted secondary structure from forming and preventing output strands in larger circuits from hybridizing with each other (crosstalk). We adopted the same sequence constraints in our RNA gate designs, limiting the gate output sequences (2-, b-, and 1-domains) to only C, A, or U bases. This constraint is particularly important for RNA circuits because G-U wobble base pairings are more energetically favored in RNA than G-T wobble pairings in DNA. Thus, even output sequences constrained to G, A, U bases could fold into undesired secondary structures. In the ctRSD gate design in which the self-cleaving ribozyme is placed opposite of the gate toehold (FIG.20B), the gate′ strand (a′-, 1′-, and b′-domains), whose sequence would be composed of G, A, U bases, would be transcribed first. Given that co-transcriptional folding of RNA is much faster than transcription, transcription of the a′-, 1′-, and b′-domains first could result in undesired secondary structure in the transcript before the complementary 1- and b-domains are transcribed, hindering the correct gate formation. For these reasons, we chose the RNA gate design in which the 2-, b-, and 1-domains (composed of only C, A, U bases) are transcribed first (FIG.20A).

Three well characterized ribozymes were considered: the hammerhead ribozyme, the hairpin ribozyme, and the hepatitis delta virus (HDV) ribozyme. The HDV ribozyme has several advantages over the hammerhead and hairpin ribozymes. First, the HDV ribozyme folds quickly into a stable structure, likely making it resistant to misfolding across different flanking sequences. Second, the rate constant for HDV ribozyme cleavage has been reported as 52 min−1in certain settings, compared to 1 min−1for the hammerhead or 0.5 min−1to 0.05 min−1for the hairpin ribozymes. Lastly, the HDV ribozyme has little sequence preference upstream of the cleavage site. Both the hammerhead and hairpin ribozymes have cleavage site sequence constraints and their cleavage sites are flanked by RNA duplexes thus requiring a dissociation step following cleavage to separate the two strands. This dissociation step is particularly problematic in our ctRSD gate designs, in which the ssRNA toehold for strand displacement must be exposed after cleavage. In our designs, the hammerhead and hairpin ribozymes require 6 and 4 bases, respectively, to dissociate after cleavage to expose the toehold for strand displacement (FIG.21). In the case of the hammerhead ribozyme, these 6 bases are likely to remain hybridized most of the time after cleavage, impeding RNA strand displacement. The HDV ribozyme does not suffer from these sequence limitations, driving this choice for our designs. We found the HDV ribozyme resulted in the desired efficient and rapid cleavage in our RNA gates (FIG.6andFIG.22). We also tested a ctRSD gate with the hairpin ribozyme, but much less cleavage was observed than with the HDV ribozyme (FIG.23).

Equilibrium Analysis with NUPACK

NUPACK 3.2.2 was used for equilibrium analysis of RNA complexes. We used the default NUPACK parameters for RNA (1.0 mol/L Na+ and 0 mol/L Mg++, dangles: some). Although there is 6 mmol/L MgCl2in our transcription buffer, there is a total of 8 mmol/L NTPs, which will sequester MgCl2, so the concentration of free Mg++ is unknown. For RNA analysis, the default salt conditions are the only options. Unless otherwise state, analysis was conducted at 37° C. with 1 μmol/L of each RNA species. Changing the equimolar concentration of the RNA species between 10 nmol/L and 100 μmol/L does not change the predicted equilibrium concentrations.

For analysis of the reaction I1+1_2 gate↔I1:gate′+O2 the strands supplied to NUPACK are shown below:

The 1_2 gate′ sequence contains the HDV ribozyme sequence. However, the HDV ribozyme structure is a pseudoknot, which NUPACK is incapable of predicting. Thus, the secondary structure of the HDV ribozyme in NUPACK does not represent its real structure. We found that the first two 5′ bases of the T7 RNAP terminator sequence n 11 (5′ CU) o were predicted to hybridize to part of the HDV ribozyme sequence on the 1_2 gate. However, this region of the ribozyme sequence is expected to be double stranded in the true ribozyme structure. To remove the influence of these spurious bases from the equilibrium analysis in NUPACK, the first C of the T7 RNAP terminator sequence was changed to an A (highlighted in yellow in the sequence above). This was done for all input sequences when analyzing these sequences in NUPACK.

Modeling ctRSD Circuit Reactions

Model Assumptions and Reactions

RNA strand displacement reactions were modeled using ordinary differential equations derived from mass action kinetics. All modeled reactions are shown inFIG.27. In our model transcription was simplified to a first order process, whereby transcription rate is linearly proportional to the template concentration (kp*[template]). This assumption ignores transcriptional loading effects that arise when the concentration of polymerase is not in excess of the total concentration of total transcription templates. Thus, in our model, the first order transcription rate constant (kp) depends both on the concentration of T7 RNAP and the total concentration of templates, i.e. transcriptional load (FIG.41). To account for these dependencies, we used an experimental calibration sample to obtain kpvalues for a given T7 RNAP concentration and total template concentration. The kpvalue obtained from this calibration sample was then used to simulate experimental samples with the same conditions (FIG.42). Because all of the transcripts in this study possess the same 5′ sequence, we assumed kpwas the same for all transcripts. Because co-transcriptional folding is 10-fold faster than transcription, we assume that the gates fold instantaneously upon transcription, unless otherwise stated.

The leak reaction in the ctRSD system was modeled by assuming that a small fraction of each ctRSD gate produced is as reactive as the designed output of the gate. Thus, a leak term was introduced in which an output is directly produced from its ctRSD gate template (kpL*[ctRSD gate template]). kpLis the first order leak transcription rate constant. For single input ctRSD gates, we found a kpLthat was 3% of kprecapitulated our experimental observations. This 3% leak transcription was used for all single input gates. We found that a 3% transcriptional leak for AND gates resulted in less leak than we observed in experiments. We reasoned this might be because each AND gate possesses two dsRNA domains. If we assume that each dsRNA stem has a 97% chance of being transcribed and folded correctly, we expect the chances an AND gate is correctly produced to be (0.97)2=94.1%. Based on this analysis, we assume a 6% transcriptional leak (kpLA) for all AND gates in the study. We also assumed the reactions between AND gates and their first inputs were irreversible because the reverse reaction is facilitated by a one base toehold. The reverse reaction between an AND gate and its final output was included in the model.

Beyond the leak reaction described above, our model ignores other potential side reactions that are not expected to significantly influence dynamics. First, any gate possessing an output complementary to another gate could react via a 0 base strand displacement mechanism. This reaction was not included in the model because it occurs two to three orders of magnitude slower than the designed RNA strand displacement reactions. Second, an input can react with an RNA strand displacement gate prior to ribozyme cleavage. However, a mutant ctRSD gate that could not cleave reacted much slower with input than the self-cleaving ctRSD gate (FIG.28). We assumed this side reaction would not greatly influence the observed kinetics at the low concentrations expected for the uncleaved ctRSD gate.

The model implementation pools output strands from gates with different input domains. For example, if both a 4_1 and 5_1 gate are present in a simulation the model only tracks the total O1 produced and does not explicitly track O1 released from the 4_1 gate and the O1 released from the 5_1 gate (FIG.27). In most of our simulations, this issue does not arise as there is only a single gate releasing a given output. However, for circuits with OR elements, the reverse reaction for each gate will be overestimated. For example, for a 4_1 and 5_1 OR element the reverse rate for the 4_1 gate is krev*[14:RSDg4]*[O1], but O1 can come from both the 4_1 and the 51 gate, and only O1 from the 4_1 gate can participate in this reverse reaction. In each OR gate experiment, the input template concentrations were equal and the gate template concentrations were equal. Thus, the overcounting of outputs would only change the reverse reaction rate 2-fold. For single-layer OR gates, the largest circuits simulated in which two gates produced the same output, a significant change in kinetics is not observed until the reverse rate constants are increased 25-fold (FIGS.29, A and B). Based on these results, the overcounting in OR gate reverse reaction rates should not influence the results for the networks simulated. A more rigorous model that tracks which gates the outputs come from could occur as ctRSD circuits expand.

Finally, the model does not consider any loss of T7 RNAP activity or depletion of NTPs during transcription. Thus, the model may become inaccurate when simulating experimental times>(4 to 5) h, as T7 RNAP activity will have decreased significantly. For slow reactions that are limited by transcription (i.e., transcription of leak products), the loss of T7 RNAP activity will eventually result in a plateau in output. The model will not capture this.

Kinetic Parameters Used to Model ctRSD Circuits

In toehold mediated DNA strand displacement (DSD), the rate of the strand displacement reaction is correlated to the binding energy of the toehold. As binding energy increases with increasing toehold length, the same trend between toehold length and strand displacement rate enhancement is predicted for RSD as for DSD. Because rate enhancement is related to toehold binding energy, toehold sequence can also greatly influence the observed rate. For example, a strong 6 base toehold with high G-C content can result in rate constants near 106L mol−1s−1, while weaker 6 base toehold sequences can result in rate constants closer to 104 L mol−1s−1. The toehold on the DNA reporter contains five A or T bases and a single G, making it a weak toehold. Thus, a rate constant of 104 L mol−1s−1was used to model the reaction between the reporter and the 1_2r strand (ksd). All reporting reactions are considered to be irreversible.

For the rate constant of the reaction between an input strand and its corresponding ctRSD gate complex (krsd), we found a value of 103L mol−1s−1best recapitulated our experimental results. This value is at least two orders of magnitude lower than expected for a 6 base toehold with moderate GC content. There is some evidence that RSD reaction rate constants can be an order of magnitude lower than DSD reaction rates for short toeholds. Additionally, the presence of the bulky HDV ribozyme structure directly upstream of the toehold on the ctRSD gate could lower the observed reaction rate (FIG.11). This bulky structure could sterically clash with the terminator hairpin at the 3′ end of the input strand and effectively decrease the strand displacement rate. In support of this hypothesis, we found the introduction of a 4 base single-stranded spacer between the HDV ribozyme motif, and the toehold increased krsdto ≥105L mol−1s−1. We assumed the krsdvalue was the same for all RSD reactions in ctRSD circuits, including for both toeholds of the AND gates. For the reaction between a fuel species and an input:gate′ complex, we assumed the same reaction rate constant as between the input and the ctRSD gate.

All the RNA strand displacement reactions in this study are reversible (FIG.13). Estimation of the reverse reaction rate constants based on toehold length and sequence alone is confounded by reverse reactions replacing a G-C pair with a G-U wobble in the first two to three bases of branch migration (FIG.13). This will reduce the rate of strand displacement, but the amount of this reduction is unknown. In DNA strand displacement, introduction of a mismatch at a similar position during branch migration can reduce the reaction rate by two to three orders of magnitude; presumably a G-U wobble would have a slightly less pronounced effect. Because we did not find an estimate for a comparable system in the literature, we estimated the reverse reaction rate constants from an equilibrium analysis of the strand displacement products. We used NUPACK 3.2.2 to calculate the equilibrium constant (Keq) for each complementary gate and input sequence. The reverse reaction rate constant (krev) was determined from the equilibrium constant as krev=krsd/Keq. Based on this analysis, we found gates with outputs possessing the b-toehold have reverse rate constants nearly three orders of magnitude lower than the forward rate constants. Gates with outputs possessing the a-toehold have reverse reaction rate constants only one order of magnitude lower than the forward rates (TABLE 2). To simplify the model, we assumed a single reverse rate constant for gates with b-toeholds (5 L mol−1s−1) and for gates with a-toeholds (270 L mol−1s−1). The reaction rate constants must be 10-fold larger than these values to begin to influence model predictions (FIG.29).

The HDV ribozyme cleavage rate constant was estimated as 0.25 min−1(FIG.22), consistent with previously reported in vitro values.

TABLE 2 lists an equilibrium analysis of RSD reactions across different ctRSD gates and inputs. All kinetic ate constants used in simulations are listed in TABLE 3, wherein the last two rate constants are used to model the leak transcription reaction for single input gates (kpL) and AND gates (kpLA).

Modeling and Characterizing Leak

In the experiments, we observed a leak in which transcription of the 1_2r gate template in the absence of the I1 template resulted in a slow increase in DNA reporter signal. This leak reaction increased with increasing concentrations of T7 RNAP, i.e., the leak increased with increasing transcription rate (FIG.30). The initial model of ctRSD circuits did not include terms capable of producing this leak (FIGS.31, A and B). To include this observed leak in the model, we investigated three potential models of leak the pathway: Models1,2, and3inFIG.31C. Both Model2and Model3can recapitulate the experimental data, but only Model3is consistent with the experimental results presented inFIG.31. Unless otherwise stated, all other simulations included the leak reactions depicted in Model3.

Two additional models for leak were eliminated. (1) Short transcripts produced during abortive cycling by T7 RNAP could include part of the output domains and react with the DNA reporter. This model was considered unlikely because short abortive transcripts typically range from (2 to 12) nucleotides but the gate transcripts possess a 17 nucleotide hairpin sequence at their 5′ end. Thus, any short transcripts produced during abortive cycling should not contain sequence complementarity with the reporter. (2) The ribozyme rapidly cleaves during transcription and releases the output before the bottom strand of the gate (gate′) is produced. The output strand could then irreversibly react with the DNA reporter before hybridizing to form a dsRNA gate. This model was considered unlikely because we measured the HDV ribozyme cleavage rate constant to be ˜0.25 per min (˜0.004 per s) in our assay conditions (FIG.22). This value is consistent with previously published values for HDV ribozyme self-cleavage in vitro. From our simulation results, the transcription rate constant in our experiments was ˜0.01 per s, indicating transcription proceeds much faster than ribozyme cleavage. The results inFIG.32are also inconsistent with this model of leak because the leak is still observed even when the RNA gate is transcribed in isolation.

(D) Simulation results (dashed lines) for Model1compared to experimental results (solid lines). In the simulations, a kleakof 15 L mol−1s−1was used for the 0 base toehold reaction between the 1_2r gate complex and the DNA reporter. This is an order of magnitude higher than reported previously. (E) Simulation results (dashed lines) for Model2compared to experimental results (solid lines). In the simulations, a kfoldof 0.15 s−1was used. Considering that co-transcriptional folding occurs much faster than transcription, the kfoldparameter may be taken as the time required to produce the transcript, during which the nascent transcript could react with the DNA reporter. A kfoldof 0.15 s−1corresponds to a transcript produced every 6.67 s, and this corresponds to the transcription rate of ˜27 nt/s for the 183 nt 1_2r gate transcript. This transcription rate is within a factor of 1.5 of previously reported transcription rates for T7 RNAP, supporting the feasibility of the kfoldparameter that recapitulates the experimental data. (F) Simulation results (dashed lines) for Model3compared to experimental results (solid lines). In the simulations, a production rate of truncated 1_2r gate products (kp,L) that was 3% of the production rate of correct products (kp) was used. The reaction between the DNA reporter and the truncated 1_2r gate product was assumed to have the same rate constant (ksd) as the reaction between the DNA reporter and the 1_2r strand. All other rate constants are in TABLE 3. The experimental results are also presented inFIG.7.

Analysis of Deviations Between Experiments and Simulations

Across our experiments there were two minor deviations from simulation predictions. Deviation 1: There was lower leak than predicted between ctRSD gates, which could be the result of steric hindrance between leak products and gates (FIG.36). Deviation 2: The gates that take 14 as an input reacted slower than the gates that take other inputs. The 4_2r gate was noticably slower than the three other single input gates tested (FIG.8A), and the 41 gate was slower than the 5_1 gate in a two-layer cascade (FIG.37B). The 4_1 gate was also slower in a logic cascade than the 5_1 gate (FIG.37D). We hypothesized that the strand displacement rate constant for gates that take 14 as an input could be lower than the other domains due to the high UA content at the start of branch migration (FIG.13). Similar sequences have been shown to significantly decrease overall strand displacement kinetics. Decreasing krsd4-fold in our simulations for just the gates that take I4 as an input resulted in better model agreement across experiments (FIG.37).

1_2r Gates with Different Toehold Lengths

The kinetics of toehold-mediated strand displacement reactions can be controlled by toehold length. Here, we explore how toehold length influenced the kinetics of ctRSD circuit reactions. The initial design for the 1_2r gate included a 6 base single-stranded input toehold, which we would expect to result in a rate constant near the maximum theoretical limit (106L mol−1s−1). However, our simulations indicated that the forward strand displacement rate constant between the 1_2r gate and 11 was only 103L mol−1s−1. We theorized steric hindrance between the ribozyme and the input strand could result in slower strand displacement because the 6 base toehold is directly adjacent to the bulky HDV ribozyme motif (FIG.11). Thus, in addition exploring the influence of toehold length on kinetics, we also explored the influence of including a single-stranded spacer sequence between the ribozyme motif and the toehold. To do this, we designed 1_2r gates with (6, 8, 10, and 12) base toeholds and 11 variants possessing (4, 6, 8, or 10) base toeholds and combinatorically tested all gate and input combinations. Schematics with sequences are presented inFIG.16. We first confirmed increasing toehold length did not influence gate folding and/or cleavage.FIG.38demonstrates that the 1_2r gate toehold variants cleaved as designed.FIG.39shows that increasing toehold length did not increase leak with the DNA reporter, suggesting proper folding.

We next evaluated RSD kinetics for all gate and input toehold length combinations. These experiments encompassed toehold lengths of 4 bases to 10 bases with spacer lengths varying from (0 to 8) bases depending on the input toehold length (FIGS.40, A and B). In these experiments, we were not able to resolve reaction rate constants greater than 105L mol−1s−1(FIG.40C). When the strand displacement reaction rate gets this high, the overall rate of output release becomes limited by transcription and gate cleavage, rather than strand displacement. Thus, we report all reaction rate constants near this 105L mol−1s−1limit as ≥105L mol−1s−1(FIG.40B).

FIG.40Dshow the kinetic traces for each gate and input toehold combination, highlighting the influence of spacer length on reaction kinetics for each input toehold length. Inclusion of a spacer generally increases reaction rate, but the spacer length that saturates the reaction rate decreases as input toehold length increases. An explanation for this observation could be: the weaker the input binding energy, the greater the influence of steric hindrance on the reaction rate. For example, the input with a 4 base toehold binds weakly to the 1_2r gate toehold, so a long spacer is required to completely remove any effect of steric hindrance. Conversely, for the input with a 10 base toehold, the same kinetics are observed for a 0 base and 2 base spacer. In this case, the input with the 10 base toehold can be viewed as an input with a 6 base toehold binding to a gate with a 4 base spacer, or an input with an 8 base toehold binding to a gate with a 2 base spacer. Both those reactions occur at a rate near the maximum value. Put another way, once the input toehold is long enough, increasing the length of the a-toehold (input) and a′-toehold (gate) together has almost the same effect as simply increasing the spacer length, i.e. increasing the a′-toehold (gate) without increasing the a-toehold (input). In support of this hypothesis, the 8 base a-toehold (input) and 8 base a′-toehold (gate) reaction rate constant is close to the 6 base a-toehold (input) and 8 base a′-toehold (gate) reaction rate constant (FIG.40B).

FIG.40Eshows the kinetic traces for each gate and input toehold combination, highlighting the influence of toehold length on reaction kinetics for each spacer length. With the exception of the input with a 4 base a-toehold, most of the changes in kinetics observed across toehold length can be attributed to the increase in a′-toehold (spacer) length. For example, with long enough spacers, inputs with (6, 8, and 10) base toeholds exhibit strand displacement constants close to the maximum value (≥105L mol−1s−1).

For traditional DNA and RNA strand displacement, in which double-stranded complexes are pre-annealed and gate toeholds have no secondary structure upstream, toeholds ≥6 bases should result in reaction rate constants at the theoretical maximum of ≈106L mol−1s−1. We found similar results for ctRSD circuits when using a long enough spacer between the HDV ribozyme and the toehold. Regarding the input with a 4 base a-toehold, the reaction between this input and any of the 1_2r gates has a much lower thermodynamic driving force than the other input toeholds tested. This is because the 1_2r gates all possess a 6 base reverse toehold, i.e. completion of the forward strand displacement reaction results in a net loss of two base pairs compared to the intact 1_2r gate. The rate constant for a DNA strand displacement reaction between an input with a 4 base toehold and a gate with a 6 base reverse toehold (b-toehold) was measured to be between (102and 103) L mol−1s−1. This aligns with our estimated rate constant of 2×102L mol−1s−1for between the 4 base input toehold variant and ctRSD gates with either a 6 base or 8 base spacers (FIG.40B). Together, these results suggest that ctRSD circuits should possess the same kinetic control of traditional toehold-mediated strand displacement, provided appropriate spacers are used.

Steric hindrance introduced by the ribozyme could also be used as an additional feature to tune strand displacement rates. Changing the spacer length adjacent to the ribozyme allows different strand displacement rate constants to be obtained, without needing to change the input's toehold length. For the 6 base a-toehold, varying spacer length changed the strand displacement rate constant by two orders of magnitude.

Potential Advantages of ctRSD Circuits Compared to DNA-Based Circuits

Should ctRSD circuits continue to prove as predictable and programmable as DNA-based circuits, ctRSD could serve as a more versatile alternative to DNA computing. Such a shift could be justified given the high fidelity and decreasing price of gene synthesis. Integrated DNA Technologies currently reports ≈80% of 30 base DNA oligonucleotides are the correct product compared to ≈100% for gBlocks of >125 bases. The low fidelity of DNA oligonucleotide synthesis requires the strands to be purified with gel electrophoresis and many DNA computing papers report the purification of individual dsDNA circuit complexes to obtain desired circuit function. For ctRSD circuits the high-fidelity gBlock synthesis is followed by a high-fidelity PCR step (<0.25% error) and high-fidelity transcription-T7 RNAP's nucleotide substitution rate is less than 1 in 17,000 bases. Further, encoding the dsRNA complex as a single transcript ensures the proper stoichiometry between the two gate strands, reducing leak pathways. Thus, ctRSD circuits remove the need for purification of circuit components before operation, greatly simplifying the workflow. Further, the per nanomole cost of a ctRSD gate template can be reduced to nearly that of analogous DNA gates with a few modifications to the protocol here.

Another advantage of using transcriptionally encoded circuits over DNA strand displacement circuits is the long-term stability of long linear DNA templates and DNA plasmids. For example, in many biosensor and diagnostic applications, circuit components are freeze dried for long-term storage and ease of transportation. These freeze-dried circuits are then activated by adding a liquid sample at the point of need. Both linear DNA templates on the order of 300 bases and DNA plasmids have been shown to remain stable for months after freeze drying. Short DNA strand displacement duplexes show significant decrease in performance only one week after freeze drying.

Experimental Conditions, Transcription Rate Calibration, and Experimental Variability

TABLE 4 lists transcription template and T7 RNAP concentrations used in DNA reporter assays. In our experiments, the transcription rate was dependent on the total transcription template and T7 RNAP concentrations (FIG.41). To ensure the same transcription load across different samples for the same ctRSD element or circuit, a template producing an unreactive input (Io) was added so that all samples had the same total template concentration. The total concentration of templates for each experiment is also presented in TABLE 4. Because the transcription rate differed across many experiments, the first order rate constant (kp) used to model transcription had to be calibrated for a given T7 RNAP and total template concentration (FIG.42). The first order rate constant (kp) calibrated for each experiment is presented in TABLE 4. This transcription rate calibration should also calibrate for batch to batch variation in T7 RNAP stocks. Other than the differences arising from different experimental conditions or T7 RNAP batches, replicate measurements of circuit kinetics varied between 2% to 5%. TABLE 4 lists transcription template and T7 RNAP concentrations used in DNA reporter experiments. 500 nmol/L of DNA reporter was used in each experiment. For experiments in which multiple input template concentrations or input template combinations were tested, each column of values represents the nominal concentrations of each input template for each experiment.

The processes and articles described herein may be embodied in, and fully automated via, software code modules executed by a computing system that includes one or more general purpose computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device. Some or all the methods may alternatively be embodied in specialized computer hardware. In addition, the components referred to herein may be implemented in hardware, software, firmware, or a combination thereof.

All ranges disclosed herein are inclusive of the endpoints, and the endpoints are independently combinable with each other. The ranges are continuous and thus contain every value and subset thereof in the range. Unless otherwise stated or contextually inapplicable, all percentages, when expressing a quantity, are weight percentages. The suffix (s) as used herein is intended to include both the singular and the plural of the term that it modifies, thereby including at least one of that term (e.g., the colorant(s) includes at least one colorants). Option, optional, or optionally means that the subsequently described event or circumstance can or cannot occur, and that the description includes instances where the event occurs and instances where it does not. As used herein, combination is inclusive of blends, mixtures, alloys, reaction products, collection of elements, and the like.

As used herein, a combination thereof refers to a combination comprising at least one of the named constituents, components, compounds, or elements, optionally together with one or more of the same class of constituents, components, compounds, or elements.

All references are incorporated herein by reference.

The use of the terms “a,” “an,” and “the” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. It can further be noted that the terms first, second, primary, secondary, and the like herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. For example, a first current could be termed a second current, and, similarly, a second current could be termed a first current, without departing from the scope of the various described embodiments. The first current and the second current are both currents, but they are not the same condition unless explicitly stated as such.

The modifier about used in connection with a quantity is inclusive of the stated value and has the meaning dictated by the context (e.g., it includes the degree of error associated with measurement of the particular quantity). The conjunction or is used to link objects of a list or alternatives and is not disjunctive; rather the elements can be used separately or can be combined together under appropriate circumstances.

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