Constrained Multistate Sequence Design for Nucleic Acid Reaction Pathway Engineering

Switching the activity of Cas12a using guide RNA strand displacement circuits

The CRISPR effector protein Cas12a has been used for a wide variety of applications such as in vivo gene editing and regulation or in vitro DNA sensing. Here, we add programmability to Cas12a-based DNA processing by combining it with strand displacement-based reaction circuits. We first establish a viable strategy for augmenting Cas12a guide RNAs (gRNAs) at their 5' end and then use such 5' extensions to construct strand displacement gRNAs (SD gRNAs) that can be activated by single-stranded RNA trigger molecules. These SD gRNAs are further engineered to exhibit a digital and orthogonal response to different trigger RNA inputs-including full length mRNAs-and to function as multi-input logic gates. We also demonstrate that SD gRNAs can be designed to work inside bacterial cells. Using such in vivo SD gRNAs and a DNase inactive version of Cas12a (dCas12a), we demonstrate logic gated transcriptional control of gene expression in E. coli.

Evolving methods for rational de novo design of functional RNA molecules

Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.

An extended dual graph library and partitioning algorithm applicable to pseudoknotted RNA structures

Exploring novel RNA topologies is imperative for understanding RNA structure and pursuing its design. Our RNA-As-Graphs (RAG) approach exploits graph theory tools and uses coarse-grained tree and dual graphs to represent RNA helices and loops by vertices and edges. Only dual graphs represent pseudoknotted RNAs fully. Here we develop a dual graph enumeration algorithm to generate an expanded library of dual graph topologies for 2-9 vertices, and extend our dual graph partitioning algorithm to identify all possible RNA subgraphs. Our enumeration algorithm connects smaller-vertex graphs, using all possible edge combinations, to build larger-vertex graphs and retain all non-isomorphic graph topologies, thereby more than doubling the size of our prior library to a total of 110,667 dual graph topologies. We apply our dual graph partitioning algorithm, which keeps pseudoknots and junctions intact, to all existing RNA structures to identify all possible substructures up to 9 vertices. In addition, our expanded dual graph library assigns graph topologies to all RNA graphs and subgraphs, rectifying prior inconsistencies. We update our RAG-3Dual database of RNA atomic fragments with all newly identified substructures and their graph IDs, increasing its size by more than 50 times. The enlarged dual graph library and RAG-3Dual database provide a comprehensive repertoire of graph topologies and atomic fragments to study yet undiscovered RNA molecules and design RNA sequences with novel topologies, including a variety of pseudoknotted RNAs.

Label-Free and Enzyme-Free Colorimetric Detection of Pb2+ Based on RNA Cleavage and Annealing-Accelerated Hybridization Chain Reaction

A label-free and enzyme-free colorimetric sensor for rapid detection of Pb²⁺ is reported, which is based on the strategy of DNAzyme-mediated RNA cleavage combined with an annealing-accelerated DNA hybridization chain reaction (HCR). As a trigger DNA, the substrate strand (S_TM) of DNAzyme can initiate HCR effectively. However, when it is cleaved by DNAzyme in the presence of Pb²⁺, the separation of DNA functional domains leads to a serious decrease in HCR efficiency. As a result, the difference in Pb²⁺ concentration converts into the difference of DNA assembly, which eventually leads to the color change of colloidal gold nanoparticles (AuNPs). In this work, a DNA strand (cGR5) completely complementary to the catalytic strand (GR5) of DNAzyme is used to improve the dissociation of S_TM to enhance the HCR efficiency. In addition, the simple operation of DNA annealing is first used to accelerate the HCR process, enabling the Pb²⁺ detection to be completed in about 30 min. As advantages of high sensitivity, good selectivity, strong anti-interference ability, and good practical performance are achieved, it is anticipated that the cheap and simple colorimetric sensor will be helpful for on-site detection of environmental and food samples.

Principles and Applications of Nucleic Acid Strand Displacement Reactions

Dynamic DNA nanotechnology, a subfield of DNA nanotechnology, is concerned with the study and application of nucleic acid strand-displacement reactions. Strand-displacement reactions generally proceed by three-way or four-way branch migration and initially were investigated for their relevance to genetic recombination. Through the use of toeholds, which are single-stranded segments of DNA to which an invader strand can bind to initiate branch migration, the rate with which strand displacement reactions proceed can be varied by more than 6 orders of magnitude. In addition, the use of toeholds enables the construction of enzyme-free DNA reaction networks exhibiting complex dynamical behavior. A demonstration of this was provided in the year 2000, in which strand displacement reactions were employed to drive a DNA-based nanomachine (Yurke, B.; et al. Nature 2000, 406, 605-608). Since then, toehold-mediated strand displacement reactions have been used with ever increasing sophistication and the field of dynamic DNA nanotechnology has grown exponentially. Besides molecular machines, the field has produced enzyme-free catalytic systems, all DNA chemical oscillators and the most complex molecular computers yet devised. Enzyme-free catalytic systems can function as chemical amplifiers and as such have received considerable attention for sensing and detection applications in chemistry and medical diagnostics. Strand-displacement reactions have been combined with other enzymatically driven processes and have also been employed within living cells (Groves, B.; et al. Nat. Nanotechnol. 2015, 11, 287-294). Strand-displacement principles have also been applied in synthetic biology to enable artificial gene regulation and computation in bacteria. Given the enormous progress of dynamic DNA nanotechnology over the past years, the field now seems poised for practical application.

Signal Transduction in Human Cell Lysate via Dynamic RNA Nanotechnology

Dynamic RNA nanotechnology with small conditional RNAs (scRNAs) offers a promising conceptual approach to introducing synthetic regulatory links into endogenous biological circuits. Here, we use human cell lysate containing functional Dicer and RNases as a testbed for engineering scRNAs for conditional RNA interference (RNAi). scRNAs perform signal transduction via conditional shape change: detection of a subsequence of mRNA input X triggers formation of a Dicer substrate that is processed to yield small interfering RNA (siRNA) output anti-Y targeting independent mRNA Y for destruction. Automated sequence design is performed using the reaction pathway designer within NUPACK to encode this conditional hybridization cascade into the scRNA sequence subject to the sequence constraints imposed by X and Y. Because it is difficult for secondary structure models to predict which subsequences of mRNA input X will be accessible for detection, here we develop the RNAhyb method to experimentally determine accessible windows within the mRNA that are provided to the designer as sequence constraints. We demonstrate the programmability of scRNA regulators by engineering scRNAs for transducing in both directions between two full-length mRNAs X and Y, corresponding to either the forward molecular logic “if X then not Y” (X  Y) or the reverse molecular logic “if Y then not X” (Y  X). In human cell lysate, we observe a strong OFF/ON conditional response with low crosstalk, corresponding to a ≈20-fold increase in production of the siRNA output in response to the cognate versus noncognate full-length mRNA input. 2′OMe-RNA chemical modifications protect signal transduction reactants and intermediates against RNase degradation while enabling Dicer processing of signal transduction products. Because diverse biological pathways interact with RNA, scRNAs that transduce between detection of endogenous RNA inputs and production of biologically active RNA outputs hold great promise as a synthetic regulatory paradigm.

Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson-Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

AptaBlocks: Designing RNA complexes and accelerating RNA-based drug delivery systems

RNA-based therapeutics, i.e. the utilization of synthetic RNA molecules to alter cellular functions, have the potential to address targets which are currently out of scope for traditional drug design pipelines. This potential however hinges on the ability to selectively deliver and internalize therapeutic RNAs into cells of interest. Cell internalizing RNA aptamers selected against surface receptors and discriminatively expressed on target cells hold particular promise as suitable candidates for such delivery agents. Specifically, these aptamers can be combined with a therapeutic cargo and facilitate internalization of the cargo into the cell of interest. A recently proposed method to obtain such aptamer-cargo constructs employs a double-stranded "sticky bridge" where the complementary strands constituting the bridge are conjugated with the aptamer and the cargo respectively. The design of appropriate sticky bridge sequences however has proven highly challenging given the structural and functional constraints imposed on them during synthesis and administration. These include, but are not limited to, guaranteed formation and stability of the complex, non-interference with the aptamer or the cargo, as well as the prevention of spurious aggregation of the molecules during incubation. In order to address these issues, we have developed AptaBlocks - a computational method to design RNA complexes that hybridize via sticky bridges. The effectiveness of our approach has been verified computationally, and experimentally in the context of drug delivery to pancreatic cancer cells. Importantly, AptaBlocks is a general method for the assembly of nucleic acid systems that, in addition to designing of RNA-based drug delivery systems, can be used in other applications of RNA nanotechnology. AptaBlocks is available at https://github.com/wyjhxq/AptaBlocks.

Ultraspecific and Amplification-Free Quantification of Mutant DNA by Single-Molecule Kinetic Fingerprinting

Conventional techniques for detecting rare DNA sequences require many cycles of PCR amplification for high sensitivity and specificity, potentially introducing significant biases and errors. While amplification-free methods exist, they rarely achieve the ability to detect single molecules, and their ability to discriminate between single-nucleotide variants is often dictated by the specificity limits of hybridization thermodynamics. Here we show that a direct detection approach using single-molecule kinetic fingerprinting can surpass the thermodynamic discrimination limit by 3 orders of magnitude, with a dynamic range of up to 5 orders of magnitude with optional super-resolution analysis. This approach detects mutations as subtle as the drug-resistance-conferring cancer mutation EGFR T790M (a single C → T substitution) with an estimated specificity of 99.99999%, surpassing even the leading PCR-based methods and enabling detection of 1 mutant molecule in a background of at least 1 million wild-type molecules. This level of specificity revealed rare, heat-induced cytosine deamination events that introduce false positives in PCR-based detection, but which can be overcome in our approach through milder thermal denaturation and enzymatic removal of damaged nucleobases.

Sequential super-resolution imaging using DNA strand displacement

Sequential labeling and imaging in fluorescence microscopy allows the imaging of multiple structures in the same cell using a single fluorophore species. In super-resolution applications, the optimal dye suited to the method can be chosen, the optical setup can be simpler and there are no chromatic aberrations between images of different structures. We describe a method based on DNA strand displacement that can be used to quickly and easily perform the labeling and removal of the fluorophores during each sequence. Site-specific tags are conjugated with unique and orthogonal single stranded DNA. Labeling for a particular structure is achieved by hybridization of antibody-bound DNA with a complimentary dye-labeled strand. After imaging, the dye is removed using toehold-mediated strand displacement, in which an invader strand competes off the dye-labeled strand than can be subsequently washed away. Labeling and removal of each DNA-species requires only a few minutes. We demonstrate the concept using sequential dSTORM super-resolution for multiplex imaging of subcellular structures.

A pipeline for computational design of novel RNA-like topologies

Designing novel RNA topologies is a challenge, with important therapeutic and industrial applications. We describe a computational pipeline for design of novel RNA topologies based on our coarse-grained RNA-As-Graphs (RAG) framework. RAG represents RNA structures as tree graphs and describes RNA secondary (2D) structure topologies (currently up to 13 vertices, ≈260 nucleotides). We have previously identified novel graph topologies that are RNA-like among these. Here we describe a systematic design pipeline and illustrate design for six broad design problems using recently developed tools for graph-partitioning and fragment assembly (F-RAG). Following partitioning of the target graph, corresponding atomic fragments from our RAG-3D database are combined using F-RAG, and the candidate atomic models are scored using a knowledge-based potential developed for 3D structure prediction. The sequences of the top scoring models are screened further using available tools for 2D structure prediction. The results indicate that our modular approach based on RNA-like topologies rather than specific 2D structures allows for greater flexibility in the design process, and generates a large number of candidate sequences quickly. Experimental structure probing using SHAPE-MaP for two sequences agree with our predictions and suggest that our combined tools yield excellent candidates for further sequence and experimental screening.

Programming Chemical Reaction Networks Using Intramolecular Conformational Motions of DNA

The programmable regulation of chemical reaction networks (CRNs) represents a major challenge toward the development of complex molecular devices performing sophisticated motions and functions. Nevertheless, regulation of artificial CRNs is generally energy- and time-intensive as compared to natural regulation. Inspired by allosteric regulation in biological CRNs, we herein develop an intramolecular conformational motion strategy (InCMS) for programmable regulation of DNA CRNs. We design a DNA switch as the regulatory element to program the distance between the toehold and branch migration domain. The presence of multiple conformational transitions leads to wide-range kinetic regulation spanning over 4 orders of magnitude. Furthermore, the process of energy-cost-free strand exchange accompanied by conformational change discriminates single base mismatches. Our strategy thus provides a simple yet effective approach for dynamic programming of complex CRNs.

Programmable and Multifunctional DNA-Based Materials for Biomedical Applications

DNA encodes the genetic information; recently, it has also become a key player in material science. Given the specific Watson-Crick base-pairing interactions between only four types of nucleotides, well-designed DNA self-assembly can be programmable and predictable. Stem-loops, sticky ends, Holliday junctions, DNA tiles, and lattices are typical motifs for forming DNA-based structures. The oligonucleotides experience thermal annealing in a near-neutral buffer containing a divalent cation (usually Mg²⁺ ) to produce a variety of DNA nanostructures. These structures not only show beautiful landscape, but can also be endowed with multifaceted functionalities. This Review begins with the fundamental characterization and evolutionary trajectory of DNA-based artificial structures, but concentrates on their biomedical applications. The coverage spans from controlled drug delivery to high therapeutic profile and accurate diagnosis. A variety of DNA-based materials, including aptamers, hydrogels, origamis, and tetrahedrons, are widely utilized in different biomedical fields. In addition, to achieve better performance and functionality, material hybridization is widely witnessed, and DNA nanostructure modification is also discussed. Although there are impressive advances and high expectations, the development of DNA-based structures/technologies is still hindered by several commonly recognized challenges, such as nuclease instability, lack of pharmacokinetics data, and relatively high synthesis cost.

Programmable DNA switches and their applications

DNA switches are ideally suited for numerous nanotechnological applications, and increasing efforts are being directed toward their engineering. In this review, we discuss how to engineer these switches starting from the selection of a specific DNA-based recognition element, to its adaptation and optimisation into a switch, with applications ranging from sensing to drug delivery, smart materials, molecular transporters, logic gates and others. We provide many examples showcasing their high programmability and recent advances towards their real life applications. We conclude with a short perspective on this exciting emerging field.

Hybridization Chain Reaction Design and Biosensor Implementation

DNA biosensors could overcome some of the common drawbacks of lab-based techniques for nucleic acids detection for diagnostics purposes. One of the main impediments for such applications of DNA biosensors is their lack of sensitivity: this can prevent their full exploitation in the diagnostic analytical field. DNA nanotechnology could enhance DNA biosensors and let them perform at the required high sensitivity. Well-designed, programmable self-assembly reactions can be triggered by a specific nucleic acid target. The Hybridization Chain Reaction (HCR) is a self-assembly strategy in which the target nucleic acid sequence triggers the formation of long nicked double-stranded DNA nanostructures. This can be performed in solution or on a surface, and the process can be coupled to different signal transduction schemes. We here describe the methods to design and test HCR reactions for the detection of different nucleic acid targets in solution and the procedures to exploit this strategy on surfaces with an electrochemical biosensing platform.

Enzyme-free nucleic acid dynamical systems

Chemistries exhibiting complex dynamics—from inorganic oscillators to gene regulatory networks—have been long known but either cannot be reprogrammed at will or rely on the sophisticated enzyme chemistry underlying the central dogma. Can simpler molecular mechanisms, designed from scratch, exhibit the same range of behaviors? Abstract chemical reaction networks have been proposed as a programming language for complex dynamics, along with their systematic implementation using short synthetic DNA molecules. We developed this technology for dynamical systems by identifying critical design principles and codifying them into a compiler automating the design process. Using this approach, we built an oscillator containing only DNA components, establishing that Watson-Crick base-pairing interactions alone suffice for complex chemical dynamics and that autonomous molecular systems can be designed via molecular programming languages.

Computational design of small transcription activating RNAs for versatile and dynamic gene regulation

A longstanding goal of synthetic biology has been the programmable control of cellular functions. Central to this is the creation of versatile regulatory toolsets that allow for programmable control of gene expression. Of the many regulatory molecules available, RNA regulators offer the intriguing possibility of de novo design-allowing for the bottom-up molecular-level design of genetic control systems. Here we present a computational design approach for the creation of a bacterial regulator called Small Transcription Activating RNAs (STARs) and create a library of high-performing and orthogonal STARs that achieve up to ~ 9000-fold gene activation. We demonstrate the versatility of these STARs-from acting synergistically with existing constitutive and inducible regulators, to reprogramming cellular phenotypes and controlling multigene metabolic pathway expression. Finally, we combine these new STARs with themselves and CRISPRi transcriptional repressors to deliver new types of RNA-based genetic circuitry that allow for sophisticated and temporal control of gene expression.

RNA structure prediction: from 2D to 3D

We summarize different levels of RNA structure prediction, from classical 2D structure to extended secondary structure and motif-based research toward 3D structure prediction of RNA. We outline the importance of classical secondary structure during all those levels of structure prediction.