Cross-Inhibitor: a time-sensitive molecular circuit based on DNA strand displacement

Scalable DNA recognition circuits based on DNA strand displacement

DNA is a kind of nano-molecule considered to be computable on the molecular level, and the precise Watson-Crick principle of base pairing has made it possible for DNA to be a novel computer material. The DNA strand displacement technology has greatly facilitated the development of DNA computing in both logical and intelligent computation. In this paper, we proposed and implemented a molecular recognition circuit based on DNA strand displacement, which can achieve recognition and summation functions. This circuit has a simple molecular composition and is easily scalable. A cross-inhibition module was integrated based upon the molecular recognition circuit to construct a molecular comparator. Considering the advantages of modularity and the experimental feasibility of a scalable recognition circuit, it could be used as a pattern signal recognition and classification module in smart molecular circuits or biosensors.

An Exo III-powered closed-loop DNA circuit architecture for biosensing/imaging

With their regulated Boolean logic operations in vitro and in vivo, DNA logic circuits have shown great promise for target recognition and disease diagnosis. However, significant obstacles must be overcome to improve their operational efficiency and broaden their range of applications. In this study, we propose an Exo III-powered closed-loop DNA circuit (ECDC) architecture that integrates four highly efficient AND logic gates. The ECDC utilizes Exo III as the sole enzyme-activated actuator, simplifying the circuit design and ensuring optimal performance. Moreover, the use of Exo III enables a self-feedback (autocatalytic) mechanism in the dynamic switching between AND logic gates within this circulating logic circuit. After validating the signal flow and examining the impact of each AND logic gate on the regulation of the circuit, we demonstrate the intelligent determination of miR-21 using the carefully designed ECDC architecture in vitro. The proposed ECDC exhibits a linear detection range for miR-21 from 0 to 300 nM, with a limit of detection (LOD) of approximately 0.01 nM, surpassing most reported methods. It also shows excellent selectivity for miR-21 detection and holds potential for identifying and imaging live cancer cells. This study presents a practical and efficient strategy for monitoring various nucleic acid-based biomarkers in vitro and in vivo through specific sequence modifications, offering significant potential for early cancer diagnosis, bioanalysis, and prognostic clinical applications.

Plug-and-Play Module for Reversible and Continuous Control of DNA Strand Displacement Kinetics

Regulatory modules for controlling the kinetics of toehold-mediated strand displacement (TMSD) play critical roles in designing dynamic and dissipative DNA chemical reaction networks (CRNs) but are hardwired into sequence designs. Herein, we introduce antitoehold (At), a plug-and-play module for reversible and continuous tuning of TMSD kinetics by temporarily occupying the toehold domain via a metastable duplex and base stacking. We demonstrate that kinetic control can be readily activated or deactivated in real time for any TMSD by simply adding At or anti-At. Continuous tuning of TMSD kinetics can also be achieved by altering the concentration of At. Moreover, the simple addition of At could readily reprogram existing TMSDs into a pulse-generation DNA CRN with continuous tunability. Our At approach also offers a new way for engineering continuously tunable DNA hybridization probes, which may find practical uses for discriminating clinically important mutations. Because of the simplicity, we anticipate that At will find wide applications for engineering DNA CRNs with diverse dynamic and dissipative behaviors, and DNA hybridization probes with tunable affinity and selectivity.

DNAzyme-Based Dissipative DNA Strand Displacement for Constructing Temporal Logic Gates

Toehold-mediated DNA strand displacement is the foundation of dynamic DNA nanotechnology, encompassing a wide range of tools with diverse functions, dynamics, and thermodynamic properties. However, a majority of these tools are limited to unidirectional reactions driven by thermodynamics. In response to the growing field of dissipative DNA nanotechnology, we present an approach: DNAzyme-based dissipative DNA strand displacement (D-DSD), which combines the principles of dynamic DNA nanotechnology and dissipative DNA nanotechnology. D-DSD introduces circular and dissipative characteristics, distinguishing it from the unidirectional reactions observed in conventional strand displacement. We investigated the reaction mechanism of D-DSD and devised temporal control elements. By substituting temporal components, we designed two distinct temporal AND gates using fewer than 10 strands, eliminating the need for complex network designs. In contrast to previous temporal logic gates, our temporal storage is not through dynamics control or cross-inhibition but through autoregressive storage, a more modular and scalable approach to memory storage. D-DSD preserves the fundamental structure of toehold-mediated strand displacement, while offering enhanced simplicity and versatility.

Temporal logic circuits implementation using a dual cross-inhibition mechanism based on DNA strand displacement

Molecular circuits crafted from DNA molecules harness the inherent programmability and biocompatibility of DNA to intelligently steer molecular machines in the execution of microscopic tasks. In comparison to combinational circuits, DNA-based temporal circuits boast supplementary capabilities, allowing them to proficiently handle the omnipresent temporal information within biochemical systems and life sciences. However, the lack of temporal mechanisms and components proficient in comprehending and processing temporal information presents challenges in advancing DNA circuits that excel in complex tasks requiring temporal control and time perception. In this study, we engineered temporal logic circuits through the design and implementation of a dual cross-inhibition mechanism, which enables the acceptance and processing of temporal information, serving as a fundamental building block for constructing temporal circuits. By incorporating the dual cross-inhibition mechanism, the temporal logic gates are endowed with cascading capabilities, significantly enhancing the inhibitory effect compared to a cross-inhibitor. Furthermore, we have introduced the annihilation mechanism into the circuit to further augment the inhibition effect. As a result, the circuit demonstrates sensitive time response characteristics, leading to a fundamental improvement in circuit performance. This architecture provides a means to efficiently process temporal signals in DNA strand displacement circuits. We anticipate that our findings will contribute to the design of complex temporal logic circuits and the advancement of molecular programming.

Programming DNA Reaction Networks Using Allosteric DNA Hairpins

The construction of DNA reaction networks with complex functions using various methods has been an important research topic in recent years. Whether the DNA reaction network can perform complex tasks and be recycled directly affects the performance of the reaction network. Therefore, it is very important to design and implement a DNA reaction network capable of multiple tasks and reversible regulation. In this paper, the hairpin allosteric method was used to complete the assembly task of different functional nucleic acids. In addition, information conversion of the network was realized. In this network, multiple hairpins were assembled into nucleic acid structures with different functions to achieve different output information through the cyclic use of trigger strands. A method of single-input dual-output information conversion was proposed. Finally, the network with signal amplification and reversible regulation was constructed. In this study, the reversible regulation of different functional nucleic acids in the same network was realized, which shows the potential of this network in terms of programmability and provides new ideas for constructing complex and multifunctional DNA reaction networks.

Mapping Temporally Ordered Inputs to Binary Message Outputs with a DNA Temporal Logic Circuit

Molecular circuits and devices with temporal signal processing capability are of great significance for the analysis of complex biological processes. Mapping temporal inputs to binary messages is a process of history-dependent signal responses, which can help understand the signal-processing behavior of organisms. Here, we propose a DNA temporal logic circuit based on DNA strand displacement reactions, which can map temporally ordered inputs to corresponding binary message outputs. The presence or absence of the output signal is determined by the type of substrate reaction with the input so that different orders of inputs correspond to different binary outputs. We demonstrate that a circuit can be generalized to more complex temporal logic circuits by increasing or decreasing the number of substrates or inputs. We also show that our circuit had excellent responsiveness to temporally ordered inputs, flexibility, and expansibility in the case of symmetrically encrypted communications. We envision that our scheme can provide some new ideas for future molecular encryption, information processing, and neural networks.

DNA-based customized functional modules for signal transformation

Information on the temporal and spatial scale of cellular molecules in biological systems is crucial for estimating life processes and may be conducive to an improved understanding of disease progression. This intracellular and extracellular information is often difficult to obtain at the same time due to the limitations of accessibility and sensing throughput. DNA is an excellent material for in vivo and in vitro applications, and can be used to build functional modules that can transform bio-information (input) into ATCG sequence information (output). Due to their small volume and highly amenable programming, DNA-based functional modules provide an opportunity to monitor a range of information, from transient molecular events to dynamic biological processes. Over the past two decades, with the advent of customized strategies, a series of functional modules based on DNA networks have been designed to gather different information about molecules, including the identity, concentration, order, duration, location, and potential interactions; the action of these modules are based on the principle of kinetics or thermodynamics. This paper summarizes the available DNA-based functional modules that can be used for biomolecular signal sensing and transformation, reviews the available designs and applications of these modules, and assesses current challenges and prospects.

Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers

As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git.

DNA strand displacement based computational systems and their applications

DNA computing has become the focus of computing research due to its excellent parallel processing capability, data storage capacity, and low energy consumption characteristics. DNA computational units can be precisely programmed through the sequence specificity and base pair principle. Then, computational units can be cascaded and integrated to form large DNA computing systems. Among them, DNA strand displacement (DSD) is the simplest but most efficient method for constructing DNA computing systems. The inputs and outputs of DSD are signal strands that can be transferred to the next unit. DSD has been used to construct logic gates, integrated circuits, artificial neural networks, etc. This review introduced the recent development of DSD-based computational systems and their applications. Some DSD-related tools and issues are also discussed.

Reconfigurable DNA triplex structure for pH responsive logic gates†

The DNA triplex is a special DNA structure often used as a logic gate substrate due to its high stability, programmability, and pH responsiveness. However, multiple triplex structures with different C−G−C⁺ proportions must be introduced into existing triplex logic gates due to the numerous logic calculations involved. This requirement complicates circuit design and results in many reaction by-products, greatly restricting the construction of large-scale logic circuits. Thus, we designed a new reconfigurable DNA triplex structure (RDTS) and constructed the pH-responsive logic gates through its conformational change that uses two types of logic calculations, ‘AND’ and ‘OR’. The use of these logic calculations necessitates fewer substrates, further enhancing the extensibility of the logic circuit. This result is expected to promote the development of the triplex in molecular computing and facilitate the completion of large-scale computing networks.

We constructed pH-responsive logic gates through substrate conformational change that uses two types of logic calculations, ‘AND’ and ‘OR’. Our logic gates necessitate fewer substrates when two types of logic calculations are needed.

Rapid microRNA detection method based on DNA strand displacement for ovarian cancer cells

The current cancer detection methods are heavily dependent on the component analysis of corresponding cancer antigens. There is a lack of effective and simple clinical methods of ovarian cancer screening, which hinders the early identification for ovarian cancer and its treatment. To develop a simple and rapid method for quantitative analysis of ovarian cancer, we developed a DNA strand displacement-based method and finished the rapid detection of miR-21 in ovarian cancer cells within 5 min by a one-step isothermal reaction. The fluorescence intensity trajectory had a good linear relationship with miR-21 concentrations in the range of 100 fM-100 nM, with a lower limit of 6.05 pM. This detection method is simple, faster, and accurate. Besides, it can be applied to detect the miRNA biomarkers of other cancers by changing the preset sequences of toehold.

SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction

Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [1] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.

DNA Strand-Displacement Temporal Logic Circuits

Molecular circuits capable of processing temporal information are essential for complex decision making in response to both the presence and history of a molecular environment. A particular type of temporal information that has been recognized to be important is the relative timing of signals. Here we demonstrate the strategy of temporal memory combined with logic computation in DNA strand-displacement circuits capable of making decisions based on specific combinations of inputs as well as their relative timing. The circuit encodes the timing information on inputs in a set of memory strands, which allows for the construction of logic gates that act on current and historical signals. We show that mismatches can be employed to reduce the complexity of circuit design and that shortening specific toeholds can be useful for improving the robustness of circuit behavior. We also show that a detailed model can provide critical insights for guiding certain aspects of experimental investigations that an abstract model cannot. We envision that the design principles explored in this study can be generalized to more complex temporal logic circuits and incorporated into other types of circuit architectures, including DNA-based neural networks, enabling the implementation of timing-dependent learning rules and opening up new opportunities for embedding intelligent behaviors into artificial molecular machines.

Massively Parallel DNA Computing Based on Domino DNA Strand Displacement Logic Gates

DNA computing has gained considerable attention due to the characteristics of high-density information storage and high parallel computing for solving computational problems. Building addressable logic gates with biomolecules is the basis for establishing biological computers. In the current calculation model, the multiinput AND operation often needs to be realized through a multilevel cascade between logic gates. Through experiments, it was found that the multilevel cascade causes signal leakage and affects the stability of the system. Using DNA strand displacement technology, we constructed a domino-like multiinput AND gate computing system instead of a cascade of operations, realizing multiinput AND computing on one logic gate and abandoning the traditional multilevel cascade of operations. Fluorescence experiments demonstrated that our methods significantly reduce system construction costs and improve the stability and robustness of the system. Finally, we proved stability and robustness of the domino AND gate by simulating the tic-tac-toe process with a massively parallel computing strategy.

An Operational DNA Strand Displacement Encryption Approach

DeoxyriboNucleic Acid (DNA) encryption is a new encryption method that appeared along with the research of DNA nanotechnology in recent years. Due to the complexity of biology in DNA nanotechnology, DNA encryption brings in an additional difficulty in deciphering and, thus, can enhance information security. As a new approach in DNA nanotechnology, DNA strand displacement has particular advantages such as being enzyme free and self-assembly. However, the existing research on DNA-strand-displacement-based encryption has mostly stayed at a theoretical or simulation stage. To this end, this paper proposes a new DNA-strand-displacement-based encryption framework. This encryption framework involves three main strategies. The first strategy was a tri-phase conversion from plaintext to DNA sequences according to a Huffman-coding-based transformation rule, which enhances the concealment of the information. The second strategy was the development of DNA strand displacement molecular modules, which produce the initial key for information encryption. The third strategy was a cyclic-shift-based operation to extend the initial key long enough, and thus increase the deciphering difficulty. The results of simulation and biological experiments demonstrated the feasibility of our scheme for encryption. The approach was further validated in terms of the key sensitivity, key space, and statistic characteristic. Our encryption framework provides a potential way to realize DNA-strand-displacement-based encryption via biological experiments and promotes the research on DNA-strand-displacement-based encryption.

DNA tile self-assembly driven by antibody-mediated four-way branch migration

The antibody-mediated four-way branch migration mechanism provides a novel idea for realizing the assembly of nanostructures, simply by attaching structures such as tiles, proteins, quantum dots, etc. to the ends of the four-way branches.

A novel strategy for programmable DNA tile self-assembly with a DNAzyme-mediated DNA cross circuit

The proposed strategy promotes the controllability and modularization of trigger elements, realizes programmable molecular self-assembly, and has broad applications for the construction of DNA nanodevices.

Programmable and scalable assembly of a flexible hexagonal DNA origami

Nanoscale structures demonstrate considerable potential utility in the construction of nanorobots, nanomachines, and many other devices. In this study, a hexagonal DNA origami ring was assembled and visualized via atomic force microscopy. The DNA origami shape could be programmed into either a hexagonal or linear shape with an open or folded pattern. The flexible origami was robust and switchable for dynamic pattern recognition. Its edges were folded by six bundles of DNA helices, which could be opened or folded in a honeycomb shape. Additionally, the edges were programmed into a concave-convex pattern, which enabled linkage between the origami and dipolymers. Furthermore, biotin-streptavidin labels were embedded at each edge for nanoscale calibration. The atomic force microscopy results demonstrated the stability and high-yield of the flexible DNA origami ring. The polymorphous nanostructure is useful for dynamic nano-construction and calibration of structural probes or sensors.

Recent Progress in DNA Hybridization Chain Reaction Strategies for Amplified Biosensing

With the continuous development of DNA nanotechnology, various spatial DNA structures and assembly techniques emerge. Hybridization chain reaction (HCR) is a typical example with exciting features and bright prospects in biosensing, which has been intensively investigated in the past decade. In this Spotlight on Applications, we summarize the assembly principles of conventional HCR and some novel forms of linear/nonlinear HCR. With advantages like great assembly kinetics, facile operation, and an enzyme-free and isothermal reaction, these strategies can be integrated with most mainstream reporters (e.g., fluorescence, electrochemistry, and colorimetry) for the ultrasensitive detection of abundant targets. Particularly, we select several representative studies to better illustrate the novel ideas and performances of HCR strategies. Theoretical and practical utilities are confirmed for a range of biosensing applications. In the end, a deep discussion is provided about the challenges and future tasks of this field.

Medical Image Protection Algorithm Based on Deoxyribonucleic Acid Chain of Dynamic Length

Current image encryption algorithms have various deficiencies in effectively protecting medical images with large storage capacity and high pixel correlation. This article proposed a new image protection algorithm based on the deoxyribonucleic acid chain of dynamic length, which achieved image encryption by DNA dynamic coding, generation of DNA dynamic chain, and dynamic operation of row chain and column chain. First, the original image is encoded dynamically according to the binary bit from a pixel, and the DNA sequence matrix is scrambled. Second, DNA sequence matrices are dynamically segmented into DNA chains of different lengths. After that, row and column deletion operation and transposition operation of DNA dynamic chain are carried out, respectively, which made DNA chain matrix double shuffle. Finally, the encrypted image is got after recombining DNA chains of different lengths. The proposed algorithm was tested on a list of medical images. Results showed that the proposed algorithm showed excellent security performance, and it is immune to noise attack, occlusion attack, and all common cryptographic attacks.