A Unified Dynamic Programming Framework for the Analysis of Interacting Nucleic Acid Strands: Enhanced Models, Scalability, and Speed

Evaluation of antifungal potentials of Albizia kalkora extract as a natural fungicide: In vitro and computational studies

The antifungal bioactivity potential of the organic extract of silk tree (Albizia kalkora) was investigated in the current study. The crude extracts of A. kalkora and methanol, n-hexane, chloroform, and ethyl acetate fractions were prepared. The antifungal activity of obtained fractions of A. kalkora was studied at different concentrations ranging from 0.39-50 µg/mL. Dimethyl sulfoxide (DMSO) was taken as a toxicity control, whereas thiophanate methyl (TM) as a positive control. All the fractions significantly reduced the FOL growth (methanolic: 9.49-94.93 %, n-hexane: 11.12-100 %, chloroform: 20.96-91.41 %, and ethyl acetate: 18.75-96.70 %). The n-hexane fraction showed 6.25 µg/mL MIC as compared to TM with 64 µg/mL MIC. The non-polar (n-hexane) fraction showed maximum antifungal bioactivity against FOL in comparison with chloroform, methanol, and ethyl acetate fractions. GC/MS analysis exhibited that the n-hexane fraction contained hexadecanoic acid, 9,12,15-octadecatrienoic acid, 9,12-octadecadienoic acid, bis(2-ethylhexyl) phthalate, methyl stearate, and [1,2,4]triazolo[1,5-a]pyrimidine-6-carboxylic acid. The results of in vitro antifungal inhibition were further reinforced by molecular docking analysis. Five virulence proteins of FOL i.e., pH-responsive PacC transcription factor (PACC), MeaB, TOR; target of rapamycin (FMK1), Signal transducing MAP kinase kinase (STE-STE7), and High Osmolarity Glycerol 1(HOG1) were docked with identified phytocompounds in the n-hexane fraction by GC/MS analysis. MEAB showed maximum binding affinities with zinnimide (-12.03 kcal/mol), HOG1 and FMK1with α-Tocospiro-B (-11.51 kcal/mol) and (-10.55 kcal/mol) respectively, STE-STE7 with docosanoic acid (-11.31 kcal/mol), and PACC with heptadecanoic acid (-9.88 kcal/mol) respectively with strong hydrophobic or hydrophilic interactions with active pocket residues. In conclusion, the n-hexane fraction of the A. kalkora can be used to manage FOL.

Expanding DNA Origami Design Freedom with De Novo Synthesized Scaffolds

The construction of DNA origami nanostructures is heavily dependent on the folding of the scaffold strand, which is typically a single-stranded DNA genome extracted from a bacteriophage (M13). Custom scaffolds can be prepared in a number of methods, but they are not widely accessible to a broad user base in the DNA nanotechnology community. Here, we explored new design and construction possibilities with custom scaffolds prepared in our cost- and time-efficient production pipeline. According to the pipeline, we de novo produced a variety of scaffolds of specified local and global sequence characteristics and consequent origami constructs of modular arrangement in morphologies and functionalities. Taking advantage of this strategy of template-free scaffold production, we also designed and produced three-letter-coded scaffolds that can fold into designated morphologies rapidly at room temperature. The expanded design and construction freedom immediately brings in many new research opportunities and invites many more on the horizon.

Scalable design of orthogonal DNA barcode libraries

Orthogonal DNA barcode library design is an essential task in bioengineering. Here we present seqwalk, an efficient method for designing barcode libraries that satisfy a sequence symmetry minimization (SSM) heuristic for orthogonality, with theoretical guarantees of maximal or near-maximal library size under certain design constraints. Seqwalk encodes SSM constraints in a de Bruijn graph representation of sequence space, enabling the application of recent advances in discrete mathematics1 to the problem of orthogonal sequence design. We demonstrate the scalability of seqwalk by designing a library of >106 SSM-satisfying barcode sequences in less than 20 s on a standard laptop.

Competition between Self-Assembly and Phase Separation Governs High-Temperature Condensation of a DNA Liquid

In many biopolymer solutions, attractive interactions that stabilize finite-sized clusters at low concentrations also promote phase separation at high concentrations. Here we study a model biopolymer system that exhibits the opposite behavior, whereby self-assembly of DNA oligonucleotides into finite-sized, stoichiometric clusters tends to inhibit phase separation. We first use microfluidics-based experiments to map a novel phase transition in which the oligonucleotides condense as the temperature increases at high concentrations of divalent cations. We then show that a theoretical model of competition between self-assembly and phase separation quantitatively predicts changes in experimental phase diagrams arising from DNA sequence perturbations. Our results point to a general mechanism by which self-assembly shapes phase boundaries in complex biopolymer solutions.

Design and analysis of self-priming extension DNA hairpin probe for miRNA detection based on a unified dynamic programming framework

MicroRNAs (miRNAs) are potential biomarkers for cancer diagnosis and prognosis, methods for detecting miRNAs with high sensitivity, selectivity, and stability are urgently needed. Various nucleic acid probes that have traditionally been for this purpose suffer several drawbacks, including inefficient signal-to-noise ratios and intensities, high cost, and time-consuming method establishment. Computing tools used for investigating the thermodynamics of DNA hybridization reactions can accurately predict the secondary structure of DNA and the interactions between DNA molecules. Herein, NUPACK was used to design a series of nucleic acid probes and develop a phosphorothioated-terminal hairpin formation and self-priming extension (PS-THSP) signal amplification strategy, which enabled the ultrasensitive detection of miR-200a in serum samples. The free and binding energies of the DNA detection probes calculated using NUPACK, as well as the biological experimental results, were considered synthetically to select the best sequence and experimental conditions. A unified dynamic programming framework, NUPACK analysis and the experimental data, were complementary and improved the designed model in all respects. Our study demonstrates the feasibility of using computer technology such as NUPACK to simplify the experimental process and provide intuitive results.

DNA Crosslinked Mucin Hydrogels Allow for On-Demand Gel Disintegration and Triggered Particle Release

Whereas hydrogels created from synthetic polymers offer a high level of control over their stability and mechanical properties, their biomedical activity is typically limited. In contrast, biopolymers have evolved over billions of years to integrate a broad range of functionalities into a single design. Thus, biopolymeric hydrogels can show remarkable capabilities such as regulatory behavior, selective barrier properties, or antimicrobial effects. Still, despite their widespread use in numerous biomedical applications, achieving a meticulous control over the physical properties of macroscopic biopolymeric networks remains a challenge. Here, a macroscopic, DNA-crosslinked mucin hydrogel with tunable viscoelastic properties that responds to two types of triggers: temperature alterations and DNA displacement strands, is presented. As confirmed with bulk rheology and single particle tracking, the hybridized base pairs governing the stability of the hydrogel can be opened, thus allowing for a precise control over the hydrogel stiffness and even enabling a full gel-to-sol transition. As those DNA-crosslinked mucin hydrogels possess tunable mechanical properties and can be disintegrated on demand, they can not only be considered for controlled cargo release but may also serve as a role model for the development of smart biomedical materials in applications such as tissue engineering and wound healing.

Synthetic eco-evolutionary dynamics in simple molecular environment

The understanding of eco-evolutionary dynamics, and in particular the mechanism of coexistence of species, is still fragmentary and in need of test bench model systems. To this aim we developed a variant of SELEX in vitro selection to study the evolution of a population of ∼1015 single-strand DNA oligonucleotide 'individuals'. We begin with a seed of random sequences which we select via affinity capture from ∼1012 DNA oligomers of fixed sequence ('resources') over which they compete. At each cycle ('generation'), the ecosystem is replenished via PCR amplification of survivors. Massive parallel sequencing indicates that across generations the variety of sequences ('species') drastically decreases, while some of them become populous and dominate the ecosystem. The simplicity of our approach, in which survival is granted by hybridization, enables a quantitative investigation of fitness through a statistical analysis of binding energies. We find that the strength of individual resource binding dominates the selection in the first generations, while inter- and intra-individual interactions become important in later stages, in parallel with the emergence of prototypical forms of mutualism and parasitism.

An ultrasensitive and multiplexed miRNA one-step real time RT-qPCR detection system and its application in esophageal cancer serum

MicroRNAs (miRNAs) are increasingly recognized as promising biomarkers for early disease diagnosis and prognosis. Therefore, the need for rapid, robust methods for multiplex miRNA detection in biological research and clinical diagnosis is crucial. This study introduces a novel multiplex miRNA detection method, SMOS-qPCR (Sensitive and Multiplexed One-Step RT-qPCR). The method integrates multiplexed reverse transcription and TaqMan-based qPCR into a single tube, employing a one-step operation on a real-time PCR system. We investigated the effect of 3' end phosphorylation of the Linker, Linker concentration and probe concentration on the SMOS-qPCR, resulted in a wide linear range from 1 fM to 0.1 zM (R2 ≥ 0.99 for each miRNA), surpassing the capabilities of stem-loop RT-qPCR and SYBR Green One-step RT-qPCR. The method showed excellent performance in distinguishing mature miRNA from miRNA precursor, and successfully detected four miRNAs in a single tube without cross-interference. Its high specificity enables precise differentiation of less than 1% nonspecific signal. Finally, we demonstrated the effectiveness of the SMOS-qPCR system in detecting circulating miRNAs in serum samples, distinguishing between esophageal cancers and health individuals with high AUC values (>0.940). In conclusion, the proposed SMOS-qPCR system offers a straightforward and promising approach for miRNA profiling in future clinical applications.

Tigerfish designs oligonucleotide-based in situ hybridization probes targeting intervals of highly repetitive DNA at the scale of genomes

Fluorescent in situ hybridization (FISH) is a powerful method for the targeted visualization of nucleic acids in their native contexts. Recent technological advances have leveraged computationally designed oligonucleotide (oligo) probes to interrogate > 100 distinct targets in the same sample, pushing the boundaries of FISH-based assays. However, even in the most highly multiplexed experiments, repetitive DNA regions are typically not included as targets, as the computational design of specific probes against such regions presents significant technical challenges. Consequently, many open questions remain about the organization and function of highly repetitive sequences. Here, we introduce Tigerfish, a software tool for the genome-scale design of oligo probes against repetitive DNA intervals. We showcase Tigerfish by designing a panel of 24 interval-specific repeat probes specific to each of the 24 human chromosomes and imaging this panel on metaphase spreads and in interphase nuclei. Tigerfish extends the powerful toolkit of oligo-based FISH to highly repetitive DNA.

3D feasibility of 2D RNA-RNA interaction paths by stepwise folding simulations

The structure of an RNA, and even more so its interactions with other RNAs, provide valuable information about its function. Secondary structure-based tools for RNA-RNA interaction predictions provide a quick way to identify possible interaction targets and structures. However, these tools ignore the effect of steric hindrance on the tertiary (3D) structure level, and do not consider whether a suitable folding pathway exists to form the interaction. As a consequence, these tools often predict interactions that are unrealistically long and could be formed (in three dimensions) only by going through highly entangled intermediates. Here, we present a computational pipeline to assess whether a proposed secondary (2D) structure interaction is sterically feasible and reachable along a plausible folding pathway. To this end, we simulate the folding of a series of 3D structures along a given 2D folding path. To avoid the complexity of large-scale atomic resolution simulations, our pipeline uses coarse-grained 3D modeling and breaks up the folding path into small steps, each corresponding to the extension of the interaction by 1 or 2 bp. We apply our pipeline to analyze RNA-RNA interaction formation for three selected RNA-RNA complexes. We find that kissing hairpins, in contrast to interactions in the exterior loop, are difficult to extend and tend to get stuck at an interaction length of 6 bp. Our tool, including source code, documentation, and sample data, is available at www.github.com/irenekb/RRI-3D.

Automated design of dynamic programming schemes for RNA folding with pseudoknots

Although RNA secondary structure prediction is a textbook application of dynamic programming (DP) and routine task in RNA structure analysis, it remains challenging whenever pseudoknots come into play. Since the prediction of pseudoknotted structures by minimizing (realistically modelled) energy is NP-hard, specialized algorithms have been proposed for restricted conformation classes that capture the most frequently observed configurations. To achieve good performance, these methods rely on specific and carefully hand-crafted DP schemes. In contrast, we generalize and fully automatize the design of DP pseudoknot prediction algorithms. For this purpose, we formalize the problem of designing DP algorithms for an (infinite) class of conformations, modeled by (a finite number of) fatgraphs, and automatically build DP schemes minimizing their algorithmic complexity. We propose an algorithm for the problem, based on the tree-decomposition of a well-chosen representative structure, which we simplify and reinterpret as a DP scheme. The algorithm is fixed-parameter tractable for the treewidth tw of the fatgraph, and its output represents a [Formula: see text] algorithm (and even possibly [Formula: see text] in simple energy models) for predicting the MFE folding of an RNA of length n. We demonstrate, for the most common pseudoknot classes, that our automatically generated algorithms achieve the same complexities as reported in the literature for hand-crafted schemes. Our framework supports general energy models, partition function computations, recursive substructures and partial folding, and could pave the way for algebraic dynamic programming beyond the context-free case.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite powered by deep neural networks to rapidly analyze single-, two- and three-color single-molecule data, especially from single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Deep-LASI automatically sorts recorded traces, determines FRET correction factors and classifies the state transitions of dynamic traces all in ~20-100 ms per trajectory. We benchmarked Deep-LASI using ground truth simulations as well as experimental data analyzed manually by an expert user and compared the results with a conventional Hidden Markov Model analysis. We illustrate the capabilities of the technique using a highly tunable L-shaped DNA origami structure and use Deep-LASI to perform titrations, analyze protein conformational dynamics and demonstrate its versatility for analyzing both total internal reflection fluorescence microscopy and confocal smFRET data.

Micro-compartmentalized strand displacement reactions with a random pool background

Toehold-mediated strand displacement (TMSD) is a widely used process in dynamic DNA nanotechnology, which has been applied for the actuation of molecular devices, in biosensor applications, and for DNA-based molecular computation. Similar processes also occur in a biological context, when RNA strands invade secondary structures or duplexes of other RNA or DNA molecules. Complex reaction environments-inside cells or synthetic cells-potentially contain a large number of competing nucleic acid molecules that transiently bind to the components of the strand displacement reaction of interest and thus slow down its kinetics. Here, we investigate the kinetics of TMSD reactions compartmentalized into water-in-oil emulsion droplets-in both the presence and absence of a random sequence background-using a droplet microfluidic 'stopped flow' set-up. The set-up enables one to determine the kinetics within thousands of droplets and easily vary experimental parameters such as the stoichiometry of the TMSD components. While the average kinetics in the droplets coincides precisely with the bulk behaviour, we observe considerable variability among the droplets. This variability is partially explained by the encapsulation procedure itself, but appears to be more pronounced in reactions involving a random pool background.

Biomolecule-Driven Two-Factor Authentication Strategy for Access Control of Molecular Devices

The rise of DNA nanotechnology is promoting the development of molecular security devices and marking an essential change in information security technology, to one that can resist the threats resulting from the increase in computing power, brute force attempts, and quantum computing. However, developing a secure and reliable access control strategy to guarantee the confidentiality of molecular security devices is still a challenge. Here, a biomolecule-driven two-factor authentication strategy for access control of molecular devices is developed. Importantly, the two-factor is realized by applying the specificity and nicking properties of the nicking enzyme and the programmable design of the DNA sequence, endowing it with the characteristic of a one-time password. To demonstrate the feasibility of this strategy, an access control module is designed and integrated to further construct a role-based molecular access control device. By constructing a command library composed of three commands (Ca, Cb, Ca and Cb), the authorized access of three roles in the molecular device is realized, in which the command Ca corresponds to the authorization of role A, Cb corresponds to the authorization of role B, and Ca and Cb corresponds to the authorization of role C. In this way, when users access the device, they not only need the correct factor but also need to apply for role authorization in advance to obtain secret information. This strategy provides a highly robust method for the research on access control of molecular devices and lays the foundation for research on the next generation of information security.

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.

Leveraging Steric Moieties for Kinetic Control of DNA Strand Displacement Reactions

DNA strand displacement networks are a critical part of dynamic DNA nanotechnology and are proven primitives for implementing chemical reaction networks. Precise kinetic control of these networks is important for their use in a range of applications. Among the better understood and widely leveraged kinetic properties of these networks are toehold sequence, length, composition, and location. While steric hindrance has been recognized as an important factor in such systems, a clear understanding of its impact and role is lacking. Here, a systematic investigation of steric hindrance within a DNA toehold-mediated strand displacement network was performed through tracking kinetic reactions of reporter complexes with incremental concatenation of steric moieties near the toehold. Two subsets of steric moieties were tested with systematic variation of structures and reaction conditions to isolate sterics from electrostatics. Thermodynamic and coarse-grained computational modeling was performed to gain further insight into the impacts of steric hindrance. Steric factors yielded up to 3 orders of magnitude decrease in the reaction rate constant. This pronounced effect demonstrates that steric moieties can be a powerful tool for kinetic control in strand displacement networks while also being more broadly informative of DNA structural assembly in both DNA-based therapeutic and diagnostic applications that possess elements of steric hindrance through DNA functionalization with an assortment of chemistries.

Machine learning-aided scoring of synthesis difficulties for designer chromosomes

Designer chromosomes are artificially synthesized chromosomes. Nowadays, these chromosomes have numerous applications ranging from medical research to the development of biofuels. However, some chromosome fragments can interfere with the chemical synthesis of designer chromosomes and eventually limit the widespread use of this technology. To address this issue, this study aimed to develop an interpretable machine learning framework to predict and quantify the synthesis difficulties of designer chromosomes in advance. Through the use of this framework, six key sequence features leading to synthesis difficulties were identified, and an eXtreme Gradient Boosting model was established to integrate these features. The predictive model achieved high-quality performance with an AUC of 0.895 in cross-validation and an AUC of 0.885 on an independent test set. Based on these results, the synthesis difficulty index (S-index) was proposed as a means of scoring and interpreting synthesis difficulties of chromosomes from prokaryotes to eukaryotes. The findings of this study emphasize the significant variability in synthesis difficulties between chromosomes and demonstrate the potential of the proposed model to predict and mitigate these difficulties through the optimization of the synthesis process and genome rewriting.

Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.

A split ribozyme that links detection of a native RNA to orthogonal protein outputs

Individual RNA remains a challenging signal to synthetically transduce into different types of cellular information. Here, we describe Ribozyme-ENabled Detection of RNA (RENDR), a plug-and-play strategy that uses cellular transcripts to template the assembly of split ribozymes, triggering splicing reactions that generate orthogonal protein outputs. To identify split ribozymes that require templating for splicing, we use laboratory evolution to evaluate the activities of different split variants of the Tetrahymena thermophila ribozyme. The best design delivers a 93-fold dynamic range of splicing with RENDR controlling fluorescent protein production in response to an RNA input. We further resolve a thermodynamic model to guide RENDR design, show how input signals can be transduced into diverse outputs, demonstrate portability across different bacteria, and use RENDR to detect antibiotic-resistant bacteria. This work shows how transcriptional signals can be monitored in situ and converted into different types of biochemical information using RNA synthetic biology.

A biophysical study of the interactions of palladium(II), platinum(II) and gold(III) complexes of aminopyridyl-2,2'-bipyridine ligands with RNAs and other nucleic acid structures

Metal compounds form an attractive class of ligands for a variety of nucleic acids. Five metal complexes bearing aminopyridyl-2,2'-bipyridine tetradentate ligands and possessing a quasi-planar geometry were challenged toward different types of nucleic acid molecules including RNA polynucleotides in the duplex or triplex form, an RNA Holliday four-way junction, natural double helix DNA and a DNA G-quadruplex. The binding process was monitored comparatively using different spectroscopic and melting methods. The binding preferences that emerge from our analysis are discussed in relation to the structural features of the metal complexes.

Toehold-Mediated Strand Displacement in Random Sequence Pools

Toehold-mediated strand displacement (TMSD) has been used extensively for molecular sensing and computing in DNA-based molecular circuits. As these circuits grow in complexity, sequence similarity between components can lead to cross-talk, causing leak, altered kinetics, or even circuit failure. For small non-biological circuits, such unwanted interactions can be designed against. In environments containing a huge number of sequences, taking all possible interactions into account becomes infeasible. Therefore, a general understanding of the impact of sequence backgrounds on TMSD reactions is of great interest. Here, we investigate the impact of random DNA sequences on TMSD circuits. We begin by studying individual interfering strands and use the obtained data to build machine learning models that estimate kinetics. We then investigate the influence of pools of random strands and find that the kinetics are determined by only a small subpopulation of strongly interacting strands. Consequently, their behavior can be mimicked by a small collection of such strands. The equilibration of the circuit with the background sequences strongly influences this behavior, leading to up to 1 order of magnitude difference in reaction speed. Finally, we compare two established and one novel technique that speed up TMSD reactions in random sequence pools: a three-letter alphabet, protection of toeholds by intramolecular secondary structure, or by an additional blocking strand. While all of these techniques were useful, only the latter can be used without sequence constraints. We expect that our insights will be useful for the construction of TMSD circuits that are robust to molecular noise.

Zur and zinc increase expression of E. coli ribosomal protein L31 through RNA-mediated repression of the repressor L31p

Bacteria can adapt in response to numerous stress conditions. One such stress condition is zinc depletion. The zinc-sensing transcription factor Zur regulates the way numerous bacterial species respond to severe changes in zinc availability. Under zinc sufficient conditions, Zn-loaded Zur (Zn2-Zur) is well-known to repress transcription of genes encoding zinc uptake transporters and paralogues of a few ribosomal proteins. Here, we report the discovery and mechanistic basis for the ability of Zur to up-regulate expression of the ribosomal protein L31 in response to zinc in E. coli. Through genetic mutations and reporter gene assays, we find that Zur achieves the up-regulation of L31 through a double repression cascade by which Zur first represses the transcription of L31p, a zinc-lacking paralogue of L31, which in turn represses the translation of L31. Mutational analyses show that translational repression by L31p requires an RNA hairpin structure within the l31 mRNA and involves the N-terminus of the L31p protein. This work uncovers a new genetic network that allows bacteria to respond to host-induced nutrient limiting conditions through a sophisticated ribosomal protein switching mechanism.

ClampFISH 2.0 enables rapid, scalable amplified RNA detection in situ

RNA labeling in situ has enormous potential to visualize transcripts and quantify their levels in single cells, but it remains challenging to produce high levels of signal while also enabling multiplexed detection of multiple RNA species simultaneously. Here, we describe clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method. We leverage the increased throughput afforded by multiplexed signal amplification and sequential detection to detect 10 different RNA species in more than 1 million cells. We also show that clampFISH 2.0 works in tissue sections. We expect that the advantages offered by clampFISH 2.0 will enable many applications in spatial transcriptomics.

DNA circuits compatible encoder and demultiplexer based on a single biomolecular platform with DNA strands as outputs

A series of multiple logic circuits based on a single biomolecular platform is constructed to perform nonarithmetic and arithmetic functions, including 4-to-2 encoder, 1-to-2 demultiplexer, 1-to-4 demultiplexer, and multi-input OR gate. The encoder to a DNA circuit is the equivalent of a sensory receptor to a reflex arc. They all function to encode information from outside the pathway (DNA circuit or reflex arc) into a form that subsequent pathways can recognize and utilize. Current molecular encoders are based on optical or electrical signals as outputs, while DNA circuits are based on DNA strands as transmission signals. The output of existing encoders cannot be recognized by subsequent DNA circuits. It is the first time the DNA-based encoder with DNA strands as outputs can be truly applied to the DNA circuit, enabling the application of DNA circuits in non-binary biological environments. Another novel feature of the designed system is that the developed nanodevices all have a simple structure, low leakage and low crosstalk, which allows them to implement higher-level encoders and demultiplexers easily. Our work is based on the idea of complex functionality in a simple form, which will also provide a new route for developing advanced molecular logic circuits.

The stability and number of nucleating interactions determine DNA hybridization rates in the absence of secondary structure

The kinetics of DNA hybridization are fundamental to biological processes and DNA-based technologies. However, the precise physical mechanisms that determine why different DNA sequences hybridize at different rates are not well understood. Secondary structure is one predictable factor that influences hybridization rates but is not sufficient on its own to fully explain the observed sequence-dependent variance. In this context, we measured hybridization rates of 43 different DNA sequences that are not predicted to form secondary structure and present a parsimonious physically justified model to quantify our observations. Accounting only for the combinatorics of complementary nucleating interactions and their sequence-dependent stability, the model achieves good correlation with experiment with only two free parameters. Our results indicate that greater repetition of Watson-Crick pairs increases the number of initial states able to proceed to full hybridization, with the stability of those pairings dictating the likelihood of such progression, thus providing new insight into the physical factors underpinning DNA hybridization rates.

Cellular Computational Logic Using Toehold Switches

The development of computational logic that carries programmable and predictable features is one of the key requirements for next-generation synthetic biological devices. Despite considerable progress, the construction of synthetic biological arithmetic logic units presents numerous challenges. In this paper, utilizing the unique advantages of RNA molecules in building complex logic circuits in the cellular environment, we demonstrate the RNA-only bitwise logical operation of XOR gates and basic arithmetic operations, including a half adder, a half subtractor, and a Feynman gate, in Escherichia coli. Specifically, de-novo-designed riboregulators, known as toehold switches, were concatenated to enhance the functionality of an OR gate, and a previously utilized antisense RNA strategy was further optimized to construct orthogonal NIMPLY gates. These optimized synthetic logic gates were able to be seamlessly integrated to achieve final arithmetic operations on small molecule inputs in cells. Toehold-switch-based ribocomputing devices may provide a fundamental basis for synthetic RNA-based arithmetic logic units or higher-order systems in cells.

Programmable siRNA pro-drugs that activate RNAi activity in response to specific cellular RNA biomarkers

Since Paul Ehrlich's introduction of the "magic bullet" concept in 1908, drug developers have been seeking new ways to target drug activity to diseased cells while limiting effects on normal tissues. In recent years, it has been proposed that coupling riboswitches capable of detecting RNA biomarkers to small interfering RNAs (siRNAs) to create siRNA pro-drugs could selectively activate RNA interference (RNAi) activity in specific cells. However, this concept has not been achieved previously. We report here that we have accomplished this goal, validating a simple and programmable new design that functions reliably in mammalian cells. We show that these conditionally activated siRNAs (Cond-siRNAs) can switch RNAi activity against different targets between clearly distinguished OFF and ON states in response to different cellular RNA biomarkers. Notably, in a rat cardiomyocyte cell line (H9C2), one version of our construct demonstrated biologically meaningful inhibition of a heart-disease-related target gene protein phosphatase 3 catalytic subunit alpha (PPP3CA) in response to increased expression of the pathological marker atrial natriuretic peptide (NPPA) messenger RNA (mRNA). Our results demonstrate the ability of synthetic riboswitches to regulate gene expression in mammalian cells, opening a new path for development of programmable siRNA pro-drugs.

CIRCprimerXL: Convenient and High-Throughput PCR Primer Design for Circular RNA Quantification

Circular RNA (circRNA) is a class of endogenous non-coding RNA characterized by a back-splice junction (BSJ). In general, large-scale circRNA BSJ detection is performed based on RNA sequencing data, followed by the selection and validation of circRNAs of interest using RT-qPCR with circRNA-specific PCR primers. Such a primer pair is convergent and functional on the circRNA template but divergent and non-functional on the linear host gene. Although a few circRNA primer design pipelines have been published, none of them offer large-scale, easy-to-use circRNA primer design. Other limitations are that these tools generally do not take into account assay specificity, secondary structures, and SNPs in the primer annealing regions. Furthermore, these tools are limited to circRNA primer design for humans (no other organisms possible), and no wet-lab validation is demonstrated. Here, we present CIRCprimerXL, a circRNA RT-qPCR assay design pipeline based on the primer design framework primerXL. CIRCprimerXL takes a circRNA BSJ position as input, and designs BSJ-spanning primers using Primer3. The user can choose to use the unspliced or spliced circRNA sequence as template. Prior to primer design, sequence regions with secondary structures and common SNPs are flagged. Next, the primers are filtered based on predicted specificity and the absence of secondary structures of the amplicon to select a suitable primer pair. Our tool is both available as a user-friendly web tool and as a stand-alone pipeline based on Docker and Nextflow, allowing users to run the pipeline on a wide range of computer infrastructures. The CIRCprimerXL Nextflow pipeline can be used to design circRNA primers for any species by providing the appropriate reference genome. The CIRCprimerXL web tool supports circRNA primer design for human, mouse, rat, zebrafish, Xenopus tropicalis, and C. elegans. The design process can easily be scaled up for the qPCR assay design of tens of thousands of circRNAs within a couple of hours. We show how CIRCprimerXL has been successfully used to design qPCR assays for over 15,000 human circRNAs of which 20 were empirically validated. CIRCprimerXL software, documentation, and test data can be found at: https://github.com/OncoRNALab/CIRCprimerXL. CIRCprimerXL is also implemented as a webtool at: https://circprimerxl.cmgg.be.

Molecular dynamics simulation‐guided toehold mediated strand displacement probe for single‐nucleotide variants detection

Single nucleotide variant (SNV) has become an emerging biomarker for various diseases such as cancers and infectious diseases. Toehold‐mediated strand displacement (TMSD), the core reaction of DNA nanotechnology, has been widely leveraged to identify SNVs. However, inappropriate choice of mismatch location results in poor discrimination ability. Here, we comprehensively investigate the effect of mismatch location on TMSD kinetics by molecular dynamic simulation tool oxDNA through umbrella sampling and forward flux sampling disclosing that mismatches at the border of the toehold and branch migration domain yield the lowest TMSD reaction rate. Nine disease‐related SNVs (SARS‐CoV‐2‐D614G, EGFR‐L858R, EGFR‐T790M, KRAS‐G12R, etc.) were tested experimentally showing a good agreement with simulation. The best choice of mismatch location enables high discrimination factor with a median of 124 for SNV and wild type. Coupling with a probe‐sink system, a low variant allele frequency of 0.1% was detected with 3 S/N. We successfully used the probes to detect SNVs with high confidence in the PCR clones of constructed plasmids. This work provides mechanistic insights into TMSD process at the single‐nucleotide level and can be a guidance for the design of TMSD system with fine‐tuning kinetics for various applications in biosensors and nanotechnology.

A biosensor based on interchain reactions for the detection of acetamipirid and the construction of basics logic gates OR and AND

An enzyme-free and label-free fluorescent DNA aptasensor was constructed with computer assistance based on thermodynamic deviation driving interchain reactions. In this work, in the presence of target acetamiprid, the released trigger strand C-apt could open hairpin Hp1, which in turn triggered the strand displacement reaction and catalyzed the self-assembly of hairpins Hp1 and Hp2, so that the guanine base rich stem in Hp2 was opened. In the presence of K+ and NMM, the G-rich moiety could form a G-quadruplex and emit strong fluorescence at a specific excitation wavelength. The proposed strategy enables sensitive detection of acetamiprid at concentrations as low as 54.3 pM. Most importantly, computer-assisted analysis of the thermodynamic properties of nucleic acid strands and simulation of the reaction process and conditions of the proposed model before conducting biological experiments theoretically proves this strategy feasible and may simplify subsequent biological experiments. In addition, basic molecular logic gates, including OR and AND, were constructed based on this detection principle, and simulation tests and biological experiments were performed. The final results show that this strategy can not only have some applications in the field of food safety and environmental monitoring, but also provide a certain way for the development of molecular logic computing.

Detection of pks Island mRNAs Using Toehold Sensors in Escherichia coli

Synthetic biologists have applied biomolecular engineering approaches toward the goal of novel biological devices and have shown progress in diverse areas of medicine and biotechnology. Especially promising is the application of synthetic biological devices towards a novel class of molecular diagnostics. As an example, a de-novo-designed riboregulator called toehold switch, with its programmability and compatibility with field-deployable devices showed promising in vitro applications for viral RNA detection such as Zika and Corona viruses. However, the in vivo application of high-performance RNA sensors remains challenging due to the secondary structure of long mRNA species. Here, we introduced ‘Helper RNAs’ that can enhance the functionality of toehold switch sensors by mitigating the effect of secondary structures around a target site. By employing the helper RNAs, previously reported mCherry mRNA sensor showed improved fold-changes in vivo. To further generalize the Helper RNA approaches, we employed automatic design pipeline for toehold sensors that target the essential genes within the pks island, an important target of biomedical research in connection with colorectal cancer. The toehold switch sensors showed fold-changes upon the expression of full-length mRNAs that apparently depended sensitively on the identity of the gene as well as the predicted local structure within the target region of the mRNA. Still, the helper RNAs could improve the performance of toehold switch sensors in many instances, with up to 10-fold improvement over no helper cases. These results suggest that the helper RNA approaches can further assist the design of functional RNA devices in vivo with the aid of the streamlined automatic design software developed here. Further, our solutions for screening and stabilizing single-stranded region of mRNA may find use in other in vivo mRNA-sensing applications such as cas13 crRNA design, transcriptome engineering, and trans-cleaving ribozymes.

Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning

Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows.

PaintSHOP enables the interactive design of transcriptome- and genome-scale oligonucleotide FISH experiments

Fluorescence in situ hybridization (FISH) allows researchers to visualize the spatial position and quantity of nucleic acids in fixed samples. Recently, considerable progress has been made in developing oligonucleotide (oligo)-based FISH methods that have enabled researchers to study the three-dimensional organization of the genome at super-resolution and visualize the spatial patterns of gene expression for thousands of genes in individual cells. However, there are few existing computational tools to support the bioinformatics workflows necessary to carry out these experiments utilizing oligo FISH probes. Here, we introduce Paint Server and Homology Optimization Pipeline (PaintSHOP), an interactive platform for the design of oligo FISH experiments. PaintSHOP enables researchers to identify probes for their experimental targets efficiently, to incorporate additional necessary sequences such as primer pairs, and to easily generate files documenting library design. PaintSHOP democratizes and standardizes the process of designing complex probe sets for the oligo FISH community.

Paint Server and Homology Optimization Pipeline (PaintSHOP), an interactive platform for the design of oligo FISH experiments, democratizes and standardizes the process of designing complex probe sets for the oligo FISH community.