A comprehensive web tool for toehold switch design

Regulatory RNAs in Bacillus subtilis: A review on regulatory mechanism and applications in synthetic biology

Bacteria exhibit a rich repertoire of RNA molecules that intricately regulate gene expression at multiple hierarchical levels, including small RNAs (sRNAs), riboswitches, and antisense RNAs. Notably, the majority of these regulatory RNAs lack or have limited protein-coding capacity but play pivotal roles in orchestrating gene expression by modulating transcription, post-transcription or translation processes. Leveraging and redesigning these regulatory RNA elements have emerged as pivotal strategies in the domains of metabolic engineering and synthetic biology. While previous investigations predominantly focused on delineating the roles of regulatory RNA in Gram-negative bacterial models such as Escherichia coli and Salmonella enterica, this review aims to summarize the mechanisms and functionalities of endogenous regulatory RNAs inherent to typical Gram-positive bacteria, notably Bacillus subtilis. Furthermore, we explore the engineering and practical applications of these regulatory RNA elements in the arena of synthetic biology, employing B. subtilis as a foundational chassis.

A comprehensive review of dengue with a focus on emerging solutions for precision and timely detection

Dengue is a global health problem, caused by the dengue virus (DENV), which belongs to the Flaviviridae family of viruses. The transmission of DENV occurs through vectors, Ae. aegypti and Ae. Albopictus mosquitoes, to the human host, classifying it as a vector-borne disease. The disease incidence is increasing at an alarming rate and needs to be tackled to reduce the morbidity and mortality caused by the disease. Environmental and clinical surveillance, detection of the virus, and diagnostics are critical tools to address this issue. In this comprehensive review, we explore various diagnostic techniques and the associated challenges within the context of dengue. While we briefly touch upon dengue's epidemiology, serotypes, and pathogenesis, our primary emphasis remains on diagnostics. We delve into the intricacies of these diagnostic methods, considering both the challenges they entail and the potential they hold in terms of accuracy and accessibility. It's important to note that the review does not extensively cover clinical aspects or regional variations of the disease.

Development of a cell-free toehold switch for hepatitis A virus type I on-site detection

Picornavirus hepatitis A virus (HAV) is a common cause of hepatitis worldwide. It is spread primarily through contaminated food and water or person-to-person contact. HAV I has been identified as the most common type of human HAV infection. Here, we have developed a cell-free toehold switch sensor for HAV I detection. We screened 10 suitable toehold switch sequences using NUPACK software, and the VP1 gene was used as the target gene. The optimal toehold switch sequence was selected by in vivo expression. The best toehold switch concentration was further found to be 20 nM in a cell-free system. 5 nM trigger RNA activated the toehold switch to generate visible green fluorescence. The minimum detection concentration decreased to 1 pM once combined with NASBA. HAV I trigger RNA could be detected accurately with excellent specificity. In addition, the cell-free toehold switch sensor was verified in HAV I entities. The successful construction of the cell-free toehold switch sensor provided a convenient, rapid, and accurate method for HAV I on-site detection, especially in developing countries, without the involvement of expensive facilities and additional professional operators.

"Toehold Switches; a foothold for Synthetic Biology"

Toehold switches are de novo designed riboregulators that contain two RNA components interacting through linear-linear RNA interactions, regulating the gene expression. These are highly versatile, exhibit excellent orthogonality, wide dynamic range, and are highly programmable, so can be used for various applications in synthetic biology. In this review, we summarized and discussed the design characteristics and benefits of toehold switch riboregulators over conventional riboregulators. We also discussed applications and recent advancements of toehold switch riboregulators in various fields like gene editing, DNA nanotechnology, translational repression, and diagnostics (detection of microRNAs and some pathogens). Toehold switches, therefore, furnished advancement in synthetic biology applications in various fields with their prominent features.

Current Metabolic Engineering Strategies for Photosynthetic Bioproduction in Cyanobacteria

Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO₂ and H₂O into O₂ and energy-rich organic compounds, thus enabling sustainable production of a wide range of bio-products. More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering.

A detailed landscape of CRISPR-Cas-mediated plant disease and pest management

Genome editing technology has rapidly evolved to knock-out genes, create targeted genetic variation, install precise insertion/deletion and single nucleotide changes, and perform large-scale alteration. The flexible and multipurpose editing technologies have started playing a substantial role in the field of plant disease management. CRISPR-Cas has reduced many limitations of earlier technologies and emerged as a versatile toolbox for genome manipulation. This review summarizes the phenomenal progress of the use of the CRISPR toolkit in the field of plant pathology. CRISPR-Cas toolbox aids in the basic studies on host-pathogen interaction, in identifying virulence genes in pathogens, deciphering resistance and susceptibility factors in host plants, and engineering host genome for developing resistance. We extensively reviewed the successful genome editing applications for host plant resistance against a wide range of biotic factors, including viruses, fungi, oomycetes, bacteria, nematodes, insect pests, and parasitic plants. Recent use of CRISPR-Cas gene drive to suppress the population of pathogens and pests has also been discussed. Furthermore, we highlight exciting new uses of the CRISPR-Cas system as diagnostic tools, which rapidly detect pathogenic microorganism. This comprehensive yet concise review discusses innumerable strategies to reduce the burden of crop protection.

Engineering Toehold-Mediated Switches for Native RNA Detection and Regulation in Bacteria

RNA switches are versatile tools in synthetic biology for sensing and regulation applications. The discoveries of RNA-mediated translational and transcriptional control have facilitated the development of complexde novodesigns of RNA switches. Specifically, RNA toehold-mediated switches, in which binding to the toehold sensing domain controls the transition between switch states via strand displacement, have been extensively adapted for coupling systems responses to specifictrans-RNA inputs. This review highlights some of the challenges associated with applying these switches for native RNA detectionin vivo, including transferability between organisms. The applicability and design considerations of toehold-mediated switches are discussed by highlighting twelve recently developed switch designs. This review finishes with future perspectives to address current gaps in the field, particularly regarding the power of structural prediction algorithms for improved in vivo functionality of RNA switches.

Toehold switch based biosensors for sensing the highly trafficked rosewood Dalbergia maritima

Nucleic acid sensing is a 3 decades old but still challenging area of application for different biological sub-domains, from pathogen detection to single cell transcriptomics analysis. The many applications of nucleic acid detection and identification are mostly carried out by PCR techniques, sequencing, and their derivatives used at large scale. However, these methods’ limitations on speed, cost, complexity and specificity have motivated the development of innovative detection methods among which nucleic acid biosensing technologies seem promising. Toehold switches are a particular class of RNA sensing devices relying on a conformational switch of secondary structure induced by the pairing of the detected trigger RNA with a de novo designed synthetic sensing mRNA molecule. Here we describe a streamlined methodology enabling the development of such a sensor for the RNA-mediated detection of an endangered plant species in a cell-free reaction system. We applied this methodology to help identify the rosewood Dalbergia maritima, a highly trafficked wood, whose protection is limited by the capacity of the authorities to distinguish protected logs from other unprotected but related species. The streamlined pipeline presented in this work is a versatile framework enabling cheap and rapid development of new sensors for custom RNA detection.

End-to-end computational approach to the design of RNA biosensors for detecting miRNA biomarkers of cervical cancer

Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Relatively scant awareness and limited access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative unified pipeline for diagnosis has been developed, beginning with identification of optimal biomarkers, concurrent design of toehold switch sensors, and finally simulation of the designed diagnostic circuits to assess performance. Using miRNA expression data in the public domain, we identified miR-21–5p and miR-20a-5p as blood-based miRNA biomarkers specific to early-stage cervical cancer employing a multi-tier algorithmic screening. Synthetic riboregulators called toehold switches specific to the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we used a generic grammar of these switches, and built a neural network model of dynamic range using thermodynamic features derived from mRNA secondary structure and interaction. Second-generation toehold switches were used to overcome the design challenges associated with miRNA biomarkers. The resultant model yielded an adj. R² ∼0.71, outperforming earlier models of toehold-switch dynamic range. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10 nM and 100 nM before saturation. Our study demonstrates an end-to-end computational workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic/nucleic-acid signatures. The approach has the potential to replace iterative experimental trial and error, and focus time, money, and efforts. All software including the toehold grammar parser, neural network model and reaction kinetics simulation are available as open-source software (https://github.com/SASTRA-iGEM2019) under GNU GPLv3 licence.

Combating Infectious Diseases with Synthetic Biology

Over the past decades, there have been numerous outbreaks, including parasitic, fungal, bacterial, and viral infections, worldwide. The rate at which infectious diseases are emerging is disproportionate to the rate of development for new strategies that could combat them. Therefore, there is an increasing demand to develop novel, specific, sensitive, and effective methods for infectious disease diagnosis and treatment. Designed synthetic systems and devices are becoming powerful tools to treat human diseases. The advancement in synthetic biology offers efficient, accurate, and cost-effective platforms for detecting and preventing infectious diseases. Herein we focus on the latest state of living theranostics and its implications.

Methods for the Development of Recombinant Microorganisms for the Production of Natural Products

Metabolic engineering strives to develop microbial strains that are capable of producing a target chemical in a biological organism. There are still many challenges to overcome in order to achieve titers, yields, and productivities necessary for industrial production. The use of recombinant microorganisms to meet these needs is the next step for metabolic engineers. In this chapter, we aim to provide insight on both the applications of metabolic engineering for natural product biosynthesis as well as optimization methods.

Detection and differentiation of respiratory syncytial virus subgroups A and B with colorimetric toehold switch sensors in a paper-based cell-free system

Respiratory syncytial virus (RSV) infection is the most common clinical infectious disease threatening the safety of human life. Herein, we provided a sensitive and specific method for detection and differentiation of RSV subgroups A (RSVA) and B (RSVB) with colorimetric toehold switch sensors in a paper-based cell-free system. In this method, we applied the toehold switch, an RNA-based riboswitch, to regulate the translation level of β-galactosidase (lacZ) gene. In the presence of target trigger RNA, the toehold switch sensor was activated and the expressed LacZ hydrolyzed chromogenic substrates to produce a colorimetric result that can be observed directly with the naked eye in a cell-free system. In addition, nucleic acid sequence-based amplification (NASBA) was used to improve the sensitivity by amplifying target trigger RNAs. Under optimal conditions, our method produced a visible result for the detection of RSVA and RSVB with the detection limit of 52 aM and 91 aM, respectively. The cross-reaction of this method was validated with other closely related respiratory viruses, including human coronavirus HKU1 (HCoV-HKU1), and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Furthermore, we used the paper-based carrier material that allows stable storage of our detection elements and rapid detection outside laboratory. In conclusion, this method can sensitively and specifically differentiate RSVA and RSVB and generate a visible colorimetric result without specialized operators and sophisticated equipment. Based on these advantages above, this method serves as a simple and portable detector in resource-poor areas and point-of-care testing (POCT) scenarios.

MoiRNAiFold: a novel tool for complex in silico RNA design

Novel tools for in silico design of RNA constructs such as riboregulators are required in order to reduce time and cost to production for the development of diagnostic and therapeutic advances. Here, we present MoiRNAiFold, a versatile and user-friendly tool for de novo synthetic RNA design. MoiRNAiFold is based on Constraint Programming and it includes novel variable types, heuristics and restart strategies for Large Neighborhood Search. Moreover, this software can handle dozens of design constraints and quality measures and improves features for RNA regulation control of gene expression, such as Translation Efficiency calculation. We demonstrate that MoiRNAiFold outperforms any previous software in benchmarking structural RNA puzzles from EteRNA. Importantly, with regard to biologically relevant RNA designs, we focus on RNA riboregulators, demonstrating that the designed RNA sequences are functional both in vitro and in vivo. Overall, we have generated a powerful tool for de novo complex RNA design that we make freely available as a web server (https://moiraibiodesign.com/design/).

Smart Nucleic Acids as Future Therapeutics

Nucleic acid therapeutics (NATs) hold promise in treating undruggable diseases and are recognized as the third major category of therapeutics in addition to small molecules and antibodies. Despite the milestones that NATs have made in clinical translation over the past decade, one important challenge pertains to increasing the specificity of this class of drugs. Activating NATs exclusively in disease-causing cells is highly desirable because it will safely broaden the application of NATs to a wider range of clinical indications. Smart NATs are triggered through a photo-uncaging reaction or a specific molecular input such as a transcript, protein, or small molecule, thus complementing the current strategy of targeting cells and tissues with receptor-specific ligands to enhance specificity. This review summarizes the programmable modalities that have been incorporated into NATs to build in responsive behaviors. We discuss the various inputs, transduction mechanisms, and output response functions that have been demonstrated to date.

Sequence-to-function deep learning frameworks for engineered riboregulators

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

A deep learning approach to programmable RNA switches

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04-0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

Domain-Specific Programming Languages for Computational Nucleic Acid Systems

The construction of models of system behavior is of great importance throughout science and engineering. In bioengineering and bionanotechnology, these often take the form of dynamic models that specify the evolution of different species over time. To ensure that scientific observations and conclusions are consistent and that systems can be reliably engineered on the basis of model predictions, it is important that models of biomolecular systems can be constructed in a reliable, principled, and efficient manner. This review focuses on efforts to address this need by using domain-specific programming languages as the basis for custom design tools for researchers working on computational nucleic acid devices, where a domain-specific language is simply a programming language tailored to a particular application domain. The underlying thesis of our review is that there is a continuum of practical implementation strategies for computational nucleic acid systems, which can all benefit from appropriate domain-specific languages and software design tools. We emphasize the need for specialized yet flexible tools that can be realized using domain-specific languages that compile to more general-purpose representations.

Developments of Riboswitches and Toehold Switches for Molecular Detection-Biosensing and Molecular Diagnostics

Riboswitches and toehold switches are considered to have potential for implementation in various fields, i.e., biosensing, metabolic engineering, and molecular diagnostics. The specific binding, programmability, and manipulability of these RNA-based molecules enable their intensive deployments in molecular detection as biosensors for regulating gene expressions, tracking metabolites, or detecting RNA sequences of pathogenic microorganisms. In this review, we will focus on the development of riboswitches and toehold switches in biosensing and molecular diagnostics. This review introduces the operating principles and the notable design features of riboswitches as well as toehold switches. Moreover, we will describe the advances and future directions of riboswitches and toehold switches in biosensing and molecular diagnostics.

Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators

RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages such as short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multilevel regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterize the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analyzed a prototype gate in vitro as well as in silico, employing parametrized ordinary differential equations (ODEs), for which parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. On the basis of this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.

Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression

Recent advances in our understanding of RNA folding and functions have facilitated the use of regulatory RNAs such as synthetic antisense RNAs (asRNAs) to modulate gene expression. However, despite the simple and universal complementarity rule, predictable asRNA-mediated repression is still challenging due to the intrinsic complexity of native asRNA-mediated gene regulation. To address this issue, we present a multivariate model, based on the change in free energy of complex formation (Δ G_CF) and percent mismatch of the target binding region, which can predict synthetic asRNA-mediated repression efficiency in diverse contexts. First, 69 asRNAs that bind to multiple target mRNAs were designed and tested to create the predictive model. Second, we showed that the same model is effective predicting repression of target genes in both plasmids and chromosomes. Third, using our model, we designed asRNAs that simultaneously modulated expression of a toxin and its antitoxin to demonstrate tunable control of cell growth. Fourth, we tested and validated the same model in two different biotechnologically important organisms: Escherichia coli Nissle 1917 and Bacillus subtilis 168. Last, multiple parameters, including target locations, the presence of an Hfq binding site, GC contents, and gene expression levels, were revisited to define the conditions under which the multivariate model should be used for accurate prediction. Together, 434 different strain-asRNA combinations were tested, validating the predictive model in a variety of contexts, including multiple target genes and organisms. The result presented in this study is an important step toward achieving predictable tunability of asRNA-mediated repression.