High-throughput cellular RNA device engineering

Non-invasive single cell aptasensing in live cells and animals

We report a genetically encoded aptamer biosensor platform for non-invasive measurement of drug distribution in cells and animals. We combined the high specificity of aptamer molecular recognition with the easy-to-detect properties of fluorescent proteins. We generated six encoded aptasensors, showcasing the platform versatility. The biosensors display high sensitivity and specificity for detecting their specific drug target over related analogs. We show dose dependent response of biosensor performance reaching saturating drug uptake levels in individual live cells. We designed our platform for integration into animal genomes; thus, we incorporated aptamer biosensors into zebrafish, an important model vertebrate. The biosensors enabled non-invasive drug biodistribution imaging in whole animals across different timepoints. To our knowledge, this is the first example of an aptamer biosensor-expressing transgenic vertebrate that is carried through generations. As such, our encoded platform addresses the need for non-invasive whole animal biosensing ideal for pharmacokinetic-pharmacodynamic analyses that can be expanded to other organisms and to detect diverse molecules of interest.

Deep generative design of RNA family sequences

RNA engineering has immense potential to drive innovation in biotechnology and medicine. Despite its importance, a versatile platform for the automated design of functional RNA is still lacking. Here, we propose RNA family sequence generator (RfamGen), a deep generative model that designs RNA family sequences in a data-efficient manner by explicitly incorporating alignment and consensus secondary structure information. RfamGen can generate novel and functional RNA family sequences by sampling points from a semantically rich and continuous representation. We have experimentally demonstrated the versatility of RfamGen using diverse RNA families. Furthermore, we confirmed the high success rate of RfamGen in designing functional ribozymes through a quantitative massively parallel assay. Notably, RfamGen successfully generates artificial sequences with higher activity than natural sequences. Overall, RfamGen significantly improves our ability to design functional RNA and opens up new potential for generative RNA engineering in synthetic biology.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.

Multifunctional wet-adhesive chitosan/acrylic conduit for sutureless repair of peripheral nerve injuries

The incidence of peripheral nerve injury (PNI) is high worldwide, and a poor prognosis is common. Surgical closure and repair of the affected area are crucial to ensure the effective treatment of peripheral nerve injuries. Despite being the standard treatment approach, reliance on sutures to seal the severed nerve ends introduces several limitations and restrictions. This technique is intricate and time-consuming, and the application of threading and punctate sutures may lead to tissue damage and heightened tension concentrations, thus increasing the risk of fixation failure and local inflammation. This study aimed to develop easily implantable chitosan-based peripheral nerve repair conduits that combine acrylic acid and cleavable N-hydroxysuccinimide to reduce nerve damage during repair. In ex vivo tissue adhesion tests, the conduit achieved maximal interfacial toughness of 705 J m-2 ± 30 J m-2, allowing continuous bridging of the severed nerve ends. Adhesive repair significantly reduces local inflammation caused by conventional sutures, and the positive charge of chitosan disrupts the bacterial cell wall and reduces implant-related infections. This promises to open new avenues for sutureless nerve repair and reliable medical implants.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Engineering U1-Based Tetracycline-Inducible Riboswitches to Control Gene Expression in Mammals

Synthetic riboswitches are promising regulatory devices due to their small size, lack of immunogenicity, and ability to fine-tune gene expression in the absence of exogenous trans-acting factors. Based on a gene inhibitory system developed at our lab, termed U1snRNP interference (U1i), we developed tetracycline (TC)-inducible riboswitches that modulate mRNA polyadenylation through selective U1 snRNP recruitment. First, we engineered different TC-U1i riboswitches, which repress gene expression unless TC is added, leading to inductions of gene expression of 3-to-4-fold. Second, we developed a technique called Systematic Evolution of Riboswitches by Exponential Enrichment (SEREX), to isolate riboswitches with enhanced U1 snRNP binding capacity and activity, achieving inducibilities of up to 8-fold. Interestingly, by multiplexing riboswitches we increased inductions up to 37-fold. Finally, we demonstrated that U1i-based riboswitches are dose-dependent and reversible and can regulate the expression of reporter and endogenous genes in culture cells and mouse models, resulting in attractive systems for gene therapy applications. Our work probes SEREX as a much-needed technology for the in vitro identification of riboswitches capable of regulating gene expression in vivo.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

Trans-acting aptazyme for conditional gene knockdown in eukaryotic cells

Trans-acting hammerhead ribozyme inherits the advantages of being the smallest and best-characterized RNA-cleaving ribozyme, offering high modularity and the ability to cleave any desired sequence without the aid of any protein, as long as the target sequence contains a cleavage site. However, achieving precise control over the trans-acting hammerhead ribozyme would enable safer and more accurate regulation of gene expression. Herein, we described an intracellular selection of hammerhead aptazyme that contains a theophylline aptamer on stem II based on toxin protein IbsC. Based on the intracellular selection, we obtained three new cis-acting hammerhead aptazymes. Moreover, the corresponding trans-acting aptazymes could be efficiently induced by theophylline to knock down different targeted genes in eukaryotic cells. Notably, the best one, T195, exhibited a ligand-dependent and dose-dependent response to theophylline, and the cleavage efficiency could be enhanced by incorporating multiplex aptazymes.

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.

Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors

Quantification of the concentration of particular cellular metabolites reports on the actual utilization of metabolic pathways in physiological and pathological conditions. Metabolite concentration also constitutes the readout for screening cell factories in metabolic engineering. However, there are no direct approaches that allow for real-time assessment of the levels of intracellular metabolites in single cells. In recent years, the modular architecture of natural bacterial RNA riboswitches has inspired the design of genetically encoded synthetic RNA devices that convert the intracellular concentration of a metabolite into a quantitative fluorescent signal. These so-called RNA-based sensors are composed of a metabolite-binding RNA aptamer as the sensor domain, connected through an actuator segment to a signal-generating reporter domain. However, at present, the variety of available RNA-based sensors for intracellular metabolites is still very limited. Here, we go through natural mechanisms for metabolite sensing and regulation in cells across all kingdoms, focusing on those mediated by riboswitches. We review the design principles underlying currently developed RNA-based sensors and discuss the challenges that hindered the development of novel sensors and recent strategies to address them. We finish by introducing the current and potential applicability of synthetic RNA-based sensors for intracellular metabolites.

RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

Engineering Ag43 Signal Peptides with Bacterial Display and Selection

Protein display, secretion, and export in prokaryotes are essential for utilizing microbial systems as engineered living materials, medicines, biocatalysts, and protein factories. To select for improved signal peptides for Escherichia coli protein display, we utilized error-prone polymerase chain reaction (epPCR) coupled with single-cell sorting and microplate titer to generate, select, and detect improved Ag43 signal peptides. Through just three rounds of mutagenesis and selection using green fluorescence from the 56 kDa sfGFP-beta-lactamase, we isolated clones that modestly increased surface display from 1.4- to 3-fold as detected by the microplate plate-reader and native SDS-PAGE assays. To establish that the functional protein was displayed extracellularly, we trypsinized the bacterial cells to release the surface displayed proteins for analysis. This workflow demonstrated a fast and high-throughput method leveraging epPCR and single-cell sorting to augment bacterial surface display rapidly that could be applied to other bacterial proteins.

Highly multiplexed selection of RNA aptamers against a small molecule library

Applications of synthetic biology spanning human health, industrial bioproduction, and ecosystem monitoring often require small molecule sensing capabilities, typically in the form of genetically encoded small molecule biosensors. Critical to the deployment of greater numbers of these systems are methods that support the rapid development of such biosensors against a broad range of small molecule targets. Here, we use a previously developed method for selection of RNA biosensors against unmodified small molecules (DRIVER) to perform a selection against a densely multiplexed mixture of small molecules, representative of those employed in high-throughput drug screening. Using a mixture of 5,120 target compounds randomly sampled from a large diversity drug screening library, we performed a 95-round selection and then analyzed the enriched RNA biosensor library using next generation sequencing (NGS). From our analysis, we identified RNA biosensors with at least 2-fold change in signal in the presence of at least 217 distinct target compounds with sensitivities down to 25 nM. Although many of these biosensors respond to multiple targets, clustering analysis indicated at least 150 different small-molecule sensing patterns. We also built a classifier that was able to predict whether the biosensors would respond to a new compound with an average precision of 0.82. Since the target compound library was designed to be representative of larger diversity compound libraries, we expect that the described approach can be used with similar compound libraries to identify aptamers against other small molecules with a similar success rate. The new RNA biosensors (or their component aptamers) described in this work can be further optimized and used in applications such as biosensing, gene control, or enzyme evolution. In addition, the data presented here provide an expanded compendium of new RNA aptamers compared to the 82 small molecule RNA aptamers published in the literature, allowing further bioinformatic analyses of the general classes of small molecules for which RNA aptamers can be found.

Engineering synthetic RNA devices for cell control

The versatility of RNA in sensing and interacting with small molecules, proteins and other nucleic acids while encoding genetic instructions for protein translation makes it a powerful substrate for engineering biological systems. RNA devices integrate cellular information sensing, processing and actuation of specific signals into defined functions and have yielded programmable biological systems and novel therapeutics of increasing sophistication. However, challenges centred on expanding the range of analytes that can be sensed and adding new mechanisms of action have hindered the full realization of the field's promise. Here, we describe recent advances that address these limitations and point to a significant maturation of synthetic RNA-based devices.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Dynamic RNA fitness landscapes of a group I ribozyme during changes to the experimental environment

Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl₂). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.

Engineered l-Lactate Responding Promoter System Operating in Glucose-Rich and Anoxic Environments

Bacteria equipped with genetically encoded lactate biosensors are promising tools for biopharmaceutical production, diagnostics, and cellular therapies. However, many applications involve glucose-rich and anoxic environments, in which current whole-cell lactate biosensors show low performance. Here we engineer an optimized, synthetic lactate biosensor system by repurposing the natural LldPRD promoter regulated by the LldR transcriptional regulator. We removed glucose catabolite and anoxic repression by designing a hybrid promoter, containing LldR operators and tuned both regulator and reporter gene expressions to optimize biosensor signal-to-noise ratio. The resulting lactate biosensor, termed ALPaGA (A Lactate Promoter Operating in Glucose and Anoxia), can operate in glucose-rich, aerobic and anoxic conditions. We show that ALPaGA works reliably in the probiotic chassisEscherichia coliNissle 1917 and can detect endogenous l-lactate produced by 3D tumor spheroids with an improved dynamic range. In the future, the ALPaGA system could be used to monitor bioproduction processes and improve the specificity of engineered bacterial cancer therapies by restricting their activity to the lactate-rich microenvironment of solid tumors.

Sweet sensation: Developing a single-cell fluorescent reporter of glycolytic heterogeneity

Conversion of in vitro selected aptamers into functional metabolic sensors is hampered by reduced in vivo aptamer binding and limited tunability of cellular metabolite levels. In this issue of Cell Chemical Biology, Ortega et al. (2021) construct RNA sensors of fructose-6-bisphosphate (FBP) that report on metabolite levels within single yeast cells.

Linking genome variants to disease: scalable approaches to test the functional impact of human mutations

The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.

Efficient splicing-based RNA regulators for tetracycline-inducible gene expression in human cell culture and C. elegans

Synthetic riboswitches gain increasing interest for controlling transgene expression in diverse applications ranging from synthetic biology, functional genomics, and pharmaceutical target validation to potential therapeutic approaches. However, existing systems often lack the pharmaceutically suited ligands and dynamic responses needed for advanced applications. Here we present a series of synthetic riboswitches for controlling gene expression through the regulation of alternative splicing. Placing the 5′-splice site into a stem structure of a tetracycline-sensing aptamer allows us to regulate the accessibility of the splice site. In the presence of tetracycline, an exon with a premature termination codon is skipped and gene expression can occur, whereas in its absence the exon is included into the coding sequence, repressing functional protein expression. We were able to identify RNA switches controlling protein expression in human cells with high dynamic ranges and different levels of protein expression. We present minimalistic versions of this system that circumvent the need to insert an additional exon. Further, we demonstrate the robustness of our approach by transferring the devices into the important research model organism Caenorhabditis elegans, where high levels of functional protein with very low background expression could be achieved.

Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae

This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.

Riboswitches for Controlled Expression of Therapeutic Transgenes Delivered by Adeno-Associated Viral Vectors

Vectors developed from adeno-associated virus (AAV) are powerful tools for in vivo transgene delivery in both humans and animal models, and several AAV-delivered gene therapies are currently approved for clinical use. However, AAV-mediated gene therapy still faces several challenges, including limited vector packaging capacity and the need for a safe, effective method for controlling transgene expression during and after delivery. Riboswitches, RNA elements which control gene expression in response to ligand binding, are attractive candidates for regulating expression of AAV-delivered transgene therapeutics because of their small genomic footprints and non-immunogenicity compared to protein-based expression control systems. In addition, the ligand-sensing aptamer domains of many riboswitches can be exchanged in a modular fashion to allow regulation by a variety of small molecules, proteins, and oligonucleotides. Riboswitches have been used to regulate AAV-delivered transgene therapeutics in animal models, and recently developed screening and selection methods allow rapid isolation of riboswitches with novel ligands and improved performance in mammalian cells. This review discusses the advantages of riboswitches in the context of AAV-delivered gene therapy, the subsets of riboswitch mechanisms which have been shown to function in human cells and animal models, recent progress in riboswitch isolation and optimization, and several examples of AAV-delivered therapeutic systems which might be improved by riboswitch regulation.

A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux

RNA-based sensors for intracellular metabolites are a promising solution to the emerging issue of metabolic heterogeneity. However, their development, i.e., the conversion of an aptamer into an in vivo-functional intracellular metabolite sensor, still harbors challenges. Here, we accomplished this for the glycolytic flux-signaling metabolite, fructose-1,6-bisphosphate (FBP). Starting from in vitro selection of an aptamer, we constructed device libraries with a hammerhead ribozyme as actuator. Using high-throughput screening in yeast with fluorescence-activated cell sorting (FACS), next-generation sequencing, and genetic-environmental perturbations to modulate the intracellular FBP levels, we identified a sensor that generates ratiometric fluorescent readout. An abrogated response in sensor mutants and occurrence of two sensor conformations-revealed by RNA structural probing-indicated in vivo riboswitching activity. Microscopy showed that the sensor can differentiate cells with different glycolytic fluxes within yeast populations, opening research avenues into metabolic heterogeneity. We demonstrate the possibility to generate RNA-based sensors for intracellular metabolites for which no natural metabolite-binding RNA element exits.

A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements

Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improved elucidation of the relationships between sequence, structure, and activity can improve our capacity for de novo rational design of ribozyme switches. Here, we generated data on the activity of hundreds of thousands of ribozyme sequences. Using automated structural analysis and machine learning, we leveraged these large data sets to develop predictive models that estimate the in vivo gene-regulatory activity of a ribozyme sequence. These models supported the de novo design of ribozyme libraries with low mean basal gene-regulatory activities and new ribozyme switches that exhibit changes in gene-regulatory activity in the presence of a target ligand, producing functional switches for four out of five aptamers. Our work examines how biases in the model and the data set that affect prediction accuracy can arise and demonstrates that machine learning can be applied to RNA sequences to predict gene-regulatory activity, providing the basis for design tools for functional RNAs.

A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Here, using aptamer-coupled ribozyme libraries and a ribozyme regeneration method, de novo rapid in vitro evolution of RNA biosensors (DRIVER) enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identify and validate biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors are applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors are also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

Biosensors are key to engineered biological systems. Here the authors demonstrate rapid de novo in vitro evolution of RNA biosensors of small molecules in a fully automated system.

Activation of Aptamers with Gain of Function by Small-Molecule-Clipping of Intramolecular Motifs

Click reactions have the advantages of high reactivity, excellent orthogonality, and synthetic accessibility. Inspired by click reactions, we propose the concept of "clipped aptamers", whose binding affinity is regulated by the "clip"-like specific interaction between a synthetic DNA-mismatch-binding small molecule (molecular glue, Z-NCTS) and the preset CGG/CGG sequences in nucleic acid sequences. In this study, we investigated a Z-NCTS-mediated de novo selection of clipped aptamers against epithelial cell adhesion molecule. The generated clipped aptamers can achieve the efficient transition from a binding-inactive state to an active state by clipping of Z-NCTS with two CGG sites, which otherwise would not hybridize. The experimental and simulation results showed that the clipped aptamer had ideal binding thermodynamics and the ability to regulate cellular adhesion. Because of this superior activated mechanism and structural diversity, clipped aptamers hold great potential in biosensing, imaging, conditional gene- and cellular behavior-regulation, and drug delivery.

Phased nucleotide inserts for sequencing low-diversity RNA samples from in vitro selection experiments

In vitro selection combined with high-throughput sequencing is a powerful experimental approach with broad application in the engineering and characterization of RNA molecules. Diverse pools of starting sequences used for selection are often flanked by fixed sequences used as primer binding sites. These low diversity regions often lead to data loss from complications with Illumina image processing algorithms. A common method to alleviate this problem is the addition of fragmented bacteriophage PhiX genome, which improves sequence quality but sacrifices a portion of usable sequencing reads. An alternative approach is to insert nucleotides of variable length and composition ("phased inserts") at the beginning of each molecule when adding sequencing adaptors. This approach preserves read depth but reduces the length of each read. Here, we test the ability of phased inserts to replace PhiX in a low-diversity sample generated for a high-throughput sequencing based ribozyme activity screen. We designed a pool of 4096 RNA sequence variants of the self-cleaving twister ribozyme from Oryza sativa For each unique sequence, we determined the fraction of ribozyme cleaved during in vitro transcription via deep sequencing on an Illumina MiSeq. We found that libraries with the phased inserts produced high-quality sequence data without the addition of PhiX. We found good agreement between previously published data on twister ribozyme variants and our data produced with phased inserts even when PhiX was omitted. We conclude that phased inserts can be implemented following in vitro selection experiments to reduce or eliminate the use of PhiX and maximize read depth.

Permutational analysis of Saccharomyces cerevisiae regulatory elements

Gene expression in Saccharomyces cerevisiae is regulated at multiple levels. Genomic and epigenomic mapping of transcription factors and chromatin factors has led to the delineation of various modular regulatory elements—enhancers (upstream activating sequences), core promoters, 5′ untranslated regions (5′ UTRs) and transcription terminators/3′ untranslated regions (3′ UTRs). However, only a few of these elements have been tested in combinations with other elements and the functional interactions between the different modular regulatory elements remain under explored. We describe a simple and rapid approach to build a combinatorial library of regulatory elements and have used this library to study 26 different enhancers, core promoters, 5′ UTRs and transcription terminators/3′ UTRs to estimate the contribution of individual regulatory parts in gene expression. Our combinatorial analysis shows that while enhancers initiate gene expression, core promoters modulate the levels of enhancer-mediated expression and can positively or negatively affect expression from even the strongest enhancers. Principal component analysis (PCA) indicates that enhancer and promoter function can be explained by a single principal component while UTR function involves multiple functional components. The PCA also highlights outliers and suggest differences in mechanisms of regulation by individual elements. Our data also identify numerous regulatory cassettes composed of different individual regulatory elements that exhibit equivalent gene expression levels. These data thus provide a catalog of elements that could in future be used in the design of synthetic regulatory circuits.

Model-driven generation of artificial yeast promoters

Promoters play a central role in controlling gene regulation; however, a small set of promoters is used for most genetic construct design in the yeast Saccharomyces cerevisiae. Generating and utilizing models that accurately predict protein expression from promoter sequences would enable rapid generation of useful promoters and facilitate synthetic biology efforts in this model organism. We measure the gene expression activity of over 675,000 sequences in a constitutive promoter library and over 327,000 sequences in an inducible promoter library. Training an ensemble of convolutional neural networks jointly on the two data sets enables very high (R² > 0.79) predictive accuracies on multiple sequence-activity prediction tasks. We describe model-guided design strategies that yield large, sequence-diverse sets of promoters exhibiting activities higher than those represented in training data and similar to current best-in-class sequences. Our results show the value of model-guided design as an approach for generating useful DNA parts.

A small set of promoters is used for most genetic construct design in S. cerevisiae. Here, the authors develop a predictive model of promoter activity trained on a data set of over one million sequences and use it to design large sets of high-activity promoters.

Intracellular Selection of Theophylline-Sensitive Hammerhead Aptazyme

Hammerhead ribozyme-based aptazyme (HHAz), inheriting the advantages of small size and high efficiency from the RNA-cleaving ribozyme and the specific recognition ability of aptamers to specific targets, exhibits the huge potential to be a transgene expression regulator. Herein, we report a selection strategy for HHAz by using a toxin protein IbsC as the reporter to offer a positive phenotype, thus realizing an easy-operating, time- and labor-saving selection of HHAz variants with desired properties. Based on this strategy, we obtained a new HHAz (TAP-1), which could react sensitively toward the extracellular regulatory molecule, theophylline, both in prokaryotic and eukaryotic systems. With fluorescent protein reporter, the intracellular switching efficiencies of TAP-1 and other reported theophylline-dependent HHAzs has been quantitatively evaluated, showing that TAP-1 not only exhibits the best downregulating ability at high concentration of theophylline but also maintains high activity with 0.1 mM theophylline, which is a safe concentration in the human body.

High-throughput identification of synthetic riboswitches by barcode-free amplicon-sequencing in human cells

Synthetic riboswitches mediating ligand-dependent RNA cleavage or splicing-modulation represent elegant tools to control gene expression in various applications, including next-generation gene therapy. However, due to the limited understanding of context-dependent structure-function relationships, the identification of functional riboswitches requires large-scale-screening of aptamer-effector-domain designs, which is hampered by the lack of suitable cellular high-throughput methods. Here we describe a fast and broadly applicable method to functionally screen complex riboswitch libraries (~1.8 × 104 constructs) by cDNA-amplicon-sequencing in transiently transfected and stimulated human cells. The self-barcoding nature of each construct enables quantification of differential mRNA levels without additional pre-selection or cDNA-manipulation steps. We apply this method to engineer tetracycline- and guanine-responsive ON- and OFF-switches based on hammerhead, hepatitis-delta-virus and Twister ribozymes as well as U1-snRNP polyadenylation-dependent RNA devices. In summary, our method enables fast and efficient high-throughput riboswitch identification, thereby overcoming a major hurdle in the development cascade for therapeutically applicable gene switches.

A Theophylline-Responsive Riboswitch Regulates Expression of Nuclear-Encoded Genes

Riboswitches are small cis-regulatory RNA elements that regulate gene expression by conformational changes in response to ligand binding. Synthetic riboswitches have been engineered as versatile and innovative tools for gene regulation by external application of their ligand in prokaryotes and eukaryotes. In plants, synthetic riboswitches were used to regulate gene expression in plastids, but the application of synthetic riboswitches for the regulation of nuclear-encoded genes in planta remains to be explored. Here, we characterize the properties of a theophylline-responsive synthetic aptazyme for control of nuclear-encoded transgenes in Arabidopsis (Arabidopsis thaliana). Activation of the aptazyme, inserted in the 3' UTR of the target gene, resulted in rapid self-cleavage and subsequent decay of the mRNA. This riboswitch allowed reversible, theophylline-dependent down-regulation of the GFP reporter gene in a dose- and time-dependent manner. Insertion of the riboswitch into the ONE HELIX PROTEIN1 gene allowed complementation of ohp1 mutants and induction of the mutant phenotype by theophylline. GFP and ONE HELIX PROTEIN1 transcript levels were downregulated by up to 90%, and GFP protein levels by 95%. These results establish artificial riboswitches as tools for externally controlled gene expression in synthetic biology in plants or functional crop design.

Aptamers in RNA-based switches of gene expression

The ability to control gene expression via small molecule effectors is important in basic research as well as in future gene therapy applications. Although transcription factor-based systems are widely used, they are not well suited for certain applications due to a lack of functionality, limited available coding space, and potential immunogenicity of the regulatory proteins. RNA-based switches fill this gap since they can be designed to respond to effector compounds utilizing ligand-sensing aptamers. These systems are very modular since the aptamer can be combined with a variety of different expression platforms. RNA-based switches have been constructed that allow for controlling gene expression in diverse contexts. Here we discuss latest developments and applications of aptamer-based gene expression switches in eukaryotes.

Contemporary Tools for Regulating Gene Expression in Bacteria

Insights from novel mechanistic paradigms in gene expression control have led to the development of new gene expression systems for bioproduction, control, and sensing applications. Coupled with a greater understanding of synthetic burden and modern creative biodesign approaches, contemporary bacterial gene expression tools and systems are emerging that permit fine-tuning of expression, enabling greater predictability and maximisation of specific productivity, while minimising deleterious effects upon cell viability. These advances have been achieved by using a plethora of regulatory tools, operating at all levels of the so-called 'central dogma' of molecular biology. In this review, we discuss these gene regulation tools in the context of their design, prototyping, integration into expression systems, and biotechnological application.

Massively parallel RNA device engineering in mammalian cells with RNA-Seq

Synthetic RNA-based genetic devices dynamically control a wide range of gene-regulatory processes across diverse cell types. However, the limited throughput of quantitative assays in mammalian cells has hindered fast iteration and interrogation of sequence space needed to identify new RNA devices. Here we report developing a quantitative, rapid and high-throughput mammalian cell-based RNA-Seq assay to efficiently engineer RNA devices. We identify new ribozyme-based RNA devices that respond to theophylline, hypoxanthine, cyclic-di-GMP, and folinic acid from libraries of ~22,700 sequences in total. The small molecule responsive devices exhibit low basal expression and high activation ratios, significantly expanding our toolset of highly functional ribozyme switches. The large datasets obtained further provide conserved sequence and structure motifs that may be used for rationally guided design. The RNA-Seq approach offers a generally applicable strategy for developing broad classes of RNA devices, thereby advancing the engineering of genetic devices for mammalian systems.

Next-level riboswitch development-implementation of Capture-SELEX facilitates identification of a new synthetic riboswitch

The development of synthetic riboswitches has always been a challenge. Although a number of interesting proof-of-concept studies have been published, almost all of these were performed with the theophylline aptamer. There is no shortage of small molecule-binding aptamers; however, only a small fraction of them are suitable for RNA engineering since a classical SELEX protocol selects only for high-affinity binding but not for conformational switching. We now implemented RNA Capture-SELEX in our riboswitch developmental pipeline to integrate the required selection for high-affinity binding with the equally necessary RNA conformational switching. Thus, we successfully developed a new paromomycin-binding synthetic riboswitch. It binds paromomycin with a KD of 20 nM and can discriminate between closely related molecules both in vitro and in vivo. A detailed structure-function analysis confirmed the predicted secondary structure and identified nucleotides involved in ligand binding. The riboswitch was further engineered in combination with the neomycin riboswitch for the assembly of an orthogonal Boolean NOR logic gate. In sum, our work not only broadens the spectrum of existing RNA regulators, but also signifies a breakthrough in riboswitch development, as the effort required for the design of sensor domains for RNA-based devices will in many cases be much reduced.

Synthetic biology strategies for microbial biosynthesis of plant natural products

Metabolic engineers endeavor to create a bio-based manufacturing industry using microbes to produce fuels, chemicals, and medicines. Plant natural products (PNPs) are historically challenging to produce and are ubiquitous in medicines, flavors, and fragrances. Engineering PNP pathways into new hosts requires finding or modifying a suitable host to accommodate the pathway, planning and implementing a biosynthetic route to the compound, and discovering or engineering enzymes for missing steps. In this review, we describe recent developments in metabolic engineering at the level of host, pathway, and enzyme, and discuss how the field is approaching ever more complex biosynthetic opportunities.

Evolving methods for rational de novo design of functional RNA molecules

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

Advances in engineered trans-acting regulatory RNAs and their application in bacterial genome engineering

Small noncoding RNAs, a large class of ancient posttranscriptional regulators, are increasingly recognized and utilized as key modulators of gene expression in a broad range of microorganisms. Owing to their small molecular size and the central role of Watson-Crick base pairing in defining their interactions, structure and function, numerous diverse types of trans-acting RNA regulators that are functional at the DNA, mRNA and protein levels have been experimentally characterized. It has become increasingly clear that most small RNAs play critical regulatory roles in many processes and are, therefore, considered to be powerful tools for genetic engineering and synthetic biology. The trans-acting regulatory RNAs accelerate this ability to establish potential framework for genetic engineering and genome-scale engineering, which allows RNA structure characterization, easier to design and model compared to DNA or protein-based systems. In this review, we summarize recent advances in engineered trans-acting regulatory RNAs that are used in bacterial genome-scale engineering and in novel cellular capabilities as well as their implementation in wide range of biotechnological, biological and medical applications.

Applications of high-throughput sequencing to analyze and engineer ribozymes

A large number of catalytic RNAs, or ribozymes, have been identified in the genomes of various organisms and viruses. Ribozymes are involved in biological processes such as regulation of gene expression and viral replication, but biological roles of many ribozymes still remain unknown. Ribozymes have also inspired researchers to engineer synthetic ribozymes that function as sensors or gene switches. To gain deeper understanding of the sequence-function relationship of ribozymes and to efficiently engineer synthetic ribozymes, a large number of ribozyme variants need to be examined which was limited to hundreds of sequences by Sanger sequencing. The advent of high-throughput sequencing technologies, however, has allowed us to sequence millions of ribozyme sequences at low cost. This review focuses on the recent applications of high-throughput sequencing to both characterize and engineer ribozymes, to highlight how the large-scale sequence data can advance ribozyme research and engineering.

Expanding the toolbox of synthetic riboswitches with guanine-dependent aptazymes

Artificial riboswitches based on ribozymes serve as versatile tools for ligand-dependent gene expression regulation. Advantages of these so-called aptazymes are their modular architecture and the comparably little coding space they require. A variety of aptamer-ribozyme combinations were constructed in the past 20 years and the resulting aptazymes were applied in diverse contexts in prokaryotic and eukaryotic systems. Most in vivo functional aptazymes are OFF-switches, while ON-switches are more advantageous regarding potential applications in e.g. gene therapy vectors. We developed new ON-switching aptazymes in the model organism Escherichia coli and in mammalian cell culture using the intensely studied guanine-sensing xpt aptamer. Utilizing a high-throughput screening based on fluorescence-activated cell sorting in bacteria we identified up to 9.2-fold ON-switches and OFF-switches with a dynamic range up to 32.7-fold. For constructing ON-switches in HeLa cells, we used a rational design approach based on existing tetracycline-sensitive ON-switches. We discovered that communication modules responding to tetracycline are also functional in the context of guanine aptazymes, demonstrating a high degree of modularity. Here, guanine-responsive ON-switches with a four-fold dynamic range were designed. Summarizing, we introduce a series of novel guanine-dependent ribozyme switches operative in bacteria and human cell culture that significantly broaden the existing toolbox.

Neomycin-dependent hammerhead ribozymes for the direct control of gene expression in Saccharomyces cerevisiae

Hammerhead ribozyme-based RNA switches have been proven to be powerful tools for conditional gene regulation in various organisms. We present neomycin-dependent hammerhead ribozymes (HHR) that influence gene expression in a ligand- and dose-dependent manner in S. cerevisiae. We utilized a novel design of fusing the aptamer domain to the HHR enabling for the first time the identification of genetic ON- and OFF-switches within the same library. For this purpose a neomycin aptamer was fused to stem 1 of a type 3 hammerhead ribozyme via an addressable three-way junction that shows high flexibility at the connection site. An in vivo screening approach identified sequences that allow to induce or repress gene expression 2- to 3-fold in response to neomycin addition. The ribozyme switches operate at neomycin concentrations that show no toxic effect on cell growth and pose powerful genetic tools to study and modulate cellular function in yeast.

RNA Switches for Synthetic Biology

In addition to coding for protein sequences, RNA molecules encode a diverse set of gene-regulatory elements. RNA switches are one class of gene-regulatory elements that control protein expression in a manner that is dependent on the concentration of specific ligand molecules. These allosteric gene-regulatory elements have been shown as useful tools in engineering diverse cell types to display novel function. In particular, RNA switches have been used as genetically encoded biosensors and conditional controllers to direct cellular decisions based on the system's changing environment. A significant focus in the field has been the generation of novel RNA switches that are tailored for different biological systems. We review approaches that have been used to generate RNA switches, which leverage the unique physical properties of RNA and the myriad ways in which RNA can modulate gene expression.

Attempted use of PACE for riboswitch discovery generates three new translational theophylline riboswitch side products

OBJECTIVE

The purpose of this project was to use an in vivo method to discover riboswitches that are activated by new ligands. We employed phage-assisted continuous evolution (PACE) to evolve new riboswitches in vivo. We started with one translational riboswitch and one transcriptional riboswitch, both of which were activated by theophylline. We used xanthine as the new target ligand during positive selection followed by negative selection using theophylline. The goal was to generate very large M13 phage populations that contained unknown mutations, some of which would result in new aptamer specificity. We discovered side products of three new theophylline translational riboswitches with different levels of protein production.

RESULTS

We used next generation sequencing to identify M13 phage that carried riboswitch mutations. We cloned and characterized the most abundant riboswitch mutants and discovered three variants that produce different levels of translational output while retaining their theophylline specificity. Although we were unable to demonstrate evolution of new riboswitch ligand specificity using PACE, we recommend careful design of recombinant M13 phage to avoid evolution of "cheaters" that short circuit the intended selection pressure.