Engineering synthetic RNA devices for cell control

Advanced strategies of targeting circular RNAs as therapeutic approaches in colorectal cancer drug resistance

Colorectal cancer (CRC) stands second in terms of mortality and third among the highest prevalent kinds of cancer globally. CRC prevalence is rising in moderately and poorly developed regions and is greater in economically advanced regions. Despite breakthroughs in targeted therapy, resistance to chemotherapeutics remains a significant challenge in the long-term management of CRC. Circular RNAs (circRNAs) have been involved in growing cancer therapy resistance, particularly in CRC, according to an increasing number of studies in recent years. CircRNAs are one of the novel subclasses of non-coding RNAs, previously thought of as viroid. According to studies, circRNAs have been recommended as biological markers for therapeutic targets and diagnostic and prognostic purposes. That is particularly notable given that the expression of circRNAs has been linked to the hallmarks of CRC since they are responsible for drug resistance in CRC patients; thereby, circRNAs are significant for chemotherapy failure. Moreover, knowledge concerning circRNAs remains relatively unclear despite using all these advanced techniques. Here, in this study, we will go over the most recent published work to highlight the critical roles of circRNAs in CRC development and drug resistance and highlight the main strategies to overcome drug resistance to improve clinical outcomes.

Synthetic biology advances towards a bio-based society in the era of artificial intelligence

Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture, or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.

Advancing in vivo reprogramming with synthetic biology

Reprogramming cells will play a fundamental role in shaping the future of cell therapies by developing new strategies to engineer cells for improved performance and higher-order physiological functions. Approaches in synthetic biology harness cells' natural ability to sense diverse signals, integrate environmental inputs to make decisions, and execute complex behaviors based on the health of the organism or tissue. In this review, we highlight strategies in synthetic biology to reprogram cells, and discuss how recent approaches in the delivery of modified mRNA have created new opportunities to alter cell function in vivo. Finally, we discuss how combining concepts from synthetic biology and the delivery of mRNA in vivo could provide a platform for innovation to advance in vivo cellular reprogramming.

CRISPR-based gene expression platform for precise regulation of bladder cancer

The development of compact CRISPR systems has facilitated delivery but has concurrently reduced gene editing efficiency, thereby limiting the further utilization of CRISPR systems. Enhancing the efficiency of CRISPR systems poses a challenging task and holds significant implications for the advancement of biotechnology. In our work, we report a synthetic dual-antibody system that can stably exist in the intracellular environment, specifically inhibiting the functions of NF-κB and β-catenin. This not only elevates the transgenic expression of the CRISPR system by suppressing the innate immune response within cells to enhance the gene editing efficiency but also demonstrates a notable tumor inhibitory effect. Based on the specific output expression regulation of CRISPR-CasΦ, we constructed a CRISPR-based gene expression platform, which includes sensor modules for detecting intracellular β-catenin and NF-κB, as well as an SDA module to enhance overall efficiency. In vitro experiments revealed that the CRISPR-based gene expression platform exhibited superior CDK5 expression inhibition efficiency and specific cytotoxicity towards tumor cells. In vitro experiments, we found that CRISPR-based gene expression platforms can selectively kill bladder cancer cells through T cell-mediated cytotoxicity. Our design holds significant assistant potential of transgene therapy and may offer the capability to treat other diseases requiring transgene therapy.

RNA-Based Sensor Systems for Affordable Diagnostics in the Age of Pandemics

In the era of the COVID-19 pandemic, the significance of point-of-care (POC) diagnostic tools has become increasingly vital, driven by the need for quick and precise virus identification. RNA-based sensors, particularly toehold sensors, have emerged as promising candidates for POC detection systems due to their selectivity and sensitivity. Toehold sensors operate by employing an RNA switch that changes the conformation when it binds to a target RNA molecule, resulting in a detectable signal. This review focuses on the development and deployment of RNA-based sensors for POC viral RNA detection with a particular emphasis on toehold sensors. The benefits and limits of toehold sensors are explored, and obstacles and future directions for improving their performance within POC detection systems are presented. The use of RNA-based sensors as a technology for rapid and sensitive detection of viral RNA holds great potential for effectively managing (dealing/coping) with present and future pandemics in resource-constrained settings.

A molecular proximity sensor based on an engineered, dual-component guide RNA

One of the goals of synthetic biology is to enable the design of arbitrary molecular circuits with programmable inputs and outputs. Such circuits bridge the properties of electronic and natural circuits, processing information in a predictable manner within living cells. Genome editing is a potentially powerful component of synthetic molecular circuits, whether for modulating the expression of a target gene or for stably recording information to genomic DNA. However, programming molecular events such as protein-protein interactions or induced proximity as triggers for genome editing remains challenging. Here we demonstrate a strategy termed "P3 editing", which links protein-protein proximity to the formation of a functional CRISPRCas9 dual-component guide RNA. By engineering the crRNA:tracrRNA interaction, we demonstrate that various known protein-protein interactions, as well as the chemically-induced dimerization of protein domains, can be used to activate prime editing or base editing in human cells. Additionally, we explore how P3 editing can incorporate outputs from ADAR-based RNA sensors, potentially allowing specific RNAs to induce specific genome edits within a larger circuit. Our strategy enhances the controllability of CRISPR-based genome editing, facilitating its use in synthetic molecular circuits deployed in living cells.

Self-feedback loop-containing synthetic mRNA switches for controlled microRNA sensing

Synthetic mRNA switches are powerful cell fate manipulation tools that sense cellular input molecules to directly control protein expression at the translational level. The lack of available switch designs that can mimic the natural sophisticated protein regulation is a fundamental issue that limits the application of synthetic mRNA switches. Here we report a new set of synthetic mRNA switches by incorporating self-feedback loop machineries to dynamically control protein expression levels upon sensing cellular microRNAs. We redesigned the coding region of the switch to express output protein along with mRNA regulatory proteins. RNA-binding proteins (RBPs) and RBP-binding RNA motifs (aptamers) guide the regulatory proteins to act on their own mRNAs, enhancing or flattening the effect of microRNA sensing. Importantly, we demonstrated that the switches with the positive feedback feature can enlarge a high-or-low microRNA effect into a nearly all-or-none pattern, substantially boosting the use of synthetic mRNA switches as high-performance microRNA sensors or binary cell regulation tools. We believe these novel mRNA switch designs provide new strategies to construct complex mRNA-based genetic circuits for future molecular sensing and cell engineering.

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.

Targeting and engineering long non-coding RNAs for cancer therapy

RNA therapeutics (RNATx) aim to treat diseases, including cancer, by targeting or employing RNA molecules for therapeutic purposes. Amongst the most promising targets are long non-coding RNAs (lncRNAs), which regulate oncogenic molecular networks in a cell type-restricted manner. lncRNAs are distinct from protein-coding genes in important ways that increase their therapeutic potential yet also present hurdles to conventional clinical development. Advances in genome editing, oligonucleotide chemistry, multi-omics and RNA engineering are paving the way for efficient and cost-effective lncRNA-focused drug discovery pipelines. In this Review, we present the emerging field of lncRNA therapeutics for oncology, with emphasis on the unique strengths and challenges of lncRNAs within the broader RNATx framework. We outline the necessary steps for lncRNA therapeutics to deliver effective, durable, tolerable and personalized treatments for cancer.

Orthogonal inducible control of Cas13 circuits enables programmable RNA regulation in mammalian cells

RNA plays an indispensable role in mammalian cell functions. Cas13, a class of RNA-guided ribonuclease, is a flexible tool for modifying and regulating coding and non-coding RNAs, with enormous potential for creating new cell functions. However, the lack of control over Cas13 activity has limited its cell engineering capability. Here, we present the CRISTAL (Control of RNA with Inducible SpliT CAs13 Orthologs and Exogenous Ligands) platform. CRISTAL is powered by a collection (10 total) of orthogonal split inducible Cas13 effectors that can be turned ON or OFF via small molecules in multiple cell types, providing precise temporal control. Also, we engineer Cas13 logic circuits that can respond to endogenous signaling and exogenous small molecule inputs. Furthermore, the orthogonality, low leakiness, and high dynamic range of our inducible Cas13d and Cas13b enable the design and construction of a robust incoherent feedforward loop, leading to near-perfect and tunable adaptation response. Finally, using our inducible Cas13 effectors, we achieve simultaneous multiplexed control of multiple genes in vitro and in mice. Together, our CRISTAL design represents a powerful platform for precisely regulating RNA dynamics to advance cell engineering and elucidate RNA biology.

Modulating the tumoral SPARC content to enhance albumin-based drug delivery for cancer therapy

Insufficient delivery of therapeutic agents into solid tumors by systemic administration remains a major challenge in cancer treatment. Secreted protein acidic and rich in cysteine (SPARC) has high binding affinity to albumin and has been shown to enhance the penetration and uptake of albumin-based drug carriers in tumors. Here, we developed a strategy to alter the tumor microenvironment (TME) by upregulating SPARC to enhance the delivery efficiency of albumin-based drug carriers into tumors. We prepared albumin nanoparticles encapsulating an NF-κB controllable CRISPR activation system (SP-NPs). SP-NPs achieved tumor-selective SPARC upregulation by responding to the highly activated NF-κB in tumor cells. Whereas a single dose of SP-NPs only modestly upregulated SPARC expression, serial administration of SP-NPs created a positive feedback loop that induced progressive increases in SPARC expression as well as tumor cell uptake and tumor penetration of the nanoparticles in vitro, in organoids, and in subcutaneous tumors in vivo. Additionally, pre-treatment with SP-NPs significantly enhanced the anti-tumor efficacy of Abraxane, a commercialized albumin-bound paclitaxel nanoformulation. Our data provide evidence that modulating SPARC in the TME can enhance the efficiency of albumin-based drug delivery to solid tumors, which may result in new strategies to increase the efficacy of nanoparticle-based cancer drugs.

2'-OH as a universal handle for studying intracellular RNAs

RNA plays pivotal roles in most cellular processes, serving as both the traditional carrier of genetic information and as a key regulator of cellular functions. The advent of chemical technologies has contributed critically to the analysis of cellular RNA structures, functions, and interactions. Many of these methods and molecules involve the utilization of chemically reactive handles in RNAs, either introduced externally or inherent within the polymer itself. Among these handles, the 2'-hydroxyl (2'-OH) group has emerged as an exceptionally well-suited and general chemical moiety for the modification and profiling of RNAs in intracellular studies. In this review, we provide an overview of the recent advancements in intracellular applications of acylation at the 2'-OH group of RNA. We outline progress made in probing RNA structure and interactomes, controlling RNA function, RNA imaging, and analyzing RNA-small molecule interactions, all achieved in living cells through this simple chemical handle on the biopolymer.

Engineered poly(A)-surrogates for translational regulation and therapeutic biocomputation in mammalian cells

Here, we present a gene regulation strategy enabling programmable control over eukaryotic translational initiation. By excising the natural poly-adenylation (poly-A) signal of target genes and replacing it with a synthetic control region harboring RNA-binding protein (RBP)-specific aptamers, cap-dependent translation is rendered exclusively dependent on synthetic translation initiation factors (STIFs) containing different RBPs engineered to conditionally associate with different eIF4F-binding proteins (eIFBPs). This modular design framework facilitates the engineering of various gene switches and intracellular sensors responding to many user-defined trigger signals of interest, demonstrating tightly controlled, rapid and reversible regulation of transgene expression in mammalian cells as well as compatibility with various clinically applicable delivery routes of in vivo gene therapy. Therapeutic efficacy was demonstrated in two animal models. To exemplify disease treatments that require on-demand drug secretion, we show that a custom-designed gene switch triggered by the FDA-approved drug grazoprevir can effectively control insulin expression and restore glucose homeostasis in diabetic mice. For diseases that require instantaneous sense-and-response treatment programs, we create highly specific sensors for various subcellularly (mis)localized protein markers (such as cancer-related fusion proteins) and show that translation-based protein sensors can be used either alone or in combination with other cell-state classification strategies to create therapeutic biocomputers driving self-sufficient elimination of tumor cells in mice. This design strategy demonstrates unprecedented flexibility for translational regulation and could form the basis for a novel class of programmable gene therapies in vivo.

Engineering redirected NF-κB/OIP5 expression programs to enhance tumor responses to chemotherapy in bladder cancer

Nuclear factor kappa-B (NF-κB), a pivotal transcriptional regulator, plays a crucial role in modulating downstream genes implicated in tumor drug resistance. We establish a programmable system within bladder cancer cells to tailor drug responses by employing a synthetic clustered regularly interspaced short palindromic repeats (CRISPR)-based expression strategy that emulates natural transcriptional regulators. Our investigation uncovers the functional significance of Opa-interacting protein 5 (OIP5), upregulated upon NF-κB activation, as a key regulator governing drug-resistance to vincristine (VCR) treatment in bladder cancer. Through engineered guide RNAs (sgRNAs) targeting OIP5 to integrate NF-κB aptamers, we construct a modular scaffold RNA that encodes both the target locus and regulatory functionality. This engineered CRISPR scaffold RNA effectively responds to VCR stimulus by binding with activated NF-κB. Intriguingly, it redirects NF-κB to attenuate OIP5 expression-a reversal of its original role-while concurrently obstructing multiple NF-κB-mediated drug resistance pathways. This dual action thwarts drug resistance development. Further enhancing therapeutic potential, we develop a versatile nanoparticle system capable of co-delivering CRISPR scaffold RNAs and VCR. This synergistic approach demonstrates potent anti-tumor effects in both in vitro and in vivo settings. Our nanoparticle-mediated combination presents a compelling proof-of-concept, showcasing the utility of engineered CRISPR-based synthetic expression programs to reconfigure cellular drug responses and heighten tumor cell susceptibility to chemotherapy.

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.

Harnessing synthetic biology for advancing RNA therapeutics and vaccine design

Recent global events have drawn into focus the diversity of options for combatting disease across a spectrum of prophylactic and therapeutic approaches. The recent success of the mRNA-based COVID-19 vaccines has paved the way for RNA-based treatments to revolutionize the pharmaceutical industry. However, historical treatment options are continuously updated and reimagined in the context of novel technical developments, such as those facilitated through the application of synthetic biology. When it comes to the development of genetic forms of therapies and vaccines, synthetic biology offers diverse tools and approaches to influence the content, dosage, and breadth of treatment with the prospect of economic advantage provided in time and cost benefits. This can be achieved by utilizing the broad tools within this discipline to enhance the functionality and efficacy of pharmaceutical agent sequences. This review will describe how synthetic biology principles can augment RNA-based treatments through optimizing not only the vaccine antigen, therapeutic construct, therapeutic activity, and delivery vector. The enhancement of RNA vaccine technology through implementing synthetic biology has the potential to shape the next generation of vaccines and therapeutics.

Versatile toolkit for highly-efficient and scarless overexpression of circular RNAs

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA that contain a unique back-splice junction (bsj) sequence created by the ligation of their 5' and 3' ends via spliceosome-catalyzed back-splicing. A key step in illuminating the cellular roles of specific circRNAs is via increasing their expression. This is frequently done by transfecting cells with plasmid DNA containing cloned exons from which the circRNA is transcribed, flanked by sequences that promote back-splicing. We observed that commonly used plasmids lead to the production of circRNAs with molecular scars at the circRNA bsj. Stepwise redesign of the cloning vector corrected this problem, ensuring bona fide circRNAs are produced with their natural bsj at high efficiency. The fidelity of circRNAs produced from this new construct was validated by RNA sequencing and also functionally validated. To increase the utility of this modified resource for expressing circRNA, we developed an expanded set of vectors incorporating this design that (i) enables selection with a variety of antibiotics and fluorescent proteins, (ii) employs a range of promoters varying in promoter strength and (iii) generated a complementary set of lentiviral plasmids for difficult-to-transfect cells. These resources provide a novel and versatile toolkit for high-efficiency and scarless overexpression of circular RNAs that fulfill a critical need for the investigation of circRNA function.

Applications of artificial intelligence and machine learning in dynamic pathway engineering

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.

Photoswitchable molecular glue for RNA: reversible photocontrol of structure and function of the ribozyme

Single-stranded RNA folds into a variety of secondary and higher-order structures. Distributions and dynamics of multiple RNA conformations are responsible for the biological function of RNA. We here developed a photoswitchable molecular glue for RNA, which could reversibly control the association of two unpaired RNA regions in response to light stimuli. The photoswitchable molecular glue, NCTA, is an RNA-binding ligand possessing a photoisomerizable azobenzene moiety. Z-NCTA is an active ligand for the target RNA containing 5'-WGG-3'/5'-WGG-3' (W = U or A) site and stabilizes its hybridized state, while its isomer E-NCTA is not. Photoreversible isomerization of NCTA enabled control of the secondary and tertiary structure of the target RNA. The RNA-cleaving activity of hammerhead ribozyme, where appropriate RNA folding is necessary, could be reversibly regulated by photoirradiation in cells treated with NCTA, demonstrating precise photocontrol of RNA structure and function by the photoswitchable molecular glue.

Dual Input-Controlled Synthetic mRNA Circuit for Bidirectional Protein Expression Regulation

Synthetic mRNA circuits manipulate cell fate by controlling output protein expression via cell-specific input molecule detection. Most current circuits either repress or enhance output production upon input binding. Such binary input-output mechanisms restrict the fine-tuning of protein expression to control complex cellular events. Here we designed mRNA circuits using enhancer/repressor modules that were independently controlled by different input molecules, resulting in bidirectional output regulation; the maximal enhancement over maximal repression was 57 fold. The circuit either enhances or represses protein production in different cells based on the difference in the expression of two microRNAs. This study examined novel bidirectional circuit designs capable of fine-tuning protein production by sensing multiple input molecules. It also broadened the scope of cell manipulation by synthetic mRNA circuits, facilitating the development of mRNA circuits for precise cell manipulation and providing cell-based solutions to biomedical problems.

Tumor targeting and therapeutic assessments of RNA nanoparticles carrying α9-nAChR aptamer and anti-miR-21 in triple-negative breast cancers

Triple-negative breast cancer (TNBC) is highly aggressive with a poor prognosis because of a lack of cell markers as drug targets. α9-Nicotinic acetylcholine receptor (nAChR) is expressed abundantly in TNBC; thus, it is a valuable biomarker for TNBC detection and treatment. In this study, we utilized thermodynamically stable three-way junction (3WJ) packaging RNA (pRNA) as the core to construct RNA nanoparticles with an α9-nAChR RNA aptamer as a targeting ligand and an anti-microRNA-21 (miR-21) as a therapeutic module. We compared the configuration of the two RNA nanoparticles and found that 3WJ-B-α9-nAChR-aptamer fluorescent RNA nanoparticles (3WJ-B-α9-apt-Alexa) exhibited better specificity for α9-nAChR in TNBC cells compared with 3WJ-C-α9-nAChR. Furthermore, 3WJ-B-α9-apt-Alexa bound more efficiently to TNBC patient-derived xenograft (PDX) tumors than 3WJ fluorescent RNA nanoparticles (3WJ-Alexa) with little or no accumulation in healthy organs after systemic injection in mice. Moreover, 3WJ-B-α9-nAChR-aptamer RNA nanoparticles carrying anti-miR-21 (3WJ-B-α9-apt-anti-miR-21) significantly suppressed TNBC-PDX tumor growth and induced cell apoptosis because of reduced miR-21 gene expression and upregulated the phosphatase and tensin homolog (PTEN) and programmed cell death 4 (PDCD4) proteins. In addition, no pathological changes were detected upon toxicity examination of treated mice. In conclusion, the 3WJ-B-α9-nAChR-aptamer RNA nanoparticles established in this study efficiently deliver therapeutic anti-miR-21, indicating their potential as a novel TNBC therapy.

Synthesis of RNA-Amphiphiles via Atom Transfer Radical Polymerization in the Organic Phase

The combination of hydrophobic polymers with nucleic acids is a fascinating way to engineer the self-assembly behavior of nucleic acids into diverse nanostructures such as micelles, vesicles, nanosheets, and worms. Here we developed a robust route to synthesize a RNA macroinitiator with protecting groups on the 2'-hydroxyl groups in the solid phase using an oligonucleotide synthesizer. The protecting groups successfully solubilized the RNA macroinitiator, enabling atom transfer radical polymerization (ATRP) of hydrophobic monomers. As a result, the RNA-polymer hybrids obtained by ATRP exhibited enhanced chemical stability by suppressing cleavage. In addition, we demonstrated evidence of controlled polymerization behavior as well as control over the molecular weight of the hydrophobic polymers grown from RNA. We envision that this methodology will expand the field of RNA-polymer conjugates while vastly enhancing the possibility to alter and engineer the properties of RNA-based polymeric materials.

Crystal structure of Escherichia coli thiamine pyrophosphate-sensing riboswitch in the apo state

The thiamine pyrophosphate (TPP)-sensing riboswitch is one of the earliest discovered and most widespread riboswitches. Numerous structural studies have been reported for this riboswitch bound with various ligands. However, the ligand-free (apo) structure remains unknown. Here, we report a 3.1 Å resolution crystal structure of Escherichia coli TPP riboswitch in the apo state, which exhibits an extended, Y-shaped conformation further supported by small-angle X-ray scattering data and driven molecular dynamics simulations. The loss of ligand interactions results in helical uncoiling of P5 and disruption of the key tertiary interaction between the sensory domains. Opening of the aptamer propagates to the gene-regulatory P1 helix and generates the key conformational flexibility needed for the switching behavior. Much of the ligand-binding site at the three-way junction is unaltered, thereby maintaining a partially preformed pocket. Together, these results paint a dynamic picture of the ligand-induced conformational changes in TPP riboswitches that confer conditional gene regulation.

RNAs as Sensors of Oxidative Stress in Bacteria

Oxidative stress is an important and pervasive physical stress encountered by all kingdoms of life, including bacteria. In this review, we briefly describe the nature of oxidative stress, highlight well-characterized protein-based sensors (transcription factors) of reactive oxygen species that serve as standards for molecular sensors in oxidative stress, and describe molecular studies that have explored the potential of direct RNA sensitivity to oxidative stress. Finally, we describe the gaps in knowledge of RNA sensors-particularly regarding the chemical modification of RNA nucleobases. RNA sensors are poised to emerge as an essential layer of understanding and regulating dynamic biological pathways in oxidative stress responses in bacteria and, thus, also represent an important frontier of synthetic biology.

Utilizing Epigenetic Modification as a Reactive Handle To Regulate RNA Function and CRISPR-Based Gene Regulation

The current methods to control RNA functions in living conditions are limited. The new RNA-controlling strategy presented in this study involves utilizing 5-formylcytidine (f5C)-directed base manipulation. This study shows that malononitrile and pyridine boranes can effectively manipulate the folding, small molecule binding, and enzyme recognition of f5C-bearing RNAs. We further demonstrate the efficiency of f5C-directed reactions in controlling two different clustered regularly interspaced short palindromic repeat (CRISPR) systems. Although further studies are needed to optimize the efficiency of these reactions in vivo, this small molecule-based approach presents exciting new opportunities for regulating CRISPR-based gene expression and other applications.

Design Approaches to Expand the Toolkit for Building Cotranscriptionally Encoded RNA Strand Displacement Circuits

Cotranscriptionally encoded RNA strand displacement (ctRSD) circuits are an emerging tool for programmable molecular computation, with potential applications spanning in vitro diagnostics to continuous computation inside living cells. In ctRSD circuits, RNA strand displacement components are continuously produced together via transcription. These RNA components can be rationally programmed through base pairing interactions to execute logic and signaling cascades. However, the small number of ctRSD components characterized to date limits circuit size and capabilities. Here, we characterize over 200 ctRSD gate sequences, exploring different input, output, and toehold sequences and changes to other design parameters, including domain lengths, ribozyme sequences, and the order in which gate strands are transcribed. This characterization provides a library of sequence domains for engineering ctRSD components, i.e., a toolkit, enabling circuits with up to 4-fold more inputs than previously possible. We also identify specific failure modes and systematically develop design approaches that reduce the likelihood of failure across different gate sequences. Lastly, we show the ctRSD gate design is robust to changes in transcriptional encoding, opening a broad design space for applications in more complex environments. Together, these results deliver an expanded toolkit and design approaches for building ctRSD circuits that will dramatically extend capabilities and potential applications.

Orthogonal inducible control of Cas13 circuits enables programmable RNA regulation in mammalian cells

RNA plays an indispensable role in mammalian cell functions. Cas13, a class of RNA-guided ribonuclease, is a flexible tool for modifying and regulating coding and non-coding RNAs, with enormous potential for creating new cell functions. However, the lack of control over Cas13 activity has limited its cell engineering capability. Here, we present the CRISTAL ( C ontrol of R NA with Inducible S pli T C A s13 Orthologs and Exogenous L igands) platform. CRISTAL is powered by a collection (10 total) of orthogonal split inducible Cas13s that can be turned ON or OFF via small molecules in multiple cell types, providing precise temporal control. Also, we engineered Cas13 logic circuits that can respond to endogenous signaling and exogenous small molecule inputs. Furthermore, the orthogonality, low leakiness, and high dynamic range of our inducible Cas13d and Cas13b enable the design and construction of a robust incoherent feedforward loop, leading to near-perfect and tunable adaptation response. Finally, using our inducible Cas13s, we achieve simultaneous multiplexed control of multiple genes in vitro and in mice. Together, our CRISTAL design represents a powerful platform for precisely regulating RNA dynamics to advance cell engineering and elucidate RNA biology.

Recent advances in sensing the inter-biomolecular interactions at the nanoscale - A comprehensive review of AFM-based force spectroscopy

Biomolecular interactions underpin most processes inside the cell. Hence, a precise and quantitative understanding of molecular association and dissociation events is crucial, not only from a fundamental perspective, but also for the rational design of biomolecular platforms for state-of-the-art biomedical and industrial applications. In this context, atomic force microscopy (AFM) appears as an invaluable experimental technique, allowing the measurement of the mechanical strength of biomolecular complexes to provide a quantitative characterization of their interaction properties from a single molecule perspective. In the present review, the most recent methodological advances in this field are presented with special focus on bioconjugation, immobilization and AFM tip functionalization, dynamic force spectroscopy measurements, molecular recognition imaging and theoretical modeling. We expect this work to significantly aid in grasping the principles of AFM-based force spectroscopy (AFM-FS) technique and provide the necessary tools to acquaint the type of data that can be achieved from this type of experiments. Furthermore, a critical assessment is done with other nanotechnology techniques to better visualize the future prospects of AFM-FS.

Thermo-Programmed Synthetic DNA-Based Receptors

Herein, we present a generalizable and versatile strategy to engineer synthetic DNA ligand-binding devices that can be programmed to load and release a specific ligand at a defined temperature. We do so by re-engineering two model DNA-based receptors: a triplex-forming bivalent DNA-based receptor that recognizes a specific DNA sequence and an ATP-binding aptamer. The temperature at which these receptors load/release their ligands can be finely modulated by controlling the entropy associated with the linker connecting the two ligand-binding domains. The availability of a set of receptors with tunable and reversible temperature dependence allows achieving complex load/release behavior such as sustained ligand release over a wide temperature range. Similar programmable thermo-responsive synthetic ligand-binding devices can be of utility in applications such as drug delivery and production of smart materials.

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Latest Trends in Nucleic Acids' Engineering Techniques Applied to Precision Medicine

Nucleic acids are paving the way for advanced therapeutics. Unveiling the genome enabled a better understanding of unique genotype-phenotype profiling. Methods for engineering and analysis of nucleic acids, from polymerase chain reaction to Cre-Lox recombination, are contributing greatly to biomarkers' discovery, mapping of cellular signaling cascades, and smart design of therapeutics in precision medicine. Investigating the different subtypes of DNA and RNA via sequencing and profiling is empowering the scientific community with valuable information, to be used in advanced therapeutics, tracking epigenetics linked to disease. Recent results from the application of nucleic acids in novel therapeutics and precision medicine are very encouraging, demonstrating great potential to treat cancer, viral infections via inoculation (e.g., SAR-COV-2 mRNA vaccines), along with metabolic and genetic disorders. Limitations posed by challenges in delivery mode are being addressed to enable efficient guided-gene-programmed precision therapies. With the focus on genetic engineering and novel therapeutics, more precisely, in precision medicine, this chapter discusses the advance enabled by knowledge derived from these innovative branches of biotechnology.

Emerging technologies for genetic control systems in cellular therapies

Progress in synthetic biology has enabled the construction of designer cells that sense biological inputs, and, in response, activate user-defined biomolecular programs. Such engineered cells provide unique opportunities for treating a wide variety of diseases. Current strategies mostly rely on cell-surface receptor systems engineered to convert binding interactions into activation of a transcriptional program. Genetic control systems are emerging as an appealing alternative to receptor-based sensors as they overcome the need for receptor engineering and result in cellular behaviors that operate over therapeutically relevant timescales. Genetic control systems include synthetic gene networks, RNA-based sensors, and post-translational tools. These technologies present fundamental challenges, including the requirement for precise integration with innate pathways, the need for parts orthogonal to existing circuitries, and the metabolic burden induced by such complex cell engineering endeavors. This review discusses the challenges in the design of genetic control systems for cellular therapies and their translational applications.

Harnessing introns to monitor gene expression

A number of new technologies have utilized synthetic RNAs which leverage the cell’s RNA splicing machinery to drive expression of gene products. Now a new study reports a technique to dynamically and non-invasively monitor gene expression by embedding reporters within introns contained in the parent gene.

G-quadruplex-guided RNA engineering to modulate CRISPR-based genomic regulation

It is important to develop small moelcule-based methods to modulate gene editing and expression in human cells. The roles of the G-quadruplex (G4) in biological systems have been widely studied. Here, G4-guided RNA engineering is performed to generate guide RNA with G4-forming units (G4-gRNA). We further demonstrate that chemical targeting of G4-gRNAs holds promise as a general approach for modulating gene editing and expression in human cells. The rich structural diversity of RNAs offers a reservoir of targets for small molecules to bind, thus creating the potential to modulate RNA biology.

Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

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.