High-Throughput Analysis and Engineering of Ribozymes and Deoxyribozymes by Sequencing

Purine Chemistry in the Early RNA World at the Origins of Life: From RNA and Nucleobases Lesions to Current Key Metabolic Routes

In early life, RNA probably played the central role and, in the corresponding RNA world, the main produced amino acids and small peptides had to react continuously with RNA, ribonucleos(t)ides and nucleobases, especially with purines. A RNA-peptide world and key metabolic pathways have emerged from the corresponding chemical modifications such as the translation process performed by the ribosome. Some interesting reactions of the purine bicycle and of the corresponding ribonucleos(t)ides are performed under plausible prebiotic conditions and described RNA chemical lesions are reviewed with the prospect to highlight their connection with some major steps of the purine and histidine biosynthetic pathways that are, in an intriguingly way, related through two key metabolites, adenosine 5'-triphosphate and the imidazole ribonucleotide 5-aminoimidazole-4-carboxamide ribonucleotide. Ring-opening reactions of purines stand out as efficient accesses to imidazole ribonucleotides and to formamidopyrimidine (Fapy) ribonucleotides suggesting that biosynthetic pathway' first steps have emerged from RNA and ribonucleos(t)ide damages. Also, are summarized the works on the formation and catalytic properties, under plausible prebiotic conditions, of N6-derivatives of the purine base adenine as potential surrogates of histidine in catalysis accordingly to their structural relationship.

CHiTA: A scarless high-throughput pipeline for characterization of ribozymes

Small self-cleaving ribozymes are catalytic RNAs that cleave their phosphodiester backbone rapidly and site-specifically, without the assistance of proteins. Their catalytic properties make them ideal targets for applications in RNA pharmaceuticals and bioengineering. Consequently, computational pipelines that predict or design thousands of self-cleaving ribozyme candidates have been developed. Traditional experimental techniques for verifying the activity of these putative ribozymes, however, are low-throughput and time intensive. High-throughput (HT) pipelines that employ next-generation sequencing (NGS) analyze the activity of these thousands of ribozymes simultaneously. Until recently, the application of these HT pipelines has been limited to studying all single and double mutants of a select representative ribozyme. Unfortunately, this prevents the exploration of candidates having different lengths, circular permutations, and auxiliary stem-loops. Moreover, pipelines that analyze ribozymes en masse often include transcription of non-native flanking sequences that preclude accurate assessment of the intrinsic rate of ribozyme self-cleavage. To overcome these limitations, we developed a HT pipeline, "Cleavage High-Throughput Assay (CHiTA)", which employs NGS and massively parallel oligonucleotide synthesis (MPOS) to characterize ribozyme activity for thousands of candidates in a scarless fashion. Herein, we describe detailed strategies and protocols to implement CHiTA to measure the activity of putative ribozymes from a wide range of ribozyme classes.

Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements

Oligopool synthesis and next-generation sequencing enable the construction and characterization of large libraries of designed genetic parts and systems. As library sizes grow, it becomes computationally challenging to optimally design large numbers of primer binding sites, barcode sequences, and overlap regions to obtain efficient assemblies and precise measurements. We present the Oligopool Calculator, an end-to-end suite of algorithms and data structures that rapidly designs many thousands of oligonucleotides within an oligopool and rapidly analyzes many billions of barcoded sequencing reads. We introduce several novel concepts that greatly increase the design and analysis throughput, including orthogonally symmetric barcode design, adaptive decision trees for primer design, a Scry barcode classifier, and efficient read packing. We demonstrate the Oligopool Calculator's capabilities across computational benchmarks and real-data projects, including the design of over four million highly unique and compact barcodes in 1.2 h, the design of universal primer binding sites for one million 200-mer oligos in 15 min, and the analysis of about 500 million deep sequencing reads per hour, all on an 8-core desktop computer. Overall, the Oligopool Calculator accelerates the creative use of massively parallel experiments by eliminating the computational complexity of their design and analysis.

Direct testing of natural twister ribozymes from over a thousand organisms reveals a broad tolerance for structural imperfections

Twister ribozymes are an extensively studied class of nucleolytic RNAs. Thousands of natural twisters have been proposed using sequence homology and structural descriptors. Yet, most of these candidates have not been validated experimentally. To address this gap, we developed Cleavage High-Throughput Assay (CHiTA), a high-throughput pipeline utilizing massively parallel oligonucleotide synthesis and next-generation sequencing to test putative ribozymes en masse in a scarless fashion. As proof of principle, we applied CHiTA to a small set of known active and mutant ribozymes. We then used CHiTA to test two large sets of naturally occurring twister ribozymes: over 1600 previously reported putative twisters and ∼1000 new candidate twisters. The new candidates were identified computationally in ∼1000 organisms, representing a massive increase in the number of ribozyme-harboring organisms. Approximately 94% of the twisters we tested were active and cleaved site-specifically. Analysis of their structural features revealed that many substitutions and helical imperfections can be tolerated. We repeated our computational search with structural descriptors updated from this analysis, whereupon we identified and confirmed the first intrinsically active twister ribozyme in mammals. CHiTA broadly expands the number of active twister ribozymes found in nature and provides a powerful method for functional analyses of other RNAs.

Direct testing of natural twister ribozymes from over a thousand organisms reveals a broad tolerance for structural imperfections

Twister ribozymes are an extensively studied class of nucleolytic RNAs. Thousands of natural twisters have been proposed using sequence homology and structural descriptors. Yet, most of these candidates have not been validated experimentally. To address this gap, we developed CHiTA (Cleavage High-Throughput Assay), a high-throughput pipeline utilizing massively parallel oligonucleotide synthesis and next-generation sequencing to test putative ribozymes en masse in a scarless fashion. As proof of principle, we applied CHiTA to a small set of known active and mutant ribozymes. We then used CHiTA to test two large sets of naturally occurring twister ribozymes: over 1, 600 previously reported putative twisters and ∼1, 000 new candidate twisters. The new candidates were identified computationally in ∼1, 000 organisms, representing a massive increase in the number of ribozyme-harboring organisms. Approximately 94% of the twisters we tested were active and cleaved site-specifically. Analysis of their structural features revealed that many substitutions and helical imperfections can be tolerated. We repeated our computational search with structural descriptors updated from this analysis, whereupon we identified and confirmed the first intrinsically active twister ribozyme in mammals. CHiTA broadly expands the number of active twister ribozymes found in nature and provides a powerful method for functional analyses of other RNAs.

GRAPHICAL ABSTRACT

Fitness Landscapes and Evolution of Catalytic RNA

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.

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.

Discovering pathways through ribozyme fitness landscapes using information theoretic quantification of epistasis

The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary "bridge" between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.

Tunable metal-organic frameworks assist in catalyzing DNAzymes with amplification platforms for biomedical applications

Various binding modes of tunable metal organic frameworks (MOFs) and functional DNAzymes (Dzs) synergistically catalyze the emergence of abundant functional nanoplatforms. Given their serial variability in formation, structural designability, and functional controllability, Dzs@MOFs tend to be excellent building blocks for the precise "intelligent" manufacture of functional materials. To present a clear outline of this new field, this review systematically summarizes the progress of Dz integration into MOFs (MOFs@Dzs) through different methods, including various surface infiltration, pore encapsulation, covalent binding, and biomimetic mineralization methods. Atomic-level and time-resolved catalytic mechanisms for biosensing and imaging are made possible by the complex interplay of the distinct molecular structure of Dzs@MOF, conformational flexibility, and dynamic regulation of metal ions. Exploiting the precision of DNAzymes, MOFs@Dzs constructed a combined nanotherapy platform to guide intracellular drug synthesis, photodynamic therapy, catalytic therapy, and immunotherapy to enhance gene therapy in different ways, solving the problems of intracellular delivery inefficiency and insufficient supply of cofactors. MOFs@Dzs nanostructures have become excellent candidates for biosensing, bioimaging, amplification delivery, and targeted cancer gene therapy while emphasizing major advancements and seminal endeavors in the fields of biosensing (nucleic acid, protein, enzyme activity, small molecules, and cancer cells), biological imaging, and targeted cancer gene delivery and gene therapy. Overall, based on the results demonstrated to date, we discuss the challenges that the emerging MOFs@Dzs might encounter in practical future applications and briefly look forward to their bright prospects in other fields.

Catalytic mechanism and pH dependence of a methyltransferase ribozyme (MTR1) from computational enzymology

A methyltransferase ribozyme (MTR1) was selected in vitro to catalyze alkyl transfer from exogenous O6-methylguanine (O6mG) to a target adenine N1, and recently, high-resolution crystal structures have become available. We use a combination of classical molecular dynamics, ab initio quantum mechanical/molecular mechanical (QM/MM) and alchemical free energy (AFE) simulations to elucidate the atomic-level solution mechanism of MTR1. Simulations identify an active reactant state involving protonation of C10 that hydrogen bonds with O6mG:N1. The deduced mechanism involves a stepwise mechanism with two transition states corresponding to proton transfer from C10:N3 to O6mG:N1 and rate-controlling methyl transfer (19.4  kcal·mol-1 barrier). AFE simulations predict the pKa for C10 to be 6.3, close to the experimental apparent pKa of 6.2, further implicating it as a critical general acid. The intrinsic rate derived from QM/MM simulations, together with pKa calculations, enables us to predict an activity-pH profile that agrees well with experiment. The insights gained provide further support for a putative RNA world and establish new design principles for RNA-based biochemical tools.

Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes

Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance.

Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters

Characterizing genotype-phenotype relationships of biomolecules (e.g. ribozymes) requires accurate ways to measure activity for a large set of molecules. Kinetic measurement using high-throughput sequencing (e.g. k-Seq) is an emerging assay applicable in various domains that potentially scales up measurement throughput to over 106 unique nucleic acid sequences. However, maximizing the return of such assays requires understanding the technical challenges introduced by sequence heterogeneity and DNA sequencing. We characterized the k-Seq method in terms of model identifiability, effects of sequencing error, accuracy and precision using simulated datasets and experimental data from a variant pool constructed from previously identified ribozymes. Relative abundance, kinetic coefficients, and measurement noise were found to affect the measurement of each sequence. We introduced bootstrapping to robustly quantify the uncertainty in estimating model parameters and proposed interpretable metrics to quantify model identifiability. These efforts enabled the rigorous reporting of data quality for individual sequences in k-Seq experiments. Here we present detailed protocols, define critical experimental factors, and identify general guidelines to maximize the number of sequences and their measurement accuracy from k-Seq data. Analogous practices could be applied to improve the rigor of other sequencing-based assays.

RNA-Cleaving Deoxyribozymes Differentiate Methylated Cytidine Isomers in RNA

Deoxyribozymes are emerging as modification‐specific endonucleases for the analysis of epigenetic RNA modifications. Here, we report RNA‐cleaving deoxyribozymes that differentially respond to the presence of natural methylated cytidines, 3‐methylcytidine (m³C), N⁴‐methylcytidine (m⁴C), and 5‐methylcytidine (m⁵C), respectively. Using in vitro selection, we found several DNA catalysts, which are selectively activated by only one of the three cytidine isomers, and display 10‐ to 30‐fold accelerated cleavage of their target m³C‐, m⁴C‐ or m⁵C‐modified RNA. An additional deoxyribozyme is strongly inhibited by any of the three methylcytidines, but effectively cleaves unmodified RNA. The m^XC‐detecting deoxyribozymes are programmable for the interrogation of natural RNAs of interest, as demonstrated for human mitochondrial tRNAs containing known m³C and m⁵C sites. The results underline the potential of synthetic functional DNA to shape highly selective active sites.

Single-round deoxyribozyme discovery

Artificial evolution experiments typically use libraries of ∼1015 sequences and require multiple rounds of selection to identify rare variants with a desired activity. Based on the simple structures of some aptamers and nucleic acid enzymes, we hypothesized that functional motifs could be isolated from significantly smaller libraries in a single round of selection followed by high-throughput sequencing. To test this idea, we investigated the catalytic potential of DNA architectures in which twelve or fifteen randomized positions were embedded in a scaffold present in all library members. After incubating in either the presence or absence of lead (which promotes the nonenzymatic cleavage of RNA), library members that cleaved themselves at an RNA linkage were purified by PAGE and characterized by high-throughput sequencing. These selections yielded deoxyribozymes with activities 8- to 30-fold lower than those previously isolated under similar conditions from libraries containing 1014 different sequences, indicating that the disadvantage of using a less diverse pool can be surprisingly small. It was also possible to elucidate the sequence requirements and secondary structures of deoxyribozymes without performing additional experiments. Due to its relative simplicity, we anticipate that this approach will accelerate the discovery of new catalytic DNA and RNA motifs.

Encapsulation of ribozymes inside model protocells leads to faster evolutionary adaptation

Functional biomolecules, such as RNA, encapsulated inside a protocellular membrane are believed to have comprised a very early, critical stage in the evolution of life, since membrane vesicles allow selective permeability and create a unit of selection enabling cooperative phenotypes. The biophysical environment inside a protocell would differ fundamentally from bulk solution due to the microscopic confinement. However, the effect of the encapsulated environment on ribozyme evolution has not been previously studied experimentally. Here, we examine the effect of encapsulation inside model protocells on the self-aminoacylation activity of tens of thousands of RNA sequences using a high-throughput sequencing assay. We find that encapsulation of these ribozymes generally increases their activity, giving encapsulated sequences an advantage over nonencapsulated sequences in an amphiphile-rich environment. In addition, highly active ribozymes benefit disproportionately more from encapsulation. The asymmetry in fitness gain broadens the distribution of fitness in the system. Consistent with Fisher's fundamental theorem of natural selection, encapsulation therefore leads to faster adaptation when the RNAs are encapsulated inside a protocell during in vitro selection. Thus, protocells would not only provide a compartmentalization function but also promote activity and evolutionary adaptation during the origin of life.

Porphyrin metalation catalyzed by DNAzymes and nanozymes

In this review, DNA and nanomaterial based catalysts for porphyrin metalation reactions are summarized, including the selection of DNAzymes, choice of nanomaterials, their catalytic mechanisms, and applications of the reactions.