Comprehensive sequence-to-function mapping of cofactor-dependent RNA catalysis in the glmS ribozyme

Optimized periphery-core interface increases fitness of the Bacillus subtilisglmS ribozyme

Like other functional RNAs, ribozymes encode a conserved catalytic center supported by peripheral domains that vary among ribozyme sub-families. To understand how core-periphery interactions contribute to ribozyme fitness, we compared the cleavage kinetics of all single base substitutions at 152 sites across the Bacillus subtilis glmS ribozyme by high-throughput sequencing (k-seq). The in vitro activity map mirrored phylogenetic sequence conservation in glmS ribozymes, indicating that biological fitness reports all biochemically important positions. The k-seq results and folding assays showed that most deleterious mutations lower activity by impairing ribozyme self-assembly. All-atom molecular dynamics simulations of the complete ribozyme revealed how individual mutations in the core or the IL4 peripheral loop introduce a non-native tertiary interface that rewires the catalytic center, eliminating activity. We conclude that the need to avoid non-native helix packing powerfully constrains the evolution of tertiary structure motifs in RNA.

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

Fluorogenic Aptamer Optimizations on a Massively Parallel Sequencing Platform

F luorogenic ap tamers (FAPs) have become an increasingly important tool in cellular sensing and pathogen diagnostics. However, fine-tuning FAPs for enhanced performance remains challenging even with the structural details provided by X-ray crystallography. Here we present a novel approach to optimize a DNA-based FAP (D-FAP), Lettuce, on repurposed Illumina next-generation sequencing (NGS) chips. When substituting its cognate chromophore, DFHBI-1T, with TO1-biotin, Lettuce not only shows a red-shifted emission peak by 53 nm (from 505 to 558 nm), but also a 4-fold bulk fluorescence enhancement. After screening 8,821 Lettuce variants complexed with TO1-biotin, the C14T mutation is found to exhibit an improved apparent dissociated constant ( vs. 0.82 µM), an increased quantum yield (QY: 0.62 vs. 0.59) and an elongated fluorescence lifetime (τ: 6.00 vs. 5.77 ns), giving 45% more ensemble fluorescence than the canonical Lettuce/TO1-biotin complex. Molecular dynamic simulations further indicate that the π-π stacking interaction is key to determining the coordination structure of TO1-biotin in Lettuce. Our screening-and-simulation pipeline can effectively optimize FAPs without any prior structural knowledge of the canonical FAP/chromophore complexes, providing not only improved molecular probes for fluorescence sensing but also insights into aptamer-chromophore interactions.

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.

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.

Systematic analysis of cotranscriptional RNA folding using transcription elongation complex display

RNA can fold into structures that mediate diverse cellular functions. Understanding how RNA primary sequence directs the formation of functional structures requires methods that can comprehensively assess how changes in an RNA sequence affect its structure and function. Here we have developed a platform for performing high-throughput cotranscriptional RNA biochemical assays, called Transcription Elongation Complex display (TECdisplay). TECdisplay measures RNA function by fractionating a TEC library based on the activity of cotranscriptionally displayed nascent RNA. In this way, RNA function is measured as the distribution of template DNA molecules between fractions of the transcription reaction. This approach circumvents typical RNA sequencing library preparation steps that can cause technical bias. We used TECdisplay to characterize the transcription antitermination activity of 32,768 variants of the Clostridium beijerinckii pfl ZTP riboswitch designed to perturb steps within its cotranscriptional folding pathway. Our findings establish TECdisplay as an accessible platform for high-throughput RNA biochemical assays.

Linking folding dynamics and function of SAM/SAH riboswitches at the single molecule level

Riboswitches are regulatory elements found in bacterial mRNAs that control downstream gene expression through ligand-induced conformational changes. Here, we used single-molecule FRET to map the conformational landscape of the translational SAM/SAH riboswitch and probe how co-transcriptional ligand-induced conformational changes affect its translation regulation function. Riboswitch folding is highly heterogeneous, suggesting a rugged conformational landscape that allows for sampling of the ligand-bound conformation even in the absence of ligand. The addition of ligand shifts the landscape, favoring the ligand-bound conformation. Mutation studies identified a key structural element, the pseudoknot helix, that is crucial for determining ligand-free conformations and their ligand responsiveness. We also investigated ribosomal binding site accessibility under two scenarios: pre-folding and co-transcriptional folding. The regulatory function of the SAM/SAH riboswitch involves kinetically favoring ligand binding, but co-transcriptional folding reduces this preference with a less compact initial conformation that exposes the Shine-Dalgarno sequence and takes min to redistribute to more compact conformations of the pre-folded riboswitch. Such slow equilibration decreases the effective ligand affinity. Overall, our study provides a deeper understanding of the complex folding process and how the riboswitch adapts its folding pattern in response to ligand, modulates ribosome accessibility and the role of co-transcriptional folding in these processes.

Translation regulation by a guanidine-II riboswitch is highly tunable in sensitivity, dynamic range, and apparent cooperativity

Riboswitches function as important translational regulators in bacteria. Comprehensive mutational analysis of transcriptional riboswitches has been used to probe the energetic intricacies of interplay between the aptamer and expression platform, but translational riboswitches have been inaccessible to massively parallel techniques. The guanidine-II (gdm-II) riboswitch is an exclusively translational class. We have integrated RelE cleavage with next-generation sequencing to quantify ligand-dependent changes in translation initiation for all single and double mutations of the Pseudomonas aeruginosa gdm-II riboswitch, a total of more than 23,000 variants. This extensive mutational analysis is consistent with the prominent features of the bioinformatic consensus. These data indicate, unexpectedly, that direct sequestration of the Shine-Dalgarno sequence is dispensable for riboswitch function. Additionally, this comprehensive data set reveals important positions not identified in previous computational and crystallographic studies. Mutations in the variable linker region stabilize alternate conformations. The double mutant data reveal the functional importance of the previously modeled P0b helix formed by the 5' and 3' tails that serves as the basis for translational control. Additional mutations to GU wobble base pairs in both P1 and P2 reveal how the apparent cooperativity of the system involves an intricate network of communication between the two binding sites. This comprehensive examination of a translational riboswitch's expression platform illuminates how the riboswitch is precisely tuned and tunable with regard to ligand sensitivity, the amplitude of expression between ON and OFF states, and the cooperativity of ligand binding.

Massively Parallel Profiling of RNA-targeting CRISPR-Cas13d

Type VI CRISPR enzymes cleave target RNAs and are widely used for gene regulation, RNA tracking, and diagnostics. However, a systematic understanding of their RNA binding specificity and cleavage activation is lacking. Here, we describe RNA chip-hybridized association-mapping platform (RNA-CHAMP), a massively parallel platform that repurposes next-generation DNA sequencing chips to measure the binding affinity for over 10,000 RNA targets containing structural perturbations, mismatches, insertions, and deletions relative to the CRISPR RNA (crRNA). Deep profiling of Cas13d, a compact and widely used RNA nuclease, reveals that it does not require a protospacer flanking sequence (PFS) but is exquisitely sensitive to secondary structure within the target RNA. Cas13d binding is strongly penalized by mismatches, insertions, and deletions in the distal crRNA-target RNA regions, while alterations in the proximal region inhibit nuclease activity without affecting binding. A biophysical model built from these data reveals that target recognition begins at the distal end of unstructured target RNAs and proceeds to the proximal end. Using this model, we designed a series of partially mismatched guide RNAs that modulate nuclease activity to detect single nucleotide polymorphisms (SNPs) in circulating SARS-CoV-2 variants. This work describes the key determinants of RNA targeting by a type VI CRISPR enzyme to improve CRISPR diagnostics and in vivo RNA editing. More broadly, RNA-CHAMP provides a quantitative platform for systematically measuring protein-RNA interactions.

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.

High-throughput biochemistry in RNA sequence space: predicting structure and function

RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.

Riboswitches as therapeutic targets: promise of a new era of antibiotics

INTRODUCTION

The growth of antibiotic resistance among bacterial pathogens is an impending global threat that can only be averted through the development of novel antibacterial drugs. A promising answer could be the targeting of riboswitches, structured RNA elements found almost exclusively in bacteria.

AREAS COVERED

This review examines the potential of riboswitches as novel antibacterial drug targets. The limited mechanisms of action of currently available antibiotics are summarized, followed by a delineation of the functional mechanisms of riboswitches. We then discuss the potential for developing novel approaches that target paradigmatic riboswitches in the context of their bacterial gene expression machinery.

EXPERT OPINION

We highlight potential advantages of targeting riboswitches in their functional form, embedded within gene expression complexes critical for bacterial survival. We emphasize the benefits of this approach, including potentially higher species specificity and lower side effects.

Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning

A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays an important role in the mutational robustness and evolvability of biomolecules. In contrast to earlier theoretical works, evidence of large neutral networks has been lacking in recent experimental studies of fitness landscapes. This suggests that evolution could be constrained globally. Here, we demonstrate that a deep learning-guided evolutionary algorithm can efficiently identify neutral genotypes within the sequence space of an RNA ligase ribozyme. Furthermore, we measure the activities of all 2¹⁶ variants connecting two active ribozymes that differ by 16 mutations and analyze mutational interactions (epistasis) up to the 16th order. We discover an extensive network of neutral paths linking the two genotypes and reveal that these paths might be predicted using only information from lower-order interactions. Our experimental evaluation of over 120,000 ribozyme sequences provides important empirical evidence that neutral networks can increase the accessibility and predictability of the fitness landscape.

Neutral networks, which are sets of genotypes connected via single mutations that share the same phenotype, are important for evolvability. Here, the authors provide experimental evidence of a neutral network in an RNA enzyme using a high-throughput assay and deep learning.

Exploring molecular biology in sequence space: The road to next-generation single-molecule biophysics

Next-generation sequencing techniques have led to a new quantitative dimension in the biological sciences. In particular, integrating sequencing techniques with biophysical tools allows sequence-dependent mechanistic studies. Using the millions of DNA clusters that are generated during sequencing to perform high-throughput binding affinity and kinetics measurements enabled the construction of energy landscapes in sequence space, uncovering relationships between sequence, structure, and function. Here, we review the approaches to perform ensemble fluorescence experiments on next-generation sequencing chips for variations of DNA, RNA, and protein sequences. As the next step, we anticipate that these fluorescence experiments will be pushed to the single-molecule level, which can directly uncover kinetics and molecular heterogeneity in an unprecedented high-throughput fashion. Molecular biophysics in sequence space, both at the ensemble and single-molecule level, leads to new mechanistic insights. The wide spectrum of applications in biology and medicine ranges from the fundamental understanding of evolutionary pathways to the development of new therapeutics.

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.

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.

The RNA structurome in the asexual blood stages of malaria pathogen plasmodium falciparum

Plasmodium falciparum is a deadly human pathogen responsible for the devastating disease called malaria. In this study, we measured the differential accumulation of RNA secondary structures in coding and non-coding transcripts from the asexual developmental cycle in P. falciparum in human red blood cells. Our comprehensive analysis that combined high-throughput nuclease mapping of RNA structures by duplex RNA-seq, SHAPE-directed RNA structure validation, immunoaffinity purification and characterization of antisense RNAs collectively measured differentially base-paired RNA regions throughout the parasite's asexual RBC cycle. Our mapping data not only aligned to a diverse pool of RNAs with known structures but also enabled us to identify new structural RNA regions in the malaria genome. On average, approximately 71% of the genes with secondary structures are found to be protein coding mRNAs. The mapping pattern of these base-paired RNAs corresponded to all regions of mRNAs, including the 5' UTR, CDS and 3' UTR as well as the start and stop codons. Histone family genes which are known to form secondary structures in their mRNAs and transcripts from genes which are important for transcriptional and post-transcriptional control, such as the unique plant-like transcription factor family, ApiAP2, DNA-/RNA-binding protein, Alba3 and proteins important for RBC invasion and malaria cytoadherence also showed strong accumulation of duplex RNA reads in various asexual stages in P. falciparum. Intriguingly, our study determined stage-specific, dynamic relationships between mRNA structural contents and translation efficiency in P. falciparum asexual blood stages, suggesting an essential role of RNA structural changes in malaria gene expression programs.

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.

Fitness landscape of a dynamic RNA structure

RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5' splice site. Following cleavage at the 5' splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3' splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape.