Structure and ligand binding of the glutamine-II riboswitch

Multidimensional Applications and Challenges of Riboswitches in Biosensing and Biotherapy

Riboswitches have received significant attention over the last two decades for their multiple functionalities and great potential for applications in various fields. This article highlights and reviews the recent advances in biosensing and biotherapy. These fields involve a wide range of applications, such as food safety detection, environmental monitoring, metabolic engineering, live cell imaging, wearable biosensors, antibacterial drug targets, and gene therapy. The discovery, origin, and optimization of riboswitches are summarized to help readers better understand their multidimensional applications. Finally, this review discusses the multidimensional challenges and development of riboswitches in order to further expand their potential for novel applications.

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Development and characterization of a glycine biosensor system for fine-tuned metabolic regulation in Escherichia coli

Background

In vivo biosensors have a wide range of applications, ranging from the detection of metabolites to the regulation of metabolic networks, providing versatile tools for synthetic biology and metabolic engineering. However, in view of the vast array of metabolite molecules, the existing number and performance of biosensors is far from sufficient, limiting their potential applications in metabolic engineering. Therefore, we developed the synthetic glycine-ON and -OFF riboswitches for metabolic regulation and directed evolution of enzyme in Escherichia coli.

Results

The results showed that a synthetic glycine-OFF riboswitch (glyOFF6) and an increased-detection-range synthetic glycine-ON riboswitch (glyON14) were successfully screened from a library based on the Bacillus subtilis glycine riboswitch using fluorescence-activated cell sorting (FACS) and tetA-based dual genetic selection. The two synthetic glycine riboswitches were successfully used in tunable regulation of lactate synthesis, dynamic regulation of serine synthesis and directed evolution of alanine-glyoxylate aminotransferase in Escherichia coli, respectively. Mutants AGXT22 and AGXT26 of alanine-glyoxylate aminotransferase with an increase of 58% and 73% enzyme activity were obtained by using a high-throughput screening platform based on the synthetic glycine-OFF riboswitch, and successfully used to increase the 5-aminolevulinic acid yield of engineered Escherichia coli.

Conclusions

A synthetic glycine-OFF riboswitch and an increased-detection-range synthetic glycine-ON riboswitch were successfully designed and screened. The developed riboswitches showed broad application in tunable regulation, dynamic regulation and directed evolution of enzyme in E. coli.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01779-4.

Developing Community Resources for Nucleic Acid Structures

In this review, we describe the creation of the Nucleic Acid Database (NDB) at Rutgers University and how it became a testbed for the current infrastructure of the RCSB Protein Data Bank. We describe some of the special features of the NDB and how it has been used to enable research. Plans for the next phase as the Nucleic Acid Knowledgebase (NAKB) are summarized.

The Biochemical Landscape of Riboswitch Ligands

More than 55 distinct classes of riboswitches that respond to small metabolites or elemental ions have been experimentally validated to date. The ligands sensed by these riboswitches are biased in favor of fundamental compounds or ions that are likely to have been relevant to ancient forms of life, including those that might have populated the "RNA World", which is a proposed biochemical era that predates the evolutionary emergence of DNA and proteins. In the following text, I discuss the various types of ligands sensed by some of the most common riboswitches present in modern bacterial cells and consider implications for ancient biological processes centered on the proven capabilities of these RNA-based sensors. Although most major biochemical aspects of metabolism are represented by known riboswitch classes, there are striking sensory gaps in some key areas. These gaps could reveal weaknesses in the performance capabilities of RNA that might have hampered RNA World evolution, or these could highlight opportunities to discover additional riboswitch classes that sense essential metabolites.

Structures of artificially designed discrete RNA nanoarchitectures at near-atomic resolution

Although advances in nanotechnology have enabled the construction of complex and functional synthetic nucleic acid–based nanoarchitectures, high-resolution discrete structures are lacking because of the difficulty in obtaining good diffracting crystals. Here, we report the design and construction of RNA nanostructures based on homooligomerizable one-stranded tiles for x-ray crystallographic determination. We solved three structures to near-atomic resolution: a 2D parallelogram, a 3D nanobracelet unexpectedly formed from an RNA designed for a nanocage, and, eventually, a bona fide 3D nanocage designed with the guidance of the two previous structures. Structural details of their constituent motifs, such as kissing loops, branched kissing loops, and T-junctions, that resemble natural RNA motifs and resisted x-ray determination are revealed, providing insights into those natural motifs. This work unveils the largely unexplored potential of crystallography in gaining high-resolution feedback for nanoarchitectural design and suggests a route to investigate RNA motif structures by configuring them into nanoarchitectures.

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network

MOTIVATION

RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition.

RESULTS

Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs.

AVAILABILITY AND IMPLEMENTATION

Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recent advances in synthetic biology of cyanobacteria for improved chemicals production

Cyanobacteria are Gram-negative photoautotrophic prokaryotes and have shown great importance to the Earth’s ecology. Based on their capability in oxygenic photosynthesis and genetic merits, they can be engineered as microbial chassis for direct conversion of carbon dioxide to value-added biofuels and chemicals. In the last decades, attempts have given to the application of synthetic biology tools and approaches in the development of cyanobacterial cell factories. Despite the successful proof-of-principle studies, large-scale application is still a technical challenge due to low yields of bioproducts. Therefore, recent efforts are underway to characterize and develop genetic regulatory parts and strategies for the synthetic biology applications in cyanobacteria. In this review, we present the recent advancements and application in cyanobacterial synthetic biology toolboxes. We also discuss the limitations and future perspectives for using such novel tools in cyanobacterial biotechnology.

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

Carbon/nitrogen homeostasis control in cyanobacteria

Carbon/nitrogen (C/N) balance sensing is a key requirement for the maintenance of cellular homeostasis. Therefore, cyanobacteria have evolved a sophisticated signal transduction network targeting the metabolite 2-oxoglutarate (2-OG), the carbon skeleton for nitrogen assimilation. It serves as a status reporter for the cellular C/N balance that is sensed by transcription factors NtcA and NdhR and the versatile PII-signaling protein. The PII protein acts as a multitasking signal-integrating regulator, combining the 2-OG signal with the energy state of the cell through adenyl-nucleotide binding. Depending on these integrated signals, PII orchestrates metabolic activities in response to environmental changes through binding to various targets. In addition to 2-OG, other status reporter metabolites have recently been discovered, mainly indicating the carbon status of the cells. One of them is cAMP, which is sensed by the PII-like protein SbtB. The present review focuses, with a main emphasis on unicellular model strains Synechoccus elongatus and Synechocystis sp. PCC 6803, on the physiological framework of these complex regulatory loops, the tight linkage to metabolism and the molecular mechanisms governing the signaling processes.

Structural Insights into RNA Dimerization: Motifs, Interfaces and Functions

In comparison with the pervasive use of protein dimers and multimers in all domains of life, functional RNA oligomers have so far rarely been observed in nature. Their diminished occurrence contrasts starkly with the robust intrinsic potential of RNA to multimerize through long-range base-pairing ("kissing") interactions, self-annealing of palindromic or complementary sequences, and stable tertiary contact motifs, such as the GNRA tetraloop-receptors. To explore the general mechanics of RNA dimerization, we performed a meta-analysis of a collection of exemplary RNA homodimer structures consisting of viral genomic elements, ribozymes, riboswitches, etc., encompassing both functional and fortuitous dimers. Globally, we found that domain-swapped dimers and antiparallel, head-to-tail arrangements are predominant architectural themes. Locally, we observed that the same structural motifs, interfaces and forces that enable tertiary RNA folding also drive their higher-order assemblies. These feature prominently long-range kissing loops, pseudoknots, reciprocal base intercalations and A-minor interactions. We postulate that the scarcity of functional RNA multimers and limited diversity in multimerization motifs may reflect evolutionary constraints imposed by host antiviral immune surveillance and stress sensing. A deepening mechanistic understanding of RNA multimerization is expected to facilitate investigations into RNA and RNP assemblies, condensates, and granules and enable their potential therapeutical targeting.

A minimum set of regulators to thrive in the ocean

Marine cyanobacteria of the genus Prochlorococcus thrive in high cell numbers throughout the euphotic zones of the world's subtropical and tropical oligotrophic oceans, making them some of the most ecologically relevant photosynthetic microorganisms on Earth. The ecological success of these free-living phototrophs suggests that they are equipped with a regulatory system competent to address many different stress situations. However, Prochlorococcus genomes are compact and streamlined, with the majority encoding only five different sigma factors, five to six two-component systems and eight types of other transcriptional regulators. Here, we summarize the existing information about the functions of these protein regulators, about transcriptomic responses to defined stress conditions, and discuss the current knowledge about riboswitches, RNA-based regulation and the roles of certain metabolites as co-regulators. We focus on the best-studied isolate, Prochlorococcus MED4, but extend to other strains and ecotypes when appropriate, and we include some information gained from metagenomic and metatranscriptomic analyses.

Regulatory RNA at the crossroads of carbon and nitrogen metabolism in photosynthetic cyanobacteria

Cyanobacteria are photosynthetic bacteria that populate widely different habitats. Accordingly, cyanobacteria exhibit a wide spectrum of lifestyles, physiologies, and morphologies and possess genome sizes and gene numbers which may vary by up to a factor of ten within the phylum. Consequently, large differences exist between individual species in the size and complexity of their regulatory networks. Several non-coding RNAs have been identified that play crucial roles in the acclimation responses of cyanobacteria to changes in the environment. Some of these regulatory RNAs are conserved throughout the cyanobacterial phylum, while others exist only in a few taxa. Here we give an overview on characterized regulatory RNAs in cyanobacteria, with a focus on regulators of photosynthesis, carbon and nitrogen metabolism. However, chances are high that these regulators represent just the tip of the iceberg.

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only [Formula: see text]250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of [Formula: see text]10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.