sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs

ChimericFragments: computation, analysis and visualization of global RNA networks

RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.

Agnodice: indexing experimentally supported bacterial sRNA-RNA interactions

In the last decade, the immense growth in the field of bacterial small RNAs (sRNAs), along with the biotechnological breakthroughs in Deep Sequencing permitted the deeper understanding of sRNA-RNA interactions. However, microbiology is currently lacking a thoroughly curated collection of this rapidly expanding universe. We present Agnodice (https://dianalab.e-ce.uth.gr/agnodice), our effort to systematically catalog and annotate experimentally supported bacterial sRNA-RNA interactions. Agnodice, for the first time, incorporates thousands of bacterial sRNA-RNA interactions derived from a diverse set of experimental methodologies including state-of-the-art Deep Sequencing interactome identification techniques. It comprises 39,600 entries which are annotated at strain-level resolution and pertain to 399 sRNAs and 12,137 target RNAs identified in 71 bacterial strains. The database content is exclusively experimentally supported, incorporating interactions derived via low yield as well as state-of-the-art high-throughput methods. The entire content of the database is freely accessible and can be directly downloaded for further analysis. Agnodice will serve as a valuable source, enabling microbiologists to form novel hypotheses, design/identify novel sRNA-based drug targets, and explore the therapeutic potential of microbiomes from the perspective of small regulatory RNAs.IMPORTANCEAgnodice (https://dianalab.e-ce.uth.gr/agnodice) is an effort to systematically catalog and annotate experimentally supported bacterial small RNA (sRNA)-RNA interactions. Agnodice, for the first time, incorporates thousands of bacterial sRNA-RNA interactions derived from a diverse set of experimental methodologies including state-of-the-art Next Generation Sequencing interactome identification techniques.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning

Small regulatory RNAs pervade prokaryotes, with the best-studied family of these non-coding genes corresponding to trans-acting regulators that bind via base pairing to their message targets. Given the increasing frequency with which these genes are being identified, it is important that methods for illuminating their regulatory targets keep pace. Using a machine learning approach, we investigate thousands of interactions between small RNAs and their targets, and we interrogate more than a hundred features indicative of these interactions. We present a new method, TargetRNA3, for predicting targets of small RNA regulators and show that it outperforms existing approaches. TargetRNA3 is available at https://cs.wellesley.edu/~btjaden/TargetRNA3 .

Dietary sugars silence the master regulator of carbohydrate utilization in human gut Bacteroides species

The mammalian gut microbiota is a critical human health determinant with therapeutic potential for remediation of many diseases. The host diet is a key factor governing the gut microbiota composition by altering nutrient availability and supporting the expansion of distinct microbial populations. Diets rich in simple sugars modify the abundance of microbial subsets, enriching for microbiotas that elicit pathogenic outcomes. We previously demonstrated that diets rich in fructose and glucose can reduce the fitness and abundance of a human gut symbiont, Bacteroides thetaiotaomicron, by silencing the production of a critical intestinal colonization protein, called Roc, via its mRNA leader through an unknown mechanism. We have now determined that dietary sugars silence Roc by reducing the activity of BT4338, a master regulator of carbohydrate utilization. Here, we demonstrate that BT4338 is required for Roc synthesis, and that BT4338 activity is silenced by glucose or fructose. We show that the consequences of glucose and fructose on orthologous transcription factors are conserved across human intestinal Bacteroides species. This work identifies a molecular pathway by which a common dietary additive alters microbial gene expression in the gut that could be harnessed to modulate targeted microbial populations for future therapeutic interventions.

Regulation of biofilm formation by non-coding RNA in prokaryotes

Biofilm refers to microbes that associate with each other or to a surface via self-synthesized exopolysaccharides and other surface-related structures. The presence of biofilms consisting of pathogenic microbes in the food and clinical environment can pose a threat to human health as microbes in biofilms are highly robust and are difficult to remove. Understanding the process of biofilm formation is crucial for the development of novel strategies to control or harness biofilm. The complex network of proteins, small RNA, and diverse molecules regulate biofilm formation at different steps in biofilm development, including triggering the switch from planktonic to sessile cells, maturation of biofilms, and eventual dispersion of microbes from the biofilms. Small non-coding RNAs are relatively small RNAs that are not translated into proteins and play diverse roles in metabolism, physiology, pathogenesis, and biofilm formation. In this review, we primarily focused on non-coding regulatory RNA that regulates biofilm formation in clinically relevant pathogens or threatens human health. Even though many ncRNA have recently been identified in Archaea, much characterization work remains. The mechanisms and regulatory processes controlled by ncRNA in prokaryotes are covered in this review.

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

Do small RNAs unlock the below ground microbiome-plant interaction mystery?

Over the past few decades, regulatory RNAs, such as small RNAs (sRNAs), have received increasing attention in the context of host-microbe interactions due to their diverse roles in controlling various biological processes in eukaryotes. In addition, studies have identified an increasing number of sRNAs with novel functions across a wide range of bacteria. What is not well understood is why cells regulate gene expression through post-transcriptional mechanisms rather than at the initiation of transcription. The finding of a multitude of sRNAs and their identified associated targets has allowed further investigation into the role of sRNAs in mediating gene regulation. These foundational data allow for further development of hypotheses concerning how a precise control of gene activity is accomplished through the combination of transcriptional and post-transcriptional regulation. Recently, sRNAs have been reported to participate in interkingdom communication and signalling where sRNAs originating from one kingdom are able to target or control gene expression in another kingdom. For example, small RNAs of fungal pathogens that silence plant genes and vice-versa plant sRNAs that mediate bacterial gene expression. However, there is currently a lack of evidence regarding sRNA-based inter-kingdom signalling across more than two interacting organisms. A habitat that provides an excellent opportunity to investigate interconnectivity is the plant rhizosphere, a multifaceted ecosystem where plants and associated soil microbes are known to interact. In this paper, we discuss how the interconnectivity of bacteria, fungi, and plants within the rhizosphere may be mediated by bacterial sRNAs with a particular focus on disease suppressive and non-suppressive soils. We discuss the potential roles sRNAs may play in the below-ground world and identify potential areas of future research, particularly in reference to the regulation of plant immunity genes by bacterial and fungal communities in disease-suppressive and non-disease-suppressive soils.

A scalable framework for the discovery of functional helicase substrates and helicase-driven regulatory switches

Helicases are ubiquitous motor enzymes that remodel nucleic acids (NA) and NA-protein complexes in key cellular processes. To explore the functional repertoire and specificity landscape of helicases, we devised a screening scheme-Helicase-SELEX (Systematic Evolution of Ligands by EXponential enrichment)-that enzymatically probes substrate and cofactor requirements at global scale. Using the transcription termination Rho helicase of Escherichia coli as a prototype for Helicase-SELEX, we generated a genome-wide map of Rho utilization (Rut) sites. The map reveals many features, including promoter- and intrinsic terminator-associated Rut sites, bidirectional Rut tandems, and cofactor-dependent Rut sites with inverted G > C skewed compositions. We also implemented an H-SELEX variant where we used a model ligand, serotonin, to evolve synthetic Rut sites operating in vitro and in vivo in a ligand-dependent manner. Altogether, our data illustrate the power and flexibility of Helicase-SELEX to seek constitutive or conditional helicase substrates in natural or synthetic NA libraries for fundamental or synthetic biology discovery.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

sRNA chaperone Hfq controls bioluminescence and other phenotypes through Qrr1-dependent and -independent mechanisms in Vibrio fischeri

Colonization of the squid Euprymna scolopes by the bacterium Vibrio fischeri depends on bacterial biofilm formation, motility, and bioluminescence. Previous work has demonstrated an inhibitory role for the small RNA (sRNA) Qrr1 in quorum-induced bioluminescence of V. fischeri, but the contribution of the corresponding sRNA chaperone, Hfq, was not examined. We thus hypothesized that V. fischeri Hfq similarly functions to inhibit bacterial bioluminescence as well as regulate other key steps of symbiosis, including bacterial biofilm formation and motility. Surprisingly, deletion of hfq increased luminescence of V. fischeri beyond what was observed for the loss of qrr1 sRNA. Epistasis experiments revealed that, while Hfq contributes to the Qrr1-dependent regulation of light production, it also functions independently of Qrr1 and its downstream target, LitR. This Hfq-dependent, Qrr1-independent regulation of bioluminescence is also independent of the major repressor of light production in V. fischeri, ArcA. We further determined that Hfq is required for full motility of V. fischeri in a mechanism that partially depends on the Qrr1/LitR regulators. Finally, Hfq also appears to function in the control of biofilm formation: loss of Hfq delayed the timing and diminished the extent of wrinkled colony development, but did not eliminate the production of SYP-polysaccharide-dependent cohesive colonies. Furthermore, loss of Hfq enhanced production of cellulose and resulted in increased Congo red binding. Together, these findings point to Hfq as an important regulator of multiple phenotypes relevant to symbiosis between V. fischeri and its squid host.

Mining RNA-seq data reveals the massive regulon of GcvB small RNA and its physiological significance in maintaining amino acid homeostasis in Escherichia coli

Bacterial small RNAs regulate the expression of multiple genes through imperfect base-pairing with target mRNAs mediated by RNA chaperone proteins such as Hfq. GcvB is the master sRNA regulator of amino acid metabolism and transport in a wide range of Gram-negative bacteria. Recently, independent RNA-seq approaches identified a plethora of transcripts interacting with GcvB in Escherichia coli. In this study, the compilation of RIL-seq, CLASH, and MAPS data sets allowed us to identify GcvB targets with high accuracy. We validated 21 new GcvB targets repressed at the posttranscriptional level, raising the number of direct targets to >50 genes in E. coli. Among its multiple seed sequences, GcvB utilizes either R1 or R3 to regulate most of these targets. Furthermore, we demonstrated that both R1 and R3 seed sequences are required to fully repress the expression of gdhA, cstA, and sucC genes. In contrast, the ilvLXGMEDA polycistronic mRNA is targeted by GcvB through at least four individual binding sites in the mRNA. Finally, we revealed that GcvB is involved in the susceptibility of peptidase-deficient E. coli strain (Δpeps) to Ala-Gln dipeptide by regulating both Dpp dipeptide importer and YdeE dipeptide exporter via R1 and R3 seed sequences, respectively.

sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction

Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programsin terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

Understanding small ORF diversity through a comprehensive transcription feature classification

Small open reading frames (small ORFs/sORFs/smORFs) are potentially coding sequences smaller than 100 codons that have historically been considered junk DNA by gene prediction software and in annotation screening; however, the advent of next-generation sequencing has contributed to the deeper investigation of junk DNA regions and their transcription products, resulting in the emergence of smORFs as a new focus of interest in systems biology. Several smORF peptides were recently reported in noncanonical mRNAs as new players in numerous biological contexts; however, their relevance is still overlooked in coding potential analysis. Hence, this review proposes a smORF classification based on transcriptional features, discussing the most promising approaches to investigate smORFs based on their different characteristics. First, smORFs were divided into nonexpressed (intergenic) and expressed (genic) smORFs. Second, genic smORFs were classified as smORFs located in noncoding RNAs (ncRNAs) or canonical mRNAs. Finally, smORFs in ncRNAs were further subdivided into sequences located in small or long RNAs, whereas smORFs located in canonical mRNAs were subdivided into several specific classes depending on their localization along the gene. We hope that this review provides new insights into large-scale annotations and reinforces the role of smORFs as essential components of a hidden coding DNA world.

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

Bacterial Cyclopropane Fatty Acid Synthase mRNA Is Targeted by Activating and Repressing Small RNAs

Altering membrane protein and lipid composition is an important strategy for maintaining membrane integrity during environmental stress. Many bacterial small RNAs (sRNAs) control membrane protein production, but sRNA-mediated regulation of membrane fatty acid composition is less well understood. The sRNA RydC was previously shown to stabilize cfa (cyclopropane fatty acid synthase) mRNA, resulting in higher levels of cyclopropane fatty acids in the cell membrane. Here, we report that additional sRNAs, ArrS and CpxQ, also directly regulate cfa posttranscriptionally. RydC and ArrS act through masking an RNase E cleavage site in the cfa mRNA 5' untranslated region (UTR), and both sRNAs posttranscriptionally activate cfa In contrast, CpxQ binds to a different site in the cfa mRNA 5' UTR and represses cfa expression. Alteration of membrane lipid composition is a key mechanism for bacteria to survive low-pH environments, and we show that cfa translation increases in an sRNA-dependent manner when cells are subjected to mild acid stress. This work suggests an important role for sRNAs in the acid stress response through regulation of cfa mRNA.IMPORTANCE Small RNAs (sRNAs) in bacteria are abundant and play important roles in posttranscriptional regulation of gene expression, particularly under stress conditions. Some mRNAs are targets for regulation by multiple sRNAs, each responding to different environmental signals. Uncovering the regulatory mechanisms governing sRNA-mRNA interactions and the relevant conditions for these interactions is an ongoing challenge. In this study, we discovered that multiple sRNAs control membrane lipid composition by regulating stability of a single mRNA target. The sRNA-dependent regulation occurred in response to changing pH and was important for cell viability under acid stress conditions. This work reveals yet another aspect of bacterial physiology controlled at the posttranscriptional level by sRNA regulators.