RNA-bioinformatics: Tools, services and databases for the analysis of RNA-based regulation

A conserved uORF in the ilvBNC mRNA of Corynebacterium species regulates ilv operon expression

Computational methods can be used to identify putative structured noncoding RNAs (ncRNAs) in bacteria, which can then be validated using various biochemical and genetic approaches. In a search for ncRNAs in Corynebacterium pseudotuberculosis, we observed a conserved region called the ilvB-II motif located upstream of the ilvB gene that is also present in other members of this genus. This gene codes for an enzyme involved in the production of branched-chain amino acids (BCAAs). The ilvB gene in some bacteria is regulated by members of a ppGpp-sensing riboswitch class, but previous and current data suggest that the ilvB-II motif regulates expression by a transcription attenuation mechanism involving protein translation from an upstream open reading frame (uORF or leader peptide). All representatives of this RNA motif carry a start codon positioned in-frame with a nearby stop codon, and the peptides resulting from translation of this uORF are enriched for BCAAs, suggesting that expression of the ilvB gene in the host cells is controlled by attenuation. Furthermore, recently discovered RNA motifs also associated with ilvB genes in other bacterial species appear to carry distinct uORFs, suggesting that transcription attenuation by uORF translation is a common mechanism for regulating ilvB genes.

A Sporulation-Specific sRNA Bvs196 Contributing to the Developing Spore in Bacillus velezensis

Many putative sRNAs have been characterized using bioinformatic analysis and high-throughput sequencing in Gram-positive Bacillus strains, but there are only a few functional studies on the sRNAs involved in the spore formation developmental process. In particular, there is no sRNA confirmed experimentally to regulate the late stages of sporulation. Bvs196 is an sRNA with a length of 294 nucleotides that is abundantly expressed in the stationary phase of several media and independently transcribed in Bacillus velezensis strain PEBA20, as validated by RNA-seq and Northern blot,. It is also confirmed, by qRT-PCR, that Bvs196 is transcribed abundantly throughout the intermediate and late stages of sporulation. Using the gfpmut3a gene transcriptional reporter demonstrates that Bvs196 is expressed specifically in the forespore during sporulation and controlled by σF and σG (mainly by σG). This was observed by fluorescence microscopy and multi-function microplate reader. Further evolutionary conservation analysis found that Bvs196 is widely present in Bacillus with a strongly conserved and stable secondary structure. Resistance phenotypic assays of spores formed from the Bvs196 deletion mutant, the overexpressed Bvs196 mutant, and the wild-type strain revealed that the absence of Bvs196 led to reduced heat and UV resistance and enhanced formaldehyde resistance. We determined, by MST analysis, that Bvs196 can directly interact with spo0A and sspN-tlp mRNAs in vitro, and that short incomplete complementary paired bases affect the binding affinity of Bvs196 to target mRNAs. Our results suggest that Bvs196 is a novel sporulation-specific sRNA of B. velezensis, 294 nt in length, independently transcribed under the control of σF and σG in the forespore during sporulation, and that it affects spore resistance, and is able to directly interact with spo0A and sspN-tlp mRNAs. The remarkable conservation and impressive expression level of Bvs196 imply that it acts as an important conservative regulator, presumably by interacting with many other unknown targets in the forespore, and therefore contributing to spore properties. This work provides new clues for further understanding of the spore formation regulatory network.

Zooming in: PAGE-Northern Blot Helps to Analyze Anti-Sense Transcripts Originating from Human rIGS under Transcriptional Stress

Ribosomal intergenic spacer (rIGS), located between the 45S rRNA coding arrays in humans, is a deep, unexplored source of small and long non-coding RNA molecules transcribed in certain conditions to help a cell generate a stress response, pass through a differentiation state or fine tune the functioning of the nucleolus as a ribosome biogenesis center of the cell. Many of the non-coding transcripts originating from the rIGS are not characterized to date. Here, we confirm the transcriptional activity of the region laying a 2 kb upstream of the rRNA promoter, and demonstrate its altered expression under transcriptional stress, induced by a wide range of known transcription inhibitors. We managed to show an increased variability of anti-sense transcripts in alpha-amanitin treated cells by applying the low-molecular RNA fraction extracted from agarose gel to PAGE-northern. Also, the fractioning of RNA by size using agarose gel slices occurred, being applicable for determining the sizes of target transcripts via RT-PCR.

Mutations in cis that affect mRNA synthesis, processing and translation

Genetic mutations that cause hereditary diseases usually affect the composition of the transcribed mRNA and its encoded protein, leading to instability of the mRNA and/or the protein. Sometimes, however, such mutations affect the synthesis, the processing or the translation of the mRNA, with similar disastrous effects. We here present an overview of mRNA synthesis, its posttranscriptional modification and its translation into protein. We then indicate which elements in these processes are known to be affected by pathogenic mutations, but we restrict our review to mutations in cis, in the DNA of the gene that encodes the affected protein. These mutations can be in enhancer or promoter regions of the gene, which act as binding sites for transcription factors involved in pre-mRNA synthesis. We also describe mutations in polyadenylation sequences and in splice site regions, exonic and intronic, involved in intron removal. Finally, we include mutations in the Kozak sequence in mRNA, which is involved in protein synthesis. We provide examples of genetic diseases caused by mutations in these DNA regions and refer to databases to help identify these regions. The over-all knowledge of mRNA synthesis, processing and translation is essential for improvement of the diagnosis of patients with genetic diseases.

IntaRNAhelix-composing RNA–RNA interactions from stable inter-molecular helices boosts bacterial sRNA target prediction

Efficient computational tools for the identification of putative target RNAs regulated by prokaryotic sRNAs rely on thermodynamic models of RNA secondary structures. While they typically predict RNA–RNA interaction complexes accurately, they yield many highly-ranked false positives in target screens. One obvious source of this low specificity appears to be the disability of current secondary-structure-based models to reflect steric constraints, which nevertheless govern the kinetic formation of RNA–RNA interactions. For example, often — even thermodynamically favorable — extensions of short initial kissing hairpin interactions are kinetically prohibited, since this would require unwinding of intra-molecular helices as well as sterically impossible bending of the interaction helix. Another source is the consideration of instable and thus unlikely subinteractions that enable better scoring of longer interactions. In consequence, the efficient prediction methods that do not consider such effects show a high false positive rate.

To increase the prediction accuracy we devise IntaRNAhelix, a dynamic programming algorithm that length-restricts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitly model the steric and kinetic effects. The novel method is implemented by extending the state-of-the-art tool IntaRNA.

Our comprehensive bacterial sRNA target prediction benchmark demonstrates significant improvements of the prediction accuracy and enables more than 40-times faster computations. These results indicate — supporting our hypothesis — that stable helix composition increases the accuracy of interaction prediction models compared to the current state-of-the-art approach.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

CMV: visualization for RNA and protein family models and their comparisons

Summary

A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest.

Availability and implementation

Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS).

Supplementary information

Supplementary data are available at Bioinformatics online.

Post-transcriptional Regulation of Colorectal Cancer: A Focus on RNA-Binding Proteins

Colorectal cancer (CRC) is a major health problem with an estimated 1. 8 million new cases worldwide. To date, most CRC studies have focused on DNA-related aberrations, leaving post-transcriptional processes under-studied. However, post-transcriptional alterations have been shown to play a significant part in the maintenance of cancer features. RNA binding proteins (RBPs) are uprising as critical regulators of every cancer hallmark, yet little is known regarding the underlying mechanisms and key downstream oncogenic targets. Currently, more than a thousand RBPs have been discovered in humans and only a few have been implicated in the carcinogenic process and even much less in CRC. Identification of cancer-related RBPs is of great interest to better understand CRC biology and potentially unveil new targets for cancer therapy and prognostic biomarkers. In this work, we reviewed all RBPs which have a role in CRC, including their control by microRNAs, xenograft studies and their clinical implications.

RNApolis: Computational Platform for RNA Structure Analysis

In the 1970s, computer scientists began to engage in research in the field of structural biology. The first structural databases, as well as models and methods supporting the analysis of biomolecule structures, started to be created. RNA was put at the centre of scientific interest quite late. However, more and more methods dedicated to this molecule are currently being developed. This paper presents RNApolis - a new computing platform, which offers access to seven bioinformatic tools developed to support the RNA structure study. The set of tools include a structural database and systems for predicting, modelling, annotating and evaluating the RNA structure. RNApolis supports research at different structural levels and allows the discovery, establishment, and validation of relationships between the primary, secondary and tertiary structure of RNAs. The platform is freely available at http://rnapolis.pl

Systems biology in inflammatory bowel diseases: on the way to precision medicine

Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.

Exploring miRNAs for developing climate-resilient crops: A perspective review

Climate changes and environmental stresses have significant implications on global crop production and necessitate developing crops that can withstand an array of climate changes and environmental perturbations such as irregular water-supplies leading to drought or water-logging, hyper soil-salinity, extreme and variable temperatures, ultraviolet radiations and metal stress. Plants have intricate molecular mechanisms to cope with these dynamic environmental changes, one of the most common and effective being the reprogramming of expression of stress-responsive genes. Plant microRNAs (miRNAs) have emerged as key post-transcriptional and translational regulators of gene-expression for modulation of stress implications. Recent reports are establishing their key roles in epigenetic regulations of stress/adaptive responses as well as in providing plants genome-stability. Several stress responsive miRNAs are being identified from different crop plants and miRNA-driven RNA-interference (RNAi) is turning into a technology of choice for improving crop traits and providing phenotypic plasticity in challenging environments. Here we presents a perspective review on exploration of miRNAs as potent targets for engineering crops that can withstand multi-stress environments via loss-/gain-of-function approaches. This review also shed a light on potential roles plant miRNAs play in genome-stability and their emergence as potent target for genome-editing. Current knowledge on plant miRNAs, their biogenesis, function, their targets, and latest developments in bioinformatics approaches for plant miRNAs are discussed. Though there are recent reviews discussing primarily the individual miRNAs responsive to single stress factors, however, considering practical limitation of this approach, special emphasis is given in this review on miRNAs involved in responses and adaptation of plants to multi-stress environments including at epigenetic and/or epigenomic levels.

MiR-652-3p inhibition enhances endothelial repair and reduces atherosclerosis by promoting Cyclin D2 expression

BACKGROUND

Atherosclerosis is a hyperlipidemia-induced condition affecting the arterial wall that damages healthy endothelial cell (EC) function, leading to enhanced risk of atherothrombotic events. Certain microRNAs regulate EC dysfunction in response to hyperlipidemia and may be suitable therapeutic targets to combat atherosclerosis.

METHODS

miRNA expression in human ECs was analyzed under various conditions to identify key microRNAs. High-cholesterol diet (HCD)-fed Mir652-/-Apoe-/- (Mir652-/-) mice and matching Mir652+/+Apoe-/- (Mir652+/+) mice were subjected to carotid injury to analyze the effects of miR-652 knockdown on endothelial repair. In silico analysis followed by in vitro and in vivo experiments were applied to identify miR-652's target gene Ccnd2 and investigate the pair's effects on ECs. miR-652-5p and miR-652-3p antagomir therapies were tested in Mir652+/+ mice under normal and HCD diet to assess their effect on endothelial repair.

FINDINGS

miR-652-3p, which is upregulated in human and murine atherosclerotic plaques, suppresses expression of the endothelial repair gene Ccnd2, thereby enhancing atherosclerotic lesion formation. Post-denudation recovery of ECs was promoted in Mir652-/- mice due to enhanced EC proliferation attributable to de-repression of miR-652-3p's (but not miR-652-5p's) regulation of Ccnd2 expression. Under hyperlipidemic conditions at non-predilection sites, miR-652-3p produces anti-proliferative effects in ECs, such that Mir652-/- mice display reduced atherosclerotic progression. In contrast, neither miR-652-3p nor Ccnd2 displayed significant effects on the endothelium at predilection sites or under disturbed flow conditions. Administration of a miR-652-3p antagomir rescued the proliferation of ECs in vivo, thereby limiting atherosclerotic development.

INTERPRETATION

miR-652-3p blockade may be a potential therapeutic strategy against atherosclerosis.

Epigenetics and Common Non Communicable Disease

Common Non communicable diseases (NCDs), such as cardiovascular disease, cancer, schizophrenia, and diabetes, have become the major cause of death in the world. They result from an interaction between genetics, lifestyle and environmental factors. The prevalence of NCDs are increasing, and researchers hopes to find efficient strategies to predict, prevent and treat them. Given the role of epigenome in the etiology of NCDs, insight into epigenetic mechanisms may offer opportunities to predict, detect, and prevent disease long before its clinical onset.Epigenetic alterations are exerted through several mechanisms including: chromatin modification, DNA methylation and controlling gene expression by non-coding RNAs (ncRNAs). In this chapter, we will discuss about NCDs, with focus on cancer, diabetes and schizophrenia. Different epigenetic mechanisms, categorized into two main groups DNA methylation and chromatin modifications and non-coding RNAs, will be separately discussed for these NCDs.

Identification and functional analyses of new sesame miRNAs (Sesamum indicum L.) and their targets

Plant microRNAs (miRNAs) have been commonly investigated during many years. Hundreds of miRNAs have been identified in many different plant species but there is very little information about the function of sesame (Sesamum indicum L.) miRNAs. For this purpose, in silico prediction of novel sesame miRNAs based on BLAST searches of the expressed sequence tag database was performed, using stringent criterias for miRNA annotation. The secondary structures of their precursor sequences, potential target genes of conserved and novel miRNAs were predicted and subjected to Gene Ontology (GO) annotation. mir447 and mir8140 were reported for the first time in sesame. Enrichment analysis of the GO with biological processes, cellular component and molecular functions revealed that these target genes were potentially involved in different metabolic pathways such as transcription factors, metabolism, growth and development, stress-related and even plant hormones. Results are valuable for figure out the gene regulation mechanism in sesame, using in the medicinal aspect of this plant species. Furthermore, these miRNAs and their profiled targets could provide the improvement of regulation and management, and even development of desirable traits in this plant.

Interactive implementations of thermodynamics-based RNA structure and RNA-RNA interaction prediction approaches for example-driven teaching

The investigation of RNA-based regulation of cellular processes is becoming an increasingly important part of biological or medical research. For the analysis of this type of data, RNA-related prediction tools are integrated into many pipelines and workflows. In order to correctly apply and tune these programs, the user has to have a precise understanding of their limitations and concepts. Within this manuscript, we provide the mathematical foundations and extract the algorithmic ideas that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. To allow the reader to change and adapt the algorithms or to play with different inputs, we provide an open-source web interface to JavaScript implementations and visualizations of each algorithm. The conceptual, teaching-focused presentation enables a high-level survey of the approaches, while providing sufficient details for understanding important concepts. This is boosted by the simple generation and study of examples using the web interface available at http://rna.informatik.uni-freiburg.de/Teaching/. In combination, we provide a valuable resource for teaching, learning, and understanding the discussed prediction tools and thus enable a more informed analysis of RNA-related effects.

RNA molecules are central players in many cellular processes. Thus, the analysis of RNA-based regulation has provided valuable insights and is often pivotal to biological and medical research. In order to correctly select appropriate algorithms and apply available RNA structure and RNA–RNA interaction prediction software, it is crucial to have a good understanding of their limitations and concepts. Such an overview is hard to achieve by end users, since most state-of-the-art tools are introduced on expert level and are not discussed in text books. Within this manuscript, we provide the mathematical means and extract the algorithmic concepts that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. The conceptual, teaching-focused presentation enables a detailed understanding of the approaches using a simplified model for didactic purposes. We support this process by providing clear examples using the web interface of our algorithm implementation. In summary, we have compiled material and web applications for teaching—and the self-study of—several state-of-the-art algorithms commonly used to investigate the role of RNA in regulatory processes.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.