dStruct: identifying differentially reactive regions from RNA structurome profiling data

Profiling human pathogenic repeat expansion regions by synergistic and multi-level impacts on molecular connections

Whilst DNA repeat expansions cause numerous heritable human disorders, their origins and underlying pathological mechanisms are often unclear. We collated a dataset comprising 224 human repeat expansions encompassing 203 different genes, and performed a systematic analysis with respect to key topological features at the DNA, RNA and protein levels. Comparison with controls without known pathogenicity and genomic regions lacking repeats, allowed the construction of the first tool to discriminate repeat regions harboring pathogenic repeat expansions (DPREx). At the DNA level, pathogenic repeat expansions exhibited stronger signals for DNA regulatory factors (e.g. H3K4me3, transcription factor-binding sites) in exons, promoters, 5'UTRs and 5'genes but were not significantly different from controls in introns, 3'UTRs and 3'genes. Additionally, pathogenic repeat expansions were also found to be enriched in non-B DNA structures. At the RNA level, pathogenic repeat expansions were characterized by lower free energy for forming RNA secondary structure and were closer to splice sites in introns, exons, promoters and 5'genes than controls. At the protein level, pathogenic repeat expansions exhibited a preference to form coil rather than other types of secondary structure, and tended to encode surface-located protein domains. Guided by these features, DPREx ( http://biomed.nscc-gz.cn/zhaolab/geneprediction/# ) achieved an Area Under the Curve (AUC) value of 0.88 in a test on an independent dataset. Pathogenic repeat expansions are thus located such that they exert a synergistic influence on the gene expression pathway involving inter-molecular connections at the DNA, RNA and protein levels.

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions.

Accurate detection of RNA stem-loops in structurome data reveals widespread association with protein binding sites

RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of patteRNA's pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets patteRNA's statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbour thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Advancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites.

Probing Transcriptome-Wide RNA Structural Changes Dependent on the DEAD-box Helicase Dbp2

RNA helicases function in all aspects of RNA biology mainly through remodeling structures of RNA and RNA-protein (RNP) complexes. Among them, DEAD-box proteins form the largest family in eukaryotes and have been shown to remodel RNA/RNP structures and clamping of RNA-binding proteins, both in vitro and in vivo. Nevertheless, for the majority of these enzymes, it is largely unclear what RNAs are targeted and where they modulate RNA/RNP structures to promote RNA metabolism. Several methods have been developed to probe secondary and tertiary structures of specific transcripts or whole transcriptomes in vivo. In this chapter, we describe a protocol for identification of RNA structural changes that are dependent on a Saccharomyces cerevisiae DEAD-box helicase Dbp2. Experiments detailed here can be adapted to the study of other RNA helicases and identification of putative remodeling targets in vivo.

RNA-mediated translation regulation in viral genomes: computational advances in the recognition of sequences and structures

RNA structures are widely distributed across all life forms. The global conformation of these structures is defined by a variety of constituent structural units such as helices, hairpin loops, kissing-loop motifs and pseudoknots, which often behave in a modular way. Their ubiquitous distribution is associated with a variety of functions in biological processes. The location of these structures in the genomes of RNA viruses is often coordinated with specific processes in the viral life cycle, where the presence of the structure acts as a checkpoint for deciding the eventual fate of the process. These structures have been found to adopt complex conformations and exert their effects by interacting with ribosomes, multiple host translation factors and small RNA molecules like miRNA. A number of such RNA structures have also been shown to regulate translation in viruses at the level of initiation, elongation or termination. The role of various computational studies in the preliminary identification of such sequences and/or structures and subsequent functional analysis has not been fully appreciated. This review aims to summarize the processes in which viral RNA structures have been found to play an active role in translational regulation, their global conformational features and the bioinformatics/computational tools available for the identification and prediction of these structures.

Stability and nuclear localization of yeast telomerase depend on protein components of RNase P/MRP

RNase P and MRP are highly conserved, multi-protein/RNA complexes with essential roles in processing ribosomal and tRNAs. Three proteins found in both complexes, Pop1, Pop6, and Pop7 are also telomerase-associated. Here, we determine how temperature sensitive POP1 and POP6 alleles affect yeast telomerase. At permissive temperatures, mutant Pop1/6 have little or no effect on cell growth, global protein levels, the abundance of Est1 and Est2 (telomerase proteins), and the processing of TLC1 (telomerase RNA). However, in pop mutants, TLC1 is more abundant, telomeres are short, and TLC1 accumulates in the cytoplasm. Although Est1/2 binding to TLC1 occurs at normal levels, Est1 (and hence Est3) binding is highly unstable. We propose that Pop-mediated stabilization of Est1 binding to TLC1 is a pre-requisite for formation and nuclear localization of the telomerase holoenzyme. Furthermore, Pop proteins affect TLC1 and the RNA subunits of RNase P/MRP in very different ways.

mRNA structural elements immediately upstream of the start codon dictate dependence upon eIF4A helicase activity

BACKGROUND

The RNA helicase eIF4A1 is a key component of the translation initiation machinery and is required for the translation of many pro-oncogenic mRNAs. There is increasing interest in targeting eIF4A1 therapeutically in cancer, thus understanding how this protein leads to the selective re-programming of the translational landscape is critical. While it is known that eIF4A1-dependent mRNAs frequently have long GC-rich 5'UTRs, the details of how 5'UTR structure is resculptured by eIF4A1 to enhance the translation of specific mRNAs are unknown.

RESULTS

Using Structure-seq2 and polysome profiling, we assess global mRNA structure and translational efficiency in MCF7 cells, with and without eIF4A inhibition with hippuristanol. We find that eIF4A inhibition does not lead to global increases in 5'UTR structure, but rather it leads to 5'UTR remodeling, with localized gains and losses of structure. The degree of these localized structural changes is associated with 5'UTR length, meaning that eIF4A-dependent mRNAs have greater localized gains of structure due to their increased 5'UTR length. However, it is not solely increased localized structure that causes eIF4A-dependency but the position of the structured regions, as these structured elements are located predominantly at the 3' end of the 5'UTR.

CONCLUSIONS

By measuring changes in RNA structure following eIF4A inhibition, we show that eIF4A remodels local 5'UTR structures. The location of these structural elements ultimately determines the dependency on eIF4A, with increased structure just upstream of the CDS being the major limiting factor in translation, which is overcome by eIF4A activity.

RNAdt: An online tutorial and data portal for the RNA structurome era

RNA is not only a passive transporter of genetic information, but also a pivotal player in all domains of life. RNA can regulate gene expression because of its involvement in transcription, mRNA modification and processing, and translation. RNA also possesses other intricate functions such as catalysis, ligand sensing, interaction with biomolecules, response to environment stresses, and information storage. The primary structure of RNA is single stranded, but it always folds into complex secondary and tertiary structures owing to base pairing and effects from the cellular environment. The importance of structure has been increasingly recognized in understanding the myriad functions of RNA. After decades of development, there is a wide range of RNA structure probing techniques. The marriage between structure probing and high-throughput sequencing (HTS) especially enables the measurement of RNA structure on a transcriptomic scale, advancing the advent of the RNA structurome era. Dozens of HTS-associated RNA structure probing methods have been published, so it is urgent to provide a user-friendly and easy-to-use resource for users who are perplexed by selecting the most suitable method for their experiments. Motivated by this demand, we collected currently available HTS-associated RNA structure probing methods and then developed RNAdt (freely accessible at http://www.zhounan.org/rnadt). RNAdt can be used as a web-based tutorial to learn fundamental knowledge of HTS-associated RNA structure probing methods. RNAdt can also be used as a data portal to access HTS data sets from previous RNA structurome studies. At the end of this work, we also provided perspectives on future development of RNA structure probing methods. Our study is expected to facilitate RNA structure probing and ultimately elucidate the connection between RNA structure and biological functions.

Genome-Wide Discovery of DEAD-Box RNA Helicase Targets Reveals RNA Structural Remodeling in Transcription Termination

RNA helicases are a class of enzymes that unwind RNA duplexes in vitro but whose cellular functions are largely enigmatic. Here, we provide evidence that the DEAD-box protein Dbp2 remodels RNA-protein complex (RNP) structure to facilitate efficient termination of transcription in Saccharomyces cerevisiae via the Nrd1-Nab3-Sen1 (NNS) complex. First, we find that loss of DBP2 results in RNA polymerase II accumulation at the 3' ends of small nucleolar RNAs and a subset of mRNAs. In addition, Dbp2 associates with RNA sequence motifs and regions bound by Nrd1 and can promote its recruitment to NNS-targeted regions. Using Structure-seq, we find altered RNA/RNP structures in dbp2∆ cells that correlate with inefficient termination. We also show a positive correlation between the stability of structures in the 3' ends and a requirement for Dbp2 in termination. Taken together, these studies provide a role for RNA remodeling by Dbp2 and further suggests a mechanism whereby RNA structure is exploited for gene regulation.