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

Identification of RNA structures and their roles in RNA functions

The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.

Late consolidation of rRNA structure during co-transcriptional assembly in E. coli by time-resolved DMS footprinting

The production of new ribosomes requires proper folding of the rRNA and the addition of more than 50 ribosomal proteins. The structures of some assembly intermediates have been determined by cryo-electron microscopy, yet these structures do not provide information on the folding dynamics of the rRNA. To visualize the changes in rRNA structure during ribosome assembly in E. coli cells, transcripts were pulse-labeled with 4-thiouridine and the structure of newly made rRNA probed at various times by dimethyl sulfate modification and mutational profiling sequencing (4U-DMS-MaPseq). The in-cell DMS modification patterns revealed that many long-range rRNA tertiary interactions and protein binding sites through the 16S and 23S rRNA remain partially unfolded 1.5 min after transcription. By contrast, the active sites were continually shielded from DMS modification, suggesting that these critical regions are guarded by cellular factors throughout assembly. Later, bases near the peptidyl tRNA site exhibited specific rearrangements consistent with the binding and release of assembly factors. Time-dependent structure-probing in cells suggests that many tertiary interactions throughout the new ribosomal subunits remain mobile or unfolded until the late stages of subunit maturation.

HY, Kalin JH, Cole PA, Lassalle D, Forde-Thomas J, Chalmers IW, Brancale A, Grunau C, Hoffmann KF. Chemical modulation of Schistosoma mansoni lysine specific demethylase 1 (SmLSD1) induces wide-scale biological and epigenomic changes

Background: Schistosoma mansoni, a parasitic worm species responsible for the neglected tropical disease schistosomiasis, undergoes strict developmental regulation of gene expression that is carefully controlled by both genetic and epigenetic processes. As inhibition of S. mansoni epigenetic machinery components impairs key transitions throughout the parasite's digenetic lifecycle, a greater understanding of how epi-drugs affect molecular processes in schistosomes could lead to the development of new anthelmintics. Methods:   In vitro whole organism assays were used to assess the anti-schistosomal activity of 39 Homo sapiens Lysine Specific Demethylase 1 (HsLSD1) inhibitors on different parasite life cycle stages. Moreover, tissue-specific stains and genomic analysis shed light on the effect of these small molecules on the parasite biology. Results: Amongst this collection of small molecules, compound 33 was the most potent in reducing ex vivo viabilities of schistosomula, juveniles, miracidia and adults. At its sub-lethal concentration to adults (3.13 µM), compound 33 also significantly impacted oviposition, ovarian as well as vitellarian architecture and gonadal/neoblast stem cell proliferation. ATAC-seq analysis of adults demonstrated that compound 33 significantly affected chromatin structure (intragenic regions > intergenic regions), especially in genes differentially expressed in cell populations (e.g., germinal stem cells, hes2 + stem cell progeny, S1 cells and late female germinal cells) associated with these ex vivo phenotypes. KEGG analyses further highlighted that chromatin structure of genes associated with sugar metabolism as well as TGF-beta and Wnt signalling were also significantly perturbed by compound 33 treatment. Conclusions: This work confirms the importance of histone methylation in S. mansoni lifecycle transitions, suggesting that evaluation of LSD1 - targeting epi-drugs may facilitate the search for next-generation anti-schistosomal drugs. The ability of compound 33 to modulate chromatin structure as well as inhibit parasite survival, oviposition and stem cell proliferation warrants further investigations of this compound and its epigenetic target SmLSD1.

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.