mRNA structure determines specificity of a polyQ-driven phase separation

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here by permission of American Association for the Advancement of Science for personal use, not for redistribution. The definitive version was published in American Association for the Advancement of Science 360 (2018): 922-927, doi:10.1126/science.aar7432.RNA promotes liquid-liquid phase separation (LLPS) to build membrane-less compartments in cells. How distinct molecular compositions are established and maintained in these liquid compartments is unknown. Here we report that secondary structure allows mRNAs to self-associate and determines if an mRNA is recruited to or excluded from liquid compartments. The polyQ-protein Whi3 induces conformational changes in RNA structure and generates distinct molecular fluctuations depending on the RNA sequence. These data support a model in which structure-based, RNA-RNA interactions promote assembly of distinct droplets and protein-driven, conformational dynamics of the RNA maintain this identity. Thus, the shape of RNA can promote the formation and coexistence of the diverse array of RNA-rich liquid compartments found in a single cell.This work was supported by NIH GM R01- GM081506, the HHMI Faculty Scholars program, R35 GM122532, ACS 130845-RSG-17-114- 01-RMC, NIH 1DP2 GM105453, and NIH R01 GM115631

RNAvigate: efficient exploration of RNA chemical probing datasets

Chemical probing technologies enable high-throughput examination of diverse structural features of RNA, including local nucleotide flexibility, RNA secondary structure, protein and ligand binding, through-space interaction networks, and multistate structural ensembles. Deep understanding of RNA structure-function relationships typically requires evaluating a system under structure- and function-altering conditions, linking these data with additional information, and visualizing multilayered relationships. Current platforms lack the broad accessibility, flexibility and efficiency needed to iterate on integrative analyses of these diverse, complex data. Here, we share the RNA visualization and graphical analysis toolset RNAvigate, a straightforward and flexible Python library that automatically parses 21 standard file formats (primary sequence annotations, per- and internucleotide data, and secondary and tertiary structures) and outputs 18 plot types. RNAvigate enables efficient exploration of nuanced relationships between multiple layers of RNA structure information and across multiple experimental conditions. Compatibility with Jupyter notebooks enables nonburdensome, reproducible, transparent and organized sharing of multistep analyses and data visualization strategies. RNAvigate simplifies and accelerates discovery and characterization of RNA-centric functions in biology

The spectral action and cosmic topology

The spectral action functional, considered as a model of gravity coupled to matter, provides, in its non-perturbative form, a slow-roll potential for inflation, whose form and corresponding slow-roll parameters can be sensitive to the underlying cosmic topology. We explicitly compute the non-perturbative spectral action for some of the main candidates for cosmic topologies, namely the quaternionic space, the Poincare' dodecahedral space, and the flat tori. We compute the corresponding slow-roll parameters and see we check that the resulting inflation model behaves in the same way as for a simply-connected spherical topology in the case of the quaternionic space and the Poincare' homology sphere, while it behaves differently in the case of the flat tori. We add an appendix with a discussion of the case of lens spaces.Comment: 55 pages, LaTe

Advances in RNA Secondary and Tertiary Structure Analysis by Chemical Probing

RNA is arguably the most versatile biological macromolecule due to its ability both to encode and to manipulate genetic information. The diverse roles of RNA depend on its ability to fold back on itself to form biologically functional structures that bind small molecules and large protein ligands, to change conformation, and to affect the cellular regulatory state. These features of RNA biology can be structurally interrogated using chemical mapping experiments. The usefulness and applications of RNA chemical probing technologies have expanded dramatically over the past five years due to several critical advances. These innovations include new sequence-independent RNA chemistries, algorithmic tools for high-throughput analysis of complex data sets composed of thousands of measurements, new approaches for interpreting chemical probing data for both secondary and tertiary structure prediction, facile methods for following time-dependent processes, and the willingness of individual research groups to tackle increasingly bold problems in RNA structural biology

Visualizing RNA structure ensembles by single-molecule correlated chemical probing

RNA molecules fold to form complex internal structures. Many of these RNA structures populate ensembles with rheostat-like properties, with each state having a distinct function. Until recently, analysis of RNA structures, especially within cells, was limited to modeling either a single averaged structure or computationally-modeled ensembles. These approaches obscure the intrinsic heterogeneity of many structured RNAs. Single-molecule correlated chemical probing (smCCP) strategies are now making it possible to measure and deconvolute RNA structure ensembles based on efficiently executed chemical probing experiments. Here, we provide an overview of fundamental single-molecule probing principles, review current ensemble deconvolution strategies, and discuss recent applications to diverse biological systems. smCCP is enabling a revolution in understanding how the plasticity of RNA structure is exploited in biological systems to respond to stimuli and alter gene function. The energetics of RNA ensembles are often subtle and a subset can likely be targeted to modulate disease-associated biological processes

The Mrs1 Splicing Factor Binds the bI3 Group I Intron at Each of Two Tetraloop-Receptor Motifs

Most large ribozymes require protein cofactors in order to function efficiently. The yeast mitochondrial bI3 group I intron requires two proteins for efficient splicing, Mrs1 and the bI3 maturase. Mrs1 has evolved from DNA junction resolvases to function as an RNA cofactor for at least two group I introns; however, the RNA binding site and the mechanism by which Mrs1 facilitates splicing were unknown. Here we use high-throughput RNA structure analysis to show that Mrs1 binds a ubiquitous RNA tertiary structure motif, the GNRA tetraloop-receptor interaction, at two sites in the bI3 RNA. Mrs1 also interacts at similar tetraloop-receptor elements, as well as other structures, in the self-folding Azoarcus group I intron and in the RNase P enzyme. Thus, Mrs1 recognizes general features found in the tetraloop-receptor motif. Identification of the two Mrs1 binding sites now makes it possible to create a model of the complete six-component bI3 ribonucleoprotein. All protein cofactors bind at the periphery of the RNA such that every long-range RNA tertiary interaction is stabilized by protein binding, involving either Mrs1 or the bI3 maturase. This work emphasizes the strong evolutionary pressure to bolster RNA tertiary structure with RNA-binding interactions as seen in the ribosome, spliceosome, and other large RNA machines

Direct identification of base-paired RNA nucleotides by correlated chemical probing

Many RNA molecules fold into complex secondary and tertiary structures that play critical roles in biological function. Among the best-established methods for examining RNA structure are chemical probing experiments, which can report on local nucleotide structure in a concise and extensible manner. While probing data are highly useful for inferring overall RNA secondary structure, these data do not directly measure through-space base-pairing interactions. We recently introduced an approach for single-molecule correlated chemical probing with dimethyl sulfate (DMS) that measures RNA interaction groups by mutational profiling (RING-MaP). RING-MaP experiments reveal diverse through-space interactions corresponding to both secondary and tertiary structure. Here we develop a framework for using RING-MaP data to directly and robustly identify canonical base pairs in RNA. When applied to three representative RNAs, this framework identified 20%–50% of accepted base pairs with a <10% false discovery rate, allowing detection of 88% of duplexes containing four or more base pairs, including pseudoknotted pairs. We further show that base pairs determined from RING-MaP analysis significantly improve secondary structure modeling. RING-MaP-based correlated chemical probing represents a direct, experimentally concise, and accurate approach for detection of individual base pairs and helices and should greatly facilitate structure modeling for complex RNAs

The SL1-SL2 (Stem-Loop) Domain Is the Primary Determinant for Stability of the Gamma Retroviral Genomic RNA Dimer

Retroviral genomes are assembled from two sense-strand RNAs by noncovalent interactions at their 5' ends, forming a dimer. The RNA dimerization domain is a potential target for antiretroviral therapy and represents a compelling RNA folding problem. The fundamental dimerization unit for the Moloney murine sarcoma gamma retrovirus spans a 170-nucleotide minimal dimerization active sequence. In the dimer, two self-complementary sequences, PAL1 and PAL2, form intermolecular duplexes, and an SL1-SL2 (stem-loop) domain forms loop-loop base pairs, mediated by GACG tetraloops, and extensive tertiary interactions. To develop a framework for assembly of the retroviral RNA dimer, we quantified the stability of and established nucleotide resolution secondary structure models for sequence variants in which each motif was compromised. Base pairing and tertiary interactions between SL1-SL2 domains contribute a large free energy increment of -10 kcal/mol. In contrast, even though the PAL1 and PAL2 intermolecular duplexes span 10 and 16 bp in the dimer, respectively, they contribute only -2.5 kcal/mol to stability, roughly equal to a single new base pair. First, these results emphasize that the energetic costs for disrupting interactions in the monomer state nearly balance the PAL1 and PAL2 base pairing interactions that form in the dimer. Second, intermolecular duplex formation plays a biological role distinct from simply stabilizing the structure of the retroviral genomic RNA dimer

Role of Context in RNA Structure: Flanking Sequences Reconfigure CAG Motif Folding in Huntingtin Exon 1 Transcripts

The length of the CAG repeat region in the huntingtin messenger RNA is predictive of Huntington’s disease. Structural studies of CAG repeat-containing RNAs suggest that these sequences form simple hairpin structures; however, in the context of the full-length huntingtin mRNA, CAG repeats may form complex structures that could be targeted for therapeutic intervention. We examined the structures of transcripts spanning the first exon of the huntingtin mRNA with both healthy and disease-prone repeat lengths. In transcripts with 17 to 70 repeats, the CAG sequences base paired extensively with bases in the 5′ UTR and with a conserved region downstream of the CCG repeat region. In huntingtin transcripts with healthy numbers of repeats, the previously observed CAG hairpin was either absent or short. In contrast, in transcripts with disease-associated numbers of repeats, a CAG hairpin was present and extended from a three-helix junction. Our findings demonstrate the profound importance of sequence context in RNA folding and identify specific structural differences between healthy and disease-inducing huntingtin alleles that may be targets for therapeutic intervention

SHAPE-directed RNA secondary structure prediction

The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA structure would inform a broader understanding of RNA biology and facilitate exploiting RNA as a biotechnological tool and therapeutic target. Determining the pattern of base pairing, or secondary structure, of RNA is a first step in these endeavors. Advances in experimental, computational, and comparative analysis approaches for analyzing secondary structure have yielded accurate structures for many small RNAs, but only a few large (>500 nts) RNAs. In addition, most current methods for determining a secondary structure require considerable effort, analytical expertise, and technical ingenuity. In this review, we outline an efficient strategy for developing accurate secondary structure models for RNAs of arbitrary length. This approach melds structural information obtained using SHAPE chemistry with structure prediction using nearest-neighbor rules and the dynamic programming algorithm implemented in the RNAstructure program. Prediction accuracies reach ≥95% for RNAs on the kilobase scale. This approach facilitates both development of new models and refinement of existing RNA structure models, which we illustrate using the Gag-Pol frameshift element in an HIV-1 M-group genome. Most promisingly, integrated experimental and computational refinement brings closer the ultimate goal of efficiently and accurately establishing the secondary structure for any RNA sequence