2 SUPPORTING DISCUSSION Prior experiments on the S4-binding elementThe original double pseudoknot (DPK) model of the S4-binding element (S4E) was developed from early experiments mapping the impact of mutations on S4 binding (Fig. S6) (1). Following these initial experiments, the same library of S4E mutants has been extensively characterized by complementary biophysical and functional experiments (2-4). These data have been interpreted through the lens of the DPK model. However, as detailed below, much of this data is ambiguous and in several cases is inconsistent with the DPK structure.The DPK structure is motivated by two pairs of mutations that disrupt and rescue S4 binding in filter-binding assays (1). Disruption of the proposed PK2 interaction by mutation 17 reduces S4 binding ~10-fold and is rescued by the compensatory mutation 21 ( Fig. S6A). Disruption of PK3 by mutation 18 reduces S4 binding ~2-fold and is rescued by the compensatory mutation 22.However, the significance of these compensatory mutations is less clear when considered in the context of other mutation data. Relative to mutations spanning the kissing loop (KL) structure that uniformly and catastrophically disrupt binding, mutations to the PK2 and PK3 regions have inconsistent and modest impact on binding (Fig. S6A). Notably, multiple mutations that should disrupt PK2 and PK3 have no impact on binding. Subsequent in vivo functional assays also are inconsistent with the DPK model (2). Significantly, what should be compensatory 18+22 mutations do not rescue S4 repressive function in vivo. Finally, biophysical characterization of different S4E mutants also reveals inconsistencies of the DPK model, with the expected compensatory mutations (17+21 and 18+22) failing to rescue S4E tertiary folding (3). Overall, the inconsistent and modest impact of mutations to the proposed PK2 and PK3 interactions and the inconsistency of compensatory rescue argue against direct PK2 and PK3 pairing. Rather, these data are more consistent with these regions interacting indirectly, as would be expected for a kissing-loop type structure where nucleotides adjacent to the KL duplex contribute to tertiary stability and S4 binding but do not directly pair ( Fig. S6D).While most of the mutations tested by prior studies are not expected to impact H3, three mutations do provide evidence supporting H3 and the kissing-loop structure. Mutation 19 (G95àA) converts a GU pair in H3 to an AU pair, and as expected, has no impact on S4 3 binding in vitro or repression function in vivo (2). Mutation 18 modestly disrupts H3 and modestly decreases S4 binding affinity, although has no impact on repression function in vivo (1). Most significantly, mutation of the AGGAG Shine-Dalgarno sequence (SD; Fig. S6A) to its sequence complement, UCCUC, completely disrupts H3 and, as expected, completely abolishes S4 binding (4). By comparison, in the context of the DPK model, the SD mutation occurs in the middle of a 25-nt single-stranded loop and thus would not be expected to have such profound impa...

2 ABSTRACT RNA structure and dynamics are critical to biological function. However, strategies for determining RNA structure in vivo are limited, with established chemical probing and newer duplex detection methods each having notable deficiencies. Here we convert the common reagent dimethyl sulfate (DMS) into a useful probe of all four RNA nucleotides. Building on this advance, we introduce PAIR-MaP, which uses single-molecule correlated chemical probing to directly detect base pairing interactions in cells. PAIR-MaP has superior resolution and accuracy compared to alternative experiments, can resolve alternative pairing interactions of structurally dynamic RNAs, and enables highly accurate structure modeling, including of RNAs containing multiple pseudoknots and extensively bound by proteins. Application of PAIR-MaP to human RNase MRP and two bacterial mRNA 5'-UTRs reveals new functionally important and complex structures undetectable by conventional analyses. PAIR-MaP is a powerful, experimentally concise, and broadly applicable strategy for directly visualizing RNA base pairs and dynamics in cells. !

No abstract

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 multi-state structural ensembles. These layers of RNA structural information are often most incisive for understanding RNA structure-function relationships when combined with each other and when evaluated under structure- and function-altering conditions. Analysis of these complex data has required, often time consuming and awkward, juggling of multiple intermediate data files and software to create visualizations that support RNA-centered hypotheses. Here, we present the RNA visualization and graphical analysis toolset RNAvigate, developed as an easy-to-use and flexible Python library. RNAvigate currently integrates seven chemical probing data formats, nine secondary and tertiary structure formats, and eleven plot types. These features enable efficient exploration of nuanced relationships between chemical probing data, RNA structures, and motif annotations across multiple experimental samples. Modularity supports integration of new data types and plotting features. Compatibility with Jupyter Notebooks facilitates reproducibility and organization of multistep analyses and makes RNAvigate an ideal, time-effective, and non-burdensome platform for sharing full analysis pipelines. RNAvigate streamlines implementation of chemical probing strategies and accelerates discovery and characterization of diverse RNA-centric functions in biology.

To survive stress, eukaryotes selectively translate stress-related transcripts while inhibiting growth-associated protein production. How this translational reprogramming occurs under biotic stress has not been systematically studied. To identify common features shared by transcripts with stress-upregulated translation efficiency (TE-up), we first performed high-resolution ribosome-sequencing in Arabidopsis during pattern-triggered immunity and found that TE-up transcripts are enriched with upstream open reading frames (uORFs). Under non-stress conditions, start codons of these uORFs (uAUGs) have higher-than-background ribosomal association. Upon immune induction, there is an overall downshift in ribosome occupancy at uAUGs, accompanied by enhanced translation of main ORFs (mORFs). Using in planta nucleotide-resolution mRNA structurome probing, we discovered that this stress-induced switch in translation is mediated by highly structured regions detected downstream of uAUGs in TE-up transcripts. Without stress, these structures are responsible for uORF-mediated inhibition of mORF translation by slowing progression of the translation preinitiation complex to initiate translation from uAUGs, instead of mAUGs. In response to immune induction, uORF-inhibition is alleviated by three Ded1p/DDX3X-homologous RNA helicases which unwind the RNA structures, allowing ribosomes to bypass the inhibitory uORFs and upregulate defence protein production. Conservation of the RNA helicases suggests that mRNA structurome remodelling is a general mechanism for stress-induced translation across kingdoms.