G-quadruplex DNA structure is a positive regulator of MYC transcription.

DNA structure can regulate genome function. Four-stranded DNA G-quadruplex (G4) structures have been implicated in transcriptional regulation; however, previous studies have not directly addressed the role of an individual G4 within its endogenous cellular context. Using CRISPR to genetically abrogate endogenous G4 structure folding, we directly interrogate the G4 found within the upstream regulatory region of the critical human MYC oncogene. G4 loss leads to suppression of MYC transcription from the P1 promoter that is mediated by the deposition of a de novo nucleosome alongside alterations in RNA polymerase recruitment. We also show that replacement of the endogenous MYC G4 with a different G4 structure from the KRAS oncogene restores G4 folding and MYC transcription. Moreover, we demonstrate that the MYC G4 structure itself, rather than its sequence, recruits transcription factors and histone modifiers. Overall, our work establishes that G4 structures are important features of transcriptional regulation that coordinate recruitment of key chromatin proteins and the transcriptional machinery through interactions with DNA secondary structure, rather than primary sequence.

MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT.

Chemical modifications are essential regulatory elements that modulate the behavior and function of cellular RNAs. Despite recent advances in sequencing-based RNA modification mapping, methods combining accuracy and speed are still lacking. Here, we introduce MRT- ModSeq for rapid, simultaneous detection of multiple RNA modifications using MarathonRT. MRT-ModSeq employs distinct divalent cofactors to generate 2-D mutational profiles that are highly dependent on nucleotide identity and modification type. As a proof of concept, we use the MRT fingerprints of well-studied rRNAs to implement a general workflow for detecting RNA modifications. MRT-ModSeq rapidly detects positions of diverse modifications across a RNA transcript, enabling assignment of m1acp3Y, m1A, m3U, m7G and 2'-OMe locations through mutation-rate filtering and machine learning. m1A sites in sparsely modified targets, such as MALAT1 and PRUNE1 could also be detected. MRT-ModSeq can be trained on natural and synthetic transcripts to expedite detection of diverse RNA modification subtypes across targets of interest.

RSCanner: rapid assessment and visualization of RNA structure content.

MOTIVATION: The increasing availability of RNA structural information that spans many kilobases of transcript sequence imposes a need for tools that can rapidly screen, identify, and prioritize structural modules of interest. RESULTS: We describe RNA Structural Content Scanner (RSCanner), an automated tool that scans RNA transcripts for regions that contain high levels of secondary structure and then classifies each region for its relative propensity to adopt stable or dynamic structures. RSCanner then generates an intuitive heatmap enabling users to rapidly pinpoint regions likely to contain a high or low density of discrete RNA structures, thereby informing downstream functional or structural investigation. AVAILABILITY AND IMPLEMENTATION: RSCanner is freely available as both R script and R Markdown files, along with full documentation and test data (https://github.com/pylelab/RSCanner).

The global and local distribution of RNA structure throughout the SARS-CoV-2 genome.

SARS-CoV-2 is the causative viral agent of COVID-19, the disease at the center of the current global pandemic. While knowledge of highly structured regions is integral for mechanistic insights into the viral infection cycle, very little is known about the location and folding stability of functional elements within the massive, ∼30kb SARS-CoV-2 RNA genome. In this study, we analyze the folding stability of this RNA genome relative to the structural landscape of other well-known viral RNAs. We present an in-silico pipeline to predict regions of high base pair content across long genomes and to pinpoint hotspots of well-defined RNA structures, a method that allows for direct comparisons of RNA structural complexity within the several domains in SARS-CoV-2 genome. We report that the SARS-CoV-2 genomic propensity for stable RNA folding is exceptional among RNA viruses, superseding even that of HCV, one of the most structured viral RNAs in nature. Furthermore, our analysis suggests varying levels of RNA structure across genomic functional regions, with accessory and structural ORFs containing the highest structural density in the viral genome. Finally, we take a step further to examine how individual RNA structures formed by these ORFs are affected by the differences in genomic and subgenomic contexts, which given the technical difficulty of experimentally separating cellular mixtures of sgRNA from gRNA, is a unique advantage of our in-silico pipeline. The resulting findings provide a useful roadmap for planning focused empirical studies of SARS-CoV-2 RNA biology, and a preliminary guide for exploring potential SARS-CoV-2 RNA drug targets.Importance The RNA genome of SARS-CoV-2 is among the largest and most complex viral genomes, and yet its RNA structural features remain relatively unexplored. Since RNA elements guide function in most RNA viruses, and they represent potential drug targets, it is essential to chart the architectural features of SARS-CoV-2 and pinpoint regions that merit focused study. Here we show that RNA folding stability of SARS-CoV-2 genome is exceptional among viral genomes and we develop a method to directly compare levels of predicted secondary structure across SARS-CoV-2 domains. Remarkably, we find that coding regions display the highest structural propensity in the genome, forming motifs that differ between the genomic and subgenomic contexts. Our approach provides an attractive strategy to rapidly screen for candidate structured regions based on base pairing potential and provides a readily interpretable roadmap to guide functional studies of RNA viruses and other pharmacologically relevant RNA transcripts.