Summary Noncoding RNAs (ncRNAs) function with associated proteins to effect complex structural and regulatory outcomes. To reveal the composition and dynamics of specific noncoding RNA- protein complexes (RNPs) in vivo, we developed comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS). ChIRP-MS analysis of four ncRNAs captures key protein interactors, including a U1-specific link to the 3′ RNA processing machinery. Xist, an essential lncRNA for X-chromosome inactivation (XCI), interacts with 81 proteins from chromatin modification, nuclear matrix, and RNA remodeling pathways. The Xist RNA-protein particle assembles in two steps coupled with the transition from pluripotency to differentiation. Specific interactors include HnrnpK that participates in Xist-mediated gene silencing and histone modifications, but not Xist localization and Drosophila Split ends homolog Spen that interacts via the A-repeat domain of Xist and is required for gene silencing. Thus, Xist lncRNA engages with proteins in a modular and developmentally controlled manner to coordinate chromatin spreading and silencing.

Visualizing the physical basis for molecular behavior inside living cells is a grand challenge in biology. RNAs are central to biological regulation, and RNA’s ability to adopt specific structures intimately controls every step of the gene expression program1. However, our understanding of physiological RNA structures is limited; current in vivo RNA structure profiles view only two of four nucleotides that make up RNA2,3. Here we present a novel biochemical approach, In Vivo Click SHAPE (icSHAPE), that enables the first global view of RNA secondary structures of all four bases in living cells. icSHAPE of mouse embryonic stem cell transcriptome versus purified RNA folded in vitro shows that the structural dynamics of RNA in the cellular environment distinguishes different classes of RNAs and regulatory elements. Structural signatures at translational start sites and ribosome pause sites are conserved from in vitro, suggesting that these RNA elements are programmed by sequence. In contrast, focal structural rearrangements in vivo reveal precise interfaces of RNA with RNA binding proteins or RNA modification sites that are consistent with atomic-resolution structural data. Such dynamic structural footprints enable accurate prediction of RNA-protein interactions and N6-methyladenosine (m6A) modification genome-wide. These results open the door for structural genomics of RNA in living cells and reveal key physiological structures controlling gene expression.

SUMMARY RNA has the intrinsic property to base pair, forming complex structures fundamental to its diverse functions. Here we develop PARIS, a method based on reversible psoralen-crosslinking for global mapping of RNA duplexes with near base-pair resolution in living cells. PARIS analysis in three human and mouse cell types reveals frequent long-range structures, higher order architectures, and RNA:RNA interactions in trans across the transcriptome. PARIS determines base-pairing interactions on an individual-molecule level, revealing pervasive alternative conformations. We used PARIS-determined helices to guide phylogenetic analysis of RNA structures, and discovered conserved long-range and alternative structures. XIST, a lncRNA essential for X chromosome inactivation, folds into evolutionarily conserved RNA structural domains that span many kilobases. XIST A-repeat forms complex inter-repeat duplexes that nucleate higher order assembly of the key epigenetic silencing protein SPEN. PARIS is a generally applicable and versatile method that provides novel insights into the RNA structurome and interactome.

In parallel to the genetic code for protein synthesis, a second layer of information is embedded in all RNA transcripts in the form of RNA structure. RNA structure influences practically every step in the gene expression program1. Yet the nature of most RNA structures or effects of sequence variation on structure are not known. Here we report the initial landscape and variation of RNA secondary structures (RSS) in a human family Trio, providing a comprehensive RSS map of human coding and noncoding RNAs. We identify unique RSS signatures that demarcate open reading frames, splicing junctions, and define authentic microRNA binding sites. Comparison of native deproteinized RNA isolated from cells versus refolded purified RNA suggests that the majority of the RSS information is encoded within RNA sequence. Over 1900 transcribed single nucleotide variants (~15% of all transcribed SNVs) alter local RNA structure. We discover simple sequence and spacing rules that determine the ability of point mutations to impact RSS. Selective depletion of RiboSNitches versus structurally synonymous variants at precise locations suggests selection for specific RNA shapes at thousands of sites, including 3’UTRs, binding sites of miRNAs and RNA binding proteins genome-wide. These results highlight the potentially broad contribution of RNA structure and its variation to gene regulation.

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our current knowledge derives from high-throughput techniques such as yeast two hybrid and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression, and phylogenetic profiles have also been developed for the genome-wide inference of protein-protein interactions (PPIs)5,6. Yet, comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7–9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, PrePPI, that combines structural information with other functional clues is comparable in accuracy to high-throughput experiments, yielding over 30,000 high confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of significant biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

tRNA-derived small RNAs (tsRNAs; also called tRNA-derived fragments (tRFs)) are an abundant class of small non-coding RNAs whose biological roles are not well defined. We show that inhibition of a specific tsRNA, LeuCAG3′tsRNA, induces apoptosis in rapidly dividing cells in vitro and in a patient-derived orthotopic hepatocellular carcinoma model in mice. This tsRNA binds at least two ribosomal protein mRNAs (for RPS28 and RPS15) to enhance their translation. Reduction of RPS28 mRNA translation blocks pre-18S ribosomal RNA processing, resulting in a decrease in the number of 40S ribosomal subunits. These data establish another post-transcriptional mechanism that can fine-tune gene expression during different physiological states and provide a potential new target for treating cancer.

The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in chromatin states using five histone modifications, cohesin, and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence, and is associated with disruptions of transcription factor binding motifs. Overall, our results provide insights into chromatin variation among humans.

SARS-CoV-2 is the cause of the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. Using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as of the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.