MicroRNAs (miRNAs) play key roles in gene regulation, but reliable bioinformatic or experimental identification of their targets remains difficult. To provide an unbiased view of human miRNA targets, ...we developed a technique for ligation and sequencing of miRNA-target RNA duplexes associated with human AGO1. Here, we report data sets of more than 18,000 high-confidence miRNA-mRNA interactions. The binding of most miRNAs includes the 5′ seed region, but around 60% of seed interactions are noncanonical, containing bulged or mismatched nucleotides. Moreover, seed interactions are generally accompanied by specific, nonseed base pairing. 18% of miRNA-mRNA interactions involve the miRNA 3′ end, with little evidence for 5′ contacts, and some of these were functionally validated. Analyses of miRNA:mRNA base pairing showed that miRNA species systematically differ in their target RNA interactions, and strongly overrepresented motifs were found in the interaction sites of several miRNAs. We speculate that these affect the response of RISC to miRNA-target binding.
Display omitted
•A experimental approach identifies in vivo targets for miRNAs•miRNAs show frequent noncanonical targeting of mRNAs•Nonseed interactions are common and functional•miRNA base pairing shows distinct patterns and overrepresented motifs
The CLASH approach identifies more than 18,000 interactions between miRNA and mRNA molecules in human cells, revealing many noncanonical interactions involving bulged or mismatched nucleotides, as well as interactions outside the proposed 5′ “seed” region of miRNAs.
Using a neural network to predict how green fluorescent proteins respond to genetic mutations illuminates properties that could help design new proteins.
A key question in nuclear RNA surveillance is how target RNAs are recognized. To address this, we identified in vivo binding sites for nuclear RNA surveillance factors, Nrd1, Nab3 and the ...Trf4/5–Air1/2–Mtr4 polyadenylation (TRAMP) complex poly(A) polymerase Trf4, by UV crosslinking. Hit clusters were reproducibly found over known binding sites on small nucleolar RNAs (snoRNAs), pre‐mRNAs and cryptic, unstable non‐protein‐coding RNAs (ncRNAs) (‘CUTs’), along with ∼642 predicted long anti‐sense ncRNAs (asRNAs), ∼178 intergenic ncRNAs and, surprisingly, ∼1384 mRNAs. Five putative asRNAs tested were confirmed to exist and were stabilized by loss of Nrd1, Nab3 or Trf4. Mapping of micro‐deletions and substitutions allowed clear definition of preferred, in vivo Nab3 and Nrd1 binding sites. Nrd1 and Nab3 were believed to be Pol II specific but, unexpectedly, bound many oligoadenylated Pol III transcripts, predominately pre‐tRNAs. Depletion of Nrd1 or Nab3 stabilized tested Pol III transcripts and their oligoadenylation was dependent on Nrd1–Nab3 and TRAMP. Surveillance targets were enriched for non‐encoded A‐rich tails. These were generally very short (1–5 nt), potentially explaining why adenylation destabilizes these RNAs while stabilizing mRNAs with long poly(A) tails.
The exosome and Trf4/5–Air1/2–Mtr4 polyadenylation (TRAMP) complexes together with the Nrd1–Nab3 RNA‐binding heterodimer have an important role in RNA surveillance. Here, the global analysis of Nrd1, Nab3 and Trf4 binding sites identifies targets for the nuclear surveillance system, including mRNAs, ncRNAs and RNA polymerase III transcripts.
Deep sequencing now provides detailed snapshots of ribosome occupancy on mRNAs. We leverage these data to parameterize a computational model of translation, keeping track of every ribosome, tRNA, and ...mRNA molecule in a yeast cell. We determine the parameter regimes in which fast initiation or high codon bias in a transgene increases protein yield and infer the initiation rates of endogenous Saccharomyces cerevisiae genes, which vary by several orders of magnitude and correlate with 5′ mRNA folding energies. Our model recapitulates the previously reported 5′-to-3′ ramp of decreasing ribosome densities, although our analysis shows that this ramp is caused by rapid initiation of short genes rather than slow codons at the start of transcripts. We conclude that protein production in healthy yeast cells is typically limited by the availability of free ribosomes, whereas protein production under periods of stress can sometimes be rescued by reducing initiation or elongation rates.
Display omitted
•Computational model of translation tracks all ribosomes, tRNAs, and mRNAs in a cell•Translation is generally limited by initiation, not elongation•Model allows inference of initiation rates for all yeast genes•Ramp of 5′ ribosomes is caused by rapid initiation of short genes
A computational model for translation in yeast quantifies translation dynamics for an entire cell and suggests that both basal and conditional expression levels are governed by availability of free ribosomes.
The exosome plays major roles in RNA processing and surveillance but the in vivo target range and substrate acquisition mechanisms remain unclear. Here we apply in vivo RNA crosslinking (CRAC) to the ...nucleases (Rrp44, Rrp6), two structural subunits (Rrp41, Csl4) and a cofactor (Trf4) of the yeast exosome. Analysis of wild-type Rrp44 and catalytic mutants showed that both the CUT and SUT classes of non-coding RNA, snoRNAs and, most prominently, pre-tRNAs and other Pol III transcripts are targeted for oligoadenylation and exosome degradation. Unspliced pre-mRNAs were also identified as targets for Rrp44 and Rrp6. CRAC performed using cleavable proteins (split-CRAC) revealed that Rrp44 endonuclease and exonuclease activities cooperate on most substrates. Mapping oligoadenylated reads suggests that the endonuclease activity may release stalled exosome substrates. Rrp6 was preferentially associated with structured targets, which frequently did not associate with the core exosome indicating that substrates follow multiple pathways to the nucleases.
Display omitted
► The in vivo target range was identified for the exosome nuclease complex ► The endonuclease and exonuclease activities of Rrp44/Dis3 function cooperatively ► Comparison of Rrp6 and core exosome suggests multiple substrate recruitment pathways ► Pre-tRNA and other RNAs transcribed by Pol III emerge as major exosome targets
The U3 small nucleolar ribonucleoprotein (snoRNP) plays an essential role in ribosome biogenesis but, like many RNA-protein complexes, its architecture is poorly understood. To address this problem, ...binding sites for the snoRNP proteins Nop1, Nop56, Nop58, and Rrp9 were mapped by UV cross-linking and analysis of cDNAs. Cross-linked protein-RNA complexes were purified under highly-denaturing conditions, ensuring that only direct interactions were detected. Recovered RNA fragments were amplified after linker ligation and cDNA synthesis. Cross-linking was successfully performed either in vitro on purified complexes or in vivo in living cells. Cross-linking sites were precisely mapped either by Sanger sequencing of multiple cloned fragments or direct, high-throughput Solexa sequencing. Analysis of RNAs associated with the snoRNP proteins revealed remarkably high signal-to-noise ratios and identified specific binding sites for each of these proteins on the U3 RNA. The results were consistent with previous data, demonstrating the reliability of the method, but also provided insights into the architecture of the U3 snoRNP. The snoRNP proteins were also cross-linked to pre-rRNA fragments, with preferential association at known sites of box C/D snoRNA function. This finding demonstrates that the snoRNP proteins directly contact the pre-rRNA substrate, suggesting roles in snoRNA recruitment. The techniques reported here should be widely applicable to analyses of RNA-protein interactions.
The dynamics of SARS-CoV-2 RNA structure and their functional relevance are largely unknown. Here we develop a simplified SPLASH assay and comprehensively map the in vivo RNA-RNA interactome of ...SARS-CoV-2 genome across viral life cycle. We report canonical and alternative structures including 5'-UTR and 3'-UTR, frameshifting element (FSE) pseudoknot and genome cyclization in both cells and virions. We provide direct evidence of interactions between Transcription Regulating Sequences, which facilitate discontinuous transcription. In addition, we reveal alternative short and long distance arches around FSE. More importantly, we find that within virions, while SARS-CoV-2 genome RNA undergoes intensive compaction, genome domains remain stable but with strengthened demarcation of local domains and weakened global cyclization. Taken together, our analysis reveals the structural basis for the regulation of replication, discontinuous transcription and translational frameshifting, the alternative conformations and the maintenance of global genome organization during the whole life cycle of SARS-CoV-2, which we anticipate will help develop better antiviral strategies.
Many protein-protein and protein-nucleic acid interactions have been experimentally characterized, whereas RNA-RNA interactions have generally only been predicted computationally. Here, we describe a ...high-throughput method to identify intramolecular and intermolecular RNA-RNA interactions experimentally by cross-linking, ligation, and sequencing of hybrids (CLASH). As validation, we identified 39 known target sites for box C/D modification-guide small nucleolar RNAs (snoRNAs) on the yeast pre-rRNA. Novel snoRNA-rRNA hybrids were recovered between snR4-5S and U14-25S. These are supported by native electrophoresis and consistent with previously unexplained data. The U3 snoRNA was found to be associated with sequences close to the 3' side of the central pseudoknot in 18S rRNA, supporting a role in formation of this structure. Applying CLASH to the yeast U2 spliceosomal snRNA led to a revised predicted secondary structure, featuring alternative folding of the 3' domain and long-range contacts between the 3' and 5' domains. CLASH should allow transcriptome-wide analyses of RNA-RNA interactions in many organisms.
Epistatic interactions play a fundamental role in molecular evolution, but little is known about the spatial distribution of these interactions within genes. To systematically survey a model ...landscape of intragenic epistasis, we quantified the fitness of ~60,000 Saccharomyces cerevisiae strains expressing randomly mutated variants of the 333-nucleotide-long U3 small nucleolar RNA (snoRNA). The fitness effects of individual mutations were correlated with evolutionary conservation and structural stability. Many mutations had small individual effects but had large effects in the context of additional mutations, which indicated negative epistasis. Clusters of negative interactions were explained by local thermodynamic threshold effects, whereas positive interactions were enriched among large-effect sites and between base-paired nucleotides. We conclude that high-throughput mapping of intragenic epistasis can identify key structural and functional features of macromolecules.
PDF
