Human and murine APOBEC3s restrict replication of koala retrovirus by different mechanisms

Background: Koala retrovirus (KoRV) is an endogenous and exogenous retrovirus of koalas that may cause lymphoma. As for many other gammaretroviruses, the KoRV genome can potentially encode an alternate form of Gag protein, glyco-gag. Results: In this study, a convenient assay for assessing KoRV infectivity in vitro was employed: the use of DERSE cells (initially developed to search for infectious xenotropic murine leukemia-like viruses). Using infection of DERSE and other human cell lines (HEK293T), no evidence for expression of glyco-gag by KoRV was found, either in expression of glyco-gag protein or changes in infectivity when the putative glyco-gag reading frame was mutated. Since glyco-gag mediates resistance of Moloney murine leukemia virus to the restriction factor APOBEC3, the sensitivity of KoRV (wt or putatively mutant for glyco-gag) to restriction by murine (mA3) or human APOBEC3s was investigated. Both mA3 and hA3G potently inhibited KoRV infectivity. Interestingly, hA3G restriction was accompanied by extensive G → A hypermutation during reverse transcription while mA3 restriction was not. Glyco-gag status did not affect the results. Conclusions: These results indicate that the mechanisms of APOBEC3 restriction of KoRV by hA3G and mA3 differ (deamination dependent vs. independent) and glyco-gag does not play a role in the restriction

RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP)

Many biological processes are RNA-mediated, but higher-order structures for most RNAs are unknown, making it difficult to understand how RNA structure governs function. Here we describe SHAPE mutational profiling (SHAPE-MaP) that makes possible de novo and large-scale identification of RNA functional motifs. Sites of 2’-hydroxyl acylation by SHAPE are encoded as non-complementary nucleotides during cDNA synthesis, as measured by massively parallel sequencing. SHAPE-MaP-guided modeling identified greater than 90% of accepted base pairs in complex RNAs of known structure and was used to define a second-generation model for the HIV-1 RNA genome. The HIV-1 model contains all known structured motifs and previously unknown elements, including experimentally validated pseudoknots. SHAPE-MaP yields accurate and high-resolution secondary structure models, enables analysis of low abundance RNAs, disentangles sequence polymorphisms in single experiments, and will ultimately democratize RNA structure analysis

Selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) for direct, versatile and accurate RNA structure analysis

SHAPE chemistries exploit small electrophilic reagents that react with the 2′-hydroxyl group to interrogate RNA structure at single-nucleotide resolution. Mutational profiling (MaP) identifies modified residues based on the ability of reverse transcriptase to misread a SHAPE-modified nucleotide and then counting the resulting mutations by massively parallel sequencing. The SHAPE-MaP approach measures the structure of large and transcriptome-wide systems as accurately as for simple model RNAs. This protocol describes the experimental steps, implemented over three days, required to perform SHAPE probing and construct multiplexed SHAPE-MaP libraries suitable for deep sequencing. These steps include RNA folding and SHAPE structure probing, mutational profiling by reverse transcription, library construction, and sequencing. Automated processing of MaP sequencing data is accomplished using two software packages. ShapeMapper converts raw sequencing files into mutational profiles, creates SHAPE reactivity plots, and provides useful troubleshooting information, often within an hour. SuperFold uses these data to model RNA secondary structures, identify regions with well-defined structures, and visualize probable and alternative helices, often in under a day. We illustrate these algorithms with the E. coli thiamine pyrophosphate riboswitch, E. coli 16S rRNA, and HIV-1 genomic RNAs. SHAPE-MaP can be used to make nucleotide-resolution biophysical measurements of individual RNA motifs, rare components of complex RNA ensembles, and entire transcriptomes. The straightforward MaP strategy greatly expands the number, length, and complexity of analyzable RNA structures

RNA folding in living cells

RNA folding is the most essential process underlying RNA function. While significant progress has been made in understanding the forces driving RNA folding in vitro, exploring the rules governing intracellular RNA structure formation is still in its infancy. The cellular environment hosts a great diversity of factors that potentially influence RNA folding in vivo. For example, the nature of transcription and translation is known to shape the folding landscape of RNA molecules. Trans-acting factors such as proteins, RNAs and metabolites, among others, are also able to modulate the structure and thus the fate of an RNA. Here we summarize the ongoing efforts to uncover how RNA folds in living cells

Roles of DEAD-box proteins in RNA and RNP folding

RNAs and RNA-protein complexes (RNPs) traverse rugged energy landscapes as they fold to their native structures, and many continue to undergo conformational rearrangements as they function. Due to the inherent stability of local RNA structure, proteins are required to assist with RNA conformational transitions during initial folding and in exchange between functional structures. DEAD-box proteins are superfamily 2 RNA helicases that are ubiquitously involved in RNA-mediated processes. Some of these proteins use an ATP-dependent cycle of conformational changes to disrupt RNA structure nonprocessively, accelerating structural transitions of RNAs and RNPs in a manner that bears a strong resemblance to the activities of certain groups of protein chaperones. This review summarizes recent work using model substrates and tractable self-splicing intron RNAs, which has given new insights into how DEAD-box proteins promote RNA folding steps and conformational transitions, and it summarizes recent progress in identifying sites and mechanisms of DEAD-box protein activity within more complex cellular targets