fSHAPE, fSHAPE-eCLIP, and SHAPE-eCLIP probe transcript regions that interact with specific proteins.

Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) structure probing techniques characterize the secondary structure of RNA molecules, which influence their functions and interactions. A variation of SHAPE, footprinting SHAPE (fSHAPE), probes RNA in the presence and absence of protein to identify RNA bases that hydrogen-bond with protein. SHAPE or fSHAPE coupled with enhanced crosslinking and immunoprecipitation (SHAPE-eCLIP or fSHAPE-eCLIP) pulls down RNAs bound by any protein of interest and returns their structure or protein interaction information, respectively. Here, we describe detailed protocols for SHAPE-eCLIP and fSHAPE-eCLIP and an analysis protocol for fSHAPE. For complete details on the use and execution of these protocols, please refer to Corley et al. (2020).

Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces.

Discovering the interaction mechanism and location of RNA-binding proteins (RBPs) on RNA is critical for understanding gene expression regulation. Here, we apply selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) on in vivo transcripts compared to protein-absent transcripts in four human cell lines to identify transcriptome-wide footprints (fSHAPE) on RNA. Structural analyses indicate that fSHAPE precisely detects nucleobases that hydrogen bond with protein. We demonstrate that fSHAPE patterns predict binding sites of known RBPs, such as iron response elements in both known loci and previously unknown loci in CDC34, SLC2A4RG, COASY, and H19. Furthermore, by integrating SHAPE and fSHAPE with crosslinking and immunoprecipitation (eCLIP) of desired RBPs, we interrogate specific RNA-protein complexes, such as histone stem-loop elements and their nucleotides that hydrogen bond with stem-loop-binding proteins. Together, these technologies greatly expand our ability to study and understand specific cellular RNA interactions in RNA-protein complexes.

How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms.

RNA-binding proteins (RBPs) comprise a large class of over 2,000 proteins that interact with transcripts in all manner of RNA-driven processes. The structures and mechanisms that RBPs use to bind and regulate RNA are incredibly diverse. In this review, we take a look at the components of protein-RNA interaction, from the molecular level to multi-component interaction. We first summarize what is known about protein-RNA molecular interactions based on analyses of solved structures. We additionally describe software currently available for predicting protein-RNA interaction and other resources useful for the study of RBPs. We then review the structure and function of seventeen known RNA-binding domains and analyze the hydrogen bonds adopted by protein-RNA structures on a domain-by-domain basis. We conclude with a summary of the higher-level mechanisms that regulate protein-RNA interactions.

An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression.

Chronic obstructive pulmonary disease (COPD) affects over 65 million individuals worldwide, where α-1-antitrypsin deficiency is a major genetic cause of the disease. The α-1-antitrypsin gene, SERPINA1, expresses an exceptional number of mRNA isoforms generated entirely by alternative splicing in the 5'-untranslated region (5'-UTR). Although all SERPINA1 mRNAs encode exactly the same protein, expression levels of the individual mRNAs vary substantially in different human tissues. We hypothesize that these transcripts behave unequally due to a posttranscriptional regulatory program governed by their distinct 5'-UTRs and that this regulation ultimately determines α-1-antitrypsin expression. Using whole-transcript selective 2'-hydroxyl acylation by primer extension (SHAPE) chemical probing, we show that splicing yields distinct local 5'-UTR secondary structures in SERPINA1 transcripts. Splicing in the 5'-UTR also changes the inclusion of long upstream ORFs (uORFs). We demonstrate that disrupting the uORFs results in markedly increased translation efficiencies in luciferase reporter assays. These uORF-dependent changes suggest that α-1-antitrypsin protein expression levels are controlled at the posttranscriptional level. A leaky-scanning model of translation based on Kozak translation initiation sequences alone does not adequately explain our quantitative expression data. However, when we incorporate the experimentally derived RNA structure data, the model accurately predicts translation efficiencies in reporter assays and improves α-1-antitrypsin expression prediction in primary human tissues. Our results reveal that RNA structure governs a complex posttranscriptional regulatory program of α-1-antitrypsin expression. Crucially, these findings describe a mechanism by which genetic alterations in noncoding gene regions may result in α-1-antitrypsin deficiency.

Selecting against accidental RNA interactions.

Random base-pairing interactions between messenger RNAs and noncoding RNAs can reduce translation efficiency.

Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark.

Ribonucleic acid (RNA) secondary structure prediction continues to be a significant challenge, in particular when attempting to model sequences with less rigidly defined structures, such as messenger and non-coding RNAs. Crucial to interpreting RNA structures as they pertain to individual phenotypes is the ability to detect RNAs with large structural disparities caused by a single nucleotide variant (SNV) or riboSNitches. A recently published human genome-wide parallel analysis of RNA structure (PARS) study identified a large number of riboSNitches as well as non-riboSNitches, providing an unprecedented set of RNA sequences against which to benchmark structure prediction algorithms. Here we evaluate 11 different RNA folding algorithms' riboSNitch prediction performance on these data. We find that recent algorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rigorously validated subsets of the benchmark data. In addition, our benchmark indicates that general structure prediction algorithms (e.g. RNAfold and RNAstructure) have overall better performance if base pairing probabilities are considered rather than minimum free energy calculations. Although overall aggregate algorithmic performance on the full set of riboSNitches is relatively low, significant improvement is possible if the highest confidence predictions are evaluated independently.

In vitro selection of RNA aptamers to a protein target by filter immobilization.

This unit describes the selection of aptamers from a pool of single-stranded RNA by binding to a protein target. Aptamers generated from this selection experiment can potentially act as protein function inhibitors, and may find applications as therapeutic or diagnostic reagents. A pool of dsDNA is used to generate an ssRNA pool, which is mixed with the protein target. Bound complexes are separated from unbound reagents by filtration, and the RNA:protein complexes are amplified by a combination of reverse transcription, PCR, and in vitro transcription.

In vitro selection of RNA aptamers to a protein target by filter immobilization.

This unit describes the selection of aptamers from a pool of single-stranded RNA by binding to a protein target. Aptamers generated from this selection experiment can potentially act as protein function inhibitors, and may find applications as therapeutic or diagnostic reagents. A pool of dsDNA is used to generate an ssRNA pool, which is mixed with the protein target. Bound complexes are separated from unbound reagents by filtration, and the RNA:protein complexes are amplified by a combination of reverse transcription, PCR, and in vitro transcription.