紫外交联免疫沉淀技术原理及其应用

紫外交联免疫沉淀（UV cross-linking immunoprecipitation,CLIP）技术最初建立于2003年。通过紫外交联、免疫沉淀、逆转录及后续的高通量测序等步骤,可在全转录组范围鉴定特定RNA结合蛋白（RNA-binding proteins,RBP）的靶标RNA序列和结合位点。在近20年的应用过程中,该技术被不断改进和完善,可操作性、实验结果的准确性都有所提升,技术的应用范围也有所拓展。本文对CLIP技术的基本原理、实验方法、实际应用进行介绍,着重比较几种主流CLIP技术的异同,并对如何选择具体的技术路线提出建议。     

RNA结合蛋白与RNA互作鉴定技术

RNA结合蛋白（RNA binding proteins,RBPs）通过与RNA相互作用,广泛参与到RNA的剪切、转运、编辑、胞内定位及翻译调控等过程中。RNA领域尤其是非编码RNA（non-coding RNA,ncRNA）研究的快速发展,催生了多种RBPs RNAs相互作用鉴定技术。这些技术反之又推动了 RNA领域的研究进程。本文对紫外交联免疫沉淀（ultraviolet crosslinking and immunoprecipitation,CLIP）,CLIP cDNA文库高通量测序 (high-throughput sequencing of CLIP cDNA library,HITS-CLIP),光活化核苷增强的CLIP(photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation,PAR-CLIP),单核苷酸分离CLIP (individual nucleotide resolution CLIP,iCLIP),TRIBE (targets of RNA-binding protein identified by editing),RNA 标记,相互作用组捕获(interactome capture,IC) 和SerIC (serial RNA interactome capture)等RBPs-RNAs相互作用鉴定技术的基本原理和优缺点以及应用进行综述。     

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are “specific”, that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non-RNA specific.” Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.     

NOVA2 regulates neural circRNA biogenesis

Circular RNAs (circRNAs) are highly expressed in the brain and their expression increases during neuronal differentiation. The factors regulating circRNAs in the developing mouse brain are unknown. NOVA1 and NOVA2 are neural-enriched RNA-binding proteins with well-characterized roles in alternative splicing. Profiling of circRNAs from RNA-seq data revealed that global circRNA levels were reduced in embryonic cortex of Nova2 but not Nova1 knockout mice. Analysis of isolated inhibitory and excitatory cortical neurons lacking NOVA2 revealed an even more dramatic reduction of circRNAs and establishes a widespread role for NOVA2 in enhancing circRNA biogenesis. To investigate the cis-elements controlling NOVA2-regulation of circRNA biogenesis, we generated a backsplicing reporter based on the Efnb2 gene. We found that NOVA2-mediated backsplicing of circEfnb2 was impaired when YCAY clusters located in flanking introns were mutagenized. CLIP (cross-linking and immunoprecipitation) and additional reporter analyses demonstrated the importance of NOVA2 binding sites located in both flanking introns of circRNA loci. NOVA2 is the first RNA-binding protein identified to globally promote circRNA biogenesis in the developing brain.     

Challenges in structural modeling of RNA-protein interactions

In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein–protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.     

RNase-mediated protein footprint sequencing reveals protein-binding sites throughout the human transcriptome

Background

Sequence specific RNA binding proteins are important regulators of gene expression. Several related crosslinking-based, high-throughput sequencing methods, including PAR-CLIP, have recently been developed to determine direct binding sites of global protein-RNA interactions. However, no studies have quantitatively addressed the contribution of background binding to datasets produced by these methods.

Results

We measured non-specific RNA background in PAR-CLIP data, demonstrating that covalently crosslinked background binding is common, reproducible and apparently universal among laboratories. We show that quantitative determination of background is essential for identifying targets of most RNA-binding proteins and can substantially improve motif analysis. We also demonstrate that by applying background correction to an RNA binding protein of unknown binding specificity, Caprin1, we can identify a previously unrecognized RNA recognition element not otherwise apparent in a PAR-CLIP study.

Conclusions

Empirical background measurements of global RNA-protein crosslinking are a necessary addendum to other experimental controls, such as performing replicates, because covalently crosslinked background signals are reproducible and otherwise unavoidable. Recognizing and quantifying the contribution of background extends the utility of PAR-CLIP and can improve mechanistic understanding of protein-RNA specificity, protein-RNA affinity and protein-RNA association dynamics.     

CLIP: a method for identifying protein-RNA interaction sites in living cells

Nucleic-acid binding proteins constitute nearly one-fourth of all functionally annotated human genes. Genome-wide analysis of protein-nucleic acid contacts has not yet been performed for most of these proteins, restricting attempts to establish a comprehensive understanding of protein function. UV cross-linking is a method typically used to determine the position of direct interactions between proteins and nucleic acids. We have developed the cross-linking and immunoprecipitation assay, which exploits the covalent protein-nucleic acid cross-linking to stringently purify a specific protein-RNA complex using immunoprecipitation followed by SDS-PAGE separation. In this way, the vast majority of non-specific contaminating RNA, which can bind to co-immunoprecipitated proteins or beads, can be removed. Here, we present an improved protocol that performs RNA linker ligation before the SDS-PAGE step, and describe its application to the specific purification and amplification of RNA ligands of Nova in neurons.     

Separating real motifs from their artifacts

The typical output of many computational methods to identify binding sites is a long list of motifs containing some real motifs (those most likely to correspond to the actual binding sites) along with a large number of random variations of these. We present a statistical method to separate real motifs from their artifacts. This produces a short list of high quality motifs that is sufficient to explain the over-representation of all motifs in the given sequences. Using synthetic data sets, we show that the output of our method is very accurate. On various sets of upstream sequences in S. cerevisiae, our program identifies several known binding sites, as well as a number of significant novel motifs.     

Using RNase sequence specificity to refine the identification of RNA-protein binding regions

Massively parallel pyrosequencing is a high-throughput technology that can sequence hundreds of thousands of DNA/RNA fragments in a single experiment. Combining it with immunoprecipitation-based biochemical assays, such as cross-linking immunoprecipitation (CLIP), provides a genome-wide method to detect the sites at which proteins bind DNA or RNA. In a CLIP-pyrosequencing experiment, the resolutions of the detected protein binding regions are partially determined by the length of the detected RNA fragments (CLIP amplicons) after trimming by RNase digestion. The lengths of these fragments usually range from 50-70 nucleotides. Many genomic regions are marked by multiple RNA fragments. In this paper, we report an empirical approach to refine the localization of protein binding regions by using the distribution pattern of the detected RNA fragments and the sequence specificity of RNase digestion. We present two regions to which multiple amplicons map as examples to demonstrate this approach.