Refining the pool of RNA-binding domains advances the classification and prediction of RNA-binding proteins.

From transcription to decay, RNA-binding proteins (RBPs) influence RNA metabolism. Using the RBP2GO database that combines proteome-wide RBP screens from 13 species, we investigated the RNA-binding features of 176 896 proteins. By compiling published lists of RNA-binding domains (RBDs) and RNA-related protein family (Rfam) IDs with lists from the InterPro database, we analyzed the distribution of the RBDs and Rfam IDs in RBPs and non-RBPs to select RBDs and Rfam IDs that were enriched in RBPs. We also explored proteins for their content in intrinsically disordered regions (IDRs) and low complexity regions (LCRs). We found a strong positive correlation between IDRs and RBDs and a co-occurrence of specific LCRs. Our bioinformatic analysis indicated that RBDs/Rfam IDs were strong indicators of the RNA-binding potential of proteins and helped predicting new RBP candidates, especially in less investigated species. By further analyzing RBPs without RBD, we predicted new RBDs that were validated by RNA-bound peptides. Finally, we created the RBP2GO composite score by combining the RBP2GO score with new quality factors linked to RBDs and Rfam IDs. Based on the RBP2GO composite score, we compiled a list of 2018 high-confidence human RBPs. The knowledge collected here was integrated into the RBP2GO database at https://RBP2GO-2-Beta.dkfz.de.

Pol3Base: a resource for decoding the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs.

RNA polymerase III (Pol III) transcribes hundreds of non-coding RNA genes (ncRNAs), which involve in a variety of cellular processes. However, the expression, functions, regulatory networks and evolution of these Pol III-transcribed ncRNAs are still largely unknown. In this study, we developed a novel resource, Pol3Base (http://rna.sysu.edu.cn/pol3base/), to decode the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs. The current release of Pol3Base includes thousands of regulatory relationships between ∼79 000 ncRNAs and transcription factors by mining 56 ChIP-seq datasets. By integrating CLIP-seq datasets, we deciphered the interactions of these ncRNAs with >240 RNA binding proteins. Moreover, Pol3Base contains ∼9700 RNA modifications located within thousands of Pol III-transcribed ncRNAs. Importantly, we characterized expression profiles of ncRNAs in >70 tissues and 28 different tumor types. In addition, by comparing these ncRNAs from human and mouse, we revealed about 4000 evolutionary conserved ncRNAs. We also identified ∼11 403 tRNA-derived small RNAs (tsRNAs) in 32 different tumor types. Finally, by analyzing somatic mutation data, we investigated the mutation map of these ncRNAs to help uncover their potential roles in diverse diseases. This resource will help expand our understanding of potential functions and regulatory networks of Pol III-transcribed ncRNAs.

scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution.

Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3'-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

scAPAatlas: an atlas of alternative polyadenylation across cell types in human and mouse.

Alternative polyadenylation (APA) has been widely recognized as a crucial step during the post-transcriptional regulation of eukaryotic genes. Recent studies have demonstrated that APA exerts key regulatory roles in many biological processes and often occurs in a tissue- and cell-type-specific manner. However, to our knowledge, there is no database incorporating information about APA at the cell-type level. Single-cell RNA-seq is a rapidly evolving and powerful tool that enable APA analysis at the cell-type level. Here, we present a comprehensive resource, scAPAatlas (http://www.bioailab.com:3838/scAPAatlas), for exploring APA across different cell types, and interpreting potential biological functions. Based on the curated scRNA-seq data from 24 human and 25 mouse normal tissues, we systematically identified cell-type-specific APA events for different cell types and examined the correlations between APA and gene expression level. We also estimated the crosstalk between cell-type-specific APA events and microRNAs or RNA-binding proteins. A user-friendly web interface has been constructed to support browsing, searching and visualizing multi-layer information of cell-type-specific APA events. Overall, scAPAatlas, incorporating a rich resource for exploration of APA at the cell-type level, will greatly help researchers chart cell type with APA and elucidate the biological functions of APA.

POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility.

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

The Landscape of RNA-Protein Interactions in Plants: Approaches and Current Status.

RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA-protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA-protein interactions in plants thus far and highlight the understanding of plant RNA-protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA-protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA-protein interactions for the study of gene regulation and RNA biology in plants.

PAR-TERRA is the main contributor to telomeric repeat-containing RNA transcripts in normal and cancer mouse cells.

Telomeric repeat-containing RNA (TERRA) molecules play important roles at telomeres, from heterochromatin regulation to telomerase activity control. In human cells, TERRA is transcribed from subtelomeric promoters located on most chromosome ends and associates with telomeres. The origin of mouse TERRA molecules is, however, unclear, as transcription from the pseudoautosomal PAR locus was recently suggested to account for the vast majority of TERRA in embryonic stem cells (ESC). Here, we confirm the production of TERRA from both the chromosome 18q telomere and the PAR locus in mouse embryonic fibroblasts, ESC, and various mouse cancer and immortalized cell lines, and we identify two novel sources of TERRA on mouse chromosome 2 and X. Using various approaches, we show that PAR-TERRA molecules account for the majority of TERRA transcripts, displaying an increase of two to four orders of magnitude compared to the telomeric 18q transcript. Finally, we present a SILAC-based pull-down screen revealing a large overlap between TERRA-interacting proteins in human and mouse cells, including PRC2 complex subunits, chromatin remodeling factors, DNA replication proteins, Aurora kinases, shelterin complex subunits, Bloom helicase, Coilin, and paraspeckle proteins. Hence, despite originating from distinct genomic regions, mouse and human TERRA are likely to play similar functions in cells.

SRSF7 maintains its homeostasis through the expression of Split-ORFs and nuclear body assembly.

SRSF7 is an essential RNA-binding protein whose misexpression promotes cancer. Here, we describe how SRSF7 maintains its protein homeostasis in murine P19 cells using an intricate negative feedback mechanism. SRSF7 binding to its premessenger RNA promotes inclusion of a poison cassette exon and transcript degradation via nonsense-mediated decay (NMD). However, elevated SRSF7 levels inhibit NMD and promote translation of two protein halves, termed Split-ORFs, from the bicistronic SRSF7-PCE transcript. The first half acts as dominant-negative isoform suppressing poison cassette exon inclusion and instead promoting the retention of flanking introns containing repeated SRSF7 binding sites. Massive SRSF7 binding to these sites and its oligomerization promote the assembly of large nuclear bodies, which sequester SRSF7 transcripts at their transcription site, preventing their export and restoring normal SRSF7 protein levels. We further show that hundreds of human and mouse NMD targets, especially RNA-binding proteins, encode potential Split-ORFs, some of which are expressed under specific cellular conditions.

The LC3-conjugation machinery specifies the loading of RNA-binding proteins into extracellular vesicles.

Traditionally viewed as an autodigestive pathway, autophagy also facilitates cellular secretion; however, the mechanisms underlying these processes remain unclear. Here, we demonstrate that components of the autophagy machinery specify secretion within extracellular vesicles (EVs). Using a proximity-dependent biotinylation proteomics strategy, we identify 200 putative targets of LC3-dependent secretion. This secretome consists of a highly interconnected network enriched in RNA-binding proteins (RBPs) and EV cargoes. Proteomic and RNA profiling of EVs identifies diverse RBPs and small non-coding RNAs requiring the LC3-conjugation machinery for packaging and secretion. Focusing on two RBPs, heterogeneous nuclear ribonucleoprotein K (HNRNPK) and scaffold-attachment factor B (SAFB), we demonstrate that these proteins interact with LC3 and are secreted within EVs enriched with lipidated LC3. Furthermore, their secretion requires the LC3-conjugation machinery, neutral sphingomyelinase 2 (nSMase2) and LC3-dependent recruitment of factor associated with nSMase2 activity (FAN). Hence, the LC3-conjugation pathway controls EV cargo loading and secretion.

Genome-Wide Identification of Cassava Serine/Arginine-Rich Proteins: Insights into Alternative Splicing of Pre-mRNAs and Response to Abiotic Stress.

Serine/arginine-rich (SR) proteins have an essential role in the splicing of pre-messenger RNA (pre-mRNA) in eukaryote. Pre-mRNA with introns can be alternatively spliced to generate multiple transcripts, thereby increasing adaptation to the external stress conditions in planta. However, pre-mRNA of SR proteins can also be alternatively spliced in different plant tissues and in response to diverse stress treatments, indicating that SR proteins might be involved in regulating plant development and adaptation to environmental changes. We identified and named 18 SR proteins in cassava and systematically studied their splicing and transcriptional changes under tissue-specific and abiotic stress conditions. Fifteen out of 18 SR genes showed alternative splicing in the tissues. 45 transcripts were found from 18 SR genes under normal conditions, whereas 55 transcripts were identified, and 21 transcripts were alternate spliced in some SR genes under salt stress, suggesting that SR proteins might participate in the plant adaptation to salt stress. We then found that overexpression of MeSR34 in Arabidopsis enhanced the tolerance to salt stress through maintaining reactive oxygen species homeostasis and increasing the expression of calcineurin B-like proteins (CBL)-CBL-interacting protein kinases and osmotic stress-related genes. Therefore, our findings highlight the critical role of cassava SR proteins as regulators of RNA splicing and salt tolerance in planta.

R-DeeP: Proteome-wide and Quantitative Identification of RNA-Dependent Proteins by Density Gradient Ultracentrifugation.

The comprehensive but specific identification of RNA-binding proteins as well as the discovery of RNA-associated protein functions remain major challenges in RNA biology. Here we adapt the concept of RNA dependence, defining a protein as RNA dependent when its interactome depends on RNA. We converted this concept into a proteome-wide, unbiased, and enrichment-free screen called R-DeeP (RNA-dependent proteins), based on density gradient ultracentrifugation. Quantitative mass spectrometry identified 1,784 RNA-dependent proteins, including 537 lacking known links to RNA. Exploiting the quantitative nature of R-DeeP, proteins were classified as not, partially, or completely RNA dependent. R-DeeP identified the transcription factor CTCF as completely RNA dependent, and we uncovered that RNA is required for the CTCF-chromatin association. Additionally, R-DeeP allows reconstruction of protein complexes based on co-segregation. The whole dataset is available at http://R-DeeP.dkfz.de, providing proteome-wide, specific, and quantitative identification of proteins with RNA-dependent interactions and aiming at future functional discovery of RNA-protein complexes.

HyPR-MS for Multiplexed Discovery of MALAT1, NEAT1, and NORAD lncRNA Protein Interactomes.

RNA-protein interactions are integral to the regulation of gene expression. RNAs have diverse functions and the protein interactomes of individual RNAs vary temporally, spatially, and with physiological context. These factors make the global acquisition of individual RNA-protein interactomes an essential endeavor. Although techniques have been reported for discovery of the protein interactomes of specific RNAs they are largely laborious, costly, and accomplished singly in individual experiments. We developed HyPR-MS for the discovery and analysis of the protein interactomes of multiple RNAs in a single experiment while also reducing design time and improving efficiencies. Presented here is the application of HyPR-MS to simultaneously and selectively isolate the interactomes of lncRNAs MALAT1, NEAT1, and NORAD. Our analysis features the proteins that potentially contribute to both known and previously undiscovered roles of each lncRNA. This platform provides a powerful new multiplexing tool for the efficient and cost-effective elucidation of specific RNA-protein interactomes.

A Gene Constellation in Avian Influenza A (H7N9) Viruses May Have Facilitated the Fifth Wave Outbreak in China.

The 2016-2017 epidemic of influenza A (H7N9) virus in China prompted concern that a genetic change may underlie increased virulence. Based on an evolutionary analysis of H7N9 viruses from all five outbreak waves, we find that additional subclades of the H7 and N9 genes have emerged. Our analysis indicates that H7N9 viruses inherited NP genes from co-circulating H7N9 instead of H9N2 viruses. Genotypic diversity among H7N9 viruses increased following wave I, peaked during wave III, and rapidly deceased thereafter with minimal diversity in wave V, suggesting that the viruses entered a relatively stable evolutionary stage. The ZJ11 genotype caused the majority of human infections in wave V. We suggest that the largest outbreak of wave V may be due to a constellation of genes rather than a single mutation. Therefore, continuous surveillance is necessary to minimize the threat of H7N9 viruses.

Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites.

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.

Structural characterisation of TNRC6A nuclear localisation signal in complex with importin-alpha.

The GW182/TNRC6 family of proteins are central scaffolds that link microRNA-associated Argonaute proteins to the cytoplasmic decay machinery for targeted mRNA degradation processes. Although nuclear roles for the GW182/TNRC6 proteins are unknown, recent reports have demonstrated nucleocytoplasmic shuttling activity that utilises the importin-α and importin-ß transport receptors for nuclear translocation. Here we describe the structure of mouse importin-α in complex with the TNRC6A nuclear localisation signal peptide. We further show that the interactions observed between TNRC6A and importin-α are conserved between mouse and human complexes. Our results highlight the ability of monopartite cNLS sequences to maximise contacts at the importin-α major binding site, as well as regions outside the main binding cavities.

Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces.

BACKGROUND: Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition. RESULTS: In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed. CONCLUSIONS: Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

A typical RNA-binding protein gene (AccRBM11) in Apis cerana cerana: characterization of AccRBM11 and its possible involvement in development and stress responses.

RNA-binding motif proteins (RBMs) belong to RNA-binding proteins that display extraordinary posttranscriptional gene regulation roles in various cellular processes, including development, growth, and stress responses. Nevertheless, only a few examples of the roles of RBMs are known in insects, particularly in Apis cerana cerana. In the present study, we characterized the novel RNA-binding motif protein 11 from Apis cerana cerana, which was named AccRBM11 and whose promoter sequence included abundant potential transcription factor binding sites that are connected to responses to adverse stress and early development. Quantitative PCR results suggested that AccRBM11 was expressed at highest levels in 1-day postemergence worker bees. AccRBM11 mRNA and protein levels were higher in the poison gland and the epidermis than in other tissues. Moreover, levels of AccRBM11 transcription were upregulated upon all the simulation of abiotic stresses. Furthermore, Western blot analysis indicated that AccRBM11 protein expression levels could be induced under some abiotic stressors, a result that did not completely in agree with the qRT-PCR results. It is also noteworthy that the expression of some genes that connected with development or stress responses were remarkably suppressed when AccRBM11 was silenced, which suggested that AccRBM11 might play a similar role in development or stress reactions with the above genes. Taken together, the data presented here provide evidence that AccRBM11 is potentially involved in the regulation of development and some abiotic stress responses. We expect that this study will promote future research on the function of RNA-binding proteins.

STARs in the CNS.

STAR (signal transduction and activation of RNA) proteins regulate splicing of target genes that have roles in neural connectivity, survival and myelination in the vertebrate nervous system. These regulated splicing targets include mRNAs such as the Neurexins (Nrxn), SMN2 (survival of motor neuron) and MAG (myelin-associated glycoprotein). Recent work has made it possible to identify and validate STAR protein splicing targets in vivo by using genetically modified mouse models. In this review, we will discuss the importance of STAR protein splicing targets in the CNS (central nervous system).