HITS-CLIP: panoramic views of protein-RNA regulation in living cells

Exportin-5 binding precedes 5'- and 3'-end processing of tRNA precursors in Drosophila

Exportin5 (Exp5) is the major microRNA (miRNA) nuclear export factor and recognizes structural features of pre-miRNA hairpins, while it also exports other minihelix-containing RNAs. In Drosophila, Exp5 is suggested to play a major role in tRNA export because the gene encoding the canonical tRNA export factor Exportin-t is missing in its genome. To understand molecular functions of fly Exp5, we studied the Exp5/RNA interactome in the cell line S2R+ using the CLIP (crosslinking and immunoprecipitation) technology. The CLIP experiment captured known substrates such as tRNAs and miRNAs, and detected candidates of novel Exp5 substrates including various mRNAs and long non-coding RNAs (lncRNAs). Some mRNAs and lncRNAs enriched PAR-CLIP tags compared to their expression levels, suggesting selective binding of Exp5 to them. Intronless mRNAs tended to enrich PAR-CLIP tags, therefore we proposed that Exp5 might play a role in export of specific classes of mRNAs/lncRNAs. This result suggested that Drosophila Exp5 might have a wider variety of substrates than initially thought. Surprisingly, Exp5 CLIP reads often contained sequences corresponding to the flanking 5'-leaders and 3'-trailers of tRNAs, which were thought to be removed prior to nuclear export. In fact, we found pre-tRNAs before end-processing were present in the cytoplasm, supporting the idea that tRNA end-processing is a cytoplasmic event. In summary, our results provide a genome-wide list of Exp5 substrate candidates, and suggest that flies may lack a mechanism to distinguish pre-tRNAs with or without the flanking sequences.

Mapping RNA-protein interactions with subcellular resolution using colocalization CLIP

RNA-binding proteins (RBPs) are essential for RNA metabolism and profoundly impact health and disease. The subcellular organization of RBP interaction networks with target RNAs remains largely unexplored. Here, we develop colocalization CLIP (coCLIP), a method that combines cross-linking and immunoprecipitation (CLIP) with proximity labeling, to explore in-depth the subcellular RNA interactions of the RBP human antigen R (HuR). Using this method, we uncover HuR's dynamic and location-specific interactions with RNA, revealing alterations in sequence preferences and interactions in the nucleus, cytosol, or stress granule (SG) compartments. We uncover HuR's unique binding preferences within SGs during arsenite stress, illuminating intricate interactions that conventional methodologies cannot capture. Overall, coCLIP provides a powerful method for revealing RBP-RNA interactions based on localization and lays the foundation for an advanced understanding of RBP models that incorporate subcellular location as a critical determinant of their functions.

CRISPR-dCas13d-based deep screening of proximal and distal splicing-regulatory elements

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is hampered by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically inactive CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied to SMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identifies not only known SREs but also a previously unknown distal intronic SRE, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.

Denaturing purifications demonstrate that PRC2 and other widely reported chromatin proteins do not appear to bind directly to RNA in vivo

Polycomb repressive complex 2 (PRC2) is reported to bind to many RNAs and has become a central player in reports of how long non-coding RNAs (lncRNAs) regulate gene expression. Yet, there is a growing discrepancy between the biochemical evidence supporting specific lncRNA-PRC2 interactions and functional evidence demonstrating that PRC2 is often dispensable for lncRNA function. Here, we revisit the evidence supporting RNA binding by PRC2 and show that many reported interactions may not occur in vivo. Using denaturing purification of in vivo crosslinked RNA-protein complexes in human and mouse cell lines, we observe a loss of detectable RNA binding to PRC2 and chromatin-associated proteins previously reported to bind RNA (CTCF, YY1, and others), despite accurately mapping bona fide RNA-binding sites across others (SPEN, TET2, and others). Taken together, these results argue for a critical re-evaluation of the broad role of RNA binding to orchestrate various chromatin regulatory mechanisms.

A mutation in the low-complexity domain of splicing factor hnRNPA1 linked to amyotrophic lateral sclerosis disrupts distinct neuronal RNA splicing networks

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disease characterized by loss of motor neurons. Human genetic studies have linked mutations in RNA-binding proteins as causative for this disease. The hnRNPA1 protein, a known pre-mRNA splicing factor, is mutated in some ALS patients. Here, two human cell models were generated to investigate how a mutation in the C-terminal low-complexity domain (LCD) of hnRNPA1 can cause splicing changes of thousands of transcripts that collectively are linked to the DNA damage response, cilium organization, and translation. We show that the hnRNPA1 D262V mutant protein binds to new binding sites on differentially spliced transcripts from genes that are linked to ALS. We demonstrate that this ALS-linked hnRNPA1 mutation alters normal RNA-dependent protein-protein interactions. Furthermore, cells expressing this hnRNPA1 mutant exhibit a cell aggregation phenotype, markedly reduced growth rates, changes in stress granule kinetics, and aberrant growth of neuronal processes. This study provides insight into how a single amino acid mutation in a splicing factor can alter RNA splicing networks of genes linked to ALS.

The multifaceted role of Fragile X-Related Protein 1 (FXR1) in cellular processes: an updated review on cancer and clinical applications

RNA-binding proteins (RBPs) modulate the expression level of several target RNAs (such as mRNAs) post-transcriptionally through interactions with unique binding sites in the 3'-untranslated region. There is mounting information that suggests RBP dysregulation plays a significant role in carcinogenesis. However, the function of FMR1 autosomal homolog 1(FXR1) in malignancies is just beginning to be unveiled. Due to the diversity of their RNA-binding domains and functional adaptability, FXR1 can regulate diverse transcript processing. Changes in FXR1 interaction with RNA networks have been linked to the emergence of cancer, although the theoretical framework defining these alterations in interaction is insufficient. Alteration in FXR1 expression or localization has been linked to the mRNAs of cancer suppressor genes, cancer-causing genes, and genes involved in genomic expression stability. In particular, FXR1-mediated gene regulation involves in several cellular phenomena related to cancer growth, metastasis, epithelial-mesenchymal transition, senescence, apoptosis, and angiogenesis. FXR1 dysregulation has been implicated in diverse cancer types, suggesting its diagnostic and therapeutic potential. However, the molecular mechanisms and biological effects of FXR1 regulation in cancer have yet to be understood. This review highlights the current knowledge of FXR1 expression and function in various cancer situations, emphasizing its functional variety and complexity. We further address the challenges and opportunities of targeting FXR1 for cancer diagnosis and treatment and propose future directions for FXR1 research in oncology. This work intends to provide an in-depth review of FXR1 as an emerging oncotarget with multiple roles and implications in cancer biology and therapy.

CsrA selectively modulates sRNA-mRNA regulator outcomes

Post-transcriptional regulation, by small RNAs (sRNAs) as well as the global Carbon Storage Regulator A (CsrA) protein, play critical roles in bacterial metabolic control and stress responses. The CsrA protein affects selective sRNA-mRNA networks, in addition to regulating transcription factors and sigma factors, providing additional avenues of cross talk between other stress-response regulators. Here, we expand the known set of sRNA-CsrA interactions and study their regulatory effects. In vitro binding assays confirm novel CsrA interactions with ten sRNAs, many of which are previously recognized as key regulatory nodes. Of those 10 sRNA, we identify that McaS, FnrS, SgrS, MicL, and Spot42 interact directly with CsrA in vivo. We find that the presence of CsrA impacts the downstream regulation of mRNA targets of the respective sRNA. In vivo evidence supports enhanced CsrA McaS-csgD mRNA repression and showcases CsrA-dependent repression of the fucP mRNA via the Spot42 sRNA. We additionally identify SgrS and FnrS as potential new sRNA sponges of CsrA. Overall, our results further support the expanding impact of the Csr system on cellular physiology via CsrA impact on the regulatory roles of these sRNAs.

Comprehensive mapping of exon junction complex binding sites reveals universal EJC deposition in Drosophila

BACKGROUND

The exon junction complex (EJC) is involved in most steps of the mRNA life cycle, ranging from splicing to nonsense-mediated mRNA decay (NMD). It is assembled by the splicing machinery onto mRNA in a sequence-independent manner. A fundamental open question is whether the EJC is deposited onto all exon‒exon junctions or only on a subset of them. Several previous studies have made observations supportive of the latter, yet these have been limited by methodological constraints.

RESULTS

In this study, we sought to overcome these limitations via the integration of two different approaches for transcriptome-wide mapping of EJCs. Our results revealed that nearly all, if not all, internal exons consistently harbor an EJC in Drosophila, demonstrating that EJC presence is an inherent consequence of the splicing reaction. Furthermore, our study underscores the limitations of eCLIP methods in fully elucidating the landscape of RBP binding sites. Our findings highlight how highly specific (low false positive) methodologies can lead to erroneous interpretations due to partial sensitivity (high false negatives).

CONCLUSIONS

This study contributes to our understanding of EJC deposition and its association with pre-mRNA splicing. The universal presence of EJC on internal exons underscores its significance in ensuring proper mRNA processing. Additionally, our observations highlight the need to consider both specificity and sensitivity in RBP mapping methodologies.

Mapping RNA-Protein Interactions with Subcellular Resolution Using Colocalization CLIP

RNA binding proteins (RBPs) are essential for RNA metabolism and profoundly impact health and disease. The subcellular organization of RBP interaction networks with target RNAs remains largely unexplored. Here, we develop colocalization CLIP, a method that combines CrossLinking and ImmunoPrecipitation (CLIP) with proximity labeling, to explore in-depth the subcellular RNA interactions of the well-studied RNA-binding protein HuR. Using this method, we uncover HuR's dynamic and location-specific interactions with RNA, revealing alterations in sequence preferences and interactions in the nucleus, cytosol, or stress granule compartments. We uncover HuR's unique binding preferences within stress granules during arsenite stress, illuminating intricate interactions that conventional methodologies cannot capture. Overall, coCLIP provides a powerful method for revealing RBP:RNA interactions based on localization and lays the foundation for an advanced understanding of RBP models that incorporate subcellular location as a critical determinant of their functions.

RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.

Advantages and limitations of UV cross-linking analysis of protein-RNA interactomes in microbes

RNA-binding proteins (RBPs) govern the lifespan of nearly all transcripts and play key roles in adaptive responses in microbes. A robust approach to examine protein-RNA interactions involves irradiating cells with UV light to form covalent adducts between RBPs and their cognate RNAs. Combined with RNA or protein purification, these procedures can provide global RBP censuses or transcriptomic maps for all target sequences of a single protein in living cells. The recent development of novel methods has quickly populated the RBP landscape in microorganisms. Here, we provide an overview of prominent UV cross-linking techniques which have been applied to investigate RNA interactomes in microbes. By assessing their advantages and caveats, this technical evaluation intends to guide the selection of appropriate methods and experimental design as well as to encourage the use of complementary UV-dependent techniques to inspect RNA-binding activity.

Deep screening of proximal and distal splicing-regulatory elements in a native sequence context

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is impeded by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically dead CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied to SMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identified not only known SREs, but also a novel distal intronic splicing enhancer, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.

Toward Identification of Functional Sequences and Variants in Noncoding DNA

Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.

Rfoot-seq: Transcriptomic RNase Footprinting for Mapping Stable RNA-Protein Complexes and Rapid Ribosome Profiling

Ribosome profiling isolates ribosome-protected fragments for sequencing and is a valuable method for studying different aspects of RNA translation. However, conventional protocols require millions of input cells and time-consuming steps to isolate translating ribosome complexes using ultracentrifugation or immunoprecipitation. These limitations have prevented their application to rare physiological samples. To address these technical barriers, we developed an RNase footprinting approach named Rfoot-seq to map stable transcriptomic RNA-protein complexes that allows rapid ribosome profiling using low-input samples (Li, Yang, Stroup, Wang, & Ji, 2022). In this assay, we treat a cell lysate with concentrated RNase without complex crosslinking and retained only RNA footprints associated with stable complexes for sequencing. The footprints in coding regions represent ribosome-protected fragments and can be used to study cytosolic and mitochondrial translation simultaneously. Rfoot-seq achieves comparable results to conventional ribosome profiling to quantify ribosome occupancy and works robustly for various cultured cells and primary tissue samples. Moreover, Rfoot-seq maps RNA fragments associated with stable non-ribosomal RNA-protein complexes in noncoding domains of small noncoding RNAs and some long noncoding RNAs. Taken together, Rfoot-seq opens an avenue to quantify transcriptomic translation and characterize functional noncoding RNA domains using low-input samples. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Harvesting and lysing adherent cells Alternate Protocol 1: Harvesting and lysing suspension cells Alternate Protocol 2: Harvesting and lysing primary tissue samples Basic Protocol 2: RNase treatment and footprint purification for low-input samples Alternate Protocol 3: RNase treatment and footprint purification for ultra-low-input samples Basic Protocol 3: Library preparation for high-throughput sequencing Support Protocol: Preparation of dsDNA markers for library size selection Basic Protocol 4: Data analysis and quality control after sequencing.

Self-Attention Based Neural Network for Predicting RNA-Protein Binding Sites

Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which play a crucial role in gene regulation. The identification of RNA-protein binding sites helps to understand the function of RBP better. Although many computational methods have been developed to predict RNA-protein binding sites, their prediction accuracy on small sample datasets needs improvement. To overcome this limitation, we propose a novel model called SA-Net, which utilizes k-mer embedding to encode RNA sequences and a self-attention-based neural network to extract sequence features. K-mer embedding assists the model to discover significant subsequence fragments associated with binding sites. The self-attention mechanism captures contextual information from the entire input sequence globally, performing well in small sample sequence learning. Experimental results demonstrate that SA-Net attains state-of-the-art results on the RBP-24 dataset. We find that 4-mer embedding aids the model to achieve optimal performance. We also show that the self-attention network outperforms the commonly used CNN and CNN-BLSTM models in sequence feature extraction.

Small noncoding RNA interactome capture reveals pervasive, carbon source-dependent tRNA engagement of yeast glycolytic enzymes

Small noncoding RNAs fulfill key functions in cellular and organismal biology, typically working in concert with RNA-binding proteins (RBPs). While proteome-wide methodologies have enormously expanded the repertoire of known RBPs, these methods do not distinguish RBPs binding to small noncoding RNAs from the rest. To specifically identify this relevant subclass of RBPs, we developed small noncoding RNA interactome capture (snRIC_2C) based on the differential RNA-binding capacity of silica matrices (2C). We define the S. cerevisiae proteome of nearly 300 proteins that specifically binds to RNAs smaller than 200 nt in length (snRBPs), identifying informative distinctions from the total RNA-binding proteome determined in parallel. Strikingly, the snRBPs include most glycolytic enzymes from yeast. With further methodological developments using silica matrices, 12 tRNAs were identified as specific binders of the glycolytic enzyme GAPDH. We show that tRNA engagement of GAPDH is carbon source-dependent and regulated by the RNA polymerase III repressor Maf1, suggesting a regulatory interaction between glycolysis and RNA polymerase III activity. We conclude that snRIC_2C and other 2C-derived methods greatly facilitate the study of RBPs, revealing previously unrecognized interactions.

A congenital hydrocephalus-causing mutation in Trim71 induces stem cell defects via inhibiting Lsd1 mRNA translation

Congenital hydrocephalus (CH) is a major cause of childhood morbidity. Mono-allelic mutations in Trim71, a conserved stem-cell-specific RNA-binding protein, cause CH; however, the molecular basis for pathogenesis mediated by these mutations remains unknown. Here, using mouse embryonic stem cells as a model, we reveal that the mouse R783H mutation (R796H in human) alters Trim71's mRNA substrate specificity and leads to accelerated stem-cell differentiation and neural lineage commitment. Mutant Trim71, but not wild-type Trim71, binds Lsd1 (Kdm1a) mRNA and represses its translation. Specific inhibition of this repression or a slight increase of Lsd1 in the mutant cells alleviates the defects in stem cell differentiation and neural lineage commitment. These results determine a functionally relevant target of the CH-causing Trim71 mutant that can potentially be a therapeutic target and provide molecular mechanistic insights into the pathogenesis of this disease.

(-)- Gossypol Inhibition of Musashi-Mediated Forgetting Improves Memory and Age-Dependent Memory Decline in Caenorhabditis elegans

Musashi RNA-binding proteins (MSIs) retain a pivotal role in stem cell maintenance, tumorigenesis, and nervous system development. Recently, we showed in C. elegans that Musashi (MSI-1) actively promotes forgetting upon associative learning via a 3'UTR-dependent translational expression of the Arp2/3 actin branching complex. Here, we investigated the evolutionary conserved role of MSI proteins and the effect of their pharmacological inhibition on memory. Expression of human Musashi 1 (MSI1) and Musashi 2 (MSI2) under the endogenous Musashi promoter fully rescued the phenotype of msi-1(lf) worms. Furthermore, pharmacological inhibition of human MSI1 and MSI2 activity using (-)- gossypol resulted in improved memory retention, without causing locomotor, chemotactic, or learning deficits. No drug effect was observed in msi-1(lf) treated worms. Using Western blotting and confocal microscopy, we found no changes in MSI-1 protein abundance following (-)- gossypol treatment, suggesting that Musashi gene expression remains unaltered and that the compound exerts its inhibitory effect post-translationally. Additionally, (-)- gossypol suppressed the previously seen rescue of the msi-1(lf) phenotype in worms expressing human MSI1 specifically in the AVA neuron, indicating that (-)- gossypol can regulate the Musashi pathway in a memory-related neuronal circuit in worms. Finally, treating aged worms with (-)- gossypol reversed physiological age-dependent memory decline. Taken together, our findings indicate that pharmacological inhibition of Musashi might represent a promising approach for memory modulation.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Metadensity: a background-aware python pipeline for summarizing CLIP signals on various transcriptomic sites

Motivation

Cross-linking and immunoprecipitation (CLIP) is a technology to map the binding sites of RNA-binding proteins (RBPs). The region where an RBP binds within RNA is often indicative of its molecular function in RNA processing. As an example, the binding sites of splicing factors are found within or proximal to alternatively spliced exons. To better reveal the function of RBPs, we developed a tool to visualize the distribution of CLIP signals around various transcript features.

Results

Here, we present Metadensity (https://github.com/YeoLab/Metadensity), a software that allows users to generate metagene plots. Metadensity allows users to input features such as branchpoints and preserves the near-nucleotide resolution of CLIP technologies by not scaling the features by length. Metadensity normalizes immunoprecipitated libraries with background controls, such as size-matched inputs, then windowing in various user-defined features. Finally, the signals are averaged across a provided set of transcripts.

Availability and implementation

Metadensity is available at https://github.com/YeoLab/Metadensity, with example notebooks at https://metadensity.readthedocs.io/en/latest/tutorial.html.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Understanding the complex and specific roles played by non-coding RNAs (ncRNAs), which comprise the bulk of the genome, is important for understanding virtually every hallmark of cancer. This large group of molecules plays pivotal roles in key regulatory mechanisms in various cellular processes. Regulatory mechanisms, mediated by long non-coding RNA (lncRNA) and RNA-binding protein (RBP) interactions, are well documented in several types of cancer. Their effects are enabled through networks affecting lncRNA and RBP stability, RNA metabolism including N⁶-methyladenosine (m⁶A) and alternative splicing, subcellular localization, and numerous other mechanisms involved in cancer. In this review, we discuss the reciprocal interplay between lncRNAs and RBPs and their involvement in epigenetic regulation via histone modifications, as well as their key role in resistance to cancer therapy. Other aspects of RBPs including their structural domains, provide a deeper knowledge on how lncRNAs and RBPs interact and exert their biological functions. In addition, current state-of-the-art knowledge, facilitated by machine and deep learning approaches, unravels such interactions in better details to further enhance our understanding of the field, and the potential to harness RNA-based therapeutics as an alternative treatment modality for cancer are discussed.

A comprehensive thermodynamic model for RNA binding by the Saccharomyces cerevisiae Pumilio protein PUF4

Genomic methods have been valuable for identifying RNA-binding proteins (RBPs) and the genes, pathways, and processes they regulate. Nevertheless, standard motif descriptions cannot be used to predict all RNA targets or test quantitative models for cellular interactions and regulation. We present a complete thermodynamic model for RNA binding to the S. cerevisiae Pumilio protein PUF4 derived from direct binding data for 6180 RNAs measured using the RNA on a massively parallel array (RNA-MaP) platform. The PUF4 model is highly similar to that of the related RBPs, human PUM2 and PUM1, with one marked exception: a single favorable site of base flipping for PUF4, such that PUF4 preferentially binds to a non-contiguous series of residues. These results are foundational for developing and testing cellular models of RNA-RBP interactions and function, for engineering RBPs, for understanding the biophysical nature of RBP binding and the evolutionary landscape of RNAs and RBPs.

Survey of the binding preferences of RNA-binding proteins to RNA editing events

Background

Adenosine-to-inosine (A-to-I) editing is an important RNA posttranscriptional process related to a multitude of cellular and molecular activities. However, systematic characterizations of whether and how the events of RNA editing are associated with the binding preferences of RNA sequences to RNA-binding proteins (RBPs) are still lacking.

Results

With the RNA-seq and RBP eCLIP-seq datasets from the ENCODE project, we quantitatively survey the binding preferences of 150 RBPs to RNA editing events, followed by experimental validations. Such analyses of the RBP-associated RNA editing at nucleotide resolution and genome-wide scale shed light on the involvement of RBPs specifically in RNA editing-related processes, such as RNA splicing, RNA secondary structures, RNA decay, and other posttranscriptional processes.

Conclusions

These results highlight the relevance of RNA editing in the functions of many RBPs and therefore serve as a resource for further characterization of the functional associations between various RNA editing events and RBPs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02741-8.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction

Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.

PIWI-interacting RNAs in human diseases: databases and computational models

PIWI-interacting RNAs (piRNAs) are short 21-35 nucleotide molecules that comprise the largest class of non-coding RNAs and found in a large diversity of species including yeast, worms, flies, plants and mammals including humans. The most well-understood function of piRNAs is to monitor and protect the genome from transposons particularly in germline cells. Recent data suggest that piRNAs may have additional functions in somatic cells although they are expressed there in far lower abundance. Compared with microRNAs (miRNAs), piRNAs have more limited bioinformatics resources available. This review collates 39 piRNA specific and non-specific databases and bioinformatics resources, describes and compares their utility and attributes and provides an overview of their place in the field. In addition, we review 33 computational models based upon function: piRNA prediction, transposon element and mRNA-related piRNA prediction, cluster prediction, signature detection, target prediction and disease association. Based on the collection of databases and computational models, we identify trends and potential gaps in tool development. We further analyze the breadth and depth of piRNA data available in public sources, their contribution to specific human diseases, particularly in cancer and neurodegenerative conditions, and highlight a few specific piRNAs that appear to be associated with these diseases. This briefing presents the most recent and comprehensive mapping of piRNA bioinformatics resources including databases, models and tools for disease associations to date. Such a mapping should facilitate and stimulate further research on piRNAs.

Ribonomics Approaches to Identify RBPome in Plants and Other Eukaryotes: Current Progress and Future Prospects

RNA-binding proteins (RBPs) form complex interactions with RNA to regulate the cell’s activities including cell development and disease resistance. RNA-binding proteome (RBPome) aims to profile and characterize the RNAs and proteins that interact with each other to carry out biological functions. Generally, RNA-centric and protein-centric ribonomic approaches have been successfully developed to profile RBPome in different organisms including plants and animals. Further, more and more novel methods that were firstly devised and applied in mammalians have shown great potential to unravel RBPome in plants such as RNA-interactome capture (RIC) and orthogonal organic phase separation (OOPS). Despise the development of various robust and state-of-the-art ribonomics techniques, genome-wide RBP identifications and characterizations in plants are relatively fewer than those in other eukaryotes, indicating that ribonomics techniques have great opportunities in unraveling and characterizing the RNA–protein interactions in plant species. Here, we review all the available approaches for analyzing RBPs in living organisms. Additionally, we summarize the transcriptome-wide approaches to characterize both the coding and non-coding RBPs in plants and the promising use of RBPome for booming agriculture.

Next-generation sequencing: A new avenue to understand viral RNA-protein interactions

The genomes of RNA viruses present an astonishing source of both sequence and structural diversity. From intracellular viral RNA-host interfaces to interactions between the RNA genome and structural proteins in virus particles themselves, almost the entire viral lifecycle is accompanied by a myriad of RNA-protein interactions that are required to fulfill their replicative potential. It is therefore important to characterize such rich and dynamic collections of viral RNA-protein interactions to understand virus evolution and their adaptation to their hosts and environment. Recent advances in next-generation sequencing technologies have allowed the characterization of viral RNA-protein interactions, including both transient and conserved interactions, where molecular and structural approaches have fallen short. In this review, we will provide a methodological overview of the high-throughput techniques used to study viral RNA-protein interactions, their biochemical mechanisms, and how they evolved from classical methods as well as one another. We will discuss how different techniques have fueled virus research to characterize how viral RNA and proteins interact, both locally and on a global scale. Finally, we will present examples on how these techniques influence the studies of clinically important pathogens such as HIV-1 and SARS-CoV-2.

The translational landscape as regulated by the RNA helicase DDX3

Continuously renewing the proteome, translation is exquisitely controlled by a number of dedicated factors that interact with the ribosome. The RNA helicase DDX3 belonging to the DEAD box family has emerged as one of the critical regulators of translation, the failure of which is frequently observed in a wide range of proliferative, degenerative, and infectious diseases in humans. DDX3 unwinds double-stranded RNA molecules with coupled ATP hydrolysis and thereby remodels complex RNA structures present in various protein-coding and noncoding RNAs. By interacting with specific features on messenger RNAs (mRNAs) and 18S ribosomal RNA (rRNA), DDX3 facilitates translation, while repressing it under certain conditions. We review recent findings underlying these properties of DDX3 in diverse modes of translation, such as cap-dependent and cap-independent translation initiation, usage of upstream open reading frames, and stress-induced ribonucleoprotein granule formation. We further discuss how disease-associated DDX3 variants alter the translation landscape in the cell.

Identification of the global miR-130a targetome reveals a role for TBL1XR1 in hematopoietic stem cell self-renewal and t(8;21) AML

Gene expression profiling and proteome analysis of normal and malignant hematopoietic stem cells (HSCs) point to shared core stemness properties. However, discordance between mRNA and protein signatures highlights an important role for post-transcriptional regulation by microRNAs (miRNAs) in governing this critical nexus. Here, we identify miR-130a as a regulator of HSC self-renewal and differentiation. Enforced expression of miR-130a impairs B lymphoid differentiation and expands long-term HSCs. Integration of protein mass spectrometry and chimeric AGO2 crosslinking and immunoprecipitation (CLIP) identifies TBL1XR1 as a primary miR-130a target, whose loss of function phenocopies miR-130a overexpression. Moreover, we report that miR-130a is highly expressed in t(8;21) acute myeloid leukemia (AML), where it is critical for maintaining the oncogenic molecular program mediated by the AML1-ETO complex. Our study establishes that identification of the comprehensive miRNA targetome within primary cells enables discovery of genes and molecular networks underpinning stemness properties of normal and leukemic cells.

SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information

Non-coding RNAs (ncRNAs) take essential effects on biological processes, like gene regulation. One critical way of ncRNA executing biological functions is interactions between ncRNA and RNA binding proteins (RBPs). Identifying proteins, involving ncRNA-protein interactions, can well understand the function ncRNA. Many high-throughput experiment have been applied to recognize the interactions. As a consequence of these approaches are time- and labor-consuming, currently, a great number of computational methods have been developed to improve and advance the ncRNA-protein interactions research. However, these methods may be not available to all RNAs and proteins, particularly processing new RNAs and proteins. Additionally, most of them cannot process well with long sequence. In this work, a computational method SAWRPI is proposed to make prediction of ncRNA-protein through sequence information. More specifically, the raw features of protein and ncRNA are firstly extracted through the k-mer sparse matrix with SVD reduction and learning nucleic acid symbols by natural language processing with local fusion strategy, respectively. Then, to classify easily, Hilbert Transformation is exploited to transform raw feature data to the new feature space. Finally, stacking ensemble strategy is adopted to learn high-level abstraction features automatically and generate final prediction results. To confirm the robustness and stability, three different datasets containing two kinds of interactions are utilized. In comparison with state-of-the-art methods and other results classifying or feature extracting strategies, SAWRPI achieved high performance on three datasets, containing two kinds of lncRNA-protein interactions. Upon our finding, SAWRPI is a trustworthy, robust, yet simple and can be used as a beneficial supplement to the task of predicting ncRNA-protein interactions.

Low-input RNase footprinting for simultaneous quantification of cytosolic and mitochondrial translation

We describe a low-input RNase footprinting approach for the rapid quantification of ribosome-protected fragments with as few as 1000 cultured cells. The assay uses a simplified procedure to selectively capture ribosome footprints based on optimized RNase digestion. It simultaneously maps cytosolic and mitochondrial translation with single-nucleotide resolution. We applied it to reveal selective functions of the elongation factor TUFM in mitochondrial translation, as well as synchronized repression of cytosolic translation after TUFM perturbation. We show the assay is applicable to small amounts of primary tissue samples with low protein synthesis rates, including snap-frozen tissues and immune cells from an individual's blood draw. We showed its feasibility to characterize the personalized immuno-translatome. Our analyses revealed that thousands of genes show lower translation efficiency in monocytes compared with lymphocytes, and identified thousands of translated noncanonical open reading frames (ORFs). Altogether, our RNase footprinting approach opens an avenue to assay transcriptome-wide translation using low-input samples from a wide range of physiological conditions.

RiPCA: An Assay for the Detection of RNA-Protein Interactions in Live Cells

Increasing interest in studying and modulating the interactions between RNAs and their RNA-binding proteins has indicated the need for enabling technologies. Existing means of detecting RNA-protein interactions (RPIs) are often limited to biochemical or post-lysis methods or cell-based methods that require the addition of an RNA-based affinity tag, such as the MS2 hairpin, precluding them from use in detecting small or highly processed RNAs. Taking advantage of bioorthogonal chemistry- and split-luciferase-based technologies, we developed an assay for the detection of RPIs in live cells. This article details the protocol and design considerations for RiPCA, or RNA interaction with Protein-mediated Complementation Assay. © 2022 Wiley Periodicals LLC.

Inferring RNA-binding protein target preferences using adversarial domain adaptation

Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences determined from these platforms share similar patterns but there are discernable differences between these datasets. Computational methods trained on one dataset do not always work well on another dataset. To address this problem which resembles the classic "domain shift" in deep learning, we adopted the adversarial domain adaptation (ADDA) technique and developed a framework (RBP-ADDA) that can extract RBP binding preferences from an integration of in vivo and vitro datasets. Compared with conventional methods, ADDA has the advantage of working with two input datasets, as it trains the initial neural network for each dataset individually, projects the two datasets onto a feature space, and uses an adversarial framework to derive an optimal network that achieves an optimal discriminative predictive power. In the first step, for each RBP, we include only the in vitro data to pre-train a source network and a task predictor. Next, for the same RBP, we initiate the target network by using the source network and use adversarial domain adaptation to update the target network using both in vitro and in vivo data. These two steps help leverage the in vitro data to improve the prediction on in vivo data, which is typically challenging with a lower signal-to-noise ratio. Finally, to further take the advantage of the fused source and target data, we fine-tune the task predictor using both data. We showed that RBP-ADDA achieved better performance in modeling in vivo RBP binding data than other existing methods as judged by Pearson correlations. It also improved predictive performance on in vitro datasets. We further applied augmentation operations on RBPs with less in vivo data to expand the input data and showed that it can improve prediction performances. Lastly, we explored the predictive interpretability of RBP-ADDA, where we quantified the contribution of the input features by Integrated Gradients and identified nucleotide positions that are important for RBP recognition.

YTHDC2 control of gametogenesis requires helicase activity but not m6A binding

Mechanisms regulating meiotic progression in mammals are poorly understood. The N⁶-methyladenosine (m⁶A) reader and 3' → 5' RNA helicase YTHDC2 switches cells from mitotic to meiotic gene expression programs and is essential for meiotic entry, but how this critical cell fate change is accomplished is unknown. Here, we provide insight into its mechanism and implicate YTHDC2 in having a broad role in gene regulation during multiple meiotic stages. Unexpectedly, mutation of the m⁶A-binding pocket of YTHDC2 had no detectable effect on gametogenesis and mouse fertility, suggesting that YTHDC2 function is m⁶A-independent. Supporting this conclusion, CLIP data defined YTHDC2-binding sites on mRNA as U-rich and UG-rich motif-containing regions within 3' UTRs and coding sequences, distinct from the sites that contain m⁶A during spermatogenesis. Complete loss of YTHDC2 during meiotic entry did not substantially alter translation of its mRNA binding targets in whole-testis ribosome profiling assays but did modestly affect their steady-state levels. Mutation of the ATPase motif in the helicase domain of YTHDC2 did not affect meiotic entry, but it blocked meiotic prophase I progression, causing sterility. Our findings inform a model in which YTHDC2 binds transcripts independent of m⁶A status and regulates gene expression during multiple stages of meiosis by distinct mechanisms.

An adipose lncRAP2-Igf2bp2 complex enhances adipogenesis and energy expenditure by stabilizing target mRNAs

lncRAP2 is a conserved cytoplasmic lncRNA enriched in adipose tissue and required for adipogenesis. Using purification and in vivo interactome analyses, we show that lncRAP2 forms complexes with proteins that stabilize mRNAs and modulate translation, among them Igf2bp2. Surveying transcriptome-wide Igf2bp2 client mRNAs in white adipocytes reveals selective binding to mRNAs encoding adipogenic regulators and energy expenditure effectors, including adiponectin. These same target proteins are downregulated when either Igf2bp2 or lncRAP2 is downregulated, hindering adipocyte lipolysis. Proteomics and ribosome profiling show this occurs predominantly through mRNA accumulation, as lncRAP2-Igf2bp2 complex binding does not impact translation efficiency. Phenome-wide association studies reveal specific associations of genetic variants within both lncRAP2 and Igf2bp2 with body mass and type 2 diabetes, and both lncRAP2 and Igf2bp2 are suppressed in adipose depots of obese and diabetic individuals. Thus, the lncRAP2-Igf2bp2 complex potentiates adipose development and energy expenditure and is associated with susceptibility to obesity-linked diabetes.

Targeted RNA editing: novel tools to study post-transcriptional regulation

RNA binding proteins (RBPs) regulate nearly all post-transcriptional processes within cells. To fully understand RBP function, it is essential to identify their in vivo targets. Standard techniques for profiling RBP targets, such as crosslinking immunoprecipitation (CLIP) and its variants, are limited or suboptimal in some situations, e.g. when compatible antibodies are not available and when dealing with small cell populations such as neuronal subtypes and primary stem cells. This review summarizes and compares several genetic approaches recently designed to identify RBP targets in such circumstances. TRIBE (targets of RNA binding proteins identified by editing), RNA tagging, and STAMP (surveying targets by APOBEC-mediated profiling) are new genetic tools useful for the study of post-transcriptional regulation and RBP identification. We describe the underlying RNA base editing technology, recent applications, and therapeutic implications.

The METTL5-TRMT112 N6-methyladenosine methyltransferase complex regulates mRNA translation via 18S rRNA methylation

Ribosomal RNAs (rRNAs) have long been known to carry chemical modifications, including 2'O-methylation, pseudouridylation, N⁶-methyladenosine (m⁶A), and N^6,6-dimethyladenosine. While the functions of many of these modifications are unclear, some are highly conserved and occur in regions of the ribosome critical for mRNA decoding. Both 28S rRNA and 18S rRNA carry single m⁶A sites, and while the methyltransferase ZCCHC4 has been identified as the enzyme responsible for the 28S rRNA m⁶A modification, the methyltransferase responsible for the 18S rRNA m⁶A modification has remained unclear. Here, we show that the METTL5-TRMT112 methyltransferase complex installs the m⁶A modification at position 1832 of human 18S rRNA. Our work supports findings that TRMT112 is required for METTL5 stability and reveals that human METTL5 mutations associated with microcephaly and intellectual disability disrupt this interaction. We show that loss of METTL5 in human cancer cell lines and in mice regulates gene expression at the translational level; additionally, Mettl5 knockout mice display reduced body size and evidence of metabolic defects. While recent work has focused heavily on m⁶A modifications in mRNA and their roles in mRNA processing and translation, we demonstrate here that deorphanizing putative methyltransferase enzymes can reveal previously unappreciated regulatory roles for m⁶A in noncoding RNAs.

Experimental MicroRNA Targeting Validation

microRNAs (miRNAs) have recently been recognized as a new dimension of posttranscriptional regulation. It is well defined that most human protein-coding genes are regulated by one or more miRNAs. Therefore, it is crucial to identify genes targeted by the miRNAs to better understand their functions. Although bioinformatics tools have the ability to identify target candidates it is still essential to identify physiological targets by experimental approaches. Currently, the majority of miRNA-target experimental validation approaches assess the changes in target expression in mRNA or protein level upon miRNA upregulation or downregulation. Additionally, finding out direct physical interactions between miRNAs and their targets is also among the experimental techniques. In this chapter we reviewed the existing experimental techniques for miRNA target identification by considering their advantages and potential drawbacks.

Alternative Splicing in Human Biology and Disease

Alternative pre-mRNA splicing allows for the production of multiple mRNAs from an individual gene, which not only expands the protein-coding potential of the genome but also enables complex mechanisms for the post-transcriptional control of gene expression. Regulation of alternative splicing entails a combinatorial interplay between an abundance of trans-acting splicing factors, cis-acting regulatory sequence elements and their concerted effects on the core splicing machinery. Given the extent and biological significance of alternative splicing in humans, it is not surprising that aberrant splicing patterns can cause or contribute to a wide range of diseases. In this introductory chapter, we outline the mechanisms that govern alternative pre-mRNA splicing and its regulation and discuss how dysregulated splicing contributes to human diseases affecting the motor system and the brain.

Denaturing cross-linking immunoprecipitation to identify footprints for RNA-binding proteins

The isolation of protein-RNA complexes in the “denaturing cross-linked RNA immunoprecipitation” (dCLIP) protocol is based on biotin-tagging proteins of interest, UV cross-linking RNA to protein in vivo, RNase protection assay, and isolating RNA-protein complexes under denaturing conditions over a streptavidin column. Insofar as conventional antibody-based CLIP assays have been challenging to apply to Polycomb complexes, dCLIP has been applied successfully and yields small RNA footprints from which de novo motif analysis can be performed to identify RNA binding motifs.

For complete details on the use and execution of this protocol, please refer to Rosenberg et al. (2017).

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dCLIP biotags a protein of interest to identify cross-linked RNA interactors in vivo

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Biotin-streptavidin purification system enables denaturing washing conditions

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dCLIP is successfully applied to chromatin-modifying protein complexes

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dCLIP allows high-resolution mapping of RNA binding sites and de novo motif analysis

Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning

RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational models have been proposed for understanding the binding preferences of RBPs. These methods required integrating multiple features with raw RNA sequences such as secondary structure and their performances can be further improved. In this paper, we propose an efficient and simple convolution neural network, RBPCNN, that relies on the combination of the raw RNA sequence and evolutionary information. We show that conservation scores (evolutionary information) for the RNA sequences can significantly improve the overall performance of the proposed predictor. In addition, the automatic extraction of the binding sequence motifs can enhance our understanding of the binding specificities of RBPs. The experimental results show that RBPCNN outperforms significantly the current state-of-the-art methods. More specifically, the average area under the receiver operator curve was improved by 2.67 percent and the mean average precision was improved by 8.03 percent. The datasets and results can be downloaded from https://home.jbnu.ac.kr/NSCL/RBPCNN.htm.

NPI-RGCNAE: Fast predicting ncRNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder

- ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice. All datasets and source codes of NPI-RGCNAE have been deposited in a public Github repository (https://github.com/Angelia0hh/NPI-RGCNAE).

Dissecting and predicting different types of binding sites in nucleic acids based on structural information

The biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective, RNA may interact with ligands through forming binding pockets and contact proteins and nucleic acids using protruding surfaces, while DNA may adopt regions closer to the middle of the chain to make contacts with other molecules. Based on structural information, we established a feature-based ensemble learning classifier to identify the binding sites by fully using the interplay among different machine learning algorithms, feature spaces and sample spaces. Meanwhile, we designed a template-based classifier by exploiting structural conservation. The complementarity between the two classifiers motivated us to build an integrative framework for improving prediction performance. Moreover, we utilized a post-processing procedure based on the random walk algorithm to further correct the integrative predictions. Our unified prediction framework yielded promising results for different binding sites and outperformed existing methods.

Transcriptome programs involved in the development and structure of the cerebellum

In the past two decades, mounting evidence has modified the classical view of the cerebellum as a brain region specifically involved in the modulation of motor functions. Indeed, clinical studies and engineered mouse models have highlighted cerebellar circuits implicated in cognitive functions and behavior. Furthermore, it is now clear that insults occurring in specific time windows of cerebellar development can affect cognitive performance later in life and are associated with neurological syndromes, such as Autism Spectrum Disorder. Despite its almost homogenous cytoarchitecture, how cerebellar circuits form and function is not completely elucidated yet. Notably, the apparently simple neuronal organization of the cerebellum, in which Purkinje cells represent the only output, hides an elevated functional diversity even within the same neuronal population. Such complexity is the result of the integration of intrinsic morphogenetic programs and extracellular cues from the surrounding environment, which impact on the regulation of the transcriptome of cerebellar neurons. In this review, we briefly summarize key features of the development and structure of the cerebellum before focusing on the pathways involved in the acquisition of the cerebellar neuron identity. We focus on gene expression and mRNA processing programs, including mRNA methylation, trafficking and splicing, that are set in motion during cerebellar development and participate to its physiology. These programs are likely to add new layers of complexity and versatility that are fundamental for the adaptability of cerebellar neurons.

Recent advances in predicting protein-lncRNA interactions using machine learning methods

Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

The expanding world of metabolic enzymes moonlighting as RNA binding proteins

RNA binding proteins play key roles in many aspects of RNA metabolism and function, including splicing, transport, translation, localization, stability and degradation. Within the past few years, proteomics studies have identified dozens of enzymes in intermediary metabolism that bind to RNA. The wide occurrence and conservation of RNA binding ability across distant branches of the evolutionary tree suggest that these moonlighting enzymes are involved in connections between intermediary metabolism and gene expression that comprise far more extensive regulatory networks than previously thought. There are many outstanding questions about the molecular structures and mechanisms involved, the effects of these interactions on enzyme and RNA functions, and the factors that regulate the interactions. The effects on RNA function are likely to be wider than regulation of translation, and some enzyme-RNA interactions have been found to regulate the enzyme's catalytic activity. Several enzyme-RNA interactions have been shown to be affected by cellular factors that change under different intracellular and environmental conditions, including concentrations of substrates and cofactors. Understanding the molecular mechanisms involved in the interactions between the enzymes and RNA, the factors involved in regulation, and the effects of the enzyme-RNA interactions on both the enzyme and RNA functions will lead to a better understanding of the role of the many newly identified enzyme-RNA interactions in connecting intermediary metabolism and gene expression.

A noncanonical microRNA derived from the snaR-A noncoding RNA targets a metastasis inhibitor

MicroRNAs (miRNAs) are small noncoding RNAs that function as critical posttranscriptional regulators in various biological processes. While most miRNAs are generated from processing of long primary transcripts via sequential Drosha and Dicer cleavage, some miRNAs that bypass Drosha cleavage can be transcribed as part of another small noncoding RNA. Here, we develop the target-oriented miRNA discovery (TOMiD) bioinformatic analysis method to identify Drosha-independent miRNAs from Argonaute crosslinking and sequencing of hybrids (Ago-CLASH) data sets. Using this technique, we discovered a novel miRNA derived from a primate specific noncoding RNA, the small NF90 associated RNA A (snaR-A). The miRNA derived from snaR-A (miR-snaR) arises independently of Drosha processing but requires Exportin-5 and Dicer for biogenesis. We identify that miR-snaR is concurrently up-regulated with the full snaR-A transcript in cancer cells. Functionally, miR-snaR associates with Ago proteins and targets NME1, a key metastasis inhibitor, contributing to snaR-A's role in promoting cancer cell migration. Our findings suggest a functional link between a novel miRNA and its precursor noncoding RNA.

A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP data

Understanding post-transcriptional gene regulation is a key challenge in today's biology. The new technologies of RNAcompete and RNA Bind-n-Seq enable the measurement of the binding intensities of one RNA-binding protein (RBP) to numerous synthetic RNA sequences in a single experiment. Recently, Van Nostrand et al. reported the results of RNA Bind-n-Seq experiments measuring binding of 78 human RBPs. Because 31 of these RBPs were also covered by RNAcompete technology, a large-scale comparison between implementations of these two in vitro technologies is now possible. Here, we assessed the similarities and differences between binding models, represented as a list of $k$-mer scores, inferred from RNAcompete and RNA Bind-n-Seq, and also measured how well these models predict in vivo binding. Our results show that RNA Bind-n-Seq- and RNAcompete-derived models agree (Pearson correlation $> 0.5$) for most RBPs (23 out of 31). RNA Bind-n-Seq-derived $k$-mer scores predict RNAcompete binding measurements quite well (average Pearson correlation 0.26), and both technologies produce $k$-mer scores that achieve comparable results in predicting in vivo binding (average AUC 0.7). When inspecting RNA structural preferences inferred from the data of RNA Bind-n-Seq and RNAcompete, we observed high concordance in binding preferences. Through our study, we developed a new $k$-mer score for RNA Bind-n-Seq and extended it to include RNA structural preferences.

Rapidly Growing Protein-Centric Technologies to Extensively Identify Protein-RNA Interactions: Application to the Analysis of Co-Transcriptional RNA Processing

During mRNA transcription, diverse RNA-binding proteins (RBPs) are recruited to RNA polymerase II (RNAP II) transcription machinery. These RBPs bind to distinct sites of nascent RNA to co-transcriptionally operate mRNA processing. Recent studies have revealed a close relationship between transcription and co-transcriptional RNA processing, where one affects the other’s activity, indicating an essential role of protein–RNA interactions for the fine-tuning of mRNA production. Owing to their limited amount in cells, the detection of protein–RNA interactions specifically assembled on the transcribing RNAP II machinery still remains challenging. Currently, cross-linking and immunoprecipitation (CLIP) has become a standard method to detect in vivo protein–RNA interactions, although it requires a large amount of input materials. Several improved methods, such as infrared-CLIP (irCLIP), enhanced CLIP (eCLIP), and target RNA immunoprecipitation (tRIP), have shown remarkable enhancements in the detection efficiency. Furthermore, the utilization of an RNA editing mechanism or proximity labeling strategy has achieved the detection of faint protein–RNA interactions in cells without depending on crosslinking. This review aims to explore various methods being developed to detect endogenous protein–RNA interaction sites and discusses how they may be applied to the analysis of co-transcriptional RNA processing.