Identification of RNA-protein interaction networks using PAR-CLIP

Oligonucleotide-Based Photoaffinity Probes: Chemical Tools and Applications for Protein Labeling

A variety of proteins interact with DNA and RNA, including polymerases, histones, ribosomes, transcription factors, and repair enzymes. However, the transient non-covalent nature of these interactions poses challenges for analysis. Introducing a covalent bond between proteins and DNA via photochemical activation of a photosensitive functional group introduced onto nucleic acids offers a means to stabilize these often weak interactions without significantly altering the binding interface. Consequently, photoactivatable oligonucleotides are powerful tools for investigating nucleic acid-protein interactions involved in numerous biological and pathological processes. In this review, we provide a comprehensive overview of the chemical tools developed so far and the different strategies used for incorporating the most commonly used photoreactive reagents into oligonucleotide probes or nucleic acids. Furthermore, we illustrate their application with several examples including protein binding site mapping, identification of protein binding partners, and in cell studies.

Chemoproteomic capture of RNA binding activity in living cells

Proteomic methods for RNA interactome capture (RIC) rely principally on crosslinking native or labeled cellular RNA to enrich and investigate RNA-binding protein (RBP) composition and function in cells. The ability to measure RBP activity at individual binding sites by RIC, however, has been more challenging due to the heterogenous nature of peptide adducts derived from the RNA-protein crosslinked site. Here, we present an orthogonal strategy that utilizes clickable electrophilic purines to directly quantify protein-RNA interactions on proteins through photoaffinity competition with 4-thiouridine (4SU)-labeled RNA in cells. Our photo-activatable-competition and chemoproteomic enrichment (PACCE) method facilitated detection of >5500 cysteine sites across ~3000 proteins displaying RNA-sensitive alterations in probe binding. Importantly, PACCE enabled functional profiling of canonical RNA-binding domains as well as discovery of moonlighting RNA binding activity in the human proteome. Collectively, we present a chemoproteomic platform for global quantification of protein-RNA binding activity in living cells.

Transcription factors and splice factors - interconnected regulators of stem cell differentiation

Purpose of review

The underlying molecular mechanisms that direct stem cell differentiation into fully functional, mature cells remain an area of ongoing investigation. Cell state is the product of the combinatorial effect of individual factors operating within a coordinated regulatory network. Here, we discuss the contribution of both gene regulatory and splicing regulatory networks in defining stem cell fate during differentiation and the critical role of protein isoforms in this process.

Recent findings

We review recent experimental and computational approaches that characterize gene regulatory networks, splice regulatory networks, and the resulting transcriptome and proteome they mediate during differentiation. Such approaches include long-read RNA sequencing, which has demonstrated high-resolution profiling of mRNA isoforms, and Cas13-based CRISPR, which could make possible high-throughput isoform screening. Collectively, these developments enable systems-level profiling of factors contributing to cell state.

Summary

Overall, gene and splice regulatory networks are important in defining cell state. The emerging high-throughput systems-level approaches will characterize the gene regulatory network components necessary in driving stem cell differentiation.

RNA-binding proteins in cancer drug discovery

RNA-binding proteins (RBPs) are crucial players in tumorigenesis and, hence, promising targets in cancer drug discovery. However, they are largely regarded as 'undruggable', because of the often noncatalytic and complex interactions between protein and RNA, which limit the discovery of specific inhibitors. Nonetheless, over the past 10 years, drug discovery efforts have uncovered RBP inhibitors with clinical relevance, highlighting the disruption of RNA-protein networks as a promising avenue for cancer therapeutics. In this review, we discuss the role of structurally distinct RBPs in cancer, and the mechanisms of RBP-directed small-molecule inhibitors (SMOIs) focusing on drug-protein interactions, binding surfaces, potency, and translational potential. Additionally, we underline the limitations of RBP-targeting drug discovery assays and comment on future trends in the field.

tatDB: a database of Ago1-mediated targets of transfer RNA fragments

tRNA-derived fragments (tRFs) are a class of emerging post-transcriptional regulators of gene expression likely binding to the transcripts of target genes. However, only a few tRFs targets have been experimentally validated, making it hard to extrapolate the functions or binding mechanisms of tRFs. The paucity of resources supporting the identification of the targets of tRFs creates a bottleneck in the fast-developing field. We have previously analyzed chimeric reads in crosslinked Argonaute1-RNA complexes to help infer the guide-target pairs and binding mechanisms of multiple tRFs based on experimental data in human HEK293 cells. To efficiently disseminate these results to the research community, we designed a web-based database tatDB (targets of tRFs DataBase) populated with close to 250 000 experimentally determined guide-target pairs with ∼23 000 tRF isoforms. tatDB has a user-friendly interface with flexible query options/filters allowing one to obtain comprehensive information on given tRFs (or targets). Modes of interactions are supported by secondary structures of potential guide-target hybrids and binding motifs, essential for understanding the targeting mechanisms of tRFs. Further, we illustrate the value of the database on an example of hypothesis-building for a tRFs potentially involved in the lifecycle of the SARS-CoV-2 virus. tatDB is freely accessible at https://grigoriev-lab.camden.rutgers.edu/tatdb.

Bidirectional Functional Effects of Staphylococcus on Carcinogenesis

As a Gram-positive cocci existing in nature, Staphylococcus has a variety of species, such as Staphylococcus aureus and Staphylococcus epidermidis, etc. Growing evidence reveals that Staphylococcus is closely related to the occurrence and development of various cancers. On the one hand, cancer patients are more likely to suffer from bacterial infection and antibiotic-resistant strain infection compared to healthy controls. On the other hand, there exists an association between staphylococcal infection and carcinogenesis. Staphylococcus often plays a pathogenic role and evades the host immune system through surface adhesion molecules, α-hemolysin, PVL (Panton-Valentine leukocidin), SEs (staphylococcal enterotoxins), SpA (staphylococcal protein A), TSST-1 (Toxic shock syndrom toxin-1) and other factors. Staphylococcal nucleases (SNases) are extracellular nucleases that serve as genomic markers for Staphylococcus aureus. Interestingly, a human homologue of SNases, SND1 (staphylococcal nuclease and Tudor domain-containing 1), has been recognized as an oncoprotein. This review is the first to summarize the reported basic and clinical evidence on staphylococci and neoplasms. Investigations on the correlation between Staphylococcus and the occurrence, development, diagnosis and treatment of breast, skin, oral, colon and other cancers, are made from the perspectives of various virulence factors and SND1.

Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.

Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.

Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.

Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

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.

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.

DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning

RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence. Facing this challenge, we present a novel method called DeepBtoD, which predicts RBPs directly from RNA sequences. First, a [Formula: see text]-BtoD encoding is designed, which takes into account the composition of [Formula: see text]-nucleotides and their relative positions and forms a local module. Second, we designed a multi-scale convolutional module embedded with a self-attentive mechanism, the ms-focusCNN, which is used to further learn more effective, diverse, and discriminative high-level features. Finally, global information is considered to supplement local modules with ensemble learning to predict whether the target RNA binds to RBPs. Our preliminary 24 independent test datasets show that our proposed method can classify RBPs with the area under the curve of 0.933. Remarkably, DeepBtoD shows competitive results across seven state-of-the-art methods, suggesting that RBPs can be highly recognized by integrating local [Formula: see text]-BtoD and global information only from RNA sequences. Hence, our integrative method may be useful to improve the power of RBPs prediction, which might be particularly useful for modeling protein-nucleic acid interactions in systems biology studies. Our DeepBtoD server can be accessed at http://175.27.228.227/DeepBtoD/.

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.

Contributions of microRNAs to Peripheral Insulin Sensitivity

An extensive literature base combined with advances in sequencing technologies demonstrate microRNA levels correlate with various metabolic diseases. Mechanistic studies also establish microRNAs regulate central metabolic pathways and thus play vital roles in maintaining organismal energy balance and metabolic homeostasis. This review highlights research progress on the roles and regulation of microRNAs in the peripheral tissues that confer insulin sensitivity. We discuss sequencing technologies used to comprehensively define the target spectrum of microRNAs in metabolic disease that complement studies reporting physiologic roles for microRNAs in the regulation of glucose and lipid metabolism in animal models. We also discuss the emerging roles of exosomal microRNAs as endocrine signals to regulate lipid and carbohydrate metabolism.

Enhanced immunoprecipitation techniques for the identification of RNA-binding protein partners: IGF2BP1 interactions in mammary epithelial cells

RNA-binding proteins (RBPs) regulate the expression of large cohorts of RNA species to produce programmatic changes in cellular phenotypes. To describe the function of RBPs within a cell, it is key to identify their mRNA-binding partners. This is often done by crosslinking nucleic acids to RBPs, followed by chemical release of the nucleic acid fragments for analysis. However, this methodology is lengthy, which involves complex processing with attendant sample losses, thus large amounts of starting materials and prone to artifacts. To evaluate potential alternative technologies, we tested “exclusion-based” purification of immunoprecipitates (IFAST or SLIDE) and report here that these methods can efficiently, rapidly, and specifically isolate RBP–RNA complexes. The analysis requires less than 1% of the starting material required for techniques that include crosslinking. Depending on the antibody used, 50% to 100% starting protein can be retrieved, facilitating the assay of endogenous levels of RBPs; the isolated ribonucleoproteins are subsequently analyzed using standard techniques, to provide a comprehensive portrait of RBP complexes. Using exclusion-based techniques, we show that the mRNA-binding partners for RBP IGF2BP1 in cultured mammary epithelial cells are enriched in mRNAs important for detoxifying superoxides (specifically glutathione peroxidase [GPX]-1 and GPX-2) and mRNAs encoding mitochondrial proteins. We show that these interactions are functionally significant, as loss of function of IGF2BP1 leads to destabilization of GPX mRNAs and reduces mitochondrial membrane potential and oxygen consumption. We speculate that this underlies a consistent requirement for IGF2BP1 for the expression of clonogenic activity in vitro.

Non-radioactive In Vivo Labeling of RNA with 4-Thiouracil

RNA molecules and their expression dynamics play essential roles in the establishment of complex cellular phenotypes and/or in the rapid cellular adaption to environmental changes. Accordingly, analyzing RNA expression remains an important step to understand the molecular basis controlling the formation of cellular phenotypes, cellular homeostasis or disease progression. Steady-state RNA levels in the cells are controlled by the sum of highly dynamic molecular processes contributing to RNA expression and can be classified in transcription, maturation and degradation. The main goal of analyzing RNA dynamics is to disentangle the individual contribution of these molecular processes to the life cycle of a given RNA under different physiological conditions. In the recent years, the use of nonradioactive nucleotide/nucleoside analogs and improved chemistry, in combination with time-dependent and high-throughput analysis, have greatly expanded our understanding of RNA metabolism across various cell types, organisms, and growth conditions.In this chapter, we describe a step-by-step protocol allowing pulse labeling of RNA with the nonradioactive nucleotide analog, 4-thiouracil , in the eukaryotic model organism Saccharomyces cerevisiae and the model archaeon Haloferax volcanii .

Robust T cell activation requires an eIF3-driven burst in T cell receptor translation

Activation of T cells requires a rapid surge in cellular protein synthesis. However, the role of translation initiation in the early induction of specific genes remains unclear. Here, we show human translation initiation factor eIF3 interacts with select immune system related mRNAs including those encoding the T cell receptor (TCR) subunits TCRA and TCRB. Binding of eIF3 to the TCRA and TCRB mRNA 3'-untranslated regions (3'-UTRs) depends on CD28 coreceptor signaling and regulates a burst in TCR translation required for robust T cell activation. Use of the TCRA or TCRB 3'-UTRs to control expression of an anti-CD19 chimeric antigen receptor (CAR) improves the ability of CAR-T cells to kill tumor cells in vitro. These results identify a new mechanism of eIF3-mediated translation control that can aid T cell engineering for immunotherapy applications.

PAR-CLIP: A Method for Transcriptome-Wide Identification of RNA Binding Protein Interaction Sites

During post-transcriptional gene regulation (PTGR), RNA binding proteins (RBPs) interact with all classes of RNA to control RNA maturation, stability, transport, and translation. Here, we describe Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP), a transcriptome-scale method for identifying RBP binding sites on target RNAs with nucleotide-level resolution. This method is readily applicable to any protein directly contacting RNA, including RBPs that are predicted to bind in a sequence- or structure-dependent manner at discrete RNA recognition elements (RREs), and those that are thought to bind transiently, such as RNA polymerases or helicases.

Studying RNP Composition with RIP

RNA is never left alone throughout its life cycle. Together with proteins, RNAs form membraneless organelles, called ribonucleoprotein particles (RNPs) where these two types of macromolecules strongly influence each other's functions and destinies. RNA immunoprecipitation is still one of the favorite techniques which allows to simultaneously study both the RNA and protein composition of the RNP complex.

Intrinsic Disorder in Human RNA-Binding Proteins

Although RNA-binding proteins (RBPs) are known to be enriched in intrinsic disorder, no previous analysis focused on RBPs interacting with specific RNA types. We fill this gap with a comprehensive analysis of the putative disorder in RBPs binding to six common RNA types: messenger RNA (mRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), non-coding RNA (ncRNA), ribosomal RNA (rRNA), and internal ribosome RNA (irRNA). We also analyze the amount of putative intrinsic disorder in the RNA-binding domains (RBDs) and non-RNA-binding-domain regions (non-RBD regions). Consistent with previous studies, we show that in comparison with human proteome, RBPs are significantly enriched in disorder. However, closer examination finds significant enrichment in predicted disorder for the mRNA-, rRNA- and snRNA-binding proteins, while the proteins that interact with ncRNA and irRNA are not enriched in disorder, and the tRNA-binding proteins are significantly depleted in disorder. We show a consistent pattern of significant disorder enrichment in the non-RBD regions coupled with low levels of disorder in RBDs, which suggests that disorder is relatively rarely utilized in the RNA-binding regions. Our analysis of the non-RBD regions suggests that disorder harbors posttranslational modification sites and is involved in the putative interactions with DNA. Importantly, we utilize experimental data from DisProt and independent data from Pfam to validate the above observations that rely on the disorder predictions. This study provides new insights into the distribution of disorder across proteins that bind different RNA types and the functional role of disorder in the regions where it is enriched.

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views' raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views' common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.

Interactions between capsid and viral RNA regulate Chikungunya virus translation in a host-specific manner

Alphaviruses are positive sense, RNA viruses commonly transmitted by an arthropod vector to a mammalian or avian host. In recent years, a number of the Alphavirus members have reemerged as public health concerns. Transmission from mosquito vector to vertebrate hosts requires an understanding of the interaction between the virus and both vertebrate and insect hosts to develop rational intervention strategies. The current study uncovers a novel role for capsid protein during Chikungunya virus replication whereby the interaction with viral RNA in the E1 coding region regulates protein synthesis processes early in infection. Studies done in both the mammalian and mosquito cells indicate that interactions between viral RNA and capsid protein have functional consequences that are host species specific. Our data support a vertebrate-specific role for capsid:vRNA interaction in temporally regulating viral translation in a manner dependent on the PI3K-AKT-mTOR pathway.

RNA methyltransferase METTL16: Targets and function

The N6-methyladenosine (m6A) RNA methyltransferase METTL16 is an emerging player in the RNA modification landscape of the human cell. Originally thought to be a ribosomal RNA methyltransferase, it has now been shown to bind and methylate the MAT2A messenger RNA (mRNA) and U6 small nuclear RNA (snRNA). It has also been shown to bind the MALAT1 long noncoding RNA and several other RNAs. METTL16's methyltransferase domain contains the Rossmann-like fold of class I methyltransferases and uses S-adenosylmethionine (SAM) as the methyl donor. It has an RNA methylation consensus sequence of UACAGARAA (modified A underlined), and structural requirements for its known RNA interactors. In addition to the methyltransferase domain, METTL16 protein has two other RNA binding domains, one of which resides in a vertebrate conserved region, and a putative nuclear localization signal. The role of METTL16 in the cell is still being explored, however evidence suggests it is essential for most cells. This is currently hypothesized to be due to its role in regulating the splicing of MAT2A mRNA in response to cellular SAM levels. However, one of the more pressing questions remaining is what role METTL16's methylation of U6 snRNA plays in splicing and potentially cellular survival. METTL16 also has several other putative coding and noncoding RNA interactors but the definitive methylation status of those RNAs and the role METTL16 plays in their life cycle is yet to be determined. Overall, METTL16 is an intriguing RNA binding protein and methyltransferase whose important functions in the cell are just beginning to be understood. This article is categorized under: RNA Processing > RNA Editing and Modification RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

A non-radioactive, improved PAR-CLIP and small RNA cDNA library preparation protocol

Crosslinking and immunoprecipitation (CLIP) methods are powerful techniques to interrogate direct protein-RNA interactions and dissect posttranscriptional gene regulatory networks. One widely used CLIP variant is photoactivatable ribonucleoside enhanced CLIP (PAR-CLIP) that involves in vivo labeling of nascent RNAs with the photoreactive nucleosides 4-thiouridine (4SU) or 6-thioguanosine (6SG), which can efficiently crosslink to interacting proteins using UVA and UVB light. Crosslinking of 4SU or 6SG to interacting amino acids changes their base-pairing properties and results in characteristic mutations in cDNA libraries prepared for high-throughput sequencing, which can be computationally exploited to remove abundant background from non-crosslinked sequences and help pinpoint RNA binding protein binding sites at nucleotide resolution on a transcriptome-wide scale. Here we present a streamlined protocol for fluorescence-based PAR-CLIP (fPAR-CLIP) that eliminates the need to use radioactivity. It is based on direct ligation of a fluorescently labeled adapter to the 3'end of crosslinked RNA on immobilized ribonucleoproteins, followed by isolation of the adapter-ligated RNA and efficient conversion into cDNA without the previously needed size fractionation on denaturing polyacrylamide gels. These improvements cut the experimentation by half to 2 days and increases sensitivity by 10-100-fold.

Computational methods for predicting hotspots at protein-RNA interfaces

Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been produced for hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.

Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

ELAVL1 primarily couples mRNA stability with the 3' UTRs of interferon-stimulated genes

Upon pathogen detection, the innate immune system triggers signaling events leading to upregulation of pro-inflammatory and anti-microbial mRNA transcripts. RNA-binding proteins (RBPs) interact with these critical mRNAs and regulate their fates at the post-transcriptional level. One such RBP is ELAVL1. Although significant progress has been made in understanding how embryonic lethal vision-like protein 1 (ELAVL1) regulates mRNAs, its target repertoire and binding distribution within an immunological context remain poorly understood. We overlap four high-throughput approaches to define its context-dependent targets and determine its regulatory impact during immune activation. ELAVL1 transitions from binding overwhelmingly intronic sites to 3′ UTR sites upon immune stimulation of cells, binding previously and newly expressed mRNAs. We find that ELAVL1 mediates the RNA stability of genes that regulate pathways essential to pathogen sensing and cytokine production. Our findings reveal the importance of examining RBP regulatory impact under dynamic transcriptomic events to understand their post-transcriptional regulatory roles within specific biological circuitries.

Rothamel et al. show that upon immune activation, the RNA-binding protein ELAVL1 accumulates in the cytoplasm and redistributes from introns to mRNA 3′ UTRs. 3′ UTR binding confers enrichment and transcript stability. Many top-ranking transcripts are interferon-stimulated genes (ISGs), indicating that ELAVL1 is a positive regulator of an innate immune response.

The pentatricopeptide repeat protein Rmd9 recognizes the dodecameric element in the 3'-UTRs of yeast mitochondrial mRNAs

Stabilization of messenger RNA is an important step in posttranscriptional gene regulation. In the nucleus and cytoplasm of eukaryotic cells it is generally achieved by 5' capping and 3' polyadenylation, whereas additional mechanisms exist in bacteria and organelles. The mitochondrial mRNAs in the yeast Saccharomyces cerevisiae comprise a dodecamer sequence element that confers RNA stability and 3'-end processing via an unknown mechanism. Here, we isolated the protein that binds the dodecamer and identified it as Rmd9, a factor that is known to stabilize yeast mitochondrial RNA. We show that Rmd9 associates with mRNA around dodecamer elements in vivo and that recombinant Rmd9 specifically binds the element in vitro. The crystal structure of Rmd9 bound to its dodecamer target reveals that Rmd9 belongs to the family of pentatricopeptide (PPR) proteins and uses a previously unobserved mode of specific RNA recognition. Rmd9 protects RNA from degradation by the mitochondrial 3'-exoribonuclease complex mtEXO in vitro, indicating that recognition and binding of the dodecamer element by Rmd9 confers stability to yeast mitochondrial mRNAs.

Use of Fluorescent Nucleotides to Map RNA-Binding Sites on Protein Surface

Currently, studies of RNA/protein interactions occupy a prominent place in molecular biology and medicine. The structures of RNA-protein complexes may be determined by X-ray crystallography or NMR for further analyses. These methods are time-consuming and difficult due to the versatility and dynamics of the RNA structure. Furthermore, due to the need to solve the "phase problem" for each dataset in crystallography, crystallographic structures of RNA are still underrepresented. Structure determination of single ribonucleotide-protein complexes is a useful tool to identify the position of single-stranded RNA-binding sites in proteins. We describe here a structural approach that incorporates affinity measurement of a protein for various single ribonucleotides, ranking the RNA/protein complexes according to their stability. This chapter describes how to perform these measurements, including a perspective for the analysis of RNA-binding sites in protein and single-nucleotide crystal soaking.

The search for RNA-binding proteins: a technical and interdisciplinary challenge

RNA-binding proteins are customarily regarded as important facilitators of gene expression. In recent years, RNA–protein interactions have also emerged as a pervasive force in the regulation of homeostasis. The compendium of proteins with provable RNA-binding function has swelled from the hundreds to the thousands astride the partnership of mass spectrometry-based proteomics and RNA sequencing. At the foundation of these advances is the adaptation of RNA-centric capture methods that can extract bound protein that has been cross-linked in its native environment. These methods reveal snapshots in time displaying an extensive network of regulation and a wealth of data that can be used for both the discovery of RNA-binding function and the molecular interfaces at which these interactions occur. This review will focus on the impact of these developments on our broader perception of post-transcriptional regulation, and how the technical features of current capture methods, as applied in mammalian systems, create a challenging medium for interpretation by systems biologists and target validation by experimental researchers.

CLIP and Massively Parallel Functional Analysis of CELF6 Reveal a Role in Destabilizing Synaptic Gene mRNAs through Interaction with 3' UTR Elements

CELF6 is a CELF-RNA-binding protein, and thus part of a protein family with roles in human disease; however, its mRNA targets in the brain are largely unknown. Using cross-linking immunoprecipitation and sequencing (CLIP-seq), we define its CNS targets, which are enriched for 3' UTRs in synaptic protein-coding genes. Using a massively parallel reporter assay framework, we test the consequence of CELF6 expression on target sequences, with and without mutating putative binding motifs. Where CELF6 exerts an effect on sequences, it is largely to decrease RNA abundance, which is reversed by mutating UGU-rich motifs. This is also the case for CELF3-5, with a protein-dependent effect on magnitude. Finally, we demonstrate that targets are derepressed in CELF6-mutant mice, and at least two key CNS proteins, FOS and FGF13, show altered protein expression levels and localization. Our works find, in addition to previously identified roles in splicing, that CELF6 is associated with repression of its CNS targets via the 3' UTR in vivo.

An emerging role of chromatin-interacting RNA-binding proteins in transcription regulation

Transcription factors (TFs) are well-established key factors orchestrating gene transcription, and RNA-binding proteins (RBPs) are mainly thought to participate in post-transcriptional control of gene. In fact, these two steps are functionally coupled, offering a possibility for reciprocal communications between transcription and regulatory RNAs and RBPs. Recently, a series of exploratory studies, utilizing functional genomic strategies, have revealed that RBPs are prevalently involved in transcription control genome-wide through their interactions with chromatin. Here, we present a refined census of RBPs to grope for such an emerging role and discuss the global view of RBP-chromatin interactions and their functional diversities in transcription regulation.

Viral crosslinking and solid-phase purification enables discovery of ribonucleoprotein complexes on incoming RNA virus genomes

The initial interactions between incoming, pre-replicated virion RNA and host protein factors are important in infection and immunity. Yet currently there are no methods to study these crucial events. We established VIR-CLASP (VIRal Cross-Linking And Solid-phase Purification) to identify the primary viral RNA-host protein interactions. First, host cells are infected with 4-thiouridine (4SU)-labeled RNA viruses and irradiated with 365 nm light to crosslink 4SU-labeled viral genomes and interacting proteins from host or virus. The crosslinked RNA binding proteins (RBPs) are purified by solid-phase reversible immobilization (SPRI) beads with protein-denaturing buffers, and then identified by proteomics. With VIR-CLASP, only the incoming virion RNAs are labeled with 4SU, so crosslinking events specifically occur between proteins and pre-replicated virion RNA. Since solid-phase purification under protein-denaturing conditions, rather than sequence-specific nucleic acid purification, is used to pull-down total RNA and crosslinked RBPs, this method facilitates investigation of potentially all RNA viruses, regardless of RNA sequence. Preparation of 4SU-labeled virus takes ∼7 days and VIR-CLASP takes 1 day.

CLIP and Massively Parallel Functional Analysis of CELF6 Reveal a Role in Destabilizing Synaptic Gene mRNAs through Interaction with 3' UTR Elements

CELF6 is a CELF-RNA-binding protein, and thus part of a protein family with roles in human disease; however, its mRNA targets in the brain are largely unknown. Using cross-linking immunoprecipitation and sequencing (CLIP-seq), we define its CNS targets, which are enriched for 3′ UTRs in synaptic protein-coding genes. Using a massively parallel reporter assay framework, we test the consequence of CELF6 expression on target sequences, with and without mutating putative binding motifs. Where CELF6 exerts an effect on sequences, it is largely to decrease RNA abundance, which is reversed by mutating UGU-rich motifs. This is also the case for CELF3–5, with a protein-dependent effect on magnitude. Finally, we demonstrate that targets are derepressed in CELF6-mutant mice, and at least two key CNS proteins, FOS and FGF13, show altered protein expression levels and localization. Our works find, in addition to previously identified roles in splicing, that CELF6 is associated with repression of its CNS targets via the 3′ UTR in vivo.

Rieger et al. assay the function of the RNA-binding protein CELF6 by defining its targets in the brain. They show that CELF6 largely binds 3′ UTRs of synaptic mRNAs. Using a massively parallel reporter assay, they further show that CELF6 and other CELFs are associated with lower mRNA abundance and that targets are derepressed in Celf6-knockout mice in vivo.

Ligation of 2', 3'-cyclic phosphate RNAs for the identification of microRNA binding sites

Identifying the targetome of a microRNA is key for understanding its functions. Cross‐linking and immunoprecipitation (CLIP) methods capture native miRNA‐mRNA interactions in cells. Some of these methods yield small amounts of chimeric miRNA‐mRNA sequences via ligation of 5′‐phosphorylated RNAs produced during the protocol. Here, we introduce chemically synthesized microRNAs (miRNAs) bearing 2′‐, 3′‐cyclic phosphate groups, as part of a new CLIP method that does not require 5′‐phosphorylation for ligation. We show in a system that models miRNAs bound to their targets, that addition of recombinant bacterial ligase RtcB increases ligation efficiency, and that the transformation proceeds via a 3′‐phosphate intermediate. By optimizing the chemistry underlying ligation, we provide the basis for an improved method to identify miRNA targetomes.

CPEB3 functions as a tumor suppressor in colorectal cancer via JAK/STAT signaling

As RNA-binding proteins, cytoplasmic polyadenylation element binding proteins (CPEBs) have drawn increasing attention for their function of controlling gene expression related to malignant transformation via post-transcriptional regulation. However, the contribution of CPEB3 to malignant development in cancers is poorly understood. In this study, we explored the clinical, biological, and mechanical role of CPEB3 in colorectal cancer progression. We showed that colorectal cancer tissues exhibited dampened CPEB3 expression which was closely associated with poor prognosis in patients with colorectal cancer (47 vs. 62 months, P = 0.035, n=99). Down-regulation CPEB3 promoted proliferation, migration, and invasion in colorectal cancer cells and vice versa. Mechanistically, CPEB3 performed as an RNA binding protein binding to 3'UTR of JAK1 mRNA to inhibit JAK/STAT pathways in colorectal cancer cells. Knockdown of CPEB3 induced active JAK-STAT signaling, thereby triggering the proliferation and metastasis capacity of colorectal cancer cells. These results suggest that CPEB3 functions as a tumor suppressor in colorectal cancer through its post-transcriptional regulation of JAK/STAT signaling. Implications: This study identified a novel role of the RNA binding protein CPEB3 in inhibiting cell proliferation and migration as well as the underlining mechanisms in colorectal cancer cells.

m6A Reader: Epitranscriptome Target Prediction and Functional Characterization of N 6-Methyladenosine (m6A) Readers

N ⁶-methyladenosine (m⁶A) is the most abundant post-transcriptional modification in mRNA, and regulates critical biological functions via m⁶A reader proteins that bind to m⁶A-containing transcripts. There exist multiple m⁶A reader proteins in the human genome, but their respective binding specificity and functional relevance under different biological contexts are not yet fully understood due to the limitation of experimental approaches. An in silico study was devised to unveil the target specificity and regulatory functions of different m⁶A readers. We established a support vector machine-based computational framework to predict the epitranscriptome-wide targets of six m⁶A reader proteins (YTHDF1-3, YTHDC1-2, and EIF3A) based on 58 genomic features as well as the conventional sequence-derived features. Our model achieved an average AUC of 0.981 and 0.893 under the full-transcript and mature mRNA model, respectively, marking a substantial improvement in accuracy compared to the sequence encoding schemes tested. Additionally, the distinct biological characteristics of each individual m⁶A reader were explored via the distribution, conservation, Gene Ontology enrichment, cellular components and molecular functions of their target m⁶A sites. A web server was constructed for predicting the putative binding readers of m⁶A sites to serve the research community, and is freely accessible at: http://m6areader.rnamd.com.

Proximity-CLIP and Expedited Non-Radioactive Library Preparation of Small RNA Footprints for Next-Generation Sequencing

During the course of their life cycle, most RNAs move between several cellular environments where they associate with different RNA binding proteins (RBPs). Reciprocally, a significant portion of RBPs reside in more than a single cellular compartment, where they can interact with discrete RNAs and even exert distinct biological roles. Proximity-CLIP combines proximity biotinylation of proteins with photoactivatable ribonucleoside-enhanced protein-RNA crosslinking to simultaneously profile the proteome, including RBPs and the RBP-bound transcriptome, in any given subcellular compartment. Here we provide a detailed experimental protocol for Proximity-CLIP along with a simplified non-radioactive, small-RNA cDNA library preparation protocol. Published 2020 U.S. Government. Basic Protocol 1: Cell culture, 4SU labeling, proximity biotinylation, and crosslinking Basic Protocol 2: Cell extraction, streptavidin affinity purification, and on-beads trypsinization Basic Protocol 3: RNA footprints cDNA library preparation Support Protocol: Preparation of RNA-seq libraries from intact RNA.

Mapping RNA-capsid interactions and RNA secondary structure within virus particles using next-generation sequencing

To characterize RNA-capsid binding sites genome-wide within mature RNA virus particles, we have developed a Next-Generation Sequencing (NGS) platform: viral Photo-Activatable Ribonucleoside CrossLinking (vPAR-CL). In vPAR-CL, 4-thiouridine is incorporated into the encapsidated genomes of virus particles and subsequently UV-crosslinked to adjacent capsid proteins. We demonstrate that vPAR-CL can readily and reliably identify capsid binding sites in genomic viral RNA by detecting crosslink-specific uridine to cytidine transitions in NGS data. Using Flock House virus (FHV) as a model system, we identified highly consistent and significant vPAR-CL signals across virus RNA genome, indicating a clear tropism of the encapsidated RNA genome. Certain interaction sites coincide with previously identified functional RNA motifs. We additionally performed dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) to generate a high-resolution profile of single-stranded genomic RNA inside viral particles. Combining vPAR-CL and DMS-MaPseq reveals that the predominant RNA-capsid interaction sites favored double-stranded RNA regions. We disrupted secondary structures associated with vPAR-CL sites using synonymous mutations, resulting in varied effects to virus replication, propagation and packaging. Certain mutations showed substantial deficiency in virus replication, suggesting these RNA-capsid sites are multifunctional. These provide further evidence to support that FHV packaging and replication are highly coordinated and inter-dependent events.

Deep neural networks for interpreting RNA-binding protein target preferences

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP targets. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors and can provide new insights about the regulatory functions of RBPs.

A Genetically Encoded Diazirine Analogue for RNA-Protein Photo-crosslinking

Ultraviolent crosslinking is a key experimental step in the numerous protocols that have been developed for capturing and dissecting RNA-protein interactions in living cells. UV crosslinking covalently stalls dynamic interactions between RNAs and the directly contacting RNA-binding proteins and enables stringent denaturing downstream purification conditions needed for the enrichment and biochemical analysis of RNA-protein complexes. Despite its popularity, conventional 254 nm UV crosslinking possesses a set of intrinsic drawbacks, with the low photochemical efficiency being the central caveat. Here we show that genetically encoded photoreactive unnatural amino acids bearing a dialkyl diazirine photoreactive group can address this problem. Using the human iron regulatory protein 1 (IRP1) as a model RNA-binding protein, we show that the photoreactive amino acids can be introduced into the protein without diminishing its RNA-binding properties. A sevenfold increase in the crosslinking efficiency compared to conventional 254 nm UV crosslinking was achieved using the diazirine-based unnatural amino acid DiAzKs. This finding opens an avenue for new applications of the unnatural amino acids in studying RNA-protein interactions.

Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions

Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.

Approaches for measuring the dynamics of RNA-protein interactions

RNA-protein interactions are pivotal for the regulation of gene expression from bacteria to human. RNA-protein interactions are dynamic; they change over biologically relevant timescales. Understanding the regulation of gene expression at the RNA level therefore requires knowledge of the dynamics of RNA-protein interactions. Here, we discuss the main experimental approaches to measure dynamic aspects of RNA-protein interactions. We cover techniques that assess dynamics of cellular RNA-protein interactions that accompany biological processes over timescales of hours or longer and techniques measuring the kinetic dynamics of RNA-protein interactions in vitro. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Evolution and Genomics > Ribonomics.

MicroRNAs and Metastasis

Metastasis, the development of secondary malignant growths at a distance from the primary site of a cancer, is associated with almost 90% of all cancer deaths, and half of all cancer patients present with some form of metastasis at the time of diagnosis. Consequently, there is a clear clinical need for a better understanding of metastasis. The role of miRNAs in the metastatic process is beginning to be explored. However, much is still to be understood. In this review, we present the accumulating evidence for the importance of miRNAs in metastasis as key regulators of this hallmark of cancer.

Discovering sequence and structure landscapes in RNA interaction motifs

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

A Brief Overview of lncRNAs in Endothelial Dysfunction-Associated Diseases: From Discovery to Characterization

Long non-coding RNAs (lncRNAs) are a novel class of regulatory RNA molecules and they are involved in many biological processes and disease developments. Several unique features of lncRNAs have been identified, such as tissue-and/or cell-specific expression pattern, which suggest that they could be potential candidates for therapeutic and diagnostic applications. More recently, the scope of lncRNA studies has been extended to endothelial biology research. Many of lncRNAs were found to be critically involved in the regulation of endothelial function and its associated disease progression. An improved understanding of endothelial biology can thus facilitate the discovery of novel biomarkers and therapeutic targets for endothelial dysfunction-associated diseases, such as abnormal angiogenesis, hypertension, diabetes, and atherosclerosis. Nevertheless, the underlying mechanism of lncRNA remains undefined in previous published studies. Therefore, in this review, we aimed to discuss the current methodologies for discovering and investigating the functions of lncRNAs and, in particular, to address the functions of selected lncRNAs in endothelial dysfunction-associated diseases.

Repression of ferritin light chain translation by human eIF3

A central problem in human biology remains the discovery of causal molecular links between mutations identified in genome-wide association studies (GWAS) and their corresponding disease traits. This challenge is magnified for variants residing in non-coding regions of the genome. Single-nucleotide polymorphisms (SNPs) in the 5ʹ untranslated region (5ʹ-UTR) of the ferritin light chain (FTL) gene that cause hyperferritinemia are reported to disrupt translation repression by altering iron regulatory protein (IRP) interactions with the FTL mRNA 5ʹ-UTR. Here, we show that human eukaryotic translation initiation factor 3 (eIF3) acts as a distinct repressor of FTL mRNA translation, and eIF3-mediated FTL repression is disrupted by a subset of SNPs in FTL that cause hyperferritinemia. These results identify a direct role for eIF3-mediated translational control in a specific human disease.

RNA Localization in the Vertebrate Oocyte: Establishment of Oocyte Polarity and Localized mRNA Assemblages

RNA localization is a fundamental mechanism for controlling cell structure and function. Early development in fish and amphibians requires the localization of specific mRNAs to establish the initial differences in cell fates prior to the onset of zygotic genome activation. RNA localization in these oocytes (e.g., Xenopus and zebrafish) requires that animal-vegetal polarity be established early in oogenesis, mediated by formation of the Balbiani body/mitochondrial cloud. This structure serves as a platform for assembly and transport of germline determinants to the future vegetal pole and also sets up the machinery for the localization of non-germline transcripts later in oogenesis. Understanding these polarization and localization mechanisms is critical for understanding the basis for early embryonic development in these organisms and also for understanding the role of RNA compartmentalization in animal gametogenesis. Here we outline recent advances in elucidating the molecular basis for the establishment of oocyte polarity at the level of Balbiani body assembly as well as the formation of RNP assemblies for early and late pathway mRNA localization in the oocyte.

Posttranscriptional regulation of colonic epithelial repair by RNA binding protein IMP1/IGF2BP1

RNA binding proteins, including IMP1/IGF2BP1, are essential regulators of intestinal development and cancer. Imp1 hypomorphic mice exhibit gastrointestinal growth defects, yet the specific role for IMP1 in colon epithelial repair is unclear. Our prior work revealed that intestinal epithelial cell-specific Imp1 deletion (Imp1 ^{Δ IEC} ) was associated with better regeneration in mice after irradiation. Here, we report increased IMP1 expression in patients with Crohn's disease and ulcerative colitis. We demonstrate that Imp1 ^{Δ IEC} mice exhibit enhanced recovery following dextran sodium sulfate (DSS)-mediated colonic injury. Imp1 ^{Δ IEC} mice exhibit Paneth cell granule changes, increased autophagy flux, and upregulation of Atg5. In silico and biochemical analyses revealed direct binding of IMP1 to MAP1LC3B, ATG3, and ATG5 transcripts. Genetic deletion of essential autophagy gene Atg7 in Imp1 ^{Δ IEC} mice revealed increased sensitivity of double-mutant mice to colonic injury compared to control or Atg7 single mutant mice, suggesting a compensatory relationship between Imp1 and the autophagy pathway. The present study defines a novel interplay between IMP1 and autophagy, where IMP1 may be transiently induced during damage to modulate colonic epithelial cell responses to damage.