Methods to study RNA-protein interactions

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.

Exploring the Utility of Long Non-Coding RNAs for Assessing the Health Consequences of Vaping

Electronic cigarette (e-cig) use, otherwise known as "vaping", is widespread among adolescent never-smokers and adult smokers seeking a less-harmful alternative to combustible tobacco products. To date, however, the long-term health consequences of vaping are largely unknown. Many toxicants and carcinogens present in e-cig vapor and tobacco smoke exert their biological effects through epigenetic changes that can cause dysregulation of disease-related genes. Long non-coding RNAs (lncRNAs) have emerged as prime regulators of gene expression in health and disease states. A large body of research has shown that lncRNAs regulate genes involved in the pathogenesis of smoking-associated diseases; however, the utility of lncRNAs for assessing the disease-causing potential of vaping remains to be fully determined. A limited but growing number of studies has shown that lncRNAs mediate dysregulation of disease-related genes in cells and tissues of vapers as well as cells treated in vitro with e-cig aerosol extract. This review article provides an overview of the evolution of e-cig technology, trends in use, and controversies on the safety, efficacy, and health risks or potential benefits of vaping relative to smoking. While highlighting the importance of lncRNAs in cell biology and disease, it summarizes the current and ongoing research on the modulatory effects of lncRNAs on gene regulation and disease pathogenesis in e-cig users and in vitro experimental settings. The gaps in knowledge are identified, priorities for future research are highlighted, and the importance of empirical data for tobacco products regulation and public health is underscored.

Rapid and Efficient Isolation of Total RNA-Bound Proteomes by Liquid Emulsion-Assisted Purification of RNA-Bound Protein (LEAP-RBP)

The critical roles of RNA-binding proteins (RBPs) in all aspects of RNA biology fostered the development of methods utilizing ultraviolet (UV) crosslinking and method-specific RNA enrichment steps for proteome-wide identification and assessment of RBP function. Despite the substantial contributions of these UV-based RNA-centric methods to our understanding of RNA-protein interaction networks, their utility is constrained by biases in RBP recovery and significant noise contributions, which can confound meaningful interpretation. To overcome these issues, we recently developed a method termed Liquid Emulsion-Assisted Purification of RNA-Bound Protein (LEAP-RBP) and introduced quantitative signal-to-noise (S:N)-based metrics for the proteome-wide identification of RNA interactomes and accurate assessment of global RBP occupancy dynamics. Compared to existing methodologies, LEAP-RBP provides significant advantages in speed, cost, efficiency, and selectivity for RNA-bound proteins. In this work, we provide a step-by-step guide for the successful application of the LEAP-RBP method for both small- and large-scale investigations of RNA-bound proteomes. Key features • Unbiased and efficient isolation of total RNA-bound protein, RNA, and protein from biological samples. • Cost-effective identification of proteome-wide RNA interactomes and validation of direct RNA-binding protein functionality. • Robust and accurate assessment of context- and/or condition-dependent RBP occupancy state dynamics.

Deciphering the Temporal-Spatial Interactive Heterogeneity of Long Non-Coding RNAs and RNA-Binding Proteins in Living Cells at Single-Cell Resolution

The investigation of long noncoding RNAs (lncRNAs) and RNA binding proteins (RBPs) interactions in living cell holds great significance for elucidating their critical roles in a variety of biological activities, but limited techniques are available to profile the temporal-spatial dynamic heterogeneity. Here, we introduced a molecular beacon-functionalized nanoneedle array designed for spatially resolved profiling of lncRNA-RBP interactions (Nano-SpatiaLR). A nanoneedle array modified with a molecular beacon is employed to selectively isolate specific intracellular lncRNAs and their associated RBPs without affecting cell viability. The RBPs are then in situ analyzed with a fluorescent labeled antibody and colocalized with lncRNA signals to get a quantitative measurement of their dynamic interactions. Additionally, leveraging the spatial distribution and nanoscale modality of the nanoneedle array, this technique provides the spatial heterogeneity information on cellular lncRNA-RBPs interaction at single cell resolution. In this study, we tracked the temporal-spatial interactive heterogeneity dynamics of lncRNA-RBPs interaction within living cells across different biological progresses. Our findings demonstrated that the interactions between lncRNA HOTAIR and RBPs EZH2 and LSD1 undergo significant changes in response to drug treatments, particularly in tumor cells. Moreover, these interactions become more intensified as tumor cells aggregate during the proliferation process.

Technological advancements in deciphering RNA-RNA interactions

RNA-RNA interactions (RRIs) can dictate RNA molecules to form intricate higher-order structures and bind their RNA substrates in diverse biological processes. To elucidate the function, binding specificity, and regulatory mechanisms of various RNA molecules, especially the vast repertoire of non-coding RNAs, advanced technologies and methods that globally map RRIs are extremely valuable. In the past decades, many state-of-the-art technologies have been developed for this purpose. This review focuses on those high-throughput technologies for the global mapping of RRIs. We summarize the key concepts and the pros and cons of different technologies. In addition, we highlight the novel biological insights uncovered by these RRI mapping methods and discuss the future challenges for appreciating the crucial roles of RRIs in gene regulation across bacteria, viruses, archaea, and mammals.

Biospecific Chemistry for Covalent Linking of Biomacromolecules

Interactions among biomacromolecules, predominantly noncovalent, underpin biological processes. However, recent advancements in biospecific chemistry have enabled the creation of specific covalent bonds between biomolecules, both in vitro and in vivo. This Review traces the evolution of biospecific chemistry in proteins, emphasizing the role of genetically encoded latent bioreactive amino acids. These amino acids react selectively with adjacent natural groups through proximity-enabled bioreactivity, enabling targeted covalent linkages. We explore various latent bioreactive amino acids designed to target different protein residues, ribonucleic acids, and carbohydrates. We then discuss how these novel covalent linkages can drive challenging protein properties and capture transient protein-protein and protein-RNA interactions in vivo. Additionally, we examine the application of covalent peptides as potential therapeutic agents and site-specific conjugates for native antibodies, highlighting their capacity to form stable linkages with target molecules. A significant focus is placed on proximity-enabled reactive therapeutics (PERx), a pioneering technology in covalent protein therapeutics. We detail its wide-ranging applications in immunotherapy, viral neutralization, and targeted radionuclide therapy. Finally, we present a perspective on the existing challenges within biospecific chemistry and discuss the potential avenues for future exploration and advancement in this rapidly evolving field.

Cut from the same cloth: RNAs transcribed from regulatory elements

A certain degree of chromatin openness is necessary for the activity of transcription-regulating regions within the genome, facilitating accessibility to RNA polymerases and subsequent synthesis of regulatory element RNAs (regRNAs) from these regions. The rapidly increasing number of studies underscores the significance of regRNAs across diverse cellular processes and diseases, challenging the paradigm that these transcripts are non-functional transcriptional noise. This review explores the multifaceted roles of regRNAs in human cells, encompassing rather well-studied entities such as promoter RNAs and enhancer RNAs (eRNAs), while also providing insights into overshadowed silencer RNAs and insulator RNAs. Furthermore, we assess notable examples of shorter regRNAs, like miRNAs, snRNAs, and snoRNAs, playing important roles. Expanding our discourse, we deliberate on the potential usage of regRNAs as biomarkers and novel targets for cancer and other human diseases.

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

Foundation models in molecular biology

Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network

SUMMARY

Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translation process, which supports the development of mRNA-based medicine or vaccines. Deep neural networks (DNN) can learn complex cis-regulatory codes from RNA sequences. However, extracting these cis-regulatory codes efficiently from DNN remains a significant challenge. Here, we propose a method based on our toolkit NeuronMotif and motif mutagenesis, which not only enables the discovery of diverse and high-quality motifs but also efficiently reveals motif interactions. By interpreting deep-learning models, we have discovered several crucial motifs that impact mRNA translation efficiency and stability, as well as some unknown motifs or motif syntax, offering novel insights for biologists. Furthermore, we note that it is challenging to enrich motif syntax in datasets composed of randomly generated sequences, and they may not contain sufficient biological signals.

AVAILABILITY AND IMPLEMENTATION

The source code and data used to produce the results and analyses presented in this manuscript are available from GitHub (https://github.com/WangLabTHU/combmotif).

RNA-binding proteins in cardiovascular biology and disease: the beat goes on

Cardiac development and function are becoming increasingly well understood from different angles, including signalling, transcriptional and epigenetic mechanisms. By contrast, the importance of the post-transcriptional landscape of cardiac biology largely remains to be uncovered, building on the foundation of a few existing paradigms. The discovery during the past decade of hundreds of additional RNA-binding proteins in mammalian cells and organs, including the heart, is expected to accelerate progress and has raised intriguing possibilities for better understanding the intricacies of cardiac development, metabolism and adaptive alterations. In this Review, we discuss the progress and new concepts on RNA-binding proteins and RNA biology and appraise them in the context of common cardiovascular clinical conditions, from cell and organ-wide perspectives. We also discuss how a better understanding of cardiac RNA-binding proteins can fill crucial knowledge gaps in cardiology and might pave the way to developing better treatments to reduce cardiovascular morbidity and mortality.

High-throughput quantitation of protein-RNA UV-crosslinking efficiencies as a predictive tool for high-confidence identification of RNA-binding proteins

UV-crosslinking has proven to be an invaluable tool for the identification of RNA-protein interactomes. The paucity of methods for distinguishing background from bona fide RNA-protein interactions, however, makes attribution of RNA-binding function on UV-crosslinking alone challenging. To address this need, we previously reported an RNA-binding protein (RBP) confidence scoring metric (RCS), incorporating both signal-to-noise (S:N) and protein abundance determinations to distinguish high- and low-confidence candidate RBPs. Although RCS has utility, we sought a direct metric for quantification and comparative evaluation of protein-RNA interactions. Here we propose the use of protein-specific UV-crosslinking efficiency (%CL), representing the molar fraction of a protein that is crosslinked to RNA, for functional evaluation of candidate RBPs. Application to the HeLa RNA interactome yielded %CL values for 1097 proteins. Remarkably, %CL values span over five orders of magnitude. For the HeLa RNA interactome, %CL values comprise a range from high efficiency, high specificity interactions, e.g., the Elav protein HuR and the Pumilio homolog Pum2, with %CL values of 45.9 and 24.2, respectively, to very low efficiency and specificity interactions, for example, the metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and alpha-enolase, with %CL values of 0.0016, 0.006, and 0.008, respectively. We further extend the utility of %CL through prediction of protein domains and classes with known RNA-binding functions, thus establishing it as a useful metric for RNA interactome analysis. We anticipate that this approach will benefit efforts to establish functional RNA interactomes and support the development of more predictive computational approaches for RBP identification.

Untacking small RNA profiling and RNA fragment footprinting: Approaches and challenges in library construction

Small RNAs (sRNAs) with sizes ranging from 15 to 50 nucleotides (nt) are critical regulators of gene expression control. Prior studies have shown that sRNAs are involved in a broad range of biological processes, such as organ development, tumorigenesis, and epigenomic regulation; however, emerging evidence unveils a hidden layer of diversity and complexity of endogenously encoded sRNAs profile in eukaryotic organisms, including novel types of sRNAs and the previously unknown post-transcriptional RNA modifications. This underscores the importance for accurate, unbiased detection of sRNAs in various cellular contexts. A multitude of high-throughput methods based on next-generation sequencing (NGS) are developed to decipher the sRNA expression and their modifications. Nonetheless, distinct from mRNA sequencing, the data from sRNA sequencing suffer frequent inconsistencies and high variations emanating from the adapter contaminations and RNA modifications, which overall skew the sRNA libraries. Here, we summarize the sRNA-sequencing approaches, and discuss the considerations and challenges for the strategies and methods of sRNA library construction. The pros and cons of sRNA sequencing have significant implications for implementing RNA fragment footprinting approaches, including CLIP-seq and Ribo-seq. We envision that this review can inspire novel improvements in small RNA sequencing and RNA fragment footprinting in future. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Processing of Small RNAs Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.

RNA: The Unsuspected Conductor in the Orchestra of Macromolecular Crowding

This comprehensive Review delves into the chemical principles governing RNA-mediated crowding events, commonly referred to as granules or biological condensates. We explore the pivotal role played by RNA sequence, structure, and chemical modifications in these processes, uncovering their correlation with crowding phenomena under physiological conditions. Additionally, we investigate instances where crowding deviates from its intended function, leading to pathological consequences. By deepening our understanding of the delicate balance that governs molecular crowding driven by RNA and its implications for cellular homeostasis, we aim to shed light on this intriguing area of research. Our exploration extends to the methodologies employed to decipher the composition and structural intricacies of RNA granules, offering a comprehensive overview of the techniques used to characterize them, including relevant computational approaches. Through two detailed examples highlighting the significance of noncoding RNAs, NEAT1 and XIST, in the formation of phase-separated assemblies and their influence on the cellular landscape, we emphasize their crucial role in cellular organization and function. By elucidating the chemical underpinnings of RNA-mediated molecular crowding, investigating the role of modifications, structures, and composition of RNA granules, and exploring both physiological and aberrant phase separation phenomena, this Review provides a multifaceted understanding of the intriguing world of RNA-mediated biological condensates.

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links

In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.

Translation machinery: the basis of translational control

Messenger RNA (mRNA) translation consists of initiation, elongation, termination, and ribosome recycling, carried out by the translation machinery, primarily including tRNAs, ribosomes, and translation factors (TrFs). Translational regulators transduce signals of growth and development, as well as biotic and abiotic stresses, to the translation machinery, where global or selective translational control occurs to modulate mRNA translation efficiency (TrE). As the basis of translational control, the translation machinery directly determines the quality and quantity of newly synthesized peptides and, ultimately, the cellular adaption. Thus, regulating the availability of diverse machinery components is reviewed as the central strategy of translational control. We provide classical signaling pathways (e.g., integrated stress responses) and cellular behaviors (e.g., liquid-liquid phase separation) to exemplify this strategy within different physiological contexts, particularly during host-microbe interactions. With new technologies developed, further understanding this strategy will speed up translational medicine and translational agriculture.

SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in live cells

RNA-protein interactions determine the cellular fate of RNA and are central to regulating gene expression outcomes in health and disease. To date, no method exists that is able to identify proteins that interact with specific regions within endogenous RNAs in live cells. Here, we develop SHIFTR (Selective RNase H-mediated interactome framing for target RNA regions), an efficient and scalable approach to identify proteins bound to selected regions within endogenous RNAs using mass spectrometry. Compared to state-of-the-art techniques, SHIFTR is superior in accuracy, captures minimal background interactions and requires orders of magnitude lower input material. We establish SHIFTR workflows for targeting RNA classes of different length and abundance, including short and long non-coding RNAs, as well as mRNAs and demonstrate that SHIFTR is compatible with sequentially mapping interactomes for multiple target RNAs in a single experiment. Using SHIFTR, we comprehensively identify interactions of cis-regulatory elements located at the 5' and 3'-terminal regions of authentic SARS-CoV-2 RNAs in infected cells and accurately recover known and novel interactions linked to the function of these viral RNA elements. SHIFTR enables the systematic mapping of region-resolved RNA interactomes for any RNA in any cell type and has the potential to revolutionize our understanding of transcriptomes and their regulation.

RNA-binding proteins in breast cancer: Biological implications and therapeutic opportunities

RNA-binding proteins (RBPs) refer to a class of proteins that participate in alternative splicing, RNA stability, polyadenylation, localization and translation of RNAs, thus regulating gene expression in post-transcriptional manner. Dysregulation of RNA-RBP interaction contributes to various diseases, including cancer. In breast cancer, disorders in RBP expression and function influence the biological characteristics of tumor cells. Targeting RBPs has fostered the development of innovative therapies for breast cancer. However, the RBP-related mechanisms in breast cancer are not completely clear. In this review, we summarize the regulatory mechanisms of RBPs and their signaling crosstalk in breast cancer. Specifically, we emphasize the potential of certain RBPs as prognostic factors due to their effects on proliferation, invasion, apoptosis, and therapy resistance of breast cancer cells. Most importantly, we present a comprehensive overview of the latest RBP-related therapeutic strategies and novel therapeutic targets that have proven to be useful in the treatment of breast cancer.

Long noncoding RNA H19: functions and mechanisms in regulating programmed cell death in cancer

Long noncoding RNAs (lncRNAs) are a group of noncoding RNAs with transcript lengths of >200 nucleotides. Mounting evidence suggests that lncRNAs are closely associated with tumorigenesis. LncRNA H19 (H19) was the first lncRNA to function as an oncogene in many malignant tumors. Apart from the established role of H19 in promoting cell growth, proliferation, invasion, migration, epithelial-mesenchymal transition (EMT), and metastasis, it has been recently discovered that H19 also inhibits programmed cell death (PCD) of cancer cells. In this review, we summarize the mechanisms by which H19 regulates PCD in cancer cells through various signaling pathways, molecular mechanisms, and epigenetic modifications. H19 regulates PCD through the Wnt/β-catenin pathway and the PI3K-Akt-mTOR pathway. It also acts as a competitive endogenous RNA (ceRNA) in PCD regulation. The interaction between H19 and RNA-binding proteins (RBP) regulates apoptosis in cancer. Moreover, epigenetic modifications, including DNA and RNA methylation and histone modifications, are also involved in H19-associated PCD regulation. In conclusion, we summarize the role of H19 signaling via PCD in cancer chemoresistance, highlighting the promising research significance of H19 as a therapeutic target. We hope that our study will contribute to a broader understanding of H19 in cancer development and treatment.

Stability-Based Proteomics for Investigation of Structured RNA-Protein Interactions

RNA-protein interactions are essential to RNA function throughout biology. Identifying the protein interactions associated with a specific RNA, however, is currently hindered by the need for RNA labeling or costly tiling-based approaches. Conventional strategies, which commonly rely on affinity pull-down approaches, are also skewed to the detection of high affinity interactions and frequently miss weaker interactions that may be biologically important. Reported here is the first adaptation of stability-based mass spectrometry methods for the global analysis of RNA-protein interactions. The stability of proteins from rates of oxidation (SPROX) and thermal protein profiling (TPP) methods are used to identify the protein targets of three RNA ligands, the MALAT1 triple helix (TH), a viral stem loop (SL), and an unstructured RNA (PolyU), in LNCaP nuclear lysate. The 315 protein hits with RNA-induced conformational and stability changes detected by TPP and/or SPROX were enriched in previously annotated RNA-binding proteins and included new proteins for hypothesis generation. Also demonstrated are the orthogonality of the SPROX and TPP approaches and the utility of the domain-specific information available with SPROX. This work establishes a novel platform for the global discovery and interrogation of RNA-protein interactions that is generalizable to numerous biological contexts and RNA targets.

Halo-RPD: searching for RNA-binding protein targets in plants

Study of RNA-protein interactions and identification of RNA targets are among the key aspects of understanding RNA biology. Currently, various methods are available to investigate these interactions with, RNA immunoprecipitation (RIP) being the most common. The search for RNA targets has largely been conducted using antibodies to an endogenous protein or to GFP-tag directly. Having to be dependent on the expression level of the target protein and having to spend time selecting highly specific antibodies make immunoprecipitation complicated. Expression of the GFP-fused protein can lead to cytotoxicity and, consequently, to improper recognition or degradation of the chimeric protein. Over the past few years, multifunctional tags have been developed. SNAP-tag and HaloTag allow the target protein to be studied from different perspectives. Labeling of the fusion protein with custom-made fluorescent dyes makes it possible to study protein expression and to localize it in the cell or the whole organism. A high-affinity substrate has been created to allow covalent binding by chimeric proteins, minimizing protein loss during protein isolation. In this paper, a HaloTag-based method, which we called Halo-RPD (HaloTag RNA PullDown), is presented. The proposed protocol uses plants with stable fusion protein expression and Magne® HaloTag® magnetic beads to capture RNA-protein complexes directly from the cytoplasmic lysate of transgenic Arabidopsis thaliana plants. The key stages described in the paper are as follows: (1) preparation of the magnetic beads; (2) tissue homogenization and collection of control samples; (3) precipitation and wash of RNA-protein complexes; (4) evaluation of protein binding efficiency; (5) RNA isolation; (6) analysis of the RNA obtained. Recommendations for better NGS assay designs are provided.

Molecular pathways in placental-fetal development and disruption

The first trimester of pregnancy ranks high in priority when minimizing harmful exposures, given the wide-ranging types of organogenesis occurring between 4- and 12-weeks' gestation. One way to quantify potential harm to the fetus in the first trimester is to measure a corollary effect on the placenta. Placental biomarkers are widely present in maternal circulation, cord blood, and placental tissue biopsied at birth or at the time of pregnancy termination. Here we evaluate ten diverse pathways involving molecules expressed in the first trimester human placenta based on their relevance to normal fetal development and to the hypothesis of placental-fetal endocrine disruption (perturbation in development that results in abnormal endocrine function in the offspring), namely: human chorionic gonadotropin (hCG), thyroid hormone regulation, peroxisome proliferator activated receptor protein gamma (PPARγ), leptin, transforming growth factor beta, epiregulin, growth differentiation factor 15, small nucleolar RNAs, serotonin, and vitamin D. Some of these are well-established as biomarkers of placental-fetal endocrine disruption, while others are not well studied and were selected based on discovery analyses of the placental transcriptome. A literature search on these biomarkers summarizes evidence of placenta-specific production and regulation of each biomarker, and their role in fetal reproductive tract, brain, and other specific domains of fetal development. In this review, we extend the theory of fetal programming to placental-fetal programming.

The Dynamic Landscape of Capsid Proteins and Viral RNA Interactions in Flavivirus Genome Packaging and Virus Assembly

The Flavivirus genus of the Flaviviridae family of enveloped single-stranded RNA viruses encompasses more than 70 members, many of which cause significant disease in humans and livestock. Packaging and assembly of the flavivirus RNA genome is essential for the formation of virions, which requires intricate coordination of genomic RNA, viral structural, and nonstructural proteins in association with virus-induced, modified endoplasmic reticulum (ER) membrane structures. The capsid (C) protein, a small but versatile RNA-binding protein, and the positive single-stranded RNA genome are at the heart of the elusive flavivirus assembly process. The nucleocapsid core, consisting of the genomic RNA encapsidated by C proteins, buds through the ER membrane, which contains viral glycoproteins prM and E organized as trimeric spikes into the lumen, forming an immature virus. During the maturation process, which involves the low pH-mediated structural rearrangement of prM and E and furin cleavage of prM in the secretory pathway, the spiky immature virus with a partially ordered nucleocapsid core becomes a smooth, mature virus with no discernible nucleocapsid. This review focuses on the mechanisms of genome packaging and assembly by examining the structural and functional aspects of C protein and viral RNA. We review the current lexicon of critical C protein features and evaluate interactions between C and genomic RNA in the context of assembly and throughout the life cycle.

Regulation of the WNT-CTNNB1 signaling pathway by severe fever with thrombocytopenia syndrome virus in a cap-snatching manner

IMPORTANCE

One of the conserved mechanisms at the stage of genome transcription of segmented negative-strand RNA viruses (sNSVs) is the cap-snatching process, which is vital for sNSVs transcription and provides drugable targets for the development of antivirals. However, the specificity of RNAs snatched by sNSV is still unclear. By transcriptomics analysis of whole blood samples from SFTS patients, we found WNT-CTNNB1 signaling pathway was regulated according to the course of the disease. We then demonstrated that L protein of severe fever with thrombocytopenia syndrome virus (SFTSV) could interact with mRNAs of WNT-CTNNB1 signaling pathway-related gene, thus affecting WNT-CTNNB1 signaling pathway through its cap-snatching activity. Activation of WNT-CTNNB1 signaling pathway enhanced SFTSV replication, while inhibition of this pathway decreased SFTSV replication in vitro and in vivo. These findings suggest that WNT-associated genes may be the substrate for SFTSV "cap-snatching", and indicate a conserved sNSVs replication mechanism involving WNT-CTNNB1 signaling.

Long Non-Coding RNAs: Discoveries, Mechanisms, and Research Strategies in Seeds

Seeds provide nutrients for the embryo and allow for dormancy in stressed environments to better adapt the plant to its environment. In addition, seeds are an essential source of food for human survival and are the basis for the formation of food production and quality. Therefore, the research on the genetic mechanism of seed development and germination will provide a theoretical basis and technical support for the improvement of crop yield and quality. Recent studies have shown that long non-coding RNAs (lncRNAs) occupy a pivotal position in seed development and germination. In this review, we describe the key processes in seed biology and examine discoveries and insights made in seed lncRNA, with emphasis on lncRNAs that regulate seed biology through multiple mechanisms. Given that thousands of lncRNAs are present in the seed transcriptome, characterization has lagged far behind identification. We provide an overview of research strategies and approaches including some exciting new techniques that may uncover the function of lncRNAs in seed. Finally, we discuss the challenges facing the field and the opening questions. All in all, we hope to provide a clear perspective on discoveries of seed lncRNA by linking discoveries, mechanisms, and technologies.

Mechanism of Dcp2/RNCR3/Dkc1/Snora62 axis regulating neuronal apoptosis in chronic cerebral ischemia

RNA-binding proteins (RBPs), long non-coding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs) were found to play crucial regulatory roles in ischemic injury. Based on GEO databases and our experimental results, we selected Dcp2, lncRNA-RNCR3, Dkc1, and Snora62 and Foxh1 as research candidates. We found that expression levels of Dcp2, RNCR3, Dkc1, Snora62, and Foxh1 were upregulated in oxygen glucose deprivation-treated HT22 cells and hippocampal tissues subject to chronic cerebral ischemia (CCI). Silencing of Dcp2, RNCR3, Dkc1, Snora62, and Foxh1 all inhibited apoptosis of oxygen glucose deprivation-treated HT22 cells. Moreover, Dcp2 promoted RNCR3 expression by increasing its stability. Importantly, RNCR3 may act as a molecular skeleton to bind to Dkc1 and recruit Dck1 to promote snoRNP assembly. Snora62 was responsible for pseudouridylation at 28S rRNA U3507 and U3509 sites. Pseudouridylation levels of 28S rRNA were reduced after knockdown of Snora62. Decreased pseudouridylation levels inhibited the translational activity of its downstream target, Foxh1. Our study further confirmed that Foxh1 transcriptionally promoted the expression of Bax and Fam162a. Notably, experiments in vivo showed that Dcp2 knockdown combined with RNCR3 knockdown and Snora62 knockdown resulted in an anti-apoptosis effect. In conclusion, this study suggests that the axis Dcp2/RNCR3/Dkc1/Snora621 is important for the regulation of neuronal apoptosis induced by CCI.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

Enzymolysis-based RNA pull-down identifies YTHDC2 as an inhibitor of antiviral innate response

The innate immune response must be terminated in a timely manner at the late stage of infection to prevent unwanted inflammation. The role of m6A-modified RNAs and their binding partners in this process is not well known. Here, we develop an enzymolysis-based RNA pull-down (eRP) method that utilizes the immunoglobulin G-degrading enzyme of Streptococcus pyogenes (IdeS) to fish out m6A-modified RNA-associated proteins. We apply eRP to capture the methylated single-stranded RNA (ssRNA) probe-associated proteins and identify YT521-B homology domain-containing 2 (YTHDC2) as the m6A-modified interferon β (IFN-β) mRNA-binding protein. YTHDC2, induced in macrophages at the late stage of virus infection, recruits IFN-stimulated exonuclease ISG20 (IFN-stimulated exonuclease gene 20) to degrade IFN-β mRNA, consequently inhibiting antiviral innate immune response. In vitro and in vivo deficiency of YTHDC2 increases IFN-β production at the late stage of viral infection. Our findings establish an eRP method to effectively identify RNA-protein interactions and add mechanistic insight to the termination of innate response for maintaining homeostasis.

Preparation of Cardiac Extracts from Embryonal Hearts to Capture RNA-protein Interactions by CLIP

The interaction of RNA with specific RNA-binding proteins (RBP) leads to the establishment of complex regulatory networks through which gene expression is controlled. Careful consideration should be given to the exact environment where a given RNA/RBP interplay occurs, as the functional responses might depend on the type of organism as well as the specific cellular or subcellular contexts. This requisite becomes particularly crucial for the study of long non-coding RNAs (lncRNA), as a consequence of their peculiar tissue-specificity and timely regulated expression. The functional characterization of lncRNAs has traditionally relied on the use of established cell lines that, although useful, are unable to fully recapitulate the complexity of a tissue or organ. Here, we detail an optimized protocol, with comments and tips, to identify the RNA interactome of given RBPs by performing cross-linking immunoprecipitation (CLIP) from mouse embryonal hearts. We tested the efficiency of this protocol on the murine pCharme, a muscle-specific lncRNA interacting with Matrin3 (MATR3) and forming RNA-enriched condensates of biological significance in the nucleus. Key features • The protocol refines previous methods of cardiac extracts preparation to use for CLIP assays. • The protocol allows the quantitative RNA-seq analysis of transcripts interacting with selected proteins. • Depending on the embryonal stage, a high number of hearts can be required as starting material. • The steps are adaptable to other tissues and biochemical assays.

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.

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.

Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes

RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein-RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.

Signal-noise metrics for RNA binding protein identification reveal broad spectrum protein-RNA interaction frequencies and dynamics

Recent efforts towards the comprehensive identification of RNA-bound proteomes have revealed a large, surprisingly diverse family of candidate RNA-binding proteins (RBPs). Quantitative metrics for characterization and validation of protein-RNA interactions and their dynamic interactions have, however, proven analytically challenging and prone to error. Here we report a method termed LEAP-RBP (Liquid-Emulsion-Assisted-Purification of RNA-Bound Protein) for the selective, quantitative recovery of UV-crosslinked RNA-protein complexes. By virtue of its high specificity and yield, LEAP-RBP distinguishes RNA-bound and RNA-free protein levels and reveals common sources of experimental noise in RNA-centric RBP enrichment methods. We introduce strategies for accurate RBP identification and signal-based metrics for quantifying protein-RNA complex enrichment, relative RNA occupancy, and method specificity. In this work, the utility of our approach is validated by comprehensive identification of RBPs whose association with mRNA is modulated in response to global mRNA translation state changes and through in-depth benchmark comparisons with current methodologies.

Methodologies for bacterial ribonuclease characterization using RNA-seq

Bacteria adjust gene expression at the post-transcriptional level through an intricate network of small regulatory RNAs and RNA-binding proteins, including ribonucleases (RNases). RNases play an essential role in RNA metabolism, regulating RNA stability, decay, and activation. These enzymes exhibit species-specific effects on gene expression, bacterial physiology, and different strategies of target recognition. Recent advances in high-throughput RNA sequencing (RNA-seq) approaches have provided a better understanding of the roles and modes of action of bacterial RNases. Global studies aiming to identify direct targets of RNases have highlighted the diversity of RNase activity and RNA-based mechanisms of gene expression regulation. Here, we review recent RNA-seq approaches used to study bacterial RNases, with a focus on the methods for identifying direct RNase targets.

Chromenopyrazole-Peptide Conjugates as Small-Molecule Based Inhibitors Disrupting the Protein-RNA Interaction of LIN28-let-7

Targeting the protein-RNA interaction of LIN28 and let-7 is a promising strategy for the development of novel anticancer therapeutics. However, a limited number of small-molecule inhibitors disrupting the LIN28-let-7 interaction with potent efficacy are available. Herein, we developed a novel LIN28-inhibiting strategy by targeting selective hotspot amino acids at the LIN28-let-7 binding interface with small-molecule-based bifunctional conjugates. Starting from reported small-molecule LIN28 inhibitors, we identified a feasible linker-attachment position after performing a structure-activity relationship exploration based on the LIN28-targeting chromenopyrazoles. In parallel, a virtual alanine scan identified hotspot residues at the protein-RNA binding interface, based on which we designed a set of peptides to enhance the interaction with the identified hotspot residues. Conjugation of the tailor-designed peptides with linker-attached chromenopyrazoles yielded a series of bifunctional small-molecule-peptide conjugates, represented by compound 83 (PH-223), as a new LIN28-targeting chemical modality. Our result demonstrated an unexplored rational design approach using bifunctional conjugates to target protein-RNA interactions.

The DNA binding high mobility group box protein family functionally binds RNA

Nucleic acid binding proteins regulate transcription, splicing, RNA stability, RNA localization, and translation, together tailoring gene expression in response to stimuli. Upon discovery, these proteins are typically classified as either DNA or RNA binding as defined by their in vivo functions; however, recent evidence suggests dual DNA and RNA binding by many of these proteins. High mobility group box (HMGB) proteins have a DNA binding HMGB domain, act as transcription factors and chromatin remodeling proteins, and are increasingly understood to interact with RNA as means to regulate gene expression. Herein, multiple layers of evidence that the HMGB family are dual DNA and RNA binding proteins is comprehensively reviewed. For example, HMGB proteins directly interact with RNA in vitro and in vivo, are localized to RNP granules involved in RNA processing, and their protein interactors are enriched in RNA binding proteins involved in RNA metabolism. Importantly, in cell-based systems, HMGB-RNA interactions facilitate protein-protein interactions, impact splicing outcomes, and modify HMGB protein genomic or cellular localization. Misregulation of these HMGB-RNA interactions are also likely involved in human disease. This review brings to light that as a family, HMGB proteins are likely to bind RNA which is essential to HMGB protein biology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Functional knockout of long non-coding RNAs with genome editing

An effective loss-of-function study is necessary to investigate the biological function of long non-coding RNA (lncRNA). Various approaches are available, including RNA silencing, antisense oligos, and CRISPR-based genome editing. CRISPR-based genome editing is the most widely used for inactivating lncRNA function at the genomic level. Knocking out the lncRNA function can be achieved by removing the promoter and the first exon (PE1), introducing pre-termination poly(A) signals, or deleting the entire locus, unlike frameshift strategies used for messenger RNA (mRNA). However, the intricate genomic interplay between lncRNA and neighbor genes makes it challenging to interpret lncRNA function accurately. This article discusses the advantages and disadvantages of each lncRNA knockout method and envisions the potential future directions to facilitate lncRNA functional study.

Role of the RNA-binding protein family in gynecologic cancers

Gynecological cancers pose a threat to women's health. Although early-stage gynecological cancers show good outcomes after standardized treatment, the prognosis of patients with advanced, met-astatic, and recurrent cancers is poor. RNA-binding proteins (RBPs) are important cellular proteins that interact with RNA through RNA-binding domains and participate extensively in post-transcriptional regulatory processes, such as mRNA alternative splicing, polyadenylation, intracellular localization and stability, and translation. Abnormal RBP expression affects the normal function of oncogenes and tumor suppressor genes in many malignancies, thus leading to the occurrence or progression of cancers. Similarly, RBPs play crucial roles in gynecological carcinogenesis. We summarize the role of RBPs in gynecological malignancies and explore their potential in the diagnosis and treatment of cancers. The findings summarized in this review may provide a guide for future research on the functions of RBPs.

PEPseq quantifies transcriptome-wide changes in protein occupancy and reveals selective translational repression after translational stress

Post-transcriptional gene regulation is accomplished by the interplay of the transcriptome with RNA-binding proteins, which occurs in a dynamic manner in response to altered cellular conditions. Recording the combined occupancy of all proteins binding to the transcriptome offers the opportunity to interrogate if a particular treatment leads to any interaction changes, pointing to sites in RNA that undergo post-transcriptional regulation. Here, we establish a method to monitor protein occupancy in a transcriptome-wide fashion by RNA sequencing. To this end, peptide-enhanced pull-down for RNA sequencing (or PEPseq) uses metabolic RNA labelling with 4-thiouridine (4SU) for light-induced protein-RNA crosslinking, and N-hydroxysuccinimide (NHS) chemistry to isolate protein-crosslinked RNA fragments across all long RNA biotypes. We use PEPseq to investigate changes in protein occupancy during the onset of arsenite-induced translational stress in human cells and reveal an increase of protein interactions in the coding region of a distinct set of mRNAs, including mRNAs coding for the majority of cytosolic ribosomal proteins. We use quantitative proteomics to demonstrate that translation of these mRNAs remains repressed during the initial hours of recovery after arsenite stress. Thus, we present PEPseq as a discovery platform for the unbiased investigation of post-transcriptional regulation.

Dynamic RNA profiles in the small intestinal epithelia of cats after Toxoplasma gondii infection

BACKGROUND

Felids are the only definitive hosts of Toxoplasma gondii. However, the biological features of the feline small intestine following T. gondii infection are poorly understood. We investigated the changes in the expression of RNAs (including mRNAs, long non-coding RNAs and circular RNAs) in the small intestinal epithelia of cats following T. gondii infection to improve our understanding of the life cycle of T. gondii and cat responses to T. gondii infection.

METHODS

Fifteen cats were randomly assigned to five groups, and the infection groups were inoculated with 600 tissue cysts of the T. gondii Pru strain by gavage. The small intestinal epithelia of cats were collected at 6, 10, 14, and 30 days post infection (DPI). Using high-throughput RNA sequencing (RNA-seq), we investigated the changes in RNA expression. The expression levels of differentially expressed (DE) genes and non-coding RNAs (ncRNAs) identified by RNA-seq were validated by quantitative reverse transcription PCR (qRT-PCR). Differential expression was determined using the DESeq R package.

RESULTS

In total, 207 annotated lncRNAs, 20,552 novel lncRNAs, 3342 novel circRNAs and 19,409 mRNAs were identified. Among these, 70 to 344 DE mRNAs, lncRNAs and circRNAs were detected, and the post-cleavage binding sites between 725 ncRNAs and 2082 miRNAs were predicted. Using the co-location method, we predicted that a total of 235 lncRNAs target 1044 protein-coding genes, while the results of co-expression analysis revealed that 174 lncRNAs target 2097 mRNAs. Pathway enrichment analyses of the genes targeted by ncRNAs suggested that most ncRNAs were significantly enriched in immune or diseases-related pathways. NcRNA regulatory networks revealed that a single ncRNA could be directly or indirectly regulated by multiple genes or ncRNAs that could influence the immune response of cats. Co-expression analysis showed that 242 circRNAs, mainly involved in immune responses, were significantly associated with T. gondii infection. In contrast, 1352 protein coding RNAs, mainly involved in nucleic acid process/repair pathways or oocyte development pathways, were negatively associated with T. gondii infection.

CONCLUSIONS

This study is the first to reveal the expression profiles of circRNAs, lncRNAs and mRNAs in the cat small intestine following T. gondii infection and will facilitate the elucidation of the role of ncRNAs in the pathogenesis of T. gondii infection in its definitive host, thereby facilitating the development of novel intervention strategies against T. gondii infection in humans and animals.

Interactome of Long Non-Coding RNAs: Transcriptomic Expression Patterns and Shaping Cancer Cell Phenotypes

RNA biology has gained extensive recognition in the last two decades due to the identification of novel transcriptomic elements and molecular functions. Cancer arises, in part, due to the accumulation of mutations that greatly contribute to genomic instability. However, the identification of differential gene expression patterns of wild-type loci has exceeded the boundaries of mutational study and has significantly contributed to the identification of molecular mechanisms that drive carcinogenic transformation. Non-coding RNA molecules have provided a novel avenue of exploration, providing additional routes for evaluating genomic and epigenomic regulation. Of particular focus, long non-coding RNA molecule expression has been demonstrated to govern and direct cellular activity, thus evidencing a correlation between aberrant long non-coding RNA expression and the pathological transformation of cells. lncRNA classification, structure, function, and therapeutic utilization have expanded cancer studies and molecular targeting, and understanding the lncRNA interactome aids in defining the unique transcriptomic signatures of cancer cell phenotypes.

SPIDR: a highly multiplexed method for mapping RNA-protein interactions uncovers a potential mechanism for selective translational suppression upon cellular stress

RNA binding proteins (RBPs) play crucial roles in regulating every stage of the mRNA life cycle and mediating non-coding RNA functions. Despite their importance, the specific roles of most RBPs remain unexplored because we do not know what specific RNAs most RBPs bind. Current methods, such as crosslinking and immunoprecipitation followed by sequencing (CLIP-seq), have expanded our knowledge of RBP-RNA interactions but are generally limited by their ability to map only one RBP at a time. To address this limitation, we developed SPIDR (Split and Pool Identification of RBP targets), a massively multiplexed method to simultaneously profile global RNA binding sites of dozens to hundreds of RBPs in a single experiment. SPIDR employs split-pool barcoding coupled with antibody-bead barcoding to increase the throughput of current CLIP methods by two orders of magnitude. SPIDR reliably identifies precise, single-nucleotide RNA binding sites for diverse classes of RBPs simultaneously. Using SPIDR, we explored changes in RBP binding upon mTOR inhibition and identified that 4EBP1 acts as a dynamic RBP that selectively binds to 5'-untranslated regions of specific translationally repressed mRNAs only upon mTOR inhibition. This observation provides a potential mechanism to explain the specificity of translational regulation controlled by mTOR signaling. SPIDR has the potential to revolutionize our understanding of RNA biology and both transcriptional and post-transcriptional gene regulation by enabling rapid, de novo discovery of RNA-protein interactions at an unprecedented scale.

PIE-seq: identifying RNA-binding protein targets by dual RNA-deaminase editing and sequencing

RNA-binding proteins (RBPs) are essential for gene regulation, but it remains a challenge to identify their RNA targets across cell types. Here we present PIE-Seq to investigate Protein-RNA Interaction with dual-deaminase Editing and Sequencing by conjugating C-to-U and A-to-I base editors to RBPs. We benchmark PIE-Seq and demonstrate its sensitivity in single cells, its application in the developing brain, and its scalability with 25 human RBPs. Bulk PIE-Seq identifies canonical binding features for RBPs such as PUM2 and NOVA1, and nominates additional target genes for most tested RBPs such as SRSF1 and TDP-43/TARDBP. Homologous RBPs frequently edit similar sequences and gene sets in PIE-Seq while different RBP families show distinct targets. Single-cell PIE-PUM2 uncovers comparable targets to bulk samples and applying PIE-PUM2 to the developing mouse neocortex identifies neural-progenitor- and neuron-specific target genes such as App. In summary, PIE-Seq provides an orthogonal approach and resource to uncover RBP targets in mice and human cells.

A method for in situ visualization of Protein-Nascent RNA interactions in single cell using Proximity Ligation Assay (IPNR-PLA) in mammalian cells

Proximity ligation assay (PLA) is an immunofluorescence assay, which determines in situ interaction of two biomolecules present within 40 nm close proximity. Here, we describe a modification of PLA for visual detection of in situ protein interactions with nascent RNA in a single cell (IPNR-PLA). In IPNR-PLA, nascent RNA is labeled by incorporating 5-fluorouridine (FU), a uridine nucleotide analogue, followed by covalent cross-linking of the interacting partners in proximity to newly synthesized RNA. By using combination of anti-BrdU antibody, which specifically binds to FU, and primary antibody against a protein of interest, the IPNR reaction results in fluorescent puncta as a positive signal, only if the candidate proteins are in proximity to nascent RNA. We have validated this method by demonstrating known CDK9 and elongating RNA pol II interaction with nascent RNA. Finally, we used this method to test for the presence of DNA double strand breaks as well as Poly (ADP-ribose) polymerase 1 (PARP1), an RNA binding protein, in the vicinity of nascent RNA in cancer cells. The capability of performing parallel IF labeling and quantifiable multiparameter measurements within heterogeneous cell populations makes IPNR-PLA very attractive for use in biological studies. Overall, we have developed the IPNR-PLA method for analysis of protein association with nascent RNA with single-cell resolution, which is highly sensitive, quantitative, efficient, and requires little starting experimental material.

A Scalable High-Throughput Isoelectric Fractionation Platform for Extracellular Nanocarriers: Comprehensive and Bias-Free Isolation of Ribonucleoproteins from Plasma, Urine, and Saliva

Extracellular nanocarriers (extracellular vesicles (EVs), lipoproteins, and ribonucleoproteins) of protein and nucleic acids mediate intercellular communication and are clinically adaptable as distinct circulating biomarkers. However, the overlapping size and density of the nanocarriers have so far prevented their efficient physical fractionation, thus impeding independent downstream molecular assays. Here, we report a bias-free high-throughput and high-yield continuous isoelectric fractionation nanocarrier fractionation technique based on their distinct isoelectric points. This nanocarrier fractionation platform is enabled by a robust and tunable linear pH profile provided by water-splitting at a bipolar membrane and stabilized by flow without ampholytes. The linear pH profile that allows easy tuning is a result of rapid equilibration of the water dissociation reaction and stabilization by flow. The platform is automated with a machine learning procedure to allow recalibration for different physiological fluids and nanocarriers. The optimized technique has a resolution of 0.3 ΔpI, sufficient to separate all nanocarriers and even subclasses of nanocarriers. Its performance is then evaluated with several biofluids, including plasma, urine, and saliva samples. Comprehensive, high-purity (plasma: >93%, urine: >95% and saliva: >97%), high-yield (plasma: >78%, urine: >87% and saliva: >96%), and probe-free isolation of ribonucleoproteins in 0.75 mL samples of various biofluids in 30 min is demonstrated, significantly outperforming affinity-based and highly biased gold standards having low yield and day-long protocols. Binary fractionation of EVs and different lipoproteins is also achieved with similar performance.

Identification of RNA-Binding Protein Targets with HyperTRIBE in Saccharomyces cerevisiae

As a master regulator in cells, RNA-binding protein (RBP) plays critical roles in organismal development, metabolism and various diseases. It regulates gene expression at various levels mostly by specific recognition of target RNA. The traditional CLIP-seq method to detect transcriptome-wide RNA targets of RBP is less efficient in yeast due to the low UV transmissivity of their cell walls. Here, we established an efficient HyperTRIBE (Targets of RNA-binding proteins Identified By Editing) in yeast, by fusing an RBP to the hyper-active catalytic domain of human RNA editing enzyme ADAR2 and expressing the fusion protein in yeast cells. The target transcripts of RBP were marked with new RNA editing events and identified by high-throughput sequencing. We successfully applied HyperTRIBE to identifying the RNA targets of two yeast RBPs, KHD1 and BFR1. The antibody-free HyperTRIBE has competitive advantages including a low background, high sensitivity and reproducibility, as well as a simple library preparation procedure, providing a reliable strategy for RBP target identification in Saccharomyces cerevisiae.

Direct and noninvasive fluorescence analysis of an RNA-protein interaction based on a CRISPR/Cas12a-powered assay

RNA-protein interactions (RPIs) play critical roles in gene transcription and protein expression, but current analytical methods for RPIs are mainly performed in an invasive manner, involving special RNA/protein labeling, hampering access to intact and precise information on RPIs. In this work, we present the first CRISPR/Cas12a-based fluorescence assay for the direct analysis of RPIs without RNA/protein labeling steps. Select vascular endothelial growth factor 165 (VEGF₁₆₅)/its RNA aptamer interaction as a model, the RNA sequence simultaneously serves as both the aptamer of VEGF₁₆₅ and crRNA of CRISPR/Cas12a system, and the presence of VEGF₁₆₅ facilitates VEGF₁₆₅/its RNA aptamer interaction, thus prohibiting the formation of Cas12a-crRNA-DNA ternary complex along with low fluorescence signal. The assay showed a detection limit of 0.23 pg mL^-1, and good performance in serum-spiked samples with an RSD of 0.4 %-13.1 %. This simple and selective strategy opens the door for establishing CRISPR/Cas-based biosensors for gaining intact information on RPIs, and shows widespread potential for other RPIs analysis.

The nexus between RNA-binding proteins and their effectors

RNA-binding proteins (RBPs) regulate essentially every event in the lifetime of an RNA molecule, from its production to its destruction. Whereas much has been learned about RNA sequence specificity and general functions of individual RBPs, the ways in which numerous RBPs instruct a much smaller number of effector molecules, that is, the core engines of RNA processing, as to where, when and how to act remain largely speculative. Here, we survey the known modes of communication between RBPs and their effectors with a particular focus on converging RBP-effector interactions and their roles in reducing the complexity of RNA networks. We discern the emerging unifying principles and discuss their utility in our understanding of RBP function, regulation of biological processes and contribution to human disease.