Addiction is a chronic and relapsing medical condition characterized by the compulsive use of drugs or alcohol despite harmful consequences. While transcriptional regulation has long been recognized for its role in addiction, recent genome-wide analyses have uncovered widespread alternative splicing changes that shift protein isoform diversity in multiple brain reward regions central to addiction. In this review, we discuss emerging research and evidence that alternative splicing is dysregulated in cocaine, alcohol, and opioid use disorders.

RNA-protein interactions are fundamental to a wide range of biological processes, and understanding these interactions in their native cellular context is both vital and challenging. Traditional methods for studying RNA-protein interactions rely on crosslinking, which can introduce artifacts. Recently, proximity labeling-based techniques have emerged as powerful alternatives, offering a crosslinking-free approach to investigate these interactions. This review highlights recent advancements in the development and application of proximity labeling methods, focusing on both RNA-centric and protein-centric strategies for profiling cellular RNA-protein interactions. By examining these innovative approaches, we aim to provide insights into their potential for enhancing our understanding of RNA-protein dynamics in various biological settings.

In yeast, tRNA modifications that are introduced by the Elongator complex are recognized by zymocin, a fungal tRNase killer toxin that cleaves the anticodon. Based on zymocin resistance conferred by mutations in KTI12, a gene coding for an Elongator interactor, we further examined the yet vaguely defined cellular role of Kti12. Guided by structural similarities between Kti12 and PSTK, a tRNA kinase involved in selenocysteine synthesis, we identified conserved basic residues in the C-terminus of Kti12, which upon site-directed mutagenesis caused progressive loss of tRNA binding in vitro. The inability of Kti12 to bind tRNA led to similar phenotypes caused by Elongator inactivation in vivo. Consistently, tRNA binding deficient kti12 mutants drastically suppressed Elongator dependent tRNA anticodon modifications and reduced the capacity of Kti12 to interact with Elongator. We further could distinguish Elongator unbound pools of Kti12 in a tRNA dependent manner from bound ones. In summary, the C-terminal domain of Kti12 is crucial for tRNA binding and Kti12 recruitment to Elongator, which are both requirements for Elongator function suggesting Kti12 is a tRNA carrier that interacts with Elongator for modification of the tRNA anticodon.

Recent advancements in molecular engineering have established RNA-based technologies as powerful tools for both fundamental research and translational applications. Among the various RNA-based technologies developed, RNA base editing has recently emerged as a groundbreaking advancement. It primarily involves the conversion of adenosine (A) to inosine (I) and cytidine (C) to uridine (U), which are mediated by ADAR and APOBEC enzymes, respectively. RNA base editing has been applied in both biological research and therapeutic contexts. It enables site-directed editing within target transcripts, offering reversible, dose-dependent effects, in contrast to the permanent or heritable changes associated with DNA base editing. Additionally, RNA editing-based profiling of RNA-binding protein (RBP) binding sites facilitates transcriptome-wide mapping of RBP-RNA interactions in specific tissues and at the single-cell level. Furthermore, RNA editing-based sensors have been utilized to express effector proteins in response to specific RNA species. As RNA base editing technologies continue to evolve, we anticipate that they will significantly drive advancements in RNA therapeutics, synthetic biology, and biological research.

Recent breakthroughs in AI-mediated protein structure prediction have significantly accelerated research and generated valuable hypotheses within the field of structural biology and beyond. Notably, AlphaFold2 has facilitated the determination of larger protein complexes for which only limited experimental data are available. De novo predictions can now be experimentally validated with relative ease compared to the pre-AlphaFold2 era. In May 2024, AlphaFold3 was launched with high expectations, promising the capability to accurately predict RNA structures and protein-RNA complexes - features that were absent in AlphaFold2. This review evaluates the extent to which AlphaFold3 fulfills this promise through specific examples. At present, AlphaFold3 falls short in reliably predicting RNA and protein-RNA complex structures, particularly for non-canonical interactions where training data remain scarce. As a result, users should exercise caution when using AlphaFold3 predictions as hypotheses generators for RNA and protein-RNA complex structures. In the interim, integrating AI-based predictors with data-driven docking tools is recommended to address these limitations. This approach can help bridge the gap until sufficient training data are available to enable the development of more reliable predictive algorithms.

Cellular RNA labeling using light-up aptamers that bind to and activate fluorogenic molecules has gained interest in recent years as an alternative to protein-based RNA labeling approaches. Aptamer-based systems are genetically encodable and cover the entire visible spectrum. However, the inherently temporary nature of the noncovalent aptamer-fluorogen interaction limits the utility of these systems in that imaging does not withstand dye washout, and dye dissociation can compromise RNA tracking. We propose that these limitations can be averted through covalent RNA labeling. Here, we describe a photoaffinity approach in which the aptamer ligand is functionalized with a photoactivatable diazirine reactive group such that irradiation with UV light results in covalent attachment to the RNA of interest. In addition to the robustness of the covalent linkage, this approach benefits from the ability to achieve spatiotemporal control over RNA labeling. To demonstrate this approach, we incorporated a photoaffinity linker into malachite green and fused a single copy of the malachite green aptamer to a Cajal body-associated small nuclear RNA of interest as well as a cytoplasmic mRNA. We observed improved sensitivity for live cell imaging of the target RNA upon UV irradiation and demonstrated visualization of RNA dynamics over a time scale of minutes. The covalent attachment uniquely enables these time-resolved experiments, whereas in noncovalent approaches, the dye molecule can be transferred between different RNA molecules, compromising tracking. We envision future applications of this method for a wide range of investigations into the cellular localization, dynamics, and protein-binding properties of cellular RNAs.

Since the discovery of RNA splicing and its role in gene expression, researchers have sought a set of rules, an algorithm or a computational model that could predict the splice isoforms, and their frequencies, produced from any transcribed gene in a specific cellular context. Over the past 30 years, these models have evolved from simple position weight matrices to deep-learning models capable of integrating sequence data across vast genomic distances. Most recently, new model architectures are moving the field closer to context-specific alternative splicing predictions, and advances in sequencing technologies are expanding the type of data that can be used to inform and interpret such models. Together, these developments are driving improved understanding of splicing regulatory mechanisms and emerging applications of the splicing code to the rational design of RNA- and splicing-based therapeutics.

A large fraction of the human genome is transcribed in RNA molecules that do not encode for proteins but that do have a crucial role in regulating almost every level of gene expression and, thus, define the specific phenotype of each cell. These non-coding RNAs include well-characterized microRNAs and thousands of less-defined longer transcripts, named long non-coding RNAs. Both types markedly affect the onset and the progression of numerous pathologies, ranging from cancer to vascular and neuro-degenerative diseases. In recent years, a substantial effort has been made to design drugs targeting ncRNAs, and promising advancements have been produced from micro-RNA mimics and inhibitors. Each ncRNA controls several targets, and the overall effect of its inhibition or overexpression depends on the function of the set of genes it regulates. Therefore, in selecting the most appropriate target, and predicting the final outcome of ncRNA-based therapies, it is crucial to have and utilize detailed and accurate knowledge of their functional interactions. In this review, I recapitulate the principal resources which collect information on microRNA and lncRNA networks, focusing on the non-homogeneity of the data that result from disparate approaches. I highlight the role of RNA identifiers and interaction evidence standardization in helping the user to filter and integrate data derived from different databases in a reliable functional web of regulative relations.

NOVA1, a neuronal RNA-binding protein expressed in the central nervous system, is essential for survival in mice and normal development in humans. A single amino acid change (I197V) in NOVA1's second RNA binding domain is unique to modern humans. To study its physiological effects, we generated mice carrying the human-specific I197V variant (Nova1hu/hu) and analyzed the molecular and behavioral consequences. While the I197V substitution had minimal impact on NOVA1's RNA binding capacity, it led to specific effects on alternative splicing, and CLIP revealed multiple binding peaks in mouse brain transcripts involved in vocalization. These molecular findings were associated with behavioral differences in vocalization patterns in Nova1hu/hu mice as pups and adults. Our findings suggest that this human-specific NOVA1 substitution may have been part of an ancient evolutionary selective sweep in a common ancestral population of Homo sapiens, possibly contributing to the development of spoken language through differential RNA regulation during brain development.

Piwi-interacting RNAs (piRNAs) are well established for monitoring and protecting the genome from transposons in germline cells. Recently, numerous studies provided evidence that piRNAs also play important roles in regulating mRNA transcript levels. Despite their significant role in regulating cellular RNA levels, the piRNA targeting rules are not well defined, especially in mammals, which poses obstacles to the elucidation of piRNA function.

Given the complexity and current limitation in understanding the mammalian piRNA targeting rules, we designed a deep learning model by selecting appropriate deep learning sub-networks based on the targeting patterns of piRNA inferred from previous experiments. Additionally, to alleviate the problem of insufficient data, a transfer learning approach was employed. Our model achieves a good discriminatory power (Accuracy: 98.5%) in predicting an independent test dataset. Finally, this model was utilized to predict the targets of all mouse and human piRNAs available in the piRNA database.

In this research, we developed a deep learning framework that significantly advances the prediction of piRNA targets, overcoming the limitations posed by insufficient data and current incomplete targeting rules. The piRNA target prediction network and results can be downloaded from https://github.com/SofiaTianjiaoZhang/piRNATarget .

RNA binding proteins (RBPs) are crucial regulators of gene expression and critically depend on the specific recognition of their target RNAs. Accordingly, a selection of methods to analyze RBP specificities has been developed, including protein-RNA crosslinking and sequencing (CLIP) and in vitro selection methods such as SELEX, RNA compete or RNA bind-n-seq. However, limitations like the availability for purified recombinant proteins and custom microarray platforms (RNAcompete) or extensive sequencing depth and sophisticated bioinformatic data processing (CLIP) may limit a broader implementation of these methods. Here, we present an RNA bind-n-seq method that uses short random RNA pools and enables multiple rounds of selection. This results in strong motif enrichment with low positional variance thus reducing sequencing depth requirements. Furthermore, we have coupled our protocol to immunoprecipitation of tagged or endogenous RBPs from cultured cells or tissue samples, eliminating the need for recombinant proteins. Our method also allows for the identification of indirect RNA motifs of proteins that are integral parts of multiprotein RNPs and result in physically more relevant RNA motifs.

The entire RNA life cycle, spanning from transcription to decay, is intricately regulated by RNA-binding proteins (RBPs). To understand their precise functions, it is crucial to identify direct targets, pinpoint their exact binding sites, and unravel the underlying specificity in vivo. Individual-nucleotide resolution UV cross-linking and immunoprecipitation 2 (iCLIP2) is a state-of-the-art technique that enables the identification of RBP-binding sites at single-nucleotide resolution. However, in the field of microbiology, optimized iCLIP protocols compared to mammalian systems are lacking. Here, we present the first microbial iCLIP2 approach using the multi-RRM domain protein Rrm4 from the fungus Ustilago maydis as an example. Key challenges, such as inherently high RNase and protease activity in fungi, were addressed by improving mechanical cell disruption and lysis buffer composition. Our modifications increased the yield of cross-link events and improved the identification of Rrm4-binding sites. Thereby, we were able to pinpoint that Rrm4 binds the stop codons of nuclear-encoded mRNAs of mitochondrial respiratory complexes I, III, and V-revealing an intimate link between endosomal mRNA transport and mitochondrial physiology. Thus, our study using U. maydis as an example might serve as a blueprint for optimizing iCLIP2 procedures in other microorganisms with high RNase/protease conditions.

RNA co-immunoprecipitation serves as a powerful technique for elucidating the interactions between RNA and RNA-binding proteins, pivotal for understanding posttranscriptional regulation mechanisms. This method captures the dynamics of protein-RNA associations across various cellular processes, applicable to both coding and noncoding RNAs. Here we describe a convenient and compelling method using nanobody coupled beads and UV-crosslinking. Importantly, this method can also be utilized to isolate short-lived complexes, for instance in RNA-degradation. The obtained RNA can be used for many downstream applications, such as qPCR or RNA-sequencing.

RNA-binding proteins (RBPs) are critical regulators of gene expression and RNA processing. Identification of their binding sites has important implications for their physiological and disease-related functions. Crosslinking and immunoprecipitation, followed by sequencing (CLIP-seq) and its derivatives, are the most commonly used methods to identify RBP binding sites, but are laborious and require a large amount of starting material. Recent advancements harnessing RNA deaminases in fusion to any RBP of interest, allow for the profiling of RBP binding sites from low-input samples in simpler procedures. Among these efforts, we developed STAMP (Surveying Targets by APOBEC-Mediated Profiling), which efficiently detects RBP-RNA interactions. This chapter describes the detailed protocol for the STAMP method, including plasmid construction, delivery and sorting, library preparation and bioinformatic data analysis.

Activity-dependent protein synthesis is crucial for long-lasting forms of synaptic plasticity. However, our understanding of translational mechanisms controlling GABAergic synapses is limited. One distinct form of inhibitory long-term potentiation (iLTP) enhances postsynaptic clusters of GABAARs and the primary inhibitory scaffold, gephyrin, to promote sustained synaptic strengthening. While we previously found that persistent iLTP requires mRNA translation, the mechanisms controlling plasticity-induced gephyrin translation remain unknown. We identify miR153 as a novel regulator of Gphn mRNA translation which controls gephyrin protein levels and synaptic clustering, ultimately impacting inhibitory synaptic structure and function. iLTP induction downregulates miR153, reversing its translational suppression of Gphn mRNA and promoting de novo gephyrin protein synthesis and synaptic clustering during iLTP. Finally, we find that reduced miR153 expression during iLTP is driven by an excitation-transcription coupling pathway involving calcineurin, NFAT and HDACs, which also controls the miRNA-dependent upregulation of GABAARs. Together, we delineate a miRNA-dependent post-transcriptional mechanism that controls the expression of the key synaptic scaffold, gephyrin, and may converge with parallel miRNA pathways to coordinate gene upregulation to maintain inhibitory synaptic plasticity.

RNA molecules in the cell are bound by a multitude of RNA-binding proteins (RBPs) with a variety of regulatory consequences. Often, interactions with these RNA-binding proteins are facilitated by the complex secondary and tertiary structures of RNA molecules. Viral RNAs especially are known to be heavily structured and interact with many RBPs, with roles including genome packaging, immune evasion, enhancing replication and transcription, and increasing translation efficiency. As such, the RNA-protein interactome represents a critical facet of the viral replication cycle. Characterization of these interactions is necessary for the development of novel therapeutics targeted at the disruption of essential replication cycle events. In this review, we aim to summarize the various roles of RNA structures in shaping the RNA-protein interactome, the regulatory roles of these interactions, as well as up-to-date methods developed for the characterization of the interactome and directions for novel, RNA-directed therapeutics.

Alternative splicing events increase the transcriptomic and proteomic complexity in cancers. Overexpression of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a highly conserved lncRNA, is widely known to promote cancer development, one mechanism for which may be through the regulation of alternative splicing and, thereby, gene expression. Its regulatory interactions with proteins have been a subject of much interest, yet little research has been carried out on the mechanisms adopted. It has been observed that MALAT1 binds to RNA-binding protein with serine-rich domain 1 (RNPS1), being colocalized in the nuclear speckles, and together, these two binding partners may regulate alternative splicing. Upregulated RNPS1 is predicted to play a key role in the pan-cancer development. Experimental tertiary structure of full-length MALAT1 is currently lacking despite the availability of the 3D structure of 3' expression and nuclear retention element. We hypothesize that the computationally modeled tertiary structures of the specific binding motifs in the M-region, E-region, and full-length structures of MALAT1 may adopt a modular structure and bind to the RNPS1 loop region of RS/P domain involved in exon skipping, interacting in a manner fully consistent with the biochemical experiments. Extensive observations using the powerful molecular dynamics (MD) simulations of MALAT1 regions bound to RNPS1 suggested that all three regions form interactive, yet stable complexes. The ranking of the MM-GBSA- and MM-PBSA-derived binding free energies between these complexes corroborated well in the MD simulations and experiments. Energy decomposition analyses suggested that arginines in the RNPS1 protein are among the major contributors toward the binding free energies as calculated by MM-GBSA present in the Amber package; while among the nucleotides, the major contributors were nucleotides with G and A nucleobases, with more contributory effect in comparison to arginines, across the bound M-region, E-region, and full-length MALAT1. This suggests that specific purines play a greater role in the complex formation, in a loop-specific manner, and the more proactive approach in complexation tilts toward MALAT1. To the best of our knowledge, our studies are the first studies taking a unique approach, utilizing the binding motifs to deduce a tertiary structure of MALAT1, toward our understanding of the lncRNA-protein interactions, stability, and binding on a structural basis. The therapeutic implications of targeting this complex formation to regulate splicing and hence, oncogenesis, is further envisaged.

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.

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

Chemical modification of messenger RNA (mRNA) has paved the way for advancing mRNA-based therapeutics. The intricate process of mRNA translation in eukaryotes is orchestrated by numerous proteins involved in complex interaction networks. Many of them bind specifically to a unique structure at the mRNA 5'-end, called 5'-cap. Depending on the 5'-terminal sequence and its methylation pattern, different proteins may be involved in the translation initiation and regulation, but a deeper understanding of these mechanisms requires specialized molecular tools to identify natural binders of mRNA 5'-end variants. Here, a series of 8 new synthetic 5'-cap analogs that allow the preparation of RNA molecules with photoreactive tags using a standard in vitro transcription reaction are reported. Two photoreactive tags and four different modification sites are selected to minimize potential interference with cap-protein contacts and to provide complementary properties regarding crosslinking chemistry and molecular interactions. The tailored modification strategy allows for the generation of specific crosslinks with model cap-binding proteins, such as eIF4E and Dcp2. The usefulness of the photoreactive cap analogs is also demonstrated for identifying the cap-binding subunit in a multi-protein complex, which makes them perfect candidates for further development of photoaffinity labeling probes to study more complex mRNA-related processes.

RNA-binding proteins (RBPs) have pivotal functions in RNA metabolism, but current methods are limited in retrieving RBP-RNA interactions within endogenous biological contexts. Here, we develop INSCRIBE (IN situ Sensitive Capture of RNA-protein Interactions in Biological Environments), circumventing the challenges through in situ RNA labeling by precisely directing a purified APOBEC1-nanobody fusion to the RBP of interest. This method enables highly specific RNA-binding site identification across a diverse range of fixed biological samples such as HEK293T cells and mouse brain tissue and accurately identifies the canonical binding motifs of RBFOX2 (UGCAUG) and TDP-43 (UGUGUG) in native cellular environments. Applicable to any RBP with available primary antibodies, INSCRIBE enables sensitive capture of RBP-RNA interactions from ultra-low input equivalent to ~5 cells. The robust, versatile, and sensitive INSCRIBE workflow is particularly beneficial for precious tissues such as clinical samples, empowering the exploration of genuine RBP-RNA interactions in RNA-related disease contexts.

Exploring the intricate interplay within and between nucleic acids, as well as their interactions with proteins, holds pivotal significance in unraveling the molecular complexities steering cancer initiation and progression. To investigate these interactions, a diverse array of highly specific and sensitive molecular techniques has been developed. The selection of a particular technique depends on the specific nature of the interactions. Typically, researchers employ an amalgamation of these different techniques to obtain a comprehensive and holistic understanding of inter- and intramolecular interactions involving DNA-DNA, RNA-RNA, DNA-RNA, or protein-DNA/RNA. Examining nucleic acid conformation reveals alternative secondary structures beyond conventional ones that have implications for cancer pathways. Mutational hotspots in cancer often lie within sequences prone to adopting these alternative structures, highlighting the importance of investigating intra-genomic and intra-transcriptomic interactions, especially in the context of mutations, to deepen our understanding of oncology. Beyond these intramolecular interactions, the interplay between DNA and RNA leads to formations like DNA:RNA hybrids (known as R-loops) or even DNA:DNA:RNA triplex structures, both influencing biological processes that ultimately impact cancer. Protein-nucleic acid interactions are intrinsic cellular phenomena crucial in both normal and pathological conditions. In particular, genetic mutations or single amino acid variations can alter a protein's structure, function, and binding affinity, thus influencing cancer progression. It is thus, imperative to understand the differences between wild-type (WT) and mutated (MT) genes, transcripts, and proteins. The review aims to summarize the frequently employed methods and techniques for investigating interactions involving nucleic acids and proteins, highlighting recent advancements and diverse adaptations of each technique.

The Rbfox proteins regulate alternative pre-mRNA splicing by binding to the RNA element GCAUG. In the nucleus, most of Rbfox is bound to LASR, a complex of RNA-binding proteins that recognize additional RNA motifs. However, it remains unclear how the different subunits of the Rbfox/LASR complex act together to bind RNA and regulate splicing. We used a nuclease-protection assay to map the transcriptome-wide footprints of Rbfox1/LASR on nascent cellular RNA. In addition to GCAUG, Rbfox1/LASR binds RNA containing motifs for LASR subunits hnRNPs M, H/F, C, and Matrin3. These elements are often arranged in tandem, forming multi-part modules of RNA motifs. To distinguish contact sites of Rbfox1 from the LASR subunits, we analyzed a mutant Rbfox1(F125A) that has lost RNA binding but remains associated with LASR. Rbfox1(F125A)/LASR complexes no longer interact with GCAUG but retain binding to RNA elements for LASR. Splicing analyses reveal that in addition to activating exons through adjacent GCAUG elements, Rbfox can also stimulate exons near binding sites for LASR subunits. Mini-gene experiments demonstrate that these diverse elements produce a combined regulatory effect on a target exon. These findings illuminate how a complex of RNA-binding proteins can decode combinatorial splicing regulatory signals by recognizing groups of tandem RNA elements.

Neuro-Oncological Ventral Antigen 1 (NOVA1) is best known for its role in mediating an alternative splicing (AS) program in neurons, yet was first discovered as an antigen expressed in breast tumors, causing rare autoimmune reactions and paraneoplastic neurological disorders (PNDs). The PND model suggests a plausible role of the tumor antigen expression in tumor suppression, whereas it has emerged that NOVA may function as an oncogene in a variety of cancers. In addition, whether NOVA mediates AS in breast cancer remains unanswered. Here we examine the AS profiles of breast invasive carcinoma (BRCA) tumor samples and demonstrate that ectopic NOVA1 expression led to the activation of neuron-like splicing patterns in many genes, including exons targeted by NOVA in the brain. The splicing dysregulation is especially prevalent in cell periphery and cytoskeleton genes related to cell-cell communication, actin-based movement, and neuronal functions. We find that NOVA1-mediated AS is most prominent in Luminal A tumors and high NOVA1 expression in this subtype is associated with poorer prognosis. Our results suggest that ectopic NOVA1 in tumors has regulatory activity affecting pathways with high relevance to tumor progression and that this might be a more general mechanism for PND antigens.

YTHDF proteins are main cytoplasmic 'reader' proteins of RNA N6-methyladenosine (m6A) methylation in mammals. They are largely responsible for m6A-mediated regulation in the cell cytosol by controlling both mRNA translation and degradation. Recent functional and mechanistic investigations of the YTHDF proteins revealed that these proteins have different functions to enable versatile regulation of the epitranscriptome. Their divergent functions largely originate from their different amino acid sequences in the low-complexity N termini. Consequently, they have different phase separation propensities and possess distinct post-translational modifications (PTMs). Different PTMs, subcellular localizations, and competition among partner proteins have emerged as three major mechanisms that control the functions of these YTHDF proteins. We also summarize recent progress on critical roles of these YTHDF proteins in anticancer immunity and the potential for targeting these proteins for developing new anticancer therapies.

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

Maturation of eukaryotic pre-mRNAs via splicing and polyadenylation is modulated across cell types and conditions by a variety of RNA-binding proteins (RBPs). Although there exist over 1,500 RBPs in human cells, their binding motifs and functions still remain to be elucidated, especially in the complex environment of tissues and in the context of diseases. To overcome the lack of methods for the systematic and automated detection of sequence motif-guided pre-mRNA processing regulation from RNA sequencing (RNA-Seq) data we have developed MAPP (Motif Activity on Pre-mRNA Processing). Applying MAPP to RBP knock-down experiments reveals that many RBPs regulate both splicing and polyadenylation of nascent transcripts by acting on similar sequence motifs. MAPP not only infers these sequence motifs, but also unravels the position-dependent impact of the RBPs on pre-mRNA processing. Interestingly, all investigated RBPs that act on both splicing and 3' end processing exhibit a consistently repressive or activating effect on both processes, providing a first glimpse on the underlying mechanism. Applying MAPP to normal and malignant brain tissue samples unveils that the motifs bound by the PTBP1 and RBFOX RBPs coordinately drive the oncogenic splicing program active in glioblastomas demonstrating that MAPP paves the way for characterizing pre-mRNA processing regulators under physiological and pathological conditions.

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.

Learning and memory require activity-induced changes in dendritic translation, but which mRNAs are involved and how they are regulated are unclear. In this study, to monitor how depolarization impacts local dendritic biology, we employed a dendritically targeted proximity labeling approach followed by crosslinking immunoprecipitation, ribosome profiling and mass spectrometry. Depolarization of primary cortical neurons with KCl or the glutamate agonist DHPG caused rapid reprogramming of dendritic protein expression, where changes in dendritic mRNAs and proteins are weakly correlated. For a subset of pre-localized messages, depolarization increased the translation of upstream open reading frames (uORFs) and their downstream coding sequences, enabling localized production of proteins involved in long-term potentiation, cell signaling and energy metabolism. This activity-dependent translation was accompanied by the phosphorylation and recruitment of the non-canonical translation initiation factor eIF4G2, and the translated uORFs were sufficient to confer depolarization-induced, eIF4G2-dependent translational control. These studies uncovered an unanticipated mechanism by which activity-dependent uORF translational control by eIF4G2 couples activity to local dendritic remodeling.

RNA-binding proteins (RBPs) are powerful and versatile regulators in living creatures, playing fundamental roles in organismal development, metabolism, and various diseases by the regulation of gene expression at multiple levels. The requirements of deep research on RBP function have promoted the rapid development of RBP-RNA interplay detection methods. Recently, the detection method of fusing RNA modification enzymes (RME) with RBP of interest has become a hot topic. Here, we reviewed RNA modification enzymes in adenosine deaminases that act on RNA (ADAR), terminal nucleotidyl transferase (TENT), and activation-induced cytosine deaminase/ApoB mRNA editing enzyme catalytic polypeptide-like (AID/APOBEC) protein family, regarding the biological function, biochemical activity, and substrate specificity originated from enzyme selves, their domains and partner proteins. In addition, we discussed the RME activity screening system, and the RME mutations with engineered enzyme activity. Furthermore, we provided a systematic overview of the basic principles, advantages, disadvantages, and applications of the RME-based and cross-linking and immunopurification (CLIP)-based RBP target profiling strategies, including targets of RNA-binding proteins identified by editing (TRIBE), RNA tagging, surveying targets by APOBEC-mediated profiling (STAMP), CLIP-seq, and their derivative technology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Processing > RNA Editing and Modification.

N 6-methyladenosine (m6A) is the most prevalent modified nucleotide in mRNA, and it has important functions in mRNA regulation. However, our understanding of the specific functions of m6A along with its cytosolic readers, the YTHDF proteins, has changed substantially in recent years. The original view was that different m6A sites within an mRNA could have different functions depending on which YTHDF paralog was bound to it, with bound YTHDF1 inducing translation, while bound YTHDF2 induced mRNA degradation. As a result, each YTHDF was proposed to have unique physiologic roles that arise from their unique binding properties and regulatory effects on mRNA. More recent data have called much of this into question, showing that all m6A sites bind all YTHDF proteins with equal ability, with a single primary function of all three YTHDF proteins to mediate mRNA degradation. Here, we describe the diverse technical concerns that led to the original model being questioned and the newer data that overturned this model and led to the new understanding of m6A and YTHDF function. We also discuss how any remaining questions about the functions of the YTHDF proteins can be readily resolved.

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

Although DNA methylation data yields highly accurate age predictors, little is known about the dynamics of this quintessential epigenomic biomarker during lifespan. To narrow the gap, we investigate the methylation trajectories of male mouse colon at five different time points of aging. Our study indicates the existence of sudden hypermethylation events at specific stages of life. Precisely, we identify two epigenomic switches during early-to-midlife (3-9 months) and mid-to-late-life (15-24 months) transitions, separating the rodents' life into three stages. These nonlinear methylation dynamics predominantly affect genes associated with the nervous system and enrich in bivalently marked chromatin regions. Based on groups of nonlinearly modified loci, we construct a clock-like classifier STageR (STage of aging estimatoR) that accurately predicts murine epigenetic stage. We demonstrate the universality of our clock in an independent mouse cohort and with publicly available datasets.

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

Congenital heart disease (CHD) is the most common birth defect affecting>1.35 million newborn babies worldwide. CHD can lead to prenatal, neonatal, postnatal lethality or life-long cardiac complications. RNA binding protein (RBP) mutations or variants are emerging as contributors to CHDs. RBPs are wizards of gene regulation and are major contributors to mRNA and protein landscape. However, not much is known about RBPs in the developing heart and their contributions to CHD. In this chapter, we will discuss our current knowledge about specific RBPs implicated in CHDs. We are in an exciting era to study RBPs using the currently available and highly successful RNA-based therapies and methodologies. Understanding how RBPs shape the developing heart will unveil their contributions to CHD. Identifying their target RNAs in the embryonic heart will ultimately lead to RNA-based treatments for congenital heart disease.

UV-crosslinking of protein and RNA in direct contacts has been widely used to study protein-RNA complexes while our understanding of the photo-crosslinking mechanisms remains poor. This knowledge gap is due to the challenge of precisely mapping the crosslink sites in protein and RNA simultaneously in their native sequence and structural contexts. Here we systematically analyze protein-RNA interactions and photo-crosslinking by bridging crosslinked nucleotides and amino acids mapped using different assays with protein-RNA complex structures. We developed a computational method PxR3D-map which reliably predicts crosslink sites using structural information characterizing protein-RNA interaction interfaces. Analysis of the informative features revealed that photo-crosslinking is facilitated by base stacking with not only aromatic residues, but also dipeptide bonds that involve glycine, and distinct mechanisms are utilized by different RNA-binding domains. Our work suggests protein-RNA photo-crosslinking is highly selective in the cellular environment, which can guide data interpretation and further technology development for UV-crosslinking-based assays.

NOVA1 is a neuronal RNA-binding protein identified as the target antigen of a rare autoimmune disorder associated with cancer and neurological symptoms, termed paraneoplastic opsoclonus-myoclonus ataxia. Despite the strong association between NOVA1 and cancer, it has been unclear how NOVA1 function might contribute to cancer biology. In this study, we find that NOVA1 acts as an oncogenic factor in a GBM (glioblastoma multiforme) cell line established from a patient. Interestingly, NOVA1 and Argonaute (AGO) CLIP identified common 3' untranslated region (UTR) targets, which were down-regulated in NOVA1 knockdown GBM cells, indicating a transcriptome-wide intersection of NOVA1 and AGO-microRNA (miRNA) targets regulation. NOVA1 binding to 3'UTR targets stabilized transcripts including those encoding cholesterol homeostasis related proteins. Selective inhibition of NOVA1-RNA interactions with antisense oligonucleotides disrupted GBM cancer cell fitness. The precision of our GBM CLIP studies point to both mechanism and precise RNA sequence sites to selectively inhibit oncogenic NOVA1-RNA interactions. Taken together, we find that NOVA1 is commonly overexpressed in GBM, where it can antagonize AGO2-miRNA actions and consequently up-regulates cholesterol synthesis, promoting cell viability.

Different regions of RNA molecules can often engage in specific interactions with distinct RNA-binding proteins (RBPs), giving rise to diverse modalities of RNA regulation and function. However, there are currently no methods for unbiased identification of RBPs that interact with specific RNA regions in living cells and under endogenous settings. Here we introduce TREX (targeted RNase H-mediated extraction of crosslinked RBPs)-a highly sensitive approach for identifying proteins that directly bind to specific RNA regions in living cells. We demonstrate that TREX outperforms existing methods in identifying known interactors of U1 snRNA, and reveals endogenous region-specific interactors of NORAD long noncoding RNA. Using TREX, we generated a comprehensive region-by-region interactome for 45S rRNA, uncovering both established and previously unknown interactions that regulate ribosome biogenesis. With its applicability to different cell types, TREX is an RNA-centric tool for unbiased positional mapping of endogenous RNA-protein interactions in living cells.

At its most fundamental level, life is a collection of synchronized cellular processes driven by interactions among biomolecules. Proximity labeling has emerged as a powerful technique to capture these interactions in native settings, revealing previously unexplored elements of biology. This review highlights recent developments in proximity labeling, focusing on methods that push the fundamental technologies beyond the classic bait-prey paradigm, such as RNA-protein interactions, ligand/small-molecule-protein interactions, cell surface protein interactions, and subcellular protein trafficking. The advancement of proximity labeling methods to address different biological problems will accelerate our understanding of the complex biological systems that make up life.

This study focuses on enhancing the prediction of regulatory functional sites in DNA and RNA sequences, a crucial aspect of gene regulation. Current methods, such as motif overrepresentation and machine learning, often lack specificity. To address this issue, the study leverages evolutionary information and introduces Graphylo, a deep-learning approach for predicting transcription factor binding sites in the human genome. Graphylo combines Convolutional Neural Networks for DNA sequences with Graph Convolutional Networks on phylogenetic trees, using information from placental mammals' genomes and evolutionary history. The research demonstrates that Graphylo consistently outperforms both single-species deep learning techniques and methods that incorporate inter-species conservation scores on a wide range of datasets. It achieves this by utilizing a species-based attention model for evolutionary insights and an integrated gradient approach for nucleotide-level model interpretability. This innovative approach offers a promising avenue for improving the accuracy of regulatory site prediction in genomics.

RNA-binding proteins (RBPs) regulate diverse cellular processes by dynamically interacting with RNA targets. However, effective methods to capture both stable and transient interactions between RBPs and their RNA targets are still lacking, especially when the interaction is dynamic or samples are limited. Here we present an assay of reverse transcription-based RBP binding site sequencing (ARTR-seq), which relies on in situ reverse transcription of RBP-bound RNAs guided by antibodies to identify RBP binding sites. ARTR-seq avoids ultraviolet crosslinking and immunoprecipitation, allowing for efficient and specific identification of RBP binding sites from as few as 20 cells or a tissue section. Taking advantage of rapid formaldehyde fixation, ARTR-seq enables capturing the dynamic RNA binding by RBPs over a short period of time, as demonstrated by the profiling of dynamic RNA binding of G3BP1 during stress granule assembly on a timescale as short as 10 minutes.

The key role of RNA-binding proteins (RBPs) in posttranscriptional regulation of gene expression is intimately tied to their subcellular localization. Here, we show a subcellular-specific RNA labeling method for efficient enrichment and deep profiling of nuclear and cytoplasmic RBPs. A total of 1221 nuclear RBPs and 1333 cytoplasmic RBPs were enriched and identified using nuclear/cytoplasm targeting enrichment probes, representing an increase of 54.4% and 85.7% compared with previous reports. The probes were further applied in the omics-level investigation of subcellular-specific RBP-RNA interactions upon ferroptosis induction. Interestingly, large-scale RBPs display enhanced interaction with RNAs in nucleus but reduced association with RNAs in cytoplasm during ferroptosis process. Furthermore, we discovered dozens of nucleoplasmic translocation candidate RBPs upon ferroptosis induction and validated representative ones by immunofluorescence imaging. The enrichment of Tricarboxylic acid cycle in the translocation candidate RBPs may provide insights for investigating their possible roles in ferroptosis induced metabolism dysregulation.