Protein-RNA interactions: new genomic technologies and perspectives

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

Structure-based insights into fluorogenic RNA aptamers

Fluorogenic RNA aptamers are in vitro-selected RNA molecules capable of binding to specific fluorophores, significantly increasing their intrinsic fluorescence. Over the past decade, the color palette of fluorescent RNA aptamers has greatly expanded. The emergence and development of these fluorogenic RNA aptamers has introduced a powerful approach for visualizing RNA localization and transport with high spatiotemporal resolution in live cells. To date, a variety of tertiary structures of fluorogenic RNA aptamers have been determined using X-ray crystallography or NMR spectroscopy. Many of these fluorogenic RNA aptamers feature base quadruples or base triples in their fluorophore-binding sites. This review summarizes the structure-based investigations of fluorogenic RNA aptamers, with a focus on their overall folds, ligand-binding pockets and fluorescence activation mechanisms. Additionally, the exploration of how structures guide rational optimization to enhance RNA visualization techniques is discussed.

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA-protein interactions

Spatiotemporal-controlled second messengers alter molecular interactions of central signaling nodes for ensuring physiological signal transmission. One prototypical second messenger molecule which modulates kinase signal transmission is the cyclic-adenosine monophosphate (cAMP). The main proteinogenic cellular effectors of cAMP are compartmentalized protein kinase A (PKA) complexes. Their cell-type specific compositions precisely coordinate substrate phosphorylation and proper signal propagation which is indispensable for numerous cell-type specific functions. Here we present evidence that TAF15, which is implicated in the etiology of amyotrophic lateral sclerosis, represents a novel nuclear PKA substrate. In cross-linking and immunoprecipitation experiments (iCLIP) we showed that TAF15 phosphorylation alters the binding to target transcripts related to mRNA maturation, splicing and protein-binding related functions. TAF15 appears to be one of multiple PKA substrates that undergo RNA-binding dynamics upon phosphorylation. We observed that the activation of the cAMP-PKA signaling axis caused a change in the composition of a collection of RNA species that interact with TAF15. This observation appears to be a broader principle in the regulation of molecular interactions, as we identified a significant enrichment of RNA-binding proteins within endogenous PKA complexes. We assume that phosphorylation of RNA-binding domains adds another layer of regulation to binary protein-RNAs interactions with consequences to RNA features including binding specificities, localization, abundance and composition.

Mapping protein-RNA binding in plants with individual-nucleotide-resolution UV cross-linking and immunoprecipitation (plant iCLIP2)

Despite crucial roles of RNA-binding proteins (RBPs) in plant physiology and development, methods for determining their transcriptome-wide binding landscape are less developed than those used in other model organisms. Cross-linking and immunoprecipitation (CLIP) methods (based on UV-mediated generation of covalent bonds between RNAs and cognate RBPs in vivo, purification of the cross-linked complexes and identification of the co-purified RNAs by high-throughput sequencing) have been applied mainly in mammalian cells growing in monolayers or in translucent tissue. We have developed plant iCLIP2, an efficient protocol for performing individual-nucleotide-resolution CLIP (iCLIP) in plants, tailored to overcome the experimental hurdles posed by plant tissue. We optimized the UV dosage to efficiently cross-link RNA and proteins in plants and expressed epitope-tagged RBPs under the control of their native promoters in loss-of-function mutants. We select epitopes for which nanobodies are available, allowing stringent conditions for immunopurification of the RNA-protein complexes to be established. To overcome the inherently high RNase content of plant cells, RNase inhibitors are added and the limited RNA fragmentation step is modified. We combine the optimized isolation of RBP-bound RNAs with iCLIP2, a streamlined protocol that greatly enhances the efficiency of library preparation for high-throughput sequencing. Plant researchers with experience in molecular biology and handling of RNA can complete this iCLIP2 protocol in ~5 d. Finally, we describe a bioinformatics workflow to determine targets of Arabidopsis RBPs from iCLIP data, covering all steps from downloading sequencing reads to identifying cross-linking events ( https://github.com/malewins/Plant-iCLIPseq ), and present the R/Bioconductor package BindingSiteFinder to extract reproducible binding sites ( https://bioconductor.org/packages/release/bioc/html/BindingSiteFinder.html ).

Protocol for detecting RBM33-binding sites in HEK293T cells using PAR-CLIP-seq

RNA-binding proteins (RBPs) regulate gene expression both co-transcriptionally and post-transcriptionally. Here, we provide a protocol for photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation followed by next-generation sequencing (PAR-CLIP-seq). PAR-CLIP-seq is a transcriptome-scale technique for identifying in vivo binding sites of RBPs at the single-nucleotide level. We detail procedures for the establishment of FLAG-RBM33 stable cell line, the sequencing library preparation, and the data analysis.

Advancing Strongyloides omics data: bridging the gap with Caenorhabditis elegans

Among nematodes, the free-living model organism Caenorhabditis elegans boasts the most advanced portfolio of high-quality omics data. The resources available for parasitic nematodes, including Strongyloides spp., however, are lagging behind. While C. elegans remains the most tractable nematode and has significantly advanced our understanding of many facets of nematode biology, C. elegans is not suitable as a surrogate system for the study of parasitism and it is important that we improve the omics resources available for parasitic nematode species. Here, we review the omics data available for Strongyloides spp. and compare the available resources to those for C. elegans and other parasitic nematodes. The advancements in C. elegans omics offer a blueprint for improving omics-led research in Strongyloides. We suggest areas of priority for future research that will pave the way for expansions in omics resources and technologies. This article is part of the Theo Murphy meeting issue 'Strongyloides: omics to worm-free populations'.

Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq

Enhanced crosslinking and immunoprecipitation (eCLIP) sequencing is a method for transcriptome-wide detection of binding sites of RNA-binding proteins (RBPs). However, identified crosslink sites can deviate from experimentally established functional elements of even well-studied RBPs. Current peak-calling strategies result in low replication and high false positive rates. Here, we present the R/Bioconductor package DEWSeq that makes use of replicate information and size-matched input controls. We benchmarked DEWSeq on 107 RBPs for which both eCLIP data and RNA sequence motifs are available and were able to more than double the number of motif-containing binding regions relative to standard eCLIP processing. The improvement not only relates to the number of binding sites (3.1-fold with known motifs for RBFOX2), but also their subcellular localization (1.9-fold of mitochondrial genes for FASTKD2) and structural targets (2.2-fold increase of stem-loop regions for SLBP. On several orthogonal CLIP-seq datasets, DEWSeq recovers a larger number of motif-containing binding sites (3.3-fold). DEWSeq is a well-documented R/Bioconductor package, scalable to adequate numbers of replicates, and tends to substantially increase the proportion and total number of RBP binding sites containing biologically relevant features.

miR-378a-5p exerts tumor-suppressive effects on esophageal squamous cell carcinoma after neoadjuvant immunotherapy by downregulating APOC1/CEP55

Genetic assessment of tumors following neoadjuvant immunotherapy helps identifying targets that mediate anti-tumor immunity. In this study, we explored dysregulated RNAs in esophageal squamous cell carcinoma samples after neoadjuvant immunotherapy using deep sequencing and high-throughput screening. We identified 584 differentially expressed messenger RNAs (mRNAs), 67 differentially expressed microRNAs (miRNAs), and 1,047 differentially expressed long non-coding RNAs (lncRNAs) using differential expression analysis. Competing endogenous RNAs closely related to esophageal squamous cell carcinoma were selected via a combined Pearson's correlation test and weighted correlation network analysis. After validation using survival analysis and dry-lab and wet-lab-based studies, we identified the I-miR-378-5p-APOC1/CEP55 as a critical pathway for esophageal squamous cell carcinoma progression after neoadjuvant immunotherapy. Tumor immune infiltration analysis showed that APOC1 and CEP55 expression is associated with immune regulatory pathways and the function of multiple infiltrating immune cells. We investigated the mechanism of esophageal squamous carcinoma progression after neoadjuvant immunotherapy from the perspective of the mRNA-miRNA-lncRNA network. Furthermore, we identified accurate novel therapeutic targets and prognostic biomarkers, introduced novel perspectives to immunotherapy studies, and laid the foundation for the clinical treatment of patients with esophageal squamous carcinoma.

Technologies to Study Genetics and Molecular Pathways

Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.

Metabolic Regulation of Single Synaptic Vesicle Exo- and Endocytosis in Hippocampal Synapses

Glucose has long been considered a primary source of energy for synaptic function. However, it remains unclear under what conditions alternative fuels, such as lactate/pyruvate, contribute to powering synaptic transmission. By detecting individual release events in cultured hippocampal synapses, we found that mitochondrial ATP production from oxidation of lactate/pyruvate regulates basal vesicle release probability and release location within the active zone (AZ) evoked by single action potentials (APs). Mitochondrial inhibition shifted vesicle release closer to the AZ center, suggesting that the energetic barrier for vesicle release is lower in the AZ center that the periphery. Mitochondrial inhibition also altered the efficiency of single AP evoked vesicle retrieval by increasing occurrence of ultrafast endocytosis, while inhibition of glycolysis had no effect. Mitochondria are sparsely distributed along hippocampal axons and we found that nerve terminals containing mitochondria displayed enhanced vesicle release and reuptake during high-frequency trains, irrespective of whether neurons were supplied with glucose or lactate. Thus, synaptic terminals can entirely bypass glycolysis to robustly maintain the vesicle cycle using oxidative fuels in the absence of glucose. These observations further suggest that mitochondrial metabolic function not only regulates several fundamental features of synaptic transmission but may also contribute to modulation of short-term synaptic plasticity.

High SNRPA1 expression leads to poor prognosis in patients with lung adenocarcinoma

OBJECTIVE

SNRPA1, a subunit of spliceosome complex, has been implicated in diverse cancers, while its biological effect in LUAD remains elusive. Therefore, we sought to decipher the relationship between SNRPA1 expression and the prognosis of patients with LUAD and reveal the underlying molecular mechanism.

MATERIALS AND METHODS

Based on the clinical data from TCGA databases, the multivariate Cox model was constructed to screen the prognostic value of SNRPA1. qRT-PCR and immunohistochemical staining were used to examine SNRPA1 mRNA and protein expression in LUAD. The effect of SNRPA1 on LUAD cell proliferation, migration, and epithelial mesenchymal transformation were examined using colony formation assays, wound healing, and western blot assays, respectively. Finally, the influence of SNRPA1 on LUAD immune microenvironment were validated from the Tumor Immune Estimation Resource database.

RESULTS

SNRPA1 was significantly upregulated in both LUAD tissues and cell lines, and highly expressed SNRPA1 contributed to poor prognosis of LUAD patients. In vitro, SNRPA1 knockdown inhibited the proliferation and migration, as well as delayed the EMT differentiation of LUAD cells. Lastly, SNRPA1 was found to be positively associated with immune infiltration and some immune-check-point markers.

CONCLUSIONS

Our findings indicate that SNRPA1 may be a new biomarker for prognostic prediction and a potential therapeutic target in the treatment of LUAD.

New insights into the centrosome-associated spliceosome components as regulators of ciliogenesis and tissue identity

Biomolecular condensates are membrane-less assemblies of proteins and nucleic acids. Centrosomes are biomolecular condensates that play a crucial role in nuclear division, cytoskeletal remodeling, and cilia formation in animal cells. Spatial omics technology is providing new insights into the dynamic exchange of spliceosome components between the nucleus and the centrosome/cilium. Intriguingly, centrosomes are emerging as cytoplasmic sites for information storage, enriched with RNA molecules and RNA-processing proteins. Furthermore, growing evidence supports the view that nuclear spliceosome components assembled at the centrosome function as potential coordinators of splicing subprograms, pluripotency, and cell differentiation. In this article, we first discuss the current understanding of the centrosome/cilium complex, which controls both stem cell differentiation and pluripotency. We next explore the molecular mechanisms that govern splicing factor assembly and disassembly around the centrosome and examine how RNA processing pathways contribute to ciliogenesis. Finally, we discuss numerous unresolved compelling questions regarding the centrosome-associated spliceosome components and transcript variants within the cytoplasm as sources of RNA-based secondary messages in the regulation of cell identity and cell fate determination. This article is categorized under: RNA-Based Catalysis > RNA Catalysis in Splicing and Translation RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Processing > Splicing Regulation/Alternative Splicing RNA Processing > RNA Processing.

Mitochondrial Transport in Symmetric and Asymmetric Axons with Multiple Branching Junctions: A Computational Study

We explore the impact of multiple branching junctions in axons on the mean age of mitochondria and their age density distributions in demand sites. The study looked at mitochondrial concentration, mean age, and age density distribution in relation to the distance from the soma. We developed models for a symmetric axon containing 14 demand sites and an asymmetric axon containing 10 demand sites. We examined how the concentration of mitochondria changes when an axon splits into two branches at the branching junction. We also studied whether mitochondria concentrations in the branches are affected by what proportion of mitochondrial flux enters the upper branch and what proportion of flux enters the lower branch. Additionally, we explored whether the distributions of mitochondria mean age and age density in branching axons are affected by how the mitochondrial flux splits at the branching junction. When the mitochondrial flux is split unevenly at the branching junction of an asymmetric axon, with a greater proportion of the flux entering the longer branch, the average age of mitochondria (system age) in the axon increases. Our findings elucidate the effects of axonal branching on mitochondria age. Mitochondria aging is the focus of this study as recent research suggests it may be involved in neurodegenerative disorders, such as Parkinson's disease.

Mutant Prion Protein Endoggresomes are Hubs for Local Axonal Organelle-Cytoskeletal Remodeling

Dystrophic axons comprising misfolded mutant prion protein (PrP) aggregates are a characteristic pathological feature in the prionopathies. These aggregates form inside endolysosomes -called endoggresomes-, within swellings that line up the length of axons of degenerating neurons. The pathways impaired by endoggresomes that result in failed axonal and consequently neuronal health, remain undefined. Here, we dissect the local subcellular impairments that occur within individual mutant PrP endoggresome swelling sites in axons. Quantitative high-resolution light and electron microscopy revealed the selective impairment of the acetylated vs tyrosinated microtubule cytoskeleton, while micro-domain image analysis of live organelle dynamics within swelling sites revealed deficits uniquely to the MT-based active transport system that translocates mitochondria and endosomes toward the synapse. Cytoskeletal and defective transport results in the retention of mitochondria, endosomes, and molecular motors at swelling sites, enhancing mitochondria-Rab7 late endosome contacts that induce mitochondrial fission via the activity of Rab7, and render mitochondria dysfunctional. Our findings point to mutant Pr Pendoggresome swelling sites as selective hubs of cytoskeletal deficits and organelle retention that drive the remodeling of organelles along axons. We propose that the dysfunction imparted locally within these axonal micro-domains spreads throughout the axon over time, leading to axonal dysfunction in prionopathies.

Screening for Primordial RNA–Peptide Interactions Using High-Density Peptide Arrays

RNA–peptide interactions are an important factor in the origin of the modern mechanism of translation and the genetic code. Despite great progress in the bioinformatics of RNA–peptide interactions due to the rapid growth in the number of known RNA–protein complexes, there is no comprehensive experimental method to take into account the influence of individual amino acids on non-covalent RNA–peptide bonds. First, we designed the combinatorial libraries of primordial peptides according to the combinatorial fusion rules based on Watson–Crick mutations. Next, we used high-density peptide arrays to investigate the interaction of primordial peptides with their cognate homo-oligonucleotides. We calculated the interaction scores of individual peptide fragments and evaluated the influence of the peptide length and its composition on the strength of RNA binding. The analysis shows that the amino acids phenylalanine, tyrosine, and proline contribute significantly to the strong binding between peptides and homo-oligonucleotides, while the sum charge of the peptide does not have a significant effect. We discuss the physicochemical implications of the combinatorial fusion cascade, a hypothesis that follows from the amino acid partition used in the work.

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer

Somatic mutations are a major source of cancer development, and many driver mutations have been identified in protein coding regions. However, the function of mutations located in miRNA and their target binding sites throughout the human genome remains largely unknown. Here, we built detailed cancer-specific miRNA regulatory networks across 30 cancer types to systematically analyze the effect of mutations in miRNAs and their target sites in 3' untranslated region (3' UTR), coding sequence (CDS), and 5' UTR regions. A total of 3,518,261 mutations from 9,819 samples were mapped to miRNA-gene interactions (mGI). Mutations in miRNAs showed a mutually exclusive pattern with mutations in their target genes in almost all cancer types. A linear regression method identified 148 candidate driver mutations that can significantly perturb miRNA regulatory networks. Driver mutations in 3'UTRs played their roles by altering RNA binding energy and the expression of target genes. Finally, mutated driver gene targets in 3' UTRs were significantly downregulated in cancer and functioned as tumor suppressors during cancer progression, suggesting potential miRNA candidates with significant clinical implications. A user-friendly, open-access web portal (mGI-map) was developed to facilitate further use of this data resource. Together, these results will facilitate novel noncoding biomarker identification and therapeutic drug design targeting the miRNA regulatory networks.

SIGNIFICANCE

A detailed miRNA-gene interaction map reveals extensive miRNA-mediated gene regulatory networks with mutation-induced perturbations across multiple cancers, serving as a resource for noncoding biomarker discovery and drug development.

PAR-CLIP Assay in Ferroptosis

Ferroptosis is a regulatory cell death process that is accompanied by large amounts of iron ion accumulation and lipid peroxidation. Photoactivated ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) is a method used to identify the binding sites of RNA-binding proteins (RBPs) on target RNAs with high resolution at the nucleotide level. By inserting photosensitive ribonucleoside analogs into new RNA transcripts of living cells, characteristic mutations can be generated during reverse transcription and be used to accurately locate the crosslinking position of RNAs and RBPs. The use of PAR-CLIP to detect interactions and determine precise crosslinking sites between RNAs and RBPs, or to search for RNAs upstream or downstream of ferroptosis pathways genes through known proteins, can help to clarify and verify the occurrence and regulation mechanisms of the various signaling pathways of ferroptosis. Furthermore, it may reveal new targets for ferroptosis detection and improve the treatment efficiency of ferroptosis-related diseases such as cancer and neurodegenerative diseases. Here, we introduce a specific PAR-CLIP protocol for monitoring the ferroptosis process.

CRIT: Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization

Circular RNAs (circRNAs) are endogenous non-coding RNAs that regulate gene expression and participate in carcinogenesis. However, the RNA-binding proteins (RBPs) involved in circRNAs biogenesis and modulation remain largely unclear. We developed the circRNA regulator identification tool (CRIT), a non-negative matrix-factorization-based pipeline to identify regulating RBPs in cancers. CRIT uncovered 73 novel regulators across thousands of samples by effectively leveraging genomics data and functional annotations. We demonstrated that known RBPs involved in circRNA control are significantly enriched in these predictions. Analysis of circRNA-RBP interactions using two large cross-linking immunoprecipitation (CLIP) databases, we validated the consistency between CRIT prediction and the CLIP experiments. Furthermore, newly discovered RBPs are functionally connected with authentic circRNA regulators by various biological associations, such as physical interaction, similar binding motifs, common transcription factor modulation, and co-expression. When analyzing RNA sequencing (RNA-seq) datasets after short hairpin RNA (shRNA)/small interfering RNA (siRNA) knockdown, we found several novel RBPs that can affect global circRNA expression, which strengthens their role in the circRNA life cycle. The above evidence provided independent confirmation that CRIT is a useful tool to capture RBPs in circRNA processing. Finally, we show that authentic regulators are more likely the core splicing proteins and peripheral factors and usually harbor more alterations in the vast majority of cancers.

Extracellular Vesicle (EVs) Associated Non-Coding RNAs in Lung Cancer and Therapeutics

Lung cancer is one of the most lethal forms of cancer, with a very high mortality rate. The precise pathophysiology of lung cancer is not well understood, and pertinent information regarding the initiation and progression of lung cancer is currently a crucial area of scientific investigation. Enhanced knowledge about the disease will lead to the development of potent therapeutic interventions. Extracellular vesicles (EVs) are membrane-bound heterogeneous populations of cellular entities that are abundantly produced by all cells in the human body, including the tumor cells. A defined class of EVs called small Extracellular Vesicles (sEVs or exosomes) carries key biomolecules such as RNA, DNA, Proteins and Lipids. Exosomes, therefore, mediate physiological activities and intracellular communication between various cells, including constituent cells of the tumor microenvironment, namely stromal cells, immunological cells, and tumor cells. In recent years, a surge in studying tumor-associated non-coding RNAs (ncRNAs) has been observed. Subsequently, studies have also reported that exosomes abundantly carry different species of ncRNAs and these exosomal ncRNAs are functionally involved in cancer initiation and progression. Here, we discuss the function of exosomal ncRNAs, such as miRNAs and long non-coding RNAs, in the pathophysiology of lung tumors. Further, the future application of exosomal-ncRNAs in clinics as biomarkers and therapeutic targets in lung cancer is also discussed due to the multifaceted influence of exosomes on cellular physiology.

Mitochondrial Dysfunction and Pharmacodynamics of Mitofusin Activation in Murine Charcot-Marie-Tooth Disease Type 2A

Mitofusin (MFN) 1 and MFN2 are dynamin GTPase family mitochondrial proteins that mediate mitochondrial fusion requiring MFN conformational shifts, formation of macromolecular complexes on and between mitochondria, and GTP hydrolysis. Damaging MFN2 mutations cause an untreatable, largely pediatric progressive peripheral neuropathy, Charcot-Marie-Tooth (CMT) disease type 2A. We used small molecule allosteric mitofusin activators that promote MFN conformations favoring fusion to interrogate the effects of MFN2 conformation and GTPase activity on MFN2-mediated mitochondrial fusion and motility in vitro. We translated these findings in vivo by defining dose-dependent pharmacodynamic and disease-modifying effects of mitofusin activators in murine CMT2A. MFN2 catalytic GTPase activity and MFN2 conformational switching are essential for mitochondrial fusion, but the two processes are separate and dissociable. We report the first concentration-response relationships for mitofusin activators to stimulate mitochondrial transport through CMT2A neuronal axons, which is similar to their stimulation of mitochondrial fusion. In CMT2A mice, intermittent (daily short acting) and sustained (twice daily long acting) mitofusin activation were equally effective in reversing neuromuscular degeneration. Moreover, acute dose-dependent pharmacodynamic effects of mitofusin activators on mitochondrial transport through CMT2A neuronal axons anticipated those for long-term reversal of neurodegenerative phenotypes. A crossover study showed that CMT2A neuronal deficits recurred after mitofusin activators are discontinued, and revealed that CMT2A can be ameliorated by mitofusin activation even in old (>74 week) mice. These data add to our understanding of mitochondrial dysfunction induced by a CMT2A MFN2 GTPase mutation and provide additional information supporting the approach of pharmacological mitofusin activation in CMT2A. SIGNIFICANCE: This study interrogated the roles of MFN2 catalytic activity and allosteric activation on impaired mitochondrial fusion and neuronal transport as they impact an untreatable peripheral neuropathy caused by MFN2 mutations, Charcot-Marie-Tooth disease type 2A. The results mechanistically link mitochondrial fusion and motility to the relaxed MFN2 protein conformation and correction of mitochondrial abnormalities to in vivo reversal of neurodegeneration in murine CMT2A.

Western blotting: a powerful staple in scientific and biomedical research

Western blotting (WB), also known as immunoblotting, is a well-known molecular biology method that biologists often use to investigate many features of the protein, ranging from basic protein analysis to disease detection. WB is simple, unique, rapid, widely used routine tool with easy interpretation and definite results. It is being used in various fields of science, research and development, diagnostic labs and hospitals. The principle of WB is to accomplish the separation of proteins based on molecular weight and charge. This review addresses in detail the individual steps involved in the WB technique, its troubleshooting, internal loading controls, total protein staining and its diverse applications in scientific research and clinical settings, along with its future perspectives.

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

Motivation

The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA–protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods.

Results

In this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA–RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA–RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results.

Availability and implementation

The PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGM

Supplementary information

Supplementary data are available at Bioinformatics online.

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

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

Protein-RNA interaction prediction with deep learning: structure matters

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

Identification of RNA-RBP Interactions in Subcellular Compartments by CLIP-Seq

Cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) allows the identification of RNA targets bound by a specific RNA-binding protein (RBP) in in vivo and ex vivo experimental models with high specificity. Due to the little RNA yield obtained after cross-linking, immunoprecipitation, polyacrylamide gel electrophoresis, membrane transfer, and RNA extraction, CLIP-seq is usually performed from relatively large amounts of starting material, like cell lysates or tissue homogenates. However, RBP binding of its specific RNA targets depends on its subcellular localization, and a different set of RNAs may be bound by the same RBP within distinct subcellular sites. To uncover these RNA subsets, preparation of CLIP-seq libraries from specific subcellular compartments and comparison to CLIP-seq datasets from total lysates is necessary, yet there are currently no available protocols for this. Here we describe the adaptation of CLIP-seq to identify the specific RNA targets of an RBP (FUS) at a small subcompartment, that is, neuronal synapses, including subcompartment isolation, RBP-RNA complex enrichment, and upscaling steps.

Advances in the identification of long non-coding RNA binding proteins

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without evident protein coding function. They play important regulatory roles in many biological processes, e.g., gene regulation, chromatin remodeling, and cell fate determination during development. Dysregulation of lncRNAs has been observed in various diseases including cancer. Interacting with proteins is a crucial way for lncRNAs to play their biological roles. Therefore, the characterization of lncRNA binding proteins is important to understand their functions and to delineate the underlying molecular mechanism. Large-scale studies based on mass spectrometry have characterized over a thousand new RNA binding proteins without known RNA-binding domains, thus revealing the complexity and diversity of RNA-protein interactions. In addition, several methods have been developed to identify the binding proteins for particular RNAs of interest. Here we review the progress of the RNA-centric methods for the identification of RNA-protein interactions, focusing on the studies involving lncRNAs, and discuss their strengths and limitations.

miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions

MicroRNAs (miRNAs) are noncoding RNAs with 18–26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA–target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update’s critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3′ untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.

Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization

BACKGROUND

Protein-RNA interactions play key roles in many processes regulating gene expression. To understand the underlying binding preference, ultraviolet cross-linking and immunoprecipitation (CLIP)-based methods have been used to identify the binding sites for hundreds of RNA-binding proteins (RBPs) in vivo. Using these large-scale experimental data to infer RNA binding preference and predict missing binding sites has become a great challenge. Some existing deep-learning models have demonstrated high prediction accuracy for individual RBPs. However, it remains difficult to avoid significant bias due to the experimental protocol. The DeepRiPe method was recently developed to solve this problem via introducing multi-task or multi-label learning into this field. However, this method has not reached an ideal level of prediction power due to the weak neural network architecture.

RESULTS

Compared to the DeepRiPe approach, our Multi-resBind method demonstrated substantial improvements using the same large-scale PAR-CLIP dataset with respect to an increase in the area under the receiver operating characteristic curve and average precision. We conducted extensive experiments to evaluate the impact of various types of input data on the final prediction accuracy. The same approach was used to evaluate the effect of loss functions. Finally, a modified integrated gradient was employed to generate attribution maps. The patterns disentangled from relative contributions according to context offer biological insights into the underlying mechanism of protein-RNA interactions.

CONCLUSIONS

Here, we propose Multi-resBind as a new multi-label deep-learning approach to infer protein-RNA binding preferences and predict novel interactions. The results clearly demonstrate that Multi-resBind is a promising tool to predict unknown binding sites in vivo and gain biology insights into why the neural network makes a given prediction.

Intrinsic Disorder in Human RNA-Binding Proteins

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

Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning

N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m6A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m6A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m6A detection. The optimized miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m6A detection. The analyses were calibrated with Mettl3 knockout cells to learn the characteristics of m6A deposition, including m6A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m6A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m6A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m6A identification.

Mitochondria in Neuronal Health: From Energy Metabolism to Parkinson's Disease

Mitochondria are the main suppliers of neuronal adenosine triphosphate and play a critical role in brain energy metabolism. Mitochondria also serve as Ca²⁺ sinks and anabolic factories and are therefore essential for neuronal function and survival. Dysregulation of neuronal bioenergetics is increasingly implicated in neurodegenerative disorders, particularly Parkinson's disease. This review describes the role of mitochondria in energy metabolism under resting conditions and during synaptic transmission, and presents evidence for the contribution of neuronal mitochondrial dysfunction to Parkinson's disease.

The phase separation-dependent FUS interactome reveals nuclear and cytoplasmic function of liquid-liquid phase separation

Liquid–liquid phase separation (LLPS) of proteins and RNAs has emerged as the driving force underlying the formation of membrane-less organelles. Such biomolecular condensates have various biological functions and have been linked to disease. The protein Fused in Sarcoma (FUS) undergoes LLPS and mutations in FUS have been causally linked to the motor neuron disease Amyotrophic Lateral Sclerosis (ALS-FUS). LLPS followed by aggregation of cytoplasmic FUS has been proposed to be a crucial disease mechanism. However, it is currently unclear how LLPS impacts the behaviour of FUS in cells, e.g. its interactome. Hence, we developed a method allowing for the purification of LLPS FUS-containing droplets from cell lysates. We observe substantial alterations in the interactome, depending on its biophysical state. While non-LLPS FUS interacts mainly with factors involved in pre-mRNA processing, LLPS FUS predominantly binds to proteins involved in chromatin remodelling and DNA damage repair. Interestingly, also mitochondrial factors are strongly enriched with LLPS FUS, providing a potential explanation for the observed changes in mitochondrial gene expression in mouse models of ALS-FUS. In summary, we present a methodology to investigate the interactomes of phase separating proteins and provide evidence that LLPS shapes the FUS interactome with implications for function and disease.

High-throughput splicing assays identify missense and silent splice-disruptive POU1F1 variants underlying pituitary hormone deficiency

Pituitary hormone deficiency occurs in ∼1:4,000 live births. Approximately 3% of the cases are due to mutations in the alpha isoform of POU1F1, a pituitary-specific transcriptional activator. We found four separate heterozygous missense variants in unrelated individuals with hypopituitarism that were predicted to affect a minor isoform, POU1F1 beta, which can act as a transcriptional repressor. These variants retain repressor activity, but they shift splicing to favor the expression of the beta isoform, resulting in dominant-negative loss of function. Using a high-throughput splicing reporter assay, we tested 1,070 single-nucleotide variants in POU1F1. We identified 96 splice-disruptive variants, including 14 synonymous variants. In separate cohorts, we found two additional synonymous variants nominated by this screen that co-segregate with hypopituitarism. This study underlines the importance of evaluating the impact of variants on splicing and provides a catalog for interpretation of variants of unknown significance in POU1F1.

NANOS2 is a sequence-specific mRNA-binding protein that promotes transcript degradation in spermatogonial stem cells

Spermatogonial stem cells (SSCs) sustain spermatogenesis and fertility throughout adult male life. The conserved RNA-binding protein NANOS2 is essential for the maintenance of SSCs, but its targets and mechanisms of function are not fully understood. Here, we generated a fully functional epitope-tagged Nanos2 mouse allele and applied the highly stringent cross-linking and analysis of cDNAs to define NANOS2 RNA occupancy in SSC lines. NANOS2 recognizes the AUKAAWU consensus motif, mostly found in the 3' untranslated region of defined messenger RNAs (mRNAs). We find that NANOS2 is a regulator of key signaling and metabolic pathways whose dosage or activity are known to be critical for SSC maintenance. NANOS2 interacts with components of CCR4-NOT deadenylase complex in SSC lines, and consequently, NANOS2 binding reduces the half-lives of target transcripts. In summary, NANOS2 contributes to SSC maintenance through the regulation of target mRNA stability and key self-renewal pathways.

Crosstalk between RNA-Binding Proteins and Immune Microenvironment Revealed Two RBP Regulatory Patterns with Distinct Immunophenotypes in Periodontitis

Periodontitis is an inflammatory disease whose pathogenesis is closely related with immunology. RNA-binding proteins (RBPs) were found to play crucial roles in immunity. Therefore, we aimed to investigate the potential impact of RBPs in the immune microenvironment in periodontitis. The differential expressions of RBPs in periodontitis and healthy samples were determined and were used to construct an RBP-based classifier for periodontitis using logistic regression. The correlations between RBPs and immune characteristics were investigated by the Spearman correlation. Unsupervised clustering was conducted to identify the RBP regulatory patterns. RBP-related genes were identified by WGCNA, while biological distinctions were revealed by GSVA and GO. 24 dysregulated RBPs were identified, from which a 12-RBP classifier was established to distinguish periodontitis with AUC of 0.942. Close protein-protein interactions and expression correlations were observed especially between SPATS2 and ISG20. ISG20 and ESRP1 were found to be highly correlated with immunocyte infiltration, immune signaling activation, and HLA expressions in periodontitis. Two distinct RBP regulatory patterns were identified with different immune and other biological characteristics in periodontitis. Our findings indicate a significant impact of RBPs in shaping the immune microenvironment in periodontitis, which might bring new insights into the understanding of immune mechanisms in the pathogenesis of periodontitis.

Immune and Metabolic Dysregulated Coding and Non-coding RNAs Reveal Survival Association in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies, but only a few biomarkers have been proven to be effective in clinical practice. Previous studies have demonstrated the important roles of non-coding RNAs (ncRNAs) in diagnosis, prognosis, and therapy selection in UCEC and suggested the significance of integrating molecules at different levels for interpreting the underlying molecular mechanism. In this study, we collected transcriptome data, including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs), of 570 samples, which were comprised of 537 UCEC samples and 33 normal samples. First, differentially expressed lncRNAs, miRNAs, and mRNAs, which distinguished invasive carcinoma samples from normal samples, were identified, and further analysis showed that cancer- and metabolism-related functions were enriched by these RNAs. Next, an integrated, dysregulated, and scale-free biological network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs was constructed. Protein-coding and ncRNA genes in this network showed potential immune and metabolic functions. A further analysis revealed two clinic-related modules that showed a close correlation with metabolic and immune functions. RNAs in the two modules were functionally validated to be associated with UCEC. The findings of this study demonstrate an important clinical application for improving outcome prediction for UCEC.

Computational methods for predicting hotspots at protein-RNA interfaces

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

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

Protein-nucleic acid interactions play important roles in various cellular processes. Missense mutations occurring in DNA- or RNA-binding proteins (termed MPDs and MPRs, respectively) could change the binding affinities of these interactions. Previous studies have compared protein-DNA and protein-RNA interactions from multifaceted viewpoints, but less attention has been given to the similarities and specific differences between the effects of MPDs and MPRs and between the methodologies for predicting the affinity changes induced by the two mutation classes. Therefore, we systematically compared their impacts and demonstrated that MPDs and MPRs could have specific preferences for binding affinity changes. These observations motivated us to construct regression models separately for MPDs and MPRs by introducing novel energy and nonenergy descriptors. Although similar frameworks were developed to estimate these two categories of mutation effects, different descriptors were selected in the regression models and further revealed the specificity of mutation classes. The interplay between the energy and nonenergy modules effectively improved prediction performance. Our algorithm can also be adopted to disentangle mutations significantly decreasing binding affinities from other mutations.

ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins

MOTIVATION

The interaction between proteins and nucleic acids plays a crucial role in gene regulation and cell function. Determining the binding preferences of nucleic acid-binding proteins (NBPs), namely RNA-binding proteins (RBPs) and transcription factors (TFs), is the key to decipher the protein-nucleic acids interaction code. Today, available NBP binding data from in vivo or in vitro experiments are still limited, which leaves a large portion of NBPs uncovered. Unfortunately, existing computational methods that model the NBP binding preferences are mostly protein specific: they need the experimental data for a specific protein in interest, and thus only focus on experimentally characterized NBPs. The binding preferences of experimentally unexplored NBPs remain largely unknown.

RESULTS

Here, we introduce ProbeRating, a nucleic acid recommender system that utilizes techniques from deep learning and word embeddings of natural language processing. ProbeRating is developed to predict binding profiles for unexplored or poorly studied NBPs by exploiting their homologs NBPs which currently have available binding data. Requiring only sequence information as input, ProbeRating adapts FastText from Facebook AI Research to extract biological features. It then builds a neural network-based recommender system. We evaluate the performance of ProbeRating on two different tasks: one for RBP and one for TF. As a result, ProbeRating outperforms previous methods on both tasks. The results show that ProbeRating can be a useful tool to study the binding mechanism for the many NBPs that lack direct experimental evidence. and implementation.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at <https://github.com/syang11/ProbeRating>.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional classification of plant long noncoding RNAs: a transcript is known by the company it keeps

The extraordinary maturation in high-throughput sequencing technologies has revealed the existence of a complex network of transcripts in eukaryotic organisms, including thousands of long noncoding (lnc) RNAs with little or no protein-coding capacity. Subsequent discoveries have shown that lncRNAs participate in a wide range of molecular processes, controlling gene expression and protein activity though direct interactions with proteins, DNA or other RNA molecules. Although significant advances have been achieved in the understanding of lncRNA biology in the animal kingdom, the functional characterization of plant lncRNAs is still in its infancy and remains a major challenge. In this review, we report emerging functional and mechanistic paradigms of plant lncRNAs and partner molecules, and discuss how cutting-edge technologies may help to identify and classify yet uncharacterized transcripts into functional groups.

Accumulation of Mitochondrial RPPH1 RNA Is Associated with Cellular Senescence

Post-transcriptional gene regulation is an important step in the regulation of eukaryotic gene expression. Subcellular compartmentalization of RNA species plays a crucial role in the control of mRNA turnover, spatial restriction of protein synthesis, and the formation of macromolecular complexes. Although long noncoding RNAs (lncRNAs) are one of the key regulators of post-transcriptional gene expression, it is not heavily studied whether localization of lncRNAs in subcellular organelles has functional consequences. Here, we report on mitochondrial lncRNAs whose expression fluctuates in the process of cellular senescence. One of the mitochondrial lncRNAs, RPPH1 RNA, is overexpressed and accumulates in mitochondria of senescent fibroblasts, possibly modulated by the RNA-binding protein AUF1. In addition, RPPH1 RNA overexpression promotes spontaneous replicative cellular senescence in proliferating fibroblasts. Using MS2 aptamer-based RNA affinity purification strategy, we identified putative target mRNAs of RPPH1 RNA and revealed that partial complementarity of RPPH1 RNA to its target mRNAs prevents those mRNAs decay in proliferating fibroblasts. Altogether, our results demonstrate the role of mitochondrial noncoding RNA in the regulation of mRNA stability and cellular senescence.

DeepA-RBPBS: A hybrid convolution and recurrent neural network combined with attention mechanism for predicting RBP binding site

It's important to infer the binding site of RNA-binding proteins (RBP) for understanding the interaction between RBP and its RNA targets and decipher the mechanisms of transcriptional regulation. However, experimental detection of RBP binding sites is still time-intensive and expensive. Algorithms based on machine learning can speed up detection of RBP binding sites. In this article, we propose a new deep learning method, DeepA-RBPBS, which can use RNA sequences and structural features to predict RBP binding site. DeepA-RBPBS uses CNN and BiGRU to extract sequences and structural features without long-term dependence issues. It also utilizes an attention mechanism to enhance the contribution of key features. The comparison shows that the performance of DeepA-RBPBS is better than that of the state-of-the-art predictors. In the testing on 31 datasets of CLIP-seq experiments over 19 proteins, MCC (AUC) is 8% (5%) higher than those of the latest method based on deep learning, iDeepS. We also apply DeepA-RBPBS to the target RNA data of RBPs related to diabetes (LIN28, RBFOX2, FTO, IGF2BP2, CELF1 and HuR). The results show that DeepA-RBPBS correctly predicted 41,693 samples, where iDeepS predicted 31,381 samples.Communicated by Ramaswamy H. Sarma.

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

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

Prediction of hot spots in protein-DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting

BACKGROUND

Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale.

RESULTS

Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP.

CONCLUSION

Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

Inhibition of RNA-binding proteins with small molecules

Protein-RNA interactions have crucial roles in various cellular activities, which, when dysregulated, can lead to a range of human diseases. The identification of small molecules that target the interaction between RNA-binding proteins (RBPs) and RNA is progressing rapidly and represents a novel strategy for the discovery of chemical probes that facilitate understanding of the cellular functions of RBPs and of therapeutic agents with new mechanisms of action. In this Review, I present a current overview of targeting emerging RBPs using small-molecule inhibitors and recent progress in this burgeoning field. Small-molecule inhibitors that were reported for three representative emerging classes of RBPs, the microRNA-binding protein LIN28, the single-stranded or double-stranded RNA-binding Toll-like receptors and the CRISPR-associated (Cas) proteins, are highlighted from a medicinal-chemistry and chemical-biology perspective. However, although this field is burgeoning, challenges remain in the discovery and characterization of small-molecule inhibitors of RBPs.

Interactome analysis reveals that lncRNA HULC promotes aerobic glycolysis through LDHA and PKM2

Interacting with proteins is a crucial way for long noncoding RNAs (lncRNAs) to exert their biological responses. Here we report a high throughput strategy to characterize lncRNA interacting proteins in vivo by combining tobramycin affinity purification and mass spectrometric analysis (TOBAP-MS). Using this method, we identify 140 candidate binding proteins for lncRNA highly upregulated in liver cancer (HULC). Intriguingly, HULC directly binds to two glycolytic enzymes, lactate dehydrogenase A (LDHA) and pyruvate kinase M2 (PKM2). Mechanistic study suggests that HULC functions as an adaptor molecule that enhances the binding of LDHA and PKM2 to fibroblast growth factor receptor type 1 (FGFR1), leading to elevated phosphorylation of these two enzymes and consequently promoting glycolysis. This study provides a convenient method to study lncRNA interactome in vivo and reveals a unique mechanism by which HULC promotes Warburg effect by orchestrating the enzymatic activities of glycolytic enzymes.

Here the authors present a quantitative proteomics strategy to identify long noncoding RNA (lncRNA)-binding proteins and demonstrate its application by characterizing the lncRNA HULC (highly upregulated in liver cancer), which is shown to interact with glycolytic enzymes and modulate their activity.

RIscoper: a tool for RNA-RNA interaction extraction from the literature

MOTIVATION

Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA-RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available.

RESULTS

RNA Interactome Scoper (RIscoper) represents the first tool for full-scale RNA interactome scanning and was developed for extracting RRIs from the literature based on the N-gram model. Notably, a reliable RRI corpus was integrated in RIscoper, and more than 13 300 manually curated sentences with RRI information were recruited. RIscoper allows users to upload full texts or abstracts, and provides an online search tool that is connected with PubMed (PMID and keyword input), and these capabilities are useful for biologists. RIscoper has a strong performance (90.4% precision and 93.9% recall), integrates natural language processing techniques and has a reliable RRI corpus.

AVAILABILITY AND IMPLEMENTATION

The standalone software and web server of RIscoper are freely available at www.rna-society.org/riscoper/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.