Explore the vast range of titles available.

The 18-kb Xist long noncoding RNA (lncRNA) is essential for X-chromosome inactivation during female eutherian mammalian development. Global structural architecture, cell-induced conformational changes, and protein–RNA interactions within Xist are poorly understood. We used selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) to examine these features of Xist at single-nucleotide resolution both in living cells and ex vivo. The Xist RNA forms complex well-defined secondary structure domains and the cellular environment strongly modulates the RNA structure, via motifs spanning one-half of all Xist nucleotides. The Xist RNA structure modulates protein interactions in cells via multiple mechanisms. For example, repeat-containing elements adopt accessible and dynamic structures that function as landing pads for protein cofactors. Structured RNA motifs create interaction domains for specific proteins and also sequester other motifs, such that only a subset of potential binding sites forms stable interactions. This work creates a broad quantitative framework for understanding structure–function interrelationships for Xist and other lncRNAs in cells.

Transcriptome-wide identification of RNA-binding proteins (RBPs) is a prerequisite for understanding the posttranscriptional gene regulation networks. However, proteomic profiling of RBPs has been mostly limited to polyadenylated mRNA-binding proteins, leaving RBPs on nonpoly(A) RNAs, including most noncoding RNAs (ncRNAs) and pre-mRNAs, largely undiscovered. Here we present a click chemistry-assisted RNA interactome capture (CARIC) strategy, which enables unbiased identification of RBPs, independent of the polyadenylation state of RNAs. CARIC combines metabolic labeling of RNAs with an alkynyl uridine analog and in vivo RNA-protein photocross-linking, followed by click reaction with azide-biotin, affinity enrichment, and proteomic analysis. Applying CARIC, we identified 597 RBPs in HeLa cells, including 130 previously unknown RBPs. These newly discovered RBPs can likely bind ncRNAs, thus uncovering potential involvement of ncRNAs in processes previously unknown to be ncRNA-related, such as proteasome function and intermediary metabolism. The CARIC strategy should be broadly applicable across various organisms to complete the census of RBPs.

Protein‐RNA interactions play a critical role in various biological processes and disease development. Proteins interact with RNAs through dynamic binding sites that exhibit specific structural patterns under various cellular conditions. While current computational methods take RNA structures in vivo into account, they fall short in capturing the structure contextual association of nucleotides, limiting predictive accuracy. Here, diPaRIS, a deep learning method, is proposed to predict dynamic protein‐RNA interactions with improved accuracy and enhanced interpretability by integrating RNA structures in vivo. diPaRIS introduces a novel encoding scheme for SHAPE‐seq to encode nucleotide correlations, providing a more comprehensive representation of RNA structures. Leveraging a U‐shaped network architecture, diPaRIS not only improves prediction performance but also enables interpretable analysis by learning sequence binding motifs and generating attribution maps. Benchmarking diPaRIS across 44 datasets shows its superiority over existing methods. The model consistently achieves the highest accuracy, AUC, AUPR, and F1‐score across all datasets. Additionally, diPaRIS excels in cross‐cell line predictions, consistently outperforming the second‐best method across all datasets. Predictions by diPaRIS reflect the conservation of protein‐RNA binding and facilitate further functional interpretation of genetic variants in complex diseases. The findings highlight that diPaRIS effectively predicts protein‐RNA interactions and interprets potential gene‐disease associations. This study presents diPaRIS, a deep learning method designed to predict dynamic protein‐ RNA interactions with enhanced accuracy. By integrating RNA structures in vivo through a novel encoding scheme for nucleotide correlations, diPaRIS improves the interpretability of binding motifs and outperforms current state‐of‐the‐art methods across multiple datasets. The results provide new insights into protein‐RNA binding preferences and their role in disease development.

The functional annotation of transcriptomes and identification of noncoding RNA (ncRNA) classes has been greatly facilitated by the advent of next-generation RNA sequencing which, by reading the nucleotide order of transcripts, theoretically allows the rapid profiling of all transcripts in a cell. However, primary sequence per se is a poor predictor of function, as ncRNAs dramatically vary in length and structure and often lack identifiable motifs. Therefore, to visualize an informative RNA landscape of organisms with potentially new RNA biology that are emerging from microbiome and environmental studies requires the use of more functionally relevant criteria. One such criterion is the association of RNAs with functionally important cognate RNA-binding proteins. Here we analyze the full ensemble of cellular RNAs using gradient profiling by sequencing (Grad-seq) in the bacterial pathogen Salmonella enterica, partitioning its coding and noncoding transcripts based on their network of RNA–protein interactions. In addition to capturing established RNA classes based on their biochemical profiles, the Grad-seq approach enabled the discovery of an overlooked large collective of structured small RNAs that form stable complexes with the conserved protein ProQ. We show that ProQ is an abundant RNA-binding protein with a wide range of ligands and a global influence on Salmonella gene expression. Given its generic ability to chart a functional RNA landscape irrespective of transcript length and sequence diversity, Grad-seq promises to define functional RNA classes and major RNA-binding proteins in both model species and genetically intractable organisms.

Predicting specific RNA–protein interactions remains a challenging task. Despite the existence of numerous methods, a unified approach has yet to emerge. Additional difficulties emerge from the properties of in vivo IP experiments and their systematic biases, such as the overrepresentation of highly expressed RNAs. Here, we present the PLERIO machine learning framework, which utilizes eCLIP data for a single protein to reconstruct the full spectrum of its potential interactions with the cellular transcriptome (i.e., both highly expressed and lowly expressed RNAs). In an effort to extrapolate our methodology to a multi-protein paradigm for de novo prediction of RNA–protein interactions on proteins lacking available eCLIP data, we extended our approach to 220 cellular proteins. We then demonstrate that this approach might not be well tailored to the limitations of current in vivo immunoprecipitation data, and may only be meaningful for in vitro experiments such as RNAcompete.

Background Resistance to ferroptosis, a regulated cell death caused by iron‐dependent excessive accumulation of lipid peroxides, has recently been linked to lung adenocarcinoma (LUAD). Intracellular antioxidant systems are required for protection against ferroptosis. The purpose of the present study was to investigate whether and how extracellular system desensitizes LUAD cells to ferroptosis. Methods Established human lung fibroblasts MRC‐5, WI38, and human LUAD H1650, PC9, H1975, H358, A549, and H1299 cell lines, tumor and matched normal adjacent tissues of LUAD, and plasma from healthy individuals and LUAD patients were used in this study. Immunohistochemistry and immunoblotting were used to analyze protein expression, and quantitative reverse transcription‐PCR was used to analyze mRNA expression. Cell viability, cell death, and the lipid reactive oxygen species generation were measured to evaluate the responses to ferroptosis. Exosomes were observed using transmission electron microscope. The localization of arachidonic acid (AA) was detected using click chemistry labeling followed by confocal microscopy. Interactions between RNAs and proteins were detected using RNA pull‐down, RNA immunoprecipitation and photoactivatable ribonucleoside‐enhanced crosslinking and immunoprecipitation methods. Proteomic analysis was used to investigate RNA‐regulated proteins, and metabolomic analysis was performed to analyze metabolites. Cell‐derived xenograft, patient‐derived xenograft, cell‐implanted intrapulmonary LUAD mouse models and plasma/tissue specimens from LUAD patients were used to validate the molecular mechanism. Results Plasma exosome from LUAD patients specifically reduced lipid peroxidation and desensitized LUAD cells to ferroptosis. A potential explanation is that exosomal circRNA_101093 (cir93) maintained an elevation in intracellular cir93 in LUAD to modulate AA, a poly‐unsaturated fatty acid critical for ferroptosis‐associated increased peroxidation in the plasma membrane. Mechanistically, cir93 interacted with and increased fatty acid‐binding protein 3 (FABP3), which transported AA and facilitated its reaction with taurine. Thus, global AA was reduced, whereas N‐arachidonoyl taurine (NAT, the product of AA and taurine) was induced. Notably, the role of NAT in suppressing AA incorporation into the plasma membrane was also revealed. In pre‐clinical in vivo models, reducing exosome improved ferroptosis‐based treatment. Conclusion Exosome and cir93 are essential for desensitizing LUAD cells to ferroptosis, and blocking exosome may be helpful for future LUAD treatment.

The double-stranded RNA binding domain (dsRBD) is a small protein domain of 65–70 amino acids adopting an αβββα fold, whose central property is to bind to double-stranded RNA (dsRNA). This domain is present in proteins implicated in many aspects of cellular life, including antiviral response, RNA editing, RNA processing, RNA transport and, last but not least, RNA silencing. Even though proteins containing dsRBDs can bind to very specific dsRNA targets in vivo, the binding of dsRBDs to dsRNA is commonly believed to be shape-dependent rather than sequence-specific. Interestingly, recent structural information on dsRNA recognition by dsRBDs opens the possibility that this domain performs a direct readout of RNA sequence in the minor groove, allowing a global reconsideration of the principles describing dsRNA recognition by dsRBDs. We review in this article the current structural and molecular knowledge on dsRBDs, emphasizing the intricate relationship between the amino acid sequence, the structure of the domain and its RNA recognition capacity. We especially focus on the molecular determinants of dsRNA recognition and describe how sequence discrimination can be achieved by this type of domain.

Background Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro. Results By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA–protein pair. This score can be used to measure the interactions between an RNA–protein pair. This method effectively discriminates interacting and non-interacting RNA–protein pairs and predicts RNA–protein interactions within a given complex. Applying this method on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins. Conclusions Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process.

Background The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions. Results In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k -mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. Conclusions The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It’s anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.

The Sm-like protein Hfq (host factor Q-beta phage) facilitates regulation by bacterial small noncoding RNAs (sRNAs) in response to stress and other environmental signals. Here, we present a low-resolution model of Escherichia coli Hfq bound to the rpoS mRNA, a bacterial stress response gene that is targeted by three different sRNAs. Selective 2′-hydroxyl acylation and primer extension, small-angle X-ray scattering, and Monte Carlo molecular dynamics simulations show that the distal face and lateral rim of Hfq interact with three sites in the rpoS leader, folding the RNA into a compact tertiary structure. These interactions are needed for sRNA regulation of rpoS translation and position the sRNA target adjacent to an sRNA binding region on the proximal face of Hfq. Our results show how Hfq specifically distorts the structure of the rpoS mRNA to enable sRNA base pairing and translational control.