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The long non-coding RNA Xist induces heterochromatinization of the X chromosome by recruiting repressive protein complexes to chromatin. Here we gather evidence, from the literature and from computational analyses, showing that Xist assemblies are similar in size, shape and composition to phase-separated condensates, such as paraspeckles and stress granules. Given the progressive sequestration of Xist’s binding partners during X-chromosome inactivation, we formulate the hypothesis that Xist uses phase separation to perform its function.

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

The combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet, the molecular determinants governing interactions in protein-RNA networks are not well understood. Here we investigated the relationship between the structure of an RNA and its ability to interact with proteins. Analysing in silico, in vitro and in vivo experiments, we find that the amount of double-stranded regions in an RNA correlates with the number of protein contacts. This relationship —which we call structure-driven protein interactivity— allows classification of RNA types, plays a role in gene regulation and could have implications for the formation of phase-separated ribonucleoprotein assemblies. We validate our hypothesis by showing that a highly structured RNA can rearrange the composition of a protein aggregate. We report that the tendency of proteins to phase-separate is reduced by interactions with specific RNAs. Interactions between protein and RNA occur on a large scale, and are often studied from a proteincentric point of view. Here the authors show that the number and strength of RNA-protein interactions correlate with the amount of RNA structure, which impacts the biological activity of the transcript.

RNA-binding proteins are central for many biological processes and their characterization has demonstrated a broad range of functions as well as a wide spectrum of target structures. RNA G-quadruplexes are important regulatory elements occurring in both coding and non-coding transcripts, yet our knowledge of their structure-based interactions is at present limited. Here, using theoretical predictions and experimental approaches, we show that many chromatin-binding proteins bind to RNA G-quadruplexes, and we classify them based on their RNA G-quadruplex-binding potential. Combining experimental identification of nuclear RNA G-quadruplex-binding proteins with computational approaches, we build a prediction tool that assigns probability score for a nuclear protein to bind RNA G-quadruplexes. We show that predicted G-quadruplex RNA-binding proteins exhibit a high degree of protein disorder and hydrophilicity and suggest involvement in both transcription and phase-separation into membrane-less organelles. Finally, we present the G4-Folded/UNfolded Nuclear Interaction Explorer System (G4-FUNNIES) for estimating RNA G4-binding propensities at http://service.tartaglialab.com/new_submission/G4FUNNIES . RNA G-quadruplexes are important regulatory elements, yet our knowledge of their structure-based interactions is at present limited. Here the authors combine experimental and computational methods to develop a predictive tool, G4-FUNNIES, to estimate proteins’ RNA G4-binding propensities.

Background Single-cell RNA sequencing (scRNA-seq) enables high-resolution studies of gene regulation, capturing gene expression at the individual cell level. We previously developed scRAPID, a computational pipeline for predicting protein–RNA interactions and identifying hub RNA-binding proteins (RBP) and RNAs through the integration of gene regulatory network (GRNs) inference from scRNA-seq data and cat RAPID predictions. To make this tool accessible to a broader audience, we introduce scRAPID-web, a user-friendly web server for the prediction of RBP–RNA and RBP–RBP interactions from scRNA-seq data across eight model organisms. Results scRAPID-web offers customizable options to preprocess the input gene expression matrix, such as count-based gene selection and cell type filtering. Users can choose from three GRN inference algorithms and decide whether to focus the analysis on specific gene types. Precompiled libraries allow fast filtering and motif-based validation of the inferred interactions. Results include detailed tables of predicted protein–RNA pairs and hubs, along with an interactive network visualization of potential RBP complexes built based on the inferred shared targets. Conclusions scRAPID-web democratizes access to GRN-based analyses, providing insights into protein–RNA interactions and regulatory complexes based on single-cell level expression data analysis and binding propensity estimation. scRAPID-web can be freely accessed at https://tools.tartaglialab.com/scrapid .

Liquid-liquid phase separation (LLPS) enables the formation of membraneless organelles, essential for cellular organization and implicated in diseases. We introduce catGRANULE 2.0 ROBOT, an algorithm integrating physicochemical properties and AlphaFold-derived structural features to predict LLPS at single-amino-acid resolution. The method achieves high performance and reliably evaluates mutation effects on LLPS propensity, providing detailed predictions of how specific mutations enhance or inhibit phase separation. Supported by experimental validations, including microscopy data, it predicts LLPS across diverse organisms and cellular compartments, offering valuable insights into LLPS mechanisms and mutational impacts. The tool is freely available at https://tools.tartaglialab.com/catgranule2 and https://doi.org/10.5281/zenodo.14205831 .

Synchronization of mitochondrial and cytoplasmic translation rates is critical for the maintenance of cellular fitness, with cancer cells being especially vulnerable to translational uncoupling. Although alterations of cytosolic protein synthesis are common in human cancer, compensating mechanisms in mitochondrial translation remain elusive. Here we show that the malignant long non-coding RNA (lncRNA) SAMMSON promotes a balanced increase in ribosomal RNA (rRNA) maturation and protein synthesis in the cytosol and mitochondria by modulating the localization of CARF, an RNA-binding protein that sequesters the exo-ribonuclease XRN2 in the nucleoplasm, which under normal circumstances limits nucleolar rRNA maturation. SAMMSON interferes with XRN2 binding to CARF in the nucleus by favoring the formation of an aberrant cytoplasmic RNA–protein complex containing CARF and p32, a mitochondrial protein required for the processing of the mitochondrial rRNAs. These data highlight how a single oncogenic lncRNA can simultaneously modulate RNA–protein complex formation in two distinct cellular compartments to promote cell growth.

Long Interspersed Nuclear Elements-1s (L1s) are transposable elements that constitute most of the genome’s transcriptional output yet have still largely unknown functions. Here we show that L1s are required for proper mouse brain corticogenesis operating as regulatory long non-coding RNAs. They contribute to the regulation of the balance between neuronal progenitors and differentiation, the migration of post-mitotic neurons and the proportions of different cell types. In cortical cultured neurons, L1 RNAs are mainly associated to chromatin and interact with the Polycomb Repressive Complex 2 (PRC2) protein subunits enhancer of Zeste homolog 2 (Ezh2) and suppressor of zeste 12 (Suz12). L1 RNA silencing influences PRC2’s ability to bind a portion of its targets and the deposition of tri-methylated histone H3 (H3K27me3) marks. Our results position L1 RNAs as crucial signalling hubs for genome-wide chromatin remodelling, enabling the fine-tuning of gene expression during brain development and evolution. Here the authors reveal that by acting as non-coding RNAs, L1s are bound by PRC2 and inhibit its activity on genes crucial for regulating proliferation and differentiation in neural progenitor cells.

Aptamers are artificial oligonucleotides binding to specific molecular targets. They have a promising role in therapeutics and diagnostics but are often difficult to design. Here, we exploited the cat RAPID algorithm to generate aptamers targeting TAR DNA-binding protein 43 (TDP-43), whose aggregation is associated with Amyotrophic Lateral Sclerosis. On the pathway to forming insoluble inclusions, TDP-43 adopts a heterogeneous population of assemblies, many smaller than the diffraction-limit of light. We demonstrated that our aptamers bind TDP-43 and used the tightest interactor, Apt-1, as a probe to visualize TDP-43 condensates with super-resolution microscopy. At a resolution of 10 nanometers, we tracked TDP-43 oligomers undetectable by standard approaches. In cells, Apt-1 interacts with both diffuse and condensed forms of TDP-43, indicating that Apt-1 can be exploited to follow TDP-43 phase transition. The de novo generation of aptamers and their use for microscopy opens a new page to study protein condensation. Here, the authors generated an artificial RNA molecule, or aptamer, specific for the Amyotrophic Lateral Sclerosis protein TDP-43. By interacting avidly with its target, the aptamer can be exploited to track TDP-43 phase transition in vitro and in cells.

Neuronal differentiation is a timely and spatially regulated process, relying on precisely orchestrated gene expression control. The sequential activation/repression of genes driving cell fate specification is achieved by complex regulatory networks, where transcription factors and noncoding RNAs work in a coordinated manner. Herein, we identify the long noncoding RNA HOTAIRM1 (HOXA Transcript Antisense RNA, Myeloid-Specific 1) as a new player in neuronal differentiation. We demonstrate that the neuronal-enriched HOTAIRM1 isoform epigenetically controls the expression of the proneural transcription factor NEUROGENIN 2 that is key to neuronal fate commitment and critical for brain development. We also show that HOTAIRM1 activity impacts on NEUROGENIN 2 downstream regulatory cascade, thus contributing to the achievement of proper neuronal differentiation timing. Finally, we identify the RNA-binding proteins HNRNPK and FUS as regulators of HOTAIRM1 biogenesis and metabolism. Our findings uncover a new regulatory layer underlying NEUROGENIN 2 transitory expression in neuronal differentiation and reveal a previously unidentified function for the neuronal-induced long noncoding RNA HOTAIRM1.