RNA-binding Motifs Research Articles

An internal loop region is responsible for inherent target specificity of bacterial Cold-shock proteins.

Cold shock proteins (Csps), of around 70 amino acids, share a protein fold for the cold shock domain (CSD) that contains RNA binding motifs, RNP1 and RNP2, and constitute one family of bacterial RNA-binding proteins. Despite similar amino acid composition, Csps have been shown to individually possess inherent specific functions. Here we identify the molecular differences in Csps that allow selective recognition of RNA targets. Using chimeras and mutants of Escherichia coli CspD and CspA, we demonstrate that Lys43-Ala44 in an internal loop of CspD and the N-terminal portion with Lys4 of CspA are important for determining their target specificities. Pull-down assays suggest these distinct specificities reflect differences in the ability to act on the target RNAs rather than differences in binding to the RNA targets. A phylogenetic tree constructed from 1,573 Csps reveals that the Csps containing Lys-Ala in the loop form a monophyletic clade, and the members in this clade are shown to have target specificities similar to E. coli CspD. The phylogenetic tree also finds a small cluster of Csps containing Lys-Glu in the loop, and these exhibit different specificity than E. coli CspD. Examination of this difference suggests a role of the loop of CspD type proteins in recognition of specific targets. Additionally, each identified type of Csp shows a different distribution pattern among bacteria. Our findings provide a basis for subclassification of Csps based on target RNA specificity, which will be useful for understanding of the functional specialization of Csps.

RNA-Binding Protein Motifs Predict microRNA Secretion and Cellular Retention in Hypothalamic and Other Cell Types.

Cellular microRNAs (miRNAs) can be selectively secreted or retained, adding another layer to their critical role in regulating human health and disease. To date, select RNA-binding proteins (RBPs) have been proposed to be a mechanism underlying miRNA localization, but the overall relevance of RBPs in systematic miRNA sorting remains unclear. This study profiles intracellular and small extracellular vesicles' (sEVs) miRNAs in NPY-expressing hypothalamic neurons. These findings were corroborated by the publicly available sEV and intracellular miRNA profiles of white and brown adipocytes, endothelium, liver, and muscle from various databases. Using experimentally determined binding motifs of 93 RBPs, our enrichment analysis revealed that sEV-originating miRNAs contained significantly different RBP motifs than those of intracellularly retained miRNAs. Multiple RBP motifs were shared across cell types; for instance, RBM4 and SAMD4 are significantly enriched in neurons, hepatocytes, skeletal muscle, and endothelial cells. Homologs of both proteins physically interact with Argonaute1/2 proteins, suggesting that they play a role in miRNA sorting. Machine learning modelling also demonstrates that significantly enriched RBP motifs could predict cell-specific preferential miRNA sorting. Non-optimized machine learning modeling of the motifs using Random Forest and Naive Bayes in all cell types except WAT achieved an area under the receiver operating characteristic (ROC) curve of 0.77-0.84, indicating a high predictive accuracy. Given that the RBP motifs have a significant predictive power, these results underscore the critical role that RBPs play in miRNA sorting within mammalian cells and reinforce the importance of miRNA sequencing in preferential localization. For the future development of small RNA therapeutics, considering these RBP-RNA interactions could be crucial to maximize delivery effectiveness and minimize off-target effects.

EVPsort: An Atlas of Small ncRNA Profiling and Sorting in Extracellular Vesicles and Particles

Extracellular vesicles and particles (EVPs) play a crucial role in mediating cell-to-cell communication by transporting various molecular cargos, with small non-coding RNAs (ncRNAs) holding particular significance. A thorough investigation into the abundance and sorting mechanisms of ncRNA within EVPs is imperative for advancing their clinical applications. We have developed EVPsort, which not only provides an extensive overview of ncRNA profiling in 3,162 samples across various biofluids, cell lines, and disease contexts but also seamlessly integrates 19 external databases and tools. This integration encompasses information on associations between ncRNAs and RNA-binding proteins (RBPs), motifs, targets, pathways, diseases, and drugs. With its rich resources and powerful analysis tools, EVPsort extends its profiling capabilities to investigate ncRNA sorting, identify relevant RBPs and motifs, and assess functional implications. EVPsort stands as a pioneering database dedicated to comprehensively addressing both the abundance and sorting of ncRNA within EVPs. It is freely accessible at https://bioinfo.vanderbilt.edu/evpsort/.

Species-aware DNA language models capture regulatory elements and their evolution

BackgroundThe rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution.ResultsHere, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution. Investigating their ability to predict masked nucleotides from context, we show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. Owing to their flexibility, DNA language models capture conserved regulatory elements over much further evolutionary distances than sequence alignment would allow. Remarkably, DNA language models reconstruct motif instances bound in vivo better than unbound ones and account for the evolution of motif sequences and their positional constraints, showing that these models capture functional high-order sequence and evolutionary context. We further show that species-aware training yields improved sequence representations for endogenous and MPRA-based gene expression prediction, as well as motif discovery.ConclusionsCollectively, these results demonstrate that species-aware DNA language models are a powerful, flexible, and scalable tool to integrate information from large compendia of highly diverged genomes.

Abstract 5683: lncRNA-protein interactions that predict response to endocrine therapy in HR+ breast cancer

Abstract Hormone receptor positive (HR+) breast cancer (BCa) is the most frequently diagnosed BCa type accounting for 70% of all breast cancer cases. A large population of HR+ BCa patients (30-40%) are resistant to mainstay endocrine therapy and develop incurable metastatic disease. Hence, it is imperative to identify viable biomarkers that stratify patients as drug responders or non-responders. Emerging scientific literature highlights the essential role of long non-coding RNAs (lncRNA) in cancer drug resistance and metastatic transformation; lncRNA include RNAs over 200 nucleotides in length that do not encode protein. Major biological functions of lncRNA rely on association with select protein interactors. As lncRNA equally bind proteins with and without canonical RNA-binding motifs, it is unclear what facilitates this lncRNA-protein interaction. Our recent studies show that SUMO post-translational modification (or SUMOylation) of target proteins potentiates binding with lncRNA, particularly at the chromatin. Using a small-scale screening, we see that SUMO-modification works to enhance association with lncRNAs for proteins with and without a canonical RNA binding domain (RBD). SUMOylation of RBD-expressing heterochromatin protein 1 α (HP1α) drives interaction with multiple chromatin-associated lncRNA both within and outside canonical heterochromatin foci. Interaction with lncRNA supports SUMOylated HP1α’s unconventional function as a transcriptional repressor at select euchromatic loci. We now report an inverse relationship in which SUMOylated HP1α directs the stability of its lncRNA binding partner. Specifically, SUMO-modified HP1α directs cell-cycle dependent recruitment of the RNA degradation machinery to the chromatin and facilitates clearance of chromatin-bound lncRNA. This dynamic lncRNA degradation process is disturbed with cancer onset and instead favors aberrant cancer cell transformation. Consistently, restoring lncRNA degradation homeostasis decreases metastatic properties of HR+ BCa cells and increases sensitivity to targeted therapeutics currently in clinical trials. Our studies define a fundamental mechanism that dictates lncRNA stability and/or degradation in BCa cells. In addition, we present viable biomarkers that predict sensitivity to ET and alternative targeted therapies. Citation Format: Kacie D. Waiters, Maram Quttina, Ashfia F. Khan, Samaneh Karami, Anthony S. Peidl, Funmi Babajide, Tasneem Bawa-Khalfe. lncRNA-protein interactions that predict response to endocrine therapy in HR+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5683.

Abstract 1606 Purification of double-stranded RNA binding motifs for crystallographic studies

Abstract 1606 Purification of double-stranded RNA binding motifs for crystallographic studies

Abstract A023: lncRNA-protein interactions that predict response to endocrine therapy in HR+ breast cancer

Abstract Hormone receptor positive (HR+) breast cancer (BCa) is the most frequently diagnosed BCa type accounting for 70% of all breast cancer cases. A large population of HR+ BCa patients (30-40%) are resistant to mainstay endocrine therapy and develop incurable metastatic disease. Hence, it is imperative to identify viable biomarkers that stratify patients as drug responders or non-responders. Emerging scientific literature highlights the essential role of long non-coding RNAs (lncRNA), a class of RNAs over 200 nucleotides in length that do not encode protein, in cancer drug resistance and metastatic transformation. Major biological functions of lncRNA rely on association with select protein interactors. As lncRNA equally bind proteins with and without canonical RNA-binding motifs, it is unclear what facilitates this lncRNA-protein interaction. Our recent studies show that SUMO post-translational modification (or SUMOylation) of target proteins potentiates binding with lncRNA, particularly at the chromatin. SUMOylation of heterochromatin protein 1 α (HP1α) drives interaction with multiple chromatin-associated lncRNA both within and outside canonical heterochromatin foci. Interaction with lncRNA supports SUMOylated HP1α’s unconventional function as a transcriptional repressor at select euchromatic loci. We now report an inverse relationship in which SUMOylated HP1α directs the stability of its lncRNA binding partner. Specifically, SUMO-modified HP1α directs cell-cycle dependent recruitment of the RNA degradation machinery to the chromatin and facilitates clearance of chromatin-bound lncRNA. This dynamic lncRNA degradation process is disturbed with cancer onset and instead favors aberrant cancer cell transformation. Consistently, restoring lncRNA degradation homeostasis decreases metastatic properties of HR+ BCa cells and increases sensitivity to targeted therapeutics currently in clinical trials. Our studies define a fundamental mechanism that dictates lncRNA stability and/or degradation in BCa cells. In addition, we present viable biomarkers that predict sensitivity to ET and alternative targeted therapies. Citation Format: Kacie D Waiters, Maram Quttina, Ashfia F Khan, Samaneh Karami, Anthony Peidl, Funmi Babajide, Tasneem Bawa-Khalfe. lncRNA-protein interactions that predict response to endocrine therapy in HR+ breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A023.

Regulatory features aid interpretation of 3′UTR variants

Our ability to determine the clinical impact of variants in 3' untranslated regions (UTRs) of genes remains poor. We provide a thorough analysis of 3' UTR variants from several datasets. Variants in putative regulatory elements, including RNA-binding protein motifs, eCLIP peaks, and microRNA sites, are up to 16 times more likely than variants not in these elements to have gene expression and phenotype associations. Variants in regulatory motifs result in allele-specific protein binding in cell lines and allele-specific gene expression differences in population studies. In addition, variants in shared regions of alternatively polyadenylated isoforms and those proximal to polyA sites are more likely to affect gene expression and phenotype. Finally, pathogenic 3' UTR variants in ClinVar are up to 20 times more likely than benign variants to fall in a regulatory site. We incorporated these findings into RegVar, a software tool that interprets regulatory elements and annotations for any 3' UTR variant and predicts whether the variant is likely to affect gene expression or phenotype. This tool will help prioritize variants for experimental studies and identify pathogenic variants in individuals.

Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing

Sequence-specific RNA-binding proteins (RBPs) play central roles in splicing decisions. Here, we describe a modular splicing architecture that leverages in vitro-derived RNA affinity models for 79 human RBPs and the annotated human genome to produce improved models of RBP binding and activity. Binding and activity are modeled by separate Motif and Aggregator components that can be mixed and matched, enforcing sparsity to improve interpretability. Training a new Adjusted Motif (AM) architecture on the splicing task not only yields better splicing predictions but also improves prediction of RBP-binding sites in vivo and of splicing activity, assessed using independent data.

RNA-binding proteins in cellular senescence

Cellular senescence is a state of irreversible cell cycle arrest that is triggered and controlled by various external and/or internal factors. Among them, the regulation of senescence-associated genes is an important molecular event that plays a role in senescence. The regulation of gene expression can be achieved by various types of modulating mechanisms, and RNA-binding proteins (RBPs) are commonly known as critical regulators targeting a global range of transcripts. RBPs bind to RNA-binding motifs of the target transcripts and are involved in post-transcriptional processes such as RNA transport, stabilization, splicing, and decay. These RBPs may also play critical roles in cellular senescence by regulating the expression of senescence-associated genes. The biological functions of RBPs in controlling cellular senescence are being actively studied. Herein, we summarized the RBPs that influence cellular senescence, particularly by regulating processes such as the senescence-associated secretory phenotype, cell cycle, and mitochondrial function.

Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

Known sequence features explain half of all human gene ends.

Cleavage and polyadenylation (CPA) sites define eukaryotic gene ends. CPA sites are associated with five key sequence recognition elements: the upstream UGUA, the polyadenylation signal (PAS), and U-rich sequences; the CA/UA dinucleotide where cleavage occurs; and GU-rich downstream elements (DSEs). Currently, it is not clear whether these sequences are sufficient to delineate CPA sites. Additionally, numerous other sequences and factors have been described, often in the context of promoting alternative CPA sites and preventing cryptic CPA site usage. Here, we dissect the contributions of individual sequence features to CPA using standard discriminative models. We show that models comprised only of the five primary CPA sequence features give highest probability scores to constitutive CPA sites at the ends of coding genes, relative to the entire pre-mRNA sequence, for 59% of all human genes. U1-hybridizing sequences provide a small boost in performance. The addition of all known RBP RNA binding motifs to the model increases this figure to only 61%, suggesting that additional factors beyond the core CPA machinery have a minimal role in delineating real from cryptic sites. To our knowledge, this high effectiveness of established features to predict human gene ends has not previously been documented.

Deciphering the RRM-RNA recognition code: A computational analysis.

RNA recognition motifs (RRM) are the most prevalent class of RNA binding domains in eucaryotes. Their RNA binding preferences have been investigated for almost two decades, and even though some RRM domains are now very well described, their RNA recognition code has remained elusive. An increasing number of experimental structures of RRM-RNA complexes has become available in recent years. Here, we perform an in-depth computational analysis to derive an RNA recognition code for canonical RRMs. We present and validate a computational scoring method to estimate the binding between an RRM and a single stranded RNA, based on structural data from a carefully curated multiple sequence alignment, which can predict RRM binding RNA sequence motifs based on the RRM protein sequence. Given the importance and prevalence of RRMs in humans and other species, this tool could help design RNA binding motifs with uses in medical or synthetic biology applications, leading towards the de novo design of RRMs with specific RNA recognition.

Diverse roles of heterogeneous nuclear ribonucleoproteins in viral life cycle

Understanding the host-virus interactions helps to decipher the viral replication strategies and pathogenesis. Viruses have limited genetic content and rely significantly on their host cell to establish a successful infection. Viruses depend on the host for a broad spectrum of cellular RNA-binding proteins (RBPs) throughout their life cycle. One of the major RBP families is the heterogeneous nuclear ribonucleoproteins (hnRNPs) family. hnRNPs are typically localized in the nucleus, where they are forming complexes with pre-mRNAs and contribute to many aspects of nucleic acid metabolism. hnRNPs contain RNA binding motifs and frequently function as RNA chaperones involved in pre-mRNA processing, RNA splicing, and export. Many hnRNPs shuttle between the nucleus and the cytoplasm and influence cytoplasmic processes such as mRNA stability, localization, and translation. The interactions between the hnRNPs and viral components are well-known. They are critical for processing viral nucleic acids and proteins and, therefore, impact the success of the viral infection. This review discusses the molecular mechanisms by which hnRNPs interact with and regulate each stage of the viral life cycle, such as replication, splicing, translation, and assembly of virus progeny. In addition, we expand on the role of hnRNPs in the antiviral response and as potential targets for antiviral drug research and development.

The genetic and biochemical determinants of mRNA degradation rates in mammals

BackgroundDegradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms.ResultsWe establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays.ConclusionsOur work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

Widespread autogenous mRNA-protein interactions detected by CLIP-seq.

Autogenous interactions between mRNAs and the proteins they encode are implicated in cellular feedback-loop regulation, but their extent and mechanistic foundation are unclear. It was recently hypothesized that such interactions may be common, reflecting the role of intrinsic nucleobase–amino acid affinities in shaping the genetic code's structure. Here we analyze a comprehensive set of CLIP-seq experiments involving multiple protocols and report on widespread autogenous interactions across different organisms. Specifically, 230 of 341 (67%) studied RNA-binding proteins (RBPs) interact with their own mRNAs, with a heavy enrichment among high-confidence hits and a preference for coding sequence binding. We account for different confounding variables, including physical (overexpression and proximity during translation), methodological (difference in CLIP protocols, peak callers and cell types) and statistical (treatment of null backgrounds). In particular, we demonstrate a high statistical significance of autogenous interactions by sampling null distributions of fixed-margin interaction matrices. Furthermore, we study the dependence of autogenous binding on the presence of RNA-binding motifs and structured domains in RBPs. Finally, we show that intrinsic nucleobase–amino acid affinities favor co-aligned binding between mRNA coding regions and the proteins they encode. Our results suggest a central role for autogenous interactions in RBP regulation and support the possibility of a fundamental connection between coding and binding.

A deep learning method for miRNA/isomiR target detection

Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR–mRNA interactions into account and applying a deep learning model to study miRNA–mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR–mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR–mRNA interactions.

The Disordered EZH2 Loop: Atomic Level Characterization by 1HN- and 1Hα-Detected NMR Approaches, Interaction with the Long Noncoding HOTAIR RNA.

The 96-residue-long loop of EZH2 is proposed to play a role in the interaction with long non-coding RNAs (lncRNAs) and to contribute to EZH2 recruitment to the chromatin. However, molecular details of RNA recognition have not been described so far. Cellular studies have suggested that phosphorylation of the Thr345 residue localized in this loop influences RNA binding; however, no mechanistic explanation has been offered. To address these issues, a systematic NMR study was performed. As the 1HN-detected NMR approach presents many challenges under physiological conditions, our earlier developed, as well as improved, 1Hα-detected experiments were used. As a result of the successful resonance assignment, the obtained chemical shift values indicate the highly disordered nature of the EZH2 loop, with some nascent helical tendency in the Ser407–Ser412 region. Further investigations conducted on the phosphomimetic mutant EZH2T345D showed that the mutation has only a local effect, and that the loop remains disordered. On the other hand, the mutation influences the cis/trans Pro346 equilibrium. Interactions of both the wild-type and the phosphomimetic mutant with the lncRNA HOTAIR140 (1–140 nt) highlight that the Thr367–Ser375 region is affected. This segment does not resemble any of the previously reported RNA-binding motifs, therefore the identified binding region is unique. As no structural changes occur in the EZH2 loop upon RNA binding, we can consider the protein–RNA interaction as a “fuzzy” complex.

Precision analysis of mutant U2AF1 activity reveals deployment of stress granules in myeloid malignancies

Splicing factor mutations are common among cancers, recently emerging as drivers of myeloid malignancies. U2AF1 carries hotspot mutations in its RNA-binding motifs; however, how they affect splicing and promote cancer remain unclear. The U2AF1/U2AF2 heterodimer is critical for 3' splice site (3'SS) definition. To specifically unmask changes in U2AF1 function invivo, we developed a crosslinking and immunoprecipitation procedure that detects contacts between U2AF1 and the 3'SS AG at single-nucleotide resolution. Our data reveal that the U2AF1 S34F and Q157R mutants establish new 3'SS contacts at -3 and+1 nucleotides, respectively. These effects compromise U2AF2-RNA interactions, resulting predominantly in intron retention and exon exclusion. Integrating RNA binding, splicing, and turnover data, we predicted that U2AF1 mutations directly affect stress granule components, which was corroborated by single-cell RNA-seq. Remarkably, U2AF1-mutant cell lines and patient-derived MDS/AML blasts displayed a heightened stress granule response, pointing to a novel role for biomolecular condensates in adaptive oncogenic strategies.

U2AF1 Mutations Enhance Stress Granule Response in Myeloid Malignancies

Somatic mutations in splicing factor genes are drivers of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). The splicing factors U2AF1 and U2AF2 form the U2AF heterodimer that is critical in the 3' splice site (3'SS) recognition and in the recruitment of U2 small nuclear ribonucleoproteins for the activation of the spliceosome complex. U2AF1 carries hotspot mutations in its two RNA binding motifs; yet the molecular mechanisms affecting the splicing process and promoting clonal advantage remain unclear, albeit necessary to develop effective targeted therapies.We applied a multi-omics approach comparing the activities of two U2AF1 mutants (S34F and Q157R) in MDS/AML cell lines and primary samples. Using a novel approach of fractionated enhanced crosslinking immunoprecipitation coupled with deep RNA-sequencing (freCLIP-seq), we mapped transcriptome-wide binding at nucleotide resolution and we identified conformational changes in mutant vs wild-type U2AF1 binding. Specifically, we observed an emergent peak in position -3 of the 3'SS for the S34F mutant and in position +1 for the Q157R mutant, matching the critical positions observed by differential splicing analysis on RNA-seq data. Altered U2AF1-RNA binding compromised U2AF2-RNA interactions, resulting predominantly in exon exclusion and intron retention. Combined binding-splicing analysis showed that while the Q157R mutant mainly exhibits loss of binding, the S34F mutant follows a gain-of-binding pattern, where splicing progression appears impaired by increased mutant binding.Functional analysis of genes affected by both binding and splicing alterations revealed that U2AF1 mutants alter RNA granule biology, affecting in particular stress granule-enriched transcripts and proteins. Stress granules are membrane-less cytoplasmic assemblies of RNAs and RNA binding proteins that improve cellular adaptation in response to stress conditions. Increased stress granule formation has been linked to tumorigenesis as a strategy exploited by cancer cells to regulate gene expression and signal transduction, enhancing their fitness under stress. To probe how aberrant binding and splicing of stress granule components affected stress granule biology, we assessed stress granule formation in U2AF1 mutant vs wild-type cells at steady state and after stress induction with sodium arsenite treatment. Immunofluorescent staining followed by confocal imaging demonstrated that U2AF1 mutations enhance stress granule formation upon arsenite stress in both cell lines and primary samples. RNA turnover analysis by TimeLapse-seq confirmed that U2AF1 S34F and Q157R mutations promote stability/synthesis of transcripts that are enriched in stress granules and determine degradation/shutdown of transcripts that are depleted in stress granules, providing a molecular explanation for the increase in stress granules observed by imaging. Finally, we were able to corroborate our observations by single-cell RNA-seq in patient-derived U2AF1-mutant MDS blasts, establishing the causal link between U2AF1 mutations and upregulation of stress granule components.Collectively, this multi-omics analysis identified biological processes directly influenced by mutant U2AF1 binding and splicing, laying the foundation for a new paradigm where splicing factor mutations enhance stress granule formation by acting on the availability of their RNA and protein components. The enhanced formation of stress granules potentially fosters the stress adaptation of U2AF1-mutant cells, contributing to their clonal advantage in MDS/AML. Stress granule perturbations may therefore represent a novel therapeutic vulnerability in U2AF1-mutant MDS/AML patients and possibly in patients carrying other splicing factor mutations. DisclosuresHunck: Boehringer Ingelheim: Other: Fellowship.