Interactome Data Research Articles

Synthesize heterogeneous biological knowledge via representation learning for Alzheimer’s disease drug repurposing

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era.

The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.

Birth, life and death of the Arabidopsis IRT1 iron transporter: the role of close friends and foes.

IRT1 intracellular dynamics and function are finely controlled through protein-protein interactions. In plants, iron uptake from the soil is tightly regulated to allow optimal growth and development. Iron acquisition in Arabidopsis root epidermal cells requires the IRT1 transporter, which also mediates the entry of non-iron metals. In this mini-review, we describe how protein-protein interactions regulate IRT1 intracellular dynamics and IRT1-mediated metal uptake to maintain iron homeostasis. Recent interactomic data provided interesting clues on IRT1 secretion and the putative involvement of COPI- and COPII-mediated pathways. Once delivered to the plasma membrane, IRT1 can interact with other components of the iron uptake machinery to form an iron acquisition complex that likely optimizes iron entrance in root epidermal cells. Then, IRT1 may be internalized from the plasma membrane. In the past decade, IRT1 endocytosis emerged as an essential mechanism to control IRT1 subcellular localization and thus to tune iron uptake. Interestingly, IRT1 endocytosis and degradation are regulated by its non-iron metal substrates in an ubiquitin-dependent manner, which requires a set of interacting-proteins including kinases, E3 ubiquitin ligases and ESCRT complex subunits. This mechanism is essential to avoid non-iron metal overload in Arabidopsis when the iron is scarce.

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., ‘drug treats disease’, ‘gene interacts with gene’). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

Clustering of Cancer Attributed Networks by Dynamically and Jointly Factorizing Multi-Layer Graphs.

The accumulated omic data provides an opportunity to exploit the mechanisms of cancers and poses a challenge for their integrative analysis. Although extensive efforts have been devoted to address this issue, the current algorithms result in undesirable performance because of the complexity of patterns and heterogeneity of data. In this study, the ultimate goal is to propose an effective and efficient algorithm (called NMF-DEC) to identify clusters by integrating the interactome and transcriptome data. By treating the expression profiles of genes as attributes of vertices in the gene interaction networks, we transform the integrative analysis of omic data into clustering of attributed networks. To circumvent the heterogeneity, we construct a similarity network for the attributes of genes and cast it into the common module detection problem in multi-layer networks. The NMF-DEC explores the relation between attributes and topological structure of networks by jointly factorizing the similarity and interaction networks with the same basis. In this optimization, the interaction network is dynamically updated and the information of attributes is dynamically incorporated, providing a better strategy to characterize the structure of modules in attributed networks. Extensive experiments indicate that compared with state-of-the-art baselines, NMF-DEC is more accurate on social network, and show better performance on cancer attributed networks, implying the superiority of the proposed methods for the integrative analysis of omic data.

Reply to Comment on "Binding Affinity Determines Substrate Specificity and Enables Discovery of substrates for N-Myristoyltransferases".

In our previously published article, an intriguing enzymology observation with the N-myristoyltransferases (NMT1 and NMT2) led us to conclude that binding affinity is important for determining in vivo substrate specificity and this can explain the vast literature that reports the coimmunoprecipitation of protein-modifying enzymes and their substrates. This understanding also provides a facile method to identify substrate proteins for such enzymes, which we demonstrated by identifying three substrate proteins using existing interactome data for NMT1 and NMT2. Dr. Meinnel recently commented on our finding, and we hope this Reply helps to clarify some of the important points we aimed to make in the original article.

Challenges and Limitations of Biological Network Analysis.

High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein–protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.

SLiMAn: An Integrative Web Server for Exploring Short Linear Motif-Mediated Interactions in Interactomes.

Cells express thousands of macromolecules, and their functioning relies on multiple networks of intermolecular interactions. These interactions can be experimentally determined at different spatial and temporal resolutions. But, physical interfaces are not often delineated directly, especially in high-throughput experiments. A large fraction of protein-protein interactions involves domain and so-called SLiMs (for Short Linear Motifs). Often, SLiMs lie in disordered regions or loops. Their small size, limited sequence conservation, and loosely folded nature prevent straightforward detection. SLiMAn (Short Linear Motif Analysis), a new web server, is provided to help thorough analysis of interactomics data. From a list of putative interactants (e.g., output from an interactomics study), SLiMs (from ELM) and SLiM-recognition domains (from Pfam) are extracted, and putative pairings are displayed. Predicted results can be filtered using motif E-values, IUPred2 scores, or BioGRID interaction matches. When structural templates are available, a given SLiM and its recognition domain can be modeled using SCWRL. We illustrate here the use of SLiMAn on distinct examples, including one real-case study. We oversee wide-range applications for SLiMAn in the context of the massive analysis of protein-protein interactions. This new web server is made freely available at https://sliman.cbs.cnrs.fr.

TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach.

Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells). Cell differentiation by direct reprogramming is determined by two types of transcription factors (TFs): pioneer factors (PFs) and cooperative TFs. PFs have the distinct ability to open chromatin aggregations, assemble a collective of cooperative TFs and activate gene expression. The experimental determination of two types of TFs is extremely difficult and costly. In this study, we developed a novel computational method, TRANSDIRE (TRANS-omics-based approach for DIrect REprogramming), to predict the TFs that induce direct reprogramming in various human cell types using multiple omics data. In the algorithm, potential PFs were predicted based on low signal chromatin regions, and the cooperative TFs were predicted through a trans-omics analysis of genomic data (e.g. enhancers), transcriptome data (e.g. gene expression profiles in human cells), epigenome data (e.g. chromatin immunoprecipitation sequencing data) and interactome data. We applied the proposed methods to the reconstruction of TFs that induce direct reprogramming from fibroblasts to six other cell types: hepatocytes, cartilaginous cells, neurons, cardiomyocytes, pancreatic cells and Paneth cells. We demonstrated that the methods successfully predicted TFs for most cell conversions with high accuracy. Thus, the proposed methods are expected to be useful for various practical applications in regenerative medicine. The source code and data are available at the following website: http://figshare.com/s/b653781a5b9e6639972b. Supplementary data are available at Bioinformatics online.

CELF1 promotes matrix metalloproteinases gene expression at transcriptional level in lens epithelial cells

BackgroundRNA binding proteins (RBPs)-mediated regulation plays important roles in many eye diseases, including the canonical RBP CELF1 in cataract. While the definite molecular regulatory mechanisms of CELF1 on cataract still remain elusive.MethodsIn this study, we overexpressed CELF1 in human cultured lens epithelial SRA01/04 cells and applied whole transcriptome sequencing (RNA-seq) method to analyze the global differences mediated by CELF1. We then analyzed public RNA-seq and CELF1-RNA interactome data to decipher the underlying mechanisms.ResultsThe results showed that transcriptome profile was globally changed by CELF1 overexpression (CELF1-OE). Functional analysis revealed CELF1 specifically increased the expression of genes in extracellular matrix disassembly, extracellular matrix organization, and proteolysis, which could be classified into matrix metalloproteinases (MMPs) family. This finding was also validated by RT-qPCR and public mouse early embryonic lens data. Integrating analysis with public CELF1-RNA interactome data revealed that no obvious CELF1-binding peak was found on the transcripts of these genes, indicating an indirectly regulatory role of CELF1 in lens epithelial cells.ConclusionsOur study demonstrated that CELF1-OE promotes transcriptional level of MMP genes; and this regulation may be completed by other ways except for binding to RNA targets. These results suggest that CELF1-OE is implicated in the development of lens, which is associated with cataract and expands our understanding of CELF1 regulatory roles as an RNA binding protein.

Gut microbiome and diet in populations with obesity: Role of the Na+/K+-ATPase transporter signaling in severe COVID-19.

The triad of obesity, a high-protein diet from animal sources, and disturbed gut microbiota have been linked to poor clinical outcomes in patients with COVID-19. In this report, the effect of oxidative stress resulting from the Na+ /K+ -ATPase transporter signaling cascade is explored as a driver of this poor clinical outcome. Protein-protein interactions with the SARS-CoV-2 proteome were identified from the interactome data for Na+ /K+ -transporting ATPase subunit α-1 (ATP1A1), epidermal growth factor receptor, and ERB-B2 receptor tyrosine kinase 2, using the curated data from the BioGRID Database of Protein Interactions. Data for the gene expression pattern of inflammatory response were from the Gene Expression Omnibus database for cardiomyocytes post SARS-CoV-2 infection (number GSE151879). The ATP1A1 subunit of the Na+ /K+ -ATPase transporter is targeted by multiple SARS-CoV-2 proteins. Furthermore, receptor proteins associated with inflammatory response, including epidermal growth factor receptor and ERB-B2 receptor tyrosine kinase 2 (which interact with ATP1A1), are also targeted by some SARS-CoV-2 proteins. This heightened interaction likely triggers a cytokine release that increases the severity of the viral infection in individuals with obesity. The similarities between the effects of SARS-CoV-2 proteins and indoxyl sulphate on the Na+ /K+ -ATPase transporter signaling cascade suggest the possibility of an augmentation of gene changes seen with COVID-19 infection that can result in a hyperinduction of cytokine release in individuals with obesity.

Using ortholog information from multiple species to predict barley protein-protein interaction network

Barley (Hordeum vulgare) is one of the most important crops worldwide; however, the information on protein level and, especially, protein-protein interaction datasets of barley is still lacking. Although there are several reports on protein expression for some model organisms under the conditions of interest, the progress of protein-protein interaction detection in plants is rather slow. Here, we present the first predicted protein-protein interaction network of barley by applying interolog method and using available interactome data and evolutionary information from a variety of species (272 organisms). The final predicted barley protein-protein interaction network contained 9530 proteins with 313,191 interactions. The quality of our network was evaluated using the GO annotation. We hope that the predicted barley interactome will help to better understand signal transduction pathways and facilitate the identification of new proteins involved in a special process. Furthermore, the availability of the barley protein-protein interaction network can boost the knowledge of protein level and help to breed barley crops.

On the cusp of cures: Breakthroughs in Batten disease research

Batten disease is a family of rare, lysosomal disorders caused by mutations in one of at least 13 genes, which encode a diverse set of lysosomal and extralysosomal proteins. Despite decades of research, the development of effective therapies has remained intractable. But now, the field is experiencing rapid, unprecedented progress on multiple fronts. New tools are providing insights into previously unsolvable problems, with molecular functions now known for nine Batten disease proteins. Protein interactome data are uncovering potential functional overlap between several Batten disease proteins, providing long-sought links between seemingly disparate proteins. Understanding of cellular etiology is elucidating contributions from and interactions between various CNS cell types. Collectively, this explosion in insight is hastening an unparalleled period of therapeutic breakthroughs, with multiple therapies showing great promise in preclinical and clinical studies. The coming years will provide a continuation of this rapid progress, with the promise of effective treatments giving patients hope.

Doxorubicin-induced transcriptome meets interactome: identification of new drug targets

The working mechanism of the chemotherapeutic drug doxorubicin, which is frequently used in cancer treatment, its effects on cell metabolism, and pathways activated solely by doxorubicin are not fully known. Understanding these principles is important both in improving existing therapies and in finding new drug targets. Here, I describe a systems-biology approach to find a generalizable working principle for doxorubicin by superimposition of human interactome over gene datasets commonly expressed among various cancer types. The common ?in at least two different diseases?transcriptional response of distinctive cancer cell lines to doxorubicin was reflected via 199 significantly and differentially expressed genes, mostly related to the regulation of transcription. Then, by integrating with interactome data, an active network was constructed allowing detection of clusters. Since each cluster defines densely connected regions, another level of understanding of functional principles is provided. Significant clusters were associated with the linked transcription factors and transcriptional factor enrichment analysis within these regulatory networks led to the proposition of Pou5f1b, Znf428, Prmt3, Znf12, Erg, Tfdp1, Foxm1, and Cenpa as new drug targets in drug development that can be applied in different cancer types.

Identifying Functional Modules Using Energy Minimization with Graph Cuts

Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Methods: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks. Other: This study can help us to identify disease-relevant modules which can not be identified by different expression analysis.

Binding Affinity Determines Substrate Specificity and Enables Discovery of Substrates for N-Myristoyltransferases.

Kinetic parameters (kcat and Km) derived from the Michaelis–Menten equation are widely used to characterize enzymes. kcat/Km is considered the catalytic efficiency or substrate specificity of an enzyme toward its substrate. N-Myristoyltransferases (NMTs) catalyze the N-terminal glycine myristoylation of numerous eukaryotic proteins. Surprisingly, we find that in vitro human NMT1 can accept acetyl-CoA and catalyze acetylation with kcat and Km values similar to that of myristoylation. However, when both acetyl-CoA and myristoyl-CoA are present in the reaction, NMT1 catalyzes almost exclusively myristoylation. This phenomenon is caused by the dramatically different binding affinities of NMT1 for myristoyl-CoA and acetyl-CoA (estimated Kd of 14.7 nM and 10.1 μM, respectively). When both are present, NMT1 is essentially entirely bound by myristoyl-CoA and thus catalyzes myristoylation exclusively. The NMT1 example highlights the crucial role of binding affinity in determining the substrate specificity of enzymes, which in contrast to the traditionally held view in enzymology that the substrate specificity is defined by kcat/Km values. This understanding readily explains the vast biological literature showing the coimmunoprecipitation of enzyme–substrate pairs for enzymes that catalyzes protein post-translational modifications (PTM), including phosphorylation, acetylation, and ubiquitination. Furthermore, this understanding allows the discovery of substrate proteins by identifying the interacting proteins of PTM enzymes, which we demonstrate by identifying three previously unknown substrate proteins (LRATD1, LRATD2, and ERICH5) of human NMT1/2 by mining available interactome data.

H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility

Genome-wide association studies (GWASs) have identified more than 200 prostate cancer (PrCa) risk regions, which provide potential insights into causal mechanisms. Multiple lines of evidence show that a significant proportion of PrCa risk can be explained by germline causal variants that dysregulate nearby target genes in prostate-relevant tissues, thus altering disease risk. The traditional approach to explore this hypothesis has been correlating GWAS variants with steady-state transcript levels, referred to as expression quantitative trait loci (eQTLs). In this work, we assess the utility of chromosome conformation capture (3C) coupled with immunoprecipitation (HiChIP) to identify target genes for PrCa GWAS risk loci. We find that interactome data confirm previously reported PrCa target genes identified through GWAS/eQTL overlap (e.g., MLPH). Interestingly, HiChIP identifies links between PrCa GWAS variants and genes well-known to play a role in prostate cancer biology (e.g., AR) that are not detected by eQTL-based methods. HiChIP predicted enhancer elements at the AR and NKX3-1 prostate cancer risk loci, and both were experimentally confirmed to regulate expression of the corresponding genes through CRISPR interference (CRISPRi) perturbation in LNCaP cells. Our results demonstrate that looping data harbor additional information beyond eQTLs and expand the number of PrCa GWAS loci that can be linked to candidate susceptibility genes.

Integration and Visualization of Regulatory Elements and Variations of the EPAS1 Gene in Human.

Endothelial PAS domain-containing protein 1 (EPAS1), also HIF2α, is an alpha subunit of hypoxia-inducible transcription factor (HIF), which mediates cellular and systemic response to hypoxia. EPAS1 has an important role in the transcription of many hypoxia-responsive genes, however, it has been less researched than HIF1α. The aim of this study was to integrate an increasing number of data on EPAS1 into a map of diverse OMICs elements. Publications, databases, and bioinformatics tools were examined, including Ensembl, MethPrimer, STRING, miRTarBase, COSMIC, and LOVD. The EPAS1 expression, stability, and activity are tightly regulated on several OMICs levels to maintain complex oxygen homeostasis. In the integrative EPAS1 map we included: 31 promoter-binding proteins, 13 interacting miRNAs and one lncRNA, and 16 post-translational modifications regulating EPAS1 protein abundance. EPAS1 has been associated with various cancer types and other diseases. The development of neuroendocrine tumors and erythrocytosis was shown to be associated with 11 somatic and 20 germline variants. The integrative map also includes 12 EPAS1 target genes and 27 interacting proteins. The study introduced the first integrative map of diverse genomics, transcriptomics, proteomics, regulomics, and interactomics data associated with EPAS1, to enable a better understanding of EPAS1 activity and regulation and support future research.

Identification of potential pan-coronavirus therapies using a computational drug repurposing platform.

In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus–human, human protein–protein and drug–protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein–protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e−04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.

R-Loop Associated Mitotic Stress Confers Vulnerabilities in Splicing Factor Mutant Leukemia

The RNA splicing factor SF3B1 is one of the most frequently mutated genes in chronic lymphocytic leukemia (CLL). Co-expression of mutant Sf3b1 and Atm deletionin murine B cellsresults in CLL development, in which CLL cells exhibit RNA splicing dysregulation, increased DNA damage and recurrent chromosomal amplification. These findings suggest a strong link between splicing factormutation, chromosomal instability (CIN), and oncogenesis, although the mechanism has yet to be elucidated.CIN in cells with defective RNA processing is triggered, at least in part, by excessive R-loop (RNA:DNA hybrid with displaced single strand DNA). Using dotblot assay, we observed R-loop augmentation in SF3B1 mutant (MT) compared to wild-type (WT) cells in both human cell lines and murine CLL cells. Along with R-loop accumulation, SF3B1 MT increased DNA double strand breaks (DSB) (measured by comet assay) and mis-segregated chromosomes during mitosis (quantified by immunofluorescence using anti-phospho-histone H3 and anti-αtubulin antibodies). Overexpression of R-loop resolving enzyme RNaseH1 in SF3B1 MT cell lines reduced DSB (t-test p-value <0.0001) and mitotic stress (t-test p-value <0.0001), confirming the causative role of R-loop in CIN.To elucidate the mechanisms underlying SF3B1 MT-associated CIN, we used DNA:RNA hybrid immunoprecipitation (DRIP) coupled with sequencing to map R-loop in SF3B1 MT and WT Nalm6 cells. Although SF3B1 MT caused minimal changes in overall R-loop distribution, it led to increased R-loop formation at centromeric regions. Proper centromeric R-loop (cen-R-loop) formation is essential for accurate chromosome segregation. Those R-loop are coated by phosphorylated replication protein A (p-RPA). p-RPA and centromere immunofluorescence (IF) co-staining revealed increased cen-R-loop in SF3B1 MT cells. Intriguingly, overexpression of RNaseH1 reduced both levels of centromeric p-RPA and percentage of cells with defective mitosis, suggesting SF3B1 MT associated cen-R-loop impacts CIN.To further discern the molecular underpinnings of R-loop mediated CIN, we investigated the effect of their accumulation on spindle geometry in metaphase cells. By analyzing the total body of chromosomes two-dimensional area, we observed one fold greater magnitude of chromosome oscillations in SF3B1 Nalm6 MT cells. Accordingly, compared to WT cells, SF3B1 MT cells displayed longer and wider spindles to accommodate an aneuploid chromosomal content. Removal of R-loops rescued mitotic spindle architecture and CIN in SF3B1 MT cells, highlighting a direct contribution of R-loop to kinetochore-microtubule organization and stability.As there is no evidence of strong binding of SF3B1 to centromeres, we tested the possibility that SF3B1 MT contributes to cen-R-loop formation through alternative splice variants. Through overlapping conserved SF3B1 MT-associated splice variants across isogenic cell lines and murine model with available R-loop interactome data, we identified SERBP1, which encodes an RNA-binding protein, as a target candidate . SF3B1 MT induces loss-of-function in SERBP1 through alternative splicing. Knockdown of SERBP1 triggered R-loop accumulation, increased centromeric p-RPA coating, and aberrant mitosis, which recapitulated the SF3B1 MT phenotype. Importantly, overexpression of SERBP1 WT alleviated both DSB and R-loop accumulation in SF3B1 MT cells. These observations strongly suggest that SF3B1 MT augments R-loop formation through dysregulated splicing of SERBP1 and possibly other genes involved in R-loop metabolism.We also investigated the possibility that the impact of RNA splicing defect on R-loop biology is a generalizable phenomenon. Examing a panel of leukemia-associated splicing factors (U2AF1, SRSF2, ZRSR2, RNU1), we found that these mutations all display R-loop dependent mitotic stress. Thus, mitotic stress caused by aberrant R-loop formation could be a convergent mechanism in splicing factor mutated leukemia cells.Altogether, we demonstrated that SF3B1 mutation promotes CIN by destabilizing mitotic spindles through aberrant R-loop accumulation at the centromere, via dysregulated splicing in genes such as SERBP1. Our study highlights an unrecognized role of cen-R-loop as a critical link between RNA splicing dysregulation and CIN, providing an opportunity for therapeutic targeting of R-loop in splicing factor mutant leukemias. DisclosuresWu: Pharmacyclics: Research Funding; BioNTech: Current equity holder in publicly-traded company.