Interactome Data Research Articles

The network interactions between the porcine deltacoronavirus nucleocapsid protein and host cellular proteins

Porcine deltacoronavirus (PDCoV) is an emerging swine coronavirus that can cause diarrhea in pigs of all ages with varying severity. Host–virus protein interactions are critical for intracellular viral replication. Elucidating the interactions between cellular and viral proteins can help us to design antiviral strategies. PDCoV N protein is the most abundant and vital regulator in virus replication. In this study, 604 host proteins were identified to interact with PDCoV N protein by Co-IP combined with LC-MS, of which 243 proteins were specifically bound to N protein. PPI analysis revealed that the N-interacting host proteins are categorized into three groups: ribonucleoprotein complex biogenesis modulation, cellular nitrogen compound metabolism, and nucleic acid binding. GO and KEGG analyses showed that the host proteins are primarily involved in mRNA splicing, stress granule assembly, spliceosomal snRNP assembly. Additionally, four host proteins-TRIM25, HNRNPUL1, RPS27A, and SLC3A2-were selected to validate the interactome data through Co-IP and Confocal assays. This study can help in designing anti-PDCoV strategies and understanding the replication mechanism of PDCoV.

When less is more - a fast TurboID knock-in approach for high-sensitivity endogenous interactome mapping.

In recent years, proximity labeling has established itself as an unbiased and powerful approach to map the interactome of specific proteins. Although physiological expression of labeling enzymes is beneficial for the mapping of interactors, generation of the desired cell lines remains time-consuming and challenging. Using our established pipeline for rapid generation of C- and N-terminal CRISPR-Cas9 knock-ins (KIs) based on antibiotic selection, we were able to compare the performance of commonly used labeling enzymes when endogenously expressed. Endogenous tagging of the µ subunit of the adaptor protein (AP)-1 complex with TurboID allowed identification of known interactors and cargo proteins that simple overexpression of a labeling enzyme fusion protein could not reveal. We used the KI strategy to compare the interactome of the different AP complexes and clathrin and were able to assemble lists of potential interactors and cargo proteins that are specific for each sorting pathway. Our approach greatly simplifies the execution of proximity labeling experiments for proteins in their native cellular environment and allows going from CRISPR transfection to mass spectrometry analysis and interactome data in just over a month.

ERα Coregulator TRIM28 Promotes Breast Cancer Progression by Activating the AKT/GSK3β Pathway.

Estrogen receptor α (ERα) promotes the growth and survival of ER-positive breast cancer (BC) cells. ER regulates ER expression target genes by directly binding to specific estrogen response elements, upon activation by estrogens. In this study, 106 proteins interacting with endogenous chromatin-bound ER in a BC cell line MCF7 were identified using the RIME method. The interactome data showed that the tripartite motif containing 28 (TRIM28) is the most significantly enriched ER-associated protein. This study provides evidence that TRIM28 expression improves ER transcriptional activity and promotes the BC cells proliferation, migration, and invasion of BC cells. The high expression of TRIM28 is associated with poor clinical outcomes in patients with ER-positive BC. Mechanistic experiments indicate that TRIM28 expression activates the AKT/GSK3β pathway. To conclude, TRIM28 acts as a regulatory protein of ER and AKT signaling; therefore, it can be a target for the therapeutic interventions of BC.

In silico and in vitro screening of selected antimicrobial compounds for inhibiting drug efflux pumps to combat threatening MRSA

The rapid emergence of antibiotic-resistant bacteria is occurring worldwide due to severe antibiotic abuse in the past few decades; among them, Methicillin-resistant Staphylococcus aureus (MRSA) is of major concern. Study examines potent antimicrobial compounds against Methicillin resistance Staphylococcus aureus (MRSA) efflux system. Interactome data serves as a primary focal point for global MRSA infection control initiatives. We employed CB Dock and Discovery Studio for performing docking analysis on specific antimicrobial compounds against NorA, NorB, and NorG. Additionally, we conducted molecular dynamics simulations using myPresto. In vitro validation was done by agar-based EtBr cartwheel method to assess the efflux pump activity of MRSA. Out of the eight screened antimicrobial compounds, quercetin exhibited favorable binding energies. Subsequently, molecular dynamics simulations were conducted on quercetin complexes with NorA, NorB, and NorG. In vitro result revealed that the quercetin is active at concentration of 60 µg/ml ±10 against MRSA to inhibit efflux mechanisms. Quercetin demonstrated the most promising binding affinity with the efflux pumps and their regulators of MRSA, making it the prime candidate for use with antibiotics. In-vivo experiments must be performed to validate the in-silico and in vitro analysis.

Intra-organ cell specific mitochondrial quantitative interactomics.

Almost every organ consists of many cell types, each with its unique functions. Proteomes of these cell types are thus unique too. But it is reasonable to assume that interactome (inter and intra molecular interactions of proteins) are also distinct since protein interactions are what ultimately carry out the function. Podocytes and tubules are two cell types within kidney with vastly different functions: podocytes envelop the blood vessels in the glomerulus and act as filters while tubules are located downstream of the glomerulus and are responsible for reabsorption of important nutrients. It has been long known that for tubules mitochondria plays an important role as they require a lot of energy to carry out their functions. In podocytes, however, it has been assumed that mitochondria might not matter as much in both normal physiology and pathology1. Here we have applied quantitative cross-linking mass spectrometry to compare mitochondrial interactomes of tubules and podocytes using a transgenic mitochondrial tagging strategy to immunoprecipitate cell-specific mitochondria directly from whole kidney. We have uncovered that mitochondrial proteomes of these cell types are quite similar, although still showing unique features that correspond to known functions, such as high energy production through TCA cycle in tubules and requirements for detoxification in podocytes which are on the frontline of filtration where they encounter toxic compounds and therefore, as a non-renewing cell type they must have ways to protect themselves from cellular toxicity. But we gained much deeper insight with the interactomics data. We were able to find pathways differentially regulated in podocytes and tubules based on changing cross-link levels and not just protein levels. Among these pathways are betaine metabolism, lysine degradation, and many others. We have also demonstrated how quantitative interactomics could be used to detect different activity levels of an enzyme even when protein abundances of it are the same between cell types. We have validated this finding with an orthogonal activity assay. Overall, this work presents a new view of mitochondrial biology for two important, but functionally distinct, cell types within the mouse kidney showing both similarities and unique features. This data can continue to be explored to find new aspects of mitochondrial biology, especially in podocytes, where mitochondria has been understudied. In the future this methodology can also be applied to other organs to uncover differences in the function of cell types.

The p21CIP1-CDK4-DREAM axis is a master regulator of genotoxic stress-induced cellular senescence.

Cellular senescence, a major driver of aging, can be stimulated by DNA damage, and is counteracted by the DNA repair machinery. Here we show that in p16INK4a-deficient cells, senescence induction by the environmental genotoxin B[a]P or ionizing radiation (IR) completely depends on p21CIP1. Immunoprecipitation-based mass spectrometry interactomics data revealed that during senescence induction and maintenance, p21CIP1 specifically inhibits CDK4 and thereby activates the DREAM complex. Genome-wide transcriptomics revealed striking similarities in the response induced by B[a]P and IR. Among the top 100 repressed genes 78 were identical between B[a]P and IR and 76 were DREAM targets. The DREAM complex transcriptionally silences the main proliferation-associated transcription factors E2F1, FOXM1 and B-Myb as well as multiple DNA repair factors. Knockdown of p21CIP1, E2F4 or E2F5 diminished both, repression of these factors and senescence. The transcriptional profiles evoked by B[a]P and IR largely overlapped with the profile induced by pharmacological CDK4 inhibition, further illustrating the role of CDK4 inhibition in genotoxic stress-induced senescence. Moreover, data obtained by live-cell time-lapse microscopy suggest the inhibition of CDK4 by p21CIP1 is especially important for arresting cells which slip through mitosis. Overall, we identified the p21CIP1/CDK4/DREAM axis as a master regulator of genotoxic stress-induced senescence.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

Time-resolved profiling of RNA binding proteins throughout the mRNA life cycle

mRNAs continually change their protein partners throughout their lifetimes, yet our understanding of mRNA-protein complex (mRNP) remodeling is limited by a lack of temporal data. Here, we present time-resolved mRNA interactome data by performing pulse metabolic labeling with photoactivatable ribonucleoside in human cells, UVA crosslinking, poly(A)+ RNA isolation, and mass spectrometry. This longitudinal approach allowed the quantification of over 700 RNA binding proteins (RBPs) across ten time points. Overall, the sequential order of mRNA binding aligns well with known functions, subcellular locations, and molecular interactions. However, we also observed RBPs with unexpected dynamics: the transcription-export (TREX) complex recruited posttranscriptionally after nuclear export factor 1 (NXF1) binding, challenging the current view of transcription-coupled mRNA export, and stress granule proteins prevalent in aged mRNPs, indicating roles in late stages of the mRNA life cycle. To systematically identify mRBPs with unknown functions, we employed machine learning to compare mRNA binding dynamics with Gene Ontology (GO) annotations. Our data can be explored at chronology.rna.snu.ac.kr.

Unlocking Overexpressed Membrane Proteins to Guide Breast Cancer Precision Medicine.

Breast cancer (BC) is a prevalent form of cancer affecting women worldwide. However, the effectiveness of current BC drugs is limited by issues such as systemic toxicity, drug resistance, and severe side effects. Consequently, there is an urgent need for new therapeutic targets and improved tumor tracking methods. This study aims to address these challenges by proposing a strategy for identifying membrane proteins in tumors that can be targeted for specific BC therapy and diagnosis. The strategy involves the analyses of gene expressions in breast tumor and non-tumor tissues and other healthy tissues by using comprehensive bioinformatics analysis from The Cancer Genome Atlas (TCGA), UALCAN, TNM Plot, and LinkedOmics. By employing this strategy, we identified four transcripts (LRRC15, EFNA3, TSPAN13, and CA12) that encoded membrane proteins with an increased expression in BC tissue compared to healthy tissue. These four transcripts also demonstrated high accuracy, specificity, and accuracy in identifying tumor samples, as confirmed by the ROC curve. Additionally, tissue microarray (TMA) analysis revealed increased expressions of the four proteins in tumor tissues across all molecular subtypes compared to the adjacent breast tissue. Moreover, the analysis of human interactome data demonstrated the important roles of these proteins in various cancer-related pathways. Taken together, these findings suggest that LRRC15, EFNA3, TSPAN13, and CA12 can serve as potential biomarkers for improving cancer diagnosis screening and as suitable targets for therapy with reduced side effects and enhanced efficacy.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein–protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Long Intergenic Non-Coding RNAs of Human Chromosome 18: Focus on Cancers.

Malignant neoplasms are characterized by high molecular heterogeneity due to multilevel deregulation of gene expression and cellular functions. It is known that non-coding RNAs, including long intergenic non-coding RNAs (lincRNAs), can play significant roles in cancer biology. The current review focuses on a systematical analysis of genomic, transcriptomic, epigenomic, interactomic, and literature data on 65 lincRNAs of human chromosome 18 in the context of pan-cancer studies. The entire group of lincRNAs can be conditionally divided into 4 subgroups depending on experimental evidence on direct or indirect involvement in cancers and the biological associations with cancers, which we found during the data-mining process: the most studied (5 lincRNAs), moderately or poorly studied (11 lincRNAs), and understudied (31 lincRNAs). For the remaining 18 lincRNAs, data for analysis were fragmentary or missing. Among the key findings were the following: Of the lincRNAs of human chromosome 18, 40% have tissue-specific expression patterns, 22% of lincRNAs are known to have gene fusions, 40% of lincRNAs are prone to gene amplifications and/or deletions in cancers at a frequency greater than 3%, and 23% of lincRNAs are differentially expressed across cancer types, whereas 7% have subtype-specific expression patterns. LincRNAs' interactomes consist of 'master' microRNAs and 47 proteins (including cancer-associated proteins and microRNAs) that can interact with 3 or more lincRNAs. Functional enrichment analysis of a set of highly co-expressed genes retrieved for 17 lincRNAs in different cancer types indicated the potential associations of these lincRNAs with cellular signaling pathways. Six lincRNAs encoded small open-reading frame (smORF) proteins with emerging roles in cancers, and microRNAs as well as proteins with known functions in molecular carcinogenesis can bind to coding regions of smORFs. We identified seven transcriptomic signatures with potential prognostic value, consisting of two to seven different lincRNAs only. Taken together, the literature, biomedical, and molecular biology data analyzed indicated that only five of all lincRNAs of human chromosome 18 are cancer-associated, while eleven other lincRNAs have the tendency to be associated with cancers.

Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts.

Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

Leave-one-out-analysis (LOOA): web-based tool to predict influential proteins and interactions in aggregate-crosslinking proteomic data.

Many age-progressive diseases are accompanied by (and likely caused by) the presence of protein aggregation in affected tissues. Protein aggregates are conjoined by complex protein-protein interactions, which remain poorly understood. Knowledge of the proteins that comprise aggregates, and their adherent interfaces, can be useful to identify therapeutic targets to treat or prevent pathology, and to discover small molecules for disease interventions. We present web-based software to evaluate and rank influential proteins and protein-protein interactions based on graph modelling of the cross linked aggregate interactome. We have used two network-graph-based techniques: Leave-One-Vertex-Out (LOVO) and Leave-One-Edge-Out (LOEO), each followed by dimension reduction and calculation of influential vertices and edges using Principal Components Analysis (PCA) implemented as an R program. This method enables researchers to quickly and accurately determine influential proteins and protein-protein interactions present in their aggregate interactome data.

Smoother: on-the-fly processing of interactome data using prefix sums.

Nucleic acid interactome data, such as chromosome conformation capture data and RNA-DNA interactome data, are currently analyzed via pipelines that must be rerun for each new parameter set. A more dynamic approach is desirable since the optimal parameter set is commonly unknown ahead of time and rerunning pipelines is a time-consuming process. We have developed an approach fast enough to process interactome data on-the-fly using a sparse prefix sum index. With this index, we created Smoother, a flexible, multifeatured visualization and analysis tool that allows interactive filtering, e.g. by mapping quality, almost instant comparisons between different normalization approaches, e.g. iterative correction, and ploidy correction. Further, Smoother can overlay other sequencing data or genomic annotations, compare different samples, and perform virtual 4C analysis. Smoother permits a novel way to interact with and explore interactome data, fostering comprehensive, high-quality data analysis. Smoother is available at https://github.com/Siegel-Lab/BioSmoother under the MIT license.

TMEM106B core deposition associates with TDP-43 pathology and is increased in risk SNP carriers for frontotemporal dementia.

Genetic variation at the transmembrane protein 106B gene (TMEM106B) has been linked to risk of frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) through an unknown mechanism. We found that presence of the TMEM106B rs3173615 protective genotype was associated with longer survival after symptom onset in a postmortem FTLD-TDP cohort, suggesting a slower disease course. The seminal discovery that filaments derived from TMEM106B is a common feature in aging and, across a range of neurodegenerative disorders, suggests that genetic variants in TMEM106B could modulate disease risk and progression through modulating TMEM106B aggregation. To explore this possibility and assess the pathological relevance of TMEM106B accumulation, we generated a new antibody targeting the TMEM106B filament core sequence. Analysis of postmortem samples revealed that the TMEM106B rs3173615 risk allele was associated with higher TMEM106B core accumulation in patients with FTLD-TDP. In contrast, minimal TMEM106B core deposition was detected in carriers of the protective allele. Although the abundance of monomeric full-length TMEM106B was unchanged, carriers of the protective genotype exhibited an increase in dimeric full-length TMEM106B. Increased TMEM106B core deposition was also associated with enhanced TDP-43 dysfunction, and interactome data suggested a role for TMEM106B core filaments in impaired RNA transport, local translation, and endolysosomal function in FTLD-TDP. Overall, these findings suggest that prevention of TMEM106B core accumulation is central to the mechanism by which the TMEM106B protective haplotype reduces disease risk and slows progression.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate. We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs. CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

Interactome Analysis Identifies the Role of BZW2 in Promoting Endoplasmic Reticulum-Mitochondria Contact and Mitochondrial Metabolism

Understanding the molecular functions of less-studied proteins is an important task of life science research. Despite reports of basic leucine zipper and W2 domain-containing protein 2 (BZW2) promoting cancer progression first emerging in 2017, little is known about its molecular function. Using a quantitative proteomic approach to identify its interacting proteins, we found that BZW2 interacts with both endoplasmic reticulum (ER) and mitochondrial proteins. We thus hypothesized that BZW2 localizes to and promotes the formation of ER-mitochondria contact sites and that such localization would promote calcium transport from ER to the mitochondria and promote ATP production. Indeed, we found that BZW2 localized to ER-mitochondria contact sites and that BZW2 knockdown decreased ER-mitochondria contact, mitochondrial calcium levels, and ATP production. These findings provide key insights into molecular functions of BZW2, the potential role of BZW2 in cancer progression, and highlight the utility of interactome data in understanding the function of less-studied proteins.

Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein–protein interactions

BackgroundProtein–protein interactions (PPIs) are heavily involved in many biological processes. Consequently, the identification of PPIs in the model plant Arabidopsis is of great significance to deeply understand plant growth and development, and then to promote the basic research of crop improvement. Although many experimental Arabidopsis PPIs have been determined currently, the known interactomic data of Arabidopsis is far from complete. In this context, developing effective machine learning models from existing PPI data to predict unknown Arabidopsis PPIs conveniently and rapidly is still urgently needed.ResultsWe used a large-scale pre-trained protein language model (pLM) called ESM-1b to convert protein sequences into high-dimensional vectors and then used them as the input of multilayer perceptron (MLP). To avoid the performance overestimation frequently occurring in PPI prediction, we employed stringent datasets to train and evaluate the predictive model. The results showed that the combination of ESM-1b and MLP (i.e., ESMAraPPI) achieved more accurate performance than the predictive models inferred from other pLMs or baseline sequence encoding schemes. In particular, the proposed ESMAraPPI yielded an AUPR value of 0.810 when tested on an independent test set where both proteins in each protein pair are unseen in the training dataset, suggesting its strong generalization and extrapolating ability. Moreover, the proposed ESMAraPPI model performed better than several state-of-the-art generic or plant-specific PPI predictors.ConclusionProtein sequence embeddings from the pre-trained model ESM-1b contain rich protein semantic information. By combining with the MLP algorithm, ESM-1b revealed excellent performance in predicting Arabidopsis PPIs. We anticipate that the proposed predictive model (ESMAraPPI) can serve as a very competitive tool to accelerate the identification of Arabidopsis interactome.

Comparative analysis of PDZ-binding motifs in the diacylglycerol kinase family.

Diacylglycerol kinases (DGKs) control local and temporal amounts of diacylglycerol (DAG) and phosphatidic acid (PA) by converting DAG to PA through phosphorylation in cells. Certain DGK enzymes possess C-terminal sequences that encode potential PDZ-binding motifs (PBMs), which could be involved in their recruitment into supramolecular signaling complexes. In this study, we used two different interactomic approaches, quantitative native holdup (nHU) and qualitative affinity purification (AP), both coupled to mass spectrometry (MS) to investigate the PDZ partners associated with the potential PBMs of DGKs. Complementing these results with site-specific affinity interactomic data measured on isolated PDZ domain fragments and PBM motifs, as well as evolutionary conservation analysis of the PBMs of DGKs, we explored functional differences within different DGK groups. All our results indicate that putative PBM sequences of type II enzymes, namely DGKδ, DGKη, and DGKκ, are likely to be nonfunctional. In contrast, type IV enzymes, namely DGKζ and DGKι, possess highly promiscuous PBMs that interact with a set of PDZ proteins with very similar affinity interactomes. The combination of various interactomic assays and evolutionary analyses provides a useful strategy for identifying functional domains and motifs within diverse enzyme families.

DNAR-08. BASE EXCISION REPAIR AS A THERAPEUTIC TARGET FOR DIFFUSE HEMISPHERIC GLIOMA, H3.3G34-MUTANT

Abstract Diffuse hemispheric gliomas with H3.3G34R/V mutations are primarily found in the cerebral hemisphere in adolescents and young adults, with a median age of 16-19 years. H3.3G34R frequently co-occurs with mutations in ATRX and TP53 as well as PDGFRα. We performed mutational signature analysis on a cohort of 28 cases of Diffuse hemispheric glioma, H3G34-mutant (7 WGS and 21 WES). Mutational signatures of defects in mismatch repair (MMR), homologous recombination (HR), and nucleotide excision repair (NER) pathways were found in 86% cases whereas only two (7%) had signatures of impaired base excision repair (BER). Analysis of H3.3G34R interactome data showed loss of association with MMR, HR, and NER pathways, with gains in association with proteins in the BER pathway compared to wildtype Histone H3. We thus hypothesise the Diffuse hemispheric glioma, H3G34-mutant may be dependent on BER over other DNA damage repair pathways. To test if BER could be a therapeutic vulnerability, we knocked down integral members of the BER pathway (UNG, APEX1, MPG, and XRCC1) via shRNA in 4 patient-derived H3.3G34R-mutant cell lines (two primary and two matched recurrent), and found that knockdown of XRCC1 and MPG, led to 99% (p< 0.0001) and 96% (p< 0.0001) cell death, respectively, after 8 days, compared to scrambled control. We next tested Triptolide, a BER inhibitor, and it led to cell death in vitro with IC50s of 1.1 to 1.7 nM. Taken together, these results indicate that inhibition of the BER pathway, specifically of the scaffolding-protein XRCC1, could be used as a novel therapeutic approach for diffuse hemispheric glioma, H3.3G34-mutant.