Missense mutations in pyruvate kinase M2 promote cancer metabolism, oxidative endurance, anchorage independence, and tumor growth in a dominant negative manner.

The present study was designed to examine the functional relevance of two heterozygous mutations (H391Y and K422R), observed earlier by us in the Bloom syndrome condition. Cells stably expressing exogenous wild-type or mutant PKM2 (K422R or H391Y) or co-expressing both wild type and mutant (PKM2-K422R or PKM2-H391Y) were assessed for cancer metabolism and tumorigenic potential. Interestingly, cells co-expressing PKM2 and mutant (K422R or H391Y) showed significantly aggressive cancer metabolism as compared with cells expressing either wild-type or mutant PKM2 independently. A similar trend was observed for oxidative endurance, tumorigenic potential, cellular proliferation, and tumor growth. These observations signify the dominant negative nature of mutations. Remarkably, PKM2-H391Y co-expressed cells showed a maximal effect on all the studied parameters. Such a dominant negative impaired function of PKM2 in tumor development is not known; this study demonstrates for the first time the possible predisposition of Bloom syndrome patients with impaired PKM2 activity to cancer and the importance of studying genetic variations in PKM2 in the future to understand their relevance in cancer in general.

Evaluation of a deep image-to-image network (DI2IN) auto-segmentation algorithm across a network of cancer centers.

PURPOSE/OBJECTIVE S: Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas-based, which required maintaining a library of self-made contours. Searching the collection was computationally intensive and could take several minutes to complete. Deep learning approaches have shown significant benefits compared to atlas-based methods in improving segmentation accuracy and efficiency in auto-segmentation algorithms. This work represents the first multi-institutional study to describe and evaluate an AI algorithm for the auto-segmentation of organs at risk (OARs) based on a deep image-to-image network (DI2IN). MATERIALS/METHODS: The AI-Rad Companion Organs RT (AIRC) algorithm (Siemens Healthineers, Erlangen, Germany) uses a two-step approach for segmentation. In the first step, the target organ region in the optimal input image is extracted using a trained deep reinforcement learning network (DRL), which is then used as input to create the contours in the second step based on DI2IN. The study was initially designed as a prospective single-center evaluation. The automated contours generated by AIRC were evaluated by three experienced board-certified radiation oncologists using a four-point scale where 4 is clinically usable and 1 requires re-contouring. After seeing favorable results in a single-center pilot study, we decided to expand the study to six additional institutions, encompassing eight additional evaluators for a total of 11 physician evaluators across seven institutions. RESULTS: One hundred and fifty-six patients and 1366 contours were prospectively evaluated. The five most commonly contoured organs were the lung (136 contours, average rating = 4.0), spinal cord (106 contours, average rating = 3.1), eye globe (80 contours, average rating = 3.9), lens (77 contours, average rating = 3.9), and optic nerve (75 contours, average rating = 4.0). The average rating per evaluator per contour was 3.6. On average, 124 contours were evaluated by each evaluator. 65% of the contours were rated as 4, and 31% were rated as 3. Only 4% of contours were rated as 1 or 2. Thirty-three organs were evaluated in the study, with 19 structures having a 3.5 or above average rating (ribs, abdominopelvic cavity, skeleton, larynx, lung, aorta, brachial plexus, lens, eye globe, glottis, heart, parotid glands, bladder, kidneys, supraglottic larynx, submandibular glands, esophagus, optic nerve, oral cavity) and the remaining organs having a rating of 3.0 or greater (female breast, proximal femur, seminal vesicles, rectum, sternum, brainstem, prostate, brain, lips, mandible, liver, optic chiasm, spinal cord, spleen). No organ had an average rating below 3. CONCLUSION: AIRC performed well with greater than 95% of contours accepted by treating physicians with no or minor edits. It supported a fully automated workflow with the potential for time savings and increased standardization with the use of AI-powered algorithms for high-quality OAR contouring.

Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis.

Purpose: The autosegmentation algorithm of Siemens Healthineers version VA 30 (AASH) (Siemens Healthineers, Erlangen, Germany) was trained and developed in the male pelvis, with no published data on its usability in the female pelvis. This is the first multi-institutional study to describe and evaluate an artificial intelligence algorithm for autosegmentation of the pelvic nodal region by gender. Methods and Materials: We retrospectively evaluated AASH pelvic nodal autosegmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AASH were evaluated by 1 board-certified radiation oncologist. A 4-point scale was used for each nodal region contour: a score of 4 is clinically usable with minimal edits; a score of 3 requires minor edits (missing nodal contour region, cutting through vessels, or including bowel loops) in 3 or fewer computed tomography slices; a score of 2 requires major edits, as previously defined but in 4 or more computed tomography slices; and a score of 1 requires complete recontouring of the region. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator, and midline presacral nodes. In addition, patients were graded based on their lowest nodal contour score. Statistical analysis was performed using Fisher exact tests and Yates-corrected χ2 tests. Results: Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. Ninety-six percent and 99% of contours required minor edits at most (score of 3 or 4) for female and male patients, respectively (P = .004 using Fisher exact test; P = .007 using Yates correction). No nodal regions had a statistically significant difference in scores between female and male patients. The percentage of patients requiring no more than minor edits was 87% (45 patients) and 92% (47 patients) for female and male patients, respectively (P = .53 using Fisher exact test; P = .55 using Yates correction). Conclusions: AASH pelvic nodal autosegmentation performed very well in both male and female pelvic nodal regions, although with better male pelvic nodal autosegmentation. As autosegmentation becomes more widespread, it may be important to have equal representation from all sexes in training and validation of autosegmentation algorithms.

The potential and pitfalls of using a large language model such as ChatGPT, GPT-4, or LLaMA as a clinical assistant.

OBJECTIVES: This study aims to evaluate the utility of large language models (LLMs) in healthcare, focusing on their applications in enhancing patient care through improved diagnostic, decision-making processes, and as ancillary tools for healthcare professionals. MATERIALS AND METHODS: We evaluated ChatGPT, GPT-4, and LLaMA in identifying patients with specific diseases using gold-labeled Electronic Health Records (EHRs) from the MIMIC-III database, covering three prevalent diseases-Chronic Obstructive Pulmonary Disease (COPD), Chronic Kidney Disease (CKD)-along with the rare condition, Primary Biliary Cirrhosis (PBC), and the hard-to-diagnose condition Cancer Cachexia. RESULTS: In patient identification, GPT-4 had near similar or better performance compared to the corresponding disease-specific Machine Learning models (F1-score ≥ 85%) on COPD, CKD, and PBC. GPT-4 excelled in the PBC use case, achieving a 4.23% higher F1-score compared to disease-specific "Traditional Machine Learning" models. ChatGPT and LLaMA3 demonstrated lower performance than GPT-4 across all diseases and almost all metrics. Few-shot prompts also help ChatGPT, GPT-4, and LLaMA3 achieve higher precision and specificity but lower sensitivity and Negative Predictive Value. DISCUSSION: The study highlights the potential and limitations of LLMs in healthcare. Issues with errors, explanatory limitations and ethical concerns like data privacy and model transparency suggest that these models would be supplementary tools in clinical settings. Future studies should improve training datasets and model designs for LLMs to gain better utility in healthcare. CONCLUSION: The study shows that LLMs have the potential to assist clinicians for tasks such as patient identification but false positives and false negatives must be mitigated before LLMs are adequate for real-world clinical assistance.

Insulin enhances metabolic capacities of cancer cells by dual regulation of glycolytic enzyme pyruvate kinase M2.

BACKGROUND: Insulin is tightly associated with cancer progression; however, mechanistic insights into such observations are poorly understood. Recent studies show that metabolic transformation is critical to cancer cell proliferation. Here, we attempt to understand the role of insulin in promotion of cancer metabolism. To this end, the role of insulin in regulating glycolytic enzyme pyruvate kinase M2 (PKM2) was examined. RESULTS: We observed that insulin up-regulated PKM2 expression, through PI3K/mTOR mediated HIF1α induction, but significantly reduced PKM2 activity independent of this pathway. Drop in PKM2 activity was attributed to subunit dissociation leading to formation of low activity PKM2 oligomers, as assessed by density gradient centrifugation. However, tyrosine 105 phosphorylation of PKM2, known for inhibiting PKM2 activity, remained unaffected on insulin treatment. Interestingly, insulin-induced ROS was found responsible for PKM2 activity reduction. The observed changes in PKM2 status led to augmented cancer metabolism. Insulin-induced PKM2 up-regulation resulted in enhanced aerobic glycolysis as confirmed by PKM2 knockdown studies. Further, PKM2 activity reduction led to characteristic pooling of glycolytic intermediates and increased accumulation of NADPH; suggesting diversion of glucose flux towards macromolecular synthesis, necessary for cancer cell growth. CONCLUSION: The study identifies new PKM2-mediated effects of insulin on cancer metabolism, thus, advancing the understanding of insulin's role in cancer.

Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification.

Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires numerical reasoning, for example, a temperature of 102°F suggests the phenotype Fever. However, while current state-of-the-art phenotyping models using natural language processing (NLP) are in general very efficient in extracting phenotypes, they struggle to extract phenotypes that require numerical reasoning. In this article, we propose a novel unsupervised method that leverages external clinical knowledge and contextualized word embeddings by ClinicalBERT for numerical reasoning in different phenotypic contexts. Experiments show that the proposed method achieves significant improvement against unsupervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 79% and 71%, respectively. Also, the proposed method outperforms supervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 70% and 44%, respectively. In addition, we validate the methodology on clinical use cases where the detected phenotypes significantly contribute to patient stratification systems for a set of diseases, namely, HIV and myocardial infarction (heart attack). Moreover, we find that these phenotypes from clinical text can be used to impute the missing values in structured data, which enrich and improve data quality.

Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use.

OBJECTIVE: Clinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which are crucial for predicting key outcomes in patients in acute care. In this study, we propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information to predict outcomes in the intensive care unit (ICU). This information is complementary to typically used vital signs and laboratory test results. METHODS: In this study, we developed a novel phenotype annotation model to extract the phenotypical features of patients, which were then used as input features of predictive models to predict ICU patient outcomes. We demonstrated and validated this approach by conducting experiments on three ICU prediction tasks, including in-hospital mortality, physiological decompensation and length of stay (LOS) for over 24 000 patients using the Medical Information Mart for Intensive Care (MIMIC-III) dataset. RESULTS: The predictive models incorporating phenotypical information achieved 0.845 (area under the curve-receiver operating characteristic (AUC-ROC)) for in-hospital mortality, 0.839 (AUC-ROC) for physiological decompensation and 0.430 (kappa) for LOS, all of which consistently outperformed the baseline models using only vital signs and laboratory test results. Moreover, we conducted a thorough interpretability study showing that phenotypes provide valuable insights at both the patient and cohort levels. CONCLUSION: The proposed approach demonstrates that phenotypical information complements traditionally used vital signs and laboratory test results and significantly improves the accuracy of outcome prediction in the ICU.

Predictors of Postoperative ICU Admission in Patients With COVID-19-Associated Mucormycosis.

BACKGROUND: Studies exploring factors predicting postoperative ICU requirement in patients with coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) were not found in the literature. The aim was to evaluate the demographic profile, comorbidities, pattern of steroid received, airway assessment, and intraoperative hemodynamic perturbations associated with ICU requirement amongst patients scheduled for sinonasal debridement. METHODS: This is a retrospective cohort study. All CAM patients of ≥18 years were included. The patients' characteristics, comorbidities, pattern of steroid received, airway assessment, intraoperative hemodynamic perturbations, and outcome data were retrieved. RESULTS: A total of 130 patients were included. Thirty got admitted to ICU, out of which 26 expired. Amongst the various comorbidities, diabetes was the most common (93.85%) and was associated with higher chances of ICU requirement. Of patients with a history of steroid intake, 71% had a significantly higher risk of ICU admission. Out of 30 patients admitted to ICU, 87% (n=26) received invasive ventilation, and the rest were admitted for observation only. CONCLUSION: Middle age, uncontrolled diabetes, history of steroid intake, increased levels of serum creatinine with low potassium, and increased total leucocyte count are the independent risk factors predicting postoperative ICU admission amongst patients with CAM scheduled for sinonasal debridement.

Dominant negative mutations affect oligomerization of human pyruvate kinase M2 isozyme and promote cellular growth and polyploidy.

This study was designed to understand the mechanism and functional implication of the two heterozygous mutations (H391Y and K422R) of human pyruvate kinase M2 isozyme (PKM(2)) observed earlier in a Bloom syndrome background. The co-expression of homotetrameric wild type and mutant PKM(2) in the cellular milieu resulting in the interaction between the two at the monomer level was substantiated further by in vitro experiments. The cross-monomer interaction significantly altered the oligomeric state of PKM(2) by favoring dimerization and heterotetramerization. In silico study provided an added support in showing that hetero-oligomerization was energetically favorable. The hetero-oligomeric populations of PKM(2) showed altered activity and affinity, and their expression resulted in an increased growth rate of Escherichia coli as well as mammalian cells, along with an increased rate of polyploidy. These features are known to be essential to tumor progression. This study provides insight in understanding the modulated role of large oligomeric multifunctional proteins such as PKM(2) by affecting cellular behavior, which is an essential observation to understand tumor sustenance and progression and to design therapeutic intervention in future.

Development of Gene Therapy Vectors: Remaining Challenges.

Almost 20 years after the tragic death of a young patient due to an experimental gene therapy trial to treat Ornithine Transcarboxylase deficiency, the FDA approved its first landmark gene therapy drug i.e. Luxturna® to treat inherited blindness, and dozens of gene therapy studies are underway. Whether it is replacing the mutant copies of the gene with the wild type one or editing the mutant one in or ex-vivo to elicit the production of functional proteins, numerous viral and non-viral vectors for delivering the gene payload are being evaluated. While, non-viral vectors avoid or mitigate limiting factors such as immunogenicity and the presence of neutralizing antibodies (NAbs), viral vectors such as recombinant adeno-associated viruses (AAVs) have shown early success as a delivery vehicle, because of the overall safety, target specificity, and long-term stability profile. Nonetheless, multiple challenges during the AAV product development and approval process are still looming. AAV serotypes are continuously being engineered which requires multiple cell-based assays to not only assess the neutralizing antibodies (NAb) seroprevalence but also to develop the in-vitro bio potency assays. Hence, we focus on some critical aspects of the AAVs that determine the path forward for pre-clinical and clinical product development.

Dosage compensation in the mouse balances up-regulation and silencing of X-linked genes.

Dosage compensation in mammals involves silencing of one X chromosome in XX females and requires expression, in cis, of Xist RNA. The X to be inactivated is randomly chosen in cells of the inner cell mass (ICM) at the blastocyst stage of development. Embryonic stem (ES) cells derived from the ICM of female mice have two active X chromosomes, one of which is inactivated as the cells differentiate in culture, providing a powerful model system to study the dynamics of X inactivation. Using microarrays to assay expression of X-linked genes in undifferentiated female and male mouse ES cells, we detect global up-regulation of expression (1.4- to 1.6-fold) from the active X chromosomes, relative to autosomes. We show a similar up-regulation in ICM from male blastocysts grown in culture. In male ES cells, up-regulation reaches 2-fold after 2-3 weeks of differentiation, thereby balancing expression between the single X and the diploid autosomes. We show that silencing of X-linked genes in female ES cells occurs on a gene-by-gene basis throughout differentiation, with some genes inactivating early, others late, and some escaping altogether. Surprisingly, by allele-specific analysis in hybrid ES cells, we also identified a subgroup of genes that are silenced in undifferentiated cells. We propose that X-linked genes are silenced in female ES cells by spreading of Xist RNA through the X chromosome territory as the cells differentiate, with silencing times for individual genes dependent on their proximity to the Xist locus.

Adeno-associated virus-mediated expression of human butyrylcholinesterase to treat organophosphate poisoning.

Rare diseases defined by genetic mutations are classic targets for gene therapy. More recently, researchers expanded the use of gene therapy in non-clinical studies to infectious diseases through the delivery of vectorized antibodies to well-defined antigens. Here, we further extend the utility of gene therapy beyond the "accepted" indications to include organophosphate poisoning. There are no approved preventives for the multi-organ damage resulting from acute or chronic exposure to organophosphates. We show that a single intramuscular injection of adeno-associated virus vector produces peak expression (~0.5 mg/ml) of active human butyrylcholinesterase (hBChE) in mice serum within 3-4 weeks post-treatment. This expression is sustained for up to 140 days post-injection with no silencing. Sustained expression of hBChE provided dose-dependent protection against VX in male and female mice despite detectable antibodies to hBChE in some mice, thereby demonstrating that expression of hBChE in vivo in mouse muscle is an effective prophylactic against organophosphate poisoning.

Implementation and relevance of FAIR data principles in biopharmaceutical R&D.

Biopharmaceutical industry R&D, and indeed other life sciences R&D such as biomedical, environmental, agricultural and food production, is becoming increasingly data-driven and can significantly improve its efficiency and effectiveness by implementing the FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management and stewardship. By so doing, the plethora of new and powerful analytical tools such as artificial intelligence and machine learning will be able, automatically and at scale, to access the data from which they learn, and on which they thrive. FAIR is a fundamental enabler for digital transformation.

In silico screening, genotyping, molecular dynamics simulation and activity studies of SNPs in pyruvate kinase M2.

Role of, 29-non-synonymous, 15-intronic, 3-close to UTR, single nucleotide polymorphisms (SNPs) and 2 mutations of Human Pyruvate Kinase (PK) M2 were investigated by in-silico and in-vitro functional studies. Prediction of deleterious substitutions based on sequence homology and structure based servers, SIFT, PANTHER, SNPs&GO, PhD-SNP, SNAP and PolyPhen, depicted that 19% emerged common between all the mentioned programs. SNPeffect and HOPE showed three substitutions (C31F, Q310P and S437Y) in-silico as deleterious and functionally important. In-vitro activity assays showed C31F and S437Y variants of PKM2 with reduced activity, while Q310P variant was catalytically inactive. The allosteric activation due to binding of fructose 1-6 bisphosphate (FBP) was compromised in case of S437Y nsSNP variant protein. This was corroborated through molecular dynamics (MD) simulation study, which was also carried out in other two variant proteins. The 5 intronic SNPs of PKM2, associated with sporadic breast cancer in a case-control study, when subjected to different computational analyses, indicated that 3 SNPs (rs2856929, rs8192381 and rs8192431) could generate an alternative transcript by influencing splicing factor binding to PKM2. We propose that these, potentially functional and important variations, both within exons and introns, could have a bearing on cancer metabolism, since PKM2 has been implicated in cancer in the recent past.

Pyruvate kinase M2: regulatory circuits and potential for therapeutic intervention.

Cancer cells are characterized by reprogramming of energy metabolism. Over the last decade, understanding of the metabolic changes that occur in cancer has increased dramatically, with great interest in targeting metabolism for cancer therapy. Pyruvate kinase isoenzyme type M2 (abbreviations: PKM2, M2-PK) plays a key role in modulating glucose metabolism to support cell proliferation. PKM2, like other PK isoforms, catalyzes the last energy-generating step in glycolysis, but is unique in its capacity to be regulated. PKM2 is regulated at several cellular levels, including gene expression, alternative splicing and post-translational modification. In addition, PKM2 is regulated by key metabolic intermediates and interacts with more than twenty different proteins. Hence, this isoenzyme is an important regulator of glycolysis, and additionally functions in other novel roles that have recently emerged. Recent evidence indicates that intervening with the complex regulatory network of PKM2 has severe consequences on tumor cell proliferation, indicating the potential of this enzyme as a target for tumor therapy.

Interplay between epigenetics & cancer metabolism.

Nutrient utilization is dramatically altered when cells receive signals to proliferate. Characteristic metabolic changes enable cells to meet the large biosynthetic demands associated with cell growth and division. Changes in rate-limiting glycolytic enzymes redirect metabolism to support growth and proliferation. Metabolic reprogramming in cancer is controlled largely by oncogenic activation of signal transduction pathways and transcription factors. Although less well understood, epigenetic mechanisms may seem to contribute to the regulation of metabolic gene expression in cancer. Reciprocally, accumulating evidence suggests that metabolic alterations may affect the epigenome. Understanding the relation between metabolism and epigenetics in cancer cells may open new avenues for anti-cancer strategies. In multi-cellular systems, molecular signals promoting cell growth and proliferation mediate the switch between catabolism and anabolism. Both normal proliferating and cancer cells must achieve high levels of macromolecular biosynthesis to provide the raw materials needed to produce new daughter cells. From a therapeutic view point, it is of great interest to determine metabolic differences that exist between normal proliferating cells and cancer cells. Cancer cells also exhibit significant alterations in the epigenome. Recent data indicate that cellular metabolism and epigenetic phenomenon are engaged in crosstalk. Considering current efforts to target both cancer metabolism and epigenetics, an understanding of the relationship between these two key features is of paramount importance. Here we discuss the role of cellular metabolism in regulation of the epigenome. Moreover, we discuss how epigenetic changes may contribute to establish cancer-specific metabolic features.

Pyruvate kinase M2 and cancer: an updated assessment.

Cancer cells are characterized by high glycolytic rates to support energy regeneration and anabolic metabolism, along with the expression of pyruvate kinase isoenzyme M2 (PKM2). The latter catalyzes the last step of glycolysis and reprograms the glycolytic flux to feed the special metabolic demands of proliferating cells. Besides, PKM2 has moonlight functions, such as gene transcription, favoring cancer. Accumulating evidence suggests a critical role played by the low-activity-dimeric PKM2 in tumor progression, supported by the identification of mutations which result in the down-regulation of its activity and tumorigenesis in a nude mouse model. This review discusses PKM2 regulation and the benefits it confers to cancer cells. Further, conflicting views on PKM2's role in cancer, its therapeutic relevance and future directions in the field are also discussed.

A BRISC-SHMT complex deubiquitinates IFNAR1 and regulates interferon responses.

Lysine63-linked ubiquitin (K63-Ub) chains represent a particular ubiquitin topology that mediates proteasome-independent signaling events. The deubiquitinating enzyme (DUB) BRCC36 segregates into distinct nuclear and cytoplasmic complexes that are specific for K63-Ub hydrolysis. RAP80 targets the five-member nuclear BRCC36 complex to K63-Ub chains at DNA double-strand breaks. The alternative four-member BRCC36 containing complex (BRISC) lacks a known targeting moiety. Here, we identify serine hydroxymethyltransferase (SHMT) as a previously unappreciated component that fulfills this function. SHMT directs BRISC activity at K63-Ub chains conjugated to the type 1 interferon (IFN) receptor chain 1 (IFNAR1). BRISC-SHMT2 complexes localize to and deubiquitinate actively engaged IFNAR1, thus limiting its K63-Ub-mediated internalization and lysosomal degradation. BRISC-deficient cells and mice exhibit attenuated responses to IFN and are protected from IFN-associated immunopathology. These studies reveal a mechanism of DUB regulation and suggest a therapeutic use of BRISC inhibitors for treating pathophysiological processes driven by elevated IFN responses.

Genes are often sheltered from the global histone hyperacetylation induced by HDAC inhibitors.

Histone deacetylase inhibitors (HDACi) are increasingly used as therapeutic agents, but the mechanisms by which they alter cell behaviour remain unclear. Here we use microarray expression analysis to show that only a small proportion of genes (∼9%) have altered transcript levels after treating HL60 cells with different HDACi (valproic acid, Trichostatin A, suberoylanilide hydroxamic acid). Different gene populations respond to each inhibitor, with as many genes down- as up-regulated. Surprisingly, HDACi rarely induced increased histone acetylation at gene promoters, with most genes examined showing minimal change, irrespective of whether genes were up- or down-regulated. Many genes seem to be sheltered from the global histone hyperacetyation induced by HDACi.

RNF4-dependent hybrid SUMO-ubiquitin chains are signals for RAP80 and thereby mediate the recruitment of BRCA1 to sites of DNA damage.

The DNA repair function of the breast cancer susceptibility protein BRCA1 depends in part on its interaction with RAP80, which targets BRCA1 to DNA double-strand breaks (DSBs) through recognition of K63-linked polyubiquitin chains. The localization of BRCA1 to DSBs also requires sumoylation. We demonstrated that, in addition to having ubiquitin-interacting motifs, RAP80 also contains a SUMO-interacting motif (SIM) that is critical for recruitment to DSBs. In combination with the ubiquitin-binding activity of RAP80, this SIM enabled RAP80 to bind with nanomolar affinity to hybrid chains consisting of ubiquitin conjugated to SUMO. Furthermore, RNF4, a SUMO-targeted ubiquitin E3 ligase that synthesizes hybrid SUMO-ubiquitin chains, localized to DSBs and was critical for the recruitment of RAP80 and BRCA1 to sites of DNA damage. Our findings, therefore, connect ubiquitin- and SUMO-dependent DSB recognition, revealing that RNF4-synthesized hybrid SUMO-ubiquitin chains are recognized by RAP80 to promote BRCA1 recruitment and DNA repair.