Citations

Abstract The oncogenic transcription factor c-Maf has been proposed as an ideal therapeutic target for multiple myeloma (MM), but how to achieve it is still elusive. In the present study, we found the Otub1/c-Maf axis could be a potential target. Otub1, an OTU family deubiquitinase, was found to interact with c-Maf by mass spectrometry. Otub1 abrogates c-Maf K48-linked polyubiquitination, thus preventing its degradation and enhancing its transcriptional activity. Specifically, this deubiquitinating activity depends on its Lys71 and the N terminus but is independent of UBE2O, a known E2 of c-Maf. Otub1 promotes MM cell survival and MM tumor growth. In contrast, silence of Otub1 leads to c-Maf degradation and c-Maf-expressing MM cell apoptosis. Therefore, the Otub1/c-Maf axis could be a therapeutic target of MM. In order to explore this concept, we performed a c-Maf recognition element–driven luciferase-based screen against US Food and Drug Administration–approved drugs and natural products, from which the generic cardiac glycoside lanatoside C (LanC) is found to prevent c-Maf deubiquitination and induces its degradation by disrupting the interaction of Otub1 and c-Maf. Consequently, LanC inhibits c-Maf transcriptional activity, induces c-Maf-expressing MM cell apoptosis, and suppresses MM growth and prolongs overall survival of model mice, but without apparent toxicity. Therefore, the present study identifies Otub1 as a novel deubiquitinase of c-Maf and establishes that the Otub1/c-Maf axis is a potential therapeutic target for MM.

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.

Abstract Cellular senescence restrains the expansion of neoplastic cells through several layers of regulation, including epigenetic decoration of chromatin structure and functional modulation of bioactive components. Here we report that expression of the histone H3-specific demethylase KDM4 is upregulated in human stromal cells upon cellular senescence. In clinical oncology, upregulated KDM4 and diminished H3K9/H3K36 methylation are correlated with adverse survival of cancer patients post-chemotherapy. Global chromatin accessibility mapping via ATAC-seq and expression profiling through RNA-seq reveal extensive reorganization of chromosomes and spatiotemporal reprogramming of the transcriptomic landscape, events responsible for development of the senescence-associated secretory phenotype (SASP). Selectively targeting KDM4 dampens the SASP of senescent stromal cells and enhances the apoptotic index of cancer cells in the treatment-damaged tumor microenvironment (TME), together prolonging overall survival of experimental animals. Our study supports the dynamic change of H3K9/H3K36 methylation marks during cellular senescence, identifies an unusually permissive chromatin state, unmasks KDM4 as a key modulator of the SASP, and presents a novel therapeutic avenue to manipulate cellular senescence and curtail age-related pathologies.

Background and Purpose: Stroke is a complex disease with multiple genetic and environmental risk factors. Blacks endure a nearly 2-fold greater risk of stroke and are 2× to 3× more likely to die from stroke than European Americans. Methods: The COMPASS (Consortium of Minority Population Genome-Wide Association Studies of Stroke) has conducted a genome-wide association meta-analysis of stroke in >22 000 individuals of African ancestry (3734 cases, 18 317 controls) from 13 cohorts. Results: In meta-analyses, we identified one single nucleotide polymorphism (rs55931441) near the HNF1A gene that reached genome-wide significance ( P =4.62×10 −8 ) and an additional 29 variants with suggestive evidence of association ( P <1×10 −6 ), representing 24 unique loci. For validation, a look-up analysis for a 100 kb region flanking the COMPASS single nucleotide polymorphism was performed in SiGN (Stroke Genetics Network) Europeans, SiGN Hispanics, and METASTROKE (Europeans). Using a stringent Bonferroni correction P value of 2.08×10 −3 (0.05/24 unique loci), we were able to validate associations at the HNF1A locus in both SiGN ( P =8.18×10 −4 ) and METASTROKE ( P =1.72×10 −3 ) European populations. Overall, 16 of 24 loci showed evidence for validation across multiple populations. Previous studies have reported associations between variants in the HNF1A gene and lipids, C-reactive protein, and risk of coronary artery disease and stroke. Suggestive associations with variants in the SFXN4 and TMEM108 genes represent potential novel ischemic stroke loci. Conclusions: These findings represent the most thorough investigation of genetic determinants of stroke in individuals of African descent, to date.

Abstract Macropinocytosis supports the metabolic requirement of RAS‐transformed pancreatic ductal adenocarcinoma cells (PDACs). However, regulators of RAS‐transformation (activation) that lead to macropinocytosis have not been identified. Herein, we report that UBAP2 (ubiquitin‐binding associated protein 2), regulates the activation of KRAS and macropinocytosis in pancreatic cancer. We demonstrate that UBAP2 is highly expressed in both pancreatic cancer cell lines and tumor tissues of PDAC patients. The expression of UBAP2 is associated with poor overall survival in several cancers, including PDAC. Silencing UBAP2 decreases the levels of activated KRAS, and inhibits macropinocytosis, and tumor growth in vivo. Using a UBAP2‐deletion construct, we demonstrate that the UBA‐domain of UBAP2 is critical for the regulation of macropinocytosis and maintaining the levels of activated KRAS. In addition, UBAP2 regulates RAS downstream signaling and helps maintain RAS in the GTP‐bound form. However, the exact mechanism by which UBAP2 regulates KRAS activation is unknown and needs further investigation. Thus, UBAP2 may be exploited as a potential therapeutic target to inhibit macropinocytosis and tumor growth in activated KRAS‐driven cancers.

Abstract Registry-based epidemiologic studies suggest associations between chronic inflammatory intestinal diseases and pancreatic ductal adenocarcinoma (PDAC). As genetic susceptibility contributes to a large proportion of chronic inflammatory intestinal diseases, we hypothesize that the genomic regions surrounding established genome-wide associated variants for these chronic inflammatory diseases are associated with PDAC. We examined the association between PDAC and genomic regions (±500 kb) surrounding established common susceptibility variants for ulcerative colitis, Crohn's disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis. We analyzed summary statistics from genome-wide association studies data for 8,384 cases and 11,955 controls of European descent from two large consortium studies using the summary data-based adaptive rank truncated product method to examine the overall association of combined genomic regions for each inflammatory disease group. Combined genomic susceptibility regions for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis were associated with PDAC at P values &lt; 0.05 (0.0040, 0.0057, 0.011, and 3.4 × 10−6, respectively). After excluding the 20 PDAC susceptibility regions (±500 kb) previously identified by GWAS, the genomic regions for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained associated with PDAC (P = 0.0029, 0.0057, and 0.0098, respectively). Genomic regions for celiac disease (P = 0.22) and primary sclerosing cholangitis (P = 0.078) were not associated with PDAC. Our results support the hypothesis that genomic regions surrounding variants associated with inflammatory intestinal diseases, particularly, ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis are associated with PDAC. Significance: The joint effects of common variants in genomic regions containing susceptibility loci for inflammatory bowel disease and chronic pancreatitis are associated with PDAC and may provide insights to understanding pancreatic cancer etiology.

ObjectiveTo compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro‐ophthalmologists in classifying optic disc appearance.MethodsThe deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro‐ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated.ResultsThe system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96–0.98), 0.96 (95% CI = 0.94–0.97), and 0.89 (95% CI = 0.87–0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67–0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68–0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61–0.70) between the system and Expert 2.InterpretationThe performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro‐ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785–795