The lncRNA1-miR6288b-3p-PpTCP4-PpD2 module regulates peach branch number by affecting brassinosteroid biosynthesis.

Branch number is one of the most important agronomic traits of fruit trees such as peach. Little is known about how LncRNA and/or miRNA modules regulate branching through transcription factors. Here, we used molecular and genetic tools to clarify the molecular mechanisms underlying brassinosteroid (BR) altering plant branching. We found that the number of sylleptic branch and BR content in pillar peach ('Zhaoshouhong') was lower than those of standard type ('Okubo'), and exogenous BR application could significantly promote branching. PpTCP4 expressed great differentially comparing 'Zhaoshouhong' with 'Okubo'. PpTCP4 could directly bind to DWARF2 (PpD2) and inhibited its expression. PpD2 was the only one differentially expressed key gene in the path of BR biosynthesis. At the same time, PpTCP4 was identified as a target of miR6288b-3p. LncRNA1 could act as the endogenous target mimic of miR6288b-3p and repress expression of miR6288b-3p. Three deletions and five SNP sites of lncRNA1 promoter were found in 'Zhaoshouhong', which was an important cause of different mRNA level of PpTCP4 and BR content. Moreover, overexpressed PpTCP4 significantly inhibited branching. A novel mechanism in which the lncRNA1-miR6288b-3p-PpTCP4-PpD2 module regulates peach branching number was proposed.

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold.

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively.

Magrolimab in Combination With Azacitidine in Patients With Higher-Risk Myelodysplastic Syndromes: Final Results of a Phase Ib Study.

PURPOSE: Magrolimab is a monoclonal antibody that blocks cluster of differentiation 47, a don't-eat-me signal overexpressed on cancer cells. Cluster of differentiation 47 blockade by magrolimab promotes macrophage-mediated phagocytosis of tumor cells and is synergistic with azacitidine, which increases expression of eat-me signals. We report final phase Ib data in patients with untreated higher-risk myelodysplastic syndromes (MDS) treated with magrolimab and azacitidine (ClinicalTrials.gov identifier: NCT03248479). PATIENTS AND METHODS: Patients with previously untreated Revised International Prognostic Scoring System intermediate-/high-/very high-risk MDS received magrolimab intravenously as a priming dose (1 mg/kg) followed by ramp-up to a 30 mg/kg once-weekly or once-every-2-week maintenance dose. Azacitidine 75 mg/m2 was administered intravenously/subcutaneously once daily on days 1-7 of each 28-day cycle. Primary end points were safety/tolerability and complete remission (CR) rate. RESULTS: Ninety-five patients were treated. Revised International Prognostic Scoring System risk was intermediate/high/very high in 27%, 52%, and 21%, respectively. Fifty-nine (62%) had poor-risk cytogenetics and 25 (26%) had TP53 mutation. The most common treatment-emergent adverse effects included constipation (68%), thrombocytopenia (55%), and anemia (52%). Median hemoglobin change from baseline to first postdose assessment was -0.7 g/dL (range, -3.1 to +2.4). CR rate and overall response rate were 33% and 75%, respectively. Median time to response, duration of CR, duration of overall response, and progression-free survival were 1.9, 11.1, 9.8, and 11.6 months, respectively. Median overall survival (OS) was not reached with 17.1-month follow-up. In TP53-mutant patients, 40% achieved CR with median OS of 16.3 months. Thirty-four patients (36%) had allogeneic stem-cell transplant with 77% 2-year OS. CONCLUSION: Magrolimab + azacitidine was well tolerated with promising efficacy in patients with untreated higher-risk MDS, including those with TP53 mutations. A phase III trial of magrolimab/placebo + azacitidine is ongoing (ClinicalTrials.gov identifier: NCT04313881 [ENHANCE]).

Epigenetic driver mutations in ARID1A shape cancer immune phenotype and immunotherapy.

Whether mutations in cancer driver genes directly affect cancer immune phenotype and T cell immunity remains a standing question. ARID1A is a core member of the polymorphic BRG/BRM-associated factor chromatin remodeling complex. ARID1A mutations occur in human cancers and drive cancer development. Here, we studied the molecular, cellular, and clinical impact of ARID1A aberrations on cancer immunity. We demonstrated that ARID1A aberrations resulted in limited chromatin accessibility to IFN-responsive genes, impaired IFN gene expression, anemic T cell tumor infiltration, poor tumor immunity, and shortened host survival in many human cancer histologies and in murine cancer models. Impaired IFN signaling was associated with poor immunotherapy response. Mechanistically, ARID1A interacted with EZH2 via its carboxyl terminal and antagonized EZH2-mediated IFN responsiveness. Thus, the interaction between ARID1A and EZH2 defines cancer IFN responsiveness and immune evasion. Our work indicates that cancer epigenetic driver mutations can shape cancer immune phenotype and immunotherapy.

The Landscape of Circular RNA in Cancer.

Circular RNAs (circRNAs) are an intriguing class of RNA due to their covalently closed structure, high stability, and implicated roles in gene regulation. Here, we used an exome capture RNA sequencing protocol to detect and characterize circRNAs across >2,000 cancer samples. When compared against Ribo-Zero and RNase R, capture sequencing significantly enhanced the enrichment of circRNAs and preserved accurate circular-to-linear ratios. Using capture sequencing, we built the most comprehensive catalog of circRNA species to date: MiOncoCirc, the first database to be composed primarily of circRNAs directly detected in tumor tissues. Using MiOncoCirc, we identified candidate circRNAs to serve as biomarkers for prostate cancer and were able to detect circRNAs in urine. We further detected a novel class of circular transcripts, termed read-through circRNAs, that involved exons originating from different genes. MiOncoCirc will serve as a valuable resource for the development of circRNAs as diagnostic or therapeutic targets across cancer types.

Epigenetic Reprogramming with Antisense Oligonucleotides Enhances the Effectiveness of Androgen Receptor Inhibition in Castration-Resistant Prostate Cancer.

Advanced prostate cancer initially responds to androgen deprivation therapy (ADT), but the disease inevitably recurs as castration-resistant prostate cancer (CRPC). Although CRPC initially responds to abiraterone and enzalutamide, the disease invariably becomes nonresponsive to these agents. Novel approaches are required to circumvent resistance pathways and to extend survival, but the mechanisms underlying resistance remain poorly defined. Our group previously showed the histone lysine-N-methyltransferase EZH2 to be overexpressed in prostate cancer and quantitatively associated with progression and poor prognosis. In this study, we screened a library of epigenetic inhibitors for their ability to render CRPC cells sensitive to enzalutamide and found that EZH2 inhibitors specifically potentiated enzalutamide-mediated inhibition of proliferation. Moreover, we identified antisense oligonucleotides (ASO) as a novel drug strategy to ablate EZH2 and androgen receptor (AR) expression, which may have advantageous properties in certain settings. RNA-seq, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing demonstrated that EZH2 inhibition altered the AR cistrome to significantly upregulate AR signaling, suggesting an enhanced dependence of CRPC cells on this pathway following inhibition of EZH2. Combination treatment with ASO targeting EZH2 and AR transcripts inhibited prostate cancer cell growth in vitro and in vivo better than single agents. In sum, this study identifies EZH2 as a critical epigenetic regulator of ADT resistance and defines ASO-based cotargeting of EZH2 and AR as a promising strategy for the treatment of CRPC.Significance: Simultaneous targeting of lysine methyltransferase EZH2 and the AR with ASO proves a novel and effective therapeutic strategy in patients with CRPC. Cancer Res; 78(20); 5731-40. ©2018 AACR.

Analysis of the androgen receptor-regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression.

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions.

Motivation: Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA. Results: Here, we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays. Availability and implementation: MechRNA is available for download at https://bitbucket.org/compbio/mechrna. Supplementary information: Supplementary data are available at Bioinformatics online.

Genomic Profiling of Penile Squamous Cell Carcinoma Reveals New Opportunities for Targeted Therapy.

Penile squamous cell carcinoma (PeSCCA) is a rare malignancy for which there are limited treatment options due to a poor understanding of the molecular alterations underlying disease development and progression. Therefore, we performed comprehensive, targeted next-generation sequencing to identify relevant somatic genomic alterations in a retrospective cohort of 60 fixed tumor samples from 43 PeSCCA cases (including 14 matched primary/metastasis pairs). We identified a median of two relevant somatic mutations and one high-level copy-number alteration per sample (range, 0-5 and 0-6, respectively). Expression of HPV and p16 was detectable in 12% and 28% of patients, respectively. Furthermore, advanced clinical stage, lack of p16 expression, and MYC and CCND1 amplifications were significantly associated with shorter time to progression or PeSCCA-specific survival. Notably, four cases harbored EGFR amplifications and one demonstrated CDK4 amplification, genes for which approved and investigational targeted therapies are available. Importantly, although paired primary tumors and lymph node metastases were largely homogeneous for relevant somatic mutations, we identified heterogeneous EGFR amplification in primary tumor/lymph node metastases in 4 of 14 cases, despite uniform EGFR protein overexpression. Likewise, activating HRAS mutations occurred in 8 of 43 cases. Taken together, we provide the first comprehensive molecular PeSCCA analysis, which offers new insight into potential precision medicine approaches for this disease, including strategies targeting EGFR.

Unbiased, scalable sampling of protein loop conformations from probabilistic priors.

BACKGROUND: Protein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. This paper presents Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC), a new Markov chain Monte Carlo algorithm for generating conformations of closed loops according to experimentally available, heterogeneous structural preferences. RESULTS: Our simulation experiments demonstrate that the method computes high-scoring conformations of large loops (>10 residues) orders of magnitude faster than standard Monte Carlo and discrete search techniques. Two new developments contribute to the scalability of the new method. First, structural preferences are specified via a probabilistic graphical model (PGM) that links conformation variables, spatial variables (e.g., atom positions), constraints and prior information in a unified framework. The method uses a sparse PGM that exploits locality of interactions between atoms and residues. Second, a novel method for sampling sub-loops is developed to generate statistically unbiased samples of probability densities restricted by loop-closure constraints. CONCLUSION: Numerical experiments confirm that SLIKMC generates conformation ensembles that are statistically consistent with specified structural preferences. Protein conformations with 100+ residues are sampled on standard PC hardware in seconds. Application to proteins involved in ion-binding demonstrate its potential as a tool for loop ensemble generation and missing structure completion.