Fowlerandreassen9792

The TMPRSS2-ERG gene fusion is the most frequent alteration observed in human prostate cancer. However, its role in disease progression is still unclear. learn more In this study, we uncover an important mechanism promoting ERG oncogenic activity. We show that ERG is methylated by Enhancer of zest homolog 2 (EZH2) at a specific lysine residue (K362) located within the internal auto-inhibitory domain. Mechanistically, K362 methylation modifies intra-domain interactions, favors DNA binding and enhances ERG transcriptional activity. In a genetically engineered mouse model of ERG fusion-positive prostate cancer (Pb-Cre4 Pten flox/flox Rosa26-ERG, ERG/PTEN), ERG K362 methylation is associated with PTEN loss and progression to invasive adenocarcinomas. In both ERG positive VCaP cells and ERG/PTEN mice, PTEN loss results in AKT activation and EZH2 phosphorylation at serine 21 that favors ERG methylation. We find that ERG and EZH2 interact and co-occupy several sites in the genome forming trans-activating complexes. Consistently, ERG/EZH2 co-regulated target genes are deregulated preferentially in tumors with concomitant ERG gain and PTEN loss and in castration-resistant prostate cancers. Collectively, these findings identify ERG methylation as a post-translational modification sustaining disease progression in ERG-positive prostate cancers.Potato is the third most important staple food crop. To address challenges associated with global food security, a hybrid potato breeding system, aimed at converting potato from a tuber-propagated tetraploid crop into a seed-propagated diploid crop through crossing inbred lines, is under development. However, given that most diploid potatoes are self-incompatible, this represents a major obstacle which needs to be addressed in order to develop inbred lines. Here, we report on a self-compatible diploid potato, RH89-039-16 (RH), which can efficiently induce a mating transition from self-incompatibility to self-compatibility, when crossed to self-incompatible lines. We identify the S-locusinhibitor (Sli) gene in RH, capable of interacting with multiple allelic variants of the pistil-specific S-ribonucleases (S-RNases). Further, Sli gene functions like a general S-RNase inhibitor, to impart SC to RH and other self-incompatible potatoes. Discovery of Sli now offers a path forward for the diploid hybrid breeding program.Unrelated cord blood transplantation (UCBT) is an effective treatment for hematopoietic disorders. However, this attractive approach is frequently accompanied by pre-engraftment syndrome (PES), severe cases of PES are associated with enhanced mortality and morbidity, but the pathogenesis of PES remains unclear. Here we show that GM-CSF produced by cord blood-derived inflammatory monocytes drives PES pathology, and that monocytes are the main source of IL-6 during PES. Further, we report the outcome of a single arm, single-center clinical study of tocilizumab in the treatment of steroid-refractory severe PES patients (www.chictr.org.cn ChiCTR1800015472). The study met the primary outcome measure since none of the patients was nonrelapse death during the 100 days follow-up. The study also met key secondary outcomes measures of neutrophil engraftment and hematopoiesis. These findings offer a therapeutic strategy with which to tackle PES and improve nonrelapse mortality.Optoacoustic vibrations in optical fibres have enabled spatially resolved sensing, but the weak electrostrictive force hinders their application. Here, we introduce photothermally induced acoustic vibrations (PTAVs) to realize high-performance fibre-based optoacoustic sensing. Strong acoustic vibrations with a wide range of axial wavenumbers kz are photothermally actuated by using a focused pulsed laser. The local transverse resonant frequency and loss coefficient can be optically measured by an intra-core acoustic sensor via spectral analysis. Spatially resolved sensing is further achieved by mechanically scanning the laser spot. The experimental results show that the PTAVs can be used to resolve the acoustic impedance of the surrounding fluid at a spatial resolution of approximately 10 μm and a frame rate of 50 Hz. As a result, PTAV-based optoacoustic sensing can provide label-free visualization of the diffusion dynamics in microfluidics at a higher spatiotemporal resolution.Electrochemical water splitting is one of the most sustainable approaches for generating hydrogen. Because of the inherent constraints associated with the architecture and materials, the conventional alkaline water electrolyzer and the emerging proton exchange membrane electrolyzer are suffering from low efficiency and high materials/operation costs, respectively. Herein, we design a membrane-free flow electrolyzer, featuring a sandwich-like architecture and a cyclic operation mode, for decoupled overall water splitting. Comprised of two physically-separated compartments with flowing H2-rich catholyte and O2-rich anolyte, the cell delivers H2 with a purity >99.1%. Its low internal ohmic resistance, highly active yet affordable bifunctional catalysts and efficient mass transport enable the water splitting at current density of 750 mA cm-2 biased at 2.1 V. The eletrolyzer works equally well both in deionized water and in regular tap water. This work demonstrates the opportunity of combining the advantages of different electrolyzer concepts for water splitting via cell architecture and materials design, opening pathways for sustainable hydrogen generation.The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.