Epigenome-wide SRC-1 mediated gene silencing represses cellular differentiation in advanced breast cancer

Abstract Purpose: Despite the clinical utility of endocrine therapies for estrogen receptor–positive (ER) breast cancer, up to 40% of patients eventually develop resistance, leading to disease progression. The molecular determinants that drive this adaptation to treatment remain poorly understood. Methylome aberrations drive cancer growth yet the functional role and mechanism of these epimutations in drug resistance are poorly elucidated. Experimental Design: Genome-wide multi-omics sequencing approach identified a differentially methylated hub of prodifferentiation genes in endocrine resistant breast cancer patients and cell models. Clinical relevance of the functionally validated methyl-targets was assessed in a cohort of endocrine-treated human breast cancers and patient-derived ex vivo metastatic tumors. Results: Enhanced global hypermethylation was observed in endocrine treatment resistant cells and patient metastasis relative to sensitive parent cells and matched primary breast tumor, respectively. Using paired methylation and transcriptional profiles, we found that SRC-1–dependent alterations in endocrine resistance lead to aberrant hypermethylation that resulted in reduced expression of a set of differentiation genes. Analysis of ER-positive endocrine-treated human breast tumors (n = 669) demonstrated that low expression of this prodifferentiation gene set significantly associated with poor clinical outcome (P = 0.00009). We demonstrate that the reactivation of these genes in vitro and ex vivo reverses the aggressive phenotype. Conclusions: Our work demonstrates that SRC-1-dependent epigenetic remodeling is a ’high level’ regulator of the poorly differentiated state in ER-positive breast cancer. Collectively these data revealed an epigenetic reprograming pathway, whereby concerted differential DNA methylation is potentiated by SRC-1 in the endocrine resistant setting. Clin Cancer Res; 24(15); 3692–703. ©2018 AACR.</jats:p

The design, construction and performance of the MICE scintillating fibre trackers

This is the Pre-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 ElsevierCharged-particle tracking in the international Muon Ionisation Cooling Experiment (MICE) will be performed using two solenoidal spectrometers, each instrumented with a tracking detector based on diameter scintillating fibres. The design and construction of the trackers is described along with the quality-assurance procedures, photon-detection system, readout electronics, reconstruction and simulation software and the data-acquisition system. Finally, the performance of the MICE tracker, determined using cosmic rays, is presented.This work was supported by the Science and Technology Facilities Council under grant numbers PP/E003214/1, PP/E000479/1, PP/E000509/1, PP/E000444/1, and through SLAs with STFC-supported laboratories. This work was also supportedby the Fermi National Accelerator Laboratory, which is operated by the Fermi Research Alliance, under contract No. DE-AC02-76CH03000 with the U.S. Department of Energy, and by the U.S. National Science Foundation under grants PHY-0301737,PHY-0521313, PHY-0758173 and PHY-0630052. The authors also acknowledge the support of the World Premier International Research Center Initiative (WPI Initiative), MEXT, Japan

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Process Analytical Technology for the Food Industry

VIII, 301 p. 107 illus., 45 illus. in color.onlin