Spectroscopy along flerovium decay chains. III. Details on experiment, analysis, 282Cn, and spontaneous fission branches

Flerovium isotopes (element Z = 114) were produced in the fusion-evaporation reactions 48Ca+242,244Pu and studied with an upgraded TASISpec decay station placed in the focal plane of the gas-filled separator TASCA at the GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt, Germany. Twenty-nine flerovium decay chains were identified by means of correlated implantation, α decay, and spontaneous fission events. Data analysis aspects and statistical assessments, primarily based on measured rates of various events, which laid the foundation for the comprehensive spectroscopic information on the flerovium decay chains, are presented in detail. Various decay scenarios of an excited state observed in 282Cn are examined in depth with the help of GEANT4 simulations and assessed by predictions of beyond mean-field calculations including triaxial shape degrees of freedom. Previous, revised, and newly derived fission probabilities of even-even superheavy nuclei are compared with various theoretical predictions

Spectroscopy along flerovium decay chains. II. Fine structure in odd-A 289Fl

Fifteen correlated α-decay chains starting from the odd-A superheavy nucleus 289Fl were observed following the fusion-evaporation reaction 48Ca+244Pu. The results call for at least two parallel α-decay sequences starting from at least two different states of 289Fl. This implies that close-lying levels in nuclei along these chains have quite different spin-parity assignments. Further, observed α-electron and α-photon coincidences, as well as the α-decay fine structure along the decay chains, suggest a change in the ground-state spin assignment between 285Cn and 281Ds. Our experimental results, on the excited level structure of the heaviest odd-N nuclei to date, provide a direct testing ground for theory. This is illustrated by comparison with new nuclear structure calculations based on the symmetry-conserving configuration mixing theory

Chronic Exposure to the Herbicide, Atrazine, Causes Mitochondrial Dysfunction and Insulin Resistance

There is an apparent overlap between areas in the USA where the herbicide, atrazine (ATZ), is heavily used and obesity-prevalence maps of people with a BMI over 30. Given that herbicides act on photosystem II of the thylakoid membrane of chloroplasts, which have a functional structure similar to mitochondria, we investigated whether chronic exposure to low concentrations of ATZ might cause obesity or insulin resistance by damaging mitochondrial function. Sprague-Dawley rats (n = 48) were treated for 5 months with low concentrations (30 or 300 µg kg−1 day−1) of ATZ provided in drinking water. One group of animals was fed a regular diet for the entire period, and another group of animals was fed a high-fat diet (40% fat) for 2 months after 3 months of regular diet. Various parameters of insulin resistance were measured. Morphology and functional activities of mitochondria were evaluated in tissues of ATZ-exposed animals and in isolated mitochondria. Chronic administration of ATZ decreased basal metabolic rate, and increased body weight, intra-abdominal fat and insulin resistance without changing food intake or physical activity level. A high-fat diet further exacerbated insulin resistance and obesity. Mitochondria in skeletal muscle and liver of ATZ-treated rats were swollen with disrupted cristae. ATZ blocked the activities of oxidative phosphorylation complexes I and III, resulting in decreased oxygen consumption. It also suppressed the insulin-mediated phosphorylation of Akt. These results suggest that long-term exposure to the herbicide ATZ might contribute to the development of insulin resistance and obesity, particularly where a high-fat diet is prevalent

Matrix Metalloproteinase-Induced Epithelial-Mesenchymal Transition in Breast Cancer

Matrix metalloproteinases (MMPs) degrade and modify the extracellular matrix (ECM) as well as cell-ECM and cell-cell contacts, facilitating detachment of epithelial cells from the surrounding tissue. MMPs play key functions in embryonic development and mammary gland branching morphogenesis, but they are also upregulated in breast cancer, where they stimulate tumorigenesis, cancer cell invasion and metastasis. MMPs have been investigated as potential targets for cancer therapy, but clinical trials using broad-spectrum MMP inhibitors yielded disappointing results, due in part to lack of specificity toward individual MMPs and specific stages of tumor development. Epithelial-mesenchymal transition (EMT) is a developmental process in which epithelial cells take on the characteristics of invasive mesenchymal cells, and activation of EMT has been implicated in tumor progression. Recent findings have implicated MMPs as promoters and mediators of developmental and pathogenic EMT processes in the breast. In this review, we will summarize recent studies showing how MMPs activate EMT in mammary gland development and in breast cancer, and how MMPs mediate breast cancer cell motility, invasion, and EMT-driven breast cancer progression. We also suggest approaches to inhibit these MMP-mediated malignant processes for therapeutic benefit

Behavioral abnormalities in mice lacking mesenchyme-specific Pten

Phosphatase and tensin homolog (Pten) is a negative regulator of cell proliferation and growth. Using a Cre-recombinase approach with Lox sequences flanking the fibroblast-specific protein 1 (Fsp1 aka S100A4; a mesenchymal marker), we probed sites of expression using a beta-galactosidase Rosa26(LoxP) reporter allele; the transgene driving deletion of Pten (exons 4-5) was found throughout the brain parenchyma and pituitary, suggesting that deletion of Pten in Fsp1-positive cells may influence behavior. Because CNS-specific deletion of Pten influences social and anxiety-like behaviors and S100A4 is expressed in astrocytes, we predicted that loss of Pten in Fsp1-expressing cells would result in deficits in social interaction and increased anxiety. We further predicted that environmental enrichment would compensate for genetic deficits in these behaviors. We conducted a battery of behavioral assays on Fsp1-Cre;Pten(LoxP/LoxP) male and female homozygous knockouts (Pten(-/-)) and compared their behavior to Pten(LoxP/LoxP) (Pten(+/+)) conspecifics. Despite extensive physical differences (including reduced hippocampal size) and deficits in sensorimotor function, Pten(-/-) mice behaved remarkably similar to control mice on nearly all behavioral tasks. These results suggest that the social and anxiety-like phenotypes observed in CNS-specific Pten(-/-) mice may depend on neuronal Pten, as lack of Pten in Fsp1-expressing cells of the CNS had little effect on these behaviors

IntLIM: integration using linear models of metabolomics and gene expression data

Abstract Background Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. Results The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. Conclusions IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub (https://github.com/mathelab/IntLIM) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results