Zinc transporter gene expression is regulated by pro-inflammatory cytokines: a potential role for zinc transporters in beta-cell apoptosis?

<p>Abstract</p> <p>Background</p> <p>β-cells are extremely rich in zinc and zinc homeostasis is regulated by zinc transporter proteins. β-cells are sensitive to cytokines, interleukin-1β (IL-1β) has been associated with β-cell dysfunction and -death in both type 1 and type 2 diabetes. This study explores the regulation of zinc transporters following cytokine exposure.</p> <p>Methods</p> <p>The effects of cytokines IL-1β, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) on zinc transporter gene expression were measured in INS-1-cells and rat pancreatic islets. Being the more sensitive transporter, we further explored ZnT8 (Slc30A8): the effect of ZnT8 over expression on cytokine induced apoptosis was investigated as well as expression of the insulin gene and two apoptosis associated genes, BAX and BCL2.</p> <p>Results</p> <p>Our results showed a dynamic response of genes responsible for β-cell zinc homeostasis to cytokines: IL-1β down regulated a number of zinc-transporters, most strikingly ZnT8 in both islets and INS-1 cells. The effect was even more pronounced when mixing the cytokines. TNF-α had little effect on zinc transporter expression. IFN-γ down regulated a number of zinc transporters. Insulin expression was down regulated by all cytokines. ZnT8 over expressing cells were more sensitive to IL-1β induced apoptosis whereas no differences were observed with IFN-γ, TNF-α, or a mixture of cytokines.</p> <p>Conclusion</p> <p>The zinc transporting system in β-cells is influenced by the exposure to cytokines. Particularly ZnT8, which has been associated with the development of diabetes, seems to be cytokine sensitive.</p

THIELE CENTRE for applied mathematics in natural science Asymptotics for Estimating Equations in Hidden Markov Models ASYMPTOTICS FOR ESTIMATING EQUATIONS IN HIDDEN MARKOV MODELS

Abstract Results on asymptotic normality for the maximum likelihood estimate in hidden Markov models are extended in two directions. The stationarity assumption is relaxed, which allows for a covariate process influencing the hidden Markov process. Furthermore a class of estimating equations is considered instead of the maximum likelihood estimate. The basic ingredients are mixing properties of the process and a general central limit theorem for weakly dependent variables. The results are illustrated with a cyclic model for the progesterone concentration in cowmilk

On oracle efficiency of the ROAD classification rule

Abstract We show that the ROAD classifier of Fan, Feng and Tong (2012) asymptotically has the same misclassification rate as the corresponding oracle based classifier

Statistical Inference in Evolutionary Models of DNA Sequences via the EM Algorithm

We describe statistical inference in continuous time Markov processes of DNA sequences related by a phylogenetic tree. The maximum likelihood estimator can be found by the expectation maximization (EM) algorithm and an expression for the information matrix is also derived. We provide explicit analytical solutions for the EM algorithm and information matrix.