Guanosine stimulates neurite outgrowth in PC12 cells via activation of heme oxygenase and cyclic GMP

Undifferentiated rat pheochromocytoma (PC12) cells extend neurites when cultured in the presence of nerve growth factor (NGF). Extracellular guanosine synergistically enhances NGF-dependent neurite outgrowth. We investigated the mechanism by which guanosine enhances NGF-dependent neurite outgrowth. Guanosine administration to PC12 cells significantly increased guanosine 3-5-cyclic monophosphate (cGMP) within the first 24 h whereas addition of soluble guanylate cyclase (sGC) inhibitors abolished guanosine-induced enhancement of NGF-dependent neurite outgrowth. sGC may be activated either by nitric oxide (NO) or by carbon monoxide (CO). \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$N^{\omega }$$ \end{document}-Nitro-l-arginine methyl ester (l-NAME), a non-isozyme selective inhibitor of nitric oxide synthase (NOS), had no effect on neurite outgrowth induced by guanosine. Neither nNOS (the constitutive isoform), nor iNOS (the inducible isoform) were expressed in undifferentiated PC12 cells, or under these treatment conditions. These data imply that NO does not mediate the neuritogenic effect of guanosine. Zinc protoporphyrin-IX, an inhibitor of heme oxygenase (HO), reduced guanosine-dependent neurite outgrowth but did not attenuate the effect of NGF. The addition of guanosine plus NGF significantly increased the expression of HO-1, the inducible isozyme of HO, after 12 h. These data demonstrate that guanosine enhances NGF-dependent neurite outgrowth by first activating the constitutive isozyme HO-2, and then by inducing the expression of HO-1, the enzymes responsible for CO synthesis, thus stimulating sGC and increasing intracellular cGMP

RATEMAKING OF DEPENDENT RISKS

We start by describing how, in some cases, we can use variance-related premium principles in ratemaking, when the claim numbers and individual claim amounts are independent. We use quasi-likelihood generalized linear models, under the assumption that the variance function is a power function of the mean of the underlying random variable. We extend this approach to the cases where the claim numbers are correlated. Some alternatives to deal with dependent risks are presented, taking explicitly into account overdispersion. We present regression models covering the bivariate Poisson, the generalized bivariate negative binomial and the bivariate Poisson–Laguerre polynomial, which nest the bivariate negative binomial. We apply these models to a portfolio of the Portuguese insurance company Tranquilidade and compare the results obtained.info:eu-repo/semantics/publishedVersio