GLUT4 - At the cross roads between membrane trafficking and signal transduction

GLUT4 is a mammalian facilitative glucose transporter that is highly expressed in adipose tissue and striated muscle. In response to insulin, GLUT4 moves from intracellular storage areas to the plasma membrane, thus increasing cellular glucose uptake. While the verification of this 'translocation hypothesis' (Cushman SW. Wardzala LJ. J Biol Chem 1980;255: 4758-4762 and Suzuki K, Kono T. Proc Natl Acad Sci 1980;77: 2542-2545) has increased our understanding of insulin-regulated glucose transport, a number of fundamental questions remain unanswered. Where is GLUT4 stored within the basal cell? How does GLUT4 move to the cell surface and what mechanism does insulin employ to accelerate this process) Ultimately we require a convergence of trafficking studies with research in signal transduction. However, despite more than 30 years of intensive research we have still not reached this point. The problem is complex, involving at least two separate signal transduction pathways which feed into what appears to be a very dynamic sorting process. Below we discuss some of these complexities and highlight new data that are bringing us closer to the resolution of these questions.

Effect of Fusobacterium nucleatum on the T and B cell responses to Porphyromonas gingivalis in a mouse model

T cell cytokine profiles and specific serum antibody levels in five groups of BALB/c mice immunized with saline alone, viable Fusobacterium nucleatum ATCC 25586, viable Porphyromonas gingivalis ATCC 33277, F. nucleatum followed by P. gingivalis and P. gingivalis followed by F nucleatum were determined. Splenic CD4 and CD8 cells were examined for intracytoplasmic interleukin (IL)-4, interferon (IFN)-gamma and IL-10 by dual colour flow cytometry and the levels of serum anti-F. nucleatum and anti-P. gingivalis antibodies determined by an ELISA. Both Th1 and Th2 responses were demonstrated by all groups, and while there were slightly lower percentages of cytokine positive T cells in mice injected with F. nucleatum alone compared with the other groups immunized with bacteria., F nucleatum had no effect on the T cell production of cytokines induced by P gingivalis in the two groups immunized with both organisms. However, the percentages of cytokine positive CD8 cells were generally significantly higher than those of the CD4 cells. Mice immunized with F nucleatum alone had high levels of serum anti-E nucleatum antibodies with very low levels of P. gingivalis antibodies, whereas mice injected with P gingivalis alone produced anti-P. gingivalis antibodies predominantly. Although the levels of anti-E nucleatum antibodies in mice injected with E nucleatum followed by P. gingivalis were the same as in mice immunized with F nucleatum alone, antibody levels to P. gingivalis were very low. In contrast, mice injected with P. gingivalis followed by F nucleatum produced equal levels of both anti-P. gingivalis and anti-F nucleatum antibodies, although at lower levels than the other three groups immunized with bacteria, respectively. Anti-Actinobacillus actitiomycetemcomitans, Bacteroides forsythus and Prevotella intermedia serum antibody levels were also determined and found to be negligible. In conclusion, F nucleatum immunization does not affect the splenic T cell cytokine response to P. gingivalis. However, F nucleatum immunization prior to that of P. gingivalis almost completely inhibited the production of anti-P gingivalis antibodies while P. gingivalis injection before F. nucleatum demonstrated a partial inhibitory effect by P. gingivalis on antibody production to F. nucleatum. The significance of these results with respect to human periodontal disease is difficult to determine. However, they may explain in part differing responses to P. gingivalis in different individuals who may or may not have had prior exposure to F. nucleatum. Finally, the results suggested that P. gingivalis and F. nucleatum do not induce the production of cross-reactive antibodies to other oral microorganisms.

The IWA Anaerobic Digestion Model No 1 (ADM1)

The IWA Anaerobic Digestion Modelling Task Group was established in 1997 at the 8th World Congress on,Anaerobic Digestion (Sendai, Japan) with the goal of developing a generalised anaerobic digestion model. The structured model includes multiple steps describing biochemical as well as physicochemical processes. The biochemical steps include disintegration from homogeneous particulates to carbohydrates, proteins and lipids; extracellular hydrolysis of these particulate substrates to sugars, amino acids, and long chain fatty acids (LCFA), respectively; acidogenesis from sugars and amino acids to volatile fatty acids (VFAs) and hydrogen; acetogenesis of LCFA and VFAs to acetate; and separate methanogenesis steps from acetate and hydrogen/CO2. The physico-chemical equations describe ion association and dissociation, and gas-liquid transfer. Implemented as a differential and algebraic equation (DAE) set, there are 26 dynamic state concentration variables, and 8 implicit algebraic variables per reactor vessel or element. Implemented as differential equations (DE) only, there are 32 dynamic concentration state variables.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Reconciling value estimates from the discounted cash flow model and the residual income model

This paper examines why practitioners and researchers get different estimates of equity value when they use a discounted cash flow (CF) model versus a residual income (RI) model. Both models are derived from the same underlying assumption -- that price is the present value of expected future net dividends discounted at the cost of equity capital -- but in practice and in research they frequently yield different estimates. We argue that the research literature devoted to comparing the accuracy of these two models is misguided; properly implemented, both models yield identical valuations for all firms in all years. We identify how prior research has applied inconsistent assumptions to the two models and show how these seemingly small errors cause surprisingly large differences in the value estimates. [ABSTRACT FROM AUTHOR]