978 resultados para protein glutamine gamma glutamyltransferase


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Several lines of evidence implicate the p38 mitogen-activated protein kinase (p38 MAPK) in the proinflammatory response to bacterial agents and cytokines. Equally, the transcription factor, nuclear factor (NF)-kappaB, is recognized to be a critical determinant of the inflammatory response in intestinal epithelial cells (IECs). However, the precise inter-relationship between the activation of p38 MAPK and activation of the transcription factor NF-kappaB in the intestinal epithelial cell (IEC) system, remains unknown. Here we show that interleukin (IL)-1beta activates all three MAPKs in Caco-2 cells. The production of IL-8 and monocyte chemotactic protein 1 (MCP-1) was attenuated by 50% when these cells were preincubated with the p38 MAPK inhibitor, SB 203580. Further investigation of the NF-kappaB signalling system revealed that the inhibitory effect was independent of the phosphorylation and degradation of IkappaBalpha, the binding partner of NF-kappaB. This effect was also independent of the DNA binding of the p65 Rel A subunit, as well as transactivation, determined by an NF-kappaB luciferase construct, using both SB 203580 and dominant-negative p38 MAPK. Evaluation of IL-8 and MCP-1 RNA messages by reverse transcription-polymerase chain reaction (RT-PCR) revealed that the inhibitory effect of SB 203580 was associated with a reduction in this parameter. Using an IL-8-luciferase promoter construct, an effect of p38 upon its activation by both pharmacological and dominant-negative p38 construct co-transfection was demonstrated. It is concluded that p38 MAPK influences the expression of chemokines in intestinal epithelial cells, through an effect upon the activation of the chemokine promoter, and does not directly involve the activation of the transcription factor NF-kappaB

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Dental pulp cells (DPCs) are capable of differentiating into odontoblasts that secrete reparative dentin after pulp injury. The molecular mechanisms governing reparative dentinogenesis are yet to be fully understood. Here we investigated the differential protein profile of human DPCs undergoing odontogenic induction for 7 days. Using two-dimensional differential gel electrophoresis coupled with matrix-assisted laser adsorption ionization time of flight mass spectrometry, 2 3 protein spots related to the early odontogenic differentiation were identified. These proteins included cytoskeleton proteins, nuclear proteins, cell membrane-bound molecules, proteins involved in matrix synthesis, and metabolic enzymes. The expression of four identified proteins, which were heteronuclear ribonuclear proteins C, annexin VI, collagen type VI, and matrilin-2, was confirmed by Western blot and real-time realtime polymerase chain reaction analyses. This study generated a proteome reference map during odontoblast- like differentiation of human DPCs, which will be valuable to better understand the underlying molecular mechanisms in odontoblast-like differentiation.

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There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

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The Arabidopsis thaliana NPR1 has been shown to be a key regulator of gene expression during the onset of a plant disease-resistance response known as systemic acquired resistance. The npr1 mutant plants fail to respond to systemic acquired resistance-inducing signals such as salicylic acid (SA), or express SA-induced pathogenesis-related (PR) genes. Using NPR1 as bait in a yeast two-hybrid screen, we identified a subclass of transcription factors in the basic leucine zipper protein family (AHBP-1b and TGA6) and showed that they interact specifically in yeast and in vitro with NPR1. Point mutations that abolish the NPR1 function in A. thaliana also impair the interactions between NPR1 and the transcription factors in the yeast two-hybrid assay. Furthermore, a gel mobility shift assay showed that the purified transcription factor protein, AHBP-1b, binds specifically to an SA-responsive promoter element of the A. thaliana PR-1 gene. These data suggest that NPR1 may regulate PR-1 gene expression by interacting with a subclass of basic leucine zipper protein transcription factors.

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This paper was retracted by the Journal of Stem Cells and Development on February 15, 2013.

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Bean golden mosaic geminivirus (BGMV) has a bipartite genome composed of two circular ssDNA components (DNA-A and DNA-B) and is transmitted by the whitefly, Bemisia tabaci. DNA-A encodes the viral replication proteins and the coat protein. To determine the role of BGMV coat protein systemic infection and whitefly transmission, two deletions and a restriction fragment inversion were introduced into the BGMV coat protein gene. All three coat protein mutants produced systemic infections when coinoculated with DNA-B onto Phaseolus vulgaris using electric discharge particle acceleration "particle gun." However, they were not sap transmissible and coat protein was not detected in mutant-infected plants. In addition, none of the mutants were transmitted by whiteflies. With all three mutants, ssDNA accumulation of DNA-A and DNA-B was reduced 25- to 50-fold and 3- to 10-fold, respectively, as compared to that of wild-type DNA. No effect on dsDNA-A accumulation was detected and there was 2- to 5-fold increase in dsDNA-B accumulation. Recombinants between the mutated DNA-A and DNA-B forms were identified when the inoculated coat protein mutant was linearized in the common region.