Apoptosis signal-regulating kinase 1 (ASK1) is a mitogen-activated protein kinase kinase kinase that can activate the c-Jun N-terminal kinase and the p38 signaling pathways. It plays a critical role in cytokine-and stressinduced apoptosis. To further characterize the mechanism of the regulation of the ASK1 signal, we searched for ASK1-interacting proteins employing the yeast twohybrid method. The yeast two-hybrid assay indicated that mouse glutathione S-transferase Mu 1-1 (mGSTM1-1), an enzyme involved in the metabolism of drugs and xenobiotics, interacted with ASK1. We subsequently confirmed that mGSTM1-1 physically associated with ASK1 both in vivo and in vitro. The in vitro binding assay indicated that the C-terminal portion of mGSTM1-1 and the N-terminal region of ASK1 were crucial for binding one another. Furthermore, mGSTM1-1 suppressed stress-stimulated ASK1 activity in cultured cells. mG-STM1-1 also blocked ASK1 oligomerization. The ASK1 inhibition by mGSTM1-1 occurred independently of the glutathione-conjugating activity of mGSTM1-1. Moreover, mGSTM1-1 repressed ASK1-dependent apoptotic cell death. Taken together, our findings suggest that mGSTM1-1 functions as an endogenous inhibitor of ASK1. This highlights a novel function for mGSTM1-1 insofar as mGSTM1-1 may modulate stress-mediated signals by repressing ASK1, and this activity occurs independently of its well-known catalytic activity in intracellular glutathione metabolism.
In this paper, the prediction skills of five ensemble methods for temperature and precipitation are discussed by considering 20 yr of simulation results (from 1989 to 2008) for four regional climate models (RCMs) driven by NCEP-Department of Energy and ECMWF Interim Re-Analysis (ERA-Interim) boundary conditions. The simulation domain is the Coordinated Regional Downscaling Experiment (CORDEX) for East Asia, and the number of grid points is 197 3 233 with a 50-km horizontal resolution. Three new performance-based ensemble averaging (PEA) methods are developed in this study using 1) bias, root-mean-square errors (RMSEs) and absolute correlation (PEA_BRC), RMSE and absolute correlation (PEA_RAC), and RMSE and original correlation (PEA_ROC). The other two ensemble methods are equal-weighted averaging (EWA) and multivariate linear regression (Mul_Reg). To derive the weighting coefficients and cross validate the prediction skills of the five ensemble methods, the authors considered 15-yr and 5-yr data, respectively, from the 20-yr simulation data. Among the five ensemble methods, the Mul_Reg (EWA) method shows the best (worst) skill during the training period. The PEA_RAC and PEA_ROC methods show skills that are similar to those of Mul_Reg during the training period. However, the skills and stabilities of Mul_Reg were drastically reduced when this method was applied to the prediction period. But, the skills and stabilities of PEA_RAC were only slightly reduced in this case. As a result, PEA_RAC shows the best skill, irrespective of the seasons and variables, during the prediction period. This result confirms that the new ensemble method developed in this study, PEA_RAC, can be used for the prediction of regional climate.
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