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Hidehito Mukai

Publications and source records attributed to Hidehito Mukai.

2 recordsLinked to original sources

Mitogen-activated protein kinases, Erk and p38, phosphorylate and regulate Foxo1.

The members of the transcription factor Foxo family regulate the expression of genes concerned with the stress response, cell cycle and gluconeogenesis. Foxo1 (FKHR) contains 15 consensus phosphorylation sites for the mitogen-activated protein kinase (MAPK) family. Therefore, we hypothesized that MAPKs could directly regulate the transcriptional activity of Foxo1 via phosphorylation. In vitro kinase assay showed that Foxo1 was phosphorylated by extracellular signal-regulated kinase (Erk) and p38 MAPK (p38) but not by c-jun NH2-terminal kinase (JNK). In NIH3T3 cells, epidermal growth factor or anisomycin increased phosphorylation of exogenous Foxo1, which was significantly inhibited by pretreatment with an MEK 1 inhibitor, PD98059, or a p38 inhibitor, SB203580. Two-dimensional phosphopeptide mapping using mutation of phosphorylation sites for MAPK revealed that the nine serine residues in Foxo1 are specifically phosphorylated by Erk and that five of the nine residues are phosphorylated by p38 in vivo. Moreover, we also found that Foxo1 interacts with Ets-1 and functions as a coactivator for Ets-1 on the fetal liver kinase (Flk)-1 promoter in bovine carotid artery endothelial cells. Mutation of the nine phosphorylation sites for Erk in Foxo1 was shown to lead to less binding and synergistic activity for Ets-1 on the Flk-1 promoter when compared with wild-type Foxo1. These results suggest that Foxo1 is specifically phosphorylated by Erk and p38, and that this phosphorylation regulates the function of Foxo1 as a coactivator for Ets-1.

Animals↗

GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN (http://griffin.cbrc.jp/) is freely available and allows users to easily execute this reliable prediction of G-proteins.

Artificial Intelligence↗