The phosphorylation status and anti-apoptotic activity of Bcl-2 are regulated by ERK and protein phosphatase 2A on the mitochondria

AbstractBcl-2 protein play important roles in the regulation of apoptosis. We previously reported that the phosphorylation of Bcl-2 was augmented by treatment with protein phosphatase 2A (PP2A) inhibitor; however, the kinase responsible for Bcl-2 phosphorylation had not yet been identified. In this study, we identified extracellular-signal-regulated kinase (ERK) as the responsible kinase for the phosphorylation of Bcl-2. We also found that the transmembrane region (TM) deleted form of Bcl-2 (Bcl-2ΔTM), which was unable to localize on the mitochondria was constitutively phosphorylated, whereas wild-type Bcl-2 that localized on the mitochondria, was present in its hypophosphorylated form. The phosphorylation of Bcl-2ΔTM was retarded by treatment with MAP kinase ERK kinase (MEK) inhibitor and PP2A did not bind to Bcl-2ΔTM. These observations suggest that Bcl-2ΔTM is constitutively phosphorylated by ERK, but is not dephosphorylated by PP2A in human tumor cell lines. The phosphorylation of Bcl-2 resulted in a reduction in anti-apoptotic function, implying that dephosphorylation promoted the anti-apoptotic activity of Bcl-2 protein in human tumor cell lines. Thus, the present findings suggest that ERK and PP2A are physiological regulators of Bcl-2 phosphorylation, and these enzymes exert an influence on the anti-apoptotic function of Bcl-2

Epistemicity and Deixis: Perspectives from Central Alaskan Yup'ik

BLS 38: General Session and Thematic Session on Language Contac

Metonymic Coercion and Relativization

時空と認知の言語学(4

A Neuron Model Capable of Learning Expansion/Contraction Movement Detection without Teacher\u27s Signal

金沢大学理工研究域電子情報学系Neuron has the characteristic of reacting to a speci c stimulus. The char- acteristic is said to be from the dendritic morphology of neuron. A neuron which reacts to a speci c stimulus has its unique dendritic morphology. Traditional McClloch-Pitts neuron model failed to include such dendritic functions. In this paper, we propose a neu- ron model that includes such nonlinear functions on dendrite and show that the model is capable of learning Expansion/Contraction movement detection without teacher\u27s sig- nals. The proposed model consists of the retina, LGN (lateral geniculate nucleus), V1 (primary visual cortex) and MST (medial superior temporal area). The neuron model of MST learns the Expansion/Contraction movement detection function by plasticity. Plas- ticity of the model neuron is expressed by back-propagation-like algorithm. Furthermore, we propose a method of creating teacher\u27s signals automatically from the output state of the neuron in MST. We initialize the model neuron with an arbitrarily dendrite randomly and use the model neuron to learn to detect the movement of Expansion/Contraction. Our simulation results show that the model neuron can learn the movement detection of Expanision/Contraction pattern without teacher\u27s signals and can develop its dendritic structure, such as the location of synapses and type of synaptic inputs by eliminating un-useful dendritic branches and synapse