Evidence for a preformed transducer complex organized by the B cell antigen receptor.

The B cell antigen receptor (BCR) consists of the membrane-bound immunoglobulin (mIg) molecule and the Ig-alpha/Ig-beta heterodimer, which functions as signaling subunit of the receptor. Stimulation of the BCR activates protein tyrosine kinases (PTKs) that phosphorylate a number of substrate proteins, including the Ig-alpha/Ig-beta heterodimer of the BCR itself. How the PTKs become activated after BCR engagement is not known at present. Here, we show that BCR-negative J558L cells treated with the protein tyrosine phosphatase inhibitor pervanadate/H2O2 display only a weak substrate phosphorylation. However, in BCR-positive transfectants of J558L, treatment with pervanadate/H2O2 induces a strong phosphorylation of several substrate proteins. Treatment with pervanadate/H2O2 does not result in receptor crosslinking, yet the pattern of protein phosphorylation is similar to that observed after BCR stimulation by antigen. The response requires cellular integrity because tyrosine phosphorylation of most substrates is not visible in cell lysates. Cells that express a BCR containing an Ig-alpha subunit with a mutated immunoreceptor tyrosine-based activation motif display a delayed response. The data suggest that, once expressed on the surface, the BCR organizes protein tyrosine phosphatases, PTKs, and their substrates into a transducer complex that can be activated by pervanadate/H202 in the absence of BCR crosslinking. Assembly of this preformed complex seems to be a prerequisite for BCR-mediated signal transduction.

Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.