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In nonneuronal cells, the cell surface protein dystroglycan links the intracellular cytoskeleton (via dystrophin or utrophin) to the extracellular matrix (via laminin, agrin, or perlecan). Impairment of this linkage is instrumental in the pathogenesis of muscular dystrophies. In brain, dystroglycan and dystrophin are expressed on neurons and astrocytes, and some muscular dystrophies cause cognitive dysfunction; however, no extracellular binding partner for neuronal dystroglycan is known. Regular components of the extracellular matrix, such as laminin, agrin, and perlecan, are not abundant in brain except in the perivascular space that is contacted by astrocytes but not by neurons, suggesting that other ligands for neuronal dystroglycan must exist. We have now identified alpha- and beta-neurexins, polymorphic neuron-specific cell surface proteins, as neuronal dystroglycan receptors. The extracellular sequences of alpha- and beta-neurexins are largely composed of laminin-neurexin-sex hormone-binding globulin (LNS)/laminin G domains, which are also found in laminin, agrin, and perlecan, that are dystroglycan ligands. Dystroglycan binds specifically to a subset of the LNS domains of neurexins in a tight interaction that requires glycosylation of dystroglycan and is regulated by alternative splicing of neurexins. Neurexins are receptors for the excitatory neurotoxin alpha-latrotoxin; this toxin competes with dystroglycan for binding, suggesting overlapping binding sites on neurexins for dystroglycan and alpha-latrotoxin. Our data indicate that dystroglycan is a physiological ligand for neurexins and that neurexins' tightly regulated interaction could mediate cell adhesion between brain cells.
Pubmed ID: 11470830 RIS Download
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NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything
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