Giant axonal neuropathy (GAN), an autosomal recessive disorder caused by mutations in GAN, is characterized cytopathologically by cytoskeletal abnormality. Based on its sequence, gigaxonin contains an NH2-terminal BTB domain followed by six kelch repeats, which are believed to be important for protein-protein interactions (Adams, J., R. Kelso, and L. Cooley. 2000. Trends Cell Biol. 10:17-24.). Here, we report the identification of a neuronal binding partner of gigaxonin. Results obtained from yeast two-hybrid screening, cotransfections, and coimmunoprecipitations demonstrate that gigaxonin binds directly to microtubule-associated protein (MAP)1B light chain (LC; MAP1B-LC), a protein involved in maintaining the integrity of cytoskeletal structures and promoting neuronal stability. Studies using double immunofluorescent microscopy and ultrastructural analysis revealed physiological colocalization of gigaxonin with MAP1B in neurons. Furthermore, in transfected cells the specific interaction of gigaxonin with MAP1B is shown to enhance the microtubule stability required for axonal transport over long distance. At least two different mutations identified in GAN patients (Bomont, P., L. Cavalier, F. Blondeau, C. Ben Hamida, S. Belal, M. Tazir, E. Demir, H. Topaloglu, R. Korinthenberg, B. Tuysuz, et al. 2000. Nat. Genet. 26:370-374.) lead to loss of gigaxonin-MAP1B-LC interaction. The devastating axonal degeneration and neuronal death found in GAN patients point to the importance of gigaxonin for neuronal survival. Our findings may provide important insights into the pathogenesis of neurodegenerative disorders related to cytoskeletal abnormalities.
Pubmed ID: 12147674 RIS Download
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View all literature mentionsNEURON 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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