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      Restoration of cytoskeleton homeostasis after gigaxonin gene transfer for giant axonal neuropathy.

      Human Gene Therapy
      Animals, Cells, Cultured, Cytoskeletal Proteins, administration & dosage, genetics, Cytoskeleton, metabolism, pathology, Dependovirus, Fibroblasts, Gene Transfer Techniques, Genetic Therapy, methods, Genetic Vectors, Giant Axonal Neuropathy, therapy, HEK293 Cells, Homeostasis, Humans, Mice, Mice, Knockout, Mutation, Missense, Primary Cell Culture, Proteome, analysis, RNA, Messenger, Transfection, Vimentin

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          Abstract

          Giant axonal neuropathy (GAN) is caused by loss of function of the gigaxonin protein. On a cellular level GAN is characterized by intermediate filament (IF) aggregation, leading to a progressive and fatal peripheral neuropathy in humans. This study sought to determine if re-introduction of the GAN gene into GAN-deficient cells and mice would restore proper cytoskeleton IF homeostasis. Treatment of primary skin fibroblast cultures from three different GAN patients with an adeno-associated virus type 2 (AAV2) vector containing a normal human GAN transgene significantly reduced the number of cells displaying vimentin IF aggregates. A proteomic analysis of these treated cells was also performed, wherein the abundance of 32 of 780 identified proteins significantly changed in response to gigaxonin gene transfer. While 29 of these responding proteins have not been directly described in association with gigaxonin, three were previously identified as being disregulated in GAN and were now shifted toward normal levels. To assess the potential application of this approach in vivo and eventually in humans, GAN mice received an intracisternal injection of an AAV9/GAN vector to globally deliver the GAN gene to the brainstem and spinal cord. The treated mice showed a nearly complete clearance of peripherin IF accumulations at 3 weeks post-injection. These studies demonstrate that gigaxonin gene transfer can reverse the cellular IF aggregate pathology associated with GAN.

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