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Tackling Obstacles for Gene Therapy Targeting Neurons: Disrupting Perineural Nets with Hyaluronidase Improves Transduction

Overview of attention for article published in PLOS ONE, January 2013
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Title
Tackling Obstacles for Gene Therapy Targeting Neurons: Disrupting Perineural Nets with Hyaluronidase Improves Transduction
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0053269
Pubmed ID
Authors

Klaus Wanisch, Stjepana Kovac, Stephanie Schorge

Abstract

Gene therapy has been proposed for many diseases in the nervous system. In most cases for successful treatment, therapeutic vectors must be able to transduce mature neurons. However, both in vivo, and in vitro, where preliminary characterisation of viral particles takes place, transduction of neurons is typically inefficient. One possible explanation is that the extracellular matrix (ECM), forming dense perineural nets (PNNs) around neurons, physically blocks access to the cell surface. We asked whether co-administration of lentiviral vectors with an enzyme that disrupts the ECM could improve transduction efficiency. Using hyaluronidase, an enzyme which degrades hyaluronic acid, a high molecular weight molecule of the ECM with mainly a scaffolding function, we show that in vitro in mixed primary cortical cultures, and also in vivo in rat cortex, hyaluronidase co-administration increased the percentage of transduced mature, NeuN-positive neurons. Moreover, hyaluronidase was effective at doses that showed no toxicity in vitro based on propidium iodide staining of treated cultures. Our data suggest that limited efficacy of neuronal transduction is partly due to PNNs surrounding neurons, and further that co-applying hyaluronidase may benefit applications where efficient transduction of neurons in vitro or in vivo is required.

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The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Spain 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 33%
Researcher 8 19%
Student > Doctoral Student 4 9%
Student > Master 3 7%
Professor 2 5%
Other 3 7%
Unknown 9 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 35%
Neuroscience 6 14%
Medicine and Dentistry 5 12%
Biochemistry, Genetics and Molecular Biology 3 7%
Chemical Engineering 1 2%
Other 4 9%
Unknown 9 21%