Shigeki Kato1, Kenta Kobayashi2, Kazuto Kobayashi3. 1. Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan. 2. Section of Viral Vector Development, National Institute of Physiological Sciences, Okazaki 444-8585, Japan. 3. Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan; Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, Kawaguchi 332-0012, Japan. Electronic address: kazuto@fmu.ac.jp.
Abstract
BACKGROUND: The vector for neuron-specific retrograde gene transfer (NeuRet) is a pseudotype of human immunodeficiency virus type 1 (HIV-1)-based vector with fusion glycoprotein type C (FuG-C), which consists of the N-terminal region of the extracellular domain of rabies virus glycoprotein (RVG) and the membrane-proximal region of the extracellular domain and the transmembrane/cytoplasmic domains of vesicular stomatitis virus glycoprotein (VSVG). The NeuRet vector shows a high efficiency of gene transfer through retrograde axonal transport and transduces selectively neuronal cells around the injection site. NEW METHOD: We aimed to improve the efficiency of retrograde gene transfer of the NeuRet vector by optimizing the junction of RVG and VSVG segments in fusion glycoproteins in their membrane-proximal region. RESULTS: We produced various types of fusion glycoproteins, in which the junction of the two glycoprotein segments diverged in the membrane-proximal region and used for pseudotyping of HIV-1-based vector to evaluate the in vivo gene transfer efficiency after intrastriatal injection. We found a novel type of fusion glycoprotein termed type E (FuG-E) that yielded enhanced efficiency of retrograde gene delivery, showing neuron-specific transduction surrounding the injection site. COMPARISON WITH EXISTING METHODS: The NeuRet vector pseudotyped with FuG-E displayed the improved efficiency of retrograde gene transfer into different neural pathways compared with the original vector pseudotyped with FuG-C. CONCLUSIONS: Our vector system with FuG-E provides a powerful tool for gene therapeutic trials of neurological and neurodegenerative diseases and for the study of the mechanisms of neural networks underlying various brain functions.
BACKGROUND: The vector for neuron-specific retrograde gene transfer (NeuRet) is a pseudotype of human immunodeficiency virus type 1 (HIV-1)-based vector with fusion glycoprotein type C (FuG-C), which consists of the N-terminal region of the extracellular domain of rabies virus glycoprotein (RVG) and the membrane-proximal region of the extracellular domain and the transmembrane/cytoplasmic domains of vesicular stomatitis virus glycoprotein (VSVG). The NeuRet vector shows a high efficiency of gene transfer through retrograde axonal transport and transduces selectively neuronal cells around the injection site. NEW METHOD: We aimed to improve the efficiency of retrograde gene transfer of the NeuRet vector by optimizing the junction of RVG and VSVG segments in fusion glycoproteins in their membrane-proximal region. RESULTS: We produced various types of fusion glycoproteins, in which the junction of the two glycoprotein segments diverged in the membrane-proximal region and used for pseudotyping of HIV-1-based vector to evaluate the in vivo gene transfer efficiency after intrastriatal injection. We found a novel type of fusion glycoprotein termed type E (FuG-E) that yielded enhanced efficiency of retrograde gene delivery, showing neuron-specific transduction surrounding the injection site. COMPARISON WITH EXISTING METHODS: The NeuRet vector pseudotyped with FuG-E displayed the improved efficiency of retrograde gene transfer into different neural pathways compared with the original vector pseudotyped with FuG-C. CONCLUSIONS: Our vector system with FuG-E provides a powerful tool for gene therapeutic trials of neurological and neurodegenerative diseases and for the study of the mechanisms of neural networks underlying various brain functions.
Authors: D Pignataro; D Sucunza; A J Rico; I G Dopeso-Reyes; E Roda; A I Rodríguez-Perez; J L Labandeira-Garcia; V Broccoli; S Kato; K Kobayashi; José L Lanciego Journal: J Neural Transm (Vienna) Date: 2017-01-27 Impact factor: 3.575
Authors: L Schoderboeck; S Riad; A M Bokor; H E Wicky; M Strauss; M Bostina; M J Oswald; R M Empson; S M Hughes Journal: Gene Ther Date: 2015-01-29 Impact factor: 5.250
Authors: Iliodora V Pop; Felipe Espinosa; Cheasequah J Blevins; Portia C Okafor; Osita W Ogujiofor; Megan Goyal; Bishakha Mona; Mark A Landy; Kevin M Dean; Channabasavaiah B Gurumurthy; Helen C Lai Journal: J Neurosci Date: 2021-12-02 Impact factor: 6.709