Abstract
Lentiviral vectors are efficient tools for the introduction of genes into a wide range of established and primary cells in vitro, ex vivo, and in vivo, and also permit efficient transgenesis in a wide range of mammalian species. Our goals have been to apply the broad capabilities of the lentiviral vector system to AD research. Using a set of vectors expressing APP and PS1 genes, we demonstrated the efficiency and fidelity of the system for in vitro biochemical analyses of genes and pathways involved in plaque deposition. These analyses were performed in cell lines and in primary neuronal cultures, which have previously been difficult to use. The methods and tools described here are applicable to the study of effects of other genes and gene combinations on APP processing, including suppression of gene activity by delivering shRNAs. We have attempted to create local plaque pathology by stereotactic injection of APP and PS1 expressing vectors into mouse brains for use as a rapid model for plaque pathology that can be used in a broad range of mammals. No amyloid or preamyloid pathology has been detected over a six-month period; the possible reasons are discussed. Lastly, we have used the vectors to create transgenic rats expressing mutant APP and mutant PS1 and have obtained the first set of positive pups with more expected. The results presented here demonstrate the utility of Lentiviral vector-based approaches to the study of AD and other neurodegenerative diseases.
Keywords: app, presenilin, transgenic rat, primary neuronal cultures
Current Alzheimer Research
Title: Lentiviral Vector-Based Models of Amyloid Pathology: From Cells to Animals
Volume: 2 Issue: 2
Author(s): Laura Shaughnessy, Mary Beth Thomas, John Wakefield, Beth Chamblin, Ayyappan Nair, Frank Koentgen and Ram Ramabhadran
Affiliation:
Keywords: app, presenilin, transgenic rat, primary neuronal cultures
Abstract: Lentiviral vectors are efficient tools for the introduction of genes into a wide range of established and primary cells in vitro, ex vivo, and in vivo, and also permit efficient transgenesis in a wide range of mammalian species. Our goals have been to apply the broad capabilities of the lentiviral vector system to AD research. Using a set of vectors expressing APP and PS1 genes, we demonstrated the efficiency and fidelity of the system for in vitro biochemical analyses of genes and pathways involved in plaque deposition. These analyses were performed in cell lines and in primary neuronal cultures, which have previously been difficult to use. The methods and tools described here are applicable to the study of effects of other genes and gene combinations on APP processing, including suppression of gene activity by delivering shRNAs. We have attempted to create local plaque pathology by stereotactic injection of APP and PS1 expressing vectors into mouse brains for use as a rapid model for plaque pathology that can be used in a broad range of mammals. No amyloid or preamyloid pathology has been detected over a six-month period; the possible reasons are discussed. Lastly, we have used the vectors to create transgenic rats expressing mutant APP and mutant PS1 and have obtained the first set of positive pups with more expected. The results presented here demonstrate the utility of Lentiviral vector-based approaches to the study of AD and other neurodegenerative diseases.
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Cite this article as:
Shaughnessy Laura, Thomas Beth Mary, Wakefield John, Chamblin Beth, Nair Ayyappan, Koentgen Frank and Ramabhadran Ram, Lentiviral Vector-Based Models of Amyloid Pathology: From Cells to Animals, Current Alzheimer Research 2005; 2 (2) . https://dx.doi.org/10.2174/1567205053585963
DOI https://dx.doi.org/10.2174/1567205053585963 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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Alzheimer's disease (AD) poses a significant global health challenge, with an increasing number of individuals affected yearly. Deep learning, a subfield of artificial intelligence, has shown immense potential in various domains, including healthcare. This thematic issue of Current Alzheimer Research explores the application of deep learning techniques in advancing our ...read more
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