Proceedings of The Physiological Society
University of Oxford (2011) Proc Physiol Soc 23, PC164
Gene expression in glycyl tRNA synthetase (GARS) related neurodegeneration
J. Li1, C. Coxon1, S. Lee1, A. Goyenvalle1, G. Weir1, K. Talbot1, K. Davies1, Z. Cader1
1. University of Oxford, Oxford, United Kingdom.
Background: GARS gene encodes Glycyl-tRNA Synthetase, which is an enzyme linking tRNA to glycine. Mutation in GARS have been shown to cause distal spinal muscular atrophy (dSMA) and Charcot-Marie-Tooth disease type 2D (CMT2D). In both conditions, the prominent early feature is wasting and weakness of the muscle of the hands and feet caused by degeneration of peripheral nerves (1). However, the function and relative role of Gars in triggering neurodegeneration has not been discovered. The aim of this study is to identify the correlated genes differently expressed between wild type and mutant of the Gars using an in vitro stable neuronal cell line model and in vivo mouse tissues. Material and method: Mouse cDNA: wildtype Gars and mutant(2) cDNA were tagged with an OneStrep tag and were cloned into pTightPuro vector. We then used Lenti-XTM Tet-On® advanced inducible expression system to generate mouse stable cell lines, which involves packaging these cDNA plasmids into lentivirual particles and cotransducing with the regulator TetOn virus into target NSC-34 cells. Monoclonal cell lines were screened with immunofluorescence and western blotting to confirm the expression of the transduced gene. RNAs were extracted from the expressed cell lines and were then carried out Affymetrix microarray study. Positive targets were validated using quantitative real-time PCR on both cell lines and different mouse tissues. Result: NSC-34 cells were successfully transduced as indicated by the expression of the Strep tag, which was tested using Immunofluorescence and western blot. When comparing wildtype and mutant type, microarray data showed a list of genes expressed differently with significant fold changes. Subsequent validation using Q-PCR showed a general correlation to microarray data with higher fold changes in some individual targets.
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