golden retriever muscular dystrophy
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PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248721
Author(s):  
Paul T. Martin ◽  
Deborah A. Zygmunt ◽  
Anna Ashbrook ◽  
Sonia Hamilton ◽  
Davin Packer ◽  
...  

We have examined the effects of intravenous (IV) delivery of rAAVrh74.MHCK7.GALGT2 in the golden retriever muscular dystrophy (GRMD) model of Duchenne Muscular Dystrophy (DMD). After baseline testing, GRMD dogs were treated at 3 months of age and reassessed at 6 months. This 3–6 month age range is a period of rapid disease progression, thus offering a relatively short window to establish treatment efficacy. Measures analyzed included muscle AAV transduction, GALGT2 transgene expression, GALGT2-induced glycosylation, muscle pathology, and muscle function. A total of five dogs were treated, 4 at 2x1014vg/kg and one at 6x1014vgkg. The 2x1014vg/kg dose led to transduction of regions of the heart with 1–3 vector genomes (vg) per nucleus, while most skeletal muscles were transduced with 0.25–0.5vg/nucleus. GALGT2-induced glycosylation paralleled levels of myofiber vg transduction, with about 90% of cardiomyocytes having increased glycosylation versus 20–35% of all myofibers across the skeletal muscles tested. Conclusions from phenotypic testing were limited by the small number of dogs. Treated dogs had less pronounced fibrosis and overall lesion severity when compared to control groups, but surprisingly no significant changes in limb muscle function measures. GALGT2-treated skeletal muscle and heart had elevated levels of utrophin protein expression and GALGT2-induced expression of glycosylated α dystroglycan, providing further evidence of a treatment effect. Serum chemistry, hematology, and cardiac function measures were largely unchanged by treatment. Cumulatively, these data show that short-term intravenous treatment of GRMD dogs with rAAVrh74.MHCK7.GALGT2 at high doses can induce muscle glycosylation and utrophin expression and may be safe over a short 3-month interval, but that such treatments had only modest effects on muscle pathology and did not significantly improve muscle strength.


2020 ◽  
Vol 30 (11) ◽  
pp. 930-937
Author(s):  
Eleanor C. Hawkins ◽  
Amanda K. Bettis ◽  
Joe N. Kornegay

2019 ◽  
Vol 60 (5) ◽  
pp. 621-628 ◽  
Author(s):  
Aydin Eresen ◽  
Noor E. Hafsa ◽  
Lejla Alic ◽  
Sharla M. Birch ◽  
John F. Griffin ◽  
...  

Author(s):  
Lee‐Jae Guo ◽  
Jonathan H. Soslow ◽  
Amanda K. Bettis ◽  
Peter P. Nghiem ◽  
Kevin J. Cummings ◽  
...  

2019 ◽  
Vol 66 (5) ◽  
pp. 1222-1230 ◽  
Author(s):  
Aydin Eresen ◽  
Sharla M. Birch ◽  
Lejla Alic ◽  
Jay F. Griffin ◽  
Joe N. Kornegay ◽  
...  

2019 ◽  
Vol 59 (3) ◽  
pp. 380-386 ◽  
Author(s):  
Aydin Eresen ◽  
Lejla Alic ◽  
Sharla M. Birch ◽  
Wade Friedeck ◽  
John F. Griffin ◽  
...  

2018 ◽  
Vol 56 (1) ◽  
pp. 121-142 ◽  
Author(s):  
Dorota Duda

Abstract The study investigates the possibility of applying texture analysis (TA) for testing Duchenne Muscular Dystrophy (DMD) therapies. The work is based on the Golden Retriever Muscular Dystrophy (GRMD) canine model, in which 3 phases of canine growth and/or dystrophy development are identified: the first phase (0–4 months of age), the second phase (from over 4 to 6 months), and the third phase (from over 6 months to death). Two differentiation problems are posed: (i) the first phase vs. the second phase and (ii) the second phase vs. the third phase. Textural features are derived from T2-weighted Magnetic Resonance Imaging (MRI) images. In total, 37 features provided by 8 different TA methods (statistical, filter-based, and model-based) have been tested. The work focuses on finding such textural features that evolve along with the dog’s growth. These features are indicated by means of statistical analyses and eliminated from further investigation, as they may disturb the correct assessment of response to treatment in dystrophy. The relative importance of each remaining feature is then assessed with the use of the Monte Carlo (MC) procedure. Furthermore, feature selection based on the MC procedure is employed to find the optimal subset of age-independent features. Finally, three classifiers are used for evaluating different sets of textural features: Adaptive Boosting (AB), back-propagation Neural Network (NN), and nonlinear Support Vector Machines (SVM). The best subsets of age-independent features ensure 80.0% and 78.5% of correctly identified phases of dystrophy progression, for the first (i) and second (ii) differentiation problem respectively.


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