BACKGROUND: Dogs with dystrophin-deficient muscular dystrophy are valuable models of the equivalent human disease, Duchenne Muscular Dystrophy (DMD): unlike the mdx mouse, these animals present a disease severity and progression that closely matches that found in human patients. Canine models are however less thoroughly characterised than the established mdx mouse in many aspects, including gene expression. Analysis of expression in muscle plays a key role in the study of DMD, allowing monitoring and assessment of disease progression, evaluation of novel biomarkers and gauging of therapeutic intervention efficacy. Appropriate normalization of expression data via carefully selected reference genes is consequently essential for accurate quantitative assessment. Unlike the expression profile of healthy skeletal muscle, the dystrophic muscle environment is highly dynamic: transcriptional profiles of dystrophic muscle might alter with age, disease progression, disease severity, genetic background and between muscle groups. OBJECTIVES: The aim of this work was to identify reference genes suitable for normalizing gene expression in healthy and dystrophic dogs under various comparative scenarios. METHODS: Using the delta-E50 MD canine model of DMD, we assessed a panel of candidate reference genes for stability of expression across healthy and dystrophic animals, at different ages and in different muscle groups. RESULTS: We show that the genes HPRT1, SDHA and RPL13a appear universally suitable for normalizing gene expression in healthy and dystrophic canine muscle, while other putative reference genes are exceptionally poor, and in the case of B2M, actively disease-correlated. CONCLUSIONS: Our findings suggest consistent cross-sample normalization is possible even throughout the dynamic progression of dystrophic pathology, and furthermore highlight the importance of empirical determination of suitable reference genes for neuromuscular diseases.
BACKGROUND:Dogs with dystrophin-deficient muscular dystrophy are valuable models of the equivalent human disease, Duchenne Muscular Dystrophy (DMD): unlike the mdx mouse, these animals present a disease severity and progression that closely matches that found in humanpatients. Canine models are however less thoroughly characterised than the established mdx mouse in many aspects, including gene expression. Analysis of expression in muscle plays a key role in the study of DMD, allowing monitoring and assessment of disease progression, evaluation of novel biomarkers and gauging of therapeutic intervention efficacy. Appropriate normalization of expression data via carefully selected reference genes is consequently essential for accurate quantitative assessment. Unlike the expression profile of healthy skeletal muscle, the dystrophic muscle environment is highly dynamic: transcriptional profiles of dystrophic muscle might alter with age, disease progression, disease severity, genetic background and between muscle groups. OBJECTIVES: The aim of this work was to identify reference genes suitable for normalizing gene expression in healthy and dystrophicdogs under various comparative scenarios. METHODS: Using the delta-E50 MD canine model of DMD, we assessed a panel of candidate reference genes for stability of expression across healthy and dystrophic animals, at different ages and in different muscle groups. RESULTS: We show that the genes HPRT1, SDHA and RPL13a appear universally suitable for normalizing gene expression in healthy and dystrophiccanine muscle, while other putative reference genes are exceptionally poor, and in the case of B2M, actively disease-correlated. CONCLUSIONS: Our findings suggest consistent cross-sample normalization is possible even throughout the dynamic progression of dystrophic pathology, and furthermore highlight the importance of empirical determination of suitable reference genes for neuromuscular diseases.
Authors: John C W Hildyard; Abbe H Crawford; Faye Rawson; Dominique O Riddell; Rachel C M Harron; Richard J Piercy Journal: Wellcome Open Res Date: 2020-07-20
Authors: Abbe H Crawford; John C W Hildyard; Sophie A M Rushing; Dominic J Wells; Maria Diez-Leon; Richard J Piercy Journal: Dis Model Mech Date: 2022-03-02 Impact factor: 5.758