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Microarray Analysis of Gene Expression by Skeletal Muscle of Three Mouse Models of Kennedy Disease/Spinal Bulbar Muscular Atrophy

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      Abstract

      BackgroundEmerging evidence implicates altered gene expression within skeletal muscle in the pathogenesis of Kennedy disease/spinal bulbar muscular atrophy (KD/SBMA). We therefore broadly characterized gene expression in skeletal muscle of three independently generated mouse models of this disease. The mouse models included a polyglutamine expanded (polyQ) AR knock-in model (AR113Q), a polyQ AR transgenic model (AR97Q), and a transgenic mouse that overexpresses wild type AR solely in skeletal muscle (HSA-AR). HSA-AR mice were included because they substantially reproduce the KD/SBMA phenotype despite the absence of polyQ AR.Methodology/Principal FindingsWe performed microarray analysis of lower hindlimb muscles taken from these three models relative to wild type controls using high density oligonucleotide arrays. All microarray comparisons were made with at least 3 animals in each condition, and only those genes having at least 2-fold difference and whose coefficient of variance was less than 100% were considered to be differentially expressed. When considered globally, there was a similar overlap in gene changes between the 3 models: 19% between HSA-AR and AR97Q, 21% between AR97Q and AR113Q, and 17% between HSA-AR and AR113Q, with 8% shared by all models. Several patterns of gene expression relevant to the disease process were observed. Notably, patterns of gene expression typical of loss of AR function were observed in all three models, as were alterations in genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis. We additionally measured changes similar to those observed in skeletal muscle of a mouse model of Huntington's Disease, and to those common to muscle atrophy from diverse causes.Conclusions/SignificanceBy comparing patterns of gene expression in three independent models of KD/SBMA, we have been able to identify candidate genes that might mediate the core myogenic features of KD/SBMA.

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      Most cited references 58

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      A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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        Primer3 on the WWW for general users and for biologist programmers.

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          Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data.

          Quantification of mRNAs using real-time polymerase chain reaction (PCR) by monitoring the product formation with the fluorescent dye SYBR Green I is being extensively used in neurosciences, developmental biology, and medical diagnostics. Most PCR data analysis procedures assume that the PCR efficiency for the amplicon of interest is constant or even, in the case of the comparative C(t) method, equal to 2. The latter method already leads to a 4-fold error when the PCR efficiencies vary over just a 0.04 range. PCR efficiencies of amplicons are usually calculated from standard curves based on either known RNA inputs or on dilution series of a reference cDNA sample. In this paper we show that the first approach can lead to PCR efficiencies that vary over a 0.2 range, whereas the second approach may be off by 0.26. Therefore, we propose linear regression on the Log(fluorescence) per cycle number data as an assumption-free method to calculate starting concentrations of mRNAs and PCR efficiencies for each sample. A computer program to perform this calculation is available on request (e-mail: bioinfo@amc.uva.nl; subject: LinRegPCR).
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            Author and article information

            Affiliations
            [1 ]Department of Psychology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
            [2 ]Department of Cell and Systems Biology, University of Toronto, Mississauga, Ontario, Canada
            [3 ]Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
            [4 ]Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
            [5 ]Institute of Advanced Research, Nagoya University, Nagoya, Japan
            [6 ]Institute of Medical Science, University of Toronto, Mississauga, Ontario, Canada
            University of Cambridge, United Kingdom
            Author notes

            Conceived and designed the experiments: KM HA JTW DAM. Performed the experiments: KM ZR. Analyzed the data: KM ZR DAM. Contributed reagents/materials/analysis tools: KM ZR PR ZY HA MK GS AL JTW. Wrote the paper: KM ZR PR ZY HA MK GS AL JTW DAM.

            Contributors
            Role: Editor
            Journal
            PLoS One
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2010
            23 September 2010
            : 5
            : 9
            2944863
            20886071
            10-PONE-RA-19166R1
            10.1371/journal.pone.0012922
            (Editor)
            Mo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 8
            Categories
            Research Article
            Neuroscience/Neurobiology of Disease and Regeneration
            Neurological Disorders/Neurogenetics
            Neurological Disorders/Neuromuscular Diseases
            Neurological Disorders/Spinal Disorders

            Uncategorized

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