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Pompe disease is an inherited metabolic myopathy caused by deficiency of acid alpha‐glucosidase (GAA), resulting in lysosomal glycogen accumulation. Residual GAA enzyme activity affects disease onset and severity, although other factors, including dysregulation of cytoplasmic glycogen metabolism, are suspected to modulate the disease course. In this study, performed in mice and patient biopsies, we found elevated protein levels of enzymes involved in glucose uptake and cytoplasmic glycogen synthesis in skeletal muscle from mice with Pompe disease, including glycogenin (GYG1), glycogen synthase (GYS1), glucose transporter 4 (GLUT4), glycogen branching enzyme 1 (GBE1), and UDP‐glucose pyrophosphorylase (UGP2). Expression levels were elevated before the loss of muscle mass and function. For first time, quantitative mass spectrometry in skeletal muscle biopsies from five adult patients with Pompe disease showed increased expression of GBE1 protein relative to healthy controls at the group level. Paired analysis of individual patients who responded well to treatment with enzyme replacement therapy (ERT) showed reduction of GYS1, GYG1, and GBE1 in all patients after start of ERT compared to baseline. These results indicate that metabolic changes precede muscle wasting in Pompe disease, and imply a positive feedforward loop in Pompe disease, in which lysosomal glycogen accumulation promotes cytoplasmic glycogen synthesis and glucose uptake, resulting in aggravation of the disease phenotype. [ABSTRACT FROM AUTHOR]

Abstract Background Serial Analysis of Gene Expression (SAGE) and microarrays have found awidespread application, but much ambiguity exists regarding the evaluation of these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance. This study evaluates these overall measures and introduces the between-ratio difference as a concordance measure pergene. Results In this study, gene expression measurements of Unigene clusters represented by both Affymetrix GeneChips HG-U133A and SAGE were compared using two independent RNA samples. After matching of the data sets the final comparison contains a small data set of 1094 unique Unigene clusters, which is unbiased with respect to expression level. Different overall correlation approaches, like Up/Down classification, contingency tables and correlation coefficients were used to compare both platforms. In addition, we introduce a novel approach to compare two platforms based on the calculation of differences between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene (with statistical probability value), as opposed to the commonly used overall concordance measures between platforms. Conclusion We can conclude that intra-platform correlations are generally good, but that overall agreement between the two platforms is modest. This might be due to the binomially distributed sampling variation in SAGE tag counts, SAGE annotation errors and the intensity variation between probe sets of a single gene in Affymetrix GeneChips. We cannot identify or advice which platform performs better since both have their (dis)-advantages. Therefore it is strongly recommended to perform follow-up studies of interesting genes using additional techniques. The newly introduced between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to detect transcripts with similar regulation on both platforms.

Serial analysis of gene expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the amalgamation of the data resulting from these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the results from different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance using different overall correlation approaches, such as Up/Down classification, contingency tables, and correlation coefficients. Here, we demonstrate an approach to compare two platforms based on the calculation of the difference between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene, as opposed to the commonly used overall concordance measures between platforms. This between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to identify transcripts with similar regulation on both platforms.

Several statistical tests have been introduced for the comparison of serial analysis of gene expression (SAGE) libraries to quantitatively analyze the differential expression of genes. As each SAGE library is only one measurement, the necessary information on biological variation or experimental precision is lacking. Therefore, each test includes its own approach to derive such a variance measure from the data set or a theoretical distribution. Because the confidence in tag counts depends on the library size, a test between two or more libraries should be based on original tag counts. When groups of libraries are compared, the test should determine that the proportion of a specific tag in all libraries is the same (null hypothesis), but also offer the possibility to detect specific differences between individual libraries and groups of libraries. The Z-test and the G-test encompass these characteristics and are described for the comparison of two libraries and (two or more) groups of libraries, respectively.