Dermatomyositis has been modeled as an autoimmune disease largely mediated by the adaptive immune system, including a local humorally mediated response with B and T helper cell muscle infiltration, antibody and complement-mediated injury of capillaries, and perifascicular atrophy of muscle fibers caused by ischemia. To further understand the pathophysiology of dermatomyositis, we used microarrays, computational methods, immunohistochemistry and electron microscopy to study muscle specimens from 67 patients, 54 with inflammatory myopathies, 14 with dermatomyositis. In dermatomyositis, genes induced by interferon-alpha/beta were highly overexpressed, and immunohistochemistry for the interferon-alpha/beta inducible protein MxA showed dense staining of perifascicular, and, sometimes all myofibers in 8/14 patients and on capillaries in 13/14 patients. Of 36 patients with other inflammatory myopathies, 1 patient had faint MxA staining of myofibers and 3 of capillaries. Plasmacytoid dendritic cells, potent CD4+ cellular sources of interferon-alpha, are present in substantial numbers in dermatomyositis and may account for most of the cells previously identified as T helper cells. In addition to an adaptive immune response, an innate immune response characterized by plasmacytoid dendritic cell infiltration and interferon-alpha/beta inducible gene and protein expression may be an important part of the pathogenesis of dermatomyositis, as it appears to be in systemic lupus erythematosus.
Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies, and Alzheimer's disease, using gene sets we compiled from various public databases. In the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-alpha/beta response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry.
A long-standing model postulates that X-chromosome dosage compensation in Drosophila occurs by twofold up-regulation of the single male X, but previous data cannot exclude an alternative model, in which male autosomes are down-regulated to balance gene expression. To distinguish between the two models, we used RNA interference to deplete Male-Specific Lethal (MSL) complexes from male-like tissue culture cells. We found that expression of many genes from the X chromosome decreased, while expression from the autosomes was largely unchanged. We conclude that the primary role of the MSL complex is to up-regulate the male X chromosome.
MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear. RESULTS: We compare 11 different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on diverse techniques such as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets and genetic algorithms. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real dataset obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.
SUMMARY: To increase compatibility between different generations of Affymetrix GeneChip arrays, we propose a method of filtering probes based on their sequences. Our method is implemented as a web-based service for downloading necessary materials for converting the raw data files (*.CEL) for comparative analysis. The user can specify the appropriate level of filtering by setting the criteria for the minimum overlap length between probe sequences and the minimum number of usable probe pairs per probe set. Our website supports a within-species comparison for human and mouse GeneChip arrays. AVAILABILITY: http://www.crosschip.org