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Autoimmunity: MS microarrays
Microarray analysis of human multiple sclerosis lesions has revealed marked differences in gene expression between autopsy tissue from MS patients compared with similar tissue from individuals without MS. Microarray analysis of human multiple sclerosis (MS) lesions has revealed marked differences in gene expression between autopsy tissue from MS patients compared with similar tissue from individuals without MS. The authors validated two of the differentially expressed genes as potential therapeutic targets in a mouse model of MS, which established the power of the microarray approach.
MS is an autoimmune disease of the central nervous system (CNS) that results from immune-cell infiltration of the CNS white matter. Inflammation and the subsequent destruction of myelin cause the well-known symptoms of progressive paralysis. Environmental factors and genetic susceptibility are both implicated in the aberrant immune response against myelin. Histologically, two types of lesions are seen: acute lesions, which are characterized by inflammation, and chronic lesions, which show scarring and demyelination. A comparison of messenger RNA transcripts from the two types of lesions uncovered several genes that were not thought previously to be important in MS, such as those that encode granulocyte colony-stimulating factor (G-CSF) and the Fc The authors tested the value of the microarray approach using the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Expression of the Fc Although EAE is a useful model for MS, clinical trials have shown that many efficacious therapies in mice have not translated well into humans. So, caution must be exercised in spite of the animal-model validation of two MS targets. However, this study provides proof of principle for the power of microarray technology to identify new therapeutic targets. An enormous amount of data is included in these types of reports. Databanks that contain the raw gene-array data will allow more-meaningful comparisons across datasets to make the most of the increasing number of these analyses. Melanie Brazil References
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