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Recent studies have shown aberrant expression of SOX11 in various types of aggressive B-cell neoplasms. To elucidate the molecular mechanisms leading to such deregulation, we performed a comprehensive SOX11 gene expression and epigenetic study in stem cells, normal hematopoietic cells and different lymphoid neoplasms. We observed that SOX11 expression is associated with unmethylated DNA and presence of activating histone marks (H3K9/14Ac and H3K4me3) in embryonic stem cells and some aggressive B-cell neoplasms. In contrast, adult stem cells, normal hematopoietic cells and other lymphoid neoplasms do not express SOX11. Such repression was associated with silencing histone marks H3K9me2 and H3K27me3. The SOX11 promoter of non-malignant cells was consistently unmethylated whereas lymphoid neoplasms with silenced SOX11 tended to acquire DNA hypermethylation. SOX11 silencing in cell lines was reversed by the histone deacetylase inhibitor SAHA but not by the DNA methyltransferase inhibitor AZA. These data indicate that, although DNA hypermethylation of SOX11 is frequent in lymphoid neoplasms, it seems to be functionally inert, as SOX11 is already silenced in the hematopoietic system. In contrast, the pathogenic role of SOX11 is associated with its de novo expression in some aggressive lymphoid malignancies, which is mediated by a shift from inactivating to activating histone modifications.
The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays
(2010)
Background: Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Results: The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls. Conclusions: ICEP is a stand-alone Windows application to obtain optimal data quality from custom-designed microarrays and is freely available here (see “Additional Files” section) and at: http://www.alice-dsl.net/evgeniy. vainshtein/ICEP/