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Leukemia stem cells (LSCs) are found in most aggressive myeloid diseases and contribute to therapeutic resistance. Leukemia cells exhibit a dysregulated developmental program as the result of genetic and epigenetic alterations. Overexpression of the RNA-binding protein Musashi2 (MSI2) has been previously shown to predict poor survival in leukemia. Here, we demonstrated that conditional deletion of Msi2 in the hematopoietic compartment results in delayed leukemogenesis, reduced disease burden, and a loss of LSC function in a murine leukemia model. Gene expression profiling of these Msi2-deficient animals revealed a loss of the hematopoietic/leukemic stem cell self-renewal program and an increase in the differentiation program. In acute myeloid leukemia patients, the presence of a gene signature that was similar to that observed in Msi2-deficent murine LSCs correlated with improved survival. We determined that MSI2 directly maintains the mixed-lineage leukemia (MLL) self-renewal program by interacting with and retaining efficient translation of Hoxa9, Myc, and Ikzf2 mRNAs. Moreover, depletion of MLL target Ikzf2 in LSCs reduced colony formation, decreased proliferation, and increased apoptosis. Our data provide evidence that MSI2 controls efficient translation of the oncogenic LSC self-renewal program and suggest MSI2 as a potential therapeutic target for myeloid leukemia.
Pubmed ID: 25664853 RIS Download
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Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
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