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Metabolic reprogramming of leukemia cells is important for survival, proliferation, and drug resistance under conditions of metabolic stress in the bone marrow. Deregulation of cellular metabolism, leading to development of leukemia, occurs through abnormally high expression of transcription factors such as MYC and Ecotropic Virus Integration site 1 protein homolog (EVI1). Overexpression of EVI1 in adults and children with mixed lineage leukemia-rearrangement acute myeloid leukemia (MLL-r AML) has a very poor prognosis. To identify a metabolic inhibitor for EVI1-induced metabolic reprogramming in MLL-r AML, we used an XFp extracellular flux analyzer to examine metabolic changes during leukemia development in mouse models of AML expressing MLL-AF9 and Evi1 (Evi1/MF9). Oxidative phosphorylation (OXPHOS) in Evi1/MF9 AML cells accelerated prior to activation of glycolysis, with a higher dependency on glutamine as an energy source. Furthermore, EVI1 played a role in glycolysis as well as driving production of metabolites in the tricarboxylic acid cycle. L-asparaginase (L-asp) exacerbated growth inhibition induced by glutamine starvation and suppressed OXPHOS and proliferation of Evi1/MF9 both in vitro and in vivo; high sensitivity to L-asp was caused by low expression of asparagine synthetase (ASNS) and L-asp-induced suppression of glutamine metabolism. In addition, samples from patients with EVI1+MF9 showed low ASNS expression, suggesting that it is a sensitive marker of L-asp treatment. Clarification of metabolic reprogramming in EVI1+ leukemia cells may aid development of treatments for EVI1+MF9 refractory leukemia.
Pubmed ID: 31649131
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
View all literature mentionsMus musculus with name C57BL/6J from IMSR.
View all literature mentionsMus musculus with name C57BL/6J from IMSR.
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