Overexpression of MN1 Confers Resistance to Chemotherapy, Accelerates Leukemia Onset, and Suppresses p53 and Bim Induction
Open Access
- 14 August 2012
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 7 (8) , e43185
Abstract
The transcriptional co-activator MN1 confers a worse prognosis for patients with acute myeloid leukemia (AML) when highly expressed; however, the mechanisms involved are unknown. We sought to model the effects of high MN1 expression in AML models to explore the underlying mechanisms. We used cell lines and a genetically defined mouse model of AML to examine the effects of MN1 overexpression on prognosis and response to cytarabine and doxorubicin in vitro and in vivo. Murine AML that was engineered to overexpress MN1 became more aggressive in vivo, leading to shortened survival in both treated and control groups. In vitro murine AML cells that overexpressed MN1 became resistant to treatment with cytarabine and highly resistant to doxorubicin. This resistant phenotype was also seen in vivo, where treatment with the combination of cytarabine and doxorubicin selected for cells expressing MN1. When therapy-induced DNA damage levels were assessed by γH2AX foci, no reduction was seen in MN1 expressing cells arguing against a drug efflux mechanism. Despite no reduction in DNA damage, MN1-expressing cells showed less apoptosis as assessed by annexin V and propidium iodide staining. Following treatment, p53 and BIM induction were markedly reduced in cells expressing MN1. Pharmacologic inhibition of the p53 E3 ligase MDM2 resulted in increased p53 levels and improved response to doxorubicin in vitro. MN1 overexpression accelerates an already aggressive leukemia, confers resistance to chemotherapy, and suppresses p53 and BIM induction, resulting in decreased apoptosis. This provides a mechanistic explanation of the poor prognosis observed with high MN1 expression and suggests that therapies directed at increasing p53 function may be useful for these patients.Keywords
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