Karakitsou, Effrosyni, Foguet, Carles, Contreras Mostazo, Miriam G., Kurrle, Nina, Schnütgen, Frank, Michaelis, Martin, Cinatl, Jindrich, Marin, Silvia, Cascante, Marta (2021) Genome-scale integration of transcriptome and metabolome unveils squalene synthase and dihydrofolate reductase as targets against AML cells resistant to chemotherapy. Computational and Structural Biotechnology Journal, 19 . pp. 4059-4066. E-ISSN 2001-0370. (doi:10.1016/j.csbj.2021.06.049) (KAR id:90380)
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Official URL: https://doi.org/10.1016/j.csbj.2021.06.049 |
Abstract
The development of resistance to chemotherapeutic agents, such as Doxorubicin (DOX) and cytarabine (AraC), is one of the greatest challenges to the successful treatment of Acute Myeloid Leukemia (AML). Such acquisition is often underlined by a metabolic reprogramming that can provide a therapeutic opportunity, as it can lead to the emergence of vulnerabilities and dependencies to be exploited as targets against the resistant cells. In this regard, genome-scale metabolic models (GSMMs) have emerged as powerful tools to integrate multiple layers of data to build cancer-specific models and identify putative metabolic vulnerabilities. Here, we use genome-scale metabolic modelling to reconstruct a GSMM of the THP1 AML cell line and two derivative cell lines, one with acquired resistance to AraC and the second with acquired resistance to DOX. We also explore how, adding to the transcriptomic layer, the metabolomic layer enhances the selectivity of the resulting condition specific reconstructions. The resulting models enabled us to identify and experimentally validate that drug-resistant THP1 cells are sensitive to the FDA-approved antifolate methotrexate. Moreover, we discovered and validated that the resistant cell lines could be selectively targeted by inhibiting squalene synthase, providing a new and promising strategy to directly inhibit cholesterol synthesis in AML drug resistant cells.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.csbj.2021.06.049 |
Uncontrolled keywords: | Metabolic models; Drug resistance; Acute Myeloid Leukemia (AML); Metabolic vulnerabilities; Drug targets |
Subjects: |
R Medicine > RC Internal medicine > RC254 Neoplasms. Tumors. Oncology R Medicine > RM Therapeutics. Pharmacology |
Divisions: | Divisions > Division of Natural Sciences > Biosciences |
Depositing User: | Martin Michaelis |
Date Deposited: | 27 Sep 2021 17:21 UTC |
Last Modified: | 14 Nov 2022 23:13 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/90380 (The current URI for this page, for reference purposes) |
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