TKTL1 Knockdown Impairs Hypoxia-Induced Glucose-6-phosphate Dehydrogenase and Glyceraldehyde-3-phosphate Dehydrogenase Overexpression.

Increased expression of transketolase (TKT) and its isoform transketolase-like-1 (TKTL1) has been related to the malignant leukemia phenotype through promoting an increase in the non-oxidative branch of the pentose phosphate pathway (PPP). Recently, it has also been described that TKTL1 can have a role in survival under hypoxic conditions and in the acquisition of radio resistance. However, TKTL1's role in triggering metabolic reprogramming under hypoxia in leukemia cells has never been characterized. Using THP-1 AML cells, and by combining metabolomics and transcriptomics techniques, we characterized the impact of TKTL1 knockdown on the metabolic reprogramming triggered by hypoxia. Results demonstrated that TKTL1 knockdown results in a decrease in TKT, glucose-6-phosphate dehydrogenase (G6PD) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) activities and impairs the hypoxia-induced overexpression of G6PD and GAPDH, all having significant impacts on the redox capacity of NADPH- and NADH-related cells. Moreover, TKTL1 knockdown impedes hypoxia-induced transcription of genes encoding key enzymes and transporters involved in glucose, PPP and amino acid metabolism, rendering cells unable to switch to enhanced glycolysis under hypoxia. Altogether, our results show that TKTL1 plays a key role in the metabolic adaptation to hypoxia in THP-1 AML cells through modulation of G6PD and GAPDH activities, both regulating glucose/glutamine consumption and the transcriptomic overexpression of key players of PPP, glucose and amino acids metabolism.

Genome-scale integration of transcriptome and metabolome unveils squalene synthase and dihydrofolate reductase as targets against AML cells resistant to chemotherapy.

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.

Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations.

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.