The differential activation of metabolic pathways in leukemic cells depending on their genotype and micro-environmental stress



Acute myeloid leukemia (AML) is characterized by a set of malignant proliferations leading to an accumulation of blasts in the bone marrow and blood. The prognosis is pejorative due to the molecular complexity and pathways implicated in leukemogenesis.


Our research was focused on comparing the metabolic profiles of leukemic cells in basal culture and deprivation conditions to investigate their behaviors under metabolic stress.


We performed untargeted metabolomics using 1H HRMAS-NMR. Five human leukemic cell lines—KG1, K562, HEL, HL60 and OCIAML3—were studied in the basal and nutrient deprivation states. A multivariate analysis of the metabolic profile was performed to find over- or under- expressed metabolites in the different cell lines, depending on the experimental conditions.


In the basal state, each leukemic cell line exhibited a specific metabolic signature related to the diversity of AML subtypes represented and their phenotypes. When cultured in a serum-free medium, they showed quick metabolic adaptation and continued to proliferate and survive despite the lack of nutrients. Low apoptosis was observed. Increased phosphocholine and glutathione was a common feature of all the observed cell lines, with the maximum increase in these metabolites at 24 h of culture, suggesting the involvement of lipid metabolism and oxidative stress regulators in the survival mechanism developed by the leukemic cells.


Our study provides new insights into the metabolic mechanisms in leukemogenesis and suggests a hierarchy of metabolic pathways activated within leukemic cells, some dependent on their genotypes and others conserved among the subtypes but commonly induced under micro-environmental stress.

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We thank Michel Bardet, the head of the laboratory of magnetic resonance at CEA Grenoble and Pierre-Alain Bayle, for his invaluable help during the experiments.

Author information

PM conducted the study design; CL pretreated the samples; JM provided help during the stress experiments; FF supervised the metabolomics design of the study; and CL performed the HRMAS-NMR and data analyses. All the authors read and approved the manuscript.

Correspondence to Caroline Lo Presti.

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Supplementary file1 Representative metabolites for each cell line: (a) HL60, (b) OCIAML3, (c) HEL, (d) KG1, (e) K562. Statistical analyses (t-tests and ANOVAs) of the NMR peak relative amplitude were performed with GraphPad Prism®. The values are mean ± SE of mean (n=2). (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001) (TIF 65973 kb)

Supplementary file2 Specific regions showing the most representative metabolites for each cell line (e.g. the metabolites with higher levels for each cell line): (a) HEL, (b) KG1, (c) HL60, (d) OCIAML3. For metabolites’ abbreviations, see Table S1 (TIF 55391 kb)

Supplementary file3 Spectrum section showing the distinction between choline (Cho, 3.20 ppm), phosphocholine (PC, 3.21 ppm) and glycerophosphocholine (GPC, 3.22 ppm) (TIF 67030 kb)

Supplementary file4 (a) 3D score plot for the HL60 cell line OPLS-DA model (R2Y = 0.94; Q2 = 0.836). (b) 3D score plot for the OCIAML3 cell line OPLS-DA model (R2Y = 0.94; Q2 = 0.818) (TIF 51307 kb)

Supplementary file5 Metabolites identified with NMR analysis with assigned resonances of detected metabolites in the 1H HRMAS NMR spectra of cells (s singlet, d doublet, dd doublet of doublets, t triplet, q quadruplet, m multiplet). Chemical shifts are relative to the right peak of alanine doublet (1.475 ppm); the peaks used for the univariate statistics are in bold. For asparagine, aspartate, glutamate, taurine and proline, only a part of the bold peak (without any overlapping with either a metabolite or a peak of contaminating compound) was considered. The buckets used in the univariate statistics are given for each metabolite we analyzed (DOCX 14 kb)

Supplementary file6 The main properties of all the OPLS-DA models (DOCX 13 kb)

Supplementary file7 The most specific metabolites for all the cell lines (DOCX 12 kb)

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Lo Presti, C., Fauvelle, F., Mondet, J. et al. The differential activation of metabolic pathways in leukemic cells depending on their genotype and micro-environmental stress. Metabolomics 16, 13 (2020).

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  • Acute myeloid leukemia
  • Metabolomics
  • Metabolic pathways