1. Cell Biology
  2. Cancer Biology
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Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer

  1. Keren Yizhak  Is a corresponding author
  2. Edoardo Gaude
  3. Sylvia Le Dévédec
  4. Yedael Y Waldman
  5. Gideon Y Stein
  6. Bob van de Water
  7. Christian Frezza
  8. Eytan Ruppin  Is a corresponding author
  1. Tel-Aviv University, Israel
  2. University of Cambridge, United Kingdom
  3. Leiden University, Netherlands
  4. Beilinson Hospital, Rabin Medical Center, Israel
  5. Tel Aviv University, Israel
Research Article
Cite this article as: eLife 2014;3:e03641 doi: 10.7554/eLife.03641
6 figures and 4 additional files

Figures

Figure 1 with 1 supplement
The PRIME pipeline and growth rate predictions obtained by different methods.

(A) Upper panel: Spearman rank correlation between the metabolic gene expression of two representative cell lines in the HapMap (left) and NCI-60 (right) datatset (the two cell lines represent the average correlation across the entire datasets); Middle panel: Spearman rank correlation between predicted and measured growth rates in the HapMap (left) and NCI-60 (right) datatset as predicted by iMAT, a method that utilizes discrete gene expression signature as input; Lower Panel: Spearman rank correlation between predicted and measured growth rates in the HapMap (left) and NCI-60 (right) datatset as predicted by E-Flux, a method that utilizes absolute gene expression levels as input. (B) A schematic overview of PRIME. As input, PRIME gets a GSMM and gene expression measurements for p cells together with their associated phenotypic measurement (e.g., proliferation rate). (Step 1): A set of genes whose expression is significantly associated with the phenotype is identified. (Step 2): A linear transformation from the expression of the phenotype-associated genes, to reactions' upper bound (maximal flux capacity) is applied (‘Materials and methods’). PRIME outputs a GSMM for each of the p input cells, such that each cell model generates a different feasible flux solution space. See also Figure 1—figure supplement 1.

https://doi.org/10.7554/eLife.03641.003
Figure 1—figure supplement 1
Biomass production as a function of flux upper bound.

A piecewise linear curve representing the change in biomass production as a function of the upper bound. The latter are modified gradually over the subset of growth-associated reactions, starting from the minimal flux necessary for growth (as estimated over the subset of essential reactions, ‘Materials and methods’), to the maximal bound in the model. The normalization range is set to the first segment of the piecewise linear curve.

https://doi.org/10.7554/eLife.03641.004
Growth rate predictions obtained by PRIME.

(A) The Spearman correlation achieved by the different methods in predicting the individualized growth rates measurements across the HapMap and NCI-60 cell lines. (CV; Cross-Validation). (B) Individual predicted vs measured growth rates in the HapMap (left) and NCI-60 (right) datasets. (C) A comparison between mean predicted and measured growth rates across the four HapMap populations. Measured growth rates are represented as bars and the predicted growth rate is represented as a line. PRIME correctly predicts the population-based order of proliferation rates: CEU < YRI < JPT < CHB. (D) A comparison between mean predicted and measured growth rates across the nine tumor types composing the NCI-60 collection. Measured growth rates are represented as bars and the predicted growth rate is represented as a line (Spearman R = 0.71, p-value = 0.03); Leukemia (LE); Breast (BR); Central Nervous System (CNS); Colon (CO); Renal (RE); Lung (LU); Ovarian (OV); Prostate (PR); Melanoma (ME).

https://doi.org/10.7554/eLife.03641.005
Figure 3 with 1 supplement
Drug response, biomarkers and selectivity analysis.

(A) A comparison between measured and predicted drug response for the HapMap, CEU (Western European ancestry) and NCI-60 datasets. Overall, significant correlations (Spearman p-value < 0.05) were obtained for 12 out of the 16 drugs examined (those marked with an asterisk). The HapMap drugs are 5-fluorouracil (5FU) and 6-mercaptopurin (6MP); the CEU drugs are Ethacrynic acid, Hexachlorophene, Digoxin, Azathioprine, Reserpine and Pyrimethamine; The NCI-60 drugs for dataset 1 include Gemcitabine, Methotrexate and Pyrimethamine; For dataset 2, Trimetrexate and Gemcitabine; For dataset 3, Methotrexate, Quinacrine HCl and Allopurinol. (B) 14 metabolites for which a significant correlation between measured and predicted uptake and secretion rates is achieved. Both the Spearman correlation coefficient (gray) and the–log(p-value) (blue) are shown. The dashed line represents the FDR corrected significance level for α = 0.05. (C) Metabolic reaction targets that are predicted to be non-selective (green) or selective (blue). The x-axis represents the selectivity score (‘Materials and methods’) and the y-axis represents the growth inhibition predicted for the normal cell lines. Non-selective targets are predicted to reduce both normal and cancer cell growth by more than 50%. The selective targets are predicted to reduce normal cell growth by less than 20% and cancer cell growth by more than 30%. MLYCD is the third ranked target with a predicted reduction of >90% in cancer cell growth and <10% in normal cell growth. See also Figure 3—figure supplement 1. (D) Growth survival (in %) for the HapMap (normal) and NCI-60 (cancer) cell lines upon MLYCD knock down, as predicted by E-Flux and PRIME. While E-Flux predicts less than 10% reduction in cellular growth for both normal and cancer cell lines in a largely indiscriminate manner, PRIME predicts a cancer selective effect.

https://doi.org/10.7554/eLife.03641.006
Figure 3—figure supplement 1
Core metabolic pathways and their association with selective and non-selective predicted targets.

Metabolic enzymes colored green are a subset of known cytostatic drug targets. Metabolic enzymes colored red are those found in current clinical trials (and thus likely to be more selective than traditional cytotoxic drugs), out of which those marked by an asterisk were identified as selective targets in our simulations as well. Metabolic enzymes colored blue denote novel selective predictions according to our simulations. αKG, α-ketoglutarate; Ac-CoA, acetyl CoA; ASN, aspargine; ASNS, asparagine synthetase; ASP, aspartate; 1,3BPG, 1,3 biphosphoglyxerate; DHF, dehydrofolate; DHFR, dehydrofolate reductase; CDP, cytosine diphosphate; dCDP, deoxycytosine diphosphate; DHAP, dehydroxyacetone phosphate; dTMP, deoxythymidine monophosphate; dUMP, deoxyuridine monophosphate; F6P, fructose-6-phosphate; FBP, fructose-1,6-bisphosphate; G3P, glyceraldehydes 3-phospate; G6P, glucose-6-phosphate; Gln, glutamine; Glu, glutamate; HK2, hexokinase 2; LDHA, lactate dehydrogenase A; Mal-CoA, malonyl coa; MCT1, monocarboxylate transporter 1,4; mTHF, 5,10-Methylenetetrahydrofolate; MYLCD, malonyl-CoA decarboxylase; 2PG, glycerate 2-phosphate; 3PG, glycerate 3-phosphate; PEP, phosphoenolpyruvate; PGAM, phosphoglycerate mutase; PKM2, pyruvate kinase M2 isoform; R5P, ribose-5-phosphate; RRM1, ribonucleotide reductase M1; SHMT1, serine hydroxymethyltransferase 1; TCA, tricarboxylic acid; THF, tetrahydrofolate; TYMS, thymidylate synthase; UDP, uridine diphophate; UMP, uridine monophosphate; UPP1, uridine phosphorylase.

https://doi.org/10.7554/eLife.03641.007
Figure 4 with 1 supplement
MLYCD depletion on normal and cancer cell lines.

(A) MLYCD mRNA expression upon nucleofection with Non Targeting Control (NTC) and three independent siRNA constructs in HapMap, RPMI-8226 and K562 cells. (B) Cell counts after 72 hr of culture of the indicated cell lines. (C) MLYCD mRNA expression upon nucleofection with Non Targeting Control (NTC) and three independent siRNA constructs in HK2, TK10 and CAKI1 cells. (D) Cell counts after 72 hr of culture of the indicated cell lines. Data are shown as mean ± s.e.m of three independent cultures. *p-value<0.05. **p-value<0.01. ***p-value < 0.001.

https://doi.org/10.7554/eLife.03641.008
Figure 4—figure supplement 1
Expression levels of MLYCD across multiple cancer and normal cell lines.

mRNA levels of MLYCD in the indicated cell lines was measured by qPCR. Data are indicated as ΔCT of MLYCD vs Actin in the indicated cell lines.

https://doi.org/10.7554/eLife.03641.009
Figure 5 with 6 supplements
Metabolic characterization of MLYCD depletion.

(A) Schematic representation of isotope tracing experiment with 13C16-Palmitate. Black-filled circles indicate 13C-carbon, whereas the white filled circles represent the unlabeled carbon. The schematic shows the expected composition of labeled carbons of the indicated metabolites. (B) Labeling incorporation from 13C-Palmitate into Palmitoyl-carnitine in non-targeting control (NTC) and MLYCD-depleted (shMLYCD) cells. Data are shown as percentage of 13C16-palmitoylcarnitine to the total pool of Palmitoyl-carnitine. (C) Labeling incorporation from 13C16-palmitate into TCA cycle intermediates of the indicated cell lines. Data are shown as percentage of the m+2 isotopologue to the total pool size of each metabolite. (D) Schematic representation of isotope tracing experiment with 13C6-Glucose. The distribution of light and heavy carbons is depicted as in A. (E) Labeling of Citrate and of (F) Palmitate after incubation with 13C6-glucose. Data are shown as percentage of the indicated isotopologue to the total pool size of each metabolite. Isotopologue distribution of citrate is indicated in Figure 5—figure supplement 6. Palmitate isotopologues above m+10 were not detected (G) Schematic representation of isotope tracing experiment with 1,2-13C2-Glucose. Ru5p: ribulose-5-phosphate. The distribution of light and heavy carbons is depicted as in A. (H) Ratio between m+1 and m+2 isotopologues of Lactate in the indicated cell lines. (I) Ratio between reduced (GSH) and oxidized (GSSG) glutathione in RPMI-8226 cells infected with the indicated constructs. (J) Cell counts after 72 hr of culture of the indicated cell lines in the presence or absence of 2 mM N-Acetyl Cysteine. Data are shown as mean ± s.e.m of three independent cultures. *p-value<0.05. **p-value<0.01. ***p-value < 0.001.

https://doi.org/10.7554/eLife.03641.010
Figure 5—figure supplement 1
Silencing of MLYCD in RPMI-8226 cells using shRNA.

MLYCD mRNA expression upon infection with Non Targeting Control (NTC) and two independent shRNA constructs targeting MLYCD (shMLYCD1 and 2) in RPMI-8226 cells.

https://doi.org/10.7554/eLife.03641.011
Figure 5—figure supplement 2
Effects of Silencing of MLYCD in RPMI-8226 cells.

Cells were treated as indicated in Figure 5—figure supplement 1 and counted after 72 hr of culture were.

https://doi.org/10.7554/eLife.03641.012
Figure 5—figure supplement 3
Isotopologue distribution of TCA cycle intermediates after incubation with 13C16-palmitate.

Labeling incorporation from 13C16-palmitate in NTC and shMLYCD2 cells. Isotopologues above m+2 were not detected. Data are presented as percentage of the indicated isotopologue to the total pool of each metabolite. Data are presented as mean ± s.e.m.

https://doi.org/10.7554/eLife.03641.013
Figure 5—figure supplement 4
LCMS analyses of TCA cycle intermediates in MLYCD-depleted cells.

Total levels of TCA cycle intermediates in RPMI-8226 cells infected with NTC or shMLYCD2. Data are presented as relative abundance of total metabolites in shMLYCD2 compared to NTC.

https://doi.org/10.7554/eLife.03641.014
Figure 5—figure supplement 5
A schematic description of the metabolic changes following MLYCD knock down.

MLYCD suppression is predicted to divert the accumulated malonyl-CoA to fatty acid biosynthesis, increasing the demand of cells for reducing power. The latter is generated by the oxidative branch of the pentose phosphate pathway, overall leading to increased oxidative stress. Green/red arrows represent increased/decreased flux, respectively.

https://doi.org/10.7554/eLife.03641.015
Figure 5—figure supplement 6
TCA cycle activity in MLYCD-depleted cells.

Labeling incorporation from 13C6-Glucose in NTC and shMLYCD2 cells. Data are presented as percentage of the indicated isotopologue to the total pool of each metabolite. Data are presented as mean ± s.e.m.

https://doi.org/10.7554/eLife.03641.016
Differential growth affects in breast cancer cell-lines and clinical data analysis.

(A) Four gene/reaction targets showing a differential effect on cancer cell growth (represented as % of growth inhibition) according to both PRIME's predictions and experimental validations via siRNA knock downs (when compared to a negative control, a siRNA that targets luciferase). Each gene was tested experimentally in two cell lines in triplicate, where the gene knock down is predicted to have the lowest and highest effect on cell growth. 11 out of the 13 top predictions tested were confirmed experimentally. Data are shown as mean ± s.e.m. For the full list see Supplementary file 2D. The genes GSR and PROSC are predicted to completely suppress the Hs578 t cell line growth (Supplementary file 2) but for presentation appear with a 0.05% height bar; (B) Kaplan-Meier plots for the two breast cancer datasets and for a lung cancer dataset. In all cases low growth rate (GR) is associated with improved survival.

https://doi.org/10.7554/eLife.03641.017

Additional files

Supplementary file 1

Supplementary files 1A–K.

https://doi.org/10.7554/eLife.03641.018
Supplementary file 2

Supplementary files 2A–D.

https://doi.org/10.7554/eLife.03641.019
Supplementary file 3

The PRIME code.

https://doi.org/10.7554/eLife.03641.020
Supplementary file 4

The HapMap and NCI-60 models as SBML.

https://doi.org/10.7554/eLife.03641.021

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