1. Four Key Steps Control Glycolytic Flux in Mammalian Cells
Lukas Bahati Tanner, Alexander G. Goglia, Monica H. Wei, Talen Sehgal, Lance R. Parsons, Junyoung O. Park, Eileen White, Jared E. Toettcher, Joshua D. Rabinowitz 
Cell Systems 
Volume 7, Issue 1, Pages e8 (July 2018)
DOI: /j.cels
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2. Cell Systems 2018 7, 49-62.e8DOI: (10.1016/j.cels.2018.06.003)
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3. Figure 1
Glycolytic Flux Is Controlled by Glucose Import, Fructose-1,6-Bisphopshate Production, and Lactate Export
(A) Experimental setup used to determine glycolytic flux control. Enzymes were individually overexpressed in iBMK cells and change in lactate secretion (glycolytic flux) was assessed 24 hr post transfection; fEJ is the ratio between lactate secretion in enzyme-overexpressing cells versus vector-control cells.
(B) Western blot showing that the expression dynamics of different glycolytic enzymes is similar (see Figure S1 for additional enzymes).
(C) Single glycolytic enzyme expression did not change the ratio of produced lactate to consumed glucose. Means of two biological replicates per condition are plotted. Parental, Akt, and Ras stably expressing iBMK cells are colored in red.
(D and E) Change in lactate secretion compared with vector control upon individual overexpression of glycolytic enzymes (in DMEM supplemented with 10% dialyzed fetal bovine serum and 5 mM or 25 mM glucose as indicated) (D) and associated flux-control coefficients according to the equation in (A) (for enzymes increasing glycolytic flux) (E). Blue and red indicate statistically significant increases and decreases, respectively (q < 0.05). Means and 95% confidence intervals (n ≥ 6 biological replicates from at least two independent experiments) are plotted.
(F) The sum of averaged flux-control coefficients for glucose import (GLUT), FBP production (PFK), and lactate secretion (MCT) agrees with the summation theorem of metabolic control analysis.
(G) Change in glycolytic flux upon combined overexpression of glucose transport (GLUT3), FBP production (PFKP), and lactate export (MCT4) in comparison with the glycolytic capacity measured upon oligomycin treatment. Data are means and 95% confidence intervals (n = 3 biological replicates from one independent experiment; pTriple vs. Oligo > 0.05; pTriple vs. GLUT3 < 0.01; pTriple vs. PFKP < 0.01; pTriple vs. MCT4 < 0.02).
(H) Cartoon highlighting in light yellow the experimentally determined glycolytic flux-controlling steps.
See also Figure S1.
Cell Systems 2018 7, e8DOI: ( /j.cels )
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4. Figure 2 Intracellular FBP Levels Mirror Glycolytic Flux
(A) Heatmap summarizing changes in intracellular metabolite levels upon overexpression of glycolytic enzymes. Red and blue indicate increased and decreased intracellular metabolites, respectively. Light-yellow-colored boxes highlight the identified flux-controlling steps from Figure 1. Glycolytic enzymes with positive glycolytic flux control are colored red.
(B) Quality of Michaelis-Menten fit of lactate secretion rate to the individual glycolytic intermediate concentration. Lighter bars show R2 across all overexpressed constructs and darker bars represent R2 excluding HK2. R2 for fits were calculated by the residual sum of squares (RSS) and total sum of squares (TSS) with R2 = 1 − RSS/TSS.
(C) Absolute glycolytic flux as a function of FBP concentration follows Michaelis-Menten kinetics (p < 10−4). Dotted lines indicate fitted KM (1.5 mM; p = 0.002) and Vmax (0.3 μmol hr−1 μL cell−1; p < 10−8) values. Gray represents the 95% confidence intervals of the fitted function. Data from HK2-overexpressing cells are shown as the lighter color dot and was omitted from the fitting.
(D) Temporal dynamics of changes in lactate secretion upon single enzyme expression.
(E) Adenosine phosphate levels upon HK2 overexpression (blue) compared with GFP (green) and vector control (gray) samples.
(F) Energy charge upon HK2 overexpression.
(G) Changes in lactate secretion as a function of DNA concentration.
Means and 95% confidence intervals (n = 3 biological replicates) are plotted in (D) to (G). See also Figure S2.
Cell Systems 2018 7, e8DOI: ( /j.cels )
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5. Figure 3
Glycolytic Flux Increase upon Ras Activation Is Controlled by Glucose Import, Glucose-6-Phosphate Production, and Lactate Export
(A) Change in glycolytic flux in 3T3 cells upon individual overexpression of enzymes catalyzing every step in glycolysis. Means and 95% confidence intervals (n = 3 biological replicates) are plotted.
(B) Experimental setup to investigate acute metabolic perturbation in 3T3 cells upon optogenetic Ras activation.
(C–D) Changes after red light exposure in (C) lactate secretion at 6 hr (n = 12 biological replicates from two independent experiments; p < 10−7) and (D) cell growth at 24 hr (n = 3 biological replicates; p < 0.02). Means and 95% confidence intervals are plotted.
(E) Changes in intracellular metabolites 4 hr and 24 hr after activating red light exposure. The 4-hr red light exposed time point was normalized to the mean of the 0-hr time point; the stimulated (Ras ON) 24-hr samples were normalized to the mean of the 24-hr non-stimulated (Ras OFF) samples.
(F) Gene expression dynamics of glycolytic enzymes (from RNA-sequencing data) upon optogenetic Ras activation (data from a recent study [Wilson et al., 2017]).
(G) Overview of results in iBMK and 3T3 cells. For the iBMK and 3T3 columns, red boxes indicate significant flux control based on enzyme overexpression experiments. For the Ras column, red boxes indicate significant transcriptional upregulation upon acute optogenetic Ras activation.
See also Figure S3.
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6. Figure 4
Increased Glucose Uptake, FBP Production, and Lactate Export Is a General Hallmark of Tumor Metabolism
(A) Expression levels of glycolytic enzymes in solid tumor tissue samples compared with paired healthy tissue samples analyzed from TCGA data.
(B) Clustering of tumor types based on glycolytic gene expression data only: bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Kidney (CP) refers to clear cell and papillary cell tumors. Kidney (CH) refers to chromophobe tumors.
(C) Rank order of glycolytic genes from the most to the least expressed relative to benign adjacent tissue, on average, for different tumor types. Red represents highly ranked gene isoforms catalyzing glucose transport, FBP production, and lactate export for each tumor type. Black indicates the highest expressed hexokinase isoform.
(D) Relative frequencies with which gene isoforms across glycolysis are among the four most upregulated glycolytic genes in an individual patient tumor. The expected frequency for each gene due to chance alone (78 occurrences across 666 tumors) is defined as a relative frequency of 1. Statistically significant enrichment of a given gene isoform in the top four most upregulated genes glycolytic genes was calculated using a Fisher's exact test with Bonferroni correction for multiple hypothesis testing. Significant enrichment was found for GLUT1, GLUT5, HK2, HK3,PFKFB4, ENO2,MCT1, and MCT4.
See also Figure S4; Tables S2 and S3.
Cell Systems 2018 7, e8DOI: ( /j.cels )
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