American Association for Cancer Research
00085472can193392-sup-231500_2_supp_6002000_q3t4qn.docx (39.03 MB)

Supplementary data from Metabolic Reprogramming in Cancer Is Induced to Increase Proton Production

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journal contribution
posted on 2023-03-31, 03:23 authored by Huiyan Sun, Yi Zhou, Michael Francis Skaro, Yiran Wu, Zexing Qu, Fenglou Mao, Suwen Zhao, Ying Xu

Supplementary results, Figures S1-S38 and Tables S25-S28. Figure S1: Serine synthesis pathway from 3PG. The gene name above each reaction-representing arrow encodes the enzyme that catalyzes the reaction. Adapted from BioCyc. Figure S2. Average expression levels (y-axis) of PSPH (blue), SLC1A4 (red) and SLC1A5 (green) in control samples (#1 on x-axis), and cancer samples of stage 1, 2, 3, 4 (#2, 3, 4, 5 on x-axis) of ESCA, KICH and LIHC, respectively. Figure S3: Tryptophan degradation pathway (adapted from BioCyc). For degradation of glutaryl-CoA, see Figure S5. Figure S4: (a) Proline synthesis pathway (adapted from BioCyc). (b) Extended proline synthesis pathway. Figure S5: Lysine degradation pathway (adapted from BioCyc). Figure S6: An illustration of the glutaminolysis pathway. Figure S7: Purine (dRN) synthesis pathways. (a, b) Salvage pathway, where (a1) and (a2) are connected at IMP. (c) De novo synthesis pathway, where (c1), (c2) and (c2), (c3) are connected at 2(formamido)-N'-(5-phospho-β-D-ribosyl)acetamidine and IMP, respectively. All are adapted from BioCyc. Figure S8: Pyrimidine (dRN) synthesis pathways. (a, b) Salvage pathways. (c) De novo synthesis. All adapted from BioCyc. Figure S9: Deoxyribonucleotide degradation pathway (adapted from BioCyc). (a) Pyrimidine. (b) Purine. Figure S10: Ribonucleotides de novo biosynthesis (adapted from BioCyc). (a) Pyrimidine. (b) Purine, where (b1) and (b2) are defined similarly to those in Figure S6. Figure S11: Ribonucleotides salvage pathway. (a) Pyrimidine. (b) Purine (see S6b). Adapted from BioCyc. Figure S12: Ribonucleotide degradation pathway (adapted from BioCyc). (a) Pyrimidine. (b) Purine. Figure 13: Reprogrammed overall lipid metabolism in cancer. Figure S14: Fatty acid biosynthesis pathway: (a) initiation; (b) elongation. Adapted from BioCyc. Figure S15: Triglyceride metabolism. (a) Synthesis. (b) Degradation. Adapted from BioCyc. Figure S16: Synthesis pathways for different phospholipids: (a) phosphatidic acid (PA); (b) phosphatidylcholine (PC); (c) phosphatidylethanolamine (PE); (d) phosphatidylinositol (PI); and (e) phosphatidylserine (PS). Adapted from BioCyc. Figure S17: Degradation pathways of phospholipids. (a) Prostaglandin H2. (b) Leukotriene A4. Figure S18: Ceramide production pathways. (a) ceramide de novo biosynthesis; (b) ceramide salvage pathway. Adapted from BioCyc. Figure S19: Metabolic pathways for protein O-glycosylation. Adapted from BioCyc. Figure S20: Metabolic pathways for protein N-glycosylation. (a) Initial phase. (b) Processing phase. (c) Complex synthesis phase. Adapted from BioCyc. Figure S21: Gluconeogenesis (left) and glycolysis (right) pathways. Figure S22: Synthesis and deployment of sialic acids. Figure S23: Metabolic pathways for glucosaminoglycan synthesis. (a) Chondroitin sulfate. (b) Heparan sulfate. (c) Keratan sulfate. (d) Hyaluronic acid. Adapted from BioCyc. Figure S24: Differential expressions of (a) kinase and (b) phosphatase genes across 14 cancer types. Figure S25: Choline synthesis and metabolic pathways. Figure 26: Predicted choline production and metabolic pathways in cancer. Figure S27: The NAD+ / NADH metabolic pathway. Figure S28: Differential expressions of genes involved in NAD+/NADH conversion, where the top section (marked with blue sidebar) is for enzyme genes whose reactions convert NAD+ to NADH and the bottom section (marked with red sidebar) is for genes whose reactions convert NADH to NAD+. Figure S29: Metabolic networks of retinol biosynthesis and metabolism. Adapted from BioCyc. Figure S30: Differential expressions of methyltransferase genes across 14 cancer types. Figure S31: Differential expressions of 2-oxoglutarate oxygenase genes31 across 14 cancer types. Figure S32. The mevalonate pathway. Adapted from BioCyc. Figure S33: Density plots of estimated ATP consumption in cancer vs. control tissues of each cancer type. Figure S34: Differential expressions of genes whose reactions produce H+ vs. those that consume H+. 219 genes, whose proteins are known to locate in cytosol based on information from GeneCards.com37 are used in this calculation. (n) Figure S35: Co-expressions between RM signature genes (rows) (Table 2) and ER stress genes (columns) across 14 cancer types, measured using the Spearman correlation coefficient. Figure S36: Co-expressions between RM signature genes (rows) (Table 2) and DNA polymerase genes (columns) across 14 cancer types, measured using the Spearman correlation coefficient. Figure S37: Heat-maps showing co-expressions of genes in ten selected reprogrammed metabolic pathways across 14 cancer types. Figure S38: Predicted levels of Fenton reactions vs. levels of top six RMs (p-value < 0.05) that are selected as the most significant contributors (ordered from left to right in the descending order of statistical significance) to the regression result in 6 cancer types: (a) BRCA, (b) COAD, (c) KIRC, (d) KIRP, (e) STAD, and (f) THCA.


National Natural Science Foundation of China



Considerable metabolic reprogramming has been observed in a conserved manner across multiple cancer types, but their true causes remain elusive. We present an analysis of around 50 such reprogrammed metabolisms (RM) including the Warburg effect, nucleotide de novo synthesis, and sialic acid biosynthesis in cancer. Analyses of the biochemical reactions conducted by these RMs, coupled with gene expression data of their catalyzing enzymes, in 7,011 tissues of 14 cancer types, revealed that all RMs produce more H+ than their original metabolisms. These data strongly support a model that these RMs are induced or selected to neutralize a persistent intracellular alkaline stress due to chronic inflammation and local iron overload. To sustain these RMs for survival, cells must find metabolic exits for the nonproton products of these RMs in a continuous manner, some of which pose major challenges, such as nucleotides and sialic acids, because they are electrically charged. This analysis strongly suggests that continuous cell division and other cancerous behaviors are ways for the affected cells to remove such products in a timely and sustained manner. As supporting evidence, this model can offer simple and natural explanations to a range of long-standing open questions in cancer research including the cause of the Warburg effect. Inhibiting acidifying metabolic reprogramming could be a novel strategy for treating cancer.

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