Metabolic Reprogramming in Cancer Is Induced to Increase Proton Production

Sun, Huiyan; Zhou, Yi; Skaro, Michael; Wu, Yiran; Qu, Zhi‐Rong; Mao, Feng; Zhao, Suwen; Xu, Ying

doi:10.1158/0008-5472.can-19-3392

Cited by 43 publications

(56 citation statements)

References 63 publications

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“…More work is needed to elucidate the effects of water activity, which is modulated by solution composition and macromolecular crowding (58), on cellular metabolism in cancer. For instance, one question that can be asked is whether the proposed cancer-specific differences in water activity, which would affect the Gibbs energy of hydrolysis of ATP (59), may alter the extent of hydrolysis reactions that are thought to contribute to the production of protons in cancer (60). Similarly, a physicochemical link between protein length and water activity can be expected, as shorter proteins have more H2O incorporated into the terminal groups in proportion to their mass.…”

Section: Discussionmentioning

confidence: 99%

Water as a reactant in the differential expression of proteins in cancer

Dick¹

2020

Preprint

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The large amounts of proteomic data now available for cancer can used to investigate whether the physicochemical conditions of tumors are reflected in patterns of protein expression and chemical composition. Compositional analysis of more than 250 datasets for differentially expressed proteins compiled from the literature reveals a clear signal of higher stoichiometric hydration state (n H 2 O , derived from the theoretical formation reactions of proteins from particular basis species) in specific cancer types compared to normal tissue; this trend is also evident in pan-cancer transcriptomic and proteomic datasets from The Cancer Genome Atlas and Human Protein Atlas. In marked contrast to cancer, n H 2 O decreases for differentially expressed proteins in hyperosmotic stress (including high glucose) experiments and 3D cell culture compared to monolayer growth. Compositional analysis combined with gene ages (phylostrata) taken from the literature shows higher n H 2 O of human proteins earlier in evolution. Further analyses using amino acid biosynthetic reactions supports the conclusion that a net increase of water going into the reactions of protein synthesis is a biochemical characteristic shared by most cancer types. These findings raise the possibility of a basic physicochemical link between increased water content in tumors and the atavistic or embryonic patterns of gene and protein expression in cancer.

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Section: Discussionmentioning

confidence: 99%

Water as a reactant in the differential expression of proteins in cancer

Dick¹

2020

Preprint

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“…In addition, we have also discovered that all cancer tissue cells harbor Fenton reactions: Fe 2+ + H 2 O 2 -> Fe 3+ + q OH + OH − in their cytosol; and the rates of OH − production can saturate the intracellular pH buffer within days, hence increasing the intracellular pH if not neutralized (9), which posts a major stress to the host cells. A linear regression analysis was conducted of the predicted level of Fenton reactions against the predicted levels of all ∼50 reprogrammed metabolisms ( Figure S1), which achieves high R 2 values with statistically significant p-values for each cancer type (8). This strongly suggests that these reprogrammed metabolisms are induced to respond to cytosolic Fenton reactions, serving as neutralizers for the OH − persistently produced by Fenton reactions.…”

Section: Introductionmentioning

confidence: 90%

“…We have recently studied 44 reprogrammed metabolisms widely observed in cancer, including persistent SA synthesis, and discovered that each of them produces more protons than its original metabolism (8). In addition, we have also discovered that all cancer tissue cells harbor Fenton reactions: Fe 2+ + H 2 O 2 -> Fe 3+ + q OH + OH − in their cytosol; and the rates of OH − production can saturate the intracellular pH buffer within days, hence increasing the intracellular pH if not neutralized (9), which posts a major stress to the host cells.…”

Section: Introductionmentioning

confidence: 99%

Elucidation of Functional Roles of Sialic Acids in Cancer Migration

et al. 2020

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Sialic acids (SA), negatively charged nine-carbon sugars, have long been implicated in cancer metastasis since 1960's but its detailed functional roles remain elusive. We present a computational analysis of transcriptomic data of cancer vs. control tissues of eight types in TCGA, aiming to elucidate the possible reason for the increased production and utilization of SAs in cancer and their possible driving roles in cancer migration. Our analyses have revealed for all cancer types: (1) the synthesis and deployment enzymes of SAs are persistently up-regulated throughout the progression for all but one cancer type; and (2) gangliosides, of which SAs are part, tend to converge to specific types that allow SAs to pack at high densities on cancer cell surface as a cancer advances. Statistical and modeling analyses suggest that (i) a highly plausible reason for the increased syntheses of SAs is to produce net protons, used for neutralizing the OH − persistently generated by elevated intracellular iron metabolism coupled with chronic inflammation in cancer tissues; (ii) the level of SA accumulation on cancer cell surface strongly correlates with the stage of cancer migration, as well as multiple migration-related characteristics such as altered cell-cell adhesion, mechanical stress, cell protrusion, and contraction; and (iii) the pattern of SA deployment correlates with the 5-year survival rate of a cancer type. Overall, our study provides strong evidence for that the continuous accumulation of SAs on cancer cell surface gives rise to increasingly stronger cell-cell repulsion due to their negative charges, leading to cell deformation by electrostatic force-induced mechanical compression, which is known to be able to drive cancer cell migration established by recent studies.

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“…As detailed in Results, the reorganized network consists of 21 super module classes of 175 modules. All reactions related to metabolism were collected from the Kyoto Encyclopedia of Genes and Genomes database (KEGG) (61). In total, 11 metabolism related super modules were manually summarized, which is comprised of glycolysis, TCA cycle, pentose phosphate, fatty acids metabolism and synthesis, metabolism of amino acids namely serine, aspartate, beta-alanine, glutamate, leucine/valine/isoleucine and urea cycle, propionyl-CoA and spermidine metabolism [52].…”

Section: Collection and Reorganization Of Human Metabolic Mapmentioning

confidence: 99%

“…An essential part of metabolic map is the biosynthesis pathways. KEGG database and literature [7,[55][56][57][58][59][60][61][62][63][64] are the main information sources used for building biosynthesis modules. We collected 69 biosynthesis modules forming 9 super modules, namely biosynthesis of hyaluronic acid, glycogen, glycosaminoglycan, N-linked glycan, O-linked glycan, Sialic acid, Glycan, Purine and Pyrimidine.…”

Section: Collection and Reorganization Of Human Metabolic Mapmentioning

confidence: 99%

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

Alghamdi

Chang

Dang

et al. 2020

Preprint

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The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. Our understanding of the intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations is unfortunately quite limited, without a mature single cell metabolomics technology. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reorganized human metabolic map as focused metabolic modules, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolites in the matched metabolomics data. We also applied scFEA on publicly available single cell melanoma and head and neck cancer datasets, and discovered different metabolic landscapes between cancer and stromal cells. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

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Metabolic Reprogramming in Cancer Is Induced to Increase Proton Production

Cited by 43 publications

References 63 publications

Water as a reactant in the differential expression of proteins in cancer

Water as a reactant in the differential expression of proteins in cancer

Elucidation of Functional Roles of Sialic Acids in Cancer Migration

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

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