Information

7.14: Introduction to Connections to Other Metabolic Pathways - Biology


What you’ll learn to do: Discuss the connections between metabolic pathways

You have learned about the catabolism of glucose, which provides energy to living cells. Many of the products in a particular pathway are reactants in other pathways.


For your body to develop properly and stay healthy, many things must work together at many different levels - from organs to cells to genes.

From both inside and outside the body, cells are constantly receiving chemical cues prompted by such things as injury, infection, stress or even the presence or lack of food. To react and adjust to these cues, cells send and receive signals through biological pathways. The molecules that make up biological pathways interact with signals, as well as with each other, to carry out their designated tasks.

Biological pathways can act over short or long distances. For example, some cells send signals to nearby cells to repair localized damage, such as a scratch on a knee. Other cells produce substances, such as hormones, that travel through the blood to distant target cells.

These biological pathways control a person's response to the world. For example, some pathways subtly affect how the body processes drugs, while others play a major role in how a fertilized egg develops into a baby. Other pathways maintain balance while a person is walking, control how and when the pupil in the eye opens or closes in response to light, and affect the skin's reaction to changing temperature.

Biological pathways do not always work properly. When something goes wrong in a pathway, the result can be a disease such as cancer or diabetes.

For your body to develop properly and stay healthy, many things must work together at many different levels - from organs to cells to genes.

From both inside and outside the body, cells are constantly receiving chemical cues prompted by such things as injury, infection, stress or even the presence or lack of food. To react and adjust to these cues, cells send and receive signals through biological pathways. The molecules that make up biological pathways interact with signals, as well as with each other, to carry out their designated tasks.

Biological pathways can act over short or long distances. For example, some cells send signals to nearby cells to repair localized damage, such as a scratch on a knee. Other cells produce substances, such as hormones, that travel through the blood to distant target cells.

These biological pathways control a person's response to the world. For example, some pathways subtly affect how the body processes drugs, while others play a major role in how a fertilized egg develops into a baby. Other pathways maintain balance while a person is walking, control how and when the pupil in the eye opens or closes in response to light, and affect the skin's reaction to changing temperature.

Biological pathways do not always work properly. When something goes wrong in a pathway, the result can be a disease such as cancer or diabetes.


Introduction

The Overall Equations of the Urea Cycle and Citric Acid Cycle

(1) (2)

Students are referred to Tables 1 and 2, to get a deeper insight into this topic.

1) mit + mit + 2ATP −4

→carbamoyl + 2ADP −3 + Pi −2 + 2H +

2) + carbamoyl

4) Citrullinecyt + ATP −4 +

+ AMP −2 + + 2 H +

Overall equation: + + 3ATP −4 + + H2O

→ureacyt + 2ADP −3 + AMP −2 + + 2 + + 5H +

  • Note. The synthesis of urea is the result of the cross talk between mitochondria and the cytosolic compartment: two mitochondrial reactions [reactions 1) and 2)], three cytosolic reactions [reactions 4), 5), and 6), and the transport of two intermediates from mithocondria to cytosol and viceversa (steps 3) and 7)].

4) α-Ketoglutarate 2– + HS-CoA + NAD 1+

5) Succinyl-CoA 1– + GDP 3– + Pi 2–

→ succinate 2– + GTP 4– + HS-CoA

Overall equation: Acetyl-S-CoAmit + FADmit + 3 + + Pi 2– mit + 4H2O → 2CO2 + FADH2mit + 6NADHmit + + 2H 1+ 2HS-CoAmit.

It has long been known that the urea cycle and the citric acid cycle are inextricably interwind. However, students should be aware that two reactions or two overall equations can be summarized only if at least one of the products of one of them becomes the substrate of the other. This is not the case for Eqs. 1) and 2. They show that there is no reaction product in the right side of Eq. 1 that is present in the left side of Eq. 2). Therefore, students are faced with the intriguing problem “how can urea cycle and citric acid cycle metabolically interact, if there is no reaction product in the right side of the urea cycle overall equation that is present also in the left side of overall equation of the citric acid cycle?

Metabolic Interrelationship between Urea Synthesis and Citric Acid Cycle Shunt

(3) (4)

(5) (6)

At first glance, this equation might appear too complicated to be discussed. Nevertheless, taking into accont the familiar formulas of urea, Glu and of OAA, it may be read as follows: during the interaction of ureagenesis and the citric acid cycle shunt, one the two nitrogen atoms of urea derives from , the other from the cytosolic (5C)Glu which generates a (4C)OAA and one CO2. The three ATP are hydrolyzed, as in the ureagenesis, to give two ADP, one AMP, one PPi and two Pi. GTP is synthesized by the succinyl-CoA synthetase. Finally, in aerobic conditions, the three NADH and the FADH2 will generate a total of 9 ATP via the oxidative phosphorylation.


Highlights

- We summarize the research progress of epigenetic mechanisms in bone metabolism and osteoporosis.

- We summarize the role of DNA methylation in the osteogenic differentiation and osteoporosis.

- We summarize the role of histone modification in the osteogenic differentiation and osteoporosis, including histone methylation and histone acetylation.

- We summarize the role of non-coding RNA in the osteogenic differentiation and osteoporosis, including lncRNAs, miRNAs, and circRNAs.


References

Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

Kampf, C. et al. The human liver-specific proteome defined by transcriptomics and antibody-based profiling. FASEB J. 28, 2901–2914 (2014).

Anstee, Q. M., Targher, G. & Day, C. P. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat. Rev. Gastroenterol. Hepatol. 10, 330–344 (2013).

Lonardo, A., Ballestri, S., Marchesini, G., Angulo, P. & Loria, P. Nonalcoholic fatty liver disease: a precursor of the metabolic syndrome. Dig. Liver Dis. 47, 181–190 (2015).

Videla, L. A., Rodrigo, R., Araya, J. & Poniachik, J. Insulin resistance and oxidative stress interdependency in non-alcoholic fatty liver disease. Trends Mol. Med. 12, 555–558 (2006).

Perry, R. J., Samuel, V. T., Petersen, K. F. & Shulman, G. I. The role of hepatic lipids in hepatic insulin resistance and type 2 diabetes. Nature 510, 84–91 (2014).

Tilg, H., Moschen, A. R. & Roden, M. NAFLD and diabetes mellitus. Nat. Rev. Gastroenterol. Hepatol. 14, 32–42 (2017).

Paschos, P. & Paletas, K. Non alcoholic fatty liver disease and metabolic syndrome. Hippokratia 13, 9–19 (2009).

Dyson, J. K., Anstee, Q. M. & McPherson, S. Non-alcoholic fatty liver disease: a practical approach to treatment. Frontline Gastroenterol. 5, 277–286 (2014).

Charlton, M. Nonalcoholic fatty liver disease: a review of current understanding and future impact. Clin. Gastroenterol. Hepatol. 2, 1048–1058 (2004).

Bosley, J. et al. Improving the economics of NASH/NAFLD treatment through the use of systems biology. Drug Discov. Today 22, 1532–1538 (2017).

Gonzalez de Castro, D., Clarke, P. A., Al-Lazikani, B. & Workman, P. Personalized cancer medicine: molecular diagnostics, predictive biomarkers, and drug resistance. Clin. Pharmacol. Ther. 93, 252–259 (2013).

Auffray, C., Chen, Z. & Hood, L. Systems medicine: the future of medical genomics and healthcare. Genome Med. 1, 2 (2009).

Nielsen, J. Systems biology of metabolism: a driver for developing personalized and precision medicine. Cell Metab. 25, 572–579 (2017).

Mardinoglu, A. & Nielsen, J. Editorial: the impact of systems medicine on human health and disease. Front. Physiol. 7, 552 (2016).

Gebhardt, R. Metabolic zonation of the liver: regulation and implications for liver function. Pharmacol. Ther. 53, 275–354 (1992).

Kietzmann, T. Metabolic zonation of the liver: the oxygen gradient revisited. Redox Biol. 11, 622–630 (2017).

Gebhardt, R. & Matz-Soja, M. Liver zonation: novel aspects of its regulation and its impact on homeostasis. World J. Gastroenterol. 20, 8491–8504 (2014).

Uhlen, M. et al. Towards a knowledge-based human protein atlas. Nat. Biotechnol. 28, 1248–1250 (2010).

Kim, M. S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).

Thul, P. J. et al. A subcellular map of the human proteome. Science 356, eaal3321 (2017).

Mann, M. & Jensen, O. N. Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21, 255–261 (2003).

Beck, H. C. et al. Quantitative proteomic analysis of post-translational modifications of human histones. Mol. Cell. Proteomics 5, 1314–1325 (2006).

GTEx. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

Yu, N. Y. et al. Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium. Nucleic Acids Res. 43, 6787–6798 (2015).

Uhlen, M. et al. Transcriptomics resources of human tissues and organs. Mol. Syst. Biol. 12, 862 (2016).

Uhlen, M. et al. A pathology atlas of the human cancer transcriptome. Science 357, eaan2507 (2017).

Chen, Z. Progress and prospects of long noncoding RNAs in lipid homeostasis. Mol. Metab. 5, 164–170 (2016).

Chen, Y., Huang, H., Xu, C., Yu, C. & Li, Y. Long non-coding RNA profiling in a non-alcoholic fatty liver disease rodent model: new insight into pathogenesis. Int. J. Mol. Sci. 18, 21 (2017).

Afonso, M. B., Rodrigues, P. M., Simao, A. L. & Castro, R. E. Circulating microRNAs as potential biomarkers in non-alcoholic fatty liver disease and hepatocellular carcinoma. J. Clin. Med. 5, 30 (2016).

Newgard, C. B. Metabolomics and metabolic diseases: where do we stand? Cell Metab. 25, 43–56 (2017).

Holmes, E., Wijeyesekera, A., Taylor-Robinson, S. D. & Nicholson, J. K. The promise of metabolic phenotyping in gastroenterology and hepatology. Nat. Rev. Gastroenterol. Hepatol. 12, 458–471 (2015).

Newgard, C. B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009).

Wurtz, P. et al. Metabolic signatures of insulin resistance in 7,098 young adults. Diabetes 61, 1372–1380 (2012).

Lee, S. et al. Integrated network analysis reveals an association between plasma mannose levels and insulin resistance. Cell Metab. 24, 172–184 (2016).

Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

Mardinoglu, A. et al. Plasma mannose levels are associated with incident type 2 diabetes and cardiovascular disease. Cell Metab. 26, 281–283 (2017).

Shah, S. H. et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ. Cardiovasc. Genet. 3, 207–214 (2010).

Magnusson, M. et al. A diabetes-predictive amino acid score and future cardiovascular disease. Eur. Heart J. 34, 1982–1989 (2013).

Kalhan, S. C. et al. Plasma metabolomic profile in nonalcoholic fatty liver disease. Metabolism 60, 404–413 (2011).

Tan, Y. et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol. Cell. Proteomics 11, M111.010694 (2012).

Ganti, S. & Weiss, R. H. Urine metabolomics for kidney cancer detection and biomarker discovery. Urol. Oncol. 29, 551–557 (2011).

McDunn, J. E. et al. Metabolomic signatures of aggressive prostate cancer. Prostate 73, 1547–1560 (2013).

Zeng, J. et al. Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry. J. Proteome Res. 13, 3420–3431 (2014).

Dumas, M. E., Kinross, J. & Nicholson, J. K. Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease. Gastroenterology 146, 46–62 (2014).

Sookoian, S. et al. Serum aminotransferases in nonalcoholic fatty liver disease are a signature of liver metabolic perturbations at the amino acid and Krebs cycle level. Am. J. Clin. Nutr. 103, 422–434 (2016).

Sookoian, S. & Pirola, C. J. Alanine and aspartate aminotransferase and glutamine-cycling pathway: their roles in pathogenesis of metabolic syndrome. World J. Gastroenterol. 18, 3775–3781 (2012).

Sookoian, S. & Pirola, C. J. Liver enzymes, metabolomics and genome-wide association studies: from systems biology to the personalized medicine. World J. Gastroenterol. 21, 711–725 (2015).

Cheng, S. et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125, 2222–2231 (2012).

Krumsiek, J. et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLOS Genet. 8, e1003005 (2012).

Lotta, L. A. et al. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a mendelian randomisation analysis. PLOS Med. 13, e1002179 (2016).

Taskinen, M. R. & Boren, J. New insights into the pathophysiology of dyslipidemia in type 2 diabetes. Atherosclerosis 239, 483–495 (2015).

Bjornson, E., Adiels, M., Taskinen, M. R. & Boren, J. Kinetics of plasma triglycerides in abdominal obesity. Curr. Opin. Lipidol 28, 11–18 (2017).

Donnelly, K. L. et al. Sources of fatty acids stored in liver and secreted via lipoproteins in patients with nonalcoholic fatty liver disease. J. Clin. Invest. 115, 1343–1351 (2005).

Kawano, Y. & Cohen, D. E. Mechanisms of hepatic triglyceride accumulation in non-alcoholic fatty liver disease. J. Gastroenterol. 48, 434–441 (2013).

Dentin, R., Girard, J. & Postic, C. Carbohydrate responsive element binding protein (ChREBP) and sterol regulatory element binding protein-1c (SREBP-1c): two key regulators of glucose metabolism and lipid synthesis in liver. Biochimie 87, 81–86 (2005).

Williams, K. J. & Wu, X. Imbalanced insulin action in chronic over nutrition: clinical harm, molecular mechanisms, and a way forward. Atherosclerosis 247, 225–282 (2016).

Adiels, M., Mardinoglu, A., Taskinen, M. R. & Boren, J. Kinetic studies to elucidate impaired metabolism of triglyceride-rich lipoproteins in humans. Front. Physiol. 6, 342 (2015).

Boren, J., Taskinen, M. R. & Adiels, M. Kinetic studies to investigate lipoprotein metabolism. J. Intern. Med. 271, 166–173 (2012).

Mardinoglu, A. et al. Personal model-assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD. Mol. Syst. Biol. 13, 916 (2017).

Backhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A. & Gordon, J. I. Host-bacterial mutualism in the human intestine. Science 307, 1915–1920 (2005).

Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).

Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

Qin, J. J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

Mardinoglu, A., Boren, J. & Smith, U. Confounding effects of metformin on the human gut microbiome in type 2 diabetes. Cell Metab. 23, 10–12 (2016).

Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).

Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

Henao-Mejia, J. et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179–185 (2012).

Arora, T. & Backhed, F. The gut microbiota and metabolic disease: current understanding and future perspectives. J. Intern. Med. 280, 339–349 (2016).

Mardinoglu, A. & Nielsen, J. Systems medicine and metabolic modelling. J. Intern. Med. 271, 142–154 (2012).

Van Regenmortel, M. H. Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological and overcome the limitations of reductionism. EMBO Rep. 5, 1016–1020 (2004).

Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

Scannell, J. W. & Bosley, J. When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLOS ONE 11, e0147215 (2016).

Karr, J. R. et al. A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401 (2012).

Bordbar, A., Monk, J. M., King, Z. A. & Palsson, B. O. Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107–120 (2014).

Mardinoglu, A., Gatto, F. & Nielsen, J. Genome-scale modeling of human metabolism — a systems biology approach. Biotechnol. J. 8, 985–996 (2013).

Shoaie, S. & Nielsen, J. Elucidating the interactions between the human gut microbiota and its host through metabolic modeling. Front. Genet. 5, 86 (2014).

Oberhardt, M. A., Yizhak, K. & Ruppin, E. Metabolically re-modeling the drug pipeline. Curr. Opin. Pharmacol. 13, 778–785 (2013).

Mardinoglu, A. & Nielsen, J. New paradigms for metabolic modeling of human cells. Curr. Opin. Biotechnol. 34, 91–97 (2015).

O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).

Yizhak, K. et al. A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Mol. Syst. Biol. 10, 744 (2014).

Yizhak, K. et al. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 3, e03641 (2014).

Yizhak, K., Gabay, O., Cohen, H. & Ruppin, E. Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nat. Commun. 4, 2632 (2013).

Zhang, C., Ji, B., Mardinoglu, A., Nielsen, J. & Hua, Q. Logical transformation of genome-scale metabolic models for gene level applications and analysis. Bioinformatics 31, 2324–2331 (2015).

Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015).

Björnson, E. et al. Stratification of hepatocellular carcinoma patients based on acetate utilization. Cell Rep. 13, 2014–2026 (2015).

Mardinoglu, A. et al. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9, 649 (2013).

Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl Acad. Sci. USA 104, 1777–1782 (2007).

Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3, 135 (2007).

Agren, R. et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLOS Comput. Biol. 8, e1002518 (2012).

Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).

Mardinoglu, A. et al. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014).

Blais, E. M. et al. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat. Commun. 8, 14250 (2017).

Yizhak, K., Chaneton, B., Gottlieb, E. & Ruppin, E. Modeling cancer metabolism on a genome scale. Mol. Syst. Biol. 11, 817 (2015).

Bordbar, A. et al. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst. Biol. 5, 180 (2011).

Wang, Y. L., Eddy, J. A. & Price, N. D. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6, 153 (2012).

Nam, H. et al. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. PLOS Comput. Biol. 10, e1003837 (2014).

Vlassis, N., Pacheco, M. P. & Sauter, T. Fast reconstruction of compact context-specific metabolic network models. PLOS Comput. Biol. 10, e1003424 (2014).

Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012).

Pechenick, D. A., Payne, J. L. & Moore, J. H. Phenotypic robustness and the assortativity signature of human transcription factor networks. PLOS Comput. Biol. 10, e1003780 (2014).

Xu, H., Ang, Y. S., Sevilla, A., Lemischka, I. R. & Ma’ayan, A. Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells. PLOS Comput. Biol. 10, e1003777 (2014).

Bossi, A. & Lehner, B. Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009).

De Las Rivas, J. & Fontanillo, C. Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLOS Comput. Biol. 6, e1000807 (2010).

Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43, D470–D478 (2015).

Goel, R., Harsha, H. C., Pandey, A. & Prasad, T. S. Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. Mol. Biosyst. 8, 453–463 (2012).

Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004).

Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–D815 (2013).

Safari-Alighiarloo, N., Taghizadeh, M., Rezaei-Tavirani, M., Goliaei, B. & Peyvandi, A. A. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol. Hepatol. Bed Bench 7, 17–31 (2014).

Helikar, T., Konvalina, J., Heidel, J. & Rogers, J. A. Emergent decision-making in biological signal transduction networks. Proc. Natl Acad. Sci. USA 105, 1913–1918 (2008).

Cui, Q. et al. A map of human cancer signaling. Mol. Syst. Biol. 3, 152 (2007).

Kim, J. et al. Robustness and evolvability of the human signaling network. PLOS Comput. Biol. 10, e1003763 (2014).

Lee, S., Mardinoglu, A., Lee, D. & Nielsen, J. Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis. Nucleic Acids Res. 44, 5529–5539 (2016).

Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401 (2010).

Gille, C. et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol. Syst. Biol. 6, 411 (2010).

Sookoian, S. & Pirola, C. J. NAFLD: metabolic make-up of NASH: from fat and sugar to amino acids. Nat. Rev. Gastroenterol. Hepatol. 11, 205–207 (2014).

Hyötyläinen, T. et al. Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat. Commun. 7, 8994 (2016).

Ghaffari, P., Mardinoglu, A. & Nielsen, J. Cancer metabolism: a modeling perspective. Front. Physiol. 6, 382 (2015).

Agren, R. et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 721 (2014).

Pinyol, R. & Llovet, J. M. Hepatocellular carcinoma: genome-scale metabolic models for hepatocellular carcinoma. Nat. Rev. Gastroenterol. Hepatol. 11, 336–337 (2014).

Ghaffari, P. et al. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling. Sci. Rep. 5, 8183 (2015).

Zur, H., Ruppin, E. & Shlomi, T. iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140–3142 (2010).

Shoaie, S. et al. Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci. Rep. 3, 2532 (2013).

Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).

Magnusdottir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).

Cotillard, A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).

Mardinoglu, A. et al. The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol. Syst. Biol. 11, 834 (2015).

Lee, S. et al. Network analyses identify liver-specific targets for treating liver diseases. Mol. Syst. Biol. 13, 938 (2017).

Lee, S. et al. TCSBN: a database of tissue and cancer specific biological networks. Nucleic Acids Res. 46, D595–D600 (2017).

Bjornson, E., Boren, J. & Mardinoglu, A. Personalized cardiovascular disease prediction and treatment-a review of existing strategies and novel systems medicine tools. Front. Physiol. 7, 2 (2016).

Mardinoglu, A. & Uhlen, M. Liver: Phenotypic and genetic variance: a systems approach to the liver. Nat. Rev. Gastroenterol. Hepatol. 13, 439–440 (2016).

Williams, E. G. et al. Systems proteomics of liver mitochondria function. Science 352, aad0189 (2016).

Zhang, C., Lee, S., Mardinoglu, A. & Hua, Q. Investigating the combinatory effects of biological networks on gene co-expression. Front. Physiol. 7, 160 (2016).

Gatto, F. et al. Glycosaminoglycan profiling in patients’ plasma and urine predicts the occurrence of metastatic clear cell renal cell carcinoma. Cell Rep. 15, 1822–1836 (2016).


DISCUSSION

These studies identify PBRM1 as the first methyl reader for the α-TubK40me3 mark on microtubules. Recognition of the SETD2 α-TubK40me3 mark and recruitment of PBAF components to microtubules by PBRM1 reveals a coordinated functional relationship between an epigenetic writer and an epigenetic reader acting on the cytoskeleton. Although PBRM1 has both acetyl-binding BD and methyl-binding BAH domains, and microtubules can be acetylated or methylated at lysine-40, our data show that PBRM1 BAH domains specifically recognize the α-TubK40me3 SETD2 methyl mark. Thus, the chromatin and cytoskeletal functions of PBRM1 use distinct protein domains, allowing integration and coordination, rather than competition, for these activities in the cell (Fig. 7).

An intriguing aspect of microtubule biology is the luminal positioning of K40, target for both the α-TubK40me3 methyl mark made by SETD2 and the α-TubK40ac acetyl mark made by α-tubulin acetyltransferase (ATAT-1). Luminal positioning of acetyl and methyl marks raises the question as to how writers, such as ATAT-1 and SETD2, access these residues to modify α-tubulin, and how luminal acetyl and methyl marks direct the function of microtubules. Whether SETD2 methylates α-tubulin before or after assembly of α-/β-tubulin dimers into microtubules is unknown. However, ATAT-1 acetylates α-tubulin after assembly into microtubules and is thought to access luminal K40 to acetylate this residue via pores in the microtubule shaft (33, 34). These and other studies have led to the growing appreciation that lattice dynamics of even stable microtubules can provide access to luminal residues along the shaft of microtubule polymers (3537). Recently, a passive breathing mechanism for microtubules in which α-/β-tubulin dimers can be lost and replaced from the microtubule shaft was proposed (37), which is thought to be facilitated by the severing enzymes katanin and spastin (38). As SWI/SNF complexes are known to evict histones and other proteins from nucleosomes during chromatin remodeling (39), our current study suggests the intriguing hypothesis that PBAF could be playing a similar role in evicting tubulin and/or other microtubule-associated proteins during microtubule remodeling.

The BAH domain crystal structure has been defined for both chicken and human PBRM1 (40), but the function of this domain has not been clearly defined. Our data now confirm a role for PBRM1 BAH domains in recognition of the α-TubK40me3 mark. Furthermore, while the loss of PBRM1 is known to cause genomic instability (30, 32), we have identified a previously unknown functional role for PBRM1 in the maintenance of genomic stability via binding to spindle microtubules and recruitment of an ATPase-competent PBAF complex to the mitotic spindle. This activity requires the BAH domain(s), as BAH domain mutations that inhibit binding to methylated α-tubulin fail to rescue the genomic instability seen with PBRM1 loss despite their ability to form transcriptionally competent PBAF complexes. Recently, the BAH domain of the origin recognition complex (Orc1) in yeast was also shown to be important in maintenance of genomic stability, an activity distinct from its function in transcriptional silencing (41). This BAH domain provides an interface between Orc1 and the nucleosome, and BAH domain mutations that disrupt this interaction increased the frequency of double-strand breaks. Both BAH1 and BAH2 domains of PBRM1 appear to be required for maintenance of genomic stability, as mutations in either BAH1 or BAH2 decreased spindle localization and caused genomic instability. This suggests that there could be cooperativity between these domains for microtubule binding, perhaps due to sequential binding of the two domains to recognize microtubules and recruit PBAF, which contains two PBRM1 molecules for each BRG1 ATPase subunit (3).

Because of their colocalization on chromosome 3p, one allele each of PBRM1 and SETD2 are co-deleted as an early event in development of ccRCC (42), with “second hits” in these epigenetic proteins the second and third most common oncogenic events in this cancer. While PBRM1 mutations occur in excess of 30% of ccRCC, this is far from the only tumor type affected by PBRM1 mutation. Cholangiocarcinoma, for example, has more than 20% mutation rate for this gene. Other tumor types with more than 5% mutation rate include uterine, stomach, melanoma, mesothelioma, and bladder cancer (43, 44). The overall prevalence of PBRM1 is 4.9% (2% as loss of function) across the 11 most frequent tumor types in TCGA (45). These mutations are distributed across the gene, including mutations in the BAH domains (46). Aside from co-occurrence with VHL loss, observed in ccRCC (25), no other known co-occurring mutations have been discovered to date. Notably, mutational exclusivity is observed with other PBAF complex mutations common in cancer (ARID2, BRD7) (45). In a recent evolutionary model of ccRCC developed by the TRACERx study (47, 48), second hits resulting in loss of the reader PBRM1 precede loss of the writer SETD2, but in no case has loss of SETD2 (and its methyl mark) preceded PBRM1 loss. While the reason for this sequence has remained obscure, our results suggest that cancer cells with second hits that inactive SETD2 will lack the cytoskeletal methyl mark read by PBRM1, thereby abrogating PBRM1 cytoskeletal function, and may gain no further (cytoskeletal) advantage by loss of PBRM1. Conversely, while early loss of PBRM1 can contribute to tumor progression by eliminating an α-TubK40me3 reader, later loss of SETD2 will result in loss of both the cytoskeletal α-TubK40me3 and chromatin H3K36me3 marks. Notably, in the setting of loss of PBRM1, repeated subclonal selection for SETD2 mutations occurs (48), suggesting that complete loss of both cytoskeletal and chromatin methyl marks may be an evolutionary bottleneck in cancer progression. Thus, our study identifies dysregulation of microtubule methylation as novel nexus of convergence in explaining how mutations in an epigenetic writer (SETD2) and reader (PBRM1) can drive genomic instability and cancer progression via cytoskeletal defects. This opens new windows into understanding how defects in components of the epigenetic machine with chromatocytoskeletal activities can drive the development of cancer, and perhaps other diseases, via disruption of the cytoskeleton, potentially even independently of disruption of the epigenome.

Last, while epigenetic inheritance is well established, less is known regarding how natural selection acting on environmentally induced phenotypes could drive epimutations and evolution of the epigenome itself (49, 50). In this regard, the concept of an epigenetic machine that coordinates hereditary components of the epigenome with function of the cytoskeleton has interesting implications for evolutionary biology (51). Epigenetic readers, writers, and erasers are responsive to the environment. Examples include the impact of diet on 1-carbon metabolism and availability of methyl donors, regulation of the activity of demethylases by oxygen levels, and kinases that respond to environmental cues, which, when activated, can phosphorylate and modulate the activity of chromatin remodelers (52). Our data suggest that changes in the environment could produce coordinate changes in methylation on both the epigenome and the cytoskeleton, with functional consequences. Evolutionary theory indicates that environmentally induced phenotypes can supplement adaptation by enabling a population to survive in a new or changing environment until potentially adaptive genetic changes become fixed in the population. In the case of evolution of the epigenome, a changing environment could cause coordinated cytoskeletal changes (producing phenotypes upon which natural selection could act) and changes in the epigenome (which could be inherited generationally) (51). This would predict that over evolutionary time, epigenetic marks on chromatin and cytoskeleton could acquire coordinated functions. This is clearly the case for methyl marks made by SETD2, which function on both chromatin and the cytoskeleton to maintain genomic stability [the present report and (14, 15, 32)]. Although still quite speculative, as the dual chromatin and cytoskeletal activity of epigenetic readers, writers, and erasers more fully emerges, this hypothesis could provide useful ways to explore evolution of the epigenome and provide insights into how epigenetic variation and phenotypic variation, the actual target of selection, may be linked.


5. Lipid Metabolism

Aberrant lipid metabolism is one of the most pronounced metabolic alterations in cancer, and it greatly contributes to cancer cell growth and tumorigenesis [68]. Lipids, including sterols, mono/di/triglycerides, phospholipids, and glycolipids, are indispensable to cells. They serve as energy sources, as components of biological membranes, and as signaling molecules [69]. The many roles of lipids are a testament to the importance of processes that regulate their levels in cancer. Several aspects of lipid metabolism are reprogrammed in cancer, including the biosynthesis and oxidation of fatty acids (FAs), the uptake of FAs from the environment, and modification of FAs and release from other molecules ( Figure 6 ) [68], of which the mechanisms will be discussed.

Lipid Metabolic Reprogramming in Cancer. An overview of lipid metabolic pathways and how they are modified in cancer. (a). Tumor cells take up fatty acids (FAs) using multiple transporters, including CD36, FA binding proteins 1-6 (FABP1-6), and low-density lipoprotein receptor (LDLR) for low-density lipoproteins (LDL). These free FAs then become a part of the cellular FA pool where they can enter the citric acid (TCA) cycle and contribute to lipid formation. Upregulation of FA uptake in cancer occurs through hypoxia-inducible factor (HIF-1)-induced FABP1-6 overexpression. (b). Upregulation of lipogenesis and cholesterol biosynthesis is achieved through sterol regulatory element binding protein (SREBP) activation. SREBP1 activation induces expression of lipogenesis genes while SREBP2 activation induces expression of cholesterol biosynthesis genes. (c). Fatty acid oxidation (FAO) can be upregulated by cMyc, depending on the cancer type as a means to counteract oxidative stress. ACC1/2 acetyl-CoA carboxylase 1/2, ACLY ATP citrate lyase, ACS acyl-CoA synthetase, α-KG alpha-ketoglutarate, CoA coenzyme A, CPT1 carnitine palmitoyltransferase 1, FADS FA desaturases, FASN fatty acid synthase, FPP farnesyl-pyrophosphate, GLUT1 glucose transporter 1, HMG-CoA hydroxy-methylglutaryl-CoA, HMGCS hydroxy-methylglutaryl-CoA synthase, HMGCR hydroxy-methylglutaryl-CoA reductase, LD lipid droplets, MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids, SCD1 stearoyl-CoA desaturase 1, SOAT sterol O-acyltransferase. Figure created with BioRender.com (accessed on 26 March 2021).

5.1. Lipid Acquisition: De Novo Lipogenesis and Lipid Uptake

Cells can acquire lipids in one of two ways, de novo synthesis or uptake [70]. Most lipids are derived from FAs, which are molecules containing long hydrocarbon chains. Adult cells normally obtain FAs from external sources, such as the diet or from lipids synthesized by the liver [68]. Cancer cells, however, reactivate de novo lipogenesis which removes their reliance on externally derived lipids and allows them to proliferate at a faster rate [69]. FA synthesis occurs using cytoplasmic acetyl-CoA that is generated from acetate, glucose, or glutamine. This acetyl-CoA is converted to malonyl-CoA and then 16-carbon saturated FA palmitate using the enzymes acetyl-CoA carboxylases (ACC1/2) and fatty acid synthase (FASN), respectively ( Figure 6 a). Palmitate can then be elongated to other FAs or desaturated using FA elongases and FA desaturases to form the cellular pool of non-essential FAs that are then further converted to form other important lipids, such as cholesterol, eicosanoids, and prostaglandins [71]. Increased FA de novo synthesis in cancer has been widely observed and this increase is essential for cancer cell growth [70,72,73,74,75,76].

Cancer cells activate de novo lipogenesis by upregulating several enzymes involved in the pathway, specifically acetyl-CoA carboxylase (ACC), fatty acid synthase (FASN), and stearoyl-CoA desaturase 1 (SCD1) [70]. These enzymes are upregulated through the activation of Sterol regulatory element-binding proteins (SREBPs), which are key transcription factors involved in lipid metabolism. SREBPs are initially translated as inactive precursors in the endoplasmic reticulum and associate with the chaperone SREBP cleavage activating protein (SCAP) [71]. Glucose uptake and low sterol concentration facilitates glucose-mediated N-glycosylation of SCAP, which allows it to transport SREBPs to the Golgi where they can become proteolytically activated and bind to the promoters of effector genes in the nucleus ( Figure 7 ) [77]. SREBP isoform SREBP-1 preferentially binds to genes involved in FA synthesis to promote their expression. SREBP activation is also regulated by upstream oncogenic signaling pathways, most predominantly by the PI3K/Akt/mTORC1 signaling axis. This axis increases the expression of enzymes needed for FA synthesis and activates ATP-citrate lyase (ACLY), which catalyzes acetyl-CoA production from citrate, which can enter into de novo lipogenesis. It also increases the production of NADPH via the activation of NRF2, which is used as a cofactor in FA synthesis reactions [68,69].

Activation of sterol regulatory element binding proteins (SREBPs) in cancer. SREBPs are the main transcription factors that regulate expression of genes involved in lipogenesis that are translated as inactive precursors in the endoplasmic reticulum associated with SREBP cleavage activating protein (SCAP) and insulin induced gene protein (INSIG). PI3K/AKT and glucose uptake results in the N-glycosylation of SREBPs, which separates the complex from INSIG and allows it to translocate to the Golgi and become proteolytically activated. Mature SREBPs bind to genes in the nucleus to induce their transcription. Mature SREBP1 preferentially binds genes involved in fatty acid (FA) synthesis while mature SREBP2 preferentially binds genes involved in cholesterol biosynthesis. The upregulation of these genes results in tumor growth. High concentrations of sterols inhibit SREBP activation. EGFR epidermal growth factor receptor, TCA cycle the citric acid cycle. Figure created with BioRender.com (accessed on 26 March 2021).

Regardless of the signaling molecules involved, increased de novo lipogenesis provides cancer cells with the ability to shunt into different biosynthetic pathways to create lipids with a wide variety of functions that allow them to adapt and respond to their surroundings and ensure continued proliferation. Specifically, increased FA synthesis reduces the number of polyunsaturated FAs (PUFAs) and increases the number of monounsaturated FAs (MUFAs). This helps provide protection from lipid peroxidation as PUFAs are subject to peroxidation in the presence of ROS. Increased FA synthesis in cancer cells also confers protection from ROS, contributes to pro-angiogenic signaling, and provides an escape from immune surveillance [78].

Besides de novo lipogenesis, cancer cells also acquire a diverse pool of lipids by increasing lipid uptake [69]. Lipid uptake can occur via multiple routes, including the use of specialized transporters such as CD36 fatty acid translocase or the fatty acid transport proteins (FATPs of the SLC27 family of solute carriers), or receptor-mediated endocytosis of low-density lipoprotein (LDL) particles via the LDL receptor (LDLR), all of which are highly expressed in various types of cancer ( Figure 6 a) [68]. The uptake of exogenous FAs also promotes migration and metastasis. Through the remodeling of cellular FA composition, cancer cells can facilitate changes in membrane fluidity that promote cell migration and cancer progression [78,79]. Additionally, the uptake of lipids from the environment allows tumors to maintain their lipid pool, even in times of stress. For example, under hypoxic conditions, the conversion of saturated FAs into monounsaturated FAs is hindered, as the enzyme catalyzing the reaction, stearoyl-CoA desaturase-1 (SCD-1), requires oxygen. Hypoxic cells can compensate by taking up exogenous lysophospholipids to survive. Exogenous FA uptake is mediated by the master regulator, HIF-1α, and its control of overexpression of lipid-binding proteins, such as FA-binding protein 4 (FABP4) [69].

5.2. Lipid Storage and Export

One consequence of increased de novo lipid synthesis and uptake is that, with an excess of lipids, cancer cells must store them. Excess lipids are stored as lipid droplets, which are produced via conversion of cellular lipids to triglycerides and cholesteryl esters in the endoplasmic reticulum by sterol O-acyltransferase 1 (SOAT1), also known as acyl-CoA acyltransferase 1 (ACAT1) [70]. Cancer cells exhibit an increased number of lipid droplets compared to normal cells. These lipid droplets help maintain lipid homeostasis, prevent lipotoxicity, regulate autophagy, maintain ER and membrane homeostasis, and also provide a source of ATP and NADPH through their breakdown by lipophagy followed by β-oxidation in times of metabolic stress [69]. An accumulation of lipid droplets is found in several types of cancer, including breast, brain, liver, cervical, prostate, colon, skin, bile duct, clear-cell renal carcinoma, ovarian, and pancreatic cancer [80].

5.3. Lipolysis

Tumor cells also acquire FAs through the breakdown of lipid droplets through a process called lipolysis. Lipolysis refers to the breakdown of lipid droplets by lipoprotein lipase (LPL) to release free FAs [81]. These free FAs can then be taken up by CD36 and used to support increased growth. Increased expression of LPL occurs in breast cancer [82], non-small cell lung cancer [83], and chronic lymphocytic leukemia [84], with breast cancer also exhibiting increased CD36 expression. Increased lipolysis is associated with cachexia, a clinical manifestation of cancer referred to as �t wasting”. Cachexia is weight loss due to muscle and adipose tissue (AT) depletion that is found in multiple types of cancer and is associated with poorer prognosis. Although efforts in the past have mainly focused on muscle loss, recent studies focus on the role of lipolysis in this process, as the loss of AT is mainly due to increased induction of lipolysis [85]. Cytokines, such as TNF-α and IL-6, and lipid mobilizing factor, zinc-㬒-glycoprotein (ZAG), play a major role in the upregulation of lipolysis in cancer, although additional research is needed to understand the mechanism underlying this change [85].

5.4. Fatty Acid Oxidation

The process of lipolysis breaks down lipid droplets to free FAs and these free FAs can then be further broken down by fatty acid oxidation (FAO), referred to as β-oxidation. While the role of FA synthesis in cancer has been widely established, the role of β-oxidation has not been as well defined and is a newer area of study cancer metabolism. As a source of ATP and NADPH, β-oxidation provides the energy and reducing power for biosynthesis and a means to counteract oxidative stress. Most research, however, has focused on the generation of ATP through the Warburg Effect. NADPH can be produced via other metabolic pathways, such as the PPP, suggesting that FAO does not play a major role in the oncogenic landscape. Furthermore, malonyl-CoA, an intermediate of lipogenesis, coordinates the activity of both lipogenesis and FAO. Malonyl CoA acts as an inhibitor of the FAO rate-limiting enzyme carnitine palmitoyltransferase 1 (CPT1), supporting the idea that FA synthesis and FAO cannot occur at the same time. However, new evidence suggests that FAO may play a greater role in cancer growth and metastasis than previously thought [86].

Recent studies have demonstrated that there is increased expression of several FAO enzymes in cancer, including CD-36, CPT1 isoforms A, B, and C, carnitine transporter CT2 [42], and Acyl-CoA synthetase long chain 3 [82,86]. Consistent with this observation, several types of cancer exhibit increased FAO, such as triple negative breast cancer (TNBC) [87], gastric cancer [88], glioma [89], and prostate cancer [90]. These types of cancer rely on FAO as a main source of ATP for rapid growth and even prefer to metastasize to tissues rich in adipocytes [86]. Non-glycolytic tumors, such as those in prostate cancer, employ FAO as their main bioenergetic pathway [90]. Increased expression of FAO enzymes and upregulation is achieved by overexpression of oncogenic c-Myc ( Figure 6 c) [86]. As a generator of NADPH, FAO also helps cancer cells respond to oxidative stress and avoid cell death [91]. Additionally, FAO has been implicated in metastasis through its potential role in the reprogramming of cancer stem cells [92]. Taken together, the data suggest that FAO plays an important role in cancer metabolism.

5.5. Mevalonate Pathway

The generation of important lipids, such as cholesterol, vitamin D, and lipoproteins, through reprogramming of the mevalonate pathway (MVA) in cancer has been extensively studied, with a focus placed on cholesterol biosynthesis. The MVA uses acetyl-CoA derived from glycolysis to generate its products, with mevalonate production catalyzed by 3-hydroxy-3-methylglutaryl-CoA reductase (scdCR) being the rate-limiting step of the entire pathway. Mevalonate is then converted to isopentenyl pyrophosphate (IPP) and later, farnesyl pyrophosphate (FPP). FPP is critical for production of squalene, the precursor to cholesterol. Cholesterol itself is an important component of cell membranes and is the precursor to hormones, bile acids, and lipid rafts [93].

Many enzymes of the MVA are often overexpressed in cancer, including HMGCR, farnesyl diphosphate synthase (FDPS), geranylgeranyl pyrophosphate synthase (GGPPS), squalene synthase, and squalene epoxidase [93]. The transcription of these enzymes is controlled by SREBPs, in manner similar to de novo lipogenesis, with isoform SREBP2 showing a preference for the promoters of MVA and cholesterol biosynthesis genes ( Figure 6 b) [68]. Again, like de novo lipogenesis, SREBP2 is mediated by the PI3K/Akt/mTORC1 signaling axis. This results in increased HMGCR expression, and thus, increased flux through the MVA. SREBP2 can also interact with mutant p53 to drive the post-translational modification of oncogenes, such as the farnesylation of Ras, and regulates mediators of epigenetic changes, such as histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) [69,94]. Increased expression of HMGCR in cancer leads to increased production of cholesterol, which provides a continuous resource for membrane synthesis in dividing cells and of estrogen and androgens to support tumorigenesis [93]. As a result, inhibition of cholesterol biosynthesis with statins greatly impairs cancer growth [95,96].

In addition to cholesterol, other products of the MVA pathway play roles in cancer cell growth. One such product is ubiquinone, a key electron transfer molecule in respiration. Oxidative phosphorylation is an active metabolic pathway in many tumors, and hence, ubiquinone is an important product of the MVA for continued cell proliferation. Ubiquinone is also a regulator of ROS, and more recently, it was reported that ubiquinone supports pyrimidine biosynthesis in colorectal and pancreatic cancer [69]. Thus, the MVA pathway contribute a number of molecules needed for cancer cell survival.


Prospects, frontiers and applications

Where do we go from here? Armed with a broader view of metabolism’s influence, it should become increasingly feasible to reverse disease states caused by genetically-determined metabolic dysfunction. One area that should benefit from this new information is the treatment of children with IEMs. Although dietary modifications and other advancements have greatly reduced morbidity and mortality in some of these diseases, the treatments are often invasive and do not fully protect patients from occasional states of severe metabolic decompensation, which can result in permanent disability. It is possible that agents developed to manipulate metabolism in cancer will find applications in other diseases that share some of the same metabolic features, like dysregulated production of lactic acid or altered TCA cycle function. For example, recent work demonstrates that tumor cells with mutations in the TCA cycle or ETC (Mullen et al., 2011), or with hypoxia-induced suppression of oxidative metabolism (Metallo et al., 2011 Scott et al., 2011 Wise et al., 2011) use reductive carboxylation of α-KG to produce citrate and other precursors during growth. In this reaction, NADPH-dependent isoforms of IDH act in reverse with respect to their conventional role as oxidative decarboxylases ( Fig. 3B ). It is possible that a similar pathway contributes to disease in patients with fixed defects in mitochondrial metabolism, or with perinatal asphyxia, stroke, cardiac ischemia and other conditions involving pathological hypoxia.

Furthermore, our understanding of the pathophysiology of IEMs still draws heavily from the traditional view of intermediary metabolism that focuses primarily on catabolism. The metabolic regulation of macromolecular synthesis, chromatin dynamics and cell renewal is just beginning to be considered, and the interplay between metabolite accumulation, cell signaling and other processes highly relevant to these diseases should be investigated further. For example, the biology of IDH1/2-mutant tumors may provide insight into the effects of global 2-HG accumulation in patients with (L)- or (D)-2-hydroxyglutaric aciduria. This point was emphasized by the recent observation that up to half of patients with (D)-2-hydroxyglutaric aciduria contain heterozygous active-site mutations in IDH2 (Kranendijk et al., 2010). It is unknown whether these patients or patients with other genetic forms of 2-HG aciduria have any of the epigenetic effects observed in IDH1/2-mutant tumors.

Conversely, it should be possible to bring nearly 100 years of clinical experience with IEMs to bear on cancer and other diseases. There is significant interest in developing agents to inhibit the Warburg effect in tumors, but because glycolysis is nearly ubiquitous among human tissues, there is concern about the toxicity of such agents. The extremely large number of well-characterized IEMs in almost every known metabolic pathway may provide a guide as to which types of toxicity to anticipate for some of these targets. SLC2A1 is a HIF-1 transcriptional target and encodes the predominant glucose transporter in cancer cells, GLUT1. GLUT1 is also the major transporter at the blood-brain barrier and is responsible for supplying glucose to the brain. GLUT1 deficiency has a wide phenotypic spectrum but its most common form involves severe central nervous system manifestations including seizures, mental retardation and poor growth of the postnatal brain. Glucose levels in the cerebrospinal fluid may be less than half of normal. It is unknown whether the increased glucose dependence of tumors relative to differentiated tissue will support an acceptable therapeutic window in cancer treatment, but clearly extreme caution would be indicated in attempts to inhibit GLUT1. Other glycolytic IEMs have less severe manifestations. LDHA is a transcriptional target of HIF-1 and c-Myc. It encodes the isoform of lactate dehydrogenase observed in most tumors and is responsible for robust lactate synthesis in cancer cell lines. Severe LDHA deficiency is also a well-characterized human disease, Glycogen Storage Disease Type XI. This disorder causes exercise intolerance, cramps and occasional myoglobinuria in adolescents and adults, but is not associated with severe dysfunction of major homeostatic organs. Perhaps inhibition of LDHA would be tolerated during intermittent cancer therapy. Ongoing population-based studies in metabolomics and genomics should uncover the full breadth of metabolic diversity in healthy humans, and this information may help predict how to target metabolic pathways safely in cancer.

Altered metabolic states in disease also present a tremendous opportunity to develop novel methods in diagnostic imaging. Several platforms for metabolic imaging are already in clinical practice, particularly FDG-PET. But with an ever-expanding knowledge of the interplay between metabolism and disease, it will become more feasible to expand the repertoire of non-invasive techniques to detect diseased tissue and monitor its response to therapy. A number of efforts in this regard are already underway and have a good chance for translation to human patients in the near future. The importance of additional nutrients besides glucose to tumor metabolism has prompted the development of several new PET agents for cancer, including glutamate and glutamine (Koglin et al., 2011 Qu et al., 2011). These agents would add another level of molecular detail to help assess individual tumors, and may improve the detection of tumors invisible on FDG-PET. Magnetic resonance spectroscopy (MRS) at 1.5-Tesla has been used for decades to produce semi-quantitative, non-invasive measurements of abundant metabolite pools in cancer (Glunde and Bhujwalla, 2011). Recently, higher-field human MRI systems have drastically improved the resolution of 1 H spectra and made it possible to detect many additional metabolites in human tumors. Successful quantification of significant metabolites like glycine and 2-HG has already been reported for human gliomas (Choi, 2011a Choi et al., 2011b), and additional metabolites will be added to the list as their relevance in cancer, IEMs, or other diseases is more firmly established. It is also now possible to image metabolites labeled with 13 C and to monitor specific enzyme activities in vivo by tracking the transfer of the label from substrate to product. This method requires that polarization of the 13 C nuclear spin state be enhanced by transferring to it the high spin polarization of an unpaired electron from a free radical, a process known as hyperpolarization of the 13 C nucleus. This produces a transient gain in sensitivity of 13 C detection by 10,000-fold or more. Hyperpolarization has been used successfully to quantify oncogene-driven metabolic activities in experimental tumors (Hu et al., 2011). Although still an investigational method, metabolic imaging using hyperpolarization of 13 C and other stable isotopes has considerable translational potential in human disease because of its high sensitivity, integration with MRI, lack of the need for radioactive tracers, and in particular its ability to probe the flux of specific enzymatic reactions in vivo (Kurhanewicz et al., 2011).

In fact one of the primary challenges now in human metabolism research – and one of the next frontiers in systems biology – is to analyze the impact of disease on metabolic flux in vivo that is, to use sensitive and efficient methods to measure the transfer of carbon, nitrogen, etc. along metabolic pathways in live subjects, with and without disease. While methods to catalog and quantify small molecules in biological fluids (metabolomics) are highly informative, they do not present a complete view of metabolism and in some cases can produce misleading results. Newborn screening programs provide a valuable example of this issue. An elevated level of a metabolite in these screening tests almost always reflects decreased rather than increased flux of the pathway, similar to the effect of a traffic light on the accumulation of automobiles ( Figure 4A ). Furthermore, because metabolism involves so many intersecting pathways, it may be impossible to infer which pathway(s) are affected when abnormal levels of a metabolite are observed. For example, it is unknown which extracellular nutrients are metabolized to 2-HG in IDH1/2-mutant tumors in vivo. One way to address these issues is to combine metabolic pathway analysis with metabolomic studies. Such studies would involve introducing isotopically-labeled nutrients into an animal model or a human patient, and then harvesting metabolites of interest from the blood, urine, breath, or tissue samples ( Figure 4B ). The total abundance of these metabolites would be measured using a metabolomics platform, then a subset of the most informative metabolites would be studied further by mass spectrometry and/or NMR spectroscopy to determine the abundance and position of isotopic label within each molecule. Similar approaches focusing on a handful of metabolites have already been used successfully in humans to measure gluconeogenesis and the urea cycle in vivo, and to compare metabolism between lung tumors and surrounding tissue (Fan et al., 2009 Landau et al., 1996 Yudkoff et al., 2010). Combined metabolomics-metabolic flux studies, despite their technical challenges, have tremendous value because they could produce a quantitative and comprehensive readout of the variation in metabolic pathway activity, leading to a deeper understanding of metabolic individuality and the biological basis of disease in humans.

(A) Analysis of metabolism is similar in principle to the analysis of traffic patterns, with many of the same uncertainties. The high 𠇏lux” on a four-lane highway leads to a low density of cars, all of which travel unimpeded southward. Upon exiting the highway, drivers experience reduced flux because of the traffic light, akin to mutation or under-expression of a metabolic enzyme. This causes an increased density of cars north of the light. Flux downstream of the block is unimpeded. Note that red cars on the two-lane road also merge with black cars, leading to a reduced fraction of red cars downstream of the intersection. The sum effect of these factors on overall flux is demonstrated by counting the cars that pass the checkered flags. On the highway, 1000 cars, all red, pass in one hour. On the two-lane road, only 200 cars pass, and only half are red.

(B) Simple schematic of metabolic flux analysis. Glucose labeled with 13 C at positions 1 and 6 (red asterisks) is given via injection or oral administration to a subject, which metabolizes it. After a period of time, tissue or body fluids are sampled to determine the abundance of various metabolites, the fraction of the metabolite that contains 13 C, and the position(s) of 13 C within the molecule. Data are acquired using mass spectrometry or NMR spectroscopy. Mathematical models are then applied to translate the data into metabolic flux. In this example, labeling of lactate and acetyl-CoA are examined. The pathways producing these two metabolites diverge at pyruvate. LDH, a highly active enzyme, rapidly converts pyruvate to lactate, resulting in a very high enrichment in the lactate pool in a short time. Meanwhile, two factors conspire to reduce enrichment in acetyl-CoA. First, this pathway involves PDH, a highly regulated and less active enzyme. Second, entry of carbon from unlabeled nutrients contributes to the acetyl-CoA pool, reducing the fraction of acetyl-CoA molecules containing 13 C from glucose.


References

Agathocleous, M., Meacham, C., Burgess, R., Piskounova, E., Zhao, Z., Crane, G., et al. (2017). Ascorbate regulates haematopoietic stem cell function and leukaemogenesis. Nature 549, 476�. doi: 10.1038/nature23876

Aiello, N., Brabletz, T., Kang, Y., Nieto, M., Weinberg, R., and Stanger, B. (2017). Upholding a role for EMT in pancreatic cancer metastasis. Nature 547, E7�. doi: 10.1038/nature22963

Al-Hajj, M., Wicha, M., Benito-Hernandez, A., Morrison, S., and Clarke, M. (2003). Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. U.S.A. 100, 3983�. doi: 10.1073/pnas.0530291100

Amente, S., Lania, L., and Majello, B. (2013). The histone LSD1 demethylase in stemness and cancer transcription programs. Biochim. Biophys. Acta 1829, 981�. doi: 10.1016/j.bbagrm.2013.05.002

Ang, Y.-S., Tsai, S.-Y., Lee, D.-F., Monk, J., Su, J., Ratnakumar, K., et al. (2011). Wdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network. Cell 145, 183�. doi: 10.1016/j.cell.2011.03.003

Anokye-Danso, F., Trivedi, C. M., Juhr, D., Gupta, M., Cui, Z., Tian, Y., et al. (2011). Highly efficient miRNA-mediated reprogramming of mouse and human somatic cells to pluripotency. Cell Stem Cell 8, 376�. doi: 10.1016/j.stem.2011.03.001

Arsenault, D. J., Yoo, B. H., Rosen, K. V., and Ridgway, N. D. (2013). ras-Induced up-regulation of CTP:phosphocholine cytidylyltransferase alpha contributes to malignant transformation of intestinal epithelial cells. J. Biol. Chem. 288, 633�. doi: 10.1074/jbc.M112.347682

Asadzadeh, Z., Mansoori, B., Mohammadi, A., Aghajani, M., Haji-Asgarzadeh, K., Safarzadeh, E., et al. (2019). microRNAs in cancer stem cells: biology, pathways, and therapeutic opportunities. J. Cell. Physiol. 234, 10002�. doi: 10.1002/jcp.27885

Barcellos-Hoff, M., Lyden, D., and Wang, T. (2013). The evolution of the cancer niche during multistage carcinogenesis. Nat. Rev. Cancer 13, 511�. doi: 10.1038/nrc3536

Batlle, E., and Clevers, H. (2017). Cancer stem cells revisited. Nat. Med. 23, 1124�. doi: 10.1038/nm.4409

Beck, B., Lapouge, G., Rorive, S., Drogat, B., Desaedelaere, K., Delafaille, S., et al. (2015). Different levels of Twist1 regulate skin tumor initiation, stemness, and progression. Cell Stem Cell 16, 67�. doi: 10.1016/j.stem.2014.12.002

Bierie, B., Pierce, S. E., Kroeger, C., Stover, D. G., Pattabiraman, D. R., Thiru, P., et al. (2017). Integrin-beta4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells. Proc. Natl. Acad. Sci. U.S.A. 114, E2337�. doi: 10.1073/pnas.1618298114

Blaschke, K., Ebata, K., Karimi, M., Zepeda-Martínez, J., Goyal, P., Mahapatra, S., et al. (2013). Vitamin C induces Tet-dependent DNA demethylation and a blastocyst-like state in ES cells. Nature 500, 222�. doi: 10.1038/nature12362

Bonnet, D., and Dick, J. E. (1997). Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730�. doi: 10.1038/nm0797-730

Bronsert, P., Enderle-Ammour, K., Bader, M., Timme, S., Kuehs, M., Csanadi, A., et al. (2014). Cancer cell invasion and EMT marker expression: a three-dimensional study of the human cancer-host interface. J. Pathol. 234, 410�. doi: 10.1002/path.4416

Calon, A., Lonardo, E., Berenguer-Llergo, A., Espinet, E., Hernando-Momblona, X., Iglesias, M., et al. (2015). Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat. Genet. 47, 320�. doi: 10.1038/ng.3225

Cao, Y., Guo, W., Tian, S., He, X., Wang, X., Liu, X., et al. (2015). miR-290/371-Mbd2-Myc circuit regulates glycolytic metabolism to promote pluripotency. EMBO J. 34, 609�. doi: 10.15252/embj.201490441

Carey, B. W., Finley, L. W. S., Cross, J. R., Allis, C. D., and Thompson, C. B. (2015). Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518, 413�. doi: 10.1038/nature13981

Cervo, P. R. D. V., Romanov, R. A., Spigolon, G., Masini, D., Martin-Montanez, E., Toledo, E. M., et al. (2017). Induction of functional dopamine neurons from human astrocytes in vitro and mouse astrocytes in a Parkinson’s disease model. Nat. Biotechnol. 35, 444�. doi: 10.1038/nbt.3835

Chaffer, C. L., Marjanovic, N. D., Lee, T., Bell, G., Kleer, C. G., Reinhardt, F., et al. (2013). Poised chromatin at the ZEB1 promoter enables breast cancer cell plasticity and enhances tumorigenicity. Cell 154, 61�. doi: 10.1016/j.cell.2013.06.005

Chen, J., Guo, L., Zhang, L., Wu, H., Yang, J., Liu, H., et al. (2013a). Vitamin C modulates TET1 function during somatic cell reprogramming. Nat. Genet. 45, 1504�. doi: 10.1038/ng.2807

Chen, J., Liu, H., Liu, J., Qi, J., Wei, B., Yang, J., et al. (2013b). H3K9 methylation is a barrier during somatic cell reprogramming into iPSCs. Nat. Genet. 45, 34�. doi: 10.1038/ng.2491

Chen, Q., Chen, Y., Bian, C., Fujiki, R., and Yu, X. (2013c). TET2 promotes histone O-GlcNAcylation during gene transcription. Nature 493, 561�. doi: 10.1038/nature11742

Choi, Y. K., and Park, K. G. (2018). Targeting glutamine metabolism for cancer treatment. Biomol. Ther. 26, 19�. doi: 10.4062/biomolther.2017.178

Cimmino, L., Dolgalev, I., Wang, Y., Yoshimi, A., Martin, G., Wang, J., et al. (2017). Restoration of TET2 function blocks aberrant self-renewal and leukemia progression. Cell 170, 1079�.e20. doi: 10.1016/j.cell.2017.07.032

Clarke, M., Dick, J., Dirks, P., Eaves, C., Jamieson, C., Jones, D., et al. (2006). Cancer stem cells–perspectives on current status and future directions: AACR Workshop on cancer stem cells. Cancer Res. 66, 9339�. doi: 10.1158/0008-5472.CAN-06-3126

Clevers, H. (2011). The cancer stem cell: premises, promises and challenges. Nat. Med. 17, 313�. doi: 10.1038/nm.2304

Clevers, H. (2015). STEM CELLS. What is an adult stem cell? Science 350, 1319�. doi: 10.1126/science.aad7016

Constable, S., Lim, J., Vaidyanathan, K., and Wells, L. (2017). O-GlcNAc transferase regulates transcriptional activity of human Oct4. Glycobiology 27, 927�. doi: 10.1093/glycob/cwx055

Costa, Y., Ding, J., Theunissen, T., Faiola, F., Hore, T., Shliaha, P., et al. (2013). NANOG-dependent function of TET1 and TET2 in establishment of pluripotency. Nature 495, 370�. doi: 10.1038/nature11925

Craene, B. D., and Berx, G. (2013). Regulatory networks defining EMT during cancer initiation and progression. Nat. Rev. Cancer 13, 97�. doi: 10.1038/nrc3447

Davalos, V., Moutinho, C., Villanueva, A., Boque, R., Silva, P., Carneiro, F., et al. (2012). Dynamic epigenetic regulation of the microRNA-200 family mediates epithelial and mesenchymal transitions in human tumorigenesis. Oncogene 31, 2062�. doi: 10.1038/onc.2011.383

Deplus, R., Delatte, B., Schwinn, M., Defrance, M., Mendez, J., Murphy, N., et al. (2013). TET2 and TET3 regulate GlcNAcylation and H3K4 methylation through OGT and SET1/COMPASS. EMBO J. 32, 645�. doi: 10.1038/emboj.2012.357

Doege, C., Inoue, K., Yamashita, T., Rhee, D., Travis, S., Fujita, R., et al. (2012). Early-stage epigenetic modification during somatic cell reprogramming by Parp1 and Tet2. Nature 488, 652�. doi: 10.1038/nature11333

Dongre, A., and Weinberg, R. (2019). New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat. Rev. Mol. Cell Biol. 20, 69�. doi: 10.1038/s41580-018-0080-4

Ebrahimi, A., Sevinc, K., Gurhan Sevinc, G., Cribbs, A. P., Philpott, M., Uyulur, F., et al. (2019). Bromodomain inhibition of the coactivators CBP/EP300 facilitate cellular reprogramming. Nat. Chem. Biol. 15, 519�. doi: 10.1038/s41589-019-0264-z

Eiriksson, F. F., Rolfsson, O., Ogmundsdottir, H. M., Haraldsson, G. G., Thorsteinsdottir, M., and Halldorsson, S. (2018). Altered plasmalogen content and fatty acid saturation following epithelial to mesenchymal transition in breast epithelial cell lines. Int. J. Biochem. Cell Biol. 103, 99�. doi: 10.1016/j.biocel.2018.08.003

Emmett, M. J., and Lazar, M. A. (2019). Integrative regulation of physiology by histone deacetylase 3. Nat. Rev. Mol. Cell Biol. 20, 102�. doi: 10.1038/s41580-018-0076-0

Evans, M. J., and Kaufman, M. H. (1981). Establishment in culture of pluripotential cells from mouse embryos. Nature 292, 154�. doi: 10.1038/292154a0

Feng, D., Sheng-Dong, L., Tong, W., and Zhen-Xian, D. (2020). O-GlcNAcylation of RAF1 increases its stabilization and induces the renal fibrosis. Biochim. Biophys. Acta 1866:165556. doi: 10.1016/j.bbadis.2019.165556

Figueroa, M., Abdel-Wahab, O., Lu, C., Ward, P., Patel, J., Shih, A., et al. (2010). Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell 18, 553�. doi: 10.1016/j.ccr.2010.11.015

Fischer, B., and Bavister, B. D. (1993). Oxygen tension in the oviduct and uterus of rhesus monkeys, hamsters and rabbits. J. Reprod. Fertil. 99, 673�. doi: 10.1530/jrf.0.0990673

Fischer, K., Durrans, A., Lee, S., Sheng, J., Li, F., Wong, S., et al. (2015). Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527, 472�. doi: 10.1038/nature15748

Folmes, C., Nelson, T., Martinez-Fernandez, A., Arrell, D., Lindor, J., Dzeja, P., et al. (2011). Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metab. 14, 264�. doi: 10.1016/j.cmet.2011.06.011

Foster, J., and Archer, S. (1979). Birth order and intelligence: an immunological interpretation. Percept. Mot. Skills 48, 79�. doi: 10.2466/pms.1979.48.1.79

Fumagalli, A., Oost, K. C., Kester, L., Morgner, J., Bornes, L., Bruens, L., et al. (2020). Plasticity of Lgr5-negative cancer cells drives metastasis in colorectal cancer. Cell Stem Cell 26, 569�.e7. doi: 10.1016/j.stem.2020.02.008

Gao, Y., Chen, J., Li, K., Wu, T., Huang, B., Liu, W., et al. (2013). Replacement of Oct4 by Tet1 during iPSC induction reveals an important role of DNA methylation and hydroxymethylation in reprogramming. Cell Stem Cell 12, 453�. doi: 10.1016/j.stem.2013.02.005

Georgakopoulos-Soares, I., Chartoumpekis, D. V., Kyriazopoulou, V., and Zaravinos, A. (2020). EMT factors and metabolic pathways in cancer. Front. Oncol. 10:499. doi: 10.3389/fonc.2020.00499

Guo, W., Keckesova, Z., Donaher, J., Shibue, T., Tischler, V., Reinhardt, F., et al. (2012). Slug and Sox9 cooperatively determine the mammary stem cell state. Cell 148, 1015�. doi: 10.1016/j.cell.2012.02.008

Harosh-Davidovich, S., and Khalaila, I. (2018). O-GlcNAcylation affects beta-catenin and E-cadherin expression, cell motility and tumorigenicity of colorectal cancer. Exp. Cell Res. 364, 42�. doi: 10.1016/j.yexcr.2018.01.024

Hay, E. (1968). “Organization and fine structure of epithelium and mesenchyme in the developing chick embryos,” in Proceedings of the 18th Hahnemann Symposium Epithelial-MesenchymalInteractions, eds R. Fleischmajer and R. E. Billingham (Baltimore, Maryland: Williams & Wilkins), 31�.

He, J., Kallin, E. M., Tsukada, Y. I., and Zhang, Y. (2008). The H3K36 demethylase Jhdm1b/Kdm2b regulates cell proliferation and senescence through p15Ink4b(Ink4b). Nat. Struct. Mol. Biol. 15, 1169�. doi: 10.1038/nsmb.1499

He, S., Chen, J., Zhang, Y., Zhang, M., Yang, X., Li, Y., et al. (2017a). Sequential EMT-MET induces neuronal conversion through Sox2. Cell Discov. 3:17017. doi: 10.1038/celldisc.2017.17

He, S., Guo, Y., Zhang, Y., Li, Y., Feng, C., Li, X., et al. (2015). Reprogramming somatic cells to cells with neuronal characteristics by defined medium both in vitro and in vivo. Cell Regen. 4:12. doi: 10.1186/s13619-015-0027-6

He, S., Sun, H., Lin, L., Zhang, Y., Chen, J., Liang, L., et al. (2017b). Passive DNA demethylation preferentially up-regulates pluripotency-related genes and facilitates the generation of induced pluripotent stem cells. J. Biol. Chem. 292, 18542�. doi: 10.1074/jbc.M117.810457

He, S., Wang, F., Zhang, Y., Chen, J., Liang, L., Li, Y., et al. (2019). Hemi-methylated CpG sites connect Dnmt1-knockdown-induced and Tet1-induced DNA demethylation during somatic cell reprogramming. Cell Discov. 5:11. doi: 10.1038/s41421-018-0074-6

Hiew, M., Cheng, H., Huang, C., Chong, K., Cheong, S., Choo, K., et al. (2018). Incomplete cellular reprogramming of colorectal cancer cells elicits an epithelial/mesenchymal hybrid phenotype. J. Biomed. Sci. 25:57. doi: 10.1186/s12929-018-0461-1

Hsu, Y. C., Wu, Y. T., Tsai, C. L., and Wei, Y. H. (2018). Current understanding and future perspectives of the roles of sirtuins in the reprogramming and differentiation of pluripotent stem cells. Exp. Biol. Med. 243, 563�. doi: 10.1177/1535370218759636

Hu, X., Zhang, L., Mao, S.-Q., Li, Z., Chen, J., Zhang, R.-R., et al. (2014). Tet and TDG Mediate DNA demethylation essential for mesenchymal-to-epithelial transition in somatic cell reprogramming. Cell Stem Cell 14, 512�. doi: 10.1016/j.stem.2014.01.001

Jiang, M., Xu, B., Li, X., Shang, Y., Chu, Y., Wang, W., et al. (2019). O-GlcNAcylation promotes colorectal cancer metastasis via the miR-101-O-GlcNAc/EZH2 regulatory feedback circuit. Oncogene 38, 301�. doi: 10.1038/s41388-018-0435-5

Jolly, M., Boareto, M., Huang, B., Jia, D., Lu, M., Ben-Jacob, E., et al. (2015). Implications of the hybrid epithelial/mesenchymal phenotype in metastasis. Front. Oncol. 5:155. doi: 10.3389/fonc.2015.00155

Jolly, M. K., Ware, K. E., Gilja, S., Somarelli, J. A., and Levine, H. (2017). EMT and MET: necessary or permissive for metastasis? Mol. Oncol. 11, 755�. doi: 10.1002/1878-0261.12083

Kang, H., Kim, H., Lee, S., Youn, H., and Youn, B. (2019). Role of metabolic reprogramming in epithelial-mesenchymal transition (EMT). Int. J. Mol. Sci. 20, 2042. doi: 10.3390/ijms20082042

Kim, N. H., Cha, Y. H., Lee, J., Lee, S. H., Yang, J. H., Yun, J. S., et al. (2017). Snail reprograms glucose metabolism by repressing phosphofructokinase PFKP allowing cancer cell survival under metabolic stress. Nat. Commun. 8:14374. doi: 10.1038/ncomms14374

Korpal, M., Lee, E. S., Hu, G., and Kang, Y. (2008). The miR-200 family inhibits epithelial-mesenchymal transition and cancer cell migration by direct targeting of E-cadherin transcriptional repressors ZEB1 and ZEB2. J. Biol. Chem. 283, 14910�. doi: 10.1074/jbc.C800074200

Lambert, A. W., Pattabiraman, D. R., and Weinberg, R. A. (2017). Emerging Biological Principles of Metastasis. Cell 168, 670�. doi: 10.1016/j.cell.2016.11.037

Lamouille, S., Xu, J., and Derynck, R. (2014). Molecular mechanisms of epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178�. doi: 10.1038/nrm3758

Lapidot, T., Sirard, C., Vormoor, J., Murdoch, B., Hoang, T., Caceres-Cortes, J., et al. (1994). A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367, 645�. doi: 10.1038/367645a0

Lee, J. Y., and Kong, G. (2016). Roles and epigenetic regulation of epithelial-mesenchymal transition and its transcription factors in cancer initiation and progression. Cell. Mol. Life Sci. 73, 4643�. doi: 10.1007/s00018-016-2313-z

Lee, Y. L., Peng, Q., Fong, S. W., Chen, A. C., Lee, K. F., Ng, E. H., et al. (2012). Sirtuin 1 facilitates generation of induced pluripotent stem cells from mouse embryonic fibroblasts through the miR-34a and p53 pathways. PLoS One 7:e45633. doi: 10.1371/journal.pone.0045633

Li, Q., Hutchins, A. P., Chen, Y., Li, S., Shan, Y., Liao, B., et al. (2017). A sequential EMT-MET mechanism drives the differentiation of human embryonic stem cells towards hepatocytes. Nat. Commun. 3:15166. doi: 10.1038/ncomms15166

Li, R., Liang, J., Ni, S., Zhou, T., Qing, X., Li, H., et al. (2010). A mesenchymal-to-epithelial transition initiates and is required for the nuclear reprogramming of mouse fibroblasts. Cell Stem Cell 7, 51�. doi: 10.1016/j.stem.2010.04.014

Liao, B., Bao, X., Liu, L., Feng, S., Zovoilis, A., Liu, W., et al. (2011). MicroRNA cluster 302-367 enhances somatic cell reprogramming by accelerating a mesenchymal-to-epithelial transition. J. Biol. Chem. 286, 17359�. doi: 10.1074/jbc.C111.235960

Lim, J., and Thiery, J. P. (2012). Epithelial-mesenchymal transitions: insights from development. Development 139, 3471�. doi: 10.1242/dev.071209

Liu, X., Sun, H., Qi, J., Wang, L., He, S., Liu, J., et al. (2013). Sequential introduction of reprogramming factors reveals a time-sensitive requirement for individual factors and a sequential EMT-MET mechanism for optimal reprogramming. Nat. Cell Biol. 15, 829�. doi: 10.1038/ncb2765

Lu, C., Ward, P., Kapoor, G., Rohle, D., Turcan, S., Abdel-Wahab, O., et al. (2012). IDH mutation impairs histone demethylation and results in a block to cell differentiation. Nature 483, 474�. doi: 10.1038/nature10860

Lucena, M., Carvalho-Cruz, P., Donadio, J., Oliveira, I., de, Q. R., Marinho-Carvalho, M., et al. (2016). Epithelial mesenchymal transition induces aberrant glycosylation through hexosamine biosynthetic pathway activation. J. Biol. Chem. 291, 12917�. doi: 10.1074/jbc.M116.729236

Luo, W., Hu, H., Chang, R., Zhong, J., Knabel, M., O’Meally, R., et al. (2011). Pyruvate kinase M2 is a PHD3-stimulated coactivator for hypoxia-inducible factor 1. Cell 145, 732�. doi: 10.1016/j.cell.2011.03.054

Macheda, M. L., Rogers, S., and Best, J. D. (2005). Molecular and cellular regulation of glucose transporter (GLUT) proteins in cancer. J. Cell. Physiol. 202, 654�. doi: 10.1002/jcp.20166

Malik, S., Villanova, L., Tanaka, S., Aonuma, M., Roy, N., Berber, E., et al. (2015). SIRT7 inactivation reverses metastatic phenotypes in epithelial and mesenchymal tumors. Sci. Rep. 5:9841. doi: 10.1038/srep09841

Martin, G. R. (1981). Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc. Natl. Acad. Sci. U.S.A. 78, 7634�. doi: 10.1073/pnas.78.12.7634

Mathieu, J., Zhou, W., Xing, Y., Sperber, H., Ferreccio, A., Agoston, Z., et al. (2014). Hypoxia-inducible factors have distinct and stage-specific roles during reprogramming of human cells to pluripotency. Cell Stem Cell 14, 592�. doi: 10.1016/j.stem.2014.02.012

Mathieu, J., Zhou, W., Xing, Y., Sperber, H., Ferreccio, A., Agoston, Z., et al. (2017). EMT and MET: Necessary or permissive for metastasis? Mol. Oncol. 11, 755�.

McCoy, E., Iwanaga, R., Jedlicka, P., Abbey, N., Chodosh, L., Heichman, K., et al. (2009). Six1 expands the mouse mammary epithelial stem/progenitor cell pool and induces mammary tumors that undergo epithelial-mesenchymal transition. J. Clin. Invest. 119, 2663�. doi: 10.1172/JCI37691

Moon, J. H., Heo, J. S., Kim, J. S., Jun, E. K., Lee, J. H., Kim, A., et al. (2011). Reprogramming fibroblasts into induced pluripotent stem cells with Bmi1. Cell Res. 21, 1305�. doi: 10.1038/cr.2011.107

Morel, A., Hinkal, G., Thomas, C., Fauvet, F., Courtois-Cox, S., Wierinckx, A., et al. (2012). EMT inducers catalyze malignant transformation of mammary epithelial cells and drive tumorigenesis towards claudin-low tumors in transgenic mice. PLoS Genet. 8:e1002723. doi: 10.1371/journal.pgen.1002723

Moussaieff, A., Rouleau, M., Kitsberg, D., Cohen, M., Levy, G., Barasch, D., et al. (2015). Glycolysis-mediated changes in acetyl-CoA and histone acetylation control the early differentiation of embryonic stem cells. Cell Metab. 21, 392�. doi: 10.1016/j.cmet.2015.02.002

Mu, W. L., Wang, Y. J., Xu, P., Hao, D. L., Liu, X. Z., Wang, T. T., et al. (2015). Sox2 Deacetylation by Sirt1 Is Involved in Mouse Somatic Reprogramming. Stem Cells 33, 2135�. doi: 10.1002/stem.2012

Najafi, M., Farhood, B., and Mortezaee, K. (2019). Cancer stem cells (CSCs) in cancer progression and therapy. J. Cell. Physiol. 234, 8381�. doi: 10.1002/jcp.27740

Nassour, M., Idoux-Gillet, Y., Selmi, A., Come, C., Faraldo, M., Deugnier, M., et al. (2012). Slug controls stem/progenitor cell growth dynamics during mammary gland morphogenesis. PLoS One 7:e53498. doi: 10.1371/journal.pone.0053498

Neri, F., Incarnato, D., Krepelova, A., Rapelli, S., Pagnani, A., Zecchina, R., et al. (2013). Genome-wide analysis identifies a functional association of Tet1 and Polycomb repressive complex 2 in mouse embryonic stem cells. Genome Biol. 14:R91. doi: 10.1186/gb-2013-14-8-r91

Ng, S.-C., Locasale, J. W., Lyssiotis, C. A., Zheng, Y., Teo, R. Y., Ratanasirintrawoot, S., et al. (2013). Influence of threonine metabolism on S-Adenosylmethionine and histone methylation. Science 339, 222�. doi: 10.1126/science.1226603

Ngo, B., Van, R. J., Cantley, L., and Yun, J. (2019). Targeting cancer vulnerabilities with high-dose vitamin C. Nat. Rev. Cancer 19, 271�. doi: 10.1038/s41568-019-0135-7

Nieto, M. A. (2013). Epithelial plasticity: a common theme in embryonic and cancer cells. Science 342:1234850. doi: 10.1126/science.1234850

Nieto, M. A., Huang, R. Y., Jackson, R. A., and Thiery, J. P. (2016). Emt: 2016. Cell 166, 21�. doi: 10.1016/j.cell.2016.06.028

Onder, T. T., Kara, N., Cherry, A., Sinha, A. U., Zhu, N., Bernt, K. M., et al. (2012). Chromatin-modifying enzymes as modulators of reprogramming. Nature 483, 598�. doi: 10.1038/nature10953

Park, S. M., Gaur, A. B., Lengyel, E., and Peter, M. E. (2008). The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Genes Dev. 22, 894�. doi: 10.1101/gad.1640608

Pastushenko, I., and Blanpain, C. (2019). EMT transition states during tumor progression and metastasis. Trends Cell Biol. 29, 212�. doi: 10.1016/j.tcb.2018.12.001

Pastushenko, I., Brisebarre, A., Sifrim, A., Fioramonti, M., Revenco, T., Boumahdi, S., et al. (2018). Identification of the tumour transition states occurring during EMT. Nature 556, 463�. doi: 10.1038/s41586-018-0040-3

Patra, K. C., Wang, Q., Bhaskar, P. T., Miller, L., Wang, Z., Wheaton, W., et al. (2013). Hexokinase 2 is required for tumor initiation and maintenance and its systemic deletion is therapeutic in mouse models of cancer. Cancer Cell 24, 213�. doi: 10.1016/j.ccr.2013.06.014

Pei, D., Shu, X., Gassama-Diagne, A., and Thiery, J. P. (2019). Mesenchymal-epithelial transition in development and reprogramming. Nat. Cell Biol. 21, 44�. doi: 10.1038/s41556-018-0195-z

Peinado, H., Ballestar, E., Esteller, M., and Cano, A. (2004). Snail mediates E-cadherin repression by the recruitment of the Sin3A/histone deacetylase 1 (HDAC1)/HDAC2 complex. Mol. Cell Biol. 24, 306�. doi: 10.1128/mcb.24.1.306-319.2004

Prigione, A., Rohwer, N., Hoffmann, S., Mlody, B., Drews, K., Bukowiecki, R., et al. (2014). HIF1alpha modulates cell fate reprogramming through early glycolytic shift and upregulation of PDK1-3 and PKM2. Stem Cells 32, 364�. doi: 10.1002/stem.1552

Quivoron, C., Couronne, L., Della Valle, V., Lopez, C. K., Plo, I., Wagner-Ballon, O., et al. (2011). TET2 inactivation results in pleiotropic hematopoietic abnormalities in mouse and is a recurrent event during human lymphomagenesis. Cancer Cell 20, 25�. doi: 10.1016/j.ccr.2011.08.006

Ryall, J. G., Cliff, T., Dalton, S., and Sartorelli, V. (2015). Metabolic Reprogramming of Stem Cell Epigenetics. Cell Stem Cell 17, 651�. doi: 10.1016/j.stem.2015.11.012

Samavarchi-Tehrani, P., Golipour, A., David, L., Sung, H. K., Beyer, T. A., Datti, A., et al. (2010). Functional genomics reveals a BMP-driven mesenchymal-to-epithelial transition in the initiation of somatic cell reprogramming. Cell Stem Cell 7, 64�. doi: 10.1016/j.stem.2010.04.015

Sampson, V., David, J., Puig, I., Patil, P., Herreros, A., Thomas, G., et al. (2014). Wilms’ tumor protein induces an epithelial-mesenchymal hybrid differentiation state in clear cell renal cell carcinoma. PLoS One 9:e102041. doi: 10.1371/journal.pone.0102041

Saunders, A., Huang, X., Fidalgo, M., Reimer, M. H. Jr., Faiola, F., Ding, J., et al. (2017). The SIN3A/HDAC corepressor complex functionally cooperates with NANOG to promote pluripotency. Cell Rep 18, 1713�. doi: 10.1016/j.celrep.2017.01.055

Schliekelman, M., Taguchi, A., Zhu, J., Dai, X., Rodriguez, J., Celiktas, M., et al. (2015). Molecular portraits of epithelial, mesenchymal, and hybrid States in lung adenocarcinoma and their relevance to survival. Cancer Res. 75, 1789�. doi: 10.1158/0008-5472.CAN-14-2535

Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., et al. (2016). Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637�. doi: 10.1038/nbt.3569

Shi, Y. J., Lan, F., Matson, C., Mulligan, P., Whetstine, J. R., Cole, P. A., et al. (2004). Histone demethylation mediated by the nuclear arnine oxidase homolog LSD1. Cell 119, 941�. doi: 10.1016/j.cell.2004.12.012

Shiraki, N., Shiraki, Y., Tsuyama, T., Obata, F., Miura, M., Nagae, G., et al. (2014). Methionine metabolism regulates maintenance and differentiation of human pluripotent stem cells. Cell Metab. 19, 780�. doi: 10.1016/j.cmet.2014.03.017

Shu, X., and Pei, D. (2014). The function and regulation of mesenchymal-to-epithelial transition in somatic cell reprogramming. Curr. Opin. Genet. Dev. 28, 32�. doi: 10.1016/j.gde.2014.08.005

Shyh-Chang, N., Daley, G., and Cantley, L. (2013). Stem cell metabolism in tissue development and aging. Development 140, 2535�. doi: 10.1242/dev.091777

Siemens, H., Jackstadt, R., Hunten, S., Kaller, M., Menssen, A., Gotz, U., et al. (2011). miR-34 and SNAIL form a double-negative feedback loop to regulate epithelial-mesenchymal transitions. Cell Cycle 10, 4256�. doi: 10.4161/cc.10.24.18552

Stefano, B. D., Sardina, J., Oevelen, C. V., Collombet, S., Kallin, E., Vicent, G., et al. (2014). C/EBPalpha poises B cells for rapid reprogramming into induced pluripotent stem cells. Nature 506, 235�. doi: 10.1038/nature12885

Sun, H., Liang, L., Li, Y., Feng, C., Li, L., Zhang, Y., et al. (2016). Lysine-specific histone demethylase 1 inhibition promotes reprogramming by facilitating the expression of exogenous transcriptional factors and metabolic switch. Sci. Rep. 6:30903. doi: 10.1038/srep30903

Sun, H., Yang, X., Liang, L., Zhang, M., Li, Y., Chen, J., et al. (2020). Metabolic switch and epithelial-mesenchymal transition cooperate to regulate pluripotency. EMBO J. 39:e102961. doi: 10.15252/embj.2019102961

Sun, N. Y., and Yang, M. H. (2020). Metabolic reprogramming and epithelial-mesenchymal plasticity: opportunities and challenges for cancer therapy. Front. Oncol. 10:792. doi: 10.3389/fonc.2020.00792

Suzuki, H. I. (2018). MicroRNA control of TGF-beta signaling. Int. J. Mol. Sci. 19:1901. doi: 10.3390/ijms19071901

Swinnen, J. V., Brusselmans, K., and Verhoeven, G. (2006). Increased lipogenesis in cancer cells: new players, novel targets. Curr. Opin. Clin. Nutr. Metab. Care 9, 358�. doi: 10.1097/01.mco.0000232894.28674.30

Takahashi, K., and Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663�. doi: 10.1016/j.cell.2006.07.024

Thankamony, A. P., Saxena, K., Murali, R., Jolly, M. K., and Nair, R. (2020). Cancer stem cell plasticity - a deadly deal. Front. Mol. Biosci. 7:79. doi: 10.3389/fmolb.2020.00079

Thiery, J. P., Acloque, H., Huang, R. Y., and Nieto, M. A. (2009). Epithelial-mesenchymal transitions in development and disease. Cell 139, 871�. doi: 10.1016/j.cell.2009.11.007

Thiery, J. P., and Sleeman, J. P. (2006). Complex networks orchestrate epithelial-mesenchymal transitions. Nat. Rev. Mol. Cell Biol. 7, 131�. doi: 10.1038/nrm1835

Tsai, Y., Chen, H., Chen, S., Cheng, W., Wang, H., Shen, Z., et al. (2014). TET1 regulates hypoxia-induced epithelial-mesenchymal transition by acting as a co-activator. Genome Biol. 15:513. doi: 10.1186/s13059-014-0513-0

Uckun, F. M., Sather, H., Reaman, G., Shuster, J., Land, V., Trigg, M., et al. (1995). Leukemic cell growth in SCID mice as a predictor of relapse in high-risk B-lineage acute lymphoblastic leukemia. Blood 85, 873�.

Vella, P., Scelfo, A., Jammula, S., Chiacchiera, F., Williams, K., Cuomo, A., et al. (2013). Tet proteins connect the O-linked N-acetylglucosamine transferase Ogt to chromatin in embryonic stem cells. Mol. Cell 49, 645�. doi: 10.1016/j.molcel.2012.12.019

Wang, F., Han, J., Wang, L., Jing, Y., Zhu, Z., Hui, D., et al. (2017). CCCTC-binding factor transcriptionally targets Wdr5 to mediate somatic cell reprogramming. Stem Cells Dev. 26, 743�. doi: 10.1089/scd.2016.0309

Wang, T., Chen, K., Zeng, X., Yang, J., Wu, Y., Shi, X., et al. (2011). The Histone Demethylases Jhdm1a/1b enhance somatic cell reprogramming in a vitamin-C-dependent manner. Cell Stem Cell 9, 575�. doi: 10.1016/j.stem.2011.10.005

Wang, Y., Dong, C., and Zhou, B. P. (2020). Metabolic reprogram associated with epithelial-mesenchymal transition in tumor progression and metastasis. Genes Dis. 7, 172�. doi: 10.1016/j.gendis.2019.09.012

Wang, Y., Qin, J., Wang, S., Zhang, W., Duan, J., Zhang, J., et al. (2016). Conversion of human gastric epithelial cells to multipotent endodermal progenitors using defined small molecules. Cell Stem Cell 19, 449�. doi: 10.1016/j.stem.2016.06.006

Warburg, O. (1956). On the origin of cancer cells. Science 123, 309�. doi: 10.1126/science.123.3191.309

Wei, T., Chen, W., Wang, X., Zhang, M., Chen, J., Zhu, S., et al. (2015). An HDAC2-TET1 switch at distinct chromatin regions significantly promotes the maturation of pre-iPS to iPS cells. Nucleic Acids Res. 43, 5409�. doi: 10.1093/nar/gkv430

Wellner, U., Schubert, J., Burk, U., Schmalhofer, O., Zhu, F., Sonntag, A., et al. (2009). The EMT-activator ZEB1 promotes tumorigenicity by repressing stemness-inhibiting microRNAs. Nat. Cell Biol. 11, 1487�. doi: 10.1038/ncb1998

Williams, E. D., Gao, D., Redfern, A., and Thompson, E. W. (2019). Controversies around epithelial-mesenchymal plasticity in cancer metastasis. Nat. Rev. Cancer 19, 716�. doi: 10.1038/s41568-019-0213-x

Wu, H., and Zhang, Y. (2014). Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell 156, 45�. doi: 10.1016/j.cell.2013.12.019

Wu, J., Ocampo, A., and Belmonte, J. (2016). Cellular Metabolism and Induced Pluripotency. Cell 166, 1371�. doi: 10.1016/j.cell.2016.08.008

Wu, X., and Zhang, Y. (2017). TET-mediated active DNA demethylation: mechanism, function and beyond. Nat. Rev. Genet. 18, 517�. doi: 10.1038/nrg.2017.33

Wu, Y., Chen, K., Xing, G., Li, L., Ma, B., Hu, Z., et al. (2019). Phospholipid remodeling is critical for stem cell pluripotency by facilitating mesenchymal-to-epithelial transition. Sci. Adv. 5:eaax7525. doi: 10.1126/sciadv.aax7525

Yang, J., Antin, P., Berx, G., Blanpain, C., Brabletz, T., Bronner, M., et al. (2020). Guidelines and definitions for research on epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 21, 341�. doi: 10.1038/s41580-020-0237-9

Yang, L., Zhang, F., Wang, X., Tsai, Y., Chuang, K. H., Keng, P. C., et al. (2016). A FASN-TGF-beta1-FASN regulatory loop contributes to high EMT/metastatic potential of cisplatin-resistant non-small cell lung cancer. Oncotarget 7, 55543�. doi: 10.18632/oncotarget.10837

Ye, X., Brabletz, T., Kang, Y., Longmore, G., Nieto, M., Stanger, B., et al. (2017). Upholding a role for EMT in breast cancer metastasis. Nature 547, E1�. doi: 10.1038/nature22816

Ye, X., Tam, W. L., Shibue, T., Kaygusuz, Y., Reinhardt, F., Ng Eaton, E., et al. (2015). Distinct EMT programs control normal mammary stem cells and tumour-initiating cells. Nature 525, 256�. doi: 10.1038/nature14897

Ye, X., and Weinberg, R. A. (2015). Epithelial-mesenchymal plasticity: a central regulator of cancer progression. Trends Cell Biol. 25, 675�. doi: 10.1016/j.tcb.2015.07.012

Yin, R., Mao, S.-Q., Zhao, B., Chong, Z., Yang, Y., Zhao, C., et al. (2013). Ascorbic acid enhances Tet-Mediated 5-methylcytosine oxidation and promotes DNA demethylation in mammals. J. Am. Chem. Soc. 135, 10396�. doi: 10.1021/ja4028346

Yu, L., Lu, M., Jia, D., Ma, J., Ben-Jacob, E., Levine, H., et al. (2017). Modeling the genetic regulation of cancer metabolism: interplay between glycolysis and oxidative phosphorylation. Cancer Res. 77, 1564�. doi: 10.1158/0008-5472.CAN-16-2074

Zaravinos, A. (2015). The regulatory role of MicroRNAs in EMT and cancer. J. Oncol. 2015:865816. doi: 10.1155/2015/865816

Zavadil, J., and Bottinger, E. P. (2005). TGF-beta and epithelial-to-mesenchymal transitions. Oncogene 24, 5764�. doi: 10.1038/sj.onc.1208927

Zhang, H., and Wang, Z. (2008). Mechanisms that mediate stem cell self-renewal and differentiation. J. Cell. Biochem. 103, 709�. doi: 10.1002/jcb.21460

Zhang, J., Nuebel, E., Daley, G., Koehler, C., and Teitell, M. (2012). Metabolic regulation in pluripotent stem cells during reprogramming and self-renewal. Cell Stem Cell 11, 589�. doi: 10.1016/j.stem.2012.10.005

Zhang, J., Tian, X., Zhang, H., Teng, Y., Li, R., Bai, F., et al. (2014). TGF-beta-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci. Signal. 7:ra91. doi: 10.1126/scisignal.2005304

Zhang, Y., and Weinberg, R. A. (2018). Epithelial-to-mesenchymal transition in cancer: complexity and opportunities. Front. Med. 12, 361�. doi: 10.1007/s11684-018-0656-6

Zheng, X., Carstens, J., Kim, J., Scheible, M., Kaye, J., Sugimoto, H., et al. (2015). Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature 527, 525�. doi: 10.1038/nature16064

Zhou, W., Choi, M., Margineantu, D., Margaretha, L., Hesson, J., Cavanaugh, C., et al. (2012). HIF1α induced switch from bivalent to exclusively glycolytic metabolism during ESC-to-EpiSC/hESC transition. EMBO J. 31, 2103�. doi: 10.1038/emboj.2012.71

Zhou, Z., Yang, X., He, J., Liu, J., Wu, F., Yu, S., et al. (2017). Kdm2b regulates somatic reprogramming through variant PRC1 complex-dependent function. Cell Rep. 21, 2160�. doi: 10.1016/j.celrep.2017.10.091

Keywords : EMT, cancer, reprogramming, energy metabolism, glycolysis, OXPHOS

Citation: Lai X, Li Q, Wu F, Lin J, Chen J, Zheng H and Guo L (2020) Epithelial-Mesenchymal Transition and Metabolic Switching in Cancer: Lessons From Somatic Cell Reprogramming. Front. Cell Dev. Biol. 8:760. doi: 10.3389/fcell.2020.00760

Received: 27 May 2020 Accepted: 20 July 2020
Published: 06 August 2020.

Marco Fiorillo, University of Salford, United Kingdom

Anup Kumar Singh, Beckman Research Institute, City of Hope, United States
Monica Montopoli, University of Padua, Italy

Copyright © 2020 Lai, Li, Wu, Lin, Chen, Zheng and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Campbell Biology / Lisa A. Urry, Mills College, Oakland, California, Michael L. Cain, Bowdoin College, Brunswick, Maine, Steven A. Wasserman, University of California, San Diego, Peter V. Minorsky, Mercy College, Dobbs Ferry, New York, Jane B. Reece, Berkeley, California.

This edition was published in 2017 by Pearson Education, Inc. in New York, NY .

Edition Description

1 volume (various pagings) : illustrations (chiefly color), color maps 29 cm


Watch the video: Intro to Metabolic Pathways (January 2022).