Global profiling strategies for mapping dysregulated metabolic pathways in cancer - PubMed
- ️Sun Jan 01 2012
Review
Global profiling strategies for mapping dysregulated metabolic pathways in cancer
Daniel I Benjamin et al. Cell Metab. 2012.
Abstract
Cancer cells possess fundamentally altered metabolism that provides a foundation to support tumorigenicity and malignancy. Our understanding of the biochemical underpinnings of cancer has benefited from the integrated utilization of large-scale profiling platforms (e.g., genomics, proteomics, and metabolomics), which, together, can provide a global assessment of how enzymes and their parent metabolic networks become altered in cancer to fuel tumor growth. This review presents several examples of how these integrated platforms have yielded fundamental insights into dysregulated metabolism in cancer. We will also discuss questions and challenges that must be addressed to more completely describe, and eventually control, the diverse metabolic pathways that support tumorigenesis.
Copyright © 2012 Elsevier Inc. All rights reserved.
Figures

The metabolic pathways that sustain the proliferative nature of a cancer cells are very much the same pathways that constitute the metabolism of normal cells. However, cancer cells are able to aberrantly rewire many of these normal pathways to meet their excessive needs for growth and proliferation. In the figure above, we see that pathways that have been revealed to be essential in cancer cells (shown in Red) are pathways that are fundamentally important for the synthesis of biological macromolecules, antioxidants, and signaling molecules that facilitate cellular growth, survival, and progression. The “Warburg effect,” the preferential shunting of pyruvate to lactate under conditions of normoxia, is facilitated by the switch from the M1 splice isoform of pyruvate kinase to the M2 splice isoform, which is in-general less active due to several modes of regulation on PKM2 activity in the form of protein-protein interactions or posttranslational modifications which all collectively inhibit PKM2 activity (Anastasiou et al., 2011; Christofk et al., 2008b; Hitosugi et al., 2009; Lv et al., 2011). The slow nature of this M2 splice isoform allows for the buildup of upstream glycolytic intermediates rather than favoring the flux of glycolytic carbon through the TCA cycle. Two of the important upstream glycolytic intermediates that are essential for synthesizing necessary biological macromolecules are glucose-6-phosphate (G6P) and 3-phosphoglycerate (3PG). The 3-step formation of Ribose-5-phosphate (R5P) via 6-phosphogluconolactone (6-PGL) and 6-phosphogluconate (6-PG), involves the production of reducing equivalent in the form of NADPH, which in-turn can be used as the primary reducing power in the production of glutathione (GSH) from its oxidized precursor glutathione disulfide (GSSG) (Anastasiou et al., 2011). It was also discovered that a significant portion of glycolytic carbon is diverted towards serine and glycine biosynthesis by phoshphoglycerate dehydrogenase (PHGDH), which catalyzes the conversion of 3-PG to phosphopyruvate. Phosphopyruvate is then converted to phosphoserine, concurrently with the anaplerotic generation of α-ketoglutarate by glutamine to supply the TCA cycle (Locasale et al., 2011a; Possemato et al., 2011). Glutaminolysis-derived α-ketoglutarate provides reducing power in the form of NAPDH and regenerates oxaloacetate (OAA) to form citrate which are both necessary for the production of fatty acids by fatty acid synthase (FASN) (DeBerardinis et al., 2007; Metallo et al., 2012). Under hypoxia or mitochondrial dysfunction, α-ketoglutarate can also undergo reductive carboxylation to isocitrate and then to citrate to support lipogenesis (Mullen et al., 2012). Upon hypoxia or if IDH1 or IDH2 is mutated, these enzymes can also form the oncometabolite 2-hydroxyglutarate (2-HG) (Dang et al., 2010; Wise et al., 2011). Upon synthesis of fatty acids and esterification onto phospholipids or triglycerides, these esterified lipids are mobilized through lipolytic processes involving enzymes such as monoacylglycerol lipase (MAGL), which can lead to the formation of oncogenic lipid signaling molecules including lysophosphatidic acid (LPA) and prostaglandins (Nomura et al., 2010b). Fatty acids can also arise from extracellular sources by hydrolysis of triglycerides by lipoprotein lipase (LPL).

Christofk et al labeled HeLa cells with heavy isotopic 13C-lysine and 13C-arginine or normal isotopic 12C-lysine and 12C-arginine. The heavy labeled cells lysates were collected and flowed over a phosphorylated peptide library while the light labeled lysates were flowed over an unphosphorylated peptide library. The eluents were then digested and subsequently analyzed by LC/MS/MS. By identifying proteins that exhibited a high ratio of heavy to light label, phosphoproteins could be identified. One such protein that exhibited a particularly high SILAC heavy to light ratio was Pyruvate Kinase M2 (PKM2) (Christofk et al., 2008b).

a. Recent studies have performed metabolic flux analysis on cancer cells labeled with [13C]glucose or [13C]glutamine to dissect dysregulated metabolism of cancer cells exposed to hypoxia or mitochondrial dysfunction. b. These studies revealed that cancer cells under normoxia can form citrate through both glycolysis (bottom row of labeled carbons in TCA cycle) and glutaminolysis (top row of labeled carbons in TCA cycle). Upon hypoxia or mitochondrial dysfunction, cancer cells undergo a metabolic switch in citrate is produced through glutamine-dependent reductive carboxylation of α–ketoglutarate (α–KG) to form isocitrate via IDH1 or IDH2 and then to citrate to support de novo lipogenesis (blue arrows). Shown in the figure are [13C]-labeled carbons arising either from glucose (in green) or glutamine (in red) after one pass through the TCA cycle. Carbon atoms arising from CO2 are labeled as black dots.

Possemato et al used an elegant negative selection in vivo RNAi screen to identify metabolic enzymes necessary to support breast tumorigenesis. A prioritized list of 133 metabolic enzymes and metabolite-transport genes were individually knocked down by shRNA lentiviral constructs in MCF10a cells, combined together and collectively injected into mice. Upon tumor formation, the resultant cancers were screened by sequencing efforts to identify genes, for which the knockdown was selected against. From this functional genomic screen, a total of 16 genes were identified, one of which was PHGDH, involved in serine and glycine biosynthesis (Possemato et al., 2011).

a) ABPP uses active site-directed probes to assess the functional state of large numbers of enzymes directly in complex proteomes (Nomura et al., 2010a). Activity-based probes (ABPs) consist of a reactive group, a spacer arm, and a detection handle (e.g. fluorophore such as a rhodamine (Rh) or biotin (B)). In a typical ABPP experiment, a proteome is reacted with the activity-based probe and readout either by fluorescence on a 1D-SDS-PAGE gel for rhodamine-ABPs (above), or by avidin enrichment, on-bead tryptic digest, and identification and quantification of peptides by Multidimensional Protein Identification Technology (MudPIT) for biotinylated-ABPs (below) (Nomura et al., 2010a). Through ABPP analysis of the serine hydrolase proteome with the serine hydrolase ABP fluorophosphonate (FP)-Rh or FP-biotin, KIAA1363 and MAGL were identified as upregulated in multiple human aggressive cancer cells and primary tumors (Jessani et al., 2002; Nomura et al., 2010b). b) ABPP can also be used in a competitive format to assess potency and selectivity of inhibitors in complex proteomes. Inhibitors can compete with the ABP and enzyme inhibition will be read-out by loss of fluorescence on a SDS-PAGE gel (using a rhodamine-ABP) or loss of spectral counts by mass spectrometry (using a biotintylated-ABP). Competitive ABPP was used to develop selective inhibitors of KIAA1363 and MAGL (Chang et al., 2011b; Chiang et al., 2006; Long et al., 2009). c) With selective inhibitors in hand, the metabolic roles of KIAA1363 and MAGL were elucidated using untargeted LC-MS-based metabolomic approaches in which metabolomes were extracted and analyzed by LCMS, broadly scanning for metabolites across a large mass range. To complement the large amount of data that arises from untargeted metabolomic analysis, powerful software tool XCMS was used for quantitation and identification of ions within LC/MS datasets (Smith et al., 2006) which aligns, quantifies, and statistically rank ions that are altered between two sets of metabolomic data. This methodology was used to annotate KIAA1363 and MAGL as enzymes that regulates ether lipid and fatty acid networks, respectively (Chiang et al., 2006; Nomura et al., 2010b).
Similar articles
-
Activity-based proteomic and metabolomic approaches for understanding metabolism.
Hunerdosse D, Nomura DK. Hunerdosse D, et al. Curr Opin Biotechnol. 2014 Aug;28:116-26. doi: 10.1016/j.copbio.2014.02.001. Epub 2014 Mar 13. Curr Opin Biotechnol. 2014. PMID: 24594637 Free PMC article. Review.
-
Medina-Cleghorn D, Nomura DK. Medina-Cleghorn D, et al. Chem Biol. 2014 Sep 18;21(9):1171-84. doi: 10.1016/j.chembiol.2014.07.007. Chem Biol. 2014. PMID: 25237861 Free PMC article. Review.
-
Merrick BA, London RE, Bushel PR, Grissom SF, Paules RS. Merrick BA, et al. IARC Sci Publ. 2011;(163):121-42. IARC Sci Publ. 2011. PMID: 22997859 Review.
-
Pyruvate kinase: Function, regulation and role in cancer.
Israelsen WJ, Vander Heiden MG. Israelsen WJ, et al. Semin Cell Dev Biol. 2015 Jul;43:43-51. doi: 10.1016/j.semcdb.2015.08.004. Epub 2015 Aug 13. Semin Cell Dev Biol. 2015. PMID: 26277545 Free PMC article. Review.
-
Characterization of Glycolytic Enzymes and Pyruvate Kinase M2 in Type 1 and 2 Diabetic Nephropathy.
Gordin D, Shah H, Shinjo T, St-Louis R, Qi W, Park K, Paniagua SM, Pober DM, Wu IH, Bahnam V, Brissett MJ, Tinsley LJ, Dreyfuss JM, Pan H, Dong Y, Niewczas MA, Amenta P, Sadowski T, Kannt A, Keenan HA, King GL. Gordin D, et al. Diabetes Care. 2019 Jul;42(7):1263-1273. doi: 10.2337/dc18-2585. Epub 2019 May 10. Diabetes Care. 2019. PMID: 31076418 Free PMC article.
Cited by
-
Lu J, Li Y, Li YA, Wang L, Zeng AR, Ma XL, Qiang JW. Lu J, et al. J Transl Med. 2022 Feb 15;20(1):92. doi: 10.1186/s12967-022-03292-z. J Transl Med. 2022. PMID: 35168606 Free PMC article.
-
Comprehensive Analysis of Metabolic Isozyme Targets in Cancer.
Marczyk M, Gunasekharan V, Casadevall D, Qing T, Foldi J, Sehgal R, Shan NL, Blenman KRM, O'Meara TA, Umlauf S, Surovtseva YV, Muthusamy V, Rinehart J, Perry RJ, Kibbey R, Hatzis C, Pusztai L. Marczyk M, et al. Cancer Res. 2022 May 3;82(9):1698-1711. doi: 10.1158/0008-5472.CAN-21-3983. Cancer Res. 2022. PMID: 35247885 Free PMC article.
-
Wang HY, Tang K, Liang TY, Zhang WZ, Li JY, Wang W, Hu HM, Li MY, Wang HQ, He XZ, Zhu ZY, Liu YW, Zhang SZ. Wang HY, et al. J Exp Clin Cancer Res. 2016 May 31;35:86. doi: 10.1186/s13046-016-0362-7. J Exp Clin Cancer Res. 2016. PMID: 27245697 Free PMC article.
-
Xiong J, Bian J, Wang L, Zhou JY, Wang Y, Zhao Y, Wu LL, Hu JJ, Li B, Chen SJ, Yan C, Zhao WL. Xiong J, et al. Blood Cancer J. 2015 Mar 13;5(3):287. doi: 10.1038/bcj.2015.10. Blood Cancer J. 2015. PMID: 25768400 Free PMC article.
-
Profiling of metabolic dysregulation in ovarian cancer tissues and biofluids.
Ohta T, Sugimoto M, Ito Y, Horikawa S, Okui Y, Sakaki H, Seino M, Sunamura M, Nagase S. Ohta T, et al. Sci Rep. 2024 Sep 16;14(1):21555. doi: 10.1038/s41598-024-72938-3. Sci Rep. 2024. PMID: 39285238 Free PMC article.
References
-
- Anastasiou D, Poulogiannis G, Asara JM, Boxer MB, Jiang JK, Shen M, Bellinger G, Sasaki AT, Locasale JW, Auld DS, Thomas CJ, Vander Heiden MG, Cantley LC. Inhibition of pyruvate kinase m2 by reactive oxygen species contributes to cellular antioxidant responses. Science. 2011;334:1278–1283. - PMC - PubMed
-
- Boxer MB, Jiang JK, Vander Heiden MG, Shen M, Skoumbourdis AP, Southall N, Veith H, Leister W, Austin CP, Park HW, Inglese J, Cantley LC, Auld DS, Thomas CJ. Evaluation of Substituted N,N '-Diarylsulfonamides as Activators of the Tumor Cell Specific M2 Isoform of Pyruvate Kinase. J Med Chem. 2010;53:1048–1055. - PMC - PubMed
-
- Buck E, Eyzaguirre A, Rosenfeld-Franklin M, Thomson S, Mulvihill M, Barr S, Brown E, O'Connor M, Yao Y, Pachter J, Miglarese M, Epstein D, Iwata KK, Haley JD, Gibson NW, Ji QS. Feedback Mechanisms Promote Cooperativity for Small Molecule Inhibitors of Epidermal and Insulin-Like Growth Factor Receptors. Cancer research. 2008;68:8322–8332. - PubMed
-
- Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nature reviews. Cancer. 2011;11:85–95. - PubMed
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources