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Applied Biochemistry and Microbiology

, Volume 53, Issue 7, pp 733–753 | Cite as

Metabolic Flux Analysis Using 13C Isotopes (13C-MFA). 1. Experimental Basis of the Method and the Present State of Investigations

  • L. I. GolubevaEmail author
  • M. S. Shupletsov
  • S. V. Mashko
Problems, Prospects

Abstract

Quantitatively characterizing the intracellular carbon flux distribution provides useful information for both fundamental and applied investigations into the cellular metabolism at the system level, such as the roles of different metabolic pathways and individual reactions, metabolic state characterization, metabolic differences between the strains, and clues regarding strategies for producer-strain improvement. A variety of methods have been developed to characterize the metabolic state of the cell by determining its intracellular flux distribution, and together, they are called metabolic flux analysis (MFA) or fluxomics. These methods, in addition to other X-omics technologies (i.e., genomics, transcriptomics, proteomics, and metabolomics) constitute a recent arsenal of the system biology estimation approaches. One of the most well-developed approaches for intracellular carbon flux estimation in vivo in (quasi) steady-state conditions is 13C-MFA, which uses substrates that are labeled with a heavy carbon (13C). Applying 13C-MFA requires the coordination of experts in biochemistry, applied mathematics and nuclear magnetic resonance (NMR) or mass spectrometry. Therefore, the authors have prepared a three-part review highlighting the different but equally important aspects of 13C-MFA. In the first part, which is presented below, the focus is on the basic principles of 13C-MFA, such as stoichiometric model development, labeling experiments and experimental data extraction. The principles of the labeling experiments modeling and quantitative carbon flux estimation and statistics are discussed in the second part. The final part reviews recent achievements in fundamental and applied investigations of bacterial metabolism achieved using 13C-MFA.

Keywords

stoichiometric metabolic model isotopomer carbon labeling experiment 

Abbreviations

a.m.u.

atomic mass unit

CoA

coenzyme A

MW

molecular weight

TKT

transketolase

P

phosphate

CM

central metabolism

NMR

nuclear magnetic resonance

ATP

adenosine triphosphate

CID

collision-induced dissociation

CLE

carbon-labeling experiment

EDP

Entner–Doudoroff Pathway (ED pathway)

EI

elector impact ionization

EMP

Embden–Meyerhof–Parnas pathway

ESI

electrospray ionization

FBA

constraints-based flux balance analysis

GLC

glucose

GS

genome-scale model

GC

gas chromatography

IDV

isotropomer distribution vector

LC

liquid chromatography

MAV

metabolite activity vector

MDV

mass distribution vector

MFA

metabolic flux analysis

MS

mass spectrometry

FEP

phosphoenol pyruvate

РРР

pentose-phosphate cycle (PP-pathway)

TAL

transaldolase

TCA

tricarboxylic acid cycle

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References

  1. 1.
    Gatherer, D., So what do we really mean when we say that system biology is holistic?, BMC Systems Biol., 2010, vol. 4, no. 22, pp. 1–12. doi 10.1186/1752-0509-4-22Google Scholar
  2. 2.
    Kalinowski, J., Battels, D., et al., The complete Corynebacterium glutamicum ATCC 13032 genome sequence and its impact on the production of L-aspartate-derived amino acids and vitamins, J. Biotechnol., 2003, vol. 104, pp. 5–25.PubMedCrossRefGoogle Scholar
  3. 3.
    Thiele, I. and Palsson, B.O., A protocol for generating a high-quality genome-scale metabolic reconstruction, Nature Protocols, 2010, vol. 5, pp. 93–121. doi 10.1038/nprot.2009.203PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Hara, Y., Kadotani, N., Izui, H., et al., The complete genome sequence of Pantoea ananatis AJ13355, an organism with great biotechnological potential, Appl. Microb. Biotechnol., 2012, vol. 93, pp. 331–341. doi 10.1007/s00253-011-3713-5Google Scholar
  5. 5.
    Heather, J.M. and Chain, B., The sequence of sequencers: the history of sequencing DNA, Genomics, 2016, vol. 107, pp. 1–8. doi 10.1016/j.ygeno.2015.11.003PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Schena, M., Shalon, D., Davis, R.W., et al., Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science, 1995, vol. 270, pp. 467–470. doi 10.1126/science.270.5235.467PubMedCrossRefGoogle Scholar
  7. 7.
    Lockhart, D.J., Dong, H., Byrne, M.C., et al., Expression monitoring by hybridization to high-density oligonucleotide arrays, Nat. Biotechnol., 1996, vol. 14, pp. 1675–1680. doi 10.1038/nbtl296-1675PubMedCrossRefGoogle Scholar
  8. 8.
    Wendisch, V.F., Genome-wide expression analysis in amino acid-producing bacteria using DNA microarrays, Appl. Biochem. Biotechnol., 2004, vol. 118, pp. 215–232. doi 10.1385/ABAB:118:1-3:215PubMedCrossRefGoogle Scholar
  9. 9.
    Johansson, L. and Liden, G., Transcriptome analysis of a shikimic acid producing strain of Escherichia coli W3110 grown under carbon-and phosphate-limited conditions, J. Biotechnol., 2006, vol. 126, pp. 528–545. doi 10.1016/j.jbiotec.2006.05.007PubMedCrossRefGoogle Scholar
  10. 10.
    Jensen, O.N., Interpreting the protein language using proteomics, Nat. Rev. Mol. Cell Biol., 2006, vol. 7, pp. 391–403. doi 10.1038/nrml939PubMedCrossRefGoogle Scholar
  11. 11.
    Bantscheff, M., Schirle, M., Sweetman, G., et al., Quantitative mass spectrometry in proteomics: a critical review, Anal. Bioanal. Chem., 2007, vol. 389, pp. 1017–1031. doi 10.1007/s00216-007-1486-6PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    de Oliveira, J.M. and de Graaf, L.H., Proteomics of industrial fungi: trends and insights for biotechnology, Appl. Microb. Biotechnol., 2011, vol. 89, pp. 225–237. doi 10.1007/S00253-010-2900-0CrossRefGoogle Scholar
  13. 13.
    Plewczynski, D. and Ginalski, K., The interactome: predicting the protein-protein interactions in cells, Cell. Mol. Biol. Lett., 2009, vol. 14, no. 1, pp. 1–22. doi 10.2478/sl1658-008-0024-7PubMedCrossRefGoogle Scholar
  14. 14.
    Williamson, M.P. and Sutcliffe, M.J., Protein–protein interactions, Biochem. Soc. Trans., 2010, vol. 38, pp. 875–878. doi 10.1042/BST0380875PubMedCrossRefGoogle Scholar
  15. 15.
    Reaves, M.L. and Rabinowitz, J.D., Metabolomics in systems microbiology, Curr. Opin. Biotechnol., 2010, vol. 22, pp. 17–25. doi 10.1016/j.copbio.2010.10.001PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Baidoo, E.E.K., Benke, P.I., and Keasling, J.D., Mass Spectrometry-Based Microbial Metabolomics: Microbial Systems Biology: Methods and Protocols. Methods in Molecular Biology, Navid, A., Ed., Germany: Springer Science+Business Media, LLC, 2012, vol. 881, pp. 215–278. doi 10.1007/978-l-61779-827-6_9Google Scholar
  17. 17.
    Sauer, U., Metabolic networks in motion: 13C-based flux analysis, Mol. Syst. Biol., 2006, vol. 2, pp. 1–10. doi 10.1038/msb4100109CrossRefGoogle Scholar
  18. 18.
    Tang, Y.J., Martin, E.G., Myers, S., et al., Advances in analysis of microbial metabolic fluxes via 13C isotopic labeling, Mass Spectrometry Rev., 2009, vol. 28, pp. 362–375. doi 10.1002/mas.20191CrossRefGoogle Scholar
  19. 19.
    Kohlstedt, M., Becker, J., and Wittmann, C., Metabolic fluxes and beyond -systems biology understanding and engineering of microbial metabolism, Appl. Microbiol. Biotechnol., 2010, vol. 88, pp. 1065–1075. doi 10.1007/s00253-010-2854-2PubMedCrossRefGoogle Scholar
  20. 20.
    Heinemann, M. and Sauer, U., Systems biology of microbial metabolism, Curr. Opin. Microbiol., 2010, vol. 13, pp. 337–343. doi 10.1016/J.MIB.2010.02.005PubMedCrossRefGoogle Scholar
  21. 21.
    Zhang, W., Li, F., and Nie, L., Integrating multiple’omics' analysis for microbial biology: application and methodologies, Microbiology, 2010, vol. 156, pp. 287–301. doi 10.1099/mic.0.034793-0PubMedCrossRefGoogle Scholar
  22. 22.
    Lee, J.W., Na, D., Park, J.M., et al., Systems metabolic engineering of microorganisms for natural and non-natural chemicals, Nature Chem. Biol., 2012, vol. 8, pp. 536–546. doi 10.1016/J.MIB.2010.02.005CrossRefGoogle Scholar
  23. 23.
    Vishwanathan, N., Le, H., Le, T., et al., Advancing biopharmaceutical process science through transcriptome analysis, Curr. Opin. Biotechnol., 2014, vol. 30, pp. 113–119. doi 10.1016/j.copbio.2014.06.011PubMedCrossRefGoogle Scholar
  24. 24.
    Heffner, K., Hizal, D., Kumar, A., et al., Exploiting the proteomics revolution in biotechnology: from disease and antibody targets to optimizing bioprocess development, Curr. Opin. Biotechnol., 2014, vol. 30, pp. 80–86. doi 10.1016/j.cop-bio.2014.06.006PubMedCrossRefGoogle Scholar
  25. 25.
    Kind, S., Kreye, S., and Wittmann, C., Metabolic engineering of cellular transport for overproduction of the platform chemical 1,5-diaminopentane in Corynebacterium glutamicum, Metab. Eng., 2011, vol. 13, pp. 617–627. doi 10.1016/j.ymben.2011.07.006PubMedCrossRefGoogle Scholar
  26. 26.
    Korneli, C. Bolten, C.J., et al., Debottlenecking recombinant protein production in Bacillus megaterium under large-scale conditions-targeted precursor feeding designed from metabolomics, Biotechnol. Bioeng., 2012, vol. 109, pp. 1538–1550. doi 10.1002/bit.24434PubMedCrossRefGoogle Scholar
  27. 27.
    Becker, J., Wittmann c systems and synthetic metabolic engineering for amino acid production—the heartbeat of industrial strain development, Curr. Opin. Biotechnol., 2012, vol. 23, pp. 718–726. doi 10.1016/j.copbio.2011.12.025PubMedCrossRefGoogle Scholar
  28. 28.
    Batth, T.S., Singh, P., Ramakrishnan, V.R., et al., A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coftproteins, Metab. Eng., 2014, vol. 26, pp. 48–56. doi 10.1016/j.ymben.2014.08.004PubMedCrossRefGoogle Scholar
  29. 29.
    Ozsolak, F., Piatt, A.R., Jones, D.R., et al., Direct RNA sequencing, Nature, 2009, vol. 461, pp. 814–818. doi 10.1038/nature08390PubMedCrossRefGoogle Scholar
  30. 30.
    Van Gulik, W.M., Fast sampling for quantitative microbial metabolomics, Curr. Opin. Biotechnol., 2010, vol. 21, pp. 27–34. doi 10.1016/j.copbio.2010.01.008PubMedCrossRefGoogle Scholar
  31. 31.
    Van Gulik, W.M., Canelas, A.B., Taymaz-Nikerel, H., et al., Fast Sampling of the Cellular Metabolome: Microbial Systems Biology: Methods and Protocols. Methods in Molecular Biology, Navid, A., Ed., Germany: Springer Science+Business Media, LLC, 2012, vol. 881, pp. 279–306. doi 10.1007/978-1-61779-827-6_10Google Scholar
  32. 32.
    Bolten, C.J., Kiefer, P., Letisse, R., et al., Sampling for metabolome analysis of microorganisms, Anal. Chem., 2007, vol. 79, pp. 3843–3849. doi 10.1021/ac0623888PubMedCrossRefGoogle Scholar
  33. 33.
    Millard, P., Massou, S., Wittmann, C., et al., Sampling of intracellular metabolites for stationary and non-stationary 13C-metabolic flux analysis in Escherichia coli, Anal. Biochem., 2014, vol. 465, pp. 38–49. doi 10.1016/j.ab.2014.07.026PubMedCrossRefGoogle Scholar
  34. 34.
    Winter, G. and Krömer, J.O., Fluxomics-connecting “omics” analysis and phenotypes, Environ. Microbiol., 2013, vol. 15, pp. 1901–1916. doi 10.1111/1462-2920.12064PubMedCrossRefGoogle Scholar
  35. 35.
    Antoniewicz, M.R., Dynamic metabolic flux analysis— tools for probing transient states of metabolic networks, Curr. Opin. Biotechnol., 2013, vol. 24, pp. 973–978. doi 10.1016/j.cop-bio.2013.03.018PubMedCrossRefGoogle Scholar
  36. 36.
    Wiechert, W. and Nöh, K., Isotopically non-stationary metabolic flux analysis: complex yet highly informative, Curr. Opin. Biotechnol., 2013, vol. 24, pp. 979–986. doi 10.1016/j.cop-bio.2013.03.024PubMedCrossRefGoogle Scholar
  37. 37.
    Antoniewicz, M.R., Methods and advances in metabolic flux analysis: a mini-review, J. Ind. Microbiol. Biotechnol., 2015, vol. 42, pp. 317–325. doi 10.1007/sl0295-015-1585-xPubMedCrossRefGoogle Scholar
  38. 38.
    Niedenföhr, S., Wiechert, W., and Nöh, K., How to measure metabolic fluxes: a taxonomic guide for 13C fluxomics, Curr. Opin. Biotechnol., 2015, vol. 34, pp. 82–90. doi 10.1016/j.cop-bio.2014.12.003CrossRefGoogle Scholar
  39. 39.
    Varma, A. and Palsson, B.O., Metabolic flux balance. basic concepts. scientific and practical use, Nat. Biotechnol., 1994, vol. 12, pp. 994–998. doi 10.1038/nbtl094-994CrossRefGoogle Scholar
  40. 40.
    Feist, A.M., Henry, C.S., Reed, J.L., et al., A genomescale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and their thermodynamic information, Mol. Syst. Biol., 2007, vol. 3, pp. 121–139. doi 10.1038/msb4100155PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Varma, A. and Palsson, B.O., Metabolic capabilities of Escherichia coli: l synthesis of biosynthetic precursors and cofactors, J. Theor. Biol., 1993, vol. 165, pp. 477–502. doi 10.1006/jtbi.1993.1202PubMedCrossRefGoogle Scholar
  42. 42.
    Varma, A., Boesch, B.W., and Palsson, B.O., Biochemical production capabilities of Escherichia coli, Biotechnol. Bioengin., 1993, vol. 42, pp. 59–73. doi 10.1002/bit260420109CrossRefGoogle Scholar
  43. 43.
    Vallino, J.J. and Stephanopoulos, G., Metabolic flux distribution in Corymbacterium glutamicum during growth and lysine overproduction, Biotechnol. Bioeng., 1993, vol. 41, pp. 633–646. doi 10.1002/bit.260410606PubMedCrossRefGoogle Scholar
  44. 44.
    Palsson, B.O. and Papin, J.A., Applications of genomescale metabolic reconstructions, Mol. Syst. Biol., 2009, vol. 5, no. 320, pp. 1–15. doi 10.1038/MSB.2009.77Google Scholar
  45. 45.
    Orth, J.D., Conrad, T.M., Na, J., et al., A comprehensive genome-scale reconstruction of Escherichia coli metabolism, Mol. Syst. Biol., 2011, vol. 7, no. 535, pp. 1–9. doi 10.1038/msb.2011.65Google Scholar
  46. 46.
    Wasylenko, T.M. and Stephanopoulos, G., Kinetic isotope effects significantly influence intracellular metabolite 13C labeling patterns and flux determination, Biotechnol. J., 2013, vol. 8, pp. 1080–1089.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Wiechert, W. and de Graaf, A.A., Bidirectional reaction steps in metabolic networks: I. Modeling and stimulation of carbon isotope labeling experiments, Biotechnol. Bioeng., 1997, vol. 55, pp. 101–117. doi 10.1002/(SICI)1097-0290(19970705)55:1<101::AIDBIT12>3.0.CO;2-PPubMedCrossRefGoogle Scholar
  48. 48.
    Shearer, G., Lee, J.C., Koo, J., et al., Quantitative estimation of channeling from early glycolytic intermediates to CO2 in intact Escherichia coli, FEBS J., 2005, vol. 272, pp. 3260–3269. 10.1111/j.1742-4658.2005.04712.xPubMedCrossRefGoogle Scholar
  49. 49.
    Zamboni, N., Fischer, E., Sauer, U., et al., FiatFlux— a software for metabolic flux analysis form 13C-glucose experiments, BMC Bioinformatics, 2005, vol. 6, no. 209, pp. 1–8. doi 10.1186/1471-2105-6-209Google Scholar
  50. 50.
    Van Ooyen, J., Noack, S., Bott, M., et al., Improved L-lysine production with Corynebacterivm glutamicum and systemic insight into citrate synthase flux and activity, Biotechnol. Bioeng., 2012, vol. 109, pp. 2070–2081. doi 10.1002/bit.24486PubMedCrossRefGoogle Scholar
  51. 51.
    Becker, J., Zelder, O., Häfner, S., et al., From zero to hero—design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production, Metab. Eng., 2011, vol. 13, pp. 159–168. doi 10.1016/j.ymben.2011.01.003PubMedCrossRefGoogle Scholar
  52. 52.
    Dauner, M., From fluxes and isotope labeling patterns towards in silico cells, Curr. Opin. Biotechnol., 2010, vol. 21, pp. 55–62. doi 10.1016/j.copbio.2010.01.014PubMedCrossRefGoogle Scholar
  53. 53.
    Zamboni, N., 13C metabolic flux analysis in complex systems, Curr. Opin. Biotechnol., 2011, vol. 22, pp. 103–108. doi 10.1016/j.copbio.2010.08.009PubMedCrossRefGoogle Scholar
  54. 54.
    Guo, W., Sheng, I., and Feng, X., 13C-metabolic flux analysis: an accurate approach to demystify microbial metabolism for biochemical production, Bioengineering, 2016, vol. 3, no. 3, pp. 1–32. doi 10.3390/bioengineering3010003Google Scholar
  55. 55.
    Boghigian, B.A., Seth, G., Kiss, R., et al., Metabolic flux analysis and pharmaceutical production, Metab. Eng., 2010, vol. 12, pp. 81–95. doi 10.1016/j.ymben.2009.10.004PubMedCrossRefGoogle Scholar
  56. 56.
    Iwatani, S., Yamada, Y., and Usuda, Y., Metabolic flux analysis in biotechnology process, Biotechnol. Lett., 2008, vol. 30, pp. 791–799. doi 10.1007/sl0529-008-9633-5PubMedCrossRefGoogle Scholar
  57. 57.
    Niklas, J., Schneider, K., and Heinzle, E., Metabolic flux analysis in eukaryotes, Curr. Opin. Biotechnol., 2010, vol. 21, pp. 63–69. doi 10.1016/j.copbio.2010.01.011PubMedCrossRefGoogle Scholar
  58. 58.
    Matsuoka, Y. and Shimizu, K., Current status of 13C-metabolic flux analysis and future perspectives, Proc. Biochem., 2010, vol. 45, pp. 1873–1881. doi 10.1016/j.procbio2010.03.025CrossRefGoogle Scholar
  59. 59.
    Gerosa, L. and Sauer, U., Regulation and control of metabolic fluxes in microbes, Curr. Opin Biotechnol., 2011, vol. 22, pp. 566–575. doi 10.1016/j.copbio.2011.04.016PubMedCrossRefGoogle Scholar
  60. 60.
    Nöh, K. and Wiechert, W., The benefits of being transient: isotope-based metabolic flux analysis at the short time scale, Appl. Microbiol. Biotechnol., 2011, vol. 91, pp. 1247–1265. doi 10.1007/s00253-011-3390-4PubMedCrossRefGoogle Scholar
  61. 61.
    Young, J.D., 13C metabolic flux analysis of recombinant expression hosts, Curr. Opin. Biotechnol., 2014, vol. 30, pp. 238–245. doi 10.1016/j.copbio.2014.10.004PubMedCrossRefGoogle Scholar
  62. 62.
    Meyer, F.M., Gerwig, J., Hammer, E., et al., Physical interactions between tricarboxylic acid cycle enzymes in bacillus subtilis: evidence for a metabolon, Metab. Eng., 2011, vol. 13, no. 1, pp. 18–27. doi 10.1016/j.ymben.2010.10.001PubMedCrossRefGoogle Scholar
  63. 63.
    Ovadi, J. and Saks, V., On the origin of intracellular compartmentation and organized metabolic systems, Mol. Cell. Biochem., 2004, nos. 256/257, pp. 5–12. doi 10.1023/B:MCBI0000009855.146482cCrossRefGoogle Scholar
  64. 64.
    Ellis, R.J., Macromolecular crowding: an important but neglected aspect of the intracellular environment, Curr. Opin. Struct. Biol., 2001, vol. 11, no. 1, pp. 114–119. doi 10.1016/S0959-440X(00)00172-XPubMedCrossRefGoogle Scholar
  65. 65.
    Stephanopoulos, G.N., Aristidou, A.A., and Nielsen, J.H., Metabolic Engineering: Principles and Methodologies, San Diego: Academic Press, 1998.CrossRefGoogle Scholar
  66. 66.
    Antoniewicz, M.R., Kraynie, D.F., Laffend, L.A., et al., Metabolic flux analysis in a nonstalionary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol, Metab. Eng., 2007, vol. 9, no. 3, pp. 277–292. doi 10.1016/j.ymben.2007.01.003PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Dauner, M. and Sauer, U., Stoichiometric growth model for riboflavin-producing Bacillus subtilis, Biotechnol. Bioeng., 2001, vol. 76, no. 2, pp. 132–143. doi 10.1002/bit.l153PubMedCrossRefGoogle Scholar
  68. 68.
    Fischer, E., Zamboni, N., and Sauer, U., Highthroughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints, Anal. Biochem., 2004, vol. 325, pp. 308–316. doi 10.1016/j.ab.2003.10.036PubMedCrossRefGoogle Scholar
  69. 69.
    Papini, M., Nookaew, I., Siewers, V., et al., Physiological characterization of recombinant Saccharomyces cerevisiae expressing the Aspergillus nidulans phosphoketolase pathway: validation of activity through 13C-based metabolic flux analysis, Appl. Microbiol. Biotechnol., 2012, vol. 95, no. 4, pp. 1001–1010. doi 10.1007/s00253-012-3936-0PubMedCrossRefGoogle Scholar
  70. 70.
    Suthers, P.F., Burgard, A.P., Dasika, M.S., et al., Metabolic flux elucidation for large-scale models using 13C labeled isotopes, Metab. Eng., 2007, vol. 9, nos. 5–6, pp. 387–405. doi 10.1016/j.ym-ben.2007.05.005PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Leighty, R.W. and Antoniewicz, M.R., COMPLETEMFA: complementary parallel labeling experiments technique for metabolic flux analysis, Metab. Eng., 2013, vol. 20, pp. 49–55. doi 10.1016/j.ymben.2013.08.006PubMedCrossRefGoogle Scholar
  72. 72.
    Feist, A.M., Herrgard, M.J., Thiele, L., et al., Reconstruction of biochemical networks in microbial organisms, Nat. Rev. Microbiol., 2009, vol. 7, pp. 129–143. doi 10.1038/nrmicrol949PubMedCrossRefGoogle Scholar
  73. 73.
    Oh, Y.K., Palsson, B.O., Park, S.M., et al., Genome scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data, J. Biol. Chem., 2007, vol. 282, pp. 28791–28799. doi 10.1074/jbc.M703759200PubMedCrossRefGoogle Scholar
  74. 74.
    Cordova, L.T. and Antoniewicz, M.R., 13C metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300, Metab. Eng., 2016, vol. 33, pp. 148–157. doi 10.1016/j.ymben.2015.06.004PubMedCrossRefGoogle Scholar
  75. 75.
    Cordova, L.T. Venkataramanan, K.P., et al., Complete genome sequence, metabolic model construction and phenotypic characterization of Feobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium, Metab. Eng., 2015, vol. 32, pp. 74–81. doi 10.1016/j.ym-ben.2015.09.009PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Pramanik, J. and Keasling, J.D., Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements, Biotechnol. Bioeng., 1997, vol. 56, no. 4, pp. 398–421. doi 10.1002/(SICI)1097-0290(19971120)56:4&lt;398::AID-BIT6& gt;3.0.CO;2-JPubMedCrossRefGoogle Scholar
  77. 77.
    Ravikirthi, P., Suthers, P.F., and Maranas, C.D., Construction of an E. coli genome-scale atom mapping model for MFA calculations, Biotechnol. Bioeng., 2011, vol. 108, pp. 1372–1382. doi 10.1002/bit.23070PubMedCrossRefGoogle Scholar
  78. 78.
    Gopalakrishnan, S. and Maranas, C.D., 13C metabolic flux analysis at a genome-scale, Metab. Eng., 2015, vol. 32, pp. 12–22. doi 10.1016/j.ymben.2015.08.006PubMedCrossRefGoogle Scholar
  79. 79.
    Zamboni, N., Fendt, S.-M., and Rühl, M., 13C-based metabolic flux analysis, Nature Protocols, 2009, vol. 4, no. 6, pp. 878–892. doi 10.1038/nprot.2009.58PubMedCrossRefGoogle Scholar
  80. 80.
    Wiechert, W., The thermodynamic meaning of metabolic exchange fluxes, Biophys. J., 2007, vol. 93, pp. 2255–2264. doi 10.1529/biophysj.106.099895PubMedPubMedCentralCrossRefGoogle Scholar
  81. 81.
    Leighty, R.W. and Antoniewicz, M.R., Parallel labeling experiments with [U-13C]glucose validate E. coli metabolic network model for 13C metabolic flux analysis, Metab. Eng., 2012, vol. 14, no. 5, pp. 533–541. doi 10.1016/j.ymben.2012.06.003PubMedCrossRefGoogle Scholar
  82. 82.
    Dutow, P., Schmidl, S.R., Ridderbusch, M., et al., Interactions between glycolytic enzymes of mycoplasma pneumonia, J. Mol. Microbiol. Biotechnol., 2010, vol. 19, no. 3, pp. 134–139. doi 10.1159/000321499PubMedCrossRefGoogle Scholar
  83. 83.
    Commichau, F.M., Rome, F.M., Herzberg, C., et al., Novel activities of glycolytic enzymes in Bacillus subtilis: interactions with essential proteins involved in mRNA processing, Mol. Cell. Proteomics, 2009, vol. 8, no. 6, pp. 1350–1360. doi 10.1074/mcp.M800546-MCP200PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Jung, I.L., Phyo, K.H., and Kim, I.G., RpoS-mediated growth-dependent expression of the Escherichia coli tkt genes encoding transketolases isoenzymes, Curr. Microbiol., 2005, vol. 50, no. 6, pp. 314–318. doi 10.1007/s00284-005-4501-lPubMedCrossRefGoogle Scholar
  85. 85.
    Dueber, J.E., Wu, G.C., Malmirchegini, G.R., et al., Synthetic protein scaffolds provide modular control over metabolic flux, Nature Biotechnol., 2009, vol. 27, pp. 753–759. doi 10.1038/nbt.1557CrossRefGoogle Scholar
  86. 86.
    Lee, H., DeLoache, W.C., and Dueber, J.E., Spatial organization of enzymes for metabolic engineering, Metab. Eng., 2012, vol. 14, pp. 242–251. doi 10.1016/j.ymben.2011.09.003PubMedCrossRefGoogle Scholar
  87. 87.
    Lee, J.H., Jung, S.-C., Bui, L.M., et al., Improved production of L-threonine in Escherichia coli by use of a DNA scaffold system, Appl. Environ. Microbiol., 2013, vol. 79, pp. 774–782. doi 10.1128/AEM.02578-12PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Wiechert, W., 13C metabolic flux analysis, Metab. Eng., 2001, vol. 3, no. 3, pp. 195–206. doi 10.1006/mben.2001.0187PubMedCrossRefGoogle Scholar
  89. 89.
    Fischer, E. and Sauer, U., A novel metabolic cycle catalyzes glucose oxidation and anaplerosis in hungry Escherichia coli, J. Biol. Chem., 2003, vol. 278, no. 47, pp. 46446–46451. doi 10.1074/jbc.M307968200PubMedCrossRefGoogle Scholar
  90. 90.
    Fong, S.S., Nanchen, A., Palsson, B.O., et al., Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes, J. Biol. Chem., 2006, vol. 281, no. 12, pp. 8024–8033. doi 10.1074/jbc.M510016200PubMedCrossRefGoogle Scholar
  91. 91.
    Emmerling, M., Dauner, M., Ponti, A., et al., Metabolic flux responses to pyruvate kinase knockout in Escherichia coli. J. Bacterial, 2002, no. 1, pp. 152–164. doi 10.1128/JB.184.1.152-164.2002CrossRefGoogle Scholar
  92. 92.
    Swamp, A., Lu, J., and DeWoody, K.C., Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile thermus thermophilus hb8, Metab. Eng., 2014, vol. 24, pp. 173–180. doi 10.1016/j.ym-ben.2014.05.013CrossRefGoogle Scholar
  93. 93.
    Au, J., Choi, J., Jones, S.W., et al., Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for 13C metabolic flux analysis, Metab. Eng., 2014, vol. 26, pp. 23–33. doi 10.1016/j.ymben.2014.08.002PubMedCrossRefGoogle Scholar
  94. 94.
    Sonntag, K., Eggeling, L., de Graaf, A.A., et al., Flux partitioning in the split pathway of lysine synthesis in Corynebacterium glutamicum—quantification by 13Cand 1H-NMR spectroscopy, Eur. J. Biochem., 1993, vol. 213, no. 3, pp. 1325–1331. doi 10.1111/j.l432-1033.1993.tbl7884.xPubMedCrossRefGoogle Scholar
  95. 95.
    Wiechert, W., Siefke, C., de Graaf, A.A., et al., Bidirectional reaction steps in metabolic networks: II. Flux estimation and statistical analysis, Biotechnol. Bioeng., 1997, vol. 55, no. 1, pp. 118–135. doi 10.1002/(SICI)1097-0290(19970705)55:l<118::AID-BIT13> 3.0.CO;2-IPubMedCrossRefGoogle Scholar
  96. 96.
    Bonarius, H.P.J., Schmidt, G., and Tramper, J., Flux analysis of undetermined metabolic systems: the quest for missing constraints, Trends Biotechnol., 1997, vol. 15, no. 8, pp. 308–314. doi 10.1016/S0167-7799(97)01067-6CrossRefGoogle Scholar
  97. 97.
    Marx, A., de Graaf, A.A., Wiechert, W., et al., Determination of the fluxes in the central metabolism of Corynebacterium glutamicum by nuclear magnetic resonance spectroscopy combined with metabolite balancing, Biotechnol. Bioeng., 1996, vol. 49, no. 2, pp. 111–129. doi 10.1002/(SICI)1097-0290(19960120)49:2<111::AIDBIT1> 3.0.CO;2-TPubMedCrossRefGoogle Scholar
  98. 98.
    Sauer, U. and Bailey, J.E., Estimation of P-to-O ratio in Bacillus subtilis and its influence on maximum riboflavin yield, Biotechnol. Bioeng., 1999, vol. 64, no. 6, pp. 750–754. doi 10.1002/(SICI)1097-0290(19990920)64:63.0.CO;2-SPubMedCrossRefGoogle Scholar
  99. 99.
    Petersen, S., de Graaf, A.A., Eggeling, L., et al., In vivo quantification of parallel and bidirectional fluxes in the anaplerosis of Corynebacteria glutamicum, J. Biol. Chem., 2000, vol. 275, no. 46, pp. 35932–35941. doi 10.1074/jbc.M908728199PubMedCrossRefGoogle Scholar
  100. 100.
    Zheng, L., Kostrewa, D., Berneche, S., et al., The mechanism of ammonia transport based on the crystal structure of AmtB of Escherichia coli, Proc. Natl. Acad. Sci. U. S. A., 2004, vol. 101, no. 49, pp. 17090–17095. doi 10.1073/pnas.0406475101PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Hsieh, Y.-J. and Wanner, B.L., Global regulation by the seven-component Pi signaling system, Curr. Opin. Microbiol., 2010, vol. 13, no. 2, pp. 198–203. doi 10.1016/j.mib.2010.01.014PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Lengeler, J.W., Drews, G., and Schlegel, H.G., Biosynthesis of Building Blocks. Biology of the Prokaryotes, Stuttgart, Germany: Georg ThiemeVerlag, 1999, pp. 110–162.Google Scholar
  103. 103.
    Szyperski, T., Biosynthetically directed fractional 13C-labeling of proteinogenic amino acids. An efficient analytical tool to investigate intermediary metabolism, Eur. J. Biochem., 1995, vol. 232, no. 2, pp. 433–448. doi 10.1111/j.l432-1033.1995.433zz.xPubMedCrossRefGoogle Scholar
  104. 104.
    Becker, J. and Wittmann, S., GC-MS-based 13C metabolic flux analysis, in Metabolic Flux Analysis: Methods and Protocols, Krömer, J.O., Nielsen, L.K., and Blank, L.M., Eds., New York: Springer Science + Business Media, 2014, vol. 1191, pp. 165–174. doi 10.1007/978-1-4939-1170-7-10Google Scholar
  105. 105.
    Wiechert, W. and de Graaf, A., A in vivo stationary flux analysis by 13C labeling experiments, Adv. Biochem. Eng. Biotechnol., 1996, vol. 54, pp. 109–154. doi 10.1007/BFb0102334PubMedGoogle Scholar
  106. 106.
    Wiechert, W., Möllney, M., Isermann, N., et al., Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems, Biotechnol. Bioeng., 1999, vol. 66, no. 2, pp. 69–85. doi 10.1002/(SICI)1097-0290 (1999)66:2<69::AID-BITl>3.0.CO;2-6PubMedCrossRefGoogle Scholar
  107. 107.
    Möllney, M., Wiechert, W., Kownatzki, D., et al., Bidirectional reaction steps in metabolic networks: IV. Optimal design of isotopomer labeling experiments, Biotechnol. Bioeng., 1999, vol. 66, no. 2, pp. 86–103. doi 10.1002/(SICI)1097-0290(1999)66:2<86::ASh-VGG2>3.0.CO;2-APubMedCrossRefGoogle Scholar
  108. 108.
    Wiechert, W., Möllney, M., Petersen, S., et al., A universal framework for 13C metabolic flux analysis, Metab. Eng., 2001, vol. 3, no. 3, pp. 265–283. doi 10.1006/mben.2001.0188PubMedCrossRefGoogle Scholar
  109. 109.
    Weitzel, M. Nöh, K., et al., 13CFLUX2—high-performance software suite for 13C-metabolic flux analysis, Bioinformatics, 2013, vol. 29, no. 1, pp. 143–145.PubMedCrossRefGoogle Scholar
  110. 110.
    Malloy, C.R., Sherry, A.D., and Jeffrey, F.M.H., Evaluation of carbon flux and substrate selection through alternate pathways involving the citric acid cycle of the heart by 13C NMR spectroscopy, J. Biol. Chem., 1988, vol. 263, no. 15, pp. 6964–6971.PubMedGoogle Scholar
  111. 111.
    Schmidt, K., Carlsen, M., Nielsen, J., et al., Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices, Biotechnol. Bioeng., 1997, vol. 55, no. 6, pp. 831–840. doi 10.1002/(SICI)1097-0290(19970920)55:6&lr,831::AID-BIT2&gt; 3.0.CO;2-HPubMedCrossRefGoogle Scholar
  112. 112.
    Choi, J., Grossbach, M.T., and Antoniewicz, M.R., Measuring complete isotopomer distribution of aspartate using gas chromatography/tandem mass spectrometry, Ami. Chem., 2012, vol. 84, no. 10, pp. 4628–4632. doi 10.1021/ac300611nGoogle Scholar
  113. 113.
    Wittmann, C. and Heinzle, E., Mass spectrometry for metabolic flux analysis, Biotechnol. Bioeng., 1999, no. 6, pp. 739–750. doi 10.1002/(SICI)1097-0290(19990320)62:6<739::AID-BIT13>3.0.CO;2-ECrossRefGoogle Scholar
  114. 114.
    Zupke, C. and Stephanopoulos, G., Modeling of isotope distributions and intracellular fluxes in metabolic networks using atom mapping matrices, Biotechnol. Prog., 1994, vol. 10, no. 5, pp. 489–498. doi 10.1021/bp00029a006CrossRefGoogle Scholar
  115. 115.
    Antoniewicz, M.R., Comprehensive analysis of metabolic pathways through the combined use of multiple isotopic tracers, PhD Thesis [G. Stephanopoulos–supervised], Massachusetts Institute of Technology, 2006. http://hdl.hand-le.net/1721.1/37457Google Scholar
  116. 116.
    Kleijn, RJ., van Winden, W.A., van Gulik, W.M., et al., Revisiting the 13C-label distribution of the nonoxidative branch of the pentose phosphate pathway based upon kinetic and genetic evidence, FEBS J., 2005, vol. 272, no. 12, pp. 4970–4982. doi 10.1111/J.1742-4658.20O5.04907.XPubMedCrossRefGoogle Scholar
  117. 117.
    van Winden, W.A., van Dam, J.C., and Ras, C., Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of (13)C-labeled primary metabolites, FEMS Yeast Res., 2005, vol. 5, nos. 6–7, pp. 559–568. 10.1016-j.femsyr.2004.10.007PubMedGoogle Scholar
  118. 118.
    Quek, L.E., Wittmann, C., Nielsen, L.K., et al., Open-FLUX: efficient modelling software for 13C-based metabolic flux analysis, Microb. Cell Fact., 2009, vol. 8, no. 25, pp. 1–15. doi 10.1186/1475-2859-8-25Google Scholar
  119. 119.
    Becker, J., Reinefeld, J., Stellmacher, R., et al., Systems-wide analysis and engineering of metabolic pathway fluxes in bio-succinate producing Basfia succiniciproducens, Biotechnol. Bioeng., 2013, vol. 110, no. 11, pp. 3013–3023. doi 10.1002/bit24963PubMedCrossRefGoogle Scholar
  120. 120.
    Mu, F. Williams, R.F., et al., Carbon-fate maps for metabolic reactions, Bioinformatics, 2007, vol. 23, no. 23, pp. 3193–3199. doi 10.1093/bioinformatics/btm498PubMedCrossRefGoogle Scholar
  121. 121.
    Szyperski, T., 13C-NMR, MS and metabolic flux balancing in biotechnology research, Q. Rev. Biophys., 1998, vol. 31, no. 1, pp. 41–106. doi 10.1017/S0033583598003412PubMedCrossRefGoogle Scholar
  122. 122.
    Schmidt, K., Nielsen, J., and Villadsen, J., Quantitative analysis of metabolic fluxes in Escherichia coli, using two-dimensional NMR spectroscopy and complete isotopomer models, J. Biotechnol., 1999, vol. 71, nos.1–3, pp. 175–189. doi 10.1016/S0168-1656(99)00021-8PubMedCrossRefGoogle Scholar
  123. 123.
    Sauer, U., Hatzimanikatis, V., Bailey, J.E., et al., Metabolic fluxes in riboflavin-producing Bacillus subtilis, Nat. Biotechnol., 1997, vol. 15, no. 5, pp. 448–452. doi 10.1038/nbt0597-448PubMedCrossRefGoogle Scholar
  124. 124.
    Fischer, E. and Sauer, U., Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC–MS, Eur. J. Biochem., 2003, vol. 270, no. 5, pp. 880–891. doi 10.1046/j.l432-1033.2003.03448.xPubMedCrossRefGoogle Scholar
  125. 125.
    Iwatani, S., Van Dien, S., Shimbo, K., et al., Determination of metabolic flux changes during fed-batch cultivation from measurements of intracellular amino acids by LC-MS/MS, J. Biotechnol., 2007, vol. 128, no. 1, pp. 93–111. doi 10.1016/j.jbio-tec.2006.09.004PubMedCrossRefGoogle Scholar
  126. 126.
    Toya, Y., Ishii, N., Nakahigashi, K., et al., 13C-metabolic flux analysis for batch culture of Escherichia coli and its pyk and pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites, Biotechnol. Prog., 2010, vol. 26, no. 4, pp. 975–992. doi 10.1002/btpr.420PubMedGoogle Scholar
  127. 127.
    Wittmann, C., Metabolic flux analysis using mass spectrometry, Adv. Biochem. Eng. Biotechnol., 2002, vol. 74, pp. 39–64. doi 10.1007/3-540-45736-4JPubMedGoogle Scholar
  128. 128.
    Nanchen, A., Fuhrer, T., and Sauer, U., Determination of metabolic flux ratio from 13C-experiments and gas chromatography–mass spectrometry data: protocol and principles, Methods Mol. Biol., 2007, vol. 358, pp. 177–197. doi 10.1007/978-l-59745-244-l_llPubMedCrossRefGoogle Scholar
  129. 129.
    Dauner, M. and Sauer, U., GC–MS analysis of amino acids rapidly provides rich information for isotopomer balancing, Biotechnol. Prog., 2000, vol. 16, no. 4, pp. 642–649. doi 10.1021/bp000058hPubMedCrossRefGoogle Scholar
  130. 130.
    van Winden, W.A., Wittmann, C., Heinzle, E., et al., Correcting mass isotopomer distributions for naturally occurring isotopes, Biotechnol. Bioeng., 2002, vol. 80, no. 4, pp. 477–479. doi 10.1002/bit.l0393PubMedCrossRefGoogle Scholar
  131. 131.
    Fernandez, C.A., Des, RosiersC., Previs, S.F., et al., Correction of 13C mass isotopomer distributions for natural stable isotope abundance, J. Mass. Spectrom., 1996, vol. 31, no. 3, pp. 255–262. doi 10.1002/(SICI)1096-9888(199603)31:3<255::AID-JMS290>3.0. CO;2-3PubMedCrossRefGoogle Scholar
  132. 132.
    Shimbo, K. Yahashi, A., et al., Precolumn derivatization reagents for high-speed analysis of amines and amino acids in biological fluid using liquid chromatography/electrospray ionization tandem mass spectrometry, Rapid Commun. Mass Spectrom., 2009, vol. 23, no. 10, pp. 1483–1492. doi 10.1002/rcm.4026PubMedCrossRefGoogle Scholar
  133. 133.
    Kiefer, P., Nicolas, C., Letisse, F., et al., Determination of carbon labeling distribution of intracellular metabolites from single fragment ions by ion chromatography tandem mass spectrometry, Anal. Bbchem., 2007, vol. 360, no. 2, pp. 182–188. doi 10.1016/j.ab.2006.06.032CrossRefGoogle Scholar
  134. 134.
    Klapa, M.I., Aont, C., and Stephanopoulos, G., Systematic quantification of complex metabolic flux networks using stable isotopes and mass spectrometry, Eur. J. Biochem., 2003, vol. 270, no. 17, pp. 3525–3542. doi 10.1046/J.1432-1033.2003.03732.XPubMedCrossRefGoogle Scholar
  135. 135.
    Antoniewicz, M.R., Kelleher, J.K., and Stephanopoulos, G., Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis, Anal. Chem., 2007, vol. 79, no. 19, pp. 7554–7559. doi 10.1021/ac0708893PubMedCrossRefGoogle Scholar
  136. 136.
    Ann, W.C. and Antoniewicz, M.R., Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry. Metab. Eng., 2011, vol. 13, no. 5, pp. 598–609. doi 10.1016/j.ymben.2011.07.002CrossRefGoogle Scholar
  137. 137.
    Antoniewicz, M.R., Tandem mass spectrometry for measuring stable-isotope labeling, Curr. Opin. Biotechnol., 2013, vol. 24, no. 1, pp. 48–53. doi 10.1016/j.copbio. 2012.10.011PubMedCrossRefGoogle Scholar
  138. 138.
    Rühl, M. Nöh, K., et al., Collisional fragmentation of central metabolites in LC-MS/MS increases precision of 13C metabolic flux analysis, Biotechnol. Bioeng., 2012, vol. 109, no. 3, pp. 763–771. doi 10.1002/bit.24344PubMedCrossRefGoogle Scholar
  139. 139.
    Jeffrey, F.M.H., Roach, J.S., Storey, C.J., et al., 13C isotopomer analysis of glutamate by tandem mass spectrometry, Anal. Biochem., 2002, vol. 300, no. 2, pp. 192–205. doi 10.1006/abio.2001.5457PubMedCrossRefGoogle Scholar
  140. 140.
    Dookeran, N.N. and Harrison, A.G., Fragmentation reactions of protonated a-amino acids, J. Mass Specr., 1996, vol. 31, no. 5, pp. 500–508. doi 10.1002/(SICI)1096-9888(199605)31:53.0.CO;2-QCrossRefGoogle Scholar
  141. 141.
    Harada, K. Ohyama, Y., et al., Quantitative analysis of anionic metabolites for Catharanthus roseus by capillary electrophoresis using sulfonated capillary coupled with electrospray ionization-tandem mass spectrometry, J. Biosci. Bioeng., 2008, vol. 105, no. 3, pp. 249–260. doi 10.1263/jbb.l05.249PubMedCrossRefGoogle Scholar
  142. 142.
    Buescher, J.M., Moco, S., Sauer, U., et al., Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites, Anal. Chem., 2010, vol. 82, no. 11, pp. 4403–4412. doi 10.1021/acl00101dPubMedCrossRefGoogle Scholar
  143. 143.
    Bajad, S.U., Lu, W., Kimball, E.H., et al., Separation and quantification of water soluble cellular metabolites by hydrophilic interaction chromatography-tandem mass spectrometry, J. Chromatogr., A, 2006, vol. 1125, no. 1, pp. 76–88. doi 10.1016/j.chroma2006.05.019CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2017

Authors and Affiliations

  • L. I. Golubeva
    • 1
    Email author
  • M. S. Shupletsov
    • 1
    • 2
  • S. V. Mashko
    • 1
    • 3
  1. 1.The Closed Joint-Stock Company Ajinomoto-GenetikaMoscowRussia
  2. 2.The Faculty of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityMoscowRussia
  3. 3.The Faculty of BiologyLomonosov Moscow State UniversityMoscowRussia

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